From 020e451ee61c83fbb3effc67a6a30781dddff900 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Thu, 17 Sep 2020 10:06:35 -0700 Subject: [PATCH 001/140] Suitably dynamic versioning (#467) * Suitably dynamic versioning The following versioning code bypasses a few problems with python module versions. The following scenarios are plausible: - A user clones `graspologic` and runs `pip install -r requirements.txt` then executes `python` in the project directory, accessing the graspologic library by python's local folder structure. - A users clones `graspologic` and runs `python setup.py install` in the environment of their choice, accessing the graspologic library either by the local folder structure or the .egg in their site-packages, depending on their current working directory. - A user clones no repository and wants to install the library solely via `pip` via the `pip install ...` command, which has 2 wings to consider: - The user wishes to try out the latest prerelease, which is going to be published with a X.Y.ZdevYYYYMMDDBUILDNUMBER style version and can be installed via `pip install graspologic --pre` - The user wishes to try out the latest release, which will be published as `X.Y.Z`. This PR supports those 4 cases (notably, it does not support `pip install .` from the root project directory, which does some super weird stuff and I gave up on trying to solve it a long time ago) The concept is this: the actual version upon a **build action**, which can be undertaken by: - CI building a snapshot build - CI building a release build - Local user building a local build These states all require the same thing: a materialized version in a file. This version should be created at the time of this build action. In the case of CI, we can populate the file in our CI build process and move on. It's the case of not being in CI where we need to consider what to do next, which leaves Local user building a local build (and local user using the filesystem as the library). In these cases, the solution is the following: if we have a populated version.txt file, we use it. If we do not, we materialize a new version based on the `__semver` in version.py and the current time in YYYYMMDDHHmmSS format. This means that if you are running on the filesystem, and you say `import graspy; print(graspy.__version__);`, it will actually tell you the version is `0.1.0dev20200926120000` as an example. However, when you close the interpreter and do it again, it will tell you that the version is `0.1.0dev20200926120500` - because it will create a version for you at the time of import. However, if you were to run `python setup.py install`, the setup.py file actually takes it on itself to either get a version number or use the materialized version described above, then to write it to version.txt. Which means that installing the graspologic library from setuptools will actually lock in the version number in perpetuity. Gotchas - version.txt must always be empty in the source tree - `pip install .` does some weird thing where it registers an entry in site-packages that is like a symlink to the local filesystem anyway so it doesn't actually make an egg which means you get a new version each time and I gave up caring at this point since we got the three primary use cases: developers, users of pre-releases, and users of releases all covered. Users who install by cloning and running pip install are just going to get a weird behavior that probably isn't that important to track down, and regardless they'll get a clear `X.Y.Zdev` in their `graspologic.__version__` which is enough for us to go on if there are any issues raised. * My testing resulted in filling this file and committing it, like I said not to do * Updated conf.py for sphinx to be able to find a version it likes. Or kinda likes. Maybe likes? * Forgot I had to add the recursive-include for the version file. * Making black happy --- MANIFEST.in | 1 + docs/conf.py | 17 ++++++++--------- graspy/__init__.py | 5 ++--- graspy/version/__init__.py | 2 ++ graspy/version/version.py | 38 ++++++++++++++++++++++++++++++++++++++ graspy/version/version.txt | 0 setup.py | 33 +++++++++++++++++++++------------ 7 files changed, 72 insertions(+), 24 deletions(-) create mode 100644 graspy/version/__init__.py create mode 100644 graspy/version/version.py create mode 100644 graspy/version/version.txt diff --git a/MANIFEST.in b/MANIFEST.in index 7e61d4334..6ab442361 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,3 @@ recursive-include graspy/datasets *.csv +recursive-include graspy/version version.txt exclude tests/* \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index a81337cdb..817a6e543 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,15 +23,14 @@ copyright = "2018" authors = u"NeuroData" -# The short X.Y version -# Find GraSPy version. -PROJECT_PATH = os.path.dirname(os.path.abspath(__file__)) -for line in open(os.path.join(PROJECT_PATH, "..", "graspy", "__init__.py")): - if line.startswith("__version__ = "): - version = line.strip().split()[2][1:-1] - -# The full version, including alpha/beta/rc tags -release = "alpha" +realpath = os.path.realpath(__file__) +dir_realpath = os.path.dirname(os.path.dirname(realpath)) +sys.path.append(dir_realpath) + +import graspy + +version = graspy.version.version.__semver +release = graspy.version.version.version # -- Extension configuration ------------------------------------------------- extensions = [ diff --git a/graspy/__init__.py b/graspy/__init__.py index 485fb922f..448c0549a 100644 --- a/graspy/__init__.py +++ b/graspy/__init__.py @@ -1,6 +1,8 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. +from .version.version import name, version as __version__ + import warnings import graspy.cluster @@ -17,6 +19,3 @@ warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=PendingDeprecationWarning) warnings.simplefilter("always", category=UserWarning) - - -__version__ = "0.3.0" diff --git a/graspy/version/__init__.py b/graspy/version/__init__.py new file mode 100644 index 000000000..9a0454564 --- /dev/null +++ b/graspy/version/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. diff --git a/graspy/version/version.py b/graspy/version/version.py new file mode 100644 index 000000000..a3c193c51 --- /dev/null +++ b/graspy/version/version.py @@ -0,0 +1,38 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import datetime +from typing import List +import pkg_resources + +__all__: List[str] = ["version", "name"] + +name = ( + "graspy" # TODO: #454 Update in https://github.com/microsoft/graspologic/issues/454 +) + +# manually updated +__semver = "0.1.0" +# full version (may be same as __semver on release) +__version_file = "version.txt" + + +def _from_resource() -> str: + version_file = pkg_resources.resource_stream(__name__, __version_file) + version_file_contents = version_file.read() + return version_file_contents.decode("utf-8").strip() + + +def local_build_number() -> str: + return datetime.datetime.today().strftime("%Y%m%d%H%M%S") + + +def get_version() -> str: + version_file_contents = _from_resource() + if len(version_file_contents) == 0: + return f"{__semver}.dev{local_build_number()}" + else: + return version_file_contents + + +version = get_version() diff --git a/graspy/version/version.txt b/graspy/version/version.txt new file mode 100644 index 000000000..e69de29bb diff --git a/setup.py b/setup.py index 348fd7ee2..769ec5ab8 100644 --- a/setup.py +++ b/setup.py @@ -4,16 +4,29 @@ import os import sys from setuptools import setup, find_packages -from sys import platform +from typing import Tuple + + +def package_metadata() -> Tuple[str, str]: + sys.path.insert(0, os.path.join("graspy", "version")) # TODO: #454 Change path in https://github.com/microsoft/graspologic/issues/454 + from version import name, version + sys.path.pop(0) + + version_path = os.path.join("graspy", "version", "version.txt") + with open(version_path, "w") as version_file: + _b = version_file.write(f"{version}") + return name, version + + +PACKAGE_NAME, VERSION = package_metadata() -PACKAGE_NAME = "graspy" DESCRIPTION = "A set of python modules for graph statistics" with open("README.md", "r") as f: LONG_DESCRIPTION = f.read() AUTHOR = ("Eric Bridgeford, Jaewon Chung, Benjamin Pedigo, Bijan Varjavand",) AUTHOR_EMAIL = "j1c@jhu.edu" URL = "https://github.com/neurodata/graspy" -MINIMUM_PYTHON_VERSION = 3, 6 # Minimum of Python 3.5 +MINIMUM_PYTHON_VERSION = 3, 6 # Minimum of Python 3.6 REQUIRED_PACKAGES = [ "networkx>=2.1", "numpy>=1.8.1", @@ -25,13 +38,6 @@ ] -# Find GraSPy version. -PROJECT_PATH = os.path.dirname(os.path.abspath(__file__)) -for line in open(os.path.join(PROJECT_PATH, "graspy", "__init__.py")): - if line.startswith("__version__ = "): - VERSION = line.strip().split()[2][1:-1] - - def check_python_version(): """Exit when the Python version is too low.""" if sys.version_info < MINIMUM_PYTHON_VERSION: @@ -48,6 +54,8 @@ def check_python_version(): long_description_content_type="text/markdown", author=AUTHOR, author_email=AUTHOR_EMAIL, + maintainer="Dwayne Pryce", + maintainer_email="dwpryce@microsoft.com", install_requires=REQUIRED_PACKAGES, url=URL, license="MIT", @@ -55,11 +63,12 @@ def check_python_version(): "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Mathematics", - "License :: OSI Approved :: Apache Software License", + "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", ], - packages=find_packages(), + packages=find_packages(exclude=["tests", "tests.*", "tests/*"]), include_package_data=True, + package_data={'version': [os.path.join('graspy', 'version', 'version.txt')]}, # TODO: #454 Also needs changed by https://github.com/microsoft/graspologic/issues/454, ) From 854c92ac280b8de341ef0a956e48c6e56cfd79ff Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Thu, 17 Sep 2020 14:52:30 -0400 Subject: [PATCH 002/140] Add note about merger to README (#469) --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 8544f5e0a..d7ff76825 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,11 @@ - [License](#license) - [Issues](#issues) +# Note: GraSPy + topologic = graspologic +The package formerly known as `GraSPy` is merging with `topologic` to become `graspologic`, so some things in this +repo my be a bit wonky for the time being (e.g. names may say `GraSPy` still instead of `graspologic`). +Please stay tuned and we'll be back up and running shortly. + # Overview A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. From 0b1613bf5813c380605d045a966b5093eef8030c Mon Sep 17 00:00:00 2001 From: Paul Adkisson Date: Fri, 18 Sep 2020 15:35:29 -0400 Subject: [PATCH 003/140] Fixes Documentation Build Warnings #291 (#426) * Fixed Documentation Warnings * Replaced tabs with 4 spaces to follow formatting guidelines * Blackened the Repo * Cleared Jupyter Notebook * Removed unneccessary \'s * Wrapped p_value_1_ and p_value_2_ in ``; removed \'s * Added Release Files to the TOC tree * Reverting to python 3.7.0 * Fixed formatting * Blackened Repo Co-authored-by: Benjamin Pedigo --- docs/conf.py | 2 +- docs/index.rst | 2 +- docs/{news.rst => release.rst} | 40 +++++++++++++----------- docs/release/release_0.0.1.rst | 4 +-- docs/release/release_0.0.2.rst | 4 +-- docs/release/release_0.0.3.rst | 4 +-- docs/release/release_0.1.rst | 4 +-- docs/release/release_0.2.rst | 4 +-- docs/release/release_0.3.rst | 4 +-- docs/release/release_template.rst | 6 ++-- graspy/cluster/gclust.py | 4 ++- graspy/embed/svd.py | 2 +- graspy/inference/latent_position_test.py | 4 ++- 13 files changed, 47 insertions(+), 37 deletions(-) rename docs/{news.rst => release.rst} (61%) diff --git a/docs/conf.py b/docs/conf.py index 817a6e543..3c90b85ba 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -82,7 +82,7 @@ # -- Options for HTML output ------------------------------------------------- # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] -html_static_path = ["_static"] +html_static_path = [] modindex_common_prefix = ["graspy."] pygments_style = "sphinx" diff --git a/docs/index.rst b/docs/index.rst index f755e6500..8f9f91f06 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -57,7 +57,7 @@ GraSPy is a graph statistics package in python. tutorial reference/index contributing - news + release license .. toctree:: diff --git a/docs/news.rst b/docs/release.rst similarity index 61% rename from docs/news.rst rename to docs/release.rst index 4f76ab9cb..0f1ab755c 100644 --- a/docs/news.rst +++ b/docs/release.rst @@ -8,53 +8,57 @@ GraSPy 0.3 Release date: 04 Aug 2020 Supports Python 3.6, and 3.7 +.. toctree:: + :maxdepth: 1 -Release notes -~~~~~~~~~~~~~ -See :doc:`release/release_0.3`. + release/release_0.3.rst GraSPy 0.2 ---------- Release date: 02 Mar 2020 Supports Python 3.5, 3.6, and 3.7 +.. toctree:: + :maxdepth: 1 -Release notes -~~~~~~~~~~~~~ -See :doc:`release/release_0.2`. + release/release_0.2.rst GraSPy 0.1 ---------- Release date: 05 Aug 2019 Supports Python 3.5, 3.6, and 3.7 -Release notes -~~~~~~~~~~~~~ -See :doc:`release/release_0.1`. +.. toctree:: + :maxdepth: 1 + + release/release_0.1.rst GraSPy 0.0.3 ------------ Release date: 11 June 2019 Supports Python 3.5, 3.6, and 3.7. -Release notes -~~~~~~~~~~~~~ -See :doc:`release/release_0.0.3`. +.. toctree:: + :maxdepth: 1 + + release/release_0.0.3.rst GraSPy 0.0.2 ------------ Release date: 26 March 2019 Supports Python 3.5, 3.6, and 3.7. -Release notes -~~~~~~~~~~~~~ -See :doc:`release/release_0.0.2`. +.. toctree:: + :maxdepth: 1 + + release/release_0.0.2.rst GraSPy 0.0.1 ------------ Release date: 14 December 2018 Supports Python 3.5, 3.6, and 3.7. -Release notes -~~~~~~~~~~~~~ -See :doc:`release/release_0.0.1`. +.. toctree:: + :maxdepth: 1 + + release/release_0.0.1.rst diff --git a/docs/release/release_0.0.1.rst b/docs/release/release_0.0.1.rst index 71c3d8b53..ac95da6e2 100644 --- a/docs/release/release_0.0.1.rst +++ b/docs/release/release_0.0.1.rst @@ -1,5 +1,5 @@ -Announcement: GraSPy 0.0.1 -========================== +Release Notes: GraSPy 0.0.1 +=========================== We're happy to announce the release of GraSPy 0.0.1! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as social networks diff --git a/docs/release/release_0.0.2.rst b/docs/release/release_0.0.2.rst index 453d5e7b8..a0e04f38e 100644 --- a/docs/release/release_0.0.2.rst +++ b/docs/release/release_0.0.2.rst @@ -1,5 +1,5 @@ -Announcement: GraSPy 0.0.2 -========================== +Release Notes: GraSPy 0.0.2 +=========================== We're happy to announce the release of GraSPy 0.0.2! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as social networks diff --git a/docs/release/release_0.0.3.rst b/docs/release/release_0.0.3.rst index 744400ea2..25b08a690 100644 --- a/docs/release/release_0.0.3.rst +++ b/docs/release/release_0.0.3.rst @@ -1,5 +1,5 @@ -Announcement: GraSPy 0.0.3 -========================== +Release Notes: GraSPy 0.0.3 +=========================== We're happy to announce the release of GraSPy 0.0.3! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as diff --git a/docs/release/release_0.1.rst b/docs/release/release_0.1.rst index 30c36fcdd..242ff0a3a 100644 --- a/docs/release/release_0.1.rst +++ b/docs/release/release_0.1.rst @@ -1,5 +1,5 @@ -Announcement: GraSPy 0.1 -========================== +Release Notes: GraSPy 0.1 +========================= We're happy to announce the release of GraSPy 0.1! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as diff --git a/docs/release/release_0.2.rst b/docs/release/release_0.2.rst index f98a5554d..82d9d8343 100644 --- a/docs/release/release_0.2.rst +++ b/docs/release/release_0.2.rst @@ -1,5 +1,5 @@ -Announcement: GraSPy 0.2 -========================== +Release Notes: GraSPy 0.2 +========================= We're happy to announce the release of GraSPy 0.2! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as diff --git a/docs/release/release_0.3.rst b/docs/release/release_0.3.rst index 3807908b4..db571a289 100644 --- a/docs/release/release_0.3.rst +++ b/docs/release/release_0.3.rst @@ -1,5 +1,5 @@ -Announcement: GraSPy 0.3 -========================== +Release Notes: GraSPy 0.3 +========================= We're happy to announce the release of GraSPy 0.3! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as diff --git a/docs/release/release_template.rst b/docs/release/release_template.rst index a8838c589..807a13ab8 100644 --- a/docs/release/release_template.rst +++ b/docs/release/release_template.rst @@ -1,5 +1,7 @@ -Announcement: GraSPy 0.0.X -========================== +:orphan: + +Release Notes: GraSPy 0.0.X +=========================== We're happy to announce the release of GraSPy 0.0.X! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as social networks diff --git a/graspy/cluster/gclust.py b/graspy/cluster/gclust.py index 8817434af..f3f6969ec 100644 --- a/graspy/cluster/gclust.py +++ b/graspy/cluster/gclust.py @@ -45,8 +45,9 @@ class GaussianCluster(BaseCluster): each component has its own diagonal covariance matrix - 'spherical' each component has its own single variance + If a list/array, it must be a list/array of strings containing only - 'spherical', 'tied', 'diag', and/or 'spherical'. + 'spherical', 'tied', 'diag', and/or 'full'. tol : float, defaults to 1e-3. The convergence threshold. EM iterations will stop when the @@ -66,6 +67,7 @@ class GaussianCluster(BaseCluster): The method used to initialize the weights, the means and the precisions. Must be one of:: + 'kmeans' : responsibilities are initialized using kmeans. 'random' : responsibilities are initialized randomly. diff --git a/graspy/embed/svd.py b/graspy/embed/svd.py index d6b3d9aa0..3dd87a062 100644 --- a/graspy/embed/svd.py +++ b/graspy/embed/svd.py @@ -86,7 +86,7 @@ def select_dimension( References ---------- - .. [#1] Zhu, M. and Ghodsi, A. (2006). + .. [1] Zhu, M. and Ghodsi, A. (2006). Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis, 51(2), pp.918-930. diff --git a/graspy/inference/latent_position_test.py b/graspy/inference/latent_position_test.py index 56c62c6df..c6a1c557f 100644 --- a/graspy/inference/latent_position_test.py +++ b/graspy/inference/latent_position_test.py @@ -47,9 +47,11 @@ class LatentPositionTest(BaseInference): .. math:: H_o: X_1 = X_2 R - 'scalar-rotation' .. math:: H_o: X_1 = c X_2 R + where :math:`c` is a scalar, :math:`c > 0` - 'diagonal-rotation' .. math:: H_o: X_1 = D X_2 R + where :math:`D` is an arbitrary diagonal matrix n_bootstraps : int, optional (default 500) @@ -71,7 +73,7 @@ class LatentPositionTest(BaseInference): The p value estimated from the null distributions from sample 1 and sample 2. p_value_ : float - The overall p value from the test; this is the max of p_value_1_ and p_value_2_ + The overall p value from the test; this is the max of ``p_value_1_`` and ``p_value_2_`` See also -------- From 4724210c58a650fe37f1f548e8eae982d730eef9 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Fri, 18 Sep 2020 13:43:38 -0700 Subject: [PATCH 004/140] Consolidated our dependencies (#470) * Consolidated our requirements into a single file, setup.py. Also updated it to include a new extra requires target that includes all of our build tools; black, pytest, pytest-cov, mypy, and sphinx-rtd-theme. * PR Feedback: Removed unused variable assignation, and also limited matplotlib to <=3.3.0 as was also done in @loftusa's #421 * Pulled in the requirements necessary for the sphinx documentation as well. Need to take a greater look at the docs folder setup next, but I'll do that in build-devops instead * Missed a comma in the dependencies list --- requirements.txt | 8 +---- setup.py | 84 +++++++++++++++++++++++++----------------------- 2 files changed, 44 insertions(+), 48 deletions(-) diff --git a/requirements.txt b/requirements.txt index 68b2d9af3..b0920f98d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,2 @@ -networkx>=2.1 -numpy>=1.8.1 -scikit-learn>=0.19.1 -scipy>=1.4.0 -seaborn>=0.9.0 -matplotlib>=3.0.0,<=3.3.0 -hyppo>=0.1.2 +-e .[dev] diff --git a/setup.py b/setup.py index 769ec5ab8..f6f28b180 100644 --- a/setup.py +++ b/setup.py @@ -4,60 +4,36 @@ import os import sys from setuptools import setup, find_packages -from typing import Tuple -def package_metadata() -> Tuple[str, str]: - sys.path.insert(0, os.path.join("graspy", "version")) # TODO: #454 Change path in https://github.com/microsoft/graspologic/issues/454 - from version import name, version - sys.path.pop(0) +MINIMUM_PYTHON_VERSION = 3, 6 # Minimum of Python 3.6 - version_path = os.path.join("graspy", "version", "version.txt") - with open(version_path, "w") as version_file: - _b = version_file.write(f"{version}") - return name, version +if sys.version_info < MINIMUM_PYTHON_VERSION: + sys.exit("Python {}.{}+ is required.".format(*MINIMUM_PYTHON_VERSION)) +sys.path.insert(0, os.path.join("graspy", "version")) +# TODO: #454 Change path in https://github.com/microsoft/graspologic/issues/454 +from version import version +sys.path.pop(0) -PACKAGE_NAME, VERSION = package_metadata() +version_path = os.path.join("graspy", "version", "version.txt") +with open(version_path, "w") as version_file: + version_file.write(f"{version}") -DESCRIPTION = "A set of python modules for graph statistics" with open("README.md", "r") as f: LONG_DESCRIPTION = f.read() -AUTHOR = ("Eric Bridgeford, Jaewon Chung, Benjamin Pedigo, Bijan Varjavand",) -AUTHOR_EMAIL = "j1c@jhu.edu" -URL = "https://github.com/neurodata/graspy" -MINIMUM_PYTHON_VERSION = 3, 6 # Minimum of Python 3.6 -REQUIRED_PACKAGES = [ - "networkx>=2.1", - "numpy>=1.8.1", - "scikit-learn>=0.19.1", - "scipy>=1.4.0", - "seaborn>=0.9.0", - "matplotlib>=3.0.0", - "hyppo>=0.1.3", -] - - -def check_python_version(): - """Exit when the Python version is too low.""" - if sys.version_info < MINIMUM_PYTHON_VERSION: - sys.exit("Python {}.{}+ is required.".format(*MINIMUM_PYTHON_VERSION)) - - -check_python_version() setup( - name=PACKAGE_NAME, - version=VERSION, - description=DESCRIPTION, + name="graspy", + version=version, + description="A set of python modules for graph statistics", long_description=LONG_DESCRIPTION, long_description_content_type="text/markdown", - author=AUTHOR, - author_email=AUTHOR_EMAIL, + author="Eric Bridgeford, Jaewon Chung, Benjamin Pedigo, Bijan Varjavand", + author_email="j1c@jhu.edu", maintainer="Dwayne Pryce", maintainer_email="dwpryce@microsoft.com", - install_requires=REQUIRED_PACKAGES, - url=URL, + url="https://github.com/microsoft/graspologic", license="MIT", classifiers=[ "Development Status :: 3 - Alpha", @@ -70,5 +46,31 @@ def check_python_version(): ], packages=find_packages(exclude=["tests", "tests.*", "tests/*"]), include_package_data=True, - package_data={'version': [os.path.join('graspy', 'version', 'version.txt')]}, # TODO: #454 Also needs changed by https://github.com/microsoft/graspologic/issues/454, + package_data={ + "version": [os.path.join("graspy", "version", "version.txt")] + }, # TODO: #454 Also needs changed by https://github.com/microsoft/graspologic/issues/454 + install_requires=[ + "networkx>=2.1", + "numpy>=1.8.1", + "scikit-learn>=0.19.1", + "scipy>=1.4.0", + "seaborn>=0.9.0", + "matplotlib>=3.0.0,<=3.3.0", + "hyppo>=0.1.3", + ], + extras_require={ + "dev": [ + "black", + "ipykernel>=5.1.0", + "ipython>=7.4.0", + "mypy", + "nbsphinx", + "numpydoc", + "pytest", + "pytest-cov", + "sphinx", + "sphinxcontrib-rawfiles", + "sphinx-rtd-theme", + ] + } ) From 0fb6d36839c9510de05ae1aa4eba2e9bf906f1bd Mon Sep 17 00:00:00 2001 From: Paul Adkisson Date: Tue, 22 Sep 2020 12:09:42 -0400 Subject: [PATCH 005/140] Fix the title (#475) --- docs/reference/subgraph.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/reference/subgraph.rst b/docs/reference/subgraph.rst index 803127b8b..0121d5ad1 100644 --- a/docs/reference/subgraph.rst +++ b/docs/reference/subgraph.rst @@ -4,6 +4,6 @@ Subgraph .. currentmodule:: graspy.subgraph Signal-Subgraph Estimators ------------------- +-------------------------- .. autoclass:: SignalSubgraph From 89d0c2016dfe88fd0125e02c040b2740af1c5a93 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 22 Sep 2020 10:54:33 -0700 Subject: [PATCH 006/140] Draft PR: Build Pipeline (#471) * Initial commit of a build, enough to prep the Azure DevOps (ADO) Pipeline * Left a dangling dependsOn in there from the copy/paste * PyTest isn't in the requirements file either. Harumph. * For some reason the coverage check isn't working, but it works for me locally with the same command * First, we'll get test coverage to work. Then, we'll figure out what the hell pytest is talking about in our OS/Python matrix * Ah, I see now - coverage is always being run any time you run tests, which doesn't work since we're doing them in different build steps * Adding component governance over all of our dependencies * After updating our setup.py to include an extra requirements target for developers, I updated the build actions to make use of that in all cases where most of our dependencies were necessary * Now we're going to try building with sphinx and failing the documentation. It's important to note that this is just going to balk if our documentation can't be built due to some sort of sphinx build failures. It isn't going to check for it being visually correct * Maybe script blocks need a display name? * Ah, it wasn't tabbed in enough. Of course that was it. * There's a special place in the netherworld for people who build ANYTHING using whitespace for blocks. This place is not pleasant. * I'm dumb. * Running sphinx build without actually failing the build. Apparently we have warnings we need to fix in our docs. * Okay, new approach - most (and the most important) build actions go through github actions. These are things that are 100% visible to everyone. The stuff we need to do for Microsoft, we put in devops. And so that is that. * Removing travis yaml - WE'RE ALMOST READY FOR YOU TO FOREVER SLEEP, MASTER TRAVIS * If I did a shot for every time I had to commit something just to make CI happy I'd be dead by now * Like I said. * @j1c pointed out that if we don't pytest graspy too the doctests won't run * New job descriptors (previous was just 'install dependencies' for every job which was not helpful) * Update .github/workflows/build.yml Co-authored-by: Benjamin Pedigo * Changing some more of the labels and also clearly noting which ones are currently optional steps * Using square angle brackets may be problematic in yaml * Maybe this will get the output I'm looking for * This should be ready for a test. I explicitly disabled the actual twine upload as we are not ready to actually publish, but we do want to validate our build process in all cases, including a merge to main * Perhaps whitespace is not allowed in expression syntax? * Okay, maybe it's double quotes you're afraid of. I can understand why, it's hard to win in a 2 on 1 battle. * It looks like we have a fix for Sphinx documentation generation, so I am no longer going to assert that the documentation validation step is broken. * Trying to make sure we have pandoc installed. We need to decide if we want to keep that or what. * This time with sudo Co-authored-by: Benjamin Pedigo --- .devops/README.md | 26 +++++++++ .devops/scan-and-register.yml | 25 ++++++++ .github/build/set_version.py | 25 ++++++++ .github/workflows/build.yml | 106 ++++++++++++++++++++++++++++++++++ .travis.yml | 30 ---------- pytest.ini | 2 +- setup.py | 1 + 7 files changed, 184 insertions(+), 31 deletions(-) create mode 100644 .devops/README.md create mode 100644 .devops/scan-and-register.yml create mode 100644 .github/build/set_version.py create mode 100644 .github/workflows/build.yml delete mode 100644 .travis.yml diff --git a/.devops/README.md b/.devops/README.md new file mode 100644 index 000000000..2cb10eb8e --- /dev/null +++ b/.devops/README.md @@ -0,0 +1,26 @@ +# Continuous Integration for `graspologic` + +Microsoft requires - and it's simply good policy - that we run continuous scans +of our builds checking for any accidental commits of credentials, as well as +keeping track of all of our dependencies, looking for security issues as well +as any legal concerns. + +However, using Azure DevOps for our entire build restricts visibility into the +steps we require be completed for a viable build. This makes processing +PRs a Microsoft-first process, instead of enabling our wonderful contributing +community to be able to see the actual build failures as they occur, instead of +relying on a Microsoft employee to convey the error conditions to them. + +To achieve this, we broke our CI process into two primary processes: +1. Community build (via Github Actions) +2. Compliance and security build (via Azure DevOps) + +We want to ensure we're up front about all our processes, so these build specification +files are committed into the repository for community inspection. + +Of the steps included in `Compliance and Security` (Component Governance and Credential Scan), +the only part that should be possible to fail a build is Credential Scan. + +Unfortunately, this will require an approved Microsoft employee to determine the actual +feedback around it, but hopefully this isolation of black box failures to an individual +task will help mitigate most problems. diff --git a/.devops/scan-and-register.yml b/.devops/scan-and-register.yml new file mode 100644 index 000000000..9cb239c2e --- /dev/null +++ b/.devops/scan-and-register.yml @@ -0,0 +1,25 @@ +jobs: + - job: cred_scan + displayName: Credential Scan + pool: + vmImage: "windows-latest" + steps: + - task: CredScan@3 + displayName: 'Run CredScan' + inputs: + outputFormat: sarif + debugMode: false + - job: component_governance + displayName: Component Governance + pool: + vmImage: "ubuntu-latest" + steps: + - task: UsePythonVersion@0 + inputs: + versionSpec: '3.8' + - script: | + pip install -U pip setuptools + pip install -r requirements.txt + pip freeze > requirements.txt + - task: ms.vss-governance-buildtask.governance-build-task-component-detection.ComponentGovernanceComponentDetection@0 + displayName: 'Component Detection' \ No newline at end of file diff --git a/.github/build/set_version.py b/.github/build/set_version.py new file mode 100644 index 000000000..46a23ba96 --- /dev/null +++ b/.github/build/set_version.py @@ -0,0 +1,25 @@ + +""" +Build script that will check some environment variables set by Github during build. +See: https://help.github.com/en/actions/configuring-and-managing-workflows/using-environment-variables#default-environment-variables +If the branch is master or dev, it will generate the appropriate content for version.txt. If it isn't, it returns nothing. Note that the version.txt file is set by bash, not by this file. +""" + +import os +from graspy.version.version import __semver + +ref_key = "GITHUB_REF" +run_id_key = "GITHUB_RUN_ID" +prerelease_ref = "refs/heads/dev" +release_ref = "refs/heads/main" +publish_refs = [prerelease_ref, release_ref] + +environ = os.environ + +if ref_key in environ and run_id_key in environ: + ref = environ[ref_key] + run_id = environ[run_id_key] + if ref == prerelease_ref: + print(f"{__semver}.dev{run_id}") + elif ref == release_ref: + print(f"{__semver}") diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 000000000..8d607e1a9 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,106 @@ +name: Graspologic Continuous Integration +on: [push] +jobs: + build-documentation: + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: "Run Documentation Generation" + run: | + sudo apt install pandoc + pip install -U pip setuptools + pip install -r requirements.txt + sphinx-build -W -a docs/ docs/_build/html + code-format-check: + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: Run Format Check + run: | + pip install -U pip setuptools + pip install -r requirements.txt + black --check --diff ./graspy ./tests + mypy-type-check: + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: "[Optional] Run Type Validation Checks" + run: | + pip install -U pip setuptools + pip install -r requirements.txt + mypy ./graspy + test-coverage: + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: Run Test Coverage + run: | + pip install -U pip setuptools + pip install -r requirements.txt + python -m pytest --co --cov=graspy + unit-and-doc-test: + runs-on: ${{matrix.os}} + continue-on-error: true + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + python_version: [3.6, 3.7, 3.8] + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{matrix.python_version}} + uses: actions/setup-python@v1 + with: + python-version: ${{matrix.python_version}} + - name: Run Unit Tests and Doctests + run: | + pip install -U pip setuptools + pip install -r requirements.txt + pytest tests # note: should be pytest graspy tests, but we're skipping doctests for now and re-enabling them in another PR + publish: + runs-on: ubuntu-latest + needs: [code-format-check, unit-and-doc-test] + if: github.event.pull_request.merged && (github.ref=='main' || github.ref=='dev') + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: Install dependencies + run: | + python -m pip install --upgrade pip setuptools wheel twine + pip install -r requirements.txt + - name: Materialize Fixed Version + run: | + python .github/build/set_version.py > graspy/version/version.txt + cat graspy/version/version.txt + - name: Build with setuptools + run: | + python setup.py build sdist + - name: Publish with twine + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + # twine upload dist/* + echo "Hello" diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index cb3991ce2..000000000 --- a/.travis.yml +++ /dev/null @@ -1,30 +0,0 @@ -os: linux -dist: xenial -language: python -python: -- '3.6' -jobs: - include: - - python: 3.6 - - python: 3.7 -cache: pip -install: -- pip install -r requirements.txt -- pip install -U pytest pytest-cov pytest-doctestplus codecov black -script: -- pytest graspy/ tests/ -- black --check --diff ./graspy ./tests -after_success: -- codecov - -deploy: -- provider: pypi - user: "j1c" - password: - secure: 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 - skip_existing: true - on: - branch: master - tags: true - repo: neurodata/graspy - python: '3.6' diff --git a/pytest.ini b/pytest.ini index 10ea575ef..cb977fcd5 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,5 +1,5 @@ [pytest] -addopts = --cov=graspy --doctest-modules +addopts = --doctest-modules filterwarnings = diff --git a/setup.py b/setup.py index f6f28b180..12d3ea86c 100644 --- a/setup.py +++ b/setup.py @@ -66,6 +66,7 @@ "mypy", "nbsphinx", "numpydoc", + "pandoc", "pytest", "pytest-cov", "sphinx", From dc1105f5ffb8918b598a1db4fd9fcd28a57eae5b Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 22 Sep 2020 11:42:57 -0700 Subject: [PATCH 007/140] Sounds like the refs name will be a full path (#476) I'm merging this because I can only test on merge to dev. --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 8d607e1a9..5154d0f5e 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -78,8 +78,8 @@ jobs: pytest tests # note: should be pytest graspy tests, but we're skipping doctests for now and re-enabling them in another PR publish: runs-on: ubuntu-latest - needs: [code-format-check, unit-and-doc-test] - if: github.event.pull_request.merged && (github.ref=='main' || github.ref=='dev') + needs: [build-documentation, code-format-check, unit-and-doc-test] + if: github.event.pull_request.merged && (github.ref=='refs/heads/main' || github.ref=='refs/heads/dev') steps: - uses: actions/checkout@v2 - name: Set up Python 3.8 From 41f0c350288b7f522006be75a0fcb31e6f69be3e Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 22 Sep 2020 12:08:08 -0700 Subject: [PATCH 008/140] I'm not sure which is failing, and I'm also not convinced we need the 'is pull request merged' check, since we'd want to publish if there is ANY push to main/dev --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 5154d0f5e..6c0720371 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -79,7 +79,7 @@ jobs: publish: runs-on: ubuntu-latest needs: [build-documentation, code-format-check, unit-and-doc-test] - if: github.event.pull_request.merged && (github.ref=='refs/heads/main' || github.ref=='refs/heads/dev') + if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' steps: - uses: actions/checkout@v2 - name: Set up Python 3.8 From ab6e8d048e070e077842f9617d5c9be5758f5816 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Thu, 24 Sep 2020 09:23:41 -0700 Subject: [PATCH 009/140] Adding `pull_request_target` to the list of events that should fire off --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 6c0720371..686254398 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,5 +1,5 @@ name: Graspologic Continuous Integration -on: [push] +on: [push, pull_request_target] jobs: build-documentation: runs-on: ubuntu-latest From 4899cf80994de50dac57f1238b14c9094c48c42e Mon Sep 17 00:00:00 2001 From: kareef928 <51966539+kareef928@users.noreply.github.com> Date: Thu, 24 Sep 2020 13:57:41 -0400 Subject: [PATCH 010/140] Remove * imports in simulations (#431) * remove * imports in simulations * only change for rdpg_corr * Remove all * * Run black * add symmetrize, cartprod imports * Get netlify working * Revert to original * inconsequential change * revert * change 2 * revert * accept change * typo Co-authored-by: Kareef Ullah Co-authored-by: Benjamin Pedigo --- graspy/simulations/__init__.py | 19 ++++++++++++++++--- tests/test_sims.py | 2 +- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/graspy/simulations/__init__.py b/graspy/simulations/__init__.py index f9f8358c4..93370577c 100644 --- a/graspy/simulations/__init__.py +++ b/graspy/simulations/__init__.py @@ -1,6 +1,19 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .simulations import * -from .simulations_corr import * -from .rdpg_corr import * +from .simulations import sample_edges, er_np, er_nm, sbm, rdpg, p_from_latent +from .simulations_corr import sample_edges_corr, er_corr, sbm_corr +from .rdpg_corr import rdpg_corr + +__all__ = [ + "sample_edges", + "er_np", + "er_nm", + "sbm", + "rdpg", + "p_from_latent", + "sample_edges_corr", + "er_corr", + "sbm_corr", + "rdpg_corr", +] diff --git a/tests/test_sims.py b/tests/test_sims.py index 0ee5d145a..a1a6fd3e7 100644 --- a/tests/test_sims.py +++ b/tests/test_sims.py @@ -6,7 +6,7 @@ import numpy as np import networkx as nx from graspy.simulations import * -from graspy.utils.utils import is_symmetric, is_loopless +from graspy.utils.utils import is_symmetric, is_loopless, symmetrize, cartprod def remove_diagonal(A): From 53b567f614e78685f620db1575da19592d597e59 Mon Sep 17 00:00:00 2001 From: Daniel Borders <32661399+dtborders@users.noreply.github.com> Date: Thu, 24 Sep 2020 13:27:21 -0500 Subject: [PATCH 011/140] Fixes Add `n_iter_` attribute after fit to GraphMatch #342 (#432) * added n_iter_ return parameter to fit * updated documentation * updated docs so the autodoc generator updates * updated documentation to include more detail about n_iter_ attribute * inconsequential commit * spelling correction * Update graspy/match/gmp.py Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> * Update graspy/match/gmp.py Co-authored-by: Daniel Borders Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Co-authored-by: Benjamin Pedigo --- graspy/match/gmp.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/graspy/match/gmp.py b/graspy/match/gmp.py index 16e764470..ab981e5cc 100644 --- a/graspy/match/gmp.py +++ b/graspy/match/gmp.py @@ -68,6 +68,10 @@ class GraphMatch(BaseEstimator): score_ : float The objective function value of for the optimal permutation found. + n_iter_ : int + Number of Frank-Wolfe iterations run. If `n_init` > 1, `n_iter_` reflects the number of + iterations performed at the initialization returned. + References ---------- @@ -285,6 +289,7 @@ def f(x): # computing the original optimization function score = score_new perm_inds = np.zeros(n, dtype=int) perm_inds[permutation_A] = permutation_B[perm_inds_new] + best_n_iter = n_iter permutation_A_unshuffle = _unshuffle(permutation_A, n) A = A[np.ix_(permutation_A_unshuffle, permutation_A_unshuffle)] @@ -294,6 +299,7 @@ def f(x): # computing the original optimization function self.perm_inds_ = perm_inds # permutation indices self.score_ = score # objective function value + self.n_iter_ = best_n_iter return self def fit_predict(self, A, B, seeds_A=[], seeds_B=[]): From 8de879125c3d7e29e6b5f5be3ac87c2cf513d3a7 Mon Sep 17 00:00:00 2001 From: spencer-loggia <36051767+spencer-loggia@users.noreply.github.com> Date: Thu, 24 Sep 2020 14:53:02 -0400 Subject: [PATCH 012/140] Adds the Option to Concatenate Embeddings of Directed Graphs (#433) * add option to concatenate directed embeddings * add concat parameter to mase implimentation of fit_transform * add concat param to init instead of fit_transform * fix minor docs formatting * black autformatting * add directed embedding tests to test_pectral_embed.py * refactor test_mase to use fit_transform w/ concat instead of np.hstack * add directed embedding test to test_omni.py * fix docs formatting * add type check for concat param * fix rebase artifact * autoformat * add type check test * autoformat * minor style fixes * fix caps Co-authored-by: Benjamin Pedigo --- graspy/embed/ase.py | 6 ++++ graspy/embed/base.py | 26 ++++++++++++----- graspy/embed/lse.py | 6 ++++ graspy/embed/mase.py | 17 +++++++++--- graspy/embed/omni.py | 17 +++++++++--- tests/test_base_embed.py | 15 ++++++++++ tests/test_mase.py | 8 ++---- tests/test_omni.py | 40 +++++++++++++++++++++++--- tests/test_spectral_embed.py | 54 ++++++++++++++++++++++++++---------- 9 files changed, 151 insertions(+), 38 deletions(-) diff --git a/graspy/embed/ase.py b/graspy/embed/ase.py index 99a41d9f8..d0eee17c0 100644 --- a/graspy/embed/ase.py +++ b/graspy/embed/ase.py @@ -64,6 +64,10 @@ class AdjacencySpectralEmbed(BaseEmbed): before embedding. Empirically, this produces latent position estimates closer to the ground truth. + concat : bool, optional (default False) + If graph is directed, whether to concatenate left and right (out and in) latent positions along axis 1. + + Attributes ---------- @@ -107,6 +111,7 @@ def __init__( n_iter=5, check_lcc=True, diag_aug=True, + concat=False, ): super().__init__( n_components=n_components, @@ -114,6 +119,7 @@ def __init__( algorithm=algorithm, n_iter=n_iter, check_lcc=check_lcc, + concat=concat, ) if not isinstance(diag_aug, bool): diff --git a/graspy/embed/base.py b/graspy/embed/base.py index 8e9b391ec..a332fe018 100644 --- a/graspy/embed/base.py +++ b/graspy/embed/base.py @@ -42,6 +42,9 @@ class BaseEmbed(BaseEstimator): Whether to check if input graph is connected. May result in non-optimal results if the graph is unconnected. Not checking for connectedness may result in faster computation. + concat : bool, optional (default = False) + If graph(s) are directed, whether to concatenate each graph's left and right (out and in) latent positions + along axis 1. Attributes ---------- @@ -60,12 +63,17 @@ def __init__( algorithm="randomized", n_iter=5, check_lcc=True, + concat=False, ): self.n_components = n_components self.n_elbows = n_elbows self.algorithm = algorithm self.n_iter = n_iter self.check_lcc = check_lcc + if not isinstance(concat, bool): + msg = "Parameter `concat` is expected to be type bool" + raise TypeError(msg) + self.concat = concat def _reduce_dim(self, A): """ @@ -126,13 +134,16 @@ def fit(self, graph, y=None): return self def _fit_transform(self, graph): - "Fits the model and returns the estimated latent positions" + "Fits the model and returns the estimated latent positions." self.fit(graph) if self.latent_right_ is None: return self.latent_left_ else: - return self.latent_left_, self.latent_right_ + if self.concat: + return np.concatenate((self.latent_left_, self.latent_right_), axis=1) + else: + return self.latent_left_, self.latent_right_ def fit_transform(self, graph, y=None): """ @@ -147,11 +158,10 @@ def fit_transform(self, graph, y=None): Returns ------- - out : np.ndarray, shape (n_vertices, n_dimension) OR tuple (len 2) - Where both elements have shape (n_vertices, n_dimension) - A single np.ndarray represents the latent position of an undirected - graph, wheras a tuple represents the left and right latent positions - for a directed graph + out : np.ndarray OR length 2 tuple of np.ndarray. + If undirected then returns single np.ndarray of latent position, shape(n_vertices, n_components). + If directed, ``concat`` is True then concatenate latent matrices on axis 1, shape(n_vertices, 2*n_components). + If directed, ``concat`` is False then tuple of the latent matrices. Each of shape (n_vertices, n_components). """ return self._fit_transform(graph) @@ -165,6 +175,7 @@ def __init__( n_iter=5, check_lcc=True, diag_aug=True, + concat=False, ): super().__init__( n_components=n_components, @@ -172,6 +183,7 @@ def __init__( algorithm=algorithm, n_iter=n_iter, check_lcc=check_lcc, + concat=concat, ) if not isinstance(diag_aug, bool): diff --git a/graspy/embed/lse.py b/graspy/embed/lse.py index 0acf0cb2c..9f7d4129f 100644 --- a/graspy/embed/lse.py +++ b/graspy/embed/lse.py @@ -60,6 +60,10 @@ class LaplacianSpectralEmbed(BaseEmbed): node degree is added. If int or float, must be >= 0. Only used when ``form`` == 'R-DAD'. + concat : bool, optional (default False) + If graph is directed, whether to concatenate left and right (out and in) latent positions along axis 1. + + Attributes ---------- latent_left_ : array, shape (n_samples, n_components) @@ -111,6 +115,7 @@ def __init__( n_iter=5, check_lcc=True, regularizer=None, + concat=False, ): super().__init__( n_components=n_components, @@ -118,6 +123,7 @@ def __init__( algorithm=algorithm, n_iter=n_iter, check_lcc=check_lcc, + concat=concat, ) self.form = form self.regularizer = regularizer diff --git a/graspy/embed/mase.py b/graspy/embed/mase.py index bb6a970c3..c5b8b9746 100644 --- a/graspy/embed/mase.py +++ b/graspy/embed/mase.py @@ -64,6 +64,11 @@ class MultipleASE(BaseEmbedMulti): a vector corresponding to the degree (or sum of edge weights for a weighted network) before embedding. + concat : bool, optional (default False) + If graph(s) are directed, whether to concatenate each graph's left and right (out and in) latent positions + along axis 1. + + Attributes ---------- n_graphs_ : int @@ -82,6 +87,7 @@ class MultipleASE(BaseEmbedMulti): scores_ : array, shape (n_samples, n_components, n_components) Estimated :math:`\hat{R}` matrices for each input graph. + Notes ----- When an input graph is directed, `n_components` of `latent_left_` may not be equal @@ -96,6 +102,7 @@ def __init__( n_iter=5, scaled=True, diag_aug=True, + concat=False, ): if not isinstance(scaled, bool): msg = "scaled must be a boolean, not {}".format(scaled) @@ -107,6 +114,7 @@ def __init__( algorithm=algorithm, n_iter=n_iter, diag_aug=diag_aug, + concat=concat, ) self.scaled = scaled @@ -226,9 +234,10 @@ def fit_transform(self, graphs, y=None): Returns ------- - out : array-like, shape (n_vertices, n_components) if input - graphs were symmetric. If graphs were directed, returns tuple of - two arrays (same shape as above) where the first corresponds to the - left latent positions, and the right to the right latent positions + out : np.ndarray or length 2 tuple of np.ndarray. + If input graphs were symmetric shape (n_vertices, n_components). + If graphs were directed and ``concat`` is False, returns tuple of two arrays (same shape as above). + The first corresponds to the left latent positions, and the second to the right latent positions. + When ``concat`` is True left and right (out and in) latent positions are concatenated along axis 1. """ return self._fit_transform(graphs) diff --git a/graspy/embed/omni.py b/graspy/embed/omni.py index fcf9e479b..1ee30f9b8 100644 --- a/graspy/embed/omni.py +++ b/graspy/embed/omni.py @@ -91,6 +91,10 @@ class OmnibusEmbed(BaseEmbedMulti): a vector corresponding to the degree (or sum of edge weights for a weighted network) before embedding. + concat : bool, optional (default False) + If graph(s) are directed, whether to concatenate each graph's left and right (out and in) latent positions + along axis 1. + Attributes ---------- n_graphs_ : int @@ -110,6 +114,7 @@ class OmnibusEmbed(BaseEmbedMulti): singular_values_ : array, shape (n_components) Singular values associated with the latent position matrices. + See Also -------- graspy.embed.selectSVD @@ -131,6 +136,7 @@ def __init__( n_iter=5, check_lcc=True, diag_aug=True, + concat=False, ): super().__init__( n_components=n_components, @@ -139,6 +145,7 @@ def __init__( n_iter=n_iter, check_lcc=check_lcc, diag_aug=diag_aug, + concat=concat, ) def fit(self, graphs, y=None): @@ -204,9 +211,11 @@ def fit_transform(self, graphs, y=None): Returns ------- - out : array-like, shape (n_graphs, n_vertices, n_components) if input - graphs were symmetric. If graphs were directed, returns tuple of - two arrays (same shape as above) where the first corresponds to the - left latent positions, and the right to the right latent positions + out : np.ndarray or length 2 tuple of np.ndarray. + If input graphs were symmetric, ndarray of shape (n_graphs, n_vertices, n_components). + If graphs were directed and ``concat`` is False, returns tuple of two arrays (same shape as above). + The first corresponds to the left latent positions, and the second to the right latent positions. + If graphs were directed and ``concat`` is True, left and right (out and in) latent positions are concatenated. + In this case one tensor of shape (n_graphs, n_vertices, 2*n_components) is returned. """ return self._fit_transform(graphs) diff --git a/tests/test_base_embed.py b/tests/test_base_embed.py index 125b29297..9715f71b9 100644 --- a/tests/test_base_embed.py +++ b/tests/test_base_embed.py @@ -39,6 +39,17 @@ def test_baseembed_er_directed(self): self.assertEqual(embed.latent_right_.shape, (n, n_components)) self.assertTrue(embed.latent_right_ is not None) + def test_baseembed_er_directed_concat(self): + n_components = 4 + embed = BaseEmbed(n_components=n_components, concat=True) + n = 10 + M = 20 + A = er_nm(n, M, directed=True) + embed._reduce_dim(A) + out = embed.fit_transform(A) + self.assertEqual(out.shape, (n, 2 * n_components)) + self.assertTrue(embed.latent_right_ is not None) + def test_baseembed(self): embed = BaseEmbed(n_components=None) n = 10 @@ -60,3 +71,7 @@ def test_algorithms(self): embed = BaseEmbed(n_components=self.n, algorithm="randomized") with self.assertRaises(ValueError): embed._reduce_dim(self.A) + + def test_input_checks(self): + with self.assertRaises(TypeError): + BaseEmbed(n_components=self.n, concat=42) diff --git a/tests/test_mase.py b/tests/test_mase.py index bc6e3660d..b9d9a8851 100644 --- a/tests/test_mase.py +++ b/tests/test_mase.py @@ -132,17 +132,15 @@ def run(diag_aug, scaled): # undirected case np.random.seed(4 + diag_aug + scaled) X = make_train_undirected(n, m) - - res = MultipleASE(n_components=2).fit(X).latent_left_ + # undirected case + res = MultipleASE(n_components=2).fit_transform(X) gmm = GaussianCluster(10, covariance_type="all").fit(res) assert gmm.n_components_ == 2 # directed case np.random.seed(5 + diag_aug + scaled) X = make_train_directed(n, m) - - mase = MultipleASE(n_components=2).fit(X) - res = np.hstack([mase.latent_left_, mase.latent_right_]) + res = MultipleASE(n_components=2, concat=True).fit_transform(X) gmm = GaussianCluster(10, covariance_type="all").fit(res) assert gmm.n_components_ == 2 diff --git a/tests/test_omni.py b/tests/test_omni.py index 276e6bb05..a070495a9 100755 --- a/tests/test_omni.py +++ b/tests/test_omni.py @@ -8,11 +8,11 @@ from numpy.testing import assert_allclose from graspy.embed.omni import OmnibusEmbed, _get_omni_matrix -from graspy.simulations.simulations import er_nm, er_np +from graspy.simulations.simulations import er_nm, er_np, sbm from graspy.utils.utils import is_symmetric, symmetrize -def generate_data(n, seed=1): +def generate_data(n, seed=1, symetric=True): """Generate data form dirichelet distribution with n numbers of points """ @@ -27,8 +27,11 @@ def generate_data(n, seed=1): P = np.dot(X, X.T) # Generate two adjacencies - A1 = symmetrize(np.random.binomial(1, P)) - A2 = symmetrize(np.random.binomial(1, P)) + A1 = np.random.binomial(1, P) + A2 = np.random.binomial(1, P) + if symetric: + A1 = symmetrize(A1) + A2 = symmetrize(A2) return (X, A1, A2) @@ -158,3 +161,32 @@ def run(diag_aug): run(diag_aug=True) run(diag_aug=False) + + +def test_omni_embed_directed(): + """ + We compare the difference of norms of OmniBar and ABar. + ABar is the lowest variance estimate of the latent positions X. + OmniBar should be reasonablly close to ABar when n_vertices is high. + """ + + def compute_bar(arr): + n = arr.shape[0] // 2 + return (arr[:n] + arr[n:]) / 2 + + def run(diag_aug): + X, A1, A2 = generate_data(n=1000, symetric=False) + Abar = (A1 + A2) / 2 + + np.random.seed(11) + omni = OmnibusEmbed(n_components=3, concat=True, diag_aug=diag_aug) + OmniBar = compute_bar(omni.fit_transform([A2, A2])) + + omni = OmnibusEmbed(n_components=3, concat=True, diag_aug=diag_aug) + ABar = compute_bar(omni.fit_transform([Abar, Abar])) + + tol = 1.0e-2 + assert allclose(norm(OmniBar, axis=1), norm(ABar, axis=1), rtol=tol, atol=tol) + + run(diag_aug=True) + run(diag_aug=False) diff --git a/tests/test_spectral_embed.py b/tests/test_spectral_embed.py index 9eecaa5f9..bffc75268 100644 --- a/tests/test_spectral_embed.py +++ b/tests/test_spectral_embed.py @@ -43,6 +43,17 @@ def _kmeans_comparison(data, labels, n_clusters): return aris +def _test_output_dim_directed(self, method): + n_components = 4 + embed = method(n_components=n_components, concat=True) + n = 10 + M = 20 + A = er_nm(n, M, directed=True) + 5 + self.assertEqual(embed.fit_transform(A).shape, (n, 8)) + self.assertEqual(embed.latent_left_.shape, (n, 4)) + self.assertEqual(embed.latent_right_.shape, (n, 4)) + + def _test_output_dim(self, method, *args, **kwargs): n_components = 4 embed = method(n_components=n_components) @@ -54,7 +65,7 @@ def _test_output_dim(self, method, *args, **kwargs): self.assertTrue(embed.latent_right_ is None) -def _test_sbm_er_binary_undirected(self, method, P, *args, **kwargs): +def _test_sbm_er_binary(self, method, P, directed=False, *args, **kwargs): np.random.seed(8888) num_sims = 50 @@ -66,24 +77,25 @@ def _test_sbm_er_binary_undirected(self, method, P, *args, **kwargs): sbm_wins = 0 er_wins = 0 for sim in range(0, num_sims): - sbm_sample = sbm(verts_per_community, P) - er = er_np(verts, 0.5) - embed_sbm = method(n_components=2) - embed_er = method(n_components=2) + sbm_sample = sbm(verts_per_community, P, directed=directed) + er = er_np(verts, 0.5, directed=directed) + embed_sbm = method(n_components=2, concat=directed) + embed_er = method(n_components=2, concat=directed) labels_sbm = np.zeros((verts), dtype=np.int8) labels_er = np.zeros((verts), dtype=np.int8) labels_sbm[100:] = 1 labels_er[100:] = 1 - embed_sbm.fit(sbm_sample) - embed_er.fit(er) - - X_sbm = embed_sbm.latent_left_ - X_er = embed_er.latent_left_ + X_sbm = embed_sbm.fit_transform(sbm_sample) + X_er = embed_er.fit_transform(er) - self.assertEqual(X_sbm.shape, (verts, communities)) - self.assertEqual(X_er.shape, (verts, communities)) + if directed: + self.assertEqual(X_sbm.shape, (verts, 2 * communities)) + self.assertEqual(X_er.shape, (verts, 2 * communities)) + else: + self.assertEqual(X_sbm.shape, (verts, communities)) + self.assertEqual(X_er.shape, (verts, communities)) aris = _kmeans_comparison((X_sbm, X_er), (labels_sbm, labels_er), communities) sbm_wins = sbm_wins + (aris[0] > aris[1]) @@ -96,9 +108,16 @@ class TestAdjacencySpectralEmbed(unittest.TestCase): def test_output_dim(self): _test_output_dim(self, AdjacencySpectralEmbed) + def test_output_dim_directed(self): + _test_output_dim_directed(self, AdjacencySpectralEmbed) + def test_sbm_er_binary_undirected(self): P = np.array([[0.8, 0.2], [0.2, 0.8]]) - _test_sbm_er_binary_undirected(self, AdjacencySpectralEmbed, P) + _test_sbm_er_binary(self, AdjacencySpectralEmbed, P, directed=False) + + def test_sbm_er_binary_directed(self): + P = np.array([[0.8, 0.2], [0.2, 0.8]]) + _test_sbm_er_binary(self, AdjacencySpectralEmbed, P, directed=True) def test_unconnected_warning(self): A = er_nm(100, 10) @@ -116,9 +135,16 @@ class TestLaplacianSpectralEmbed(unittest.TestCase): def test_output_dim(self): _test_output_dim(self, LaplacianSpectralEmbed) + def test_output_dim_directed(self): + _test_output_dim_directed(self, LaplacianSpectralEmbed) + def test_sbm_er_binary_undirected(self): P = np.array([[0.8, 0.2], [0.2, 0.3]]) - _test_sbm_er_binary_undirected(self, LaplacianSpectralEmbed, P) + _test_sbm_er_binary(self, LaplacianSpectralEmbed, P, directed=False) + + def test_sbm_er_binary_directed(self): + P = np.array([[0.8, 0.2], [0.2, 0.3]]) + _test_sbm_er_binary(self, LaplacianSpectralEmbed, P, directed=True) def test_different_forms(self): f = np.array([[1, 2], [2, 1]]) From 16ee8fdc9d18000b55206de0068647d443a20729 Mon Sep 17 00:00:00 2001 From: Jingyan230 <69659070+Jingyan230@users.noreply.github.com> Date: Fri, 25 Sep 2020 04:02:57 +0800 Subject: [PATCH 013/140] Add a link for the descriptions of `palette` (#427) * Add * add * improvement on adding links * Improvement on adding a link 2.0 * Improvement of adding a link 2.1 * Add Matplotlib in intersphinx * Add the link * Changing the link * Changing the link 1.2 * Changing the conf * fix the error * rerun * add matpoltlib in conf * Change the forms of adding links * fix error in conf * change the link in conf * change the form of the links * fix error in conf * change the link in conf * Change the form of link in plot * change the form of links in plot * change the form of links in plot * change the form in conf and plot * Change the links in conf and plot * Change the form of links in conf and plot * change the form of links in plot * Change the form of links in plot * Change the form of link to colormaps in plot.py * Change the link form of marker in pairplot * Confirm the links * Slightly modify wording * minor grammar changes * Minor change I missed Co-authored-by: Benjamin Pedigo --- docs/conf.py | 1 + graspy/plot/plot.py | 14 +++++++++++--- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 3c90b85ba..8194330ee 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -71,6 +71,7 @@ "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), "sklearn": ("http://scikit-learn.org/dev", None), "hyppo": ("https://hyppo.neurodata.io", None), + "matplotlib": ('https://matplotlib.org', None) } # -- sphinx options ---------------------------------------------------------- diff --git a/graspy/plot/plot.py b/graspy/plot/plot.py index 482197833..2ab66561a 100644 --- a/graspy/plot/plot.py +++ b/graspy/plot/plot.py @@ -404,7 +404,9 @@ def gridplot( elements. palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should - be values in the hue variable + be values in the hue variable. + For acceptable string arguments, see the palette options at + :doc:`Choosing Colormaps in Matplotlib `. alpha : float [0, 1], default : 0.7 Alpha value of plotted gridplot points sizes : length 2 tuple, default: (10, 200) @@ -568,13 +570,15 @@ def pairplot( palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should be values in the hue variable. + For acceptable string arguments, see the palette options at + :doc:`Choosing Colormaps in Matplotlib `. alpha : float, optional, default: 0.7 Opacity value of plotter markers between 0 and 1 size : float or int, optional, default: 50 Size of plotted markers. marker : string, optional, default: '.' - Matplotlib style marker specification - https://matplotlib.org/api/markers_api.html + Matplotlib marker specifier, see the marker options at + :doc:`Matplotlib style marker specification ` """ _check_common_inputs( height=height, @@ -744,6 +748,8 @@ def degreeplot( palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should be values in the hue variable. + For acceptable string arguments, see the palette options at + :doc:`Choosing Colormaps in Matplotlib `. figsize : tuple of length 2, default (10, 5) Size of the figure (width, height) @@ -811,6 +817,8 @@ def edgeplot( palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should be values in the hue variable. + For acceptable string arguments, see the palette options at + :doc:`Choosing Colormaps in Matplotlib `. figsize : tuple of length 2, default (10, 5) Size of the figure (width, height) From e8f4e26f2dc4433657dfe1c7ed47eced2b87a3bb Mon Sep 17 00:00:00 2001 From: bdpedigo Date: Thu, 24 Sep 2020 16:28:01 -0400 Subject: [PATCH 014/140] fix some formatting issues I somehow missed --- docs/conf.py | 2 +- graspy/plot/plot.py | 2 +- setup.py | 3 ++- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 8194330ee..dbf86bd45 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -71,7 +71,7 @@ "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), "sklearn": ("http://scikit-learn.org/dev", None), "hyppo": ("https://hyppo.neurodata.io", None), - "matplotlib": ('https://matplotlib.org', None) + "matplotlib": ("https://matplotlib.org", None), } # -- sphinx options ---------------------------------------------------------- diff --git a/graspy/plot/plot.py b/graspy/plot/plot.py index 2ab66561a..efd49ce72 100644 --- a/graspy/plot/plot.py +++ b/graspy/plot/plot.py @@ -577,7 +577,7 @@ def pairplot( size : float or int, optional, default: 50 Size of plotted markers. marker : string, optional, default: '.' - Matplotlib marker specifier, see the marker options at + Matplotlib marker specifier, see the marker options at :doc:`Matplotlib style marker specification ` """ _check_common_inputs( diff --git a/setup.py b/setup.py index 12d3ea86c..7a7aee435 100644 --- a/setup.py +++ b/setup.py @@ -14,6 +14,7 @@ sys.path.insert(0, os.path.join("graspy", "version")) # TODO: #454 Change path in https://github.com/microsoft/graspologic/issues/454 from version import version + sys.path.pop(0) version_path = os.path.join("graspy", "version", "version.txt") @@ -73,5 +74,5 @@ "sphinxcontrib-rawfiles", "sphinx-rtd-theme", ] - } + }, ) From e9a9d6f81451a2b65882bf0231d49140b5617151 Mon Sep 17 00:00:00 2001 From: Ze Ou <38440136+zeou1@users.noreply.github.com> Date: Sun, 27 Sep 2020 18:03:17 -0400 Subject: [PATCH 015/140] Added n_features_in_ to three of the embedded class estimators (continued from #434) (#462) * added n_features_in_ to all four estimator classes * to trigger netlify * revert back for netlify * Fix grammar Co-authored-by: zeou1 Co-authored-by: Benjamin Pedigo --- graspy/embed/ase.py | 3 +++ graspy/embed/base.py | 2 ++ graspy/embed/lse.py | 4 ++++ graspy/embed/mds.py | 4 ++++ 4 files changed, 13 insertions(+) diff --git a/graspy/embed/ase.py b/graspy/embed/ase.py index d0eee17c0..03be2dd3c 100644 --- a/graspy/embed/ase.py +++ b/graspy/embed/ase.py @@ -71,6 +71,8 @@ class AdjacencySpectralEmbed(BaseEmbed): Attributes ---------- + n_features_in_: int + Number of features passed to the fit method. latent_left_ : array, shape (n_samples, n_components) Estimated left latent positions of the graph. latent_right_ : array, shape (n_samples, n_components), or None @@ -154,5 +156,6 @@ def fit(self, graph, y=None): if self.diag_aug: A = augment_diagonal(A) + self.n_features_in_ = len(A) self._reduce_dim(A) return self diff --git a/graspy/embed/base.py b/graspy/embed/base.py index a332fe018..5973e0525 100644 --- a/graspy/embed/base.py +++ b/graspy/embed/base.py @@ -50,6 +50,8 @@ class BaseEmbed(BaseEstimator): ---------- n_components_ : int Dimensionality of the embedded space. + n_features_in_: int + Number of features passed to the fit method. See Also -------- diff --git a/graspy/embed/lse.py b/graspy/embed/lse.py index 9f7d4129f..cb65c9a47 100644 --- a/graspy/embed/lse.py +++ b/graspy/embed/lse.py @@ -66,6 +66,9 @@ class LaplacianSpectralEmbed(BaseEmbed): Attributes ---------- + n_features_in_: int + Number of features passed to the fit method. + latent_left_ : array, shape (n_samples, n_components) Estimated left latent positions of the graph. @@ -157,6 +160,7 @@ def fit(self, graph, y=None): ) warnings.warn(msg, UserWarning) + self.n_features_in_ = len(A) L_norm = to_laplace(A, form=self.form, regularizer=self.regularizer) self._reduce_dim(L_norm) return self diff --git a/graspy/embed/mds.py b/graspy/embed/mds.py index 0ffb67398..34f9ede31 100644 --- a/graspy/embed/mds.py +++ b/graspy/embed/mds.py @@ -60,6 +60,9 @@ class ClassicalMDS(BaseEstimator): Equals the parameter n_components. If input n_components was None, then equals the optimal embedding dimension. + n_features_in_: int + Number of features passed to the fit method. + components_ : array, shape (n_components, n_features) Principal axes in feature space. @@ -192,6 +195,7 @@ def fit(self, X, y=None): self.components_ = U self.singular_values_ = D ** 0.5 self.dissimilarity_matrix_ = dissimilarity_matrix + self.n_features_in_ = X.shape[1] return self From d1d3d83c7b97050450bafd2b145bf7a0d784b992 Mon Sep 17 00:00:00 2001 From: dfrancisco1998 <40680427+dfrancisco1998@users.noreply.github.com> Date: Sun, 27 Sep 2020 18:50:48 -0400 Subject: [PATCH 016/140] Fix to Remove * imports in subgraph, utils (#488) * Update .travis.yml to incorporate python 3.8 * removed import * * removed python 3.8 from .travis.yml * added in the MS lic and fix #461 * hopefully able to merge now Co-authored-by: Dwayne Pryce Co-authored-by: Benjamin Pedigo --- graspy/subgraph/__init__.py | 5 ++++- graspy/utils/__init__.py | 41 +++++++++++++++++++++++++++++++++++-- 2 files changed, 43 insertions(+), 3 deletions(-) diff --git a/graspy/subgraph/__init__.py b/graspy/subgraph/__init__.py index 2b47f93d0..4e839321b 100644 --- a/graspy/subgraph/__init__.py +++ b/graspy/subgraph/__init__.py @@ -1,4 +1,7 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .sg import * + +from .sg import SignalSubgraph + +__all__ = [SignalSubgraph] diff --git a/graspy/utils/__init__.py b/graspy/utils/__init__.py index dc17f9145..ac247bdee 100644 --- a/graspy/utils/__init__.py +++ b/graspy/utils/__init__.py @@ -1,5 +1,42 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .utils import * -from .ptr import * +from .utils import ( + import_graph, + import_edgelist, + is_symmetric, + is_loopless, + is_unweighted, + is_almost_symmetric, + symmetrize, + remove_loops, + to_laplace, + is_fully_connected, + get_lcc, + get_multigraph_union_lcc, + get_multigraph_intersect_lcc, + augment_diagonal, + binarize, + cartprod, +) +from .ptr import pass_to_ranks + +__all__ = [ + import_graph, + import_edgelist, + is_symmetric, + is_loopless, + is_unweighted, + is_almost_symmetric, + symmetrize, + remove_loops, + to_laplace, + is_fully_connected, + get_lcc, + get_multigraph_union_lcc, + get_multigraph_intersect_lcc, + augment_diagonal, + binarize, + cartprod, + pass_to_ranks, +] From 240b913560c6dc7437750fc6a519645453556b3c Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 28 Sep 2020 10:14:16 -0700 Subject: [PATCH 017/140] THE GRAND RENAMING HAS BEGUN (#481) * THE GRAND RENAMING HAS BEGUN but holy crap it still doesn't work because of some nbsphinx thing that I don't know how to even begin troubleshooting * Update .github/PULL_REQUEST_TEMPLATE.md I am the goo0dest typer Co-authored-by: Benjamin Pedigo * Update README.md Co-authored-by: Benjamin Pedigo * Make the build status badge less obnoxious * Made a sentence actually make sense * Ah the last merge from dev must have overwritten some of the changes I made. This should be fixed now. * Found another instance of graspy in the issue template * Some last second changes, including a fix to the utils init file because the __all__ value was being populated by identifier names not string representations of those identifier names * I approve of black hating the single quotes for a string because I also hate it but it's still pythonic even if I wish it weren't so Co-authored-by: Benjamin Pedigo --- .github/ISSUE_TEMPLATE.md | 6 +- .github/PULL_REQUEST_TEMPLATE.md | 3 +- .github/build/set_version.py | 2 +- .github/workflows/build.yml | 14 +- CONTRIBUTING.md | 4 +- MANIFEST.in | 6 +- Makefile | 18 ++ README.md | 85 ++++--- docs/conf.py | 26 +-- docs/contributing.rst | 169 +------------- docs/index.rst | 39 ++-- docs/install.rst | 29 ++- docs/license.rst | 216 ++---------------- docs/reference/cluster.rst | 4 +- docs/reference/datasets.rst | 4 +- docs/reference/embed.rst | 4 +- docs/reference/inference.rst | 4 +- docs/reference/match.rst | 2 +- docs/reference/models.rst | 4 +- docs/reference/pipeline.rst | 2 +- docs/reference/plotting.rst | 4 +- docs/reference/simulations.rst | 2 +- docs/reference/subgraph.rst | 2 +- docs/reference/utils.rst | 2 +- docs/release.rst | 4 + .../embedding/AdjacencySpectralEmbed.ipynb | 6 +- docs/tutorials/embedding/Omnibus.ipynb | 8 +- .../inference/latent_distribution_test.ipynb | 10 +- .../inference/latent_position_test.ipynb | 12 +- docs/tutorials/matching/faq.ipynb | 6 +- docs/tutorials/matching/sgm.ipynb | 6 +- docs/tutorials/models/models.ipynb | 20 +- docs/tutorials/plotting/gridplot.ipynb | 6 +- docs/tutorials/plotting/heatmaps.ipynb | 6 +- docs/tutorials/plotting/pairplot.ipynb | 8 +- docs/tutorials/simulations/corr.ipynb | 8 +- docs/tutorials/simulations/erdos_renyi.ipynb | 6 +- docs/tutorials/simulations/rdpg.ipynb | 8 +- docs/tutorials/simulations/rdpg_corr.ipynb | 6 +- docs/tutorials/simulations/sbm.ipynb | 6 +- docs/tutorials/subgraph/subgraph.ipynb | 4 +- {graspy => graspologic}/__init__.py | 20 +- {graspy => graspologic}/cluster/__init__.py | 0 {graspy => graspologic}/cluster/autogmm.py | 4 +- {graspy => graspologic}/cluster/base.py | 0 {graspy => graspologic}/cluster/gclust.py | 0 {graspy => graspologic}/cluster/kclust.py | 0 {graspy => graspologic}/datasets/__init__.py | 0 {graspy => graspologic}/datasets/base.py | 0 .../datasets/drosophila/left_adjacency.csv | 0 .../datasets/drosophila/left_cell_labels.csv | 0 .../datasets/drosophila/right_adjacency.csv | 0 .../datasets/drosophila/right_cell_labels.csv | 0 {graspy => graspologic}/embed/__init__.py | 0 {graspy => graspologic}/embed/ase.py | 12 +- {graspy => graspologic}/embed/base.py | 2 +- {graspy => graspologic}/embed/lse.py | 14 +- {graspy => graspologic}/embed/mase.py | 4 +- {graspy => graspologic}/embed/mds.py | 4 +- {graspy => graspologic}/embed/omni.py | 10 +- {graspy => graspologic}/embed/svd.py | 4 +- {graspy => graspologic}/inference/__init__.py | 0 {graspy => graspologic}/inference/base.py | 0 .../inference/latent_distribution_test.py | 2 +- .../inference/latent_position_test.py | 6 +- {graspy => graspologic}/match/__init__.py | 0 {graspy => graspologic}/match/gmp.py | 0 {graspy => graspologic}/match/skp.py | 0 {graspy => graspologic}/models/__init__.py | 0 {graspy => graspologic}/models/base.py | 0 {graspy => graspologic}/models/er.py | 12 +- {graspy => graspologic}/models/rdpg.py | 10 +- {graspy => graspologic}/models/sbm.py | 20 +- {graspy => graspologic}/pipeline/__init__.py | 0 {graspy => graspologic}/pipeline/mug2vec.py | 8 +- {graspy => graspologic}/plot/__init__.py | 0 {graspy => graspologic}/plot/plot.py | 0 .../simulations/__init__.py | 0 .../simulations/rdpg_corr.py | 2 +- .../simulations/simulations.py | 0 .../simulations/simulations_corr.py | 2 +- {graspy => graspologic}/subgraph/__init__.py | 0 {graspy => graspologic}/subgraph/sg.py | 0 {graspy => graspologic}/utils/__init__.py | 34 +-- {graspy => graspologic}/utils/ptr.py | 0 {graspy => graspologic}/utils/utils.py | 0 {graspy => graspologic}/version/__init__.py | 0 {graspy => graspologic}/version/version.py | 4 +- graspologic/version/version.txt | 1 + graspy/version/version.txt | 0 setup.py | 11 +- tests/cluster/test_autogmm.py | 12 +- tests/cluster/test_gclust.py | 8 +- tests/cluster/test_kclust.py | 2 +- tests/match/test_match.py | 4 +- tests/pipeline/test_mug2vec.py | 6 +- tests/test_base_embed.py | 6 +- tests/test_datasets.py | 2 +- tests/test_io.py | 2 +- tests/test_latentdistributiontest.py | 6 +- tests/test_latentpositiontest.py | 6 +- tests/test_mase.py | 6 +- tests/test_mds.py | 2 +- tests/test_models.py | 6 +- tests/test_omni.py | 6 +- tests/test_plot.py | 4 +- tests/test_ptr.py | 2 +- tests/test_rdpg_corr.py | 4 +- tests/test_select_dimension.py | 4 +- tests/test_sg.py | 2 +- tests/test_sims.py | 6 +- tests/test_sims_corr.py | 6 +- tests/test_sklearn.py | 8 +- tests/test_spectral_embed.py | 6 +- tests/test_svd.py | 4 +- tests/test_utils.py | 2 +- 116 files changed, 399 insertions(+), 690 deletions(-) create mode 100644 Makefile rename {graspy => graspologic}/__init__.py (56%) rename {graspy => graspologic}/cluster/__init__.py (100%) rename {graspy => graspologic}/cluster/autogmm.py (99%) rename {graspy => graspologic}/cluster/base.py (100%) rename {graspy => graspologic}/cluster/gclust.py (100%) rename {graspy => graspologic}/cluster/kclust.py (100%) rename {graspy => graspologic}/datasets/__init__.py (100%) rename {graspy => graspologic}/datasets/base.py (100%) rename {graspy => graspologic}/datasets/drosophila/left_adjacency.csv (100%) rename {graspy => graspologic}/datasets/drosophila/left_cell_labels.csv (100%) rename {graspy => graspologic}/datasets/drosophila/right_adjacency.csv (100%) rename {graspy => graspologic}/datasets/drosophila/right_cell_labels.csv (100%) rename {graspy => graspologic}/embed/__init__.py (100%) rename {graspy => graspologic}/embed/ase.py (93%) rename {graspy => graspologic}/embed/base.py (99%) rename {graspy => graspologic}/embed/lse.py (93%) rename {graspy => graspologic}/embed/mase.py (98%) rename {graspy => graspologic}/embed/mds.py (98%) rename {graspy => graspologic}/embed/omni.py (96%) rename {graspy => graspologic}/embed/svd.py (98%) rename {graspy => graspologic}/inference/__init__.py (100%) rename {graspy => graspologic}/inference/base.py (100%) rename {graspy => graspologic}/inference/latent_distribution_test.py (99%) rename {graspy => graspologic}/inference/latent_position_test.py (98%) rename {graspy => graspologic}/match/__init__.py (100%) rename {graspy => graspologic}/match/gmp.py (100%) rename {graspy => graspologic}/match/skp.py (100%) rename {graspy => graspologic}/models/__init__.py (100%) rename {graspy => graspologic}/models/base.py (100%) rename {graspy => graspologic}/models/er.py (95%) rename {graspy => graspologic}/models/rdpg.py (95%) rename {graspy => graspologic}/models/sbm.py (96%) rename {graspy => graspologic}/pipeline/__init__.py (100%) rename {graspy => graspologic}/pipeline/mug2vec.py (97%) rename {graspy => graspologic}/plot/__init__.py (100%) rename {graspy => graspologic}/plot/plot.py (100%) rename {graspy => graspologic}/simulations/__init__.py (100%) rename {graspy => graspologic}/simulations/rdpg_corr.py (98%) rename {graspy => graspologic}/simulations/simulations.py (100%) rename {graspy => graspologic}/simulations/simulations_corr.py (99%) rename {graspy => graspologic}/subgraph/__init__.py (100%) rename {graspy => graspologic}/subgraph/sg.py (100%) rename {graspy => graspologic}/utils/__init__.py (56%) rename {graspy => graspologic}/utils/ptr.py (100%) rename {graspy => graspologic}/utils/utils.py (100%) rename {graspy => graspologic}/version/__init__.py (100%) rename {graspy => graspologic}/version/version.py (89%) create mode 100644 graspologic/version/version.txt delete mode 100644 graspy/version/version.txt diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md index 6e084e9af..5c385e57d 100644 --- a/.github/ISSUE_TEMPLATE.md +++ b/.github/ISSUE_TEMPLATE.md @@ -1,5 +1,5 @@ ## Expected Behavior @@ -9,7 +9,7 @@ Tell us what should happen Tell us what happens instead ## Template Code -```GraSPy +```graspologic Paste the template code (ideally a minimal example) that causes the issue ``` @@ -22,4 +22,4 @@ Paste the full traceback in case there is an exception ## Your Environment * Python version: -* GraSPy version: +* graspologic version: diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 739f8744f..47ccd570a 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,6 +1,6 @@ #### Reference Issues/PRs @@ -16,4 +16,3 @@ is merged. See https://github.com/blog/1506-closing-issues-via-pull-requests #### Any other comments? - diff --git a/.github/build/set_version.py b/.github/build/set_version.py index 46a23ba96..7ef21e5e0 100644 --- a/.github/build/set_version.py +++ b/.github/build/set_version.py @@ -6,7 +6,7 @@ """ import os -from graspy.version.version import __semver +from graspologic.version.version import __semver ref_key = "GITHUB_REF" run_id_key = "GITHUB_RUN_ID" diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 686254398..662e76048 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,4 +1,4 @@ -name: Graspologic Continuous Integration +name: graspologic CI on: [push, pull_request_target] jobs: build-documentation: @@ -29,7 +29,7 @@ jobs: run: | pip install -U pip setuptools pip install -r requirements.txt - black --check --diff ./graspy ./tests + black --check --diff ./graspologic ./tests mypy-type-check: runs-on: ubuntu-latest continue-on-error: true @@ -43,7 +43,7 @@ jobs: run: | pip install -U pip setuptools pip install -r requirements.txt - mypy ./graspy + mypy ./graspologic test-coverage: runs-on: ubuntu-latest continue-on-error: true @@ -57,7 +57,7 @@ jobs: run: | pip install -U pip setuptools pip install -r requirements.txt - python -m pytest --co --cov=graspy + python -m pytest --co --cov=graspologic unit-and-doc-test: runs-on: ${{matrix.os}} continue-on-error: true @@ -75,7 +75,7 @@ jobs: run: | pip install -U pip setuptools pip install -r requirements.txt - pytest tests # note: should be pytest graspy tests, but we're skipping doctests for now and re-enabling them in another PR + pytest tests # note: should be pytest graspologic tests, but we're skipping doctests for now and re-enabling them in another PR publish: runs-on: ubuntu-latest needs: [build-documentation, code-format-check, unit-and-doc-test] @@ -92,8 +92,8 @@ jobs: pip install -r requirements.txt - name: Materialize Fixed Version run: | - python .github/build/set_version.py > graspy/version/version.txt - cat graspy/version/version.txt + python .github/build/set_version.py > graspologic/version/version.txt + cat graspologic/version/version.txt - name: Build with setuptools run: | python setup.py build sdist diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6daad36c6..37b27dcfa 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -51,7 +51,7 @@ follow these guidelines! This will make it a lot faster for us to respond to you import platform; print(platform.platform()) import sys; print(f"Python {sys.version}") - import graspy; print(f"GraSPy {graspy.__version__}") + import graspologic; print(f"graspologic {graspologic.__version__}") - Please ensure all **code snippets and error messages are formatted in appropriate code blocks**. See @@ -114,7 +114,7 @@ We recommended that your contribution complies with the following rules before y - All functions and classes must have unit tests. These should include, at the very least, type checking and ensuring correct computation/outputs. - All functions and classes should be rigorously typed with Python 3.5+ - [`typehinting`](https://docs.python.org/3/library/typing.html). Validate your typehinting by running `mypy ./graspy` + [`typehinting`](https://docs.python.org/3/library/typing.html). Validate your typehinting by running `mypy ./graspologic` - All code should be automatically formatted by `black`. You can run this formatter by calling: ```bash pip install black diff --git a/MANIFEST.in b/MANIFEST.in index 6ab442361..b151080ed 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,3 @@ -recursive-include graspy/datasets *.csv -recursive-include graspy/version version.txt -exclude tests/* \ No newline at end of file +recursive-include graspologic/datasets *.csv +recursive-include graspologic/version version.txt +exclude tests/* diff --git a/Makefile b/Makefile new file mode 100644 index 000000000..29cf82fc7 --- /dev/null +++ b/Makefile @@ -0,0 +1,18 @@ +docs: + sphinx-build -W -a docs/ docs/_build/html + +coverage: + python -m pytest --co --cov=graspologic + +lint: + black --check --diff ./graspologic ./tests + +test: + pytest tests + +type-check: + mypy ./graspologic + +validate: lint coverage docs test + +.PHONY: docs coverage lint test type-check validate \ No newline at end of file diff --git a/README.md b/README.md index d7ff76825..979c0905d 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,12 @@ -# GraSPy +# graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) -[![Downloads shield](https://img.shields.io/pypi/dm/graspy.svg)](https://pypi.org/project/graspy/) -[![Build Status](https://travis-ci.org/neurodata/graspy.svg?branch=master)](https://travis-ci.org/neurodata/graspy) +[![Downloads shield](https://img.shields.io/pypi/dm/graspologic.svg)](https://pypi.org/project/graspologic/) +![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) [![codecov](https://codecov.io/gh/neurodata/graspy/branch/master/graph/badge.svg)](https://codecov.io/gh/neurodata/graspy) [![DOI](https://zenodo.org/badge/147768493.svg)](https://zenodo.org/badge/latestdoi/147768493) -[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) - -**Gra**ph **S**tatistics in **Py**thon is a package for graph statistical algorithms. +## `graspologic` is a package for graph statistical algorithms. - [Overview](#overview) - [Documentation](#documentation) @@ -17,10 +16,10 @@ - [License](#license) - [Issues](#issues) -# Note: GraSPy + topologic = graspologic -The package formerly known as `GraSPy` is merging with `topologic` to become `graspologic`, so some things in this -repo my be a bit wonky for the time being (e.g. names may say `GraSPy` still instead of `graspologic`). -Please stay tuned and we'll be back up and running shortly. +# Notice: `graspologic` is the merger project of `GraSPy` and `topologic` +We're actively merging these projects into one, but you may see some references to `graspy` or `topologic` from time to time in documentation and +issues. If you notice anything in the documentation referencing either graspy or topologic, please raise an issue (if one does not already exist) +noting the missed titles so we can address all of them. # Overview A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. @@ -32,56 +31,82 @@ Please visit the [tutorial section](https://graspy.neurodata.io/tutorial.html) i # System Requirements ## Hardware requirements -`GraSPy` package requires only a standard computer with enough RAM to support the in-memory operations. +`graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. ## Software requirements ### OS Requirements -This package is supported for *Linux* and *macOS*. The package has been tested on the following systems: -+ Linux: Ubuntu 16.04 -+ macOS: Mojave (10.14.1) -+ Windows: 10 +`graspologic` is tested on the following OSes: +- Linux x64 +- macOS x64 +- Windows 10 x64 + +And across the following versions of Python: +- 3.6 (x64) +- 3.7 (x64) +- 3.8 (x64) -### Python Requirements -This package is written for Python3. Currently, it is supported for Python 3.6 and 3.7. +If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, +please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users +if we have concrete examples of things not working! ### Python Dependencies -`GraSPy` mainly depends on the Python scientific stack. +`graspologic` has the following direct dependencies: ``` +hyppo +matplotlib networkx numpy -pandas +seaborn scikit-learn scipy -seaborn ``` +Developers of `graspologic` will also have the following dependencies: +``` +black +ipykernel +ipython +myp +nbsphinx +numpydoc +pandoc +pytest +pytest-cov +sphinx +sphinxcontrib-rawfiles +sphinx-rtd-theme +``` + +Please note that `pandoc` will also [need to be installed for your system](https://pandoc.org/installing.html). + # Installation Guide ## Install from pip ``` -pip install graspy +pip install graspologic ``` ## Install from Github ``` -git clone https://github.com/neurodata/graspy -cd graspy +git clone https://github.com/microsoft/graspologic +cd graspologic +python3 -m venv venv +source venv/bin/activate python3 setup.py install ``` # Contributing -We welcome contributions from anyone. Please see our [contribution guidelines](https://graspy.neurodata.io/contributing.html) before making a pull request. Our -[issues](https://github.com/neurodata/graspy/issues) page is full of places we could use help! +We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our +[issues](https://github.com/microsoft/graspologic/issues) page is full of places we could use help! If you have an idea for an improvement not listed there, please -[make an issue](https://github.com/neurodata/graspy/issues/new) first so you can discuss with the -developers. +[make an issue](https://github.com/microsoft/graspologic/issues/new) first so you can discuss with the developers. # License This project is covered under the MIT License. # Issues -We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/neurodata/graspy/issues) page if you have questions or ideas. +We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/microsoft/graspologic/issues) page if you have questions or ideas. -# Citing GraSPy -If you find GraSPy useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) +# Citing `graspologic` +If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) > Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. diff --git a/docs/conf.py b/docs/conf.py index dbf86bd45..2efd5f646 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,18 +19,18 @@ # -- Project information ----------------------------------------------------- -project = "GraSPy" -copyright = "2018" -authors = u"NeuroData" +project = "graspologic" +copyright = "2020" +authors = "Microsoft, NeuroData" realpath = os.path.realpath(__file__) dir_realpath = os.path.dirname(os.path.dirname(realpath)) sys.path.append(dir_realpath) -import graspy +import graspologic -version = graspy.version.version.__semver -release = graspy.version.version.version +version = graspologic.version.version.__semver +release = graspologic.version.version.version # -- Extension configuration ------------------------------------------------- extensions = [ @@ -84,7 +84,7 @@ # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] html_static_path = [] -modindex_common_prefix = ["graspy."] +modindex_common_prefix = ["graspologic."] pygments_style = "sphinx" smartquotes = False @@ -114,7 +114,7 @@ # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. -htmlhelp_basename = "graspydoc" +htmlhelp_basename = "graspologicdoc" # -- Options for LaTeX output ------------------------------------------------ @@ -137,14 +137,14 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, "graspy.tex", "GraSPy Documentation", authors, "manual") + (master_doc, "graspologic.tex", "graspologic Documentation", authors, "manual") ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [(master_doc, "graspy", "graspy Documentation", [authors], 1)] +man_pages = [(master_doc, "graspologic", "graspologic Documentation", [authors], 1)] # -- Options for Texinfo output ---------------------------------------------- @@ -154,10 +154,10 @@ texinfo_documents = [ ( master_doc, - "graspy", - "graspy Documentation", + "graspologic", + "graspologic Documentation", authors, - "graspy", + "graspologic", "One line description of project.", "Miscellaneous", ) diff --git a/docs/contributing.rst b/docs/contributing.rst index 4b82f97d5..26740265c 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -1,168 +1,5 @@ -Contributing to GraSPy -====================== +Contributing to graspologic +=========================== -(adopted from scikit-learn) +Please see: `Contributing to graspologic `_. -Submitting a bug report or a feature request --------------------------------------------- - -We use GitHub issues to track all bugs and feature requests; feel free to open -an issue if you have found a bug or wish to see a feature implemented. - -In case you experience issues using this package, do not hesitate to submit a -ticket to the -`Bug Tracker `_. You are -also welcome to post feature requests or pull requests. - -It is recommended to check that your issue complies with the -following rules before submitting: - -- Verify that your issue is not being currently addressed by other - `issues `_ - or `pull requests `_. - -- If you are submitting a bug report, we strongly encourage you to follow the guidelines in - :ref:`filing_bugs`. - -.. _filing_bugs: - -How to make a good bug report -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -When you submit an issue to `Github -`__, please do your best to -follow these guidelines! This will make it a lot easier to provide you with good -feedback: - -- The ideal bug report contains a **short reproducible code snippet**, this way - anyone can try to reproduce the bug easily (see `this - `_ for more details). If your snippet is - longer than around 50 lines, please link to a `gist - `_ or a github repo. - -- If not feasible to include a reproducible snippet, please be specific about - what **estimators and/or functions are involved and the shape of the data**. - -- If an exception is raised, please **provide the full traceback**. - -- Please include your **operating system type and version number**, as well as - your **Python and graspy versions**. This information - can be found by running the following code snippet:: - - import platform; print(platform.platform()) - import sys; print("Python", sys.version) - import graspy; print("GraSPy", graspy.__version__) - -- Please ensure all **code snippets and error messages are formatted in - appropriate code blocks**. See `Creating and highlighting code blocks - `_ - for more details. - -Contributing Code ------------------ - -The preferred workflow for contributing to GraSPy is to fork the main -repository on GitHub, clone, and develop on a branch. Steps: - -1. Fork the `project repository `__ by clicking - on the ‘Fork’ button near the top right of the page. This creates a copy - of the code under your GitHub user account. For more details on how to - fork a repository see `this - guide `__. - -2. Clone your fork of the GraSPy repo from your GitHub account to your - local disk: - - .. code:: bash - - $ git clone git@github.com:YourLogin/GraSPy.git - $ cd GraSPy - -3. Create a ``feature`` branch to hold your development changes: - - .. code:: bash - - $ git checkout -b my-feature - - Always use a ``feature`` branch. It’s good practice to never work on - the ``master`` branch! - -4. Develop the feature on your feature branch. Add changed files using - ``git add`` and then ``git commit`` files: - - .. code:: bash - - $ git add modified_files - $ git commit - - to record your changes in Git, then push the changes to your GitHub - account with: - - .. code:: bash - - $ git push -u origin my-feature - -Pull Request Checklist -~~~~~~~~~~~~~~~~~~~~~~ - -We recommended that your contribution complies with the following rules -before you submit a pull request: - -- Follow the `coding-guidelines <#guidelines>`__. -- Give your pull request a helpful title that summarises what your contribution does. - In some cases ``Fix `` is enough. ``Fix #`` is not enough. -- All public methods should have informative docstrings with sample - usage presented as doctests when appropriate. -- At least one paragraph of narrative documentation with links to references in - the literature (with PDF links when possible) and the example. -- All functions and classes must have unit tests. These should include, - at the very least, type checking and ensuring correct computation/outputs. -- Ensure all tests are passing locally using ``pytest``. Install the necessary - packages by: - - .. code:: bash - - $ pip install pytest pytest-cov - - then run - - .. code:: bash - - $ pytest - - or you can run pytest on a single test file by - - .. code:: bash - - $ pytest path/to/test.py - -- Run an autoformatter. We use ``black`` and would like for you to - format all files using ``black``. You can run the following lines to - format your files. - - .. code:: bash - - $ pip install black - $ black path/to/module.py - -Guidelines ----------- - -Coding Guidelines -~~~~~~~~~~~~~~~~~ - -Uniformly formatted code makes it easier to share code ownership. GraSPy -package closely follows the official Python guidelines detailed in -`PEP8 `__ that detail how -code should be formatted and indented. Please read it and follow it. - -Docstring Guidelines -~~~~~~~~~~~~~~~~~~~~ - -Properly formatted docstrings is required for documentation generation -by Sphinx. The pygraphstats package closely follows the numpydoc -guidelines. Please read and follow the -`numpydoc `__ -guidelines. Refer to the -`example.py `__ -provided by numpydoc. diff --git a/docs/index.rst b/docs/index.rst index 8f9f91f06..b5df5343b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -2,12 +2,12 @@ .. _contents: -Overview of GraSPy_ -=================== +Overview of graspologic_ +======================== -.. _GraSPy: https://graspy.neurodata.io +.. _graspologic: https://graspy.neurodata.io -GraSPy is a Python package for analysis of graphs, or networks. +graspologic is a Python package for analysis of graphs, or networks. Motivation ---------- @@ -27,28 +27,35 @@ Python Python is a powerful programming language that allows concise expressions of network algorithms. Python has a vibrant and growing ecosystem of packages that -GraSPy uses to provide more features such as numerical linear algebra and -plotting. In order to make the most out of GraSPy you will want to know how +graspologic uses to provide more features such as numerical linear algebra and +plotting. In order to make the most out of graspologic you will want to know how to write basic programs in Python. Among the many guides to Python, we recommend the `Python documentation `_. Free software ------------- -GraSPy is free software; you can redistribute it and/or modify it under the -terms of the :doc:`Apache-2.0 `. We welcome contributions. -Join us on `GitHub `_. +graspologic is free software; you can redistribute it and/or modify it under the +terms of the :doc:`MIT ` license. We welcome contributions. +Join us on `GitHub `_. History ------- -GraSPy was born in September 2018. The original version was designed and written by Jaewon Chung, -Benjamin Pedigo, and Eric Bridgeford. +``graspologic`` first released in September 2020, but it got its start as a pair of Python libraries +written by Johns Hopkins University's NeuroData lab and Microsoft Research's Project Essex. +Both teams worked on many of the same algorithms, shared research, findings, and generally duplicated a lot of effort. + +``GraSPy`` - the NeuroData library - and ``topologic`` - the Microsoft Research library began merging in September of 2020, but both got their starts far earlier, with GraSPy starting in September 2018 and topologic starting just a short time later, on October 2nd, 2018. + +GraSPy was originally designed and written by Jaewon Chung, Benjamin Pedigo, and Eric Bridgeford. + +Topologic was originally designed and written by Patrick Bourke, Jonathan McLean, Nick Caurvina, and Dwayne Pryce. Documentation ============= -GraSPy is a graph statistics package in python. +graspologic is a graph statistics package in python. .. toctree:: :maxdepth: 1 @@ -64,13 +71,13 @@ GraSPy is a graph statistics package in python. :maxdepth: 1 :caption: Useful Links - GraSPy @ GitHub - GraSPy @ PyPI - Issue Tracker + graspologic @ GitHub + graspologic @ PyPI + Issue Tracker Indices and tables ================== * :ref:`genindex` -* :ref:`search` \ No newline at end of file +* :ref:`search` diff --git a/docs/install.rst b/docs/install.rst index 8e743249d..53280d90b 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -3,7 +3,7 @@ Install Below we assume you have the default Python environment already configured on -your computer and you intend to install ``graspy`` inside of it. If you want +your computer and you intend to install ``graspologic`` inside of it. If you want to create and work with Python virtual environments, please follow instructions on `venv `_ and `virtual environments `_. @@ -15,30 +15,27 @@ installed. If you do not, refer to the `Pip documentation Install the released version ---------------------------- -Install the current release of ``graspy`` with ``pip``:: +Install the current release of ``graspologic`` with ``pip``:: - $ pip install graspy + $ pip install graspologic To upgrade to a newer release use the ``--upgrade`` flag:: - $ pip install --upgrade graspy + $ pip install --upgrade graspologic If you do not have permission to install software systemwide, you can install into your user directory using the ``--user`` flag:: - $ pip install --user graspy + $ pip install --user graspologic -Alternatively, you can manually download ``graspy`` from -`GitHub `_ or -`PyPI `_. -To install one of these versions, unpack it and run the following from the -top-level source directory using the Terminal:: - - $ pip install . +Alternatively, you can manually download ``graspologic`` from +`PyPI `_. +To install in this way, navigate to the download folder in your terminal and run +`pip install graspologic-VERSION.tar.gz` Python package dependencies --------------------------- -GraSPy requires the following packages: +graspologic requires the following packages: - networkx - numpy @@ -50,7 +47,7 @@ GraSPy requires the following packages: Hardware requirements --------------------- -`GraSPy` package requires only a standard computer with enough RAM to support the in-memory operations. +`graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. OS Requirements --------------- @@ -63,6 +60,6 @@ This package is supported for *Linux* and *macOS*. However, the package has been Testing ------- -GraSPy uses the Python ``pytest`` testing package. If you don't already have +graspologic uses the Python ``pytest`` testing package. If you don't already have that package installed, follow the directions on the `pytest homepage -`_. \ No newline at end of file +`_. diff --git a/docs/license.rst b/docs/license.rst index 1571173bf..3823e66cf 100644 --- a/docs/license.rst +++ b/docs/license.rst @@ -1,207 +1,27 @@ License ======= -GraSPy is distributed with Apache 2.0 license. +graspologic is distributed with MIT license. :: - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ + Copyright (c) Microsoft Corporation and contributors. - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + MIT License - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [2018] [Jaewon Chung, Benjamin Pedigo, Eric Bridgeford] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. \ No newline at end of file + THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. diff --git a/docs/reference/cluster.rst b/docs/reference/cluster.rst index 75630da15..ac3414351 100644 --- a/docs/reference/cluster.rst +++ b/docs/reference/cluster.rst @@ -2,7 +2,7 @@ Clustering ********** -.. currentmodule:: graspy.cluster +.. currentmodule:: graspologic.cluster K-Means Clustering ------------------ @@ -12,4 +12,4 @@ Gaussian Mixture Models Clustering ---------------------------------- .. autoclass:: GaussianCluster -.. autoclass:: AutoGMMCluster \ No newline at end of file +.. autoclass:: AutoGMMCluster diff --git a/docs/reference/datasets.rst b/docs/reference/datasets.rst index 847ebf146..0f72e4113 100644 --- a/docs/reference/datasets.rst +++ b/docs/reference/datasets.rst @@ -1,11 +1,11 @@ Datasets ======== -.. currentmodule:: graspy.datasets +.. currentmodule:: graspologic.datasets Drosophila larval mushroom body ------------------------------- .. autofunction:: load_drosophila_left -.. autofunction:: load_drosophila_right \ No newline at end of file +.. autofunction:: load_drosophila_right diff --git a/docs/reference/embed.rst b/docs/reference/embed.rst index 96ade3db3..d800b87c1 100644 --- a/docs/reference/embed.rst +++ b/docs/reference/embed.rst @@ -1,7 +1,7 @@ Embedding ========= -.. currentmodule:: graspy.embed +.. currentmodule:: graspologic.embed Decomposition ------------- @@ -25,4 +25,4 @@ Multiple graph embedding Dissimilarity graph embedding ----------------------------- -.. autoclass:: ClassicalMDS \ No newline at end of file +.. autoclass:: ClassicalMDS diff --git a/docs/reference/inference.rst b/docs/reference/inference.rst index 025b4e8fe..5e0792651 100644 --- a/docs/reference/inference.rst +++ b/docs/reference/inference.rst @@ -1,11 +1,11 @@ Inference ========= -.. currentmodule:: graspy.inference +.. currentmodule:: graspologic.inference Two-graph hypothesis testing ---------------------------- .. autoclass:: LatentPositionTest -.. autoclass:: LatentDistributionTest \ No newline at end of file +.. autoclass:: LatentDistributionTest diff --git a/docs/reference/match.rst b/docs/reference/match.rst index 5bb1b6c64..3521f6dcc 100644 --- a/docs/reference/match.rst +++ b/docs/reference/match.rst @@ -1,7 +1,7 @@ Matching ======== -.. currentmodule:: graspy.match +.. currentmodule:: graspologic.match Graph Matching -------------------- diff --git a/docs/reference/models.rst b/docs/reference/models.rst index 06130d4bd..18d90ab07 100644 --- a/docs/reference/models.rst +++ b/docs/reference/models.rst @@ -1,7 +1,7 @@ Models ====== -.. currentmodule:: graspy.models +.. currentmodule:: graspologic.models Erdos-Reyni models ------------------ @@ -20,4 +20,4 @@ Stochastic block models Latent position models ---------------------- -.. autoclass:: RDPGEstimator \ No newline at end of file +.. autoclass:: RDPGEstimator diff --git a/docs/reference/pipeline.rst b/docs/reference/pipeline.rst index 97551d917..44af42aaa 100644 --- a/docs/reference/pipeline.rst +++ b/docs/reference/pipeline.rst @@ -1,7 +1,7 @@ Pipeline ======== -.. currentmodule:: graspy.pipeline +.. currentmodule:: graspologic.pipeline Multigraph Embeddings --------------------- diff --git a/docs/reference/plotting.rst b/docs/reference/plotting.rst index 08e4f0c06..019ed4b07 100644 --- a/docs/reference/plotting.rst +++ b/docs/reference/plotting.rst @@ -2,7 +2,7 @@ Plotting ******** -.. currentmodule:: graspy.plot +.. currentmodule:: graspologic.plot Heatmap ------- @@ -26,4 +26,4 @@ Edgeplot Screeplot --------- -.. autofunction:: screeplot \ No newline at end of file +.. autofunction:: screeplot diff --git a/docs/reference/simulations.rst b/docs/reference/simulations.rst index 24bf6da91..484feeb99 100644 --- a/docs/reference/simulations.rst +++ b/docs/reference/simulations.rst @@ -2,7 +2,7 @@ Simulations *********** -.. currentmodule:: graspy.simulations +.. currentmodule:: graspologic.simulations diff --git a/docs/reference/subgraph.rst b/docs/reference/subgraph.rst index 0121d5ad1..b64a7e11f 100644 --- a/docs/reference/subgraph.rst +++ b/docs/reference/subgraph.rst @@ -1,7 +1,7 @@ Subgraph ======== -.. currentmodule:: graspy.subgraph +.. currentmodule:: graspologic.subgraph Signal-Subgraph Estimators -------------------------- diff --git a/docs/reference/utils.rst b/docs/reference/utils.rst index 867c1dcc0..0b628a98e 100644 --- a/docs/reference/utils.rst +++ b/docs/reference/utils.rst @@ -1,7 +1,7 @@ Utility ======= -.. currentmodule:: graspy.utils +.. currentmodule:: graspologic.utils Transformations --------------- diff --git a/docs/release.rst b/docs/release.rst index 0f1ab755c..75cb7a8e9 100644 --- a/docs/release.rst +++ b/docs/release.rst @@ -3,6 +3,10 @@ Release Log =========== +graspologic 0.1.0 +----------------- +Release date: ? + GraSPy 0.3 ---------- Release date: 04 Aug 2020 diff --git a/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb b/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb index 5f0a49a99..bb67801f5 100644 --- a/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb +++ b/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb @@ -19,9 +19,9 @@ "np.random.seed(8889)\n", "from sklearn.metrics import adjusted_rand_score\n", "from sklearn.cluster import KMeans\n", - "from graspy.embed import AdjacencySpectralEmbed\n", - "from graspy.simulations import sbm\n", - "from graspy.plot import heatmap, pairplot\n", + "from graspologic.embed import AdjacencySpectralEmbed\n", + "from graspologic.simulations import sbm\n", + "from graspologic.plot import heatmap, pairplot\n", "\n", "%matplotlib inline" ] diff --git a/docs/tutorials/embedding/Omnibus.ipynb b/docs/tutorials/embedding/Omnibus.ipynb index fe9341c18..5730525e4 100644 --- a/docs/tutorials/embedding/Omnibus.ipynb +++ b/docs/tutorials/embedding/Omnibus.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -49,7 +49,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm\n", + "from graspologic.simulations import sbm\n", "\n", "n = [25, 25]\n", "P1 = [[.3, .1], \n", @@ -79,7 +79,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "heatmap(G1, figsize=(7, 7), title='Visualization of Graph 1')\n", "heatmap(G2, figsize=(7, 7), title='Visualization of Graph 2')" @@ -114,7 +114,7 @@ }, "outputs": [], "source": [ - "from graspy.embed import OmnibusEmbed\n", + "from graspologic.embed import OmnibusEmbed\n", "\n", "embedder = OmnibusEmbed()\n", "Zhat = embedder.fit_transform([G1, G2])\n", diff --git a/docs/tutorials/inference/latent_distribution_test.ipynb b/docs/tutorials/inference/latent_distribution_test.ipynb index 65bbc1098..152c4acc3 100644 --- a/docs/tutorials/inference/latent_distribution_test.ipynb +++ b/docs/tutorials/inference/latent_distribution_test.ipynb @@ -19,11 +19,11 @@ "import matplotlib.pyplot as plt\n", "np.random.seed(8888)\n", "\n", - "from graspy.inference import LatentDistributionTest\n", - "from graspy.embed import AdjacencySpectralEmbed\n", - "from graspy.simulations import sbm, rdpg\n", - "from graspy.utils import symmetrize\n", - "from graspy.plot import heatmap, pairplot\n", + "from graspologic.inference import LatentDistributionTest\n", + "from graspologic.embed import AdjacencySpectralEmbed\n", + "from graspologic.simulations import sbm, rdpg\n", + "from graspologic.utils import symmetrize\n", + "from graspologic.plot import heatmap, pairplot\n", "\n", "%matplotlib inline" ] diff --git a/docs/tutorials/inference/latent_position_test.ipynb b/docs/tutorials/inference/latent_position_test.ipynb index 02fe91a2c..9aa3ff3a1 100644 --- a/docs/tutorials/inference/latent_position_test.ipynb +++ b/docs/tutorials/inference/latent_position_test.ipynb @@ -16,11 +16,11 @@ "import numpy as np\n", "np.random.seed(88889999)\n", "\n", - "from graspy.inference import LatentPositionTest\n", - "from graspy.embed import AdjacencySpectralEmbed\n", - "from graspy.simulations import sbm, rdpg\n", - "from graspy.utils import symmetrize\n", - "from graspy.plot import heatmap, pairplot\n", + "from graspologic.inference import LatentPositionTest\n", + "from graspologic.embed import AdjacencySpectralEmbed\n", + "from graspologic.simulations import sbm, rdpg\n", + "from graspologic.utils import symmetrize\n", + "from graspologic.plot import heatmap, pairplot\n", "\n", "%matplotlib inline" ] @@ -112,7 +112,7 @@ "metadata": {}, "source": [ "## Latent position test where null is true\n", - "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance. In this case, we will use the `LatentPositionTest` in `GraSPy` because we know the true alignment between the vertices of the two graphs we are testing. In other words, node $i$ in graph 1 can be thought of as equivalent to node $i$ in graph 2 because of the way we generated these graphs. \n", + "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance. In this case, we will use the `LatentPositionTest` in `graspologic` because we know the true alignment between the vertices of the two graphs we are testing. In other words, node $i$ in graph 1 can be thought of as equivalent to node $i$ in graph 2 because of the way we generated these graphs. \n", "\n", "In other words, we are testing $$ H_0: X_1 = X_2 R$$$$ H_a: X_1 \\neq X_2 R$$\n", "\n", diff --git a/docs/tutorials/matching/faq.ipynb b/docs/tutorials/matching/faq.ipynb index fa0aed458..b28658954 100644 --- a/docs/tutorials/matching/faq.ipynb +++ b/docs/tutorials/matching/faq.ipynb @@ -63,8 +63,8 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.match import GraphMatch as GMP\n", - "from graspy.simulations import er_np" + "from graspologic.match import GraphMatch as GMP\n", + "from graspologic.simulations import er_np" ] }, { @@ -108,7 +108,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "heatmap(G1, cbar=False, title = 'G1 [ER-NP(50, 0.3) Simulation]')\n", "heatmap(G2, cbar=False, title = 'G2 [G1 Randomly Shuffled]')" ] diff --git a/docs/tutorials/matching/sgm.ipynb b/docs/tutorials/matching/sgm.ipynb index df5be08f7..04dad7750 100644 --- a/docs/tutorials/matching/sgm.ipynb +++ b/docs/tutorials/matching/sgm.ipynb @@ -33,9 +33,9 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.match import GraphMatch as GMP\n", - "from graspy.plot import heatmap\n", - "from graspy.simulations import er_corr, sbm, sbm_corr" + "from graspologic.match import GraphMatch as GMP\n", + "from graspologic.plot import heatmap\n", + "from graspologic.simulations import er_corr, sbm, sbm_corr" ] }, { diff --git a/docs/tutorials/models/models.ipynb b/docs/tutorials/models/models.ipynb index fdf78795d..4f5708556 100644 --- a/docs/tutorials/models/models.ipynb +++ b/docs/tutorials/models/models.ipynb @@ -23,7 +23,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load some data from _GraSPy_" + "## Load some data from _graspologic_" ] }, { @@ -40,9 +40,9 @@ "outputs": [], "source": [ "import numpy as np \n", - "from graspy.datasets import load_drosophila_right\n", - "from graspy.plot import heatmap\n", - "from graspy.utils import binarize, symmetrize\n", + "from graspologic.datasets import load_drosophila_right\n", + "from graspologic.plot import heatmap\n", + "from graspologic.utils import binarize, symmetrize\n", "%matplotlib inline\n", "\n", "adj, labels = load_drosophila_right(return_labels=True)\n", @@ -79,7 +79,7 @@ "\n", "### For each graph model we will show: \n", "- how the model is formulated \n", - "- how to fit the model using GraSPy\n", + "- how to fit the model using graspologic\n", "- the P matrix that was fit by the model \n", "- a single sample from the fit model" ] @@ -105,7 +105,7 @@ }, "outputs": [], "source": [ - "from graspy.models import EREstimator\n", + "from graspologic.models import EREstimator\n", "er = EREstimator(directed=True,loops=False)\n", "er.fit(adj)\n", "print(f\"ER \\\"p\\\" parameter: {er.p_}\")\n", @@ -143,7 +143,7 @@ }, "outputs": [], "source": [ - "from graspy.models import DCEREstimator\n", + "from graspologic.models import DCEREstimator\n", "dcer = DCEREstimator(directed=True,loops=False)\n", "dcer.fit(adj)\n", "print(f\"DCER \\\"p\\\" parameter: {dcer.p_}\")\n", @@ -179,7 +179,7 @@ }, "outputs": [], "source": [ - "from graspy.models import SBMEstimator\n", + "from graspologic.models import SBMEstimator\n", "sbme = SBMEstimator(directed=True,loops=False)\n", "sbme.fit(adj, y=labels)\n", "print(\"SBM \\\"B\\\" matrix:\")\n", @@ -218,7 +218,7 @@ }, "outputs": [], "source": [ - "from graspy.models import DCSBMEstimator\n", + "from graspologic.models import DCSBMEstimator\n", "dcsbme = DCSBMEstimator(directed=True,loops=False)\n", "dcsbme.fit(adj, y=labels)\n", "print(\"DCSBM \\\"B\\\" matrix:\")\n", @@ -260,7 +260,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.models import RDPGEstimator\n", + "from graspologic.models import RDPGEstimator\n", "rdpge = RDPGEstimator(loops=False)\n", "rdpge.fit(adj, y=labels)\n", "heatmap(rdpge.p_mat_,\n", diff --git a/docs/tutorials/plotting/gridplot.ipynb b/docs/tutorials/plotting/gridplot.ipynb index 7a830bb77..c0ebb3518 100644 --- a/docs/tutorials/plotting/gridplot.ipynb +++ b/docs/tutorials/plotting/gridplot.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import numpy as np\n", "%matplotlib inline" @@ -46,7 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm\n", + "from graspologic.simulations import sbm\n", "\n", "n_communities = [50, 50]\n", "p = np.array([[0.25, 0.05], [0.05, 0.25]])\n", @@ -73,7 +73,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import gridplot\n", + "from graspologic.plot import gridplot\n", "\n", "X = [A_unif1, A_unif2]\n", "labels = [\"Uniform(1, 10)\", \"Uniform(2, 5)\"]\n", diff --git a/docs/tutorials/plotting/heatmaps.ipynb b/docs/tutorials/plotting/heatmaps.ipynb index dde5f7fab..be05c85e4 100644 --- a/docs/tutorials/plotting/heatmaps.ipynb +++ b/docs/tutorials/plotting/heatmaps.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import numpy as np\n", "%matplotlib inline" @@ -45,7 +45,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm\n", + "from graspologic.simulations import sbm\n", "\n", "n_communities = [50, 50]\n", "p = [[0.8, 0.2], \n", @@ -73,7 +73,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "title = 'Weighted Stochastic Block Model with Poisson(3)'\n", "\n", diff --git a/docs/tutorials/plotting/pairplot.ipynb b/docs/tutorials/plotting/pairplot.ipynb index a74c39b1c..cbfb1330a 100644 --- a/docs/tutorials/plotting/pairplot.ipynb +++ b/docs/tutorials/plotting/pairplot.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import numpy as np\n", "%matplotlib inline" @@ -46,7 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm\n", + "from graspologic.simulations import sbm\n", "\n", "n_communities = [50, 50, 50]\n", "p = [[0.5, 0.1, 0.05], \n", @@ -72,7 +72,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.embed import AdjacencySpectralEmbed\n", + "from graspologic.embed import AdjacencySpectralEmbed\n", "\n", "ase = AdjacencySpectralEmbed()\n", "X = ase.fit_transform(A)\n", @@ -95,7 +95,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import pairplot\n", + "from graspologic.plot import pairplot\n", "\n", "labels = ['Block 1'] * 50 + ['Block 2'] * 50 + ['Block 3'] * 50\n", "\n", diff --git a/docs/tutorials/simulations/corr.ipynb b/docs/tutorials/simulations/corr.ipynb index 1558e24e0..81c946ccd 100644 --- a/docs/tutorials/simulations/corr.ipynb +++ b/docs/tutorials/simulations/corr.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" @@ -50,7 +50,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import er_corr" + "from graspologic.simulations import er_corr" ] }, { @@ -80,7 +80,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "heatmap(G1, title = 'Correlated ER Simulation Graph 1')\n", "heatmap(G2, title = 'Correlated ER Simulation Graph 2')" ] @@ -116,7 +116,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm_corr" + "from graspologic.simulations import sbm_corr" ] }, { diff --git a/docs/tutorials/simulations/erdos_renyi.ipynb b/docs/tutorials/simulations/erdos_renyi.ipynb index f32888266..0d8d66f4e 100644 --- a/docs/tutorials/simulations/erdos_renyi.ipynb +++ b/docs/tutorials/simulations/erdos_renyi.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -38,7 +38,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import er_nm, er_np\n", + "from graspologic.simulations import er_nm, er_np\n", "\n", "n = 50\n", "m = 250\n", @@ -62,7 +62,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "heatmap(G1, title = 'ER-NP(50, 0.3) Simulation')\n", "heatmap(G2, title = 'ER-NM(50, 250) Simulation')" diff --git a/docs/tutorials/simulations/rdpg.ipynb b/docs/tutorials/simulations/rdpg.ipynb index c41ff0e4e..109d567de 100644 --- a/docs/tutorials/simulations/rdpg.ipynb +++ b/docs/tutorials/simulations/rdpg.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import numpy as np\n", "%matplotlib inline" @@ -40,7 +40,7 @@ }, "outputs": [], "source": [ - "from graspy.simulations import rdpg\n", + "from graspologic.simulations import rdpg\n", "\n", "# Create a latent position matrix\n", "X = np.full((100, 2), 0.5)\n", @@ -69,7 +69,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "heatmap(A, title='ER_NP(100, 0.5) Using RDPG')" ] @@ -125,7 +125,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm\n", + "from graspologic.simulations import sbm\n", "\n", "n = [50, 50]\n", "p = [[0.33, 0.09], [0.09, 0.03]]\n", diff --git a/docs/tutorials/simulations/rdpg_corr.ipynb b/docs/tutorials/simulations/rdpg_corr.ipynb index 2eaaf367d..c12651a7e 100644 --- a/docs/tutorials/simulations/rdpg_corr.ipynb +++ b/docs/tutorials/simulations/rdpg_corr.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations.rdpg_corr import rdpg_corr\n", + "from graspologic.simulations.rdpg_corr import rdpg_corr\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", @@ -77,7 +77,7 @@ }, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(10, 5))\n", "heatmap(G1, ax=axs[0], cbar=False, title = 'Corr. RDPG 1')\n", @@ -120,7 +120,7 @@ }, "outputs": [], "source": [ - "from graspy.simulations import sbm_corr\n", + "from graspologic.simulations import sbm_corr\n", "\n", "np.random.seed(123)\n", "directed = False\n", diff --git a/docs/tutorials/simulations/sbm.ipynb b/docs/tutorials/simulations/sbm.ipynb index 9a312d27d..08fea27b1 100644 --- a/docs/tutorials/simulations/sbm.ipynb +++ b/docs/tutorials/simulations/sbm.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy\n", + "import graspologic\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -47,7 +47,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.simulations import sbm\n", + "from graspologic.simulations import sbm\n", "\n", "n = [50, 50]\n", "p = [[0.5, 0.2],\n", @@ -70,7 +70,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "heatmap(G, title ='SBM Simulation')" ] diff --git a/docs/tutorials/subgraph/subgraph.ipynb b/docs/tutorials/subgraph/subgraph.ipynb index 3bf7a5d1f..91a6f133d 100644 --- a/docs/tutorials/subgraph/subgraph.ipynb +++ b/docs/tutorials/subgraph/subgraph.ipynb @@ -22,7 +22,7 @@ "metadata": {}, "outputs": [], "source": [ - "import graspy.subgraph as sg\n", + "import graspologic.subgraph as sg\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] @@ -69,7 +69,7 @@ "metadata": {}, "outputs": [], "source": [ - "from graspy.plot import heatmap\n", + "from graspologic.plot import heatmap\n", "\n", "verts = 70\n", "sedges = 20\n", diff --git a/graspy/__init__.py b/graspologic/__init__.py similarity index 56% rename from graspy/__init__.py rename to graspologic/__init__.py index 448c0549a..0260f697b 100644 --- a/graspy/__init__.py +++ b/graspologic/__init__.py @@ -5,16 +5,16 @@ import warnings -import graspy.cluster -import graspy.datasets -import graspy.embed -import graspy.inference -import graspy.models -import graspy.pipeline -import graspy.plot -import graspy.simulations -import graspy.utils -import graspy.subgraph +import graspologic.cluster +import graspologic.datasets +import graspologic.embed +import graspologic.inference +import graspologic.models +import graspologic.pipeline +import graspologic.plot +import graspologic.simulations +import graspologic.utils +import graspologic.subgraph warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=PendingDeprecationWarning) diff --git a/graspy/cluster/__init__.py b/graspologic/cluster/__init__.py similarity index 100% rename from graspy/cluster/__init__.py rename to graspologic/cluster/__init__.py diff --git a/graspy/cluster/autogmm.py b/graspologic/cluster/autogmm.py similarity index 99% rename from graspy/cluster/autogmm.py rename to graspologic/cluster/autogmm.py index f2a88f139..797fe296c 100644 --- a/graspy/cluster/autogmm.py +++ b/graspologic/cluster/autogmm.py @@ -189,8 +189,8 @@ class AutoGMMCluster(BaseCluster): See Also -------- - graspy.cluster.GaussianCluster - graspy.cluster.KMeansCluster + graspologic.cluster.GaussianCluster + graspologic.cluster.KMeansCluster Notes ----- diff --git a/graspy/cluster/base.py b/graspologic/cluster/base.py similarity index 100% rename from graspy/cluster/base.py rename to graspologic/cluster/base.py diff --git a/graspy/cluster/gclust.py b/graspologic/cluster/gclust.py similarity index 100% rename from graspy/cluster/gclust.py rename to graspologic/cluster/gclust.py diff --git a/graspy/cluster/kclust.py b/graspologic/cluster/kclust.py similarity index 100% rename from graspy/cluster/kclust.py rename to graspologic/cluster/kclust.py diff --git a/graspy/datasets/__init__.py b/graspologic/datasets/__init__.py similarity index 100% rename from graspy/datasets/__init__.py rename to graspologic/datasets/__init__.py diff --git a/graspy/datasets/base.py b/graspologic/datasets/base.py similarity index 100% rename from graspy/datasets/base.py rename to graspologic/datasets/base.py diff --git a/graspy/datasets/drosophila/left_adjacency.csv b/graspologic/datasets/drosophila/left_adjacency.csv similarity index 100% rename from graspy/datasets/drosophila/left_adjacency.csv rename to graspologic/datasets/drosophila/left_adjacency.csv diff --git a/graspy/datasets/drosophila/left_cell_labels.csv b/graspologic/datasets/drosophila/left_cell_labels.csv similarity index 100% rename from graspy/datasets/drosophila/left_cell_labels.csv rename to graspologic/datasets/drosophila/left_cell_labels.csv diff --git a/graspy/datasets/drosophila/right_adjacency.csv b/graspologic/datasets/drosophila/right_adjacency.csv similarity index 100% rename from graspy/datasets/drosophila/right_adjacency.csv rename to graspologic/datasets/drosophila/right_adjacency.csv diff --git a/graspy/datasets/drosophila/right_cell_labels.csv b/graspologic/datasets/drosophila/right_cell_labels.csv similarity index 100% rename from graspy/datasets/drosophila/right_cell_labels.csv rename to graspologic/datasets/drosophila/right_cell_labels.csv diff --git a/graspy/embed/__init__.py b/graspologic/embed/__init__.py similarity index 100% rename from graspy/embed/__init__.py rename to graspologic/embed/__init__.py diff --git a/graspy/embed/ase.py b/graspologic/embed/ase.py similarity index 93% rename from graspy/embed/ase.py rename to graspologic/embed/ase.py index 03be2dd3c..a9d3ee2c4 100644 --- a/graspy/embed/ase.py +++ b/graspologic/embed/ase.py @@ -20,7 +20,7 @@ class AdjacencySpectralEmbed(BaseEmbed): The adjacency spectral embedding (ASE) is a k-dimensional Euclidean representation of the graph based on its adjacency matrix. It relies on an SVD to reduce the dimensionality to the specified k, or if k is unspecified, can find a number of - dimensions automatically (see :class:`~graspy.embed.selectSVD`). + dimensions automatically (see :class:`~graspologic.embed.selectSVD`). Read more in the :ref:`tutorials ` @@ -30,11 +30,11 @@ class AdjacencySpectralEmbed(BaseEmbed): Desired dimensionality of output data. If "full", n_components must be <= min(X.shape). Otherwise, n_components must be < min(X.shape). If None, then optimal dimensions will be chosen by - :func:`~graspy.embed.select_dimension` using ``n_elbows`` argument. + :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 If ``n_components=None``, then compute the optimal embedding dimension using - :func:`~graspy.embed.select_dimension`. Otherwise, ignored. + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional SVD solver to use: @@ -83,8 +83,8 @@ class AdjacencySpectralEmbed(BaseEmbed): See Also -------- - graspy.embed.selectSVD - graspy.embed.select_dimension + graspologic.embed.selectSVD + graspologic.embed.select_dimension Notes ----- @@ -149,7 +149,7 @@ def fit(self, graph, y=None): msg = ( "Input graph is not fully connected. Results may not" + "be optimal. You can compute the largest connected component by" - + "using ``graspy.utils.get_lcc``." + + "using ``graspologic.utils.get_lcc``." ) warnings.warn(msg, UserWarning) diff --git a/graspy/embed/base.py b/graspologic/embed/base.py similarity index 99% rename from graspy/embed/base.py rename to graspologic/embed/base.py index 5973e0525..44187d588 100644 --- a/graspy/embed/base.py +++ b/graspologic/embed/base.py @@ -55,7 +55,7 @@ class BaseEmbed(BaseEstimator): See Also -------- - graspy.embed.selectSVD, graspy.embed.select_dimension + graspologic.embed.selectSVD, graspologic.embed.select_dimension """ def __init__( diff --git a/graspy/embed/lse.py b/graspologic/embed/lse.py similarity index 93% rename from graspy/embed/lse.py rename to graspologic/embed/lse.py index cb65c9a47..a07ab9dd6 100644 --- a/graspy/embed/lse.py +++ b/graspologic/embed/lse.py @@ -27,11 +27,11 @@ class LaplacianSpectralEmbed(BaseEmbed): Desired dimensionality of output data. If "full", n_components must be <= min(X.shape). Otherwise, n_components must be < min(X.shape). If None, then optimal dimensions will be chosen by - :func:`~graspy.embed.select_dimension` using ``n_elbows`` argument. + :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 If ``n_components=None``, then compute the optimal embedding dimension using - :func:`~graspy.embed.select_dimension`. Otherwise, ignored. + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional SVD solver to use: @@ -81,9 +81,9 @@ class LaplacianSpectralEmbed(BaseEmbed): See Also -------- - graspy.embed.selectSVD - graspy.embed.select_dimension - graspy.utils.to_laplace + graspologic.embed.selectSVD + graspologic.embed.select_dimension + graspologic.utils.to_laplace Notes ----- @@ -142,7 +142,7 @@ def fit(self, graph, y=None): Parameters ---------- graph : array_like or networkx.Graph - Input graph to embed. see graspy.utils.import_graph + Input graph to embed. see graspologic.utils.import_graph Returns ------- @@ -156,7 +156,7 @@ def fit(self, graph, y=None): msg = ( "Input graph is not fully connected. Results may not" + "be optimal. You can compute the largest connected component by" - + "using ``graspy.utils.get_lcc``." + + "using ``graspologic.utils.get_lcc``." ) warnings.warn(msg, UserWarning) diff --git a/graspy/embed/mase.py b/graspologic/embed/mase.py similarity index 98% rename from graspy/embed/mase.py rename to graspologic/embed/mase.py index c5b8b9746..a9d7018c1 100644 --- a/graspy/embed/mase.py +++ b/graspologic/embed/mase.py @@ -33,11 +33,11 @@ class MultipleASE(BaseEmbedMulti): Desired dimensionality of output data. If "full", n_components must be <= min(X.shape). Otherwise, n_components must be < min(X.shape). If None, then optimal dimensions will be chosen by - :func:`~graspy.embed.select_dimension` using ``n_elbows`` argument. + :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 If ``n_components=None``, then compute the optimal embedding dimension using - :func:`~graspy.embed.select_dimension`. Otherwise, ignored. + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional SVD solver to use: diff --git a/graspy/embed/mds.py b/graspologic/embed/mds.py similarity index 98% rename from graspy/embed/mds.py rename to graspologic/embed/mds.py index 34f9ede31..fded0deac 100644 --- a/graspy/embed/mds.py +++ b/graspologic/embed/mds.py @@ -43,7 +43,7 @@ class ClassicalMDS(BaseEstimator): n_elbows : int, or None (default=2) If ``n_components=None``, then compute the optimal embedding dimension using - `:func:`~graspy.embed.select_dimension`. Otherwise, ignored. + `:func:`~graspologic.embed.select_dimension`. Otherwise, ignored. dissimilarity : 'euclidean' | 'precomputed', optional, default: 'euclidean' Dissimilarity measure to use: @@ -74,7 +74,7 @@ class ClassicalMDS(BaseEstimator): See Also -------- - graspy.embed.select_dimension + graspologic.embed.select_dimension References ---------- diff --git a/graspy/embed/omni.py b/graspologic/embed/omni.py similarity index 96% rename from graspy/embed/omni.py rename to graspologic/embed/omni.py index 1ee30f9b8..3e059dbc1 100644 --- a/graspy/embed/omni.py +++ b/graspologic/embed/omni.py @@ -59,11 +59,11 @@ class OmnibusEmbed(BaseEmbedMulti): Desired dimensionality of output data. If "full", n_components must be <= min(X.shape). Otherwise, n_components must be < min(X.shape). If None, then optimal dimensions will be chosen by - :func:`~graspy.embed.select_dimension` using ``n_elbows`` argument. + :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 If ``n_components=None``, then compute the optimal embedding dimension using - :func:`~graspy.embed.select_dimension`. Otherwise, ignored. + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional SVD solver to use: @@ -117,8 +117,8 @@ class OmnibusEmbed(BaseEmbedMulti): See Also -------- - graspy.embed.selectSVD - graspy.embed.select_dimension + graspologic.embed.selectSVD + graspologic.embed.select_dimension References ---------- @@ -172,7 +172,7 @@ def fit(self, graphs, y=None): msg = ( "Input graphs are not fully connected. Results may not" + "be optimal. You can compute the largest connected component by" - + "using ``graspy.utils.get_multigraph_union_lcc``." + + "using ``graspologic.utils.get_multigraph_union_lcc``." ) warnings.warn(msg, UserWarning) diff --git a/graspy/embed/svd.py b/graspologic/embed/svd.py similarity index 98% rename from graspy/embed/svd.py rename to graspologic/embed/svd.py index 3dd87a062..cf66a2d12 100644 --- a/graspy/embed/svd.py +++ b/graspologic/embed/svd.py @@ -187,10 +187,10 @@ def selectSVD(X, n_components=None, n_elbows=2, algorithm="randomized", n_iter=5 Desired dimensionality of output data. If "full", n_components must be <= min(X.shape). Otherwise, n_components must be < min(X.shape). If None, then optimal dimensions will be chosen by - :func:`~graspy.embed.select_dimension` using ``n_elbows`` argument. + :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 If ``n_components=None``, then compute the optimal embedding dimension using - :func:`~graspy.embed.select_dimension`. Otherwise, ignored. + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional SVD solver to use: diff --git a/graspy/inference/__init__.py b/graspologic/inference/__init__.py similarity index 100% rename from graspy/inference/__init__.py rename to graspologic/inference/__init__.py diff --git a/graspy/inference/base.py b/graspologic/inference/base.py similarity index 100% rename from graspy/inference/base.py rename to graspologic/inference/base.py diff --git a/graspy/inference/latent_distribution_test.py b/graspologic/inference/latent_distribution_test.py similarity index 99% rename from graspy/inference/latent_distribution_test.py rename to graspologic/inference/latent_distribution_test.py index 31ced2e53..659a8573e 100644 --- a/graspy/inference/latent_distribution_test.py +++ b/graspologic/inference/latent_distribution_test.py @@ -58,7 +58,7 @@ class LatentDistributionTest(BaseInference): n_components : int or None (default=None) Number of embedding dimensions. If None, the optimal embedding dimensions are found by the Zhu and Godsi algorithm. - See :func:`~graspy.embed.selectSVD` for more information. + See :func:`~graspologic.embed.selectSVD` for more information. This argument is ignored if input_graph=False. n_bootstraps : int (default=200) diff --git a/graspy/inference/latent_position_test.py b/graspologic/inference/latent_position_test.py similarity index 98% rename from graspy/inference/latent_position_test.py rename to graspologic/inference/latent_position_test.py index c6a1c557f..4bbf11ac8 100644 --- a/graspy/inference/latent_position_test.py +++ b/graspologic/inference/latent_position_test.py @@ -77,9 +77,9 @@ class LatentPositionTest(BaseInference): See also -------- - graspy.embed.AdjacencySpectralEmbed - graspy.embed.OmnibusEmbed - graspy.embed.selectSVD + graspologic.embed.AdjacencySpectralEmbed + graspologic.embed.OmnibusEmbed + graspologic.embed.selectSVD References ---------- diff --git a/graspy/match/__init__.py b/graspologic/match/__init__.py similarity index 100% rename from graspy/match/__init__.py rename to graspologic/match/__init__.py diff --git a/graspy/match/gmp.py b/graspologic/match/gmp.py similarity index 100% rename from graspy/match/gmp.py rename to graspologic/match/gmp.py diff --git a/graspy/match/skp.py b/graspologic/match/skp.py similarity index 100% rename from graspy/match/skp.py rename to graspologic/match/skp.py diff --git a/graspy/models/__init__.py b/graspologic/models/__init__.py similarity index 100% rename from graspy/models/__init__.py rename to graspologic/models/__init__.py diff --git a/graspy/models/base.py b/graspologic/models/base.py similarity index 100% rename from graspy/models/base.py rename to graspologic/models/base.py diff --git a/graspy/models/er.py b/graspologic/models/er.py similarity index 95% rename from graspy/models/er.py rename to graspologic/models/er.py index 9625957e6..b726fd00b 100644 --- a/graspy/models/er.py +++ b/graspologic/models/er.py @@ -42,9 +42,9 @@ class EREstimator(SBMEstimator): See also -------- - graspy.models.DCEREstimator - graspy.models.SBMEstimator - graspy.simulations.er_np + graspologic.models.DCEREstimator + graspologic.models.SBMEstimator + graspologic.simulations.er_np References ---------- @@ -117,9 +117,9 @@ class DCEREstimator(DCSBMEstimator): See also -------- - graspy.models.DCSBMEstimator - graspy.models.EREstimator - graspy.simulations.er_np + graspologic.models.DCSBMEstimator + graspologic.models.EREstimator + graspologic.simulations.er_np References ---------- diff --git a/graspy/models/rdpg.py b/graspologic/models/rdpg.py similarity index 95% rename from graspy/models/rdpg.py rename to graspologic/models/rdpg.py index 92de0046c..61a4601ce 100644 --- a/graspy/models/rdpg.py +++ b/graspologic/models/rdpg.py @@ -24,7 +24,7 @@ class RDPGEstimator(BaseGraphEstimator): where :math:`x_i` is the left latent position of node :math:`i`, and :math:`y_j` is the right latent position of node :math:`j`. If the graph being modeled is is undirected, then :math:`x_i = y_i`. Latent positions can be estimated via - :class:`~graspy.embed.AdjacencySpectralEmbed`. + :class:`~graspologic.embed.AdjacencySpectralEmbed`. Read more in the :ref:`tutorials ` @@ -40,7 +40,7 @@ class RDPGEstimator(BaseGraphEstimator): ase_kws : dict, optional (default={}) Dictionary of keyword arguments passed down to - :class:`~graspy.embed.AdjacencySpectralEmbed`, which is used to fit the model. + :class:`~graspologic.embed.AdjacencySpectralEmbed`, which is used to fit the model. diag_aug_weight : int or float, optional (default=1) Weighting used for diagonal augmentation, which is a form of regularization for @@ -66,9 +66,9 @@ class RDPGEstimator(BaseGraphEstimator): See also -------- - graspy.simulations.rdpg - graspy.embed.AdjacencySpectralEmbed - graspy.utils.augment_diagonal + graspologic.simulations.rdpg + graspologic.embed.AdjacencySpectralEmbed + graspologic.utils.augment_diagonal References ---------- diff --git a/graspy/models/sbm.py b/graspologic/models/sbm.py similarity index 96% rename from graspy/models/sbm.py rename to graspologic/models/sbm.py index 1d8e3e7aa..1204fdac5 100644 --- a/graspy/models/sbm.py +++ b/graspologic/models/sbm.py @@ -73,7 +73,7 @@ class SBMEstimator(BaseGraphEstimator): n_components : int, optional (default=None) Desired dimensionality of embedding for clustering to find communities. ``n_components`` must be ``< min(X.shape)``. If None, then optimal dimensions - will be chosen by :func:`~graspy.embed.select_dimension``. + will be chosen by :func:`~graspologic.embed.select_dimension``. min_comm : int, optional (default=1) The minimum number of communities (blocks) to consider. @@ -82,10 +82,10 @@ class SBMEstimator(BaseGraphEstimator): The maximum number of communities (blocks) to consider (inclusive). cluster_kws : dict, optional (default={}) - Additional kwargs passed down to :class:`~graspy.cluster.GaussianCluster` + Additional kwargs passed down to :class:`~graspologic.cluster.GaussianCluster` embed_kws : dict, optional (default={}) - Additional kwargs passed down to :class:`~graspy.embed.AdjacencySpectralEmbed` + Additional kwargs passed down to :class:`~graspologic.embed.AdjacencySpectralEmbed` Attributes ---------- @@ -107,8 +107,8 @@ class SBMEstimator(BaseGraphEstimator): See also -------- - graspy.models.DCSBMEstimator - graspy.simulations.sbm + graspologic.models.DCSBMEstimator + graspologic.simulations.sbm References ---------- @@ -265,7 +265,7 @@ class DCSBMEstimator(BaseGraphEstimator): n_components : int, optional (default=None) Desired dimensionality of embedding for clustering to find communities. ``n_components`` must be ``< min(X.shape)``. If None, then optimal dimensions - will be chosen by :func:`~graspy.embed.select_dimension``. + will be chosen by :func:`~graspologic.embed.select_dimension``. min_comm : int, optional (default=1) The minimum number of communities (blocks) to consider. @@ -274,10 +274,10 @@ class DCSBMEstimator(BaseGraphEstimator): The maximum number of communities (blocks) to consider (inclusive). cluster_kws : dict, optional (default={}) - Additional kwargs passed down to :class:`~graspy.cluster.GaussianCluster` + Additional kwargs passed down to :class:`~graspologic.cluster.GaussianCluster` embed_kws : dict, optional (default={}) - Additional kwargs passed down to :class:`~graspy.embed.LaplacianSpectralEmbed` + Additional kwargs passed down to :class:`~graspologic.embed.LaplacianSpectralEmbed` Attributes ---------- @@ -305,8 +305,8 @@ class DCSBMEstimator(BaseGraphEstimator): See also -------- - graspy.models.SBMEstimator - graspy.simulations.sbm + graspologic.models.SBMEstimator + graspologic.simulations.sbm Notes ----- diff --git a/graspy/pipeline/__init__.py b/graspologic/pipeline/__init__.py similarity index 100% rename from graspy/pipeline/__init__.py rename to graspologic/pipeline/__init__.py diff --git a/graspy/pipeline/mug2vec.py b/graspologic/pipeline/mug2vec.py similarity index 97% rename from graspy/pipeline/mug2vec.py rename to graspologic/pipeline/mug2vec.py index ff01d2f4a..b45f396aa 100644 --- a/graspy/pipeline/mug2vec.py +++ b/graspologic/pipeline/mug2vec.py @@ -76,10 +76,10 @@ class mug2vec(BaseEstimator): See also -------- - graspy.utils.pass_to_ranks - graspy.embed.OmnibusEmbed - graspy.embed.ClassicalMDS - graspy.embed.select_dimension + graspologic.utils.pass_to_ranks + graspologic.embed.OmnibusEmbed + graspologic.embed.ClassicalMDS + graspologic.embed.select_dimension """ def __init__( diff --git a/graspy/plot/__init__.py b/graspologic/plot/__init__.py similarity index 100% rename from graspy/plot/__init__.py rename to graspologic/plot/__init__.py diff --git a/graspy/plot/plot.py b/graspologic/plot/plot.py similarity index 100% rename from graspy/plot/plot.py rename to graspologic/plot/plot.py diff --git a/graspy/simulations/__init__.py b/graspologic/simulations/__init__.py similarity index 100% rename from graspy/simulations/__init__.py rename to graspologic/simulations/__init__.py diff --git a/graspy/simulations/rdpg_corr.py b/graspologic/simulations/rdpg_corr.py similarity index 98% rename from graspy/simulations/rdpg_corr.py rename to graspologic/simulations/rdpg_corr.py index 856909db8..d561c4233 100644 --- a/graspy/simulations/rdpg_corr.py +++ b/graspologic/simulations/rdpg_corr.py @@ -2,7 +2,7 @@ # Licensed under the MIT License. import numpy as np -from graspy.simulations import p_from_latent, sample_edges_corr +from graspologic.simulations import p_from_latent, sample_edges_corr def rdpg_corr(X, Y, r, rescale=False, directed=False, loops=False): diff --git a/graspy/simulations/simulations.py b/graspologic/simulations/simulations.py similarity index 100% rename from graspy/simulations/simulations.py rename to graspologic/simulations/simulations.py diff --git a/graspy/simulations/simulations_corr.py b/graspologic/simulations/simulations_corr.py similarity index 99% rename from graspy/simulations/simulations_corr.py rename to graspologic/simulations/simulations_corr.py index 549819113..d2f2a44bd 100644 --- a/graspy/simulations/simulations_corr.py +++ b/graspologic/simulations/simulations_corr.py @@ -2,7 +2,7 @@ # Licensed under the MIT License. import numpy as np -from graspy.simulations import sample_edges +from graspologic.simulations import sample_edges def check_dirloop(directed, loops): diff --git a/graspy/subgraph/__init__.py b/graspologic/subgraph/__init__.py similarity index 100% rename from graspy/subgraph/__init__.py rename to graspologic/subgraph/__init__.py diff --git a/graspy/subgraph/sg.py b/graspologic/subgraph/sg.py similarity index 100% rename from graspy/subgraph/sg.py rename to graspologic/subgraph/sg.py diff --git a/graspy/utils/__init__.py b/graspologic/utils/__init__.py similarity index 56% rename from graspy/utils/__init__.py rename to graspologic/utils/__init__.py index ac247bdee..354ca392d 100644 --- a/graspy/utils/__init__.py +++ b/graspologic/utils/__init__.py @@ -22,21 +22,21 @@ from .ptr import pass_to_ranks __all__ = [ - import_graph, - import_edgelist, - is_symmetric, - is_loopless, - is_unweighted, - is_almost_symmetric, - symmetrize, - remove_loops, - to_laplace, - is_fully_connected, - get_lcc, - get_multigraph_union_lcc, - get_multigraph_intersect_lcc, - augment_diagonal, - binarize, - cartprod, - pass_to_ranks, + "import_graph", + "import_edgelist", + "is_symmetric", + "is_loopless", + "is_unweighted", + "is_almost_symmetric", + "symmetrize", + "remove_loops", + "to_laplace", + "is_fully_connected", + "get_lcc", + "get_multigraph_union_lcc", + "get_multigraph_intersect_lcc", + "augment_diagonal", + "binarize", + "cartprod", + "pass_to_ranks", ] diff --git a/graspy/utils/ptr.py b/graspologic/utils/ptr.py similarity index 100% rename from graspy/utils/ptr.py rename to graspologic/utils/ptr.py diff --git a/graspy/utils/utils.py b/graspologic/utils/utils.py similarity index 100% rename from graspy/utils/utils.py rename to graspologic/utils/utils.py diff --git a/graspy/version/__init__.py b/graspologic/version/__init__.py similarity index 100% rename from graspy/version/__init__.py rename to graspologic/version/__init__.py diff --git a/graspy/version/version.py b/graspologic/version/version.py similarity index 89% rename from graspy/version/version.py rename to graspologic/version/version.py index a3c193c51..5a5e6af52 100644 --- a/graspy/version/version.py +++ b/graspologic/version/version.py @@ -7,9 +7,7 @@ __all__: List[str] = ["version", "name"] -name = ( - "graspy" # TODO: #454 Update in https://github.com/microsoft/graspologic/issues/454 -) +name = "graspologic" # manually updated __semver = "0.1.0" diff --git a/graspologic/version/version.txt b/graspologic/version/version.txt new file mode 100644 index 000000000..61282e7c9 --- /dev/null +++ b/graspologic/version/version.txt @@ -0,0 +1 @@ +0.1.0.dev20200928085109 \ No newline at end of file diff --git a/graspy/version/version.txt b/graspy/version/version.txt deleted file mode 100644 index e69de29bb..000000000 diff --git a/setup.py b/setup.py index 7a7aee435..fba289494 100644 --- a/setup.py +++ b/setup.py @@ -11,13 +11,12 @@ if sys.version_info < MINIMUM_PYTHON_VERSION: sys.exit("Python {}.{}+ is required.".format(*MINIMUM_PYTHON_VERSION)) -sys.path.insert(0, os.path.join("graspy", "version")) -# TODO: #454 Change path in https://github.com/microsoft/graspologic/issues/454 +sys.path.insert(0, os.path.join("graspologic", "version")) from version import version sys.path.pop(0) -version_path = os.path.join("graspy", "version", "version.txt") +version_path = os.path.join("graspologic", "version", "version.txt") with open(version_path, "w") as version_file: version_file.write(f"{version}") @@ -25,7 +24,7 @@ LONG_DESCRIPTION = f.read() setup( - name="graspy", + name="graspologic", version=version, description="A set of python modules for graph statistics", long_description=LONG_DESCRIPTION, @@ -48,8 +47,8 @@ packages=find_packages(exclude=["tests", "tests.*", "tests/*"]), include_package_data=True, package_data={ - "version": [os.path.join("graspy", "version", "version.txt")] - }, # TODO: #454 Also needs changed by https://github.com/microsoft/graspologic/issues/454 + "version": [os.path.join("graspologic", "version", "version.txt")] + }, install_requires=[ "networkx>=2.1", "numpy>=1.8.1", diff --git a/tests/cluster/test_autogmm.py b/tests/cluster/test_autogmm.py index 9a6f4f813..1cf837c84 100644 --- a/tests/cluster/test_autogmm.py +++ b/tests/cluster/test_autogmm.py @@ -6,9 +6,9 @@ from numpy.testing import assert_allclose, assert_equal from sklearn.exceptions import NotFittedError -from graspy.cluster.autogmm import AutoGMMCluster -from graspy.embed.ase import AdjacencySpectralEmbed -from graspy.simulations.simulations import sbm +from graspologic.cluster.autogmm import AutoGMMCluster +from graspologic.embed.ase import AdjacencySpectralEmbed +from graspologic.simulations.simulations import sbm def test_inputs(): @@ -37,7 +37,7 @@ def test_inputs(): # affinity is not in ['euclidean', 'manhattan', 'cosine', 'none'] with pytest.raises(ValueError): - AutoGMM = AutoGMMCluster(min_components=1, affinity="graspy") + AutoGMM = AutoGMMCluster(min_components=1, affinity="graspologic") # linkage is not an array, string or list with pytest.raises(TypeError): @@ -45,7 +45,7 @@ def test_inputs(): # linkage is not in ['single', 'average', 'complete', 'ward'] with pytest.raises(ValueError): - AutoGMM = AutoGMMCluster(min_components=1, linkage="graspy") + AutoGMM = AutoGMMCluster(min_components=1, linkage="graspologic") # euclidean is not an affinity option when ward is a linkage option with pytest.raises(ValueError): @@ -57,7 +57,7 @@ def test_inputs(): # covariance type is not in ['spherical', 'diag', 'tied', 'full'] with pytest.raises(ValueError): - AutoGMM = AutoGMMCluster(min_components=1, covariance_type="graspy") + AutoGMM = AutoGMMCluster(min_components=1, covariance_type="graspologic") # min_cluster > n_samples when max_cluster is None with pytest.raises(ValueError): diff --git a/tests/cluster/test_gclust.py b/tests/cluster/test_gclust.py index 922c6aad3..cc8cb14b3 100644 --- a/tests/cluster/test_gclust.py +++ b/tests/cluster/test_gclust.py @@ -6,9 +6,9 @@ from numpy.testing import assert_allclose, assert_equal from sklearn.exceptions import NotFittedError -from graspy.cluster.gclust import GaussianCluster -from graspy.embed.ase import AdjacencySpectralEmbed -from graspy.simulations.simulations import sbm +from graspologic.cluster.gclust import GaussianCluster +from graspologic.embed.ase import AdjacencySpectralEmbed +from graspologic.simulations.simulations import sbm def test_inputs(): @@ -37,7 +37,7 @@ def test_inputs(): # covariance type is not in ['spherical', 'diag', 'tied', 'full'] with pytest.raises(ValueError): - gclust = GaussianCluster(min_components=1, covariance_type="graspy") + gclust = GaussianCluster(min_components=1, covariance_type="graspologic") # min_cluster > n_samples when max_cluster is None with pytest.raises(ValueError): diff --git a/tests/cluster/test_kclust.py b/tests/cluster/test_kclust.py index d9f89495b..470602c6a 100644 --- a/tests/cluster/test_kclust.py +++ b/tests/cluster/test_kclust.py @@ -6,7 +6,7 @@ from numpy.testing import assert_allclose, assert_equal from sklearn.exceptions import NotFittedError -from graspy.cluster.kclust import KMeansCluster +from graspologic.cluster.kclust import KMeansCluster def test_inputs(): diff --git a/tests/match/test_match.py b/tests/match/test_match.py index 2a686a703..e1af213c5 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -5,8 +5,8 @@ import numpy as np import math import random -from graspy.match import GraphMatch as GMP -from graspy.match import SinkhornKnopp as SK +from graspologic.match import GraphMatch as GMP +from graspologic.match import SinkhornKnopp as SK class TestGMP: diff --git a/tests/pipeline/test_mug2vec.py b/tests/pipeline/test_mug2vec.py index 252a997b6..032d747aa 100644 --- a/tests/pipeline/test_mug2vec.py +++ b/tests/pipeline/test_mug2vec.py @@ -5,9 +5,9 @@ import pytest from numpy.testing import assert_equal -from graspy.cluster import GaussianCluster -from graspy.pipeline import mug2vec -from graspy.simulations import sbm +from graspologic.cluster import GaussianCluster +from graspologic.pipeline import mug2vec +from graspologic.simulations import sbm def generate_data(): diff --git a/tests/test_base_embed.py b/tests/test_base_embed.py index 9715f71b9..761e2f665 100644 --- a/tests/test_base_embed.py +++ b/tests/test_base_embed.py @@ -5,9 +5,9 @@ import numpy as np -import graspy as gs -from graspy.embed.base import BaseEmbed -from graspy.simulations.simulations import er_nm, er_np +import graspologic as gs +from graspologic.embed.base import BaseEmbed +from graspologic.simulations.simulations import er_nm, er_np class TestBaseEmbed(unittest.TestCase): diff --git a/tests/test_datasets.py b/tests/test_datasets.py index fbe25dd23..475d3e107 100644 --- a/tests/test_datasets.py +++ b/tests/test_datasets.py @@ -2,7 +2,7 @@ # Licensed under the MIT License. import pytest -from graspy.datasets import * +from graspologic.datasets import * def test_drosphila_left(): diff --git a/tests/test_io.py b/tests/test_io.py index c7e157958..1570b296b 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -8,7 +8,7 @@ import numpy as np import pytest -import graspy as gs +import graspologic as gs class TestImportGraph: diff --git a/tests/test_latentdistributiontest.py b/tests/test_latentdistributiontest.py index 2f50f7f7d..a01a168d1 100644 --- a/tests/test_latentdistributiontest.py +++ b/tests/test_latentdistributiontest.py @@ -7,9 +7,9 @@ import networkx as nx from sklearn.metrics import pairwise_distances -from graspy.embed import AdjacencySpectralEmbed -from graspy.inference import LatentDistributionTest -from graspy.simulations import er_np, sbm +from graspologic.embed import AdjacencySpectralEmbed +from graspologic.inference import LatentDistributionTest +from graspologic.simulations import er_np, sbm class TestLatentDistributionTest(unittest.TestCase): diff --git a/tests/test_latentpositiontest.py b/tests/test_latentpositiontest.py index 8c8285d57..1d7b2eee7 100644 --- a/tests/test_latentpositiontest.py +++ b/tests/test_latentpositiontest.py @@ -3,9 +3,9 @@ import unittest import numpy as np -from graspy.inference import LatentPositionTest -from graspy.simulations import er_np, sbm -from graspy.utils import * +from graspologic.inference import LatentPositionTest +from graspologic.simulations import er_np, sbm +from graspologic.utils import * class TestLatentPositionTest(unittest.TestCase): diff --git a/tests/test_mase.py b/tests/test_mase.py index b9d9a8851..abc2256b1 100644 --- a/tests/test_mase.py +++ b/tests/test_mase.py @@ -3,9 +3,9 @@ import numpy as np import pytest -from graspy.cluster.gclust import GaussianCluster -from graspy.embed.mase import MultipleASE -from graspy.simulations.simulations import er_np, sbm +from graspologic.cluster.gclust import GaussianCluster +from graspologic.embed.mase import MultipleASE +from graspologic.simulations.simulations import er_np, sbm from numpy import array_equal diff --git a/tests/test_mds.py b/tests/test_mds.py index db4eafcb3..e46e9c635 100644 --- a/tests/test_mds.py +++ b/tests/test_mds.py @@ -5,7 +5,7 @@ import numpy as np from numpy.testing import assert_almost_equal, assert_equal -from graspy.embed.mds import ClassicalMDS +from graspologic.embed.mds import ClassicalMDS def test_input(): diff --git a/tests/test_models.py b/tests/test_models.py index a8f6f06e2..3a2383310 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -4,15 +4,15 @@ import pytest import numpy as np from numpy.testing import assert_allclose -from graspy.models import ( +from graspologic.models import ( EREstimator, DCSBMEstimator, SBMEstimator, RDPGEstimator, DCEREstimator, ) -from graspy.simulations import er_np, sbm, sample_edges -from graspy.utils import cartprod, is_symmetric +from graspologic.simulations import er_np, sbm, sample_edges +from graspologic.utils import cartprod, is_symmetric from sklearn.metrics import adjusted_rand_score from sklearn.exceptions import NotFittedError diff --git a/tests/test_omni.py b/tests/test_omni.py index a070495a9..5d1c969ac 100755 --- a/tests/test_omni.py +++ b/tests/test_omni.py @@ -7,9 +7,9 @@ from numpy.linalg import norm from numpy.testing import assert_allclose -from graspy.embed.omni import OmnibusEmbed, _get_omni_matrix -from graspy.simulations.simulations import er_nm, er_np, sbm -from graspy.utils.utils import is_symmetric, symmetrize +from graspologic.embed.omni import OmnibusEmbed, _get_omni_matrix +from graspologic.simulations.simulations import er_nm, er_np, sbm +from graspologic.utils.utils import is_symmetric, symmetrize def generate_data(n, seed=1, symetric=True): diff --git a/tests/test_plot.py b/tests/test_plot.py index b9ce28cb5..c793b3104 100644 --- a/tests/test_plot.py +++ b/tests/test_plot.py @@ -4,8 +4,8 @@ import numpy as np import pytest -from graspy.plot.plot import heatmap, gridplot, pairplot, _sort_inds -from graspy.simulations.simulations import er_np, sbm +from graspologic.plot.plot import heatmap, gridplot, pairplot, _sort_inds +from graspologic.simulations.simulations import er_np, sbm def test_common_inputs(): diff --git a/tests/test_ptr.py b/tests/test_ptr.py index bf334d903..31cd1d1f4 100644 --- a/tests/test_ptr.py +++ b/tests/test_ptr.py @@ -3,7 +3,7 @@ import unittest import numpy as np -from graspy.utils import pass_to_ranks, is_unweighted +from graspologic.utils import pass_to_ranks, is_unweighted class TestPTR(unittest.TestCase): diff --git a/tests/test_rdpg_corr.py b/tests/test_rdpg_corr.py index 2e0871cc7..16095d596 100644 --- a/tests/test_rdpg_corr.py +++ b/tests/test_rdpg_corr.py @@ -2,8 +2,8 @@ # Licensed under the MIT License. import unittest -from graspy.simulations.simulations import sample_edges, p_from_latent -from graspy.simulations.rdpg_corr import rdpg_corr +from graspologic.simulations.simulations import sample_edges, p_from_latent +from graspologic.simulations.rdpg_corr import rdpg_corr import numpy as np import pytest import warnings diff --git a/tests/test_select_dimension.py b/tests/test_select_dimension.py index 431c9a904..19381ba38 100644 --- a/tests/test_select_dimension.py +++ b/tests/test_select_dimension.py @@ -6,8 +6,8 @@ from numpy.testing import assert_equal from scipy.linalg import orth -from graspy.embed.svd import select_dimension -from graspy.simulations.simulations import sbm +from graspologic.embed.svd import select_dimension +from graspologic.simulations.simulations import sbm def generate_data(n=10, elbows=3, seed=1): diff --git a/tests/test_sg.py b/tests/test_sg.py index 837057caf..06fa7b84e 100644 --- a/tests/test_sg.py +++ b/tests/test_sg.py @@ -1,7 +1,7 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -import graspy.subgraph as sg +import graspologic.subgraph as sg import unittest import numpy as np from numpy.testing import assert_equal diff --git a/tests/test_sims.py b/tests/test_sims.py index a1a6fd3e7..6dc441928 100644 --- a/tests/test_sims.py +++ b/tests/test_sims.py @@ -2,11 +2,11 @@ # Licensed under the MIT License. import unittest -import graspy as gs +import graspologic as gs import numpy as np import networkx as nx -from graspy.simulations import * -from graspy.utils.utils import is_symmetric, is_loopless, symmetrize, cartprod +from graspologic.simulations import * +from graspologic.utils.utils import is_symmetric, is_loopless, symmetrize, cartprod def remove_diagonal(A): diff --git a/tests/test_sims_corr.py b/tests/test_sims_corr.py index 8d08a69a3..0407ce7fb 100644 --- a/tests/test_sims_corr.py +++ b/tests/test_sims_corr.py @@ -2,7 +2,11 @@ # Licensed under the MIT License. import unittest -from graspy.simulations.simulations_corr import sample_edges_corr, er_corr, sbm_corr +from graspologic.simulations.simulations_corr import ( + sample_edges_corr, + er_corr, + sbm_corr, +) import numpy as np import pytest import warnings diff --git a/tests/test_sklearn.py b/tests/test_sklearn.py index a2e2b6026..00dd7e203 100644 --- a/tests/test_sklearn.py +++ b/tests/test_sklearn.py @@ -1,11 +1,11 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -import graspy +import graspologic import numpy as np from sklearn.utils.estimator_checks import check_estimator -check_estimator(graspy.embed.AdjacencySpectralEmbed) -check_estimator(graspy.embed.LaplacianSpectralEmbed) -check_estimator(graspy.embed.ClassicalMDS) +check_estimator(graspologic.embed.AdjacencySpectralEmbed) +check_estimator(graspologic.embed.LaplacianSpectralEmbed) +check_estimator(graspologic.embed.ClassicalMDS) diff --git a/tests/test_spectral_embed.py b/tests/test_spectral_embed.py index bffc75268..58d7bc2d0 100644 --- a/tests/test_spectral_embed.py +++ b/tests/test_spectral_embed.py @@ -3,9 +3,9 @@ import unittest import numpy as np -from graspy.embed.ase import AdjacencySpectralEmbed -from graspy.embed.lse import LaplacianSpectralEmbed -from graspy.simulations.simulations import er_np, er_nm, sbm +from graspologic.embed.ase import AdjacencySpectralEmbed +from graspologic.embed.lse import LaplacianSpectralEmbed +from graspologic.simulations.simulations import er_np, er_nm, sbm from sklearn.cluster import KMeans from sklearn.metrics import adjusted_rand_score diff --git a/tests/test_svd.py b/tests/test_svd.py index 1bf6a25f6..e1c0a318c 100644 --- a/tests/test_svd.py +++ b/tests/test_svd.py @@ -6,8 +6,8 @@ from numpy.testing import assert_equal, assert_allclose from scipy.spatial import procrustes -from graspy.embed.svd import selectSVD -from graspy.simulations.simulations import er_np +from graspologic.embed.svd import selectSVD +from graspologic.simulations.simulations import er_np def test_bad_inputs(): diff --git a/tests/test_utils.py b/tests/test_utils.py index efdb55e5c..5af2e8949 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -4,7 +4,7 @@ import unittest import numpy as np import networkx as nx -from graspy.utils import utils as gus +from graspologic.utils import utils as gus from math import sqrt from numpy.testing import assert_equal From d37d199ff8712974f1c2f0b9d6b33fd752213ebc Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 28 Sep 2020 10:16:31 -0700 Subject: [PATCH 018/140] Update build.yml `pull_request_target` doesn't do quite what we intended it to do, and is giving us a false sense of security over what pull requests are passing and which ones are broken. I'm taking it off for now until I have a chance to look into this and get it working correctly for forked repositories. --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 662e76048..7a1cb518b 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,5 +1,5 @@ name: graspologic CI -on: [push, pull_request_target] +on: [push] jobs: build-documentation: runs-on: ubuntu-latest From 13ca4fc95963e84827c146437408e7c525ec8cfd Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 28 Sep 2020 10:36:25 -0700 Subject: [PATCH 019/140] Publishing 0.1.0 prerelease to PyPI --- .github/workflows/build.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 7a1cb518b..ef1c0e894 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -102,5 +102,4 @@ jobs: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | - # twine upload dist/* - echo "Hello" + twine upload dist/* From 796bd663b392f715ed1db3976ac146ecf7bc9e71 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Mon, 28 Sep 2020 17:32:14 -0400 Subject: [PATCH 020/140] Add CODEOWNERS (#500) * add skeleton codeowners from jhu side * trailing line --- .github/CODEOWNERS | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 .github/CODEOWNERS diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 000000000..015908622 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,15 @@ +# graspologic package +/graspologic/cluster @bdpedigo @j1c +/graspologic/datasets @bdpedigo +/graspologic/embed @j1c @bdpedigo +/graspologic/inference @alyakin314 @bdpedigo +/graspologic/match @asaadeldin11 @bdpedigo +/graspologic/models @bdpedigo @j1c +/graspologic/pipeline @j1c @bdpedigo +/graspologic/plot @j1c @bdpedigo +/graspologic/subgraph @j1c +/graspologic/utils/ @j1c @bdpedigo + +# tutorials + +# tests From 886fd4e9377dc602b59c1e1fc44aef54e26f8a1c Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 29 Sep 2020 09:34:02 -0700 Subject: [PATCH 021/140] Run build on PR In theory, simply adding pull_request to the list of triggers is all we need to run the build workflow on a forked repository. --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ef1c0e894..6ae7b867a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,5 +1,5 @@ name: graspologic CI -on: [push] +on: [push, pull_request] jobs: build-documentation: runs-on: ubuntu-latest From 0a90805066db94b8f9c7cf2bcb0c7a7a6242bf7f Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 29 Sep 2020 14:10:28 -0700 Subject: [PATCH 022/140] Update version.txt Ugh this never should have been committed like this. --- graspologic/version/version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graspologic/version/version.txt b/graspologic/version/version.txt index 61282e7c9..8b1378917 100644 --- a/graspologic/version/version.txt +++ b/graspologic/version/version.txt @@ -1 +1 @@ -0.1.0.dev20200928085109 \ No newline at end of file + From 534b804cc39d3bb565755d19e030c8a8f5ea0f80 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Thu, 1 Oct 2020 12:40:52 -0400 Subject: [PATCH 023/140] remove star import in test latent position test (#494) * remove star import in test latent position test * fix import rename issue --- tests/test_latentpositiontest.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_latentpositiontest.py b/tests/test_latentpositiontest.py index 1d7b2eee7..bc76ddea5 100644 --- a/tests/test_latentpositiontest.py +++ b/tests/test_latentpositiontest.py @@ -5,7 +5,6 @@ import numpy as np from graspologic.inference import LatentPositionTest from graspologic.simulations import er_np, sbm -from graspologic.utils import * class TestLatentPositionTest(unittest.TestCase): From 57cb5afa7d53cc9408cbb062193562e07bc9c56f Mon Sep 17 00:00:00 2001 From: alyakin314 Date: Tue, 6 Oct 2020 00:49:10 +0300 Subject: [PATCH 024/140] create align module (#419) * seedless procrustes added as a part of the align module * created base aligner * seedless procrustes and sign flips modified such that they work with BaseAlign * sign flips modified * preliminary sign flips and seedless procrustes done; sign flips works as intended * wrote many sign flips test, slightly changed the behavior of transform, overloaded fit_transform in sign flips * sign flips done, with tests yayayay * forgot to add a test for max criteria; also fixed a bug * test sign flips changed to have 100% coverage * created tests for seedless procrustes (unpopulated yet) * created orthogonal procrustes file * sign flips and orthogonal procrustes now have checks and doc strings * blacked files * created tests for orthogonal procrustes * init bug * orthogonal procrustes passes corresponding tests * black orthogonal procrustes tests file * massively simplified seedless procrustes code; speeded it up by no longer doing 100 iterations" " * added ot (optimal transport) to requirements * wrong name of the package * set-up tiny fix * seedless procrustes code reviewed, doc strings added * seedless procrustes checks and tests improved * checking initital matrix in seedless procrustes as well now * Seedless procrustes passes bad inputs tests * tests for all possible initializations of seedless procrustes * forgot to black tests * latent distribution test now uses sign flips imported from align * added a test showing that seedles procrustes works * added sign flips and seedless procrustes to latent distribution test with tsts * making sure that aligner kws are passed through * me forgot to black * increased coverage in LatentDistributionTest * misprint lft -> ldt * latent distribution test should pass now * orthogonal procrustes handles other use cases * docstring of orthogonal procurstes made proper * created a test for scaling-orthogonal * bug fix in orthogonal procurstes * freeze Y by default in orthogonal procrustes * commented out diagonal-orthogonal, because it doesn't fit into here * blacked files; all but special cases of orthogonalprocrustes done * changed comment in test orthogonal procrust * removed diagonal-orthogonal and scaling-orthogonal options from the orthogonal procrustes * comments massively changed; lpt uses align module * meesa idiot, meesa no black files * minor typos and make orthogonal check less strict * run black * try new black * initialization changed to init in seedless procrustes * initialization changed to init in seedless procrustes * changed doc in seedless, freeze_Y removed from seedless * freeze_Y removed from orthogonal procrustes * accept_sparse removed throughout align module * freeze_Y depreciated * Q_Y removed from base and sign flips * orthogonal procrustes no longer has Q_Y * seedless no longer uses Q_Y * latent position test now works after removing Q_Y things * finished refactoring aligns attributes * refactored codein align such that we now check array correctness in the base * docs changed in the inference and align modules * docs changed for seedless and orthogonal * refactored seedless procrustes ever so slightly * removed a doc from sign flips that is no longer true * latent distribution test is now safer to use * latent distribution test code refactored such that it's safer * error message fix and checking for X dimensions match Q in transform of align * bug fixed, seedless doc changed * seedless now checks that initial_q and initial_p aren't provided at the same time; need tests * test_seedless_procrustes.py * meesa no black again... * added math to seeedless * removed iterative eps completly * initializations docs changed to be more detailed (read: make Ben happy) * refernces added in seedless procrustes * lisence changed, i think in all places * Errors and some docs capitalized appropriately * inconsequential commit * no longer breaking with reps = 0 * easy misrpints and docs fixed throughout align * some more docs, reps = 0 no longer breaks * more docstring polishing in seedless and latent distribution test * orthogonal procrustes math explained better * more docstrings * changed criteria to criterion * align added to references * docstrings slightly improved * orthogonal procrustes fit transform calls super * bugfix * added align to references * references updated in procrustes (bugfixes) * useless line removed * minor docs changes * renamed graspy to graspologic everywhere in my files, let's see how this breaks * small init bug with no longer importing star * (some) Ben's suggestions incorporated * bug fixesss * fix docs building? * initial_Q _> selected_initial_Q * orthogonal procrustes now has an objective function * changed the doubly stochastic to soft assignemnt everywhere * fit and fit predict docstrings changed * meesa no blacks * trying to fix docs ci failing * add trailing under to attribute * escaped trailing undescores * only escape underscore after P * weird spacing fix * escaping afte rscore * escape initially selected Q * this should work?? * docs offset issue fixed Co-authored-by: bdpedigo --- .github/CODEOWNERS | 1 + README.md | 1 + docs/reference/align.rst | 17 + docs/reference/index.rst | 3 +- graspologic/__init__.py | 3 +- graspologic/align/__init__.py | 8 + graspologic/align/base.py | 122 ++++++ graspologic/align/orthogonal_procrustes.py | 117 ++++++ graspologic/align/seedless_procrustes.py | 364 ++++++++++++++++++ graspologic/align/sign_flips.py | 106 +++++ graspologic/inference/base.py | 1 + .../inference/latent_distribution_test.py | 205 +++++----- graspologic/inference/latent_position_test.py | 16 +- graspologic/utils/__init__.py | 2 + graspologic/utils/utils.py | 56 +++ runtime.txt | 2 +- setup.py | 1 + tests/test_latentdistributiontest.py | 95 ++++- tests/test_orthogonal_procrustes.py | 111 ++++++ tests/test_seedless_procrustes.py | 160 ++++++++ tests/test_sign_flips.py | 100 +++++ 21 files changed, 1366 insertions(+), 125 deletions(-) create mode 100644 docs/reference/align.rst create mode 100644 graspologic/align/__init__.py create mode 100644 graspologic/align/base.py create mode 100644 graspologic/align/orthogonal_procrustes.py create mode 100644 graspologic/align/seedless_procrustes.py create mode 100644 graspologic/align/sign_flips.py create mode 100644 tests/test_orthogonal_procrustes.py create mode 100644 tests/test_seedless_procrustes.py create mode 100644 tests/test_sign_flips.py diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 015908622..7fe892d95 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,4 +1,5 @@ # graspologic package +/graspologic/align @alyakin314 @bdpedigo /graspologic/cluster @bdpedigo @j1c /graspologic/datasets @bdpedigo /graspologic/embed @j1c @bdpedigo diff --git a/README.md b/README.md index 979c0905d..52b4fa282 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ hyppo matplotlib networkx numpy +POT seaborn scikit-learn scipy diff --git a/docs/reference/align.rst b/docs/reference/align.rst new file mode 100644 index 000000000..2c854b3b7 --- /dev/null +++ b/docs/reference/align.rst @@ -0,0 +1,17 @@ +******** +Aligning +******** + +.. currentmodule:: graspologic.align + +Sign flips +---------- +.. autoclass:: SignFlips + +Orthogonal Procrustes +--------------------- +.. autoclass:: OrthogonalProcrustes + +Seedless Procrustes +------------------- +.. autoclass:: SeedlessProcrustes diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 7add0ef2d..85c231372 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -16,4 +16,5 @@ Reference utils pipeline match - subgraph \ No newline at end of file + subgraph + align diff --git a/graspologic/__init__.py b/graspologic/__init__.py index 0260f697b..bb133425a 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -5,6 +5,7 @@ import warnings +import graspologic.align import graspologic.cluster import graspologic.datasets import graspologic.embed @@ -13,8 +14,8 @@ import graspologic.pipeline import graspologic.plot import graspologic.simulations -import graspologic.utils import graspologic.subgraph +import graspologic.utils warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=PendingDeprecationWarning) diff --git a/graspologic/align/__init__.py b/graspologic/align/__init__.py new file mode 100644 index 000000000..dd8d3ddbd --- /dev/null +++ b/graspologic/align/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +from .orthogonal_procrustes import OrthogonalProcrustes +from .seedless_procrustes import SeedlessProcrustes +from .sign_flips import SignFlips + +__all__ = ["OrthogonalProcrustes", "SeedlessProcrustes", "SignFlips"] diff --git a/graspologic/align/base.py b/graspologic/align/base.py new file mode 100644 index 000000000..78481ff7b --- /dev/null +++ b/graspologic/align/base.py @@ -0,0 +1,122 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import numpy as np +from abc import abstractmethod +from sklearn.utils import check_array +from sklearn.base import BaseEstimator + + +class BaseAlign(BaseEstimator): + """ + Base class for align tasks such as sign flipping, procrustes and seedless + procrustes. + + Attributes + ---------- + Q_ : array, size (d, d) + Final orthogonal matrix, used to modify `X` passed to transform + + """ + + def __init__(self): + pass + + def _check_datasets(self, X, Y): + """ + Ensures that the datasets are numpy, 2d, finite, and have the same + number of components. Does not check for same number of vertices. + Returns copies of these datasets. + """ + # check for numpy-ness + if not isinstance(X, np.ndarray): + msg = f"First dataset is a {type(X)}, not an np.ndarray! " + raise TypeError(msg) + if not isinstance(Y, np.ndarray): + msg = f"Second dataset is a {type(Y)}, not an np.ndarray! " + raise TypeError(msg) + # check for 2-dness and finiteness + X = check_array(X, copy=True) + Y = check_array(Y, copy=True) + # check for equal components + if X.shape[1] != Y.shape[1]: + msg = "Two datasets have different number of components!" + raise ValueError(msg) + return X, Y + + @abstractmethod + def fit(self, X, Y): + """ + Uses the two datasets to learn the matrix `self.Q_` that aligns the + first dataset with the second. + + Parameters + ---------- + X : np.ndarray, shape (n, d) + Dataset to be mapped to `Y`, must have same number of dimensions + (axis 1) as `Y`. + + Y : np.ndarray, shape (m, d) + Target dataset, must have same number of dimensions (axis 1) as `X`. + + Returns + ------- + self : returns an instance of self + """ + pass + + def transform(self, X): + """ + Transforms the dataset `X` using the learned matrix `self.Q_`. This may + be the same as the first dataset as in .fit(), or a new dataset. + For example, additional samples from the same dataset. + + Parameters + ---------- + X : np.ndarray, shape(m, d) + Dataset to be transformed, must have same number of dimensions + (axis 1) as `X` and `Y` that were passed to fit. + + Returns + ------- + X_prime : np.ndarray, shape (n, d) + First dataset of vectors, aligned to second. Equal to + `X` @ `self.Q_`. + """ + if not isinstance(X, np.ndarray): + msg = f"Dataset is a {type(X)}, not an np.ndarray! " + raise TypeError(msg) + X = check_array(X) + if not X.shape[1] == self.Q_.shape[0]: + msg = ( + "Dataset needs to have the same number of dimensions, d, " + "as datasets X and Y used in fit. Currently, vectors in " + f"the dataset to transform have {X.shape[1]} dimensions, " + f"while vectors in fit had {self.Q_.shape[0]} dimensions." + ) + raise ValueError(msg) + return X @ self.Q_ + + def fit_transform(self, X, Y): + """ + Uses the two datasets to learn the matrix `self.Q_` that aligns the + first dataset with the second. Then, transforms the first dataset `X` + using the learned matrix `self.Q_`. + + Parameters + ---------- + X : np.ndarray, shape (n, d) + Dataset to be mapped to `Y`, must have same number of dimensions + (axis 1) as `Y`. + + Y : np.ndarray, shape (m, d) + Target dataset, must have same number of dimensions (axis 1) as `X`. + + Returns + ------- + X_prime : np.ndarray, shape (n, d) + First dataset of vectors, aligned to second. Equal to + `X` @ `self.Q_`. + """ + self.fit(X, Y) + return self.transform(X) diff --git a/graspologic/align/orthogonal_procrustes.py b/graspologic/align/orthogonal_procrustes.py new file mode 100644 index 000000000..3dc7a3980 --- /dev/null +++ b/graspologic/align/orthogonal_procrustes.py @@ -0,0 +1,117 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import numpy as np +from scipy.linalg import orthogonal_procrustes + +from .base import BaseAlign + + +class OrthogonalProcrustes(BaseAlign): + """ + Computes the matrix solution of the classical orthogonal Procrustes [1]_ + problem, which is that given two matrices `X` and `Y` of equal shape + (n, d), find an orthogonal matrix `self.Q_` that most closely maps `X` to + `Y`. Subsequently, uses that matrix to transform either the original `X`, + or a different dataset in the same space. + + Note that when used to match two datasets, this method unlike + :class:`~graspologic.align.SeedlessProcrustes`, not only requires that the + datasets have the same number of entries, but also that there is some + correspondence between the entries. In graph embeddings, this usually + corresponds to the assumption that the vertex i in graph X has the same + latent position as the vertex i in graph Y. + + Attributes + ---------- + Q_ : array, size (d, d) + Final orthogonal matrix, used to modify `X`. + + score_ : float + Final value of the objective function: :math:`|| X Q - Y ||_F` + Lower means the datasets have been matched together better. + + References + ---------- + + .. [1] https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem + + .. [2] Peter H. Schonemann, "A generalized solution of the orthogonal + Procrustes problem", Psychometrica -- Vol. 31, No. 1, March, 1996. + + Notes + ----- + Formally, minimizes :math:`|| X Q - Y ||_F`, which has a closed form + solution, whenever :math:`Q` is constrained to be an orthogonal matrix, + that is a matrix that satisfies :math:`Q^T Q = Q Q^T = I`. For the more + details, including the proof of the closed-form solution see [1]_. + + Implementation-wise, this class is a wrapper of the + `scipy.linalg.orthogonal_procrustes`, which itself uses an algorithm + described in find the optimal solution algorithm [2]_. + + """ + + def __init__( + self, + ): + + super().__init__() + + def fit(self, X, Y): + """ + Uses the two datasets to learn the matrix `self.Q_` that aligns the + first dataset with the second. + + Parameters + ---------- + X : np.ndarray, shape (n, d) + Dataset to be mapped to `Y`, must have the same shape as `Y`. + + Y : np.ndarray, shape (m, d) + Target dataset, must have the same shape as `X`. + + + Returns + ------- + self : returns an instance of self + + """ + X, Y = self._check_datasets(X, Y) + + _, d = X.shape + if X.shape[0] != Y.shape[0]: + msg = ( + "Two datasets have different number of entries! " + "OrthogonalProcrustes assumes that entries of the two " + "datasets are matched. consider using SeedlessProcrustes " + "instead." + ) + raise ValueError(msg) + + _, d = X.shape + self.Q_, _ = orthogonal_procrustes(X, Y) + self.score_ = np.linalg.norm(X @ self.Q_ - Y, ord="fro") + return self + + def fit_transform(self, X, Y): + """ + Uses the two datasets to learn the matrix `self.Q_` that aligns the + first dataset with the second. Then, transforms the first dataset `X` + using the learned matrix `self.Q_`. + + Parameters + ---------- + X : np.ndarray, shape (n, d) + Dataset to be mapped to `Y`, must have the same shape as `Y`. + + Y : np.ndarray, shape (m, d) + Target dataset, must have the same shape as `X`. + + Returns + ------- + X_prime : np.ndarray, shape (n, d) + First dataset of vectors, aligned to second. Equal to + `X` @ `self.Q_`. + """ + return super().fit_transform(X, Y) diff --git a/graspologic/align/seedless_procrustes.py b/graspologic/align/seedless_procrustes.py new file mode 100644 index 000000000..ddfbbb671 --- /dev/null +++ b/graspologic/align/seedless_procrustes.py @@ -0,0 +1,364 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import ot +import numpy as np +from sklearn.utils import check_array + +from .base import BaseAlign +from .sign_flips import SignFlips +from .orthogonal_procrustes import OrthogonalProcrustes + + +class SeedlessProcrustes(BaseAlign): + """ + Matches two datasets using an orthogonal matrix. Unlike + :class:`~graspologic.align.OrthogonalProcrustes`, this does not require a + matching between entries. It can even be used in the settings where the two + datasets do not have the same number of entries. + + In graph setting, it is used to align the embeddings of two different + graphs, when it requires some simultaneous inference task and no 1-1 + matching between the vertices of the two graphs can be established, for + example, inside of the test for the equivalence of the latent distributions + (see: :class:`~graspologic.inference.LatentDistributionTest`). + + Parameters + ---------- + optimal_transport_lambda : float (default=0.1), optional + Regularization term of the Sinkhorn optimal transport algorithm. + + optimal_transport_eps : float (default=0.01), optional + Tolerance parameter for the each Sinkhorn optimal transport algorithm. + I.e. tolerance for each "E-step". + + optimal_transport_num_reps : int (default=1000), optional + Number of repetitions in each iteration of the iterative optimal + transport problem. I.e. maximum number of repetitions in each "E-step". + + iterative_num_reps : int (default=100), optional + Number of reps in each iteration of the iterative optimal transport + problem. I.e. maxumum number of total iterations the whole "EM" + algorithm. + + init : string, {'2d' (default), 'sign_flips', 'custom'}, optional + + - '2d' + Uses :math:`2^d` different restarts, where :math:`d` is the + dimension of the datasets. In particular, tries all matrices that + are simultaneously diagonal and orthogonal. In other words, these + are diagonal matrices with all entries on the diagonal being either + +1 or -1. This is motivated by the fact that spectral graph + embeddings have two types of orthogonal non-identifiability, one of + which is captured by the orthogonal diagonal matrices. The final + result is picked based on the final values of the objective + function. For more on this, see [2]_. + - 'sign_flips' + Initial alignment done by making the median value in each dimension + have the same sign. The motivation is similar to that in '2d', + except this is a heuristic that can save time, but can sometimes + yield suboptimal results. + - 'custom' + Expects either an initial guess for `self.Q_` or an initial guess + for `self.P_`, but not both. See `initial_Q` and `initial_P`, + respectively. If neither is provided, initializes `initial_Q` to an + identity with an appropriate number of dimensions. + + initial_Q : np.ndarray, shape (d, d) or None, optional (default=None) + An initial guess for the alignment matrix, `self.Q_`, if such exists. + Only one of `initial_Q`, `initial_P` can be provided at the same time, + and only if `init` argument is set to 'custom'. If None, and + `initial_P` is also None - initializes `initial_Q` to identity matrix. + Must be an orthogonal matrix, if provided. + + initial_P : np.ndarray, shape (n, m) or None, optional (default=None) + Initial guess for the optimal transport matrix, `self.P_`, if such + exists. Only one of `initial_Q`, `initial_P` can be provided at the + same time, and only if `init` argument is set to 'custom'. If None, and + `initial_Q` is also None - initializes `initial_Q` to identity matrix. + Must be a soft assignment matrix if provided (rows sum up to 1/n, cols + sum up to 1/m.) + + Attributes + ---------- + Q_ : array, size (d, d) + Final orthogonal matrix, used to modify `X`. + + P_ : array, size (n, m) where n and m are the sizes of two datasets + Final matrix of optimal transports, represent soft matching weights + from points in one dataset to the other, normalized such that all rows + sum to 1/n and all columns sum to 1/m. + + score_ : float + Final value of the objective function: :math:`|| X Q - P Y ||_F` + Lower means the datasets have been matched together better. + + selected_initial_Q_ : array, size (d, d) + Initial orthogonal matrix which was used as the initialization. + If `init` was set to `2d` or `sign_flips`, then it is the adaptively + selected matrix. + If `init` was set to custom, and `initial_Q` was provided, then equal + to that. If it was not provided, but `initial_P` was, then it is the + matrix after the first procrustes performed. If neither was provided, + then it is the identity matrix. + + References + ---------- + .. [1] Agterberg, J. + # TODO Cite the Seedless Procrustes preprint whenever available. + + .. [2] Agterberg, J., Tang, M., Priebe., C. E. (2020). + "On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models" + arXiv:2003.14250 + + Notes + ----- + In essence, the goal of this procedure is to simultaneously obtain a, not + necessarily 1-to-1, correspondence between the vertices of the two data + sets, and an orthogonal alignment between two datasets. If the two datasets + are represented with matrices :math:`X \in M_{n, d}` and + :math:`Y \in M_{m, d}`, then the correspondence is a matrix + :math:`P \in M_{n, m}` that is soft assignment matrix (that is, its rows + sum to :math:`1/n`, and columns sum to :math:`1/m`) and the orthogonal + alignment is an orthogonal matrix :math:`Q \in M_{d, d}` (an orthogonal + matrix is any matrix that satisfies :math:`Q^T Q = Q Q^T = I`). The global + objective function is :math:`|| X Q - P Y ||_F`. + + Note that both :math:`X` and :math:`PY` are matrices in :math:`M_{n, d}`. + Thus, if one knew :math:`P`, it would be simple to obtain an estimate for + :math:`Q`, using the regular orthogonal procrustes. On the other hand, if + :math:`Q` was known, then :math:`XQ` and :math:`Y` could be thought of + distributions over a finite number of masses, each with weight :math:`1/n` + or :math:`1/m`, respectively. These distributions could be "matched" via + solving an optimal transport problem. + + However, both :math:`Q` and :math:`P` are simultaneously unknown here. So + the algorithm performs a sequence of alternating steps, obtaining + iteratively improving estimates of :math:`Q` and :math:`P`, similarly to an + expectation-maximization (EM) procedure. It is not known whether this + procedure is formally an EM, but the analogy can be drawn as follows: after + obtaining an initial guess of of :math:`\hat{Q}_{0}`, obtaining an + assignment matrix :math:`\hat{P}_{i+1} | \hat{Q}_{i}` ("E-step") is done by + solving an optimal transport problem via Sinkhorn algorithm, whereas + obtaining an orthogonal alignment matrix :math:`Q_{i+1} | P_{i}` ("M-step") + is done via regular orthogonal procurstes. These alternating steps are + performed until `iterative_num_reps` is reached. + + For more on how the initial guess can be performed, see `init`. + + """ + + def __init__( + self, + optimal_transport_lambda=0.1, + optimal_transport_eps=0.01, + optimal_transport_num_reps=1000, + iterative_num_reps=100, + init="2d", + initial_Q=None, + initial_P=None, + ): + # check optimal_transport_lambda argument + if type(optimal_transport_lambda) is not float: + msg = "Optimal_transport_lambda must be a float, not {}".format( + type(optimal_transport_lambda) + ) + raise TypeError(msg) + if optimal_transport_lambda < 0: + msg = "{} is an invalud value of the optimal_transport_lambda, must be non-negative".format( + optimal_transport_lambda + ) + raise ValueError(msg) + # check optimal_transport_lambda argument + if type(optimal_transport_eps) is not float: + msg = "Optimal_transport_eps must be a float, not {}".format( + type(optimal_transport_eps) + ) + raise TypeError(msg) + if optimal_transport_eps <= 0: + msg = "{} is an invalid value of the optimal transport eps, must be postitive".format( + optimal_transport_eps + ) + raise ValueError(msg) + # check optimal_transport_num_reps argument + if type(optimal_transport_num_reps) is not int: + msg = "Optimal_transport_num_reps must be a int, not {}".format( + type(optimal_transport_num_reps) + ) + raise TypeError(msg) + if optimal_transport_num_reps < 1: + msg = "{} is an invalid number of repetitions, must be non-negative".format( + iterative_num_reps + ) + raise ValueError(msg) + # check iterative_num_reps argument + if type(iterative_num_reps) is not int: + msg = "Iterative_num_reps must be a int, not {}".format( + type(iterative_num_reps) + ) + raise TypeError(msg) + if iterative_num_reps < 0: + msg = "{} is an invalid number of repetitions, must be non-negative".format( + iterative_num_reps + ) + raise ValueError(msg) + # check init argument + if type(init) is not str: + msg = "Init must be a str, not {}".format(type(init)) + raise TypeError(msg) + inits_supported = ["2d", "sign_flips", "custom"] + if init not in inits_supported: + msg = "Supported inits are {}".format(inits_supported) + raise ValueError(msg) + # check that initial_Q and intial_P aren't provided when shouldn't be + if initial_Q is not None and init != "custom": + msg = "Initial_Q can only be provided if init is set to custom" + raise ValueError(msg) + if initial_P is not None and init != "custom": + msg = "Initial_P can only be provided if init is set to custom" + raise ValueError(msg) + if initial_Q is not None and initial_P is not None: + msg = "Initial_Q and initial_P cannot be provided simultaneously" + raise ValueError(msg) + # check initial_Q argument + if initial_Q is not None: + if not isinstance(initial_Q, np.ndarray): + msg = f"Initial_Q must be np.ndarray or None, not {type(initial_Q)}" + raise TypeError(msg) + initial_Q = check_array(initial_Q, copy=True) + if initial_Q.shape[0] != initial_Q.shape[1]: + msg = "Initial_Q must be a square orthogonal matrix" + raise ValueError(msg) + if not np.allclose(initial_Q.T @ initial_Q, np.eye(initial_Q.shape[0])): + msg = "Initial_Q must be a square orthogonal matrix" + raise ValueError(msg) + # check initial_P argument + if initial_P is not None: + if not isinstance(initial_P, np.ndarray): + msg = f"Initial_P must be np.ndarray or None, not {type(initial_P)}" + raise TypeError(msg) + initial_P = check_array(initial_P, copy=True) + n, m = initial_P.shape + if not ( + np.allclose(initial_P.sum(axis=0), np.ones(m) / m) + and np.allclose(initial_P.sum(axis=1), np.ones(n) / n) + ): + msg = ( + "Initial_P must be a soft assignment matrix " + "(rows add up to (1/number of cols) " + "and columns add up to (1/number of rows))" + ) + raise ValueError(msg) + + super().__init__() + + self.optimal_transport_eps = optimal_transport_eps + self.optimal_transport_num_reps = optimal_transport_num_reps + self.optimal_transport_lambda = optimal_transport_lambda + self.iterative_num_reps = iterative_num_reps + self.init = init + self.initial_Q = initial_Q + self.initial_P = initial_P + + def _optimal_transport(self, X, Y, Q): + # "E step" of the SeedlessProcrustes. + n, d = X.shape + m, _ = Y.shape + # initialize probability mass arrays & the cost matrix ; run sinkhorn + probability_mass_X = np.ones(n) / n + probability_mass_Y = np.ones(m) / m + cost_matrix = ( + np.linalg.norm((X @ Q).reshape(n, 1, d) - Y.reshape(1, m, d), axis=2) ** 2 + ) + P = ot.sinkhorn( + a=probability_mass_X, + b=probability_mass_Y, + M=cost_matrix, + reg=self.optimal_transport_lambda, + numItermax=self.optimal_transport_eps, + stopThr=self.optimal_transport_eps, + ) + return P + + def _procrustes(self, X, Y, P): + # "M step" of the SeedlessProcurstes. + aligner = OrthogonalProcrustes() + Q = aligner.fit(X, P @ Y).Q_ + return Q + + def _iterative_ot(self, X, Y, Q): + # this P is not used. it is set to default in case numreps=0 + P = np.ones((X.shape[0], Y.shape[0])) / (X.shape[0] * Y.shape[0]) + for i in range(self.iterative_num_reps): + P = self._optimal_transport(X, Y, Q) + Q = self._procrustes(X, Y, P) + return P, Q + + def _compute_objective(self, X, Y, Q=None, P=None): + if Q is None: + Q = self.Q_ + if P is None: + P = self.P_ + return np.linalg.norm(X @ Q - P @ Y, ord="fro") + + def fit(self, X, Y): + """ + Uses the two datasets to learn the matrix `self.Q_` that aligns the + first dataset with the second. + + Parameters + ---------- + X : np.ndarray, shape (n, d) + Dataset to be mapped to `Y`, must have same number of dimensions + (axis 1) as `Y`. + + Y : np.ndarray, shape (m, d) + Target dataset, must have same number of dimensions (axis 1) as `X`. + + Returns + ------- + self : returns an instance of self + """ + X, Y = self._check_datasets(X, Y) + n, d = X.shape + m, _ = Y.shape + + if self.init == "2d": + P_matrices = np.zeros((2 ** d, n, m)) + Q_matrices = np.zeros((2 ** d, d, d)) + objectives = np.zeros(2 ** d) + # try 2^d different initializations + for i in range(2 ** d): + initial_Q = _sign_flip_matrix_from_int(i, d) + P_matrices[i], Q_matrices[i] = P, Q = self._iterative_ot( + X, Y, initial_Q + ) + objectives[i] = self._compute_objective(X, Y, Q, P) + # pick the best one, using the objective function value + best = np.argmin(objectives) + self.selected_initial_Q_ = _sign_flip_matrix_from_int(best, d) + self.P_, self.Q_ = P_matrices[best], Q_matrices[best] + elif self.init == "sign_flips": + aligner = SignFlips() + self.selected_initial_Q_ = aligner.fit(X, Y).Q_ + self.P_, self.Q_ = self._iterative_ot(X, Y, self.selected_initial_Q_) + else: + # determine initial Q if "custom" + if self.initial_Q is not None: + self.selected_initial_Q_ = self.initial_Q + elif self.initial_P is not None: + # use initial P, if provided + self.selected_initial_Q_ = self._procrustes(X, Y, self.initial_P) + else: + # set to initial Q to identity if neither Q nor P provided + self.selected_initial_Q_ = np.eye(d) + self.P_, self.Q_ = self._iterative_ot(X, Y, self.selected_initial_Q_) + self.score_ = self._compute_objective(X, Y) + + return self + + +def _sign_flip_matrix_from_int(val_int, d): + val_bin = bin(val_int)[2:] + val_bin = "0" * (d - len(val_bin)) + val_bin + return np.diag(np.array([(float(i) - 0.5) * -2 for i in val_bin])) diff --git a/graspologic/align/sign_flips.py b/graspologic/align/sign_flips.py new file mode 100644 index 000000000..3b95203b4 --- /dev/null +++ b/graspologic/align/sign_flips.py @@ -0,0 +1,106 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import numpy as np + +from .base import BaseAlign + + +class SignFlips(BaseAlign): + """ + Flips the signs of all entries in one dataset, `X` along some of the + dimensions. In particular, it does so in a way that brings this dataset to + the same orthant as the second dataset, `Y`, according to some criterion, + computed along each dimension. The two critera currently available are the + median and the maximum (in magnitude) value along each dimension. + + This module can also be used to bring the dataset to the first orthant + (i.e. with all criteras being positive) by providing the identity matrix as + the second dataset. + + Parameters + ---------- + criterion : string, {'median' (default), 'max'}, optional + String describing the criterion used to choose whether to flip signs. + Two options are currently supported: + + - 'median' + Uses the median along each dimension + - 'max' + Uses the maximum (in magintude) along each dimension + + Attributes + ---------- + Q_ : array, size (d, d) + Final orthogonal matrix, used to modify `X`. + + """ + + def __init__( + self, + criterion="median", + ): + # checking criterion argument + if type(criterion) is not str: + raise TypeError("Criterion must be str") + if criterion not in ["median", "max"]: + raise ValueError(f"{criterion} is not a valid criterion.") + + super().__init__() + + self.criterion = criterion + + def set_criterion_function(self): + # perform a check, in case it was modified directly + if self.criterion not in ["median", "max"]: + raise ValueError(f"{self.criterion} is not a valid criterion") + + if self.criterion == "median": + + def median_criterion(X): + return np.median(X, axis=0) + + self.criterion_function_ = median_criterion + if self.criterion == "max": + + def max_criterion(X): + return X[np.argmax(np.abs(X), axis=0), np.arange(X.shape[1])] + + self.criterion_function_ = max_criterion + + def fit(self, X, Y): + """ + Uses the two datasets to learn the matrix `self.Q_` that aligns the + first dataset with the second. + + In sign flips, `self.Q_` is an diagonal orthogonal matrices (i.e. a + matrix with 1 or -1 in each entry on diagonal and 0 everywhere else) + picked such that all dimensions of `X` @ `self.Q_` and `Y` are in the + same orthant using some critera (median or max magnitude). + + Parameters + ---------- + X : np.ndarray, shape (n, d) + Dataset to be mapped to `Y`, must have same number of dimensions + (axis 1) as `Y`. + + Y : np.ndarray, shape (m, d) + Target dataset, must have same number of dimensions (axis 1) as `X`. + + Returns + ------- + self : returns an instance of self + + """ + X, Y = self._check_datasets(X, Y) + _, d = X.shape + + self.set_criterion_function() + X_criterias = self.criterion_function_(X) + Y_criterias = self.criterion_function_(Y) + + val = np.multiply(X_criterias, Y_criterias) + t_X = (val >= 0) * 2 - 1 + + self.Q_ = np.diag(t_X) + return self diff --git a/graspologic/inference/base.py b/graspologic/inference/base.py index b6059a11b..a576e004d 100644 --- a/graspologic/inference/base.py +++ b/graspologic/inference/base.py @@ -72,6 +72,7 @@ def fit(self, A1, A2): def fit_predict(self, A1, A2): """ Fits the model and returns the p-value + Parameters ---------- A1, A2 : nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray diff --git a/graspologic/inference/latent_distribution_test.py b/graspologic/inference/latent_distribution_test.py index 659a8573e..72885a1b7 100644 --- a/graspologic/inference/latent_distribution_test.py +++ b/graspologic/inference/latent_distribution_test.py @@ -7,7 +7,9 @@ from scipy import stats from ..embed import select_dimension, AdjacencySpectralEmbed -from ..utils import import_graph +from ..utils import import_graph, fit_plug_in_variance_estimator +from ..align import SignFlips +from ..align import SeedlessProcrustes from .base import BaseInference from sklearn.utils import check_array from sklearn.metrics import pairwise_distances @@ -59,7 +61,7 @@ class LatentDistributionTest(BaseInference): Number of embedding dimensions. If None, the optimal embedding dimensions are found by the Zhu and Godsi algorithm. See :func:`~graspologic.embed.selectSVD` for more information. - This argument is ignored if input_graph=False. + This argument is ignored if `input_graph` is False. n_bootstraps : int (default=200) Number of bootstrap iterations for the backend hypothesis test. @@ -69,7 +71,7 @@ class LatentDistributionTest(BaseInference): Number of workers to use. If more than 1, parallelizes the code. Supply -1 to use all cores available to the Process. - size_correction: bool (default=True) + size_correction : bool (default=True) Ignored when the two graphs have the same number of vertices. The test degrades in validity as the number of vertices of the two graphs diverge from each other, unless a correction is performed. @@ -81,9 +83,9 @@ class LatentDistributionTest(BaseInference): - False Does not perform any modifications (not recommended). - pooled: bool (default=False) + pooled : bool (default=False) Ignored whenever the two graphs have the same number of vertices or - size_correction is set to False. In order to correct the adjacency + `size_correction` is set to False. In order to correct the adjacency spectral embedding used in the test, it is needed to estimate the variance for each of the latent position estimates in the larger graph, which requires to compute different sample moments. These moments can @@ -93,6 +95,40 @@ class LatentDistributionTest(BaseInference): power or less power under which alternatives. Generally not recomended, as it is untested and included for experimental purposes. + align_type : str, {'sign_flips' (default), 'seedless_procrustes'} or None + Random dot product graphs have an inherent non-identifiability, + associated with their latent positions. Thus, two embeddings of + different graphs may not be orthogonally aligned. Without this accounted + for, two embeddings of different graphs may appear different, even + if the distributions of the true latent positions are the same. + There are several options in terms of how this can be addresssed: + + - 'sign_flips' + A simple heuristic that flips the signs of one of the embeddings, + if the medians of the two embeddings in that dimension differ from + each other. See :class:`~graspologic.align.SignFlips` for more + information on this procedure. In the limit, this is guaranteed to + lead to a valid test, as long as matrix :math:`X^T X`, where + :math:`X` is the latent positions does not have repeated non-zero + eigenvalues. This may, however, result in an invalid test in the + finite sample case if the some eigenvalues are same or close. + - 'seedless_procrustes' + An algorithm that learns an orthogonal alignment matrix. This + procedure is slower than sign flips, but is guaranteed to yield a + valid test in the limit, and also makes the test more valid in some + finite sample cases, in which the eigenvalues are very close to + each other. See `~graspologic.align.SignFlips` for more information + on the procedure. + - None + Do not use any alignment technique. This is strongly not + recommended, as it may often result in a test that is not valid. + + align_kws : dict + Keyword arguments for the aligner of choice, either + `~graspologic.align.SignFlips` or + `~graspologic.align.SeedlessProcrustes`, depending on the align_type. + See respective classes for more information. + input_graph : bool (default=True) Flag whether to expect two full graphs, or the embeddings. @@ -107,6 +143,9 @@ class LatentDistributionTest(BaseInference): Attributes ---------- + metric_func_ : callable + A callable associated with the specified metric. See `metric`. + null_distribution_ : ndarray, shape (n_bootstraps, ) The distribution of T statistics generated under the null. @@ -127,7 +166,7 @@ class LatentDistributionTest(BaseInference): "hyppo: A Comprehensive Multivariate Hypothesis Testing Python Package." arXiv:1907.02088. - .. [3] Alyakin, A., Agterberg, J., Helm, H., Priebe, C. (2020). + .. [3] Alyakin, A. A., Agterberg, J., Helm, H. S., Priebe, C. E. (2020). "Correcting a Nonparametric Two-sample Graph Hypothesis Test for Graphs with Different Numbers of Vertices" arXiv:2008.09434 @@ -142,55 +181,84 @@ def __init__( workers=1, size_correction=True, pooled=False, + align_type="sign_flips", + align_kws={}, input_graph=True, ): - + # check test argument if not isinstance(test, str): msg = "test must be a str, not {}".format(type(test)) raise TypeError(msg) elif test not in _VALID_TESTS: msg = "Unknown test {}. Valid tests are {}".format(test, _VALID_TESTS) raise ValueError(msg) - - if not isinstance(metric, str) and not callable(metric): - msg = "Metric must be str or callable, not {}".format(type(metric)) - raise TypeError(msg) - elif metric not in _VALID_METRICS and not callable(metric): - msg = "Unknown metric {}. Valid metrics are {}, or a callable".format( - metric, _VALID_METRICS - ) - raise ValueError(msg) - + # metric argument is checked when metric_func_ is instantiated + # check n_components argument if n_components is not None: if not isinstance(n_components, int): msg = "n_components must be an int, not {}.".format(type(n_components)) raise TypeError(msg) - + # check n_bootstraps argument if not isinstance(n_bootstraps, int): msg = "n_bootstraps must be an int, not {}".format(type(n_bootstraps)) raise TypeError(msg) elif n_bootstraps < 0: msg = "{} is invalid number of bootstraps, must be non-negative" raise ValueError(msg.format(n_bootstraps)) - + # check workers argument if not isinstance(workers, int): msg = "workers must be an int, not {}".format(type(workers)) raise TypeError(msg) - + # check size_correction argument if not isinstance(size_correction, bool): msg = "size_correction must be a bool, not {}".format(type(size_correction)) raise TypeError(msg) - + # check pooled argument if not isinstance(pooled, bool): msg = "pooled must be a bool, not {}".format(type(pooled)) raise TypeError(msg) - + # check align_type argument + if (not isinstance(align_type, str)) and (align_type is not None): + msg = "align_type must be a string or None, not {}".format(type(align_type)) + raise TypeError(msg) + align_types_supported = ["sign_flips", "seedless_procrustes", None] + if align_type not in align_types_supported: + msg = "supported align types are {}".format(align_types_supported) + raise ValueError(msg) + # check align_kws argument + if not isinstance(align_kws, dict): + msg = "align_kws must be a dictionary of keyword arguments, not {}".format( + type(align_kws) + ) + raise TypeError(msg) + # check input_graph argument if not isinstance(input_graph, bool): msg = "input_graph must be a bool, not {}".format(type(input_graph)) raise TypeError(msg) super().__init__(n_components=n_components) + self.test = test + self.metric = metric + self.metric_func_ = self._instantiate_metric_func(metric, test) + self.n_bootstraps = n_bootstraps + self.workers = workers + self.size_correction = size_correction + self.pooled = pooled + self.input_graph = input_graph + self.align_type = align_type + self.align_kws = align_kws + + def _instantiate_metric_func(self, metric, test): + # check metric argument + if not isinstance(metric, str) and not callable(metric): + msg = "Metric must be str or callable, not {}".format(type(metric)) + raise TypeError(msg) + elif metric not in _VALID_METRICS and not callable(metric): + msg = "Unknown metric {}. Valid metrics are {}, or a callable".format( + metric, _VALID_METRICS + ) + raise ValueError(msg) if callable(metric): metric_func = metric else: @@ -230,12 +298,7 @@ def metric_func(X, Y=None, metric=metric, workers=None): def metric_func(X, Y=None, metric=metric, workers=None): return pairwise_kernels(X, Y, metric=metric, n_jobs=workers) - self.test = KSample(test, compute_distance=metric_func) - self.n_bootstraps = n_bootstraps - self.workers = workers - self.size_correction = size_correction - self.pooled = pooled - self.input_graph = input_graph + return metric_func def _embed(self, A1, A2): if self.n_components is None: @@ -275,9 +338,9 @@ def _sample_modified_ase(self, X, Y, pooled=False): # estimate the central limit theorem variance if pooled: two_samples = np.concatenate([X, Y], axis=0) - get_sigma = _fit_plug_in_variance_estimator(two_samples) + get_sigma = fit_plug_in_variance_estimator(two_samples) else: - get_sigma = _fit_plug_in_variance_estimator(X) + get_sigma = fit_plug_in_variance_estimator(X) X_sigmas = get_sigma(X) * (N - M) / (N * M) # increase the variance of X by sampling from the asy dist @@ -314,6 +377,7 @@ def fit(self, A1, A2): Returns ------- self + """ if self.input_graph: A1 = import_graph(A1) @@ -366,18 +430,26 @@ def fit(self, A1, A2): X1_hat = check_array(A1) X2_hat = check_array(A2) - X1_hat, X2_hat = _median_sign_flips(X1_hat, X2_hat) + if self.align_type == "sign_flips": + aligner = SignFlips(**self.align_kws) + X1_hat = aligner.fit_transform(X1_hat, X2_hat) + elif self.align_type == "seedless_procrustes": + aligner = SeedlessProcrustes(**self.align_kws) + X1_hat = aligner.fit_transform(X1_hat, X2_hat) if self.size_correction: X1_hat, X2_hat = self._sample_modified_ase( X1_hat, X2_hat, pooled=self.pooled ) - data = self.test.test( + self.metric_func_ = self._instantiate_metric_func(self.metric, self.test) + test_obj = KSample(self.test, compute_distance=self.metric_func_) + + data = test_obj.test( X1_hat, X2_hat, reps=self.n_bootstraps, workers=self.workers, auto=False ) - self.null_distribution_ = self.test.indep_test.null_dist + self.null_distribution_ = test_obj.indep_test.null_dist self.sample_T_statistic_ = data[0] self.p_value_ = data[1] @@ -407,75 +479,10 @@ def fit_predict(self, A1, A2): Returns - ------ + ------- p_value_ : float The overall p value from the test """ # abstract method overwritten in order to have a custom doc string self.fit(A1, A2) return self.p_value_ - - -def _median_sign_flips(X1, X2): - X1_medians = np.median(X1, axis=0) - X2_medians = np.median(X2, axis=0) - val = np.multiply(X1_medians, X2_medians) - t = (val > 0) * 2 - 1 - X1 = np.multiply(t.reshape(-1, 1).T, X1) - return X1, X2 - - -def _fit_plug_in_variance_estimator(X): - """ - Takes in ASE of a graph and returns a function that estimates - the variance-covariance matrix at a given point using the - plug-in estimator from the RDPG Central Limit Theorem. - - Parameters - ---------- - X : np.ndarray, shape (n, d) - adjacency spectral embedding of a graph - - Returns - ------- - plug_in_variance_estimtor: functions - a function that estimates variance (see below) - """ - - n = len(X) - - # precompute the Delta and the middle term matrix part - delta = 1 / (n) * (X.T @ X) - delta_inverse = np.linalg.inv(delta) - middle_term_matrix = np.einsum("bi,bo->bio", X, X) - - def plug_in_variance_estimator(x): - """ - Takes in a point of a matrix of points in R^d and returns an - estimated covariance matrix for each of the points - - Parameters: - ----------- - x: np.ndarray, shape (n, d) - points to estimate variance at - if 1-dimensional - reshaped to (1, d) - - Returns: - ------- - covariances: np.ndarray, shape (n, d, d) - n estimated variance-covariance matrices of the points provided - """ - if x.ndim < 2: - x = x.reshape(1, -1) - # the following two lines are a properly vectorized version of - # middle_term = 0 - # for i in range(n): - # middle_term += np.multiply.outer((x @ X[i] - (x @ X[i]) ** 2), - # np.outer(X[i], X[i])) - # where the matrix part does not involve x and has been computed above - middle_term_scalar = x @ X.T - (x @ X.T) ** 2 - middle_term = np.tensordot(middle_term_scalar, middle_term_matrix, axes=1) - covariances = delta_inverse @ (middle_term / n) @ delta_inverse - return covariances - - return plug_in_variance_estimator diff --git a/graspologic/inference/latent_position_test.py b/graspologic/inference/latent_position_test.py index 4bbf11ac8..03c49a9c1 100644 --- a/graspologic/inference/latent_position_test.py +++ b/graspologic/inference/latent_position_test.py @@ -4,6 +4,7 @@ import numpy as np from scipy.linalg import orthogonal_procrustes +from ..align import OrthogonalProcrustes from ..embed import AdjacencySpectralEmbed, OmnibusEmbed, select_dimension from ..simulations import rdpg from ..utils import import_graph, is_symmetric @@ -134,11 +135,10 @@ def _bootstrap(self, X_hat): def _difference_norm(self, X1, X2): if self.embedding in ["ase"]: if self.test_case == "rotation": - R = orthogonal_procrustes(X1, X2)[0] - return np.linalg.norm(X1 @ R - X2) + pass elif self.test_case == "scalar-rotation": - R, s = orthogonal_procrustes(X1, X2) - return np.linalg.norm(s / np.sum(X1 ** 2) * X1 @ R - X2) + X1 = X1 / np.linalg.norm(X1, ord="fro") + X2 = X2 / np.linalg.norm(X2, ord="fro") elif self.test_case == "diagonal-rotation": normX1 = np.sum(X1 ** 2, axis=1) normX2 = np.sum(X2 ** 2, axis=1) @@ -146,11 +146,9 @@ def _difference_norm(self, X1, X2): normX2[normX2 <= 1e-15] = 1 X1 = X1 / np.sqrt(normX1[:, None]) X2 = X2 / np.sqrt(normX2[:, None]) - R = orthogonal_procrustes(X1, X2)[0] - return np.linalg.norm(X1 @ R - X2) - else: - # in the omni case we don't need to align - return np.linalg.norm(X1 - X2) + aligner = OrthogonalProcrustes() + X1 = aligner.fit_transform(X1, X2) + return np.linalg.norm(X1 - X2) def _embed(self, A1, A2, check_lcc=True): if self.embedding == "ase": diff --git a/graspologic/utils/__init__.py b/graspologic/utils/__init__.py index 354ca392d..cddfcf8cb 100644 --- a/graspologic/utils/__init__.py +++ b/graspologic/utils/__init__.py @@ -18,6 +18,7 @@ augment_diagonal, binarize, cartprod, + fit_plug_in_variance_estimator, ) from .ptr import pass_to_ranks @@ -39,4 +40,5 @@ "binarize", "cartprod", "pass_to_ranks", + "fit_plug_in_variance_estimator", ] diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index 64803ea07..ea31b1721 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -645,3 +645,59 @@ def cartprod(*arrays): return np.transpose( np.meshgrid(*arrays, indexing="ij"), np.roll(np.arange(N + 1), -1) ).reshape(-1, N) + + +def fit_plug_in_variance_estimator(X): + """ + Takes in ASE of a graph and returns a function that estimates + the variance-covariance matrix at a given point using the + plug-in estimator from the RDPG Central Limit Theorem. + + Parameters + ---------- + X : np.ndarray, shape (n, d) + adjacency spectral embedding of a graph + + Returns + ------- + plug_in_variance_estimtor: functions + a function that estimates variance (see below) + """ + + n = len(X) + + # precompute the Delta and the middle term matrix part + delta = 1 / (n) * (X.T @ X) + delta_inverse = np.linalg.inv(delta) + middle_term_matrix = np.einsum("bi,bo->bio", X, X) + + def plug_in_variance_estimator(x): + """ + Takes in a point of a matrix of points in R^d and returns an + estimated covariance matrix for each of the points + + Parameters: + ----------- + x: np.ndarray, shape (n, d) + points to estimate variance at + if 1-dimensional - reshaped to (1, d) + + Returns: + ------- + covariances: np.ndarray, shape (n, d, d) + n estimated variance-covariance matrices of the points provided + """ + if x.ndim < 2: + x = x.reshape(1, -1) + # the following two lines are a properly vectorized version of + # middle_term = 0 + # for i in range(n): + # middle_term += np.multiply.outer((x @ X[i] - (x @ X[i]) ** 2), + # np.outer(X[i], X[i])) + # where the matrix part does not involve x and has been computed above + middle_term_scalar = x @ X.T - (x @ X.T) ** 2 + middle_term = np.tensordot(middle_term_scalar, middle_term_matrix, axes=1) + covariances = delta_inverse @ (middle_term / n) @ delta_inverse + return covariances + + return plug_in_variance_estimator diff --git a/runtime.txt b/runtime.txt index 548d71365..475ba515c 100644 --- a/runtime.txt +++ b/runtime.txt @@ -1 +1 @@ -3.7 \ No newline at end of file +3.7 diff --git a/setup.py b/setup.py index fba289494..f163650c3 100644 --- a/setup.py +++ b/setup.py @@ -57,6 +57,7 @@ "seaborn>=0.9.0", "matplotlib>=3.0.0,<=3.3.0", "hyppo>=0.1.3", + "POT>=0.7.0", ], extras_require={ "dev": [ diff --git a/tests/test_latentdistributiontest.py b/tests/test_latentdistributiontest.py index a01a168d1..dd95e8aa5 100644 --- a/tests/test_latentdistributiontest.py +++ b/tests/test_latentdistributiontest.py @@ -43,26 +43,55 @@ def metric_func(X, Y=None, workers=None): ldt.fit(self.A1, self.A2) def test_bad_kwargs(self): + # check test argument + with self.assertRaises(TypeError): + LatentDistributionTest(test=0) with self.assertRaises(ValueError): LatentDistributionTest(test="foo") + # check metric argument + with self.assertRaises(TypeError): + LatentDistributionTest(metric=0) with self.assertRaises(ValueError): - LatentDistributionTest(test="dcorr", n_components=-100) - with self.assertRaises(ValueError): - LatentDistributionTest(test="dcorr", n_bootstraps=-100) + LatentDistributionTest(metric="some_kind_of_kernel") + # check metric argument modified with self.assertRaises(TypeError): - LatentDistributionTest(test=0) + ldt = LatentDistributionTest() + ldt.metric = 0 + ldt.fit(self.A1, self.A2) + with self.assertRaises(ValueError): + ldt = LatentDistributionTest() + ldt.metric = "some_kind_of_kernel" + ldt.fit(self.A1, self.A2) + # check n_components argument with self.assertRaises(TypeError): - LatentDistributionTest(test="dcorr", distance=0) + LatentDistributionTest(n_components=0.5) + with self.assertRaises(ValueError): + LatentDistributionTest(n_components=-100) + # check n_bootstraps argument with self.assertRaises(TypeError): - LatentDistributionTest(test="dcorr", n_bootstraps=0.5) + LatentDistributionTest(n_bootstraps=0.5) + with self.assertRaises(ValueError): + LatentDistributionTest(n_bootstraps=-100) + # check workers argument with self.assertRaises(TypeError): - LatentDistributionTest(test="dcorr", workers=0.5) + LatentDistributionTest(workers=0.5) + # check size_correction argument with self.assertRaises(TypeError): LatentDistributionTest(size_correction=0) + # check pooled argument with self.assertRaises(TypeError): LatentDistributionTest(pooled=0) + # check align_type argument + with self.assertRaises(ValueError): + LatentDistributionTest(align_type="foo") + with self.assertRaises(TypeError): + LatentDistributionTest(align_type={"not a": "string"}) + # check align_kws argument with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph="hello") + LatentDistributionTest(align_kws="foo") + # check input_graph argument + with self.assertRaises(TypeError): + LatentDistributionTest(input_graph="hello") def test_n_bootstraps(self): for test in self.tests.keys(): @@ -146,12 +175,23 @@ def test_pooled(self): ldt.fit(A1, A2) def test_distances_and_kernels(self): + np.random.seed(123) + A1 = er_np(20, 0.3) + A2 = er_np(100, 0.3) # some valid combinations of test and metric - with pytest.warns(None) as record: - for test in self.tests.keys(): - ldt = LatentDistributionTest(test, self.tests[test]) - ldt = LatentDistributionTest("hsic", "rbf") - assert len(record) == 0 + # # would love to do this, but currently FutureWarning breaks this + # with pytest.warns(None) as record: + # for test in self.tests.keys(): + # ldt = LatentDistributionTest(test, self.tests[test]) + # ldt.fit(A1, A2) + # ldt = LatentDistributionTest("hsic", "rbf") + # ldt.fit(A1, A2) + # assert len(record) == 0 + for test in self.tests.keys(): + ldt = LatentDistributionTest(test, self.tests[test]) + ldt.fit(A1, A2) + ldt = LatentDistributionTest("hsic", "rbf") + ldt.fit(A1, A2) # some invalid combinations of test and metric with pytest.warns(UserWarning): ldt = LatentDistributionTest("hsic", "euclidean") @@ -174,7 +214,7 @@ def test_directed_inputs(self): C = er_np(100, 0.3, directed=False) # two directed graphs is okay - ldt = LatentDistributionTest("dcorr") + ldt = LatentDistributionTest() ldt.fit(A, B) # an undirected and a direced graph is not okay @@ -248,6 +288,33 @@ def test_different_sizes_null(self): self.assertTrue(p_corrected_1 <= 0.05) self.assertTrue(p_corrected_2 <= 0.05) + def test_different_aligners(self): + np.random.seed(314) + A1 = er_np(100, 0.8) + A2 = er_np(100, 0.8) + ase_1 = AdjacencySpectralEmbed(n_components=2) + X1 = ase_1.fit_transform(A1) + ase_2 = AdjacencySpectralEmbed(n_components=2) + X2 = ase_2.fit_transform(A2) + X2 = -X2 + + ldt_1 = LatentDistributionTest(input_graph=False, align_type=None) + p_val_1 = ldt_1.fit_predict(X1, X2) + self.assertTrue(p_val_1 < 0.05) + + ldt_2 = LatentDistributionTest(input_graph=False, align_type="sign_flips") + p_val_2 = ldt_2.fit_predict(X1, X2) + self.assertTrue(p_val_2 >= 0.05) + + # also checking that kws are passed through + ldt_3 = LatentDistributionTest( + input_graph=False, + align_type="seedless_procrustes", + align_kws={"init": "sign_flips"}, + ) + p_val_3 = ldt_3.fit_predict(X1, X2) + self.assertTrue(p_val_3 >= 0.05) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_orthogonal_procrustes.py b/tests/test_orthogonal_procrustes.py new file mode 100644 index 000000000..fd4aa4856 --- /dev/null +++ b/tests/test_orthogonal_procrustes.py @@ -0,0 +1,111 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest + +import numpy as np + +from graspologic.align import OrthogonalProcrustes + + +class TestOrthogonalProcrustes(unittest.TestCase): + def test_bad_datasets(self): + X = np.arange(6).reshape(6, 1) + Y = np.arange(6).reshape(6, 1) + Y_wrong_d = np.arange(12).reshape(6, 2) + Y_wrong_n = np.arange(12).reshape(12, 1) + # check passing weird stuff as input (caught by us) + with self.assertRaises(TypeError): + aligner = OrthogonalProcrustes() + aligner.fit("hello there", Y) + with self.assertRaises(TypeError): + aligner = OrthogonalProcrustes() + aligner.fit(X, "hello there") + with self.assertRaises(TypeError): + aligner = OrthogonalProcrustes() + aligner.fit({"hello": "there"}, Y) + with self.assertRaises(TypeError): + aligner = OrthogonalProcrustes() + aligner.fit(X, {"hello": "there"}) + # check passing arrays of weird ndims (caught by check_array) + with self.assertRaises(ValueError): + aligner = OrthogonalProcrustes() + aligner.fit(X, Y.reshape(3, 2, 1)) + with self.assertRaises(ValueError): + aligner = OrthogonalProcrustes() + aligner.fit(X.reshape(3, 2, 1), Y) + # check passing arrays with different dimensions (caught by us) + with self.assertRaises(ValueError): + aligner = OrthogonalProcrustes() + aligner.fit(X, Y_wrong_d) + # check passing arrays with different number of vertices (caught by us) + with self.assertRaises(ValueError): + aligner = OrthogonalProcrustes() + aligner.fit(X, Y_wrong_n) + # check passing array with wrong dimensions to transform (caught by us) + with self.assertRaises(ValueError): + aligner = OrthogonalProcrustes() + aligner.fit(X, Y) + aligner.transform(Y_wrong_d) + # passing array with different number of vertices to fit is okay + aligner = OrthogonalProcrustes() + aligner.fit(X, Y) + aligner.transform(Y_wrong_n) + + def test_identity(self): + Y = np.array([[1234, 19], [6798, 18], [9876, 17], [4321, 16]]) + + aligner = OrthogonalProcrustes() + aligner.fit(Y, Y) + + assert np.all(np.isclose(aligner.Q_, np.eye(2))) + + def test_two_datasets(self): + # A very simple example with a true existing solution + # X: Y: + # | | + # | 2 + # | 1 | + # 2 | | + # | | + # | | + # ------+------ -1----+----3- + # | | + # | | + # | 4 | + # 3 | | + # | 4 + # | | + # + # solution is + # _ _ _ _ + # | | | | + # | 3 / 5 - 4 / 5 | | -1 0 | + # | | times | | + # | 3 / 5 3 / 5 | | 0 1 | + # |_ _| |_ _| + # because it is just rotation times reflection + X = np.array([[3, 4], [-4, 3], [-3, -4], [4, -3]]) + Y = np.array([[-5, 0], [0, 5], [5, 0], [0, -5]]) + Q_answer = np.array([[-0.6, -0.8], [-0.8, 0.6]]) + X_answer = X.copy() @ Q_answer + + # first, do fit and transform separately + aligner_1 = OrthogonalProcrustes() + aligner_1.fit(X, Y) + Q_test_1 = aligner_1.Q_ + X_test_1 = aligner_1.transform(X) + self.assertTrue(np.isclose(0, aligner_1.score_)) + self.assertTrue(np.all(np.isclose(Q_test_1, Q_answer))) + self.assertTrue(np.all(np.isclose(X_test_1, X_answer))) + # now, do fit_transform + aligner_2 = OrthogonalProcrustes() + X_test_2 = aligner_2.fit_transform(X, Y) + Q_test_2 = aligner_2.Q_ + self.assertTrue(np.isclose(0, aligner_2.score_)) + self.assertTrue(np.all(np.isclose(Q_test_2, Q_answer))) + self.assertTrue(np.all(np.isclose(X_test_2, X_answer))) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_seedless_procrustes.py b/tests/test_seedless_procrustes.py new file mode 100644 index 000000000..be39ed52f --- /dev/null +++ b/tests/test_seedless_procrustes.py @@ -0,0 +1,160 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest + +import numpy as np +from scipy import stats +from graspologic.align import SignFlips +from graspologic.align import SeedlessProcrustes + + +class TestSeedlessProcrustes(unittest.TestCase): + def test_bad_kwargs(self): + # type errors for all but initial Q and initial P + with self.assertRaises(TypeError): + SeedlessProcrustes(optimal_transport_lambda="oops") + with self.assertRaises(TypeError): + SeedlessProcrustes(optimal_transport_eps="oops") + with self.assertRaises(TypeError): + SeedlessProcrustes(optimal_transport_num_reps=3.14) + with self.assertRaises(TypeError): + SeedlessProcrustes(iterative_num_reps=3.14) + with self.assertRaises(TypeError): + SeedlessProcrustes(init=["hi", "there"]) + + # value errors for all but initial Q and initial P + with self.assertRaises(ValueError): + SeedlessProcrustes(optimal_transport_lambda=-0.01) + with self.assertRaises(ValueError): + SeedlessProcrustes(optimal_transport_eps=-0.01) + with self.assertRaises(ValueError): + SeedlessProcrustes(optimal_transport_num_reps=-1) + with self.assertRaises(ValueError): + SeedlessProcrustes(iterative_num_reps=-1) + with self.assertRaises(ValueError): + SeedlessProcrustes(init="hi") + + # initial Q and initial P passed when shouldn't be + with self.assertRaises(ValueError): + SeedlessProcrustes( + init="2d", + initial_Q=np.eye(2), + ) + with self.assertRaises(ValueError): + SeedlessProcrustes(init="2d", initial_P=np.ones((100, 100)) / 10000) + with self.assertRaises(ValueError): + SeedlessProcrustes( + init="custom", + initial_Q=np.eye(2), + initial_P=np.ones((100, 100)) / 10000, + ) + + # initial Q and initial P specific values + # pass bad types + with self.assertRaises(TypeError): + SeedlessProcrustes(init="custom", initial_Q="hello there") + with self.assertRaises(TypeError): + SeedlessProcrustes(init="custom", initial_P="hello there") + with self.assertRaises(TypeError): + SeedlessProcrustes(init="custom", initial_Q={"hello": "there"}) + with self.assertRaises(TypeError): + SeedlessProcrustes(init="custom", initial_P={"hello": "there"}) + # pass non ndim=2 matrices (cuaght by check_array) + with self.assertRaises(ValueError): + SeedlessProcrustes(init="custom", initial_Q=np.eye(5).reshape(5, 5, 1)) + with self.assertRaises(ValueError): + SeedlessProcrustes(init="custom", initial_P=np.eye(5).reshape(5, 5, 1)) + # pass not an orthogonal matrix as a Q + with self.assertRaises(ValueError): + SeedlessProcrustes(init="custom", initial_Q=np.eye(3)[:-1]) + with self.assertRaises(ValueError): + SeedlessProcrustes(init="custom", initial_Q=np.ones((3, 3))) + # pass not a soft assignment matrix as P + with self.assertRaises(ValueError): + SeedlessProcrustes(init="custom", initial_P=np.ones((3, 2))) + + def test_bad_datasets(self): + X = np.arange(6).reshape(6, 1) + Y = np.arange(6).reshape(6, 1) + Y_wrong_d = np.arange(12).reshape(6, 2) + # check passing weird stuff as input (caught by us) + with self.assertRaises(TypeError): + aligner = SeedlessProcrustes() + aligner.fit("hello there", Y) + with self.assertRaises(TypeError): + aligner = SeedlessProcrustes() + aligner.fit(X, "hello there") + with self.assertRaises(TypeError): + aligner = SeedlessProcrustes() + aligner.fit({"hello": "there"}, Y) + with self.assertRaises(TypeError): + aligner = SeedlessProcrustes() + aligner.fit(X, {"hello": "there"}) + # check passing arrays of weird ndims (caught by check_array) + with self.assertRaises(ValueError): + aligner = SeedlessProcrustes() + aligner.fit(X, Y.reshape(3, 2, 1)) + with self.assertRaises(ValueError): + aligner = SeedlessProcrustes() + aligner.fit(X.reshape(3, 2, 1), Y) + # check passing arrays with different dimensions (caught by us) + with self.assertRaises(ValueError): + aligner = SeedlessProcrustes() + aligner.fit(X, Y_wrong_d) + # check passing array with wrong dimensions to transform (caught by us) + with self.assertRaises(ValueError): + aligner = SeedlessProcrustes() + aligner.fit(X, Y) + aligner.transform(Y_wrong_d) + + def test_different_inits(self): + np.random.seed(314) + mean = np.ones(3) * 5 + cov = np.eye(3) * 0.1 + X = stats.multivariate_normal.rvs(mean, cov, 100) + print(X.shape) + Y = stats.multivariate_normal.rvs(mean, cov, 100) + W = stats.ortho_group.rvs(3) + Y = Y @ W + + aligner_1 = SeedlessProcrustes(init="2d") + aligner_1.fit(X, Y) + + aligner_2 = SeedlessProcrustes(init="sign_flips") + aligner_2.fit(X, Y) + test_sign_flips = SignFlips() + self.assertTrue( + np.all(test_sign_flips.fit(X, Y).Q_ == aligner_2.selected_initial_Q_) + ) + + aligner_3 = SeedlessProcrustes(init="custom") + aligner_3.fit(X, Y) + self.assertTrue(np.all(np.eye(3) == aligner_3.selected_initial_Q_)) + + aligner_4 = SeedlessProcrustes(init="custom", initial_Q=-np.eye(3)) + aligner_4.fit(X, Y) + self.assertTrue(np.all(-np.eye(3) == aligner_4.selected_initial_Q_)) + + aligner_5 = SeedlessProcrustes( + init="custom", initial_P=np.ones((100, 100)) / 10000 + ) + aligner_5.fit(X, Y) + + def test_aligning_datasets(self): + np.random.seed(314) + n, d = 250, 2 + mean = np.ones(d) * 5 + cov = np.ones((d, d)) * 0.02 + np.eye(d) * 0.8 + X = stats.multivariate_normal.rvs(mean, cov, n) + Y = np.concatenate([X, X]) + W = stats.ortho_group.rvs(d) + Y = Y @ W + + aligner = SeedlessProcrustes(init="2d") + Q = aligner.fit(X, Y).Q_ + self.assertTrue(np.linalg.norm(Y.mean(axis=0) - (X @ Q).mean(axis=0)) < 0.1) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_sign_flips.py b/tests/test_sign_flips.py new file mode 100644 index 000000000..a8dc58e8f --- /dev/null +++ b/tests/test_sign_flips.py @@ -0,0 +1,100 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest + +import numpy as np + +from graspologic.align import SignFlips + + +class TestSignFlips(unittest.TestCase): + def test_bad_kwargs(self): + with self.assertRaises(TypeError): + SignFlips(criterion={"this is a": "dict"}) + with self.assertRaises(ValueError): + SignFlips(criterion="cep") + # check delayed ValueError + with self.assertRaises(ValueError): + aligner = SignFlips(criterion="median") + X = np.arange(6).reshape(6, 1) + Y = np.arange(6).reshape(6, 1) + aligner.criterion = "something" + aligner.fit(X, Y) + + def test_bad_datasets(self): + X = np.arange(6).reshape(6, 1) + Y = np.arange(6).reshape(6, 1) + Y_wrong_d = np.arange(12).reshape(6, 2) + # check passing weird stuff as input (caught by us) + with self.assertRaises(TypeError): + aligner = SignFlips() + aligner.fit("hello there", Y) + with self.assertRaises(TypeError): + aligner = SignFlips() + aligner.fit(X, "hello there") + with self.assertRaises(TypeError): + aligner = SignFlips() + aligner.fit({"hello": "there"}, Y) + with self.assertRaises(TypeError): + aligner = SignFlips() + aligner.fit(X, {"hello": "there"}) + # check passing arrays of weird ndims (caught by check_array) + with self.assertRaises(ValueError): + aligner = SignFlips() + aligner.fit(X, Y.reshape(3, 2, 1)) + with self.assertRaises(ValueError): + aligner = SignFlips() + aligner.fit(X.reshape(3, 2, 1), Y) + # check passing arrays with different dimensions (caught by us) + with self.assertRaises(ValueError): + aligner = SignFlips() + aligner.fit(X, Y_wrong_d) + # check passing array with wrong dimensions to transform (caught by us) + with self.assertRaises(ValueError): + aligner = SignFlips() + aligner.fit(X, Y) + aligner.transform(Y_wrong_d) + + def test_two_datasets(self): + X = np.arange(6).reshape(3, 2) * (-1) + Y = np.arange(6).reshape(3, 2) @ np.diag([1, -1]) + 0.5 + # X flips sign in the first dimension + Q_answer = np.array([[-1, 0], [0, 1]]) + X_answer = X.copy() @ Q_answer + # first, do fit and transform separately + aligner_1 = SignFlips() + aligner_1.fit(X, Y) + Q_test = aligner_1.Q_ + X_test = aligner_1.transform(X) + self.assertTrue(np.all(Q_test == Q_answer)) + self.assertTrue(np.all(X_test == X_answer)) + # now, do fit_transform + aligner_2 = SignFlips() + X_test = aligner_2.fit_transform(X, Y) + Q_test = aligner_2.Q_ + self.assertTrue(np.all(Q_test == Q_answer)) + self.assertTrue(np.all(X_test == X_answer)) + # try giving a different matrix as the sole input (I) + I_test = aligner_2.transform(np.eye(2)) + I_answer = np.diag([-1, 1]) + self.assertTrue(np.all(I_test == I_answer)) + + def test_max_criterion(self): + X = np.arange(6).reshape(3, 2) * (-1) + Y = np.arange(6).reshape(3, 2) @ np.diag([1, -1]) + 0.5 + # in this case, Y should be unchanged, and X matched to Y + # so X flips sign in the first dimension + Q_answer = np.array([[-1, 0], [0, 1]]) + X_answer = X.copy() @ Q_answer + # set criterion to "max", see if that works + aligner = SignFlips(criterion="max") + aligner.fit(X, Y) + Q_test = aligner.Q_ + X_test = aligner.transform(X) + self.assertTrue(np.all(Q_test == Q_answer)) + self.assertTrue(np.all(X_test == X_answer)) + + +if __name__ == "__main__": + unittest.main() From 7f0576c4a7fe1a9f688bc2d84375eb0be2a60da1 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Date: Fri, 2 Oct 2020 11:00:41 -0400 Subject: [PATCH 025/140] Padded sgm (#509) * add padded sgm to gmp.py * resolve some docs issues * add tests * remove excessive gmp tests for performance * doc changes * add tutorial, remove math from docs * Update tutorial.rst * Update gmp.py * Update padded_sgm.ipynb Co-authored-by: Benjamin Pedigo --- docs/tutorial.rst | 1 + docs/tutorials/matching/padded_sgm.ipynb | 162 +++++++++++++++++++++++ graspologic/match/gmp.py | 47 ++++++- tests/match/test_match.py | 91 ++++++------- 4 files changed, 244 insertions(+), 57 deletions(-) create mode 100644 docs/tutorials/matching/padded_sgm.ipynb diff --git a/docs/tutorial.rst b/docs/tutorial.rst index dc335e28c..769eebfe2 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -81,6 +81,7 @@ including an introduction to the module, and how to utilize the seeding feature. tutorials/matching/faq tutorials/matching/sgm + tutorials/matching/padded_sgm .. _subgraph_tutorials: diff --git a/docs/tutorials/matching/padded_sgm.ipynb b/docs/tutorials/matching/padded_sgm.ipynb new file mode 100644 index 000000000..69ffa1704 --- /dev/null +++ b/docs/tutorials/matching/padded_sgm.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Padded Graph Matching\n", + "Consider the scenario where one would like to match graphs $A$ and $B$ with $n_1$ and $n_2$ nodes, respectively, where \n", + "$n_1 < n_2$. The most straightforward fashion to 'pad' $A$, such that $A$ and $B$ have the same shape, is to add $n_2 - n_1$ isolated nodes to $A$. This padding scheme is known as $\\textit{naive padding}$, substituting $A \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $B$ in place of $A$ and $B$, respectively.\n", + "\n", + "The effect of this is that one matches $A$ to the best subgraph of $B$. That is, the isolated vertices added to $A$ through padding have an affinity to the low-density subgraphs of $B$. Since the isolated vertices and non adjacent core vertices both have the same weight, the isolated vertices carry a lot of false signal. Instead, we desire to match $A$ to the best fitting induced subgraph of $B$. This padding scheme is known as $\\textit{adopted padding}$, and is achieved by substituting $\\tilde{A} \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $\\tilde{B}$ in place of $A$ and $B$, respectively, where $\\tilde{A} = 2A - 1_{n_1}1_{n_1}^T$ and $\\tilde{B} = 2B - 1_{n_2}1_{n_2}^T$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspy.match import GraphMatch as GMP\n", + "from graspy.simulations import sbm_corr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To demonstrate the difference between the two padding schemes, we sample two graph's $G_1'$ and $G_2$, each having 400 vertices, from a $0.5 \\sim SBM(4,b,\\Lambda)$, where b assigns 100 vertices to each of the k = 4 blocks, and\n", + "\n", + "\\begin{align*}\n", + "\\Lambda &= \\begin{bmatrix} \n", + "0.9 & 0.4 & 0.3 & 0.2\\\\\n", + "0.4 & 0.9 & 0.4 & 0.3\\\\\n", + "0.3 & 0.4 & 0.9 & 0.4\\\\\n", + "0.2 & 0.3 & 0.4 & 0.7\n", + "\\end{bmatrix}\\\\\n", + "\\end{align*}\n", + "\n", + "We realize $G_1$ from $G_1'$ by removing 25 nodes from each block of $G_1'$, yielding a 300 node graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SBM correlated graph pairs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# simulating G1', G2, deleting 25 vertices\n", + "from graspy.plot import heatmap\n", + "directed = False\n", + "loops = False\n", + "block_probs = [[0.9,0.4,0.3,0.2],\n", + " [0.4,0.9,0.4,0.3],\n", + " [0.3,0.4,0.9,0.4],\n", + " [0.2,0.3,0.4,0.7]]\n", + "n =100\n", + "n_blocks = 4\n", + "rho = 0.5\n", + "block_members = np.array(n_blocks * [n])\n", + "n_verts = block_members.sum()\n", + "G1p, G2 = sbm_corr(block_members,block_probs, rho, directed, loops)\n", + "G1 = np.zeros((300,300))\n", + "c = np.copy(G1p)\n", + "keep = [np.random.randint(100*i, 100*(i+1)-1, 75) for i in range(4)]\n", + "delete = [np.setdiff1d(np.arange(100*i, 100*(i+1)-1), keep[i]) for i in range(4)]\n", + "\n", + "for i in range(5):\n", + " stepx = 100*i\n", + " c[75+stepx:stepx+100,:] = -1\n", + " c[:, 75+stepx:stepx+100] = -1\n", + " for j in range(5):\n", + " stepy = 100*j\n", + " G1[(75*i):(75*(i+1)),(75*j):(75*(j+1))] = G1p[stepx:(stepx+75),stepy:(stepy+75)]\n", + " \n", + "topleft_G1 = np.zeros((400,400))\n", + "topleft_G1[:300,:300] = G1\n", + "fig, axs = plt.subplots(1, 4, figsize=(20, 10))\n", + "heatmap(G1p, ax=axs[0], cbar=False, title=\"G1'\")\n", + "heatmap(G2, ax=axs[1], cbar=False, title=\"G2\")\n", + "heatmap(c, ax=axs[2], cbar=False, title=\"G1\")\n", + "heatmap(topleft_G1, ax=axs[3], cbar=False, title=\"G1 (to top left corner)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Naive vs Adopted Padding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "\n", + "gmp_naive = GMP(padding='naive')\n", + "seed1 = np.random.choice(np.arange(300),10)\n", + "seed2 = [int(x/75)*25 + x for x in seed1]\n", + "gmp_naive = gmp_naive.fit(G2, G1, seed2, seed1)\n", + "G1_naive = topleft_G1[gmp_naive.perm_inds_][:, gmp_naive.perm_inds_]\n", + "\n", + "gmp_adopted = GMP(padding='adopted')\n", + "gmp_adopted = gmp_adopted.fit(G2, G1, seed2, seed1)\n", + "G1_adopted = topleft_G1[gmp_adopted.perm_inds_][:, gmp_adopted.perm_inds_]\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(14, 7))\n", + "heatmap(G1_naive, ax=axs[0], cbar=False, title=\"Naive Padding\")\n", + "heatmap(G1_adopted, ax=axs[1], cbar=False, title=\"Adopted Padding\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the two padding schemes perform as expected. The naive scheme permutes $G_1$ such that it matches a subgraph of $G_2$, specifically the subgraph of the first three blocks. Additionally, (almost) all isolated vertices of $G_1$ are permuted to the fourth block of $G_2$.\n", + "\n", + "On the other hand, we see that adopted padding preserves the common block structure between $G_1$ and $G_2$." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/graspologic/match/gmp.py b/graspologic/match/gmp.py index ab981e5cc..b5bd179c8 100644 --- a/graspologic/match/gmp.py +++ b/graspologic/match/gmp.py @@ -57,6 +57,17 @@ class GraphMatch(BaseEstimator): Gives users the option to solve QAP rather than the Graph Matching Problem (GMP). This is accomplished through trivial negation of the objective function. + padding : string (default = 'adopted') + Allows user to specify padding scheme if `A` and `B` are not of equal size. + Say that `A` and `B` have :math:`n_1` and :math:`n_2` nodes, respectively, and + :math:`n_1 < n_2`. + + "adopted" : matches `A` to the best fitting induced subgraph of `B`. Reduces the + affinity between isolated vertices added to `A` through padding and low-density + subgraphs of `B`. + + "naive" : matches `A` to the best fitting subgraph of `B`. + Attributes ---------- @@ -95,6 +106,7 @@ def __init__( shuffle_input=True, eps=0.1, gmp=True, + padding="adopted", ): if type(n_init) is int and n_init > 0: @@ -130,6 +142,11 @@ def __init__( else: msg = '"gmp" must be a boolean' raise TypeError(msg) + if padding in {"adopted", "naive"}: + self.padding = padding + else: + msg = 'Invalid "padding" parameter string' + raise ValueError(msg) def fit(self, A, B, seeds_A=[], seeds_B=[]): """ @@ -160,10 +177,7 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): seeds_A = column_or_1d(seeds_A) seeds_B = column_or_1d(seeds_B) - if A.shape[0] != B.shape[0]: - msg = "Adjacency matrices must be of equal size" - raise ValueError(msg) - elif A.shape[0] != A.shape[1] or B.shape[0] != B.shape[1]: + if A.shape[0] != A.shape[1] or B.shape[0] != B.shape[1]: msg = "Adjacency matrix entries must be square" raise ValueError(msg) elif seeds_A.shape[0] != seeds_B.shape[0]: @@ -182,6 +196,10 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): msg = "Seed array entries must be less than or equal to n-1" raise ValueError(msg) + # pads A and B according to section 2.5 of [2] + if A.shape[0] != B.shape[0]: + A, B = _adj_pad(A, B, self.padding) + n = A.shape[0] # number of vertices in graphs n_seeds = seeds_A.shape[0] # number of seeds n_unseed = n - n_seeds @@ -332,6 +350,27 @@ def fit_predict(self, A, B, seeds_A=[], seeds_B=[]): return self.perm_inds_ +def _adj_pad(A, B, method): + def pad(X, n): + X_pad = np.zeros((n[1], n[1])) + X_pad[: n[0], : n[0]] = X + return X_pad + + A_n = A.shape[0] + B_n = B.shape[0] + n = np.sort([A_n, B_n]) + if method == "adopted": + A = 2 * A - np.ones((A_n, A_n)) + B = 2 * B - np.ones((B_n, B_n)) + + if A.shape[0] == n[0]: + A = pad(A, n) + else: + B = pad(B, n) + + return A, B + + def _unshuffle(array, n): unshuffle = np.array(range(n)) unshuffle[array] = np.array(range(n)) diff --git a/tests/match/test_match.py b/tests/match/test_match.py index e1af213c5..88e954ae6 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -7,6 +7,9 @@ import random from graspologic.match import GraphMatch as GMP from graspologic.match import SinkhornKnopp as SK +from graspy.simulations import sbm_corr + +np.random.seed(0) class TestGMP: @@ -14,6 +17,7 @@ class TestGMP: def setup_class(cls): cls.barycenter = GMP(gmp=False) cls.rand = GMP(n_init=100, init_method="rand", gmp=False) + cls.barygm = GMP(gmp=True) def test_SGM_inputs(self): with pytest.raises(TypeError): @@ -28,6 +32,8 @@ def test_SGM_inputs(self): GMP(eps=-1) with pytest.raises(TypeError): GMP(gmp="hey") + with pytest.raises(ValueError): + GMP(padding="hey") with pytest.raises(ValueError): GMP().fit( np.random.random((3, 4)), @@ -35,13 +41,6 @@ def test_SGM_inputs(self): np.arange(2), np.arange(2), ) - with pytest.raises(ValueError): - GMP().fit( - np.random.random((3, 3)), - np.random.random((4, 4)), - np.arange(2), - np.arange(2), - ) with pytest.raises(ValueError): GMP().fit( np.random.random((3, 4)), @@ -73,25 +72,6 @@ def _get_AB(self, qap_prob): return A, B def test_barycenter_SGM(self): - A, B = self._get_AB("lipa20a") - lipa20a = self.barycenter.fit(A, B) - score = lipa20a.score_ - assert 3683 <= score < 3900 - - A, B = self._get_AB("lipa20b") - lipa20b = self.barycenter.fit(A, B) - score = lipa20b.score_ - assert score == 27076 - - A, B = self._get_AB("lipa30a") - lipa30a = self.barycenter.fit(A, B) - score = lipa30a.score_ - assert 13178 <= score < 13650 - - A, B = self._get_AB("lipa30b") - lipa30b = self.barycenter.fit(A, B) - score = lipa30b.score_ - assert score == 151426 A, B = self._get_AB("chr12c") n = A.shape[0] @@ -108,21 +88,6 @@ def test_barycenter_SGM(self): score = chr12c.score_ assert 11156 == score - A, B = self._get_AB("chr15a") - n = A.shape[0] - pi = np.array([5, 10, 8, 13, 12, 11, 14, 2, 4, 6, 7, 15, 3, 1, 9]) - [1] * n - W1 = [0, 5, 11, 14] - W2 = [pi[z] for z in W1] - chr15a = self.barycenter.fit(A, B, W1, W2) - score = chr15a.score_ - assert 9896 <= score < 20000 - - W1 = np.sort(random.sample(list(range(n)), n - 1)) - W2 = [pi[z] for z in W1] - chr15a = self.barycenter.fit(A, B, W1, W2) - score = chr15a.score_ - assert 9896 == score - def test_rand_SGM(self): A, B = self._get_AB("chr12c") chr12c = self.rand.fit(A, B) @@ -137,18 +102,38 @@ def test_rand_SGM(self): score = chr12c.score_ assert 11156 <= score < 12500 - A, B = self._get_AB("chr15a") - chr15a = self.rand.fit(A, B) - score = chr15a.score_ - assert 9896 <= score < 12000 - - n = A.shape[0] - pi = np.array([5, 10, 8, 13, 12, 11, 14, 2, 4, 6, 7, 15, 3, 1, 9]) - [1] * n - W1 = [0, 5, 11, 14] - W2 = [pi[z] for z in W1] - chr15a = self.rand.fit(A, B, W1, W2) - score = chr15a.score_ - assert 9896 <= score < 10000 + def test_padding(self): + directed = False + loops = False + block_probs = [ + [0.9, 0.4, 0.3, 0.2], + [0.4, 0.9, 0.4, 0.3], + [0.3, 0.4, 0.9, 0.4], + [0.2, 0.3, 0.4, 0.7], + ] + n = 100 + n_blocks = 4 + rho = 0.5 + block_members = np.array(n_blocks * [n]) + n_verts = block_members.sum() + G1p, G2 = sbm_corr(block_members, block_probs, rho, directed, loops) + G1 = np.zeros((300, 300)) + + for i in range(5): + stepx = 100 * i + for j in range(5): + stepy = 100 * j + G1[(75 * i) : (75 * (i + 1)), (75 * j) : (75 * (j + 1))] = G1p[ + stepx : (stepx + 75), stepy : (stepy + 75) + ] + seed1 = np.random.randint(0, 300, 10) + seed2 = [int(x / 75) * 25 + x for x in seed1] + res = self.barygm.fit(G2, G1, seed2, seed1) + matching = np.concatenate( + [res.perm_inds_[x * 100 : (x * 100) + 75] for x in range(n_blocks)] + ) + + assert 1.0 == (sum(matching == np.arange(300)) / 300) class TestSinkhornKnopp: From a8305859c0c502ae9c7f1f4a1fffd48c7f011790 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin Date: Fri, 2 Oct 2020 11:18:47 -0400 Subject: [PATCH 026/140] Update test_match.py --- tests/match/test_match.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/match/test_match.py b/tests/match/test_match.py index 88e954ae6..cd3a43c44 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -7,7 +7,7 @@ import random from graspologic.match import GraphMatch as GMP from graspologic.match import SinkhornKnopp as SK -from graspy.simulations import sbm_corr +from graspologic.simulations import sbm_corr np.random.seed(0) From 98a94dbf3f84ff46aa876777ac8ce7253c28fa29 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin Date: Fri, 2 Oct 2020 15:26:00 -0400 Subject: [PATCH 027/140] simplify padded tests --- docs/tutorials/matching/padded_sgm.ipynb | 19 ++++------- tests/match/test_match.py | 43 ++++++------------------ 2 files changed, 18 insertions(+), 44 deletions(-) diff --git a/docs/tutorials/matching/padded_sgm.ipynb b/docs/tutorials/matching/padded_sgm.ipynb index 69ffa1704..3096405ec 100644 --- a/docs/tutorials/matching/padded_sgm.ipynb +++ b/docs/tutorials/matching/padded_sgm.ipynb @@ -6,7 +6,10 @@ "metadata": {}, "outputs": [], "source": [ - "%pylab inline" + "import graspologic\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" ] }, { @@ -20,16 +23,6 @@ "The effect of this is that one matches $A$ to the best subgraph of $B$. That is, the isolated vertices added to $A$ through padding have an affinity to the low-density subgraphs of $B$. Since the isolated vertices and non adjacent core vertices both have the same weight, the isolated vertices carry a lot of false signal. Instead, we desire to match $A$ to the best fitting induced subgraph of $B$. This padding scheme is known as $\\textit{adopted padding}$, and is achieved by substituting $\\tilde{A} \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $\\tilde{B}$ in place of $A$ and $B$, respectively, where $\\tilde{A} = 2A - 1_{n_1}1_{n_1}^T$ and $\\tilde{B} = 2B - 1_{n_2}1_{n_2}^T$.\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from graspy.match import GraphMatch as GMP\n", - "from graspy.simulations import sbm_corr" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -63,7 +56,9 @@ "source": [ "\n", "# simulating G1', G2, deleting 25 vertices\n", - "from graspy.plot import heatmap\n", + "from graspologic.match import GraphMatch as GMP\n", + "from graspologic.simulations import sbm_corr\n", + "from graspologic.plot import heatmap\n", "directed = False\n", "loops = False\n", "block_probs = [[0.9,0.4,0.3,0.2],\n", diff --git a/tests/match/test_match.py b/tests/match/test_match.py index cd3a43c44..75dc69108 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -7,7 +7,7 @@ import random from graspologic.match import GraphMatch as GMP from graspologic.match import SinkhornKnopp as SK -from graspologic.simulations import sbm_corr +from graspologic.simulations import er_np np.random.seed(0) @@ -103,37 +103,16 @@ def test_rand_SGM(self): assert 11156 <= score < 12500 def test_padding(self): - directed = False - loops = False - block_probs = [ - [0.9, 0.4, 0.3, 0.2], - [0.4, 0.9, 0.4, 0.3], - [0.3, 0.4, 0.9, 0.4], - [0.2, 0.3, 0.4, 0.7], - ] - n = 100 - n_blocks = 4 - rho = 0.5 - block_members = np.array(n_blocks * [n]) - n_verts = block_members.sum() - G1p, G2 = sbm_corr(block_members, block_probs, rho, directed, loops) - G1 = np.zeros((300, 300)) - - for i in range(5): - stepx = 100 * i - for j in range(5): - stepy = 100 * j - G1[(75 * i) : (75 * (i + 1)), (75 * j) : (75 * (j + 1))] = G1p[ - stepx : (stepx + 75), stepy : (stepy + 75) - ] - seed1 = np.random.randint(0, 300, 10) - seed2 = [int(x / 75) * 25 + x for x in seed1] - res = self.barygm.fit(G2, G1, seed2, seed1) - matching = np.concatenate( - [res.perm_inds_[x * 100 : (x * 100) + 75] for x in range(n_blocks)] - ) - - assert 1.0 == (sum(matching == np.arange(300)) / 300) + n = 50 + p = 0.4 + + np.random.seed(1) + G1 = er_np(n=n, p=p) + G2 = G1[:48,:48] # remove two nodes + gmp_adopted = GMP(padding='adopted') + res = gmp_adopted.fit(G1, G2) + + assert 1.0 == (sum(res.perm_inds_ == np.arange(n)) / n) class TestSinkhornKnopp: From 1189e41d34cf6ca892347f75217960cd6a3881f1 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin Date: Fri, 2 Oct 2020 15:40:17 -0400 Subject: [PATCH 028/140] Update test_match.py --- tests/match/test_match.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/match/test_match.py b/tests/match/test_match.py index 75dc69108..28fc28ce4 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -108,8 +108,8 @@ def test_padding(self): np.random.seed(1) G1 = er_np(n=n, p=p) - G2 = G1[:48,:48] # remove two nodes - gmp_adopted = GMP(padding='adopted') + G2 = G1[:48, :48] # remove two nodes + gmp_adopted = GMP(padding="adopted") res = gmp_adopted.fit(G1, G2) assert 1.0 == (sum(res.perm_inds_ == np.arange(n)) / n) From 055c72bedd5d94455b7f59cf1650d14e58739dfa Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin Date: Sat, 3 Oct 2020 18:46:05 -0400 Subject: [PATCH 029/140] tutorial fixes --- docs/tutorials/matching/padded_sgm.ipynb | 93 +++++++++++++++++++----- graspologic/match/gmp.py | 8 +- 2 files changed, 77 insertions(+), 24 deletions(-) diff --git a/docs/tutorials/matching/padded_sgm.ipynb b/docs/tutorials/matching/padded_sgm.ipynb index 3096405ec..09b9f18c2 100644 --- a/docs/tutorials/matching/padded_sgm.ipynb +++ b/docs/tutorials/matching/padded_sgm.ipynb @@ -2,11 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import graspologic\n", + "# import graspologic\n", + "import sys\n", + "sys.path.insert(0,'/Users/asaadeldin/Downloads/Github/graspologic')\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt" @@ -18,9 +20,11 @@ "source": [ "# Padded Graph Matching\n", "Consider the scenario where one would like to match graphs $A$ and $B$ with $n_1$ and $n_2$ nodes, respectively, where \n", - "$n_1 < n_2$. The most straightforward fashion to 'pad' $A$, such that $A$ and $B$ have the same shape, is to add $n_2 - n_1$ isolated nodes to $A$. This padding scheme is known as $\\textit{naive padding}$, substituting $A \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $B$ in place of $A$ and $B$, respectively.\n", + "$n_1 < n_2$. The most straightforward fashion to 'pad' $A$, such that $A$ and $B$ have the same shape, is to add $n_2 - n_1$ isolated nodes to $A$ (represented as empty row/columns in the adjacency matrix). This padding scheme is known as $\\textit{naive padding}$, substituting $A \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $B$ in place of $A$ and $B$, respectively.\n", "\n", - "The effect of this is that one matches $A$ to the best subgraph of $B$. That is, the isolated vertices added to $A$ through padding have an affinity to the low-density subgraphs of $B$. Since the isolated vertices and non adjacent core vertices both have the same weight, the isolated vertices carry a lot of false signal. Instead, we desire to match $A$ to the best fitting induced subgraph of $B$. This padding scheme is known as $\\textit{adopted padding}$, and is achieved by substituting $\\tilde{A} \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $\\tilde{B}$ in place of $A$ and $B$, respectively, where $\\tilde{A} = 2A - 1_{n_1}1_{n_1}^T$ and $\\tilde{B} = 2B - 1_{n_2}1_{n_2}^T$.\n" + "The effect of this is that one matches $A$ to the best subgraph of $B$. That is, the isolated vertices added to $A$ through padding have an affinity to the low-density subgraphs of $B$, in effect giving the isolates a false signal. \n", + "\n", + "Instead, we may desire to match $A$ to the best fitting induced subgraph of $B$. This padding scheme is known as $\\textit{adopted padding}$, and is achieved by substituting $\\tilde{A} \\oplus 0_{(n_2-n_1)x(n_2-n_1)}$ and $\\tilde{B}$ in place of $A$ and $B$, respectively, where $\\tilde{A} = 2A - 1_{n_1}1_{n_1}^T$ and $\\tilde{B} = 2B - 1_{n_2}1_{n_2}^T$.\n" ] }, { @@ -38,7 +42,9 @@ "\\end{bmatrix}\\\\\n", "\\end{align*}\n", "\n", - "We realize $G_1$ from $G_1'$ by removing 25 nodes from each block of $G_1'$, yielding a 300 node graph." + "We realize $G_1$ from $G_1'$ by removing 25 nodes from each block of $G_1'$, yielding a 300 node graph.\n", + "\n", + "The goal of the matching in this case is to recover $G_1$ by matching the right most figure below and $G_2$. That is, we seek to recover the shared community structure common between two graphs of differing shapes." ] }, { @@ -50,15 +56,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WFhYWFhYWFo7A+omahSOwfqJmYWFhYWFhYWFhYWFhYWFh4Ylg/UTNwsLCwsLCwsLCwsLCwsLCwhPB+omahYWFhYWFhYWFhYWFhYWFhSeC9aBmYWFhYWFhYWFhYWFhYWFh4YlgPahZWFhYWFhYWFhYWFhYWFhYeCJYD2oWFhYWFhYWFhYWFhYWFhYWngjWg5qFhYWFhYWFhYWFhYWFhYWFJ4L1oGZhYWFhYWFhYWFhYWFhYWHhiWA9qFlYWFhYWFhYWFhYWFhYWFh4IlgPahYWFhYWFhYWFhYWFhYWFhaeCNaDmoWFhYWFhYWFhYWFhYWFhYUngvWgZmFhYWFhYWFhYWFhYWFhYeGJYD2oWVhYWFhYWFhYWFhYWFhYWHgi+Dp388MPPri8ePPTrbXWXr76Suvv94LZctfU2FU7VX9evvrKn7mGn9X1OJa6jvcym24stia9jfuM99Q4bL2xD/ND7YWbH/N5xDcXF66/G0OhaieO79ZJrWkVo/P6xBtvbGXjTxCRixAj/ODauPYqzit9ZuDOl4vv2D+2YfZVfxabzCc2FkN2LtQ1tFHlk+q5cJzv1prtv4uLiq8j11SbOFaFi6KvmQ8OVf/OwkWf2z51uYbdqgYa4ZXe9kuf+ZZdvr3/7tuvXaucQ6dhoo9or6pX2Fi97545v//u25afKrm8ylkM2ZlScbJ3zs6HzK+RNZppz/DJz35+dJofw9e+/KWb5qLWrsdHCBZ/M1yEtqLNp8wnTrPg2M7WqH6Ktlz7zN8RjNQ7cSz8fFS98xzgdJH9iRpVBOwFs+WuZYHy8tVXdvkZ+1QICP3q/9R1ZevFm59OEzC7zgijX1ckUCmmok3nO9qMtuNeZPOI6xR9wHYINp6aJ0sObi/UuDhm1iZ+Zq9sHdAfhUpcHnlenwIwVvBea/oBQ2a30l5xkTobM9fw3ogwYlwUzzSeE5xTbIdnUAlxZo/5ps4Fjo3zY2ue8UOlMMqKhtiOzUmdYxafLibZnNSeqzmqsRUPOu5Gm1W4M5mJy1tFNp9ZcayuKZ7KeOSoPFDJLcg56IPSF8r3GV3E+u6FGkNxDNNnWaHDuNjxXx/n2ucqO8cqN8R7GQ+rNa3WCc8dWc45KkaYRq5yEbZ9SD5h2nGUT5RNHHtvraTWdqRWquT0rEaYrXeijyP1zoJH+qtPuKh7FzgTFup+VkSxBDE6viOPeE2Ng9edyGKJj40f2ylRwBIb8x8LOJynKpLQTgWVw62ITfmE75VtRQqZ7yOxnSUY5ofy1a2D2ie2jrg+ZyfDLPnumX+FJ7L+6gz1a47rlBhhgl75y7jBFRfsfoX3mA3si9cr9mJb7JcVoRWxos6QmhPLg4p/0b7iLbfeGZhoQ7tu310cIBgXOaE4O6dbQaaLRgqYrM0sF7lcOQMWQ8gP7DyoAtxxA7PP7s/oxQrw3MR8XPGTxYYrVipcpM5yhUcr2LNmqiiLtqt5k72P15gmes7INHqVi/bGUeVsIj/cCp+wXKryobJdQcYJFZ/ivdm8gnDrxnTIaL3Dxlj4CNvlon9qbvRXn1yRfQRG7M2MXQlcJqzjeHhPJVtGNmz9Yh+85my7Nu5atY9bj0y0ZgSSkU3WH21kPlf6ujWq+pP54uZaGcPhTL9uMHu2H4uLZvtWz0qFQ1ifTKSMFpuZP5Uzo9r2axkXxb7u7OC9kfOZndnKnPZghI/d/VlOmZ1PH+9MXDQDltf32stsXeNXnxyqcVYtMJwuUrb3/FpM/9Un5lMlJzudqGzM6CLsf9SvPo1qx5FcsVfLRKxffXqdj0brpNaO46IqZnMHvlftrsEn2OYatVJljKwtrhXOSXFTbDdb78zoKXX/uWHXrz7F93EBXYD1+yOCrtKmWtywtpnvOE7vj6+jY7N+WdHQ/8X1yw5e7BttsjnE18q+4lzi+kQ/qwfPxQ7OI96L/dm+RL8cen9sy9aRjVGZD/ZjPrsEgvu8p6A6G1TMVhNa1pbdc4l+xE61DfOTxSsCOUTdz8ZXXBTvK25ja614OTtb2J9xmuLJ7Gyhj4xLsrVyXIQYOY+ubSbu2D12n+UQ9zn22yO0z4YRbdEaj62KbXXNaZ0jweKhoo+cLqrEI+MipneUrzNA+yPnprdh/rP70a46g8wG4qh9nymgXK7I/Kzkln5tRI8/R4zWXUdz0cj9W+IT7FedW6VWYm3ZHJjtSq2E9Q6rJdXcK/WO01XMX9bXzXch+Yma/qTWJZgKMc+03YMsISp/+mcU+47IKqTIDogay0H5kx0A1db1y+Yz6ne36dZ05Fq0h/PEMbIiJhtTraWaKyPCyrgVm1l/df0s/4tdPdt78VBcVfGjNR1LGOOsDWtfuVdpW22T+VY9hyM8m9nubRUfR1uVs1zhZzbu6Bp0jBZTVc4Y5eiR+60tLqriSG219ydq3nrvnVLOH82Vqt1MfsS9OPKLdbOx3fuKz9HvTJMp7mrt2C9QVj4z36J/rH2VY0b3fW9cn/Enaq6Ba9R5D8UnlZoG22d1Q+Y3a1ON7UxPMFtKV7h2mS1s53zM5re33nkOmP6Jmo64cZVFfPnKf+lnFdGGssmA/lbasc99PEyQzk9n58Wbn6aHkLVxPsd+LOHHcV3BwOZVJUx14FkftnfdRvyHfVWsqbhwohnXLJsf9sVxlA30jdlnYyh/1P5eozB4ymCx6uaPZ3eU+NV5Yu+vBTxn7iwwropx6ngmK5iwbUVYsbOgzkOViyo8rASNO394XxUP1TyQ8bPj6+waW8cKFyv7bC/VmVH7znzI8tgZMMpFe+w72w+dC6p6B8HasbOnxnDnzuXeh0KluGst11uMm3GcjBdmwHg48zVqI6d7K4VgVltgDDzHYm4vrs1F1bbOpyP4BPPvXj6pngfWZi8nVGolpw+z2mGm3sk4d7TewbksfBylBzUdWZHfMXJQoyh0wrAq/PZuMktUsThRPrjAZ0LfFZDqoUK0wYqC2J4dGkZisY0rwtThZqShfHCiIysy3Lqr9UUyqRS6OBauVbVoU/Yz21X/2D49J5JzvNFabW2dTYaM95S9GTAOUHNh55j5oM6m8hP5hvGI4yrlA9pAMEGCvldzDHKAEjvRT7f2Dpn4wdfKPBhfq5xQ8QuvVzh6RqQfUTjeCrKzNFq4RDtOgHfbTjAfBVVMqTzk2mEcM25RPqj4zIq9GTiuYGOp/ItFFhaUbO9c3t+ruRWcHzhGxvlVXRTbZxz9XPhkD67BRc5evD6qLZhfR/FJNn6/V+WT6jlkdpX2R07IbLtaaUQPsfkoe6P1Do4zW+8sfBxDD2pw0Z2AwM+uoFJBUBXkIwmiYicWIGhbjVXxISv04n1WNMR2WdHFDrsS0ZEARoRSXCfmu5sXaxfXPBP62JYhW2NWBLF5Rl8zn7Lx1XqptURbTgy6vb91qFju71kcjJJ+Zc3Uma/YG/GnIhbY9bgOWACpAiFLoMjhWGzg2RhZR8dFDu589Nc4BlsvxUU4pyyucB2USMNz6c6sWh91DeeOc1O2nV1sp3KDW8ez8RACz1hVF8Xrbi/xvTpjlf3dC1ZYINh5dn4xLoprGu87/YV9j5g7cpHSCur8Z7qoX2PzxzZxLHbvSGSxNzp+xWecf8ZFZ+eVGTAuUu2cjZHxWqtxEYPiE3a+mH9VPsHXGT5Bf+K5Z2A5V2l/1hfvOc1R0Sdx7piX2Tycf2qcPfXOOs8eQw9qWvv4wXGiRCVcBhUk1YR79CYz/+P1+JkFmxLf7OA5EVYh2ooow0SfkTm77gQ6I4vRPUECVT6pdUYbyi+0qYoQJKpq0qvMOyPXrFhi4zKh8xzIz52VakJUNrHNyF4z29XYUJ8x9hlvqqKxwkUOigfjueyfWfw6jq+cLccx2T6z69m5Ri7K9h7PH1tvzJ3YP4uZPVw0ut8KI4VSxr9nADuPqFuqZzqDi0fn21HAXOkKMjzfihfweqVwUWOxe7Ng+4KcquD0R5aH2BlW+obd3wMWW06PqTGrup2h2u/svDIDdh4r50vZcFBcNLIvik+Utun+PRSfoB/XqpVGY5nVSowTlAZhfj92vTOiK54jhh/UsARdFR4uINWByDbuSMJmB84Vc+6QoI+YTJXAw6ID10IlRif82X5VyIUh20NVMFX3kfmnCo5qolD94/09cVYV6M4v1r/byHy7RkF2i2Dr5x4UVGJ+hItUvxFUkrzjGxQ92RlxfOU4giVaF8+MF2aSPs7JcbQTabguyOdVsYZrPcJFam7X4KIsjl3cZe0qOLsAc7Ff2dOKXae7ENfi/njmRvKb4hlsy84T4zg21tGCf7ToVGOqvKR4lvmg1vvIfVYPiUbizOVghsfgoucE3MPKWXK21Ht37rL4uTafoJ9VPon3ZmKtWithn721kqrBZsZQvrM5HFXvLLyO0l99qqBa+Oy1MTrOjF9KzHcwEaxEcSWRqbEUAbI2TgwwVIsd50vsW7GR+VsZO5uLGmdkPGe36qcad9QPFy8jfn3x8tWb/usGD8FFD92vYtOd6WrcY5vYlt1jXONiuyLOsmKlckZm+WyUGxVm+rB+TAS6eeEaj/Css18Zt9q+isVF/vqIjREc/Vef0LdKrp3hon5PnR839ic/+/nZ6aZ/Acn55taCXR/VGc6PvXNGe2qsjlkuGtFF7t6e+ba2/upTaw/PRdFWa3O1Tez3EHwyUkeM1kpZfj6yVsrazI51RJ0y0/5M2P1Xn1qrPS07QsS5cWdIYsQvRhz9c7+mHp5gn5ev+P/uxGtZwlPXYt/eRq0NjsNsx77Z+vb5K0LN7ER/M6hClN1XxYxbH7V/zq7yMY4xUsCijXjNjVs5j88Vs3Mf6adicqRf5kulUM7iGs9qvK84ivXL+KK3xzOTcaSak2qv5sn8UueK2VLtkGvdfJxf7jP2UTzG9hJt4PxUvmJ+9P7V2HNQ+3AGEabOBUM1RjIbLoYqa/rWe++Ux3N+KK5ROW/0DFfWRemPUTuzUHouflYPY7Bff6/aZeMze3vhdFHUf87njIsy/xUXZbroOWM2BvZy0aw/ik+chnbnyPFT5qeqZ1jbqm2slTJOUGMdVStVdMxMvRPfZ3yPNirXFwYe1KigrTwZjO3d5+q4oxi14cRxxWd8GKMe2FQLlNh+hNjYoc0OIfrABAjODdeLjR0PceaLIhQcX/mO15W9SiGK9xDKx5GYwzXt1xzJVQrG7N4tIUta7Dr2qXCTi4lrwM0nEyWqOHEFvYqH7Oxg4e3WZiTxOz/Zucj6Rs5W+z7CRdinEhPObyaosjww4i/jZyd80ScVO1Uuiu1HRP0tIePeChc5uLYjBX3E3p+o6WNn58txjzoXrPjHewiMWXY+94KttTpHKofH+46znW3VtqoBRuH2lvlT5YsRXcRqi3j9LLrmSIysbXzN2o7kBLZnFdtH8En8fASfqDminWqtxObGxpiplVgbtY5MxygfERUNkUHlgdnc9hww/B01rfkFdclSidYqqkVGRQSPjIWCF69lB189KHBCHK/HMXH9s+RVLQbZQXbJ2e2zOviVg5kRJVujjDSzMREjgi8jtgrY2WCx5QQUW/OzFEojCStei7Hk9nzUfhZf7B7rN5uUFJew9y6BZ23Rdzz3rmhRnzO/2XnOeMBxK/qSnQmMGyf68NVxmZuHE71MxKoiBvtme+PaqpzE/HNzOgsHVeGEbOW8Z+teGa+1h1l3tfeOR6Neie/xHDEewPfRj2hn79yvoYvcOWPch21wzZy2PBKKvyqczK5X+ireP2p/z4bRAjm+OqCWz/rt5SIWx0+BT7DdkbWS8mGkVmJ1obKvtMPIHik/q3B6fZ3t1zH0HTXswcS1ksO1Ek/FLntQkBUM2dpUgq/aH5MzS9jqGo6j3rPP1euZfbVezE6ce9U/5+uRSb5qq7qO2brs8dH9/uMtgP0eNos1vMfaKzsKo5wx01/168AzgfcjsnPmzjyz5cYb4RK0w4qMo5M0i4/RM6fWs7J2rt/RBUdlDrPXq3Ot4GxcdGTBfM2Ce89P1fRfnXK6ojUeB+rc4TWFkfMTr+35/hKc78g5ndUcLF8xu06b7p1zNS84jPTN9jCz9+LNT++K67N9R80oF11Dt1ShdMBT5ZOZWqnqg7J/VK00i1HdMNPnaB10qzjsO2p6MLx48+NPKNUi9+uVTVCH0fWftTvSD4lKfVYFgVsf5ldfV+zP1hzHxgOtDq16j/NTa8D6qP1Swi62x7iK7eLclT/KfzeHEeD82NzcGNX4jfs246/y8WzAPWX7g/Ho1tTF9xFixfXP9ofFl0uQaDsTFMgtsZ3jYDyT0Rba7Z8zYRP3VZ05N994ja0V42In0PAz49fK3h59BnGOOF52r4KKCGW5z+Gaov8xoXKA4/2qLjgqdo761acIx6dKm0Rb1bmxvqiXRorT6pgj3Bw/I4+p9thX6TKmnxivHXm+qntT1UUVbaTym+O1I+L6TBjlon6vEu/X1pIqrz0FPsk0QrXOyGol5ROO6WqlEX/YfHD+rA3WnxGj9foZa5Sjsfs7ahwJVwVctM8OnArsSmE6epjcQwxWbMTrLLgz0sGxWBGK9xlxqKJlRHCw9XEkgfNX66sIiPmliiC2Lyz+lEhya5AVhWpeTlxFu4rQsG02ptrjjFTPViBV54OxMCM+sTAfwVHFK56X6JPiEsUR7Hw4TnNzcz6wNtn5YXPFexURh3PB9my8TGwou3id2UffHE9kXKTmilBjoJ2RhwVqjWYF8lmR6YxMM6nPI+t8NBj3ZMDiJ+MCVihkGitqD1ZE7IGyp85SvFfRXoyz49wYFN+M7Iuz7eaJcPoCdVHGedhHjbOKujlUuMhxD37ey0WOT1wcPBafRLvKR8YJLMZdrcQ4oVIrORsM7lyP1jtKB1a4g11/DhphFIf9ee4MbPNVQnWJlhXwR24sEggK+Xgtwgl+dfiQbNAHZlcVXvGaG4f1Qz9UH7ZGzGfme0YmlSTs2oyOpXyeQRYbzE/l11H+oU9n+ZO4R5z3rJDCtkqwu89VP0fnw85pvB6heIb1G+EifM/6OBvKX2ZXtXG+sTYZj2Jb5iNbI7fulX4Zp2XzdO0dJzK/9u5JJTZaa6fhoiMww0UjOiNi768+Zboky8FVPeX0iOIpnHN/f9ScK7pIzQdROcNo0/WPn4/61SfmV+aH67NHF2Xj7ZnzmX71aVYbOV45oo9qcxSfKL+wLfbL5qD03J5a6aj6ZTQfu7FHsbfeGdGwzwnuV592PagZPdit1QTJ7Bh7MJtYXR83RlZEOTFTKbaUP1lxk4ko7FclvgqBon9szApJOLKr7KPzcwaVNeyozqcyXux3tu+F2IO9gubayIqceD22z86Ds5PxXx+HjYl+V8+lEvIZv43OsyL+srV182BrVDnvM1w02ybr3/3J9nNvwdba7XPRhx98cHE8MFPwjGK0395fEXn/3bdfuzZTiDN9kJ0LdzYdb+yZc5+v4j6n39R8q0XJiDbAfkc+jGP2Mz9GuMhxSXWsvXF9pgc1j4UKF6kzH/HU+YTZPbpWUvOf0Q8j7Z5SvfNccch31HSwhY3Xjlj42aBx7SrBimTDDmi/pohJBSEjs/g5zpMd+moRw+yiD/2fIomMLJlf7FXNR/mO/sT1VnZxjnE857cah7XDcbK9Z/7gWLiGM+dG+YHrfkao9dpT/OI1dn5H9qnSVgkW9FWdVyYWej+29y6GGT+586cSsvvM5ucSe+QsFddKLFW4qMN9jpzpYgfHxbkw+27NmR8IFhd4X11T3J+tDfqZxYCa660iyy3s2h5tgu2cPmB9jhbDbv5Ke7j+7FzFGHV6UOmivWC6SfmAvsT3jovwrLPzxPZPcfs19pnlm+hrfO/2EPt0e3jP8RD6tlDHUVzE4rnCRTGfs3usLd4b4RP0Yy+fqFpJ2WHtRjhhT60Ux3M6I1uPar3j5nxkvfOcUfqJmiMToAv+vfaOAhZGbAx1UCvFnCosmD3VB987/9l15XM2rjvUI4evOr9qX7dnLjlUfa7MdXQNKmPt9anj1v8X+xr/azTLHdfqp3gmG0vFfJW7OmbjLvOBzaPCRThm1fdRnsp4oirKKnM9giMq83H8pwTnyFyr91nbs3CREsEzuLYu2vuTB+xXn9zYsc1ITKnCRNlzXLP3J2qqfo7wTeVsjugibL/n14DiTxGNai/Xb8RepU/Envm2dq6fqDmqFmI5ea89Vad0XJNPsgcRVT65Vq1UzeeZj249KvWO8iM7o3s1zVF1061j+idqjhQiHX1TRgoI599RvvWx4ysGYv+HUAfJjZUVJXHt2SFj/kb/svmgPfSZfUZfle8VW8w2ziW2Q7vss1qTOGacuyu23Nyr/RRUWyZSXZ+RAunWcQ0idwnC7ffsemb9MDbZecvOOb5H7mKc2c9S5TwzznY+sHPtBA/6no2P41V4i92P6xJfWV/FTWquuB6xj5sXm3+0wXxBe2xdcB8qgpXNlwn52Jb5fQYu6mB7k6FSUMzooqM4KrOPse54CD9XdIfKy8qPh4KK6azww/5qndBWB/Ilu694YhYj+mavLprJ6w+997eCI7lIxfuMXaddGZ8oTYGfq3zi5lDlk0xD4OeRWmnGNtNXcaxqvcN8rNQ7rB/zV11D31y7hQf8MuGIPRtzpNiLCZAdGBSrrE+05YB9sqSmxkRbbD3UgVNzy3xm/qOfFXGCc1Tju3XN9om1qX7O1iQjVedP/1xZ98pYbk6s71n+F/up4eji052xaluEOh+VeGZtFRdmvrMxGedlRUz1vGbzYGuDfaJvmU3Fd+5z5i/6WM0jqnjMeCLzJV6rjM/GXF8mzHE0l0SbR35HTVUXjRThmYaI9irjv3z1lUN+uqTqd+VBRsZBql9sk63pkXN2mhOvVXy7Bhetn6i546NrcMfRcDmS3X9qfFId7zFqpYo+YddVX5xTBlUTz+zdc8Wh31GTIduQvnkjQRAxQkbVxIGv/f3LV/x/M9SBw/79H/riChXls3pgwXxTc43/Yp+KyIj20U9nB/2p+IrtKmAkq3yIiPsY55EVxnhf7aPaH9U/OxuK+LKk9BwxGj8PPabrw84nnkF2DeMI24/6yARTh+MNNV7kfxXLjkfwvLq4r8R/hXMr9/GcM1RyRrTNuJr55ARdthexP9vfagEaX5FHzybYZs94lu/cGs2u29E5INNFbGwW45lfLJ9W9IW6NgMX/2zMjGfxzCA/o42qhsG2R0BxldOEGZguQltqHmfij2ugwtPxmjs3mVY4kotuhU+YrlL99tRKqo7IckcVTD/trXfQh5l6Z8Fj+kFNVkgqsIchs2NlyJ5Iqs+KCDCosrlmB8E9KEJ/HNGoQ6CKG+ang5o3rhcjGuYX3nOH1a0xjuvELhuDxV8kV9ZHFUpZMef2Vtll880ETbdRPVu3gpFEVV2jiMoeqn7x3kzSUZwShUuMjxiXTrw4Pss+u7PKrjNb6jwygV+J/wrUPjLhUOEcbJ9xrRq3v2dcjn7HBxyVuFf7U4XiiyoXqb2rCs1bwwivxD6VXNbfZ2frMYAxGuM5e6jguEG1mdFFrP0s1PmLfrGzxwpB7JOd7W7HcVS25rNwuijjwexatFcZ17Vf8FB6vFKPuLNUPV9ON7FxrsUne+osbJ+d25laieXSo2slHCcizqla72QYrXecRl3Y8aDmqEKQBWl1rEzEVg4VsxWTkrIR7TtRpcgI7bB5scTP+mIRh22zwr1ahMQCgt1nNtV4rr8ieFw/VgSx8V38ZGIC58ras/1XRIXjZsTsbDjsLd6eGqpFTtbWtakkwOwcIUaFJyvcM18qot8Jhv7ZiRTkQuQaldAZbyn72AfnG8dFn2djfFR0ZueK+VNph5xQyVsjXJSdHzUvldtU+1kReRYcqYsqeMgCNp5llYddTne6SPVhn5H3mKY6It6ywk5xp9NFzrfsrDKN6vycxYiuzuYakfFQtm/PhUOOwowummnbkdVBrD3jE2ZjlE9Qqygb2F7xSXbW9tRKGSdk68DeszVl41R1GbbBsdC/0XpnpLZ5jrDfUfPhBx9c1EZXCkEU8w4jbWOfzAf02/XHg5u9qnEzcavEOfNlxJ+suKvMj/nPkI3J2h4JFpdqjJHx1dpX2+9pd6QtxNm+oyYjf8QMF42g2mcPJ2ZnvSLwERVuwvsZZ7hCP7ap+JX5z3yqjLeHk9QaRSiB5jg28zHj56O4agSVdcVri4uuz0URL9485jtqVM5Vc6lySPSTtYv3FU+xsfZ+X8uIxkEfmZ7M+lTaZn33/qUrZrfyOV7vyPS1Q3Ud1nfUfJyPkIuOrLv2cBHTC7fGJ2oeGWZ4ZA+ys+buz2iI6rWKreeM6e+oycTHizf19wyw9h3Yds/DguhDNo9q/z62e8VD3P+p8XqfkQIF27vx4yvOSRVR7LPax6ydW+MRkaP8i6/dZv+niMLFL/MXCazie7aXI4mtQmzYLo4xmkRvESzB7OGASsFc8am69tW9qnIQO+uRi9S5GOFZxl2uH/qAYEIIz3e0o+7H8dA+G8/1YT5WYoatNbtfWfNKYZStO87B3Udk/FgBG+OsYszpImyj7jN72HZ0/eIZPzInZOea3WdnWHHSiC6KdiqcNAqlO9S1jqxgUdpQ9VfjjPD3EajqItRl2L/DxeURPPRcMbpeI+1n9oKdm3heGZ+4+4/JJ2weribG+bCxmd0KKpygzpHS0MxXBLPt6t6KrYUcu371yX3uUMGMn2PB/RgbWC3m8boKdCWUsNBC4mFJzvkS76tCHsdXhYSyi33Y/NX6YZGCNlxhhnHB1ovZQ98wWahCMSuoqrGRiTG3Bsy221csFEfj5paginvVlqFSVGUiU9ncUzDgmWQxq8ZDG0yosHOmBAfzBd8rvmPjjcwfxZkSd5kN7JuJIVxvxTM4FruuuIjxm/IrK4z2FjMuBzMBp/q5c8b8OhMfIRT/znARs/mYwpadbeRLjHeWSyu6CK8xXTSi12bgCsSoRSIcTyguqvBRVpwqjpiF0lVMCzqo9an0V2us/Fz4OJSmd1xULdKdnUrfip55CnzC5qA0F9rFuTGfkBP21ErRDpsj451szo5bM31QqXcyTbbwEXb/eW5X5OP9rG1v0xoPjL0bWvWVvXaf3OFgc2AHpdIHofyI99GWA5tfxRbzg7XP1q/Sxu0F+uoSeyaWs/XK1lbNi7U5wsdRUfbizfUncWf54wje2QPFFYxTItTZQjvxenZe0UZFRLOYZ+LIJXZ39l0Bw9q5a2oeznfn8+h8RqD4ZIQrsr0e8aMyXsfZfvWJYUQXVbGHi/b+6tNb772T5uLW+NmOn7FtpoX651Fd9OLNfb8G1OfLfK2ebbcWFV1ZAfLxUb/uVcWIv3t1GBt3b1yf7VefWuMPd/dwkdMgM5its9jnCMUn6O8Mn7DP8Trz6SFrJfSF+cN8nbVRqZlm653niqv+eW51WBlBZAe7Gjyu74ivmXivFjkvX/FfNcBr7F4cC8fon/HV+d0Ph7MX2+H1OB+2pupabI/+xGuKRNX7zGflZwVVslDr0ddYESraiK9sDOYfG6+CakH9XKD2GuMt44S9cLZijPR/bL/jGYjt8Dr2iXYd10Z/GEdgvCuOZ/0zEabGYTzshB3zJ46Ba6vWGdcG107xERtX8Vzk6+yM45rhGjsucvlvJsYzLmdtZ8X9rSGb58g6OC7I9u0o7nK6iJ1bzI3srDF9xM4b4xB19pRGmkFlvd14OB+mbarnwvHY0fmpqjPc+GrtsnqhgufCIbPAs1aNrYy7O/ZyUaXOemp8gmNhuz21kprnSK3E/FEaxOkQ5mO8rzTFNeqdhY9gH9Rki+wO5syijySuWbBDgvNhxOUOnSI6V6SgDUcqWLghwTDSY4WAIsfKHPC9aoM28HBnxRfaZGusSCIrOKviQ8WgK8rQP5d41FgZ2VXOXey7iO/jcHHW32dtjvSBnVP0A7mBxUA885hIVUJk9zOOcK+xfcZ3cV6ZCMwEnfKFjRnHjWMxP/F+BDv/0S9lT/mo1tQVO2yN2dqxPcy4yLXBe2qe6sxUuOuWUBXoe+D4p1pkvfXeO4f4ooS+OuesD/rnzpd6ZecY3++FssXyPfMzK7RYH9bG8eiR83a8y3SR04psDaq6iPWLUHr5uWMvF83Gj6ubnH1Wq+zhE4xT5afjE1dv4OejaiV1lh0nMJuZ76pv5qvTO4rzZrT2Os8apS8TZtdHg2l2E6r9nDjudpjwxsIC2+J1FmzVJOna4eGvFBDYX5Ee+stsZIUFm2dVmLB2WVJnc2PjsuJSkY8jXfQ3KzLZdfZZ9WFjKXHjBBR7zz4/B2Rc4faiYm+kX6UtnsesmHFCI44dz6oTLWp8HM8VU9FWv66EUOzn4lMJoBEbFbFSeRjhxBjri3mmspZxnEo+ZZyqfFQc5/x282X7i+1crjgb2NqOcG+VK1g/5we23fsrInEcd0acXmBtsD/7jH2cjol+HgHUGszH6jwz/5iWcf2w7VFnjO1HhXPc+MiBWS6unKPnqHEcjih83dmb9QNt4Zk6ik9G/FV8ovyvaPAjaiXln/PHXa/y1J56p9InXhupdxbuMPUTNa1xseyCxD10cHAJYESsqPuVayqgRsD6ssSF42YPHvDwMeKLbfHgjhSfWTJmBMZe2bhM6Du7FRJhBUuVdNCOKg6V2GRt8V6lr9sjRezK5i2jEnfV81/lokr8VPpmbXv7PQmZ9c/OmfKb9WNcpHiGjaXGdUJCFWOzQoH5kp3bypqqucT22Xl3PvTPav2r/lXGzNZWnTU192j7OaAyT5VbVBu0WyliR32q2lHxwc7M6FpU+7MzhT7uzX+KFxlcEYm2MK+7nMT4sFpMzWDGRsZTI/7hfmbcfDaNswcjmsL1q2gQx0WVei7TYej3Xj7J9GGVT6r1655aiXHCaK3k5lvhj0q9w67tqXeY/wsfIf2Jmiw4q4KYfWbXso2a3cSZQ46HJl6LAY4Hh7XNxBVLcI4kor14Px5S5n98dQUJO6xVgegKOOYTtmFrUgGuPRaXmShU8VolDzUfVXwqjJBVtoZngUuybE0rgiG7ppJpZq8S13i/Eg/sPDG+ieOwpO6EBJ5P5Dr3UILZUnbRv4wb8SxVCkY1luMiNT/k5wzObtbf7Y3LJS4GnY/MHvpb5eDngiN1UaWNE+BKfEcc8atPGBPIvS5O8OzgmVY8rvr0dsidR8Yr4wx2NqsPIxQX4d6i/2w9HvNMom/4OVsPd91xEev7/rtvjzl/QlTWrJovq+NV+1dqOeQT1O7MzgifqH6zfKI0Rnx/ZK3EznqlVnqoeofVnqoNw1lrliNR/qtPLCkjlJA4ClUfZskHyYKNVRFgSGTMZmznCpHKuDgWG1vN0fmvCgZsqxJ3tlaKSJhf1bXK5lSFm3tmP1u3qi8jbTOc6S+tVM74Q3KR8mmWi3rf1viZjrbZPF3R45I3to99Mt7Iiq6sGGPzqZ4pZcPZcWM5uPmp+Wbjx3sRezhr7zyd7VEf8P5z/At0e7hgxLYaZ++vPvW/COTGH+Gd7FxUc7u7tvcvILlxFDJdNMNpI/ePmvNeVPOWap/Z7rb2zLe1c/7VpwxV/bSnjjrah6fAJ3g9s8faznDCEbVSpV+cj8JoPfVY9c4t4pC/+hQDjB2wHnRVsTmzIaowUX6OjMeE7ctX/H8H+hj42tujD3g4o91omx3oaDP+w/HV2uDByooE5pcCW5sMirTcfqJPqrDMbOK6OR/dWrL9jHDrweKovx8t1K4l/J86FP9gm9GkMuqD2kt3reqP850JEGyb8STeU+vHONHFKXs4wHgyclnlwUVWsGRQPMjWUe0lrgPOzxXM2F/NEfOD8ktxUb/GuLkSEwojAk7F7nMWYtfk6koePWIMFp+uyGDnHW2yc9mR5eAYa+osPwaqushpgX4/4yKc/zUxw0Xxs1uXLJfG60xzLdTxFM7HGfjkGrWSmy9ej2D+VmvyuBfVeqeac46sd54zdv957o6MXKvXK+PMPuSpgNlWIl89lGEFVJZ0VTuVnHAd3AMLZksdlMxHHJv5F+fB+rEx1J7iOIw8nH1cUxc/rMBg75VPzJaLf7Sd+eX8zPrcOiqFonuokdme5ZQsbvF975chOyeMi5R9JWhcX1XUVwoE5IF4DduNFlY4bhQZzG9cJ+SBkYcJrsBQwlDlk2zeTKThe7ym/By9X+G1+NnlIjfuc8Oe+Vf6XnN9s+IazyCLOZabW9MP3vF8s76znD8CV8BkZ4vpMlUAVYsXxat797+SJzMuymyP6iIcf8Tf54pMv+J7hmrxrfplbbJc+tT4RNljcxutlaKWQX1SrZWUBqrUFTP1DuM1h6zeWWfZ47AHNQ8BDEC8ztpmthAsoTACQFLB4of5rPxgfbCIqhQeSBKuUMiA9tx6u7mh76pA7O3iWuD4kXjYQUe7ihgyf6uoCBScX+zL9qxavDnblULw1lCN2dbaa3FR6TciOrEvE+cV0cveM/vMTzWeS9Ks4FBntL/Hc+PObibCs+Ss+MYVMKr4ycS+41XmG+MlFSuqEGPvXZ8q984UaEwQVs8A9nX54EwcNIss71TARD6zj4J9Jjbc+PGzilkshpQ2ia8Maq7uvOPZPAqMAzNknMx8dueJnasj51uxUc1b3bdK26ouWlxSg+Pfh+Si+Bnf3xqfZLpcaZdKrcQ4YbRWiraRE65d7yj7I/XO0Xx9NtjvqPnwgw8uew60Smh4vRpQTjwf6RcWK3hQKsGlDpeaD3vowN47PyvjKtuuHZsb88PZzdYP7bh5sDVTY1XvoX1XTDm/s3HZPJlN5cNIm4gzfUfNDBzHMD6qJLdqn8wv1y87U1kcZDyrhFUW1y6mmT03RtVv107NrXIuK3yVFWqzeaI6L7xW4d0ZOFE40kfNobXFRU5/tDbGK1Xe2fsdNW+9947NYZVz1K91v+PneI21y8B82TNn9h01M7pv5ExmBVmlaDtqzsy31uqaq+pvlkMyLlrfUbOPj1itw+6xz85WdezW5vJwvIa20D5rl6HCbWwuzE6/P5Ozj6iVZjWWGxvbVfSSq3dmOfNMcLqo/GXCDiMH1ImUaxQ6ozaqxUm81vupdlWhz8Zj97KkGe9lyZChSh6jvrExHAGOigI1fpVUqz5eE5WxZgrL51Ac7eWCUWGi+rt+VTGkEnmW8KKdeI0JGTXHiihQCZiN787xSJKu8q+7p/imUlQwW8o+66fynsJRvDNjpxITo2N3m7f+ZcLV/8A6UhfNclHHUV8mPCLI3VlSPFcR9Erj4JgP8cW6jk/wGrteKfDUNXa29uxzfBin4DTptTSS4/j1oKaujVp7ndexDYspdn8PF7Gx+niqFtvDJ2wd1HjM7h6M1kqVeVZsK/tVn0f67Fmnh6ytnjp2PagZKTqy6w57Dn/l/ch4VYJQ10YKJlfEKLhCLmvDCCzzabTAyOYyUxCNzLfqh7I9W9iMiK69tl28s3HO8KCmIjYQVV6Z4awRO0dwUWv6oaS7jvZiG7w2IhLUeVXI+KVyrrO1HeGArM+1hE52nqv9juDCjFurY46Mf+tc9OEHH1xa0zx0FJfM2FL656ifqHHFDfrRrzvOiW2qBRqzy3zaU8SznyBSPlQLLuV3vKfsVLnomg+nZjRrpYgeXdvYZm9cn+FBzd7ffKjiSF6L9q7NJ2w8RBaf1fptplYa5ZE9tdJR9U42H/Sr0ifj27PjkL/6xKAO7cxhZocqE5vxPTu86rC78XobfFX9mG32mbVHf5jtfl0VVuhTb9sPoBNB/V9sp0hQrQfzl61DnAOzEe0rIo3+s+u4Bsy+65fN0e0Re+/GZ/ZG+mKfaxDuU8Qot1TbuwRbjX1nx/mR8RTG6Gjhhu/xrDN/IidgX+Y3OxvxWhyTcXfkIbyXcRGbD46N7Stnha0RmyO+V30ch1ViK/bLcp7bm5FCi/mA/XCM585B2Vo7VPVGZeyjiyp8VXncCe5sfmwcpzOdLtoD1EVqPHWmq3pDcRvayXQRvp8B40z0RWnoCpdm15k9l6POzjNH46G5qGL7IfgENcMsn7BaifUZrZVwDa5VK6G9x653Mr8WPkLpV59GiwOFGTsjffb6iYTABH4chwkDVjBjW2Yb7bu+7r2yw+yN+BevZ6KEtc/8cInX+exeu2+zth32CIVRf/aM0V9v/X+xj+YiBWdfJT4lajtm/HUcg/YZ2PnLzjkbr3JG1Rly/jpbsX3GZW4MNs4oNzJU+N3NKbMd21Z4rcJhI3tXuTeDxUX7oXJw1mfvr4jEX31SxYzSPugL3sv0xMj5idj70yUzHFj1tdKu2ibeP2LOM3yQccWMjq3YX7/6tP87s2Yxy0VZLeG0+wyfuHajUPk32hutlTK/RmslN84t1DtH645bwfSvPuGP1LFDlOFIMeOKIiXw1bVsnN7ePSDA9nGMkcOWCQJnk/ng/K3ObaaIGi1SsiJKFZbZeo8UZw6K7LMiuGJ3piBUY1fsnOF7IVrjydhxjGq3l5ey/opzRsd1hXgcp187QnygXdY2tnFnk33OeMMJNfSlyiszZ2ZkDdS9WY6o+jPCRS6nzHDS7Hxu/UHN13/TW49SGC0sLByLMzyoidpoRpeoBxsjDx1n6ooRXXZkHTlre3ZeI2sZ+2N7t87ZAw/mR1aTjoyVzWdEM6wHNa/D/upTVjgrsKA9AspW5suLN+t/2q9aPLj+2CZ7uNP7KD+wPV5HP+N8mS/Rn2rRwfxifjP/4riqnbKP19m6uLXNHhSpazg/54saM5ujaqOKcIVsXc9AejFu++d4r2pjtE9H5UGJGqtynY3F4gzbxHhV47Czh/ERx2KcEV9jexQrjnuUYGH9KudVxUIm9rL1QP/wvtqXGJ+KI6o8yHyvQvEGW2dlX/lTFZ17ztrCwsLCwjhUnnL1RNS6SvNGDVCtt1Czxdyh8kMlx49g9MHITFtVf6A2YmsTbSobaIvNK5sn+hHn4+p8NxbTUlkdk2lLHHvhDqXvqFEFkmsfUQmiUbgCadRfZT8rdpEAVdHkiA99Z6+s8GAHn43BDicT5coGjok+VYU/O9yq4HPFVma/0lbFCCu0lO0suTCCZcVhRpLow8hDorMVSOocOIy2r9qp2KwUtNWxKnvpHlxkQk3xFxMISgxi7KIdl4TdgwLkBMaP0c6oqFF+M7GZcTLaV7zN7lXPLuONCme7dozLj+KPvbl4YWFhYcHD/edAf89yIeN6lgeULnEPfVQ/9FNhT/02m8OyWsJpJRzf2WbaCWsQtTeu3mQ1BZsX7hMiG4c99FGxV9EgCxrlLxPeI7L2HNSKTVVIV4sCdh0LAyy82QMa9rRQHTLWVh1UnDPaxQPtDqA6vJUDi2ukxskONwMmmSw5uPeswMKxWCE3+kAm2nPzxZhBP5QtnNMozkaIsw8w9rSv2NvDRQqswGbnH5M9chH2Yw9o2ENAx0Uu3tlDEnzQoQScEjFKsCiuYHNn81dwnBjXma0lfmZ5QvnC5h+hBJdq54S140Wcq/LfgeWts/HRwsLCwlOE0pEMKm8pfcDssbqG1UrZg59RVGqWkX6970iuUjnVPUCp6ETXnmm0aINpQaxlFZj2inbiWNlaMR/ZWNh+4eMY/qtPlQV3hW/EURviBHzlQDDBnwVrVpC7AiPzX9nP+jMSZMI7+lnZA9eWkS6uFysE3Vxw/fp1J/gjQTkixHkpH9hcXSHFCttsHOabKmQrCU6Nf1ZUyX+PPVdMM7i9GfGBPWiI1/FsxL7K51kuUn66eHRrOSIa4yvOu9/DeVfOYbbObK5ZG2Vf7SH2PwozXKREuMrrLGdGMD5bWFhYWLg+VI0TgXrZ5SX2yjS/qhFm/pPkmsg0S0XHqRqP6bHsoRmrWVBjZWvI6tZsDDY3p3Uwplh9yXyqPjhbeoFj+EFNpUgcfWJ65OawQ+EK++gn68v+qX540NXDEuYz++weLCjRzIhO+VJ5GopwD6hUG3YdbaN/eLAjITI70Y9K3FWKLrenleILfc0K2+rDoSyes/mdBVmcjSb9yhmpJBv12flS4SL0lZ2Z2I+dpUrRrmwrn5GLkCerD2gYbzuOUmeO2c2EB/MRHzY4EeR8i4IujjWKyn45zqlywoh/ak7Iy8+BjxYWFhYeA5gD3EOT+Br7Vop49dmBPdhnbZR/DkfllaxuqNRCaAvnkul3fF/RN1nNUZ0L3q9oDea3etiUxQDTpwsfYehBTUWczvZ3QV8pLjK/2JPA7KAxP1hxwgLXBTrzKd5zfSIJVwofPCToW0bQEc5f9vAk+pAVvDgnLPpUMsFxj3pgwZKdKrbUGjLCU22zfcrmMvtw4RahEolar9H5O1HiCu5MzLD4dFzkEia7p+J/RARlhXeWmF18o102BuNXBhXj7IFXxvfMNuOzaFcJ3tiHPSRxImXELxZDERgLlYdLzh8XQ5WHYZWHdQsLCwsL44h5zuWr2B77sdwSr2U5MV532mBvMb6nv9KN1QdISv8pvYR9nF9OWykbWE8yrcM0YH9f2VP0E2Mlq82wHmR+jOiz54ihBzUzC5ltfrXgyoRedh9tZEUGHhLlOztQ7mENHiZGANk48VCqg4afM/FdKSDwQDniwDllvmJ7vOYIEsmKzc/Ng/nPxspiTwH3mRV3leRTTVAuDs6AamI4EirRjYznilzVXj3sUe0dh7jr7Eyx4rw6fvUBC96rcpGCyxXMd9wTx8M4P8bJjP+YeGaclc0RH3g4PnLrzOaN/Z0PrE1VgJ+NixYWFhYeG0orMP2NbdVnxulVjY31TZbnstogw6gGU3UJ2mJ6z91TtUhsh/vg9g3bYe2o9jerJdlcZjRWtFuZv6rxmI2Fj1B6UDN6cFBQOmTFshOUmR3WplJ4RzCiYoWLE+yZqI5tYtAyQsz8Vw8ElB/qkDBiVT64IhbbK9/VPiub7OEHrqVKNtEvR9KsPQPGRNavkugUsiTn1u8MqJzfkT2twq3j7BpnhTTjABbnyhfkouxBhYphPPdOHFTiT/GhevDG5szOFONj5QP2Y9ervFbhurgHmWCt5iU3vnugxvxkcYD3nC0lOhcWFhYWrg+Ve1BDYO5QOYPpAdUP+zAfVO5m/o9qttm8g7VNv6b+Q8LZwHWaqQUwDyvdgA9sKmPjmKrGZH7iGKz+cutRsc/6LNyh/Oe5Wxt7ajka7DPI7KqCuPq0kT19jMGNxUHmExZD7OBhezcvJdRx/RXRMp+ZD64Ai2ugSIfZxvuq+Iu+qj5qD6qJqwpVzGBMMOD6MBsjfrA4inbOSngjXORERUUsZD5gvyofVbio36+KG1yXysMKdj4V96kCnnEg2sf1Z2eB7QsTLupMx77KNt6L19wDFsXvincZlEBzIppBxZ4T1VWoPKTsOH5X7c7KSwsLCwuPAVZAs8Ja5a6ssI45XtnK8pmqR3AeLo/OattKHeq0C9pBX5T+Z+uNekLpE9QCLq+zdpkOZHZVHLC4YD64tXD24vVRzfJccPivPrEDXBWvbLxR0RrtKoHIhD0GWpUMqgWfE+Hq4URWaKHvsbDAueE42K+P4Qph5Rub90isKLJghx7HxSSBxZWyh+Ph/NlntqZsrhnpK18YRs7OniLtFuASDoOLQbVWro8rYCv7xM6Pmwf2xThiogjt472MG5RfSjSgv3gGo21cB5wn9ovnBOejRIXyB9dQAeNi5MwzLo7+4r+Mi9ScHLLzkO1d1hfXh8UJ+q/icmFhYWFhH1yeYrpZ8bDLASyvs3yiPlcLeaUN2D01P7zHtMLowx43frXWQU2k9B37XMGsPmb7oupJZyfzRfVduiDHYX+euyM7uNEOBi4b54gD5Q5zb4+HpCrQcQzWjh1mfJCgCJW94jX1sML54vxjJMGKHjd391CE+a4KJDXnSmGQPZhB31mSy/qz+We+zpASI15WkFd8u1Wwta/ygyvsZziG9avYdrzmRBJ7YMHGyN67VxRTmSBTIsXxgVojlqyztcpEI/bN+B99cPsb58P2BX3HNXU5Qd1HP0bmFn2I96qCKvqveJzNH8eaPWsLCwsLCxrIy1hw93tM47IHMb29us5ymcvz0R766eY08rAhg8rTo1DaIMuhrg/zjdVlrG7EPVd1INsrNg7TgGz/s/VnGpTVe+x14SMc9ue5Z+1kYvSITVNjMdJC8Y/3lJ34PlujKqnFseMrHg4V6Eygq6JJFYPuIQr6gv6y+bIHFzhXRWCVfo6g2Hyy4i4rQNQ8ca4OlfGQYJkv2VyeE2aS9t52lfYj++G4qF9zMYbnGF+rhb7ynXGJ4m0lLNCGeoDBfIhzYQ9L4j3ml8sxbF3dXjgosYRcpfKR8tc9ZHE5xglD5Tdrp3KG6lPlw4WFhYWFGlwec8U/y7OsveJ3zGX9mtL4yla1XqrMy+XEbKyKjnI5McttrDZUeiyO5eqfrNZhbZQGY2MrZNqBjcX06EIN9kHNNRezWkiNBszofVUoqCeGo2I29nXjq0KHPQSqkAL2yZ5mMp+UHUYG7PDHMdiaqvHdgXYPxbCAUMXZCCmpIseRrpubGsP5xvZOxcXCHSoPRLCYde2O4CLXD8+24hqMBxfbWdxmZ88V9FVRE9viHBnfKr/cdQXFMxkXsocozg8nvnAct5ZZjLn71XwzgmyeypfK2VtYWFhY2I+Yq9hDk96mX2P5RuU6/I+EOKbTClgvoE30382NtWH1BbOtxlD1FWvPxsIagz0wyfQaPlzCe0wnuba4FrEde7hTrf3UejgbrL+KI+y79MPrsA9qjlwwFqDsszogeA/buAchI2BjIvlVija0hYe2Uizg+EgOakxGqO7hh/M3tme2siKlt8nGZw96MsJQULGkCETtAfuMa3GtM+L8Uz6yZHGmhzfZXKpzZVw0WyBHVBKR64fXmPhiDz5G9xzn7MRN9pBhZF3UucI5Yd/qeipudevjHqaws+feV/fC7VG2xkqwMr5WdtVaZ3C8GP2pzm1hYWFhYT9Ql8dr2QP32Darp3p/VmOw/IljsvyQ1TOuzUwtFj/P1o0u31bWsALUaKj71NiVPI332d6pepCNg34yTa1sM38WPo7tcrnImx9+8MGlNb0p7KECtulQD2fcQcEAcoeciU/2kAXbsiBlD1FUcB1drM/YHvXBEXJcE7Xnat0cVLGB+5PZ2huLbO9d34qflTiu2qrawLkxe/H9J954Y7ODPHEwLlIxmQmBbO3ZGqq4yvY2junixr2P/R2eAhfN9mProvZhZC0ZXDw4v5StKv9V8tBIrprhYeXXzDzcNXf9TFzUmtcN7H5s43SFQxbvONYnP/v54uw43n/37ZvSRXvn3OdbifURPYAY0VsVH46a86wu6qjq++yaQh/rS5/5llJ7ha99+Us3zUWtva6NVH7rUGdH9c1yb7RZ1UrX4oYMGXe4+66myPJzhatn644ZDmE2Rto63TqqBxbu4HRR+qDGiZGISnD3dhURnRG8CqzKQagcpiOwhxQeGqNFkNqH/p71ddeYbbwXbY+SVnUc5SMT3qofa+f8q5yJkWKK9f/i5as3LUg+t33qouIoEwHVOJgR5pXiy4mdLNEdxR8VLsrGG8Estyke6b65PFTJTdn5qYifUX6p8Gc2X3fNje/mPOMf83eEy1o7x4OakXzUUYntLK5YX9Um2jryQQ3D0VqG2RsdY08R/9Z773xszFHuVvEx0qejUgAdOWfmA0PGBVlbvO9yn+qzHtTcaaPWPBf1+26ds7xZ4XnVhvGhwlOtm2Y4INuTbjeiWttUeCNer/BYBVVdkfmRrcNzg9NF6ZcJs4Bxi9zvZ+2izfjK+r14U/8KQEYeKqhYoOMYe8HIRB3Wo1GdQ1zv1l4/RLgebB2VMFFr2dvjusfxs7Vz+xk/d5txfsqGGksRj/IR1wT9ycbC1zgnhPIlrukZCBDjql9jMYQcFPmjQyXD/j5ec/GJ8Yb8FP13553589BcdC0+UnNgZzgC1z2+sjzhcg677s4O7isTu26+SgQpAZOJNeV7BhV3ii9Uvmf5Hfuwsxg/P4a4vhaUkO6fYzvGBawd3mO8w/aT2VFieBZZzO8dg+mQ6hjXyG9qrfs9lgfYvscz4rRNnIc6R8hFvY3TB6Nw83Y+R18UrzKeY+2UbbYeCz6Pulhh+jCuM+Muxz3sOtNJGY7kmixPz8SQys3uLPRr8VXpU3f+mA5V+5PNj+2J4h7Vl/mHOYzpNGbvTPrgaEx/Rw0rYlgx5Wwq0lWErAoZNn70kSVRtMfmxnyrwtme6TfqizoUVaKMa8fssnEyMerax2uO9ByRZETJfKuQAytS+mdWkDg7rtBj19n6V8TKbBJ6qlDJDfeGCVm83z8zEdzfszYsOToRztplXKS4zK3JDCqJcU/8qD1QbdTYcW1jTDPhrsZiOYqNFd+jYFXzQvGliit2T42HYDHseEHFWGyLPIb92Foj3FoybXAWPlJxy7ihYsPltaxIQt5BnlG5ZgZHCGmni2YKg9732rGF+6N4G8HOWbyH9t25ZBzm9MQoMFehb6q90udZ/ziWyrmY3xc4nA7sry7ns3uY23AcFyeV/OH8dfl6BFnsjZwdp9sRqAkVDyhtw1A5AyPn1uV0Zqe6J4qn2LiVXLlQ+KtPrphBUaKKDFbUMEER28QxWBHkxLUSTErY40FBP5i4Vsk39p+BE9iV+6p95XAxImBzZ3uU2RstCrO9doUKi6UR8mB22DjOHpuHOh94pli8M/9HYuXWkYnV+F5xA/vsYkUlFDU2a4diAPc6wp1Vd34Zf2WoFEUjYmmGi1TMKrHAitGqMGHr48ZHbmC5L5s3nl+0x+KW5S4nsNVc2Nq42Iv+unzpruO+jezRLWFUFyGYcEcb6hqOwfJx9KsSOzPzz65fQxfheim7IzxYAYtrlrsrsY75g31mYytc4zxlOliNqdbC7ZfjIsW3Z+CQa8BxeofSMSzvKW5B7om2UafFfkfMqZLDK5jJSRVdxupPlwuwr7qG5yGO4eyO1AiM0/qrq0lYHzcmW4/qGj1XlL5MuEMJSCVcXaHDApqNg7YY0ShbLLiYry7gn3oAuYOaHWJXiMTrHTOFyoiIzYhE3Xd7r9qwGFO2szbRPzVXZ7cyTsU/d5Zu/Xsh2O9hR2T762LR7VO0r1DhsIwnKzzjOOoaxZiK7VH/qu3YmB2Ko0e4yNlUHBL7ZPzoxla+j/SrnHW0Pcoto3Nyn7sf6NcZuCiLiYxvOqp6ysWOy6/92t7v8ojfXxLHeixUxt8zZ/xOntFYz7RKbO9io6Jt4+ue7yJ66713duuVTEOPclHsz7D3u5fO8B01sU5Ta5bpJdY34xzFgTiuq/MyMP/2YA9vjdQL2ZpEX6p7pfxX2rOqX3HMKre5GmtGU8/WQWfBru+oicDN7+/dge//MsFSCRQWFLH97CbPiGhl56HbOdLJCCkTGDg+3sv8U8Ixs4GxgDHU22AcMJETx2QJqLfB6zhftZY4BydKGNHFsfG9GpPtW5Z0bx1snVlsZPyk4lvFQTUG1DV3rtwes7OhRHB2zmdiYEbMVIWXa1fllbjvjB8yv1hMKIEa2yuRgmOxGMKYZWOp2MOYUWtU3Ws3LruH84r8Gj/3NnGOOOdbB+Ohii5i66HWD6/huOw6G+9a4neEH/aMr7h1hIv3jM3OqdJFFS5SPrrY6FD57Kg9dpqc+cryF+M4Ngd2zem42HYmNz0XxNhhfIJQmpdxuMtNjptcPsnAtPCeWJ+p85QOczqdrQnT/0q7MhtK/zI/4lhOw2axwbiO1V5Og+Pc2bpcK0+dBUN/nluJ09bqT/3QlgqUPYUnG0Mlz2xerN1MQVLt99BQ86+IIbZHWTJw68hEh/IN76Pfykf0KyMo1iYjFXZe3FxU+5kx1b1b/6tP7C+ttOYFdGzj9iNbfxd3CiP8pWKBIeOph8I1+Myds0rhM5vsK9xRyW3K7ypHZr4rLmI+ZH2zXF61VY3zGNdn4KLW9MOSCCXK1f2OkdxUiZ29P3mAP1HT2hwX7eGNTGMijviJGpdTmE94L0OF+ys5o7/f++e5u+0jdNFsTMd5Zfl5/UTN67/50NpYDPb2GY+P1D3qLOypoWbbH6VVKhqxt3P3Mr5QecXthdMsyv9KnZTFhdpfBVavqbGeGw79iZrWXn+ij+9V0LrDEu/19/havRf9Qf/xX7UYqBCMm2tWeKGP2RisTTXIVbGaFSPMV2aHjYdr3NujXfQl2ldzjLYcEWHMqP1SRKJ8ZHbV2GxN2J4qX1m8M5/iuGchP7UX6vxWRDCD45S4pmyt+3u2H7EPXnfnGn17qD2tJNyRPhlUbKNdXC+172ytsV3kjjhO7KvyCnIX7o0rQNDXSp50Qo7NLb53OS6LP8ebbH4u9s/ARers9s/Z9fiK9pxucOuH8cri9xpwebKjqgEdqtrrqPjKtAMbm31WXITcw2KKccu191P5j9edLkJk3MfGjuvv1nNBx5jSG9gvtlX7rXJgf8/iON5jNhlmuEKNHe24MzqCqqbvbeJ1lgOZvlS1Vjz/0aZaX2zj8ofimugjgvmKMafyF+r2dZ490r/6pASrKkY6Rg4kfmYHH4PSCevof2U+rC3zN2szKkTV4XHjVIk38zP60F8VWSgiiL5kSSGOUSk4cBx1sJk9RX74nrVlcOKZ9cX1yIogdX6YWHRnLo6hCP9Woc4r46UOtQdxLeO9aLe/V0KaAcfIBEoloalxKnw1cs/5pzDDQW5N2D1WwKg9ZbkhEycZd7B2ig+if4rXHUdluau/Kg5mQM6oxHzFDvMNUYnTW0XGRdhG9UGo2GX7rvIzy93sJ2L2Av1Tc8nmqjCTkyv5vALFRcg/7H1sV+U45A42HvZTGvhIqHVkXKb8jX2cLmKciL5knP6ckemOvmdOc6q8EG0xblI2cGw2JrYbBcu3md1Mw7uxYh/U5WiTnec4njtfjDtG87YDclacC8shmaaJdqO/6KeLPVfbPGekf/Wpv2KQuM1QwchEKfZ35Iz3WXJmh0cdCheA2J7Nj8ElZ0UeLNkrP0ZR6Tsyt/ga11cVEOyzE3iM9PAAq+STXXdjIjDWWTLK4j7aduuqEpvzhyVEJoLU/G4VTBCzBOZihdlxY0Q72XliMVc5XyyhuTEye644Yv1nhcsIKnMcLbiUvYx/mG/xPiuesgIBRQZ+jrHq7Lqz7bgozo3lPhd72MaJbyVKcfz4qvrdMtQa414pTeN0UYc6B+rcYt/ux94vE2Zj4/hK36j72XU8g9V+bOwZMJ3KfGAx7/Y/tq3oInXGo4/ufI/Ml33OCiv1inPNNIniwGpefK6o6lqVC9T6snPneD/+i+1ZLlX21dwqbfbEhuJo1oadC6e3+n1mj+X++Kpsqn4MyE8ux6g8w3xT2luN6/ysaNrnjPRXnyqLxopk1y47tCyQVOHTr7mk6ILACQGFLDG6+Wd2mZ8V30Z9ifeZUMT3cYwqsTjSZ2SO+4yvcY8ZWTHCUfdx7thGCShnC18V2WMbNS7uC2tbibUzEB/uqRJy/X6/rgRjlbMyn7JiJBPfrB2zw5Ji7Ffd4+o898SP8oedI9YX32O8Z/yD58vllfjeiS0Wf4yzsr1gnMoKHJcDmU3Mo05EZkIz+qLOEI6rOArfn4GLWqs9XK+K6VFdxPIHa3dEATMCJszZ/T399nJ2FXFPlBbI/MZ72XmKNrEPtnc8OQtnx3EjvrK1y2LT8RXuwUPH9S0BtY07cyqWGL+gLaahWf5DneZ0m/KTzVG1OSI2WA7t150uc/p8hLey8+c4wtUR7DPLO1X/2Li41+gHu858XPg4yr/6hNfVfbURrb0erCrocQxmH+3Fzyiqo68qUVSE1+j9bteJ7ApU36NEizo0lQJQEVp/VUWCIve4TxXSqcRDJlyzmFPxzGyoz2wsJ+Kzzy7puTN4BrDi0MVVVeiy/nE85kcmnNm+4T8mblQyVqLEnRE19xlOqvRRAqXfy/qqMatjM15kOUG1ccUVilgUto4L2XXluxPMjNec+HLxwPbJxQnuLZ4Xxfdn4iK2bm5+uJfsfrzH4rAj8p3SUywW+hfFHoEjzrCaw8hYFV10LYzwIDv7qoCJ15guQvvXnKs6/+gD0x7Ohrqv1kvZbq0dGte3DJYrUIc4bmYagsWtslOJw6Ni1XFCVndmUHNmbVjdkOVqZputP+uD+QFzMYsBVk+hz8yvTPNW6xfFHWpdzly37IH9q0+f2z51yYRIJiZUO5aoYjtVgMV7+D7adclwtI+DazMTdFmfawQyrkn18I4kVNVGFbOVa+q+EtEstlgcsdirCAjVRonqrB/6rs6T8j22cd8ofguocFFrPAFksev4AD9nnKb21fkzy0XX5qtszKy9Gsu1U+vG1nVkLxwcDzjbypbyN95HuxXOU3tRFaIZL470V+fpOXBR/CsrVS5y/I9g66z0k+Oo2OfIv/pU4ZC9umiU29j9Pb/u9dZ776RcpN6PaheGylll4+79q09KK8X3e3VR9XqFZ/f+St8Z/urT57ZPfayIq+hkt8/sM7NzxJhHItMGe8cbyeGV9XHnyPF5RaOwe06LjfITA+Prit3R2DsrnC6aelCjyDXerwQQtncCr0IwWbC7A7EHlSJq1MZof9enInJaGzvYsR2iQizZ2LFf5Z7zB8etxo4TXBmRxnszYs6dgZE5dtx6cRS5qDWfCF3csP5qf7JCCseuiFb0z8XTDCqFUtVG/Bx9dG33wJ0/xSnZHu8RkBWbjtsqc8rGmCly+riODxQHxfbZfCpg49w6F+Gfw81yULUocrlCxWRVI+0taN2XEY9wwFH8VsFRD2riuBUdM1NkZLooXlP9X7467s9z9/EqOW0k7zG7FT2krq8/z/26NkJU9gnbt1Z/oIrjOBsuV8Y2lXH31kuzqNQsEVkdg7ZZPnDzreztyFlVPDdSK8W+VT4cjdOz4ZA/z/3ylf9Cs8rGxXtoL96PY754k/9IXkYm7H4MhHif+TGCIwIL5zBDOqrPiIiK64P7FA+RIhu8z9amf47XcezYz42n7uEra5fFkVvPLGZwjrh+GbA/I08l7nAuD5XAHgoYm/E6xk28F8H2B8HWm42drTXrr5LhLJeoYm0GI1x0dGzhHuKad2TcUDkvEYqjMk5FDsk4wa0tmzsbT/VHHxiH9FfmS7bGs3F5Rv6JwPzY2+CeqXVAPmBrrfadnXv05dpQ3IrvrzHWQwD3tV9j+VZxUYVDWV7B2FK55sh1zvZP5bj4T/VXusbF0LXmeRZk+ndEd3Z7I1yE4yhNnY3vagbs7/KoGsdpvSrUnNz5Zz6o60y/qjzMcn23kfFklZuQ45g2Y3Nle6j2zOn2hTukf/UpKwrxGhakjrgdsbPk5YSO898FeAWVAMLAHCkKHgLZGrWmn57ivQ4lRJ1IVYfXja3aVQtKFWcqIWSJIksijqCU4GLkp+yza4o0RxL0UwZbt9Z0XGC8Yf/qeWUFUGyv9l3xpRKqyFGjQHHk7MyMUS2QKkXa6Ph4NjIuwvmrfFQRNyrvsHhS83PnnwkgNm+1Jmq/XXyyGGR9KnuuziQrFKox9NQxqkVUPKqcV1krjN0sP1wTLt5HdNFTAnKGOjNqv+M912aPLsoKoRm42HM5LcthiqPUurL3txI7j4EsHhUXMC2K11UNFdux3OLqAzcH1abKiZn92ThC3sfPVc2HbVluqPqutEG2P/2eWzN25lCvxH9sTioese0ZapRrovxXn0ZFtwoMFKms+HF2ezslTJRgd4GeJQFV3FX9mml3ROCOCiSXSONnRezdBpJEv14hUFZ4INGomMyIOiMER6h7hIIizSy5jhQDKim4db9FVHgI99nFnUq8bG8Ud7hiONp15+uIOEM7TpxFHJkkM7Hlrmft2Pmt8Eu8H0WFK4LUOqq9ZnHCfGX31Hscg/nG+rD5sPdqDRCjManW8kxcVD0zmLsUrzDxG++jTTfWNc+3QqaLjrDzGIK+wtsVro2cM1qkVDn9SM2Y6evRfOX4UWlwFst78+PZwQpztm8dWRwzjYJ9Xc5icPmqgqqumbUf22c5kemKeF99ZvaUtnHzYFoG71e0UaZBMKaYn5Vx4niq7ll4HemDGleUM+HH+ipBG9tFMnBtUIzEA+CKBFUsjSKz4e5XisfqfWYTr40GPR44RgKVNVSCU/ntSBHHU8IHCZPZnik4mK3RAif6rPxEoe7sZdfPVBAxZImExSvGkYuT+KrWXBXhihsZZ6nxjypG1Bis3RHYI8AyPxW/V+ak+qk9Z3ZVfsOYy84wG0+dadaeXc/EEYvLyM+Mh9g6oX+Ox9QYR8X2YwPPMl7LNAIrpli7Sp7E+FB7fi2oIg993KuLRvP3EWBrGa+pM5gVTrGN0629D+Ia61CxyTijwmNMnyCHsLVRYy58BOSSfq3CS0qru32O9pk99j5q32q+i+8VR1TzCqsHEWwMdsYxTtn7bB4sp7O8UOE85TdrU6kznP9Ku7LrLP4YKrXUc0f5y4QzuIBVG6FE58ihY0WYux8PG4p9V+zi54hK/yqq/TJ/qmNnIgDFnyL6+B7XmF1TPrD2mW3VV9lm883GVPPO+rpxnR/Mpuqjxo33v3j56k1/ad41uKi1PL4zmyPCmu2ta38UZrlor73s3LP2EWqdlS111hVPRLvunKN9Fi8jfFDBDBdVxhnldmyX+Vzx99a/TBj/yELlvLO2it/Z9WyPHa+1duxffWLjKIzoKIeZfnu+TDh+sa7as0xnZuewopVYP3fviDmjj+r9aJ5ldtRcmA9o++WrfV+e3No5vky4f7n5qM7s7yPcHvb7jvNcG6Z9GI7WK7NwOqMSx1W+d3C5gHFRZc2zeUU7mU6NY+B7NydXvz1X7P4y4Rdv+u8GiNf7e7bBbBNevtI/zq7G7X0UqTib8V587wKUfY79mY+zQVclqGsTWZxbNQGz6xUR2q/Hfcc9HCGgSkwpX3E+bP7VhKP8ZPNWhMjGRttqD5iNWwfjGryP6x3Xje216sfGYEmJxZvyLxPisV0Gdz4yP/ZghKOy2HT2cU6Oa1zCr4rX/srOY3aGkSujr5gTHRepOTk+zUQSmwvai3kscpyat+NR5e9TEN9HgBWsrA3uM8tX/X4HE9/4mWkOx0NHwIl0bOdy0mgMzPDHkWD6cI8uUmC6Gc8j40XWbxZMW+B7d9/pGDZWdj87XwvjYDlZ1VPxutLT+BnzSb83Ug899lnPaho8c4rHMw5QegDPutJB/b3L+yzP4ytymtO32VnM9jjbW6eJnjNKD2oqAsAVl+x9hDoQGERMRFf8qxDNrCBw7dlBYb65zzNgyZLZxX1iyTGuPR5Yt2/uQDOfKoQQ26k5M9+Zfda3OhbGJ66NshXHY2vq5uIKJyXi2OdbB+OZaqxh0cySYXZOVbLGe0rcKnuICg9VuHUkiY5gRnRlc8Z+GPvsLI+sHzs3aCfjqIh49l2x4sbAM46+qkJMnfeq0GFngc2Z8Q7jLcVJ1cLtlsD4WWGPdmLtGGepwuHI9XbzVLnNtR/1Dftci9cY8Fzimc90UcbTTO/Ge6MxNAunQ7AdW3+VR0d8c7oo2lvF3EfI+CjTt0rLY82FMa94R401c+8h4DQda8vyHq4Js6n0O47p6hTHLWwvnX7IagU1TzY2Q6ZJFa88djw8RZR/oqY1/pS0X+/XMBAdqSqBi2MgcatgYcThilp3zcEdOARbk2hHHeyj4MiHkYjak0pB5EhEfWYiz/muxD/GoJsrmzvzrwJlN0tMLobYXJi9TJBXRPOtgXFDBApmJ6qZbcZvOG4fRxWw0Q+83u+hCFLjZsgEMc7hqCSo4tbFKTu3zK7iIueLigN1n+1tHC/2czEWfe73svyEfRlGRJS6nnERzje2dbw6IuBYrJ8FMVbZHN269v7KJuZgxhGM3+J9N86RqOQ8bF/looyP8f2RQK7v79GvjuxMMr5gY2a6KF4/sqhR2qqyvlXtlxWDLIfg56PnfetgPIM8lO0PXlM5DW3hnrkYV+d+5vzO7r/TSCM2q3kex2V6BtcZbcV1Y31jnsC9quSdLDcrrZLpMsZhjFMeOl/dIsp/njuCienePr5GO3hdCWUVaK6PEizMF5xHlfQriVP1U2OwwHW+VInEFUuuPVsPtQesmGJt+meVgBkRYVvlDwpWJm7j+Ky/inG234qwYjuXCBghKb+cKGVkp8QMW/dbhUpu2EbNHeOE2e2flTBxe8QKrAiMO3Ydr2WoJrgR0R37jMSOmnNFJKKf2N/tJ/NTxUoWQ7h3bg2QA905deee+Z3lg4q4UqIvzjPb3+oYLnaVYLxlOPHqzpriErwWr6s4ZcKdCfprrrnjUddnROSzzyN+zULtI9MpjIuUvqjcZ3o24+O9c8b5VuIHdRfe668VXcRiyeWOkVx2ZjiOx/vV86r2QsVyv6/yM2uP10ZyRBabbMyMc6rxlNUB/R7j4kq/7F605zSQs1vJJdiW1VZqLMWZat+yGHnusF8m3L+kCsGSCN5z/Zy9DrSP77Fdf5/5oO6PFjAZyamAV3N8jKSjSDsT8RWxPyISVTyx/XdtlM3oK3tf8W1mXs7vylgVO25Osc+tf5lwhYta0/9j4vgks+s4rsIDzqdoo2JzFop7XJvs+mibzBfFyWqd8NoeXspyiOKiSh/lV+U9+jjCpzOYyd+sXWv6LN46F6kvNq9wRsZFWd8OFYvxftyHa32ZsBqT3ev+Vvvt0UV75xy/WLeqGyrnPLZTdmK7LK/j9Wt8gbLyDf2f0Xx7260vE36dj0byYG+nrimM6ueKbo14jJooq4dYu9i+Woe4NqomzDRStD2yj24OLkcp+1m9k+mLUZ/PBPdlwqUHNSpoXaFREZ3RZmzrDrFLDBkZVAS/Gvco7LWtfN47dkVkRVQLnn5tJinPCobYNyu0lT1mcy9cIVcpPrEPu67Gu/W/tPLhBx9cFBdkcVNJHsyuapcVSapNVdxfg4uyYq7Sfk+7vf70+wzqzFbOiEJFJEWbGR+qOewRLjPcxvJrdnYqbSu2O87ARa3VdRFeY3ok0yJKw0Q4PbangG/t7kFN5azPtjlKF0Ube+aMD6YyLdpR5aIRjPTd8+Ci7/GMD4oLRnSfenXj7I3rszyoYdddvdMxwtuu7hvZa/TxWrXWCJj+Ylqsw3Fypd5EjNTQ7qzN1j0V/9k6KLuZXnLXR3TambD7rz71RWMbyIKwEgy9XbcZ7cbr8RrzqzJuH5P5GufAfMzmM9pubxB2f2fITYk79Kdfi/fiGqp1VFBkHK+xPWBjszbOjzg2xpnzl8W4Alsb9sqIP15XSYEVAeifOh9nAuOHDrfOESrmVByh3XifcQ87S2x8xTmu2BuBih82JkOl2Jr1TwkgdzarXIP32R6puMFrMSYy0aL4gnFpHEeJliq3qXlnfIzvVXvlN7ODPp+NfxBOF2Gbfp2tDeYolndjH2YH7WFb/NPLM6icdaUxVJu9PNdtzGoiZzMi7os7E9g3y0OOl6ta6+i5K1S5KOMlxpEupkdzxXMDxiWri5SOVxzudFI13qr1RrxXQSW/OB3m7mU5LNPp0QbTEcoW4+zK3NSZRD2S2cT9Rl/cmo/UHip+1rn2KH+ZsBIlWVJyApa1ia+VA4nFMRM8jPjjtSwwqkmwGmTXDsYKKVUPMjtQlTXD/jNFCtvP2IYBk40qqF2Bhn44P1lRhnOsjBV9Y2QWryG5K2H0EOLtIaFERlYgsUIq7k1l/XBv4n12xlTsslcmsPbunSocGUYEzygqnKI4Bt+rosXlItwHbMf2lfnm1tBxBeMJ9DsbIxPGTOzhWjgBjmdArYM7A8xXtvZngjvXI5ohwolwxntZzPQ2e39FZA+c3nHzH7F/NNiZUment1NcpHxlZ3KkL/p45DpUdJHiCZWfO9Dfqi7C8Rc+DscF7LPSSEx/M7D+3X7Vhtp7h5mc4sZBH5XPLsez+yy/V3I8rqWqe9w6KC3M9AjyiPPb6RnlA4sL5hP6uvAR0l99YhvgEgkrinqf7H78XB0vtnGCMwY/vmf+K8wkRJz7Hlsj41Wuu/Xq19TeuCJMET/6kY2jSHFU+LHkzvxxc3NQ9lXcZ7aUD5VxGM7w6wYqjrJ1ymLe7YnaCxevKu7RBzePCheNoMJrai6zdkfuVc45rrnaf7V2bt+Z7cwPZ4P5Uck7o1zk4lNdz/J3dn3GRsRZuAhR0UWxXTUG4/3KeKzt3l8Ref/dtw/XKlnM7x3vqO9r6f50ON2AbV0fp4GrXIT2j/6OGhW7FYxoNbW+rE+8vr6j5qNffapqYtU2O4vVuq9ae6g2I2d/z3ijqMToyDrhNRwna8faZhrJ5SI115F5ZKi2e47Y9R01TgiPQAVi/5yRyUjyGBWcVXKYOfzXIIxrjF0pTCJc0eyI3q15VrwyOILLxNHIGI6kq4VLRbhUCsKRvh1nKo5cAVPhApVoog3HRxm/VBJYVgCgT8yXa/DVCI4QVKpta/yhb7+uzokbywmdOB5eZ2MiMl8VT1bFDfMJ7Wc+V+PS9Yl+VIo3Nu+zcNGomK22q5xr7M/eY/u931+yB1nuZ2334ojvqHFc5Di8YyTPZOc7s/vy1f4vUB6NW7yWzYN9zrSLi5v1HTX5f2K5vFjJKVV92z8z2yPaZhSujnDtZ2o/N6d4XfEDg/LFrWuWl10tVa2FKpoiu67m6No8V0x/R81IYLrPvU//F6+xdswPZbfqc7TtyGzEdgVZIo7Xjw7SvUVURYjE+7i3LBGz19gW31fmo8Zi46l5xhirrFtfsxg/+A/HqM4H1x33p/L+KLH7VODOHvKKs8FiMb7P1q3Cdc7nuLdxrKqQGRE6M/s/wkWZGHJt3V66OB4paKMNFC4sZhxXVDhQ9Xf5xsVTjNdMHKHNjM+UD2xNcN3YnHG+zPczcZKad7xf0QuOi9R4cdyYe9CfuPbX+NWnEZ9xjVzMsfz3GFCxz7RK3AOldaLdGB8ufzGdhOfpyAIH90zxiON31o/FabRVySXZuj5XxLWu8m2MQbfHlcIaYx/PRbSnwHJLBqUDshzjuKg6HuoQZYvtAdYITgeqa+o9rrfiZXZ+cT0dp6u6o/JZ3RvVdc8J5T/PHQ9lJhpV25GEMjKeaq8OTVZEjKKSaLIxVJsjxG1G2KxIZNczZHugyGE0TtQeZoJPtVWFoVqP2G4kRtV9RVbsfQbV9uz/i41JbyR+VTxUYqxyXSXBzD8Vlw57uOZIG3vGbm3sf2Dc+VFxUd3bGbAxXVvHWzMxrOIu49gRv0cR7d06F7E/z61yBoNrW4mXDqV/mO2H+PPcozhC2zjbR/x0Sbc1sr+ICodV2ldi44hffYr2Wsvn7HhY+VmxyWzhWOsnasZ/FbO1en5UZ8DVKpkmdn66wn2mVhnREfie3VM4QlM5VNbf9Y1QWiG2zfSC461MiytbR2uOW8Puv/rEiNjd74vdr/V/uDnRHtpWG4r3EehH/4d+9jZxjNkgGSm4mL+uDVsX9loFa68Isa9dXCv0R+0v2sNrLqm7NUGSyWKI+armj/PG8WI8qbiJY+DaqTnF8ZjvLNZVvKjzcga4WMR9YfuI//r1DuSNCJdEMqHB2mb+Iap7WWmXxU7GZ73N0VyEMc/asPVl64h23Zpma5ZxM+5b9Mf5NSICq236e5wbW2uMfRb3GdhZysT4rQM1RLyObVrz8cNioLLP6uwoLjrirz4djWyejA8yHBVnn/zs518bP8vJrXFOYPGi8nklzygNtnfujDNc4YZ+qc9Kb6v1iFyE64xx/hTj+jGAOSeLBcYXbH/ZGXB5jtlSvrLciPeYPVfDRbgYddyT8bAbS63JXi2uzkpFk2Y2Rtqx2qZfd/yIPOLW+Gx1yxFIH9TgYVAipbXXEwkSrmrf36siDG1m/Vnxk42rAgv7OQJxCUUdKOebG4P5XIFL8moNM4FSEVVxHWZ9ZoTsEo1aW5eQKqIjXsdx1BmpzL9SkFXEvTo7t46q2FPimHERS6yRN5hgcTzF/GKJCcdCP7I9y3hiFCPCqmOGi1Sij/fiZ7ZubH/Q1/iZCYrMZ9x79CPGoBOe6LsaS8WkA66PE7xunrNQZ0PlRKUbzgBWPMR7rL3iInfOWA50+aT7dc2/+sRycFZAZPFdiRVVjMxoDMRb773zWp5RhYnTpJFrYozE670ti504nru2h/vRZiW3sBh0Wli1z3Ke28eXr/b/pNgZkOUq9V6tt6oFOlyOZboL+7LPKnaYfeaH0nw4NxxXxbube0Wfq3kcAbdW6KOqj9yeMk3MNDKrQZiWUXUQu3ZWfbAXpV99YsWFCnQnxJVodKSg7DKxzQSjOohuDIWHCqRsnGv4gWul9sPtdxXZfsV7SpgqAoj2nR1sw+JW+aPGnCViFavsnM2u/63/uoHiIgfFRY47VOyoz6owyGKDje98uSayeTwkF7niK7vO9sUh22tnzxWEo/lQ+ZbFB9rHvpntbFwX/yP7hHiuXOT0D7uOyAoKdx6e4q8+HQ08Y0d9mfAID2T6yaGipTPdc42/+qR8qOiQGS5i+v5acX2GX31iv4qZYTRvuLjfo0uV7d63oonRnziuyl3VMV2eH4XTV9esLdWcHLL9je3iOKo/XlefnzN2/+oTQonG/rlfi6+9T9+Y+A/bRGAfFhjRjiKg/o/Zjv0ccGycJ1uPEVQOkjvQ0afqWGrdGYnF8eM6Zwe46mfc5/gZ9ykSgBMGFWJhMcRssT6q0GMxxd7H2FY+ukKPrXH12q3BzUGdYbanLln29+oaxj2LJRZz7hqeP4x3Z4Pdmzn/bP2iX6M2R6Hin61JlYsy4YN5KF7DsZmPyEEV/nD5qrImKp5VXmRxxGwzP1xMsRhVvFPhrFtGFruxHYsp1EXxuootdQ7QhxE9sAdHjeE0T6XNkbGFa8d4QPEl9s90AI6Bn9nZd+d+L9g48TMrLEd0kRorItNxCx/BrWHWR+XTSq7FsVkf5pPK73gt83s03ljcYn8c28Wg81HNHe2N1J+VsUf6VuLDjYPzUXNxMcZsnk0jHAH7EzWf2z71Z24q0Y6Fc7/W+7DPeA0PEF5jY6vreD+OFW0zsOSjDvdD4JrjqjWN9+P1rJ2LA7QXoWKFXasKE+cfGzdLDmqMrDBT10buo6+jfvS+X7x89ab/56jKRdimv8d2Kr5VHKtrbHzHkeh/lYvUNdYP12IWzOdrQvF7vKfywsgaO+5Tc1bxNFIouTlV/GK28L0ac4+vI+eH2cG53fpP1Izqot4O70cbeF1pn4pd5tORP1HzWHpoZPyXr/Z/mXBHpkEjKnyFfmL/LL85XXTETxEhqmddoVo3ZP0Zl60vE/74H33pGOGM3qajsr8jmnkm16JPCJb7VB5lZ09pjYqWdj7tbbPnvro3e84qtUu3n11T2izeV/2fC6Z/oublK/3dI05cRsTA6e/ZNTa2SnLs4L140/9uYJxL/ByvYT9l7xpQ5Il7MNLfgc0Vx8r2yRFhjA+1V3FMJNF+Df3CfcM9iu1V7OJnRs4xnhAqjqPPGeLcmC30Ve0vWwckxTOQXtz/+BrXG89ytmbxM9s3VTQ54eFiQO0F4yKGqnC5Bl/N2NzDSUqYV8UQ++y4CH1AflBczLgmyxsZF2F/xhFObLMzwXxVfmYCjYkw5kcc80xQuQXXXfGEghKv2BdjEbkfbR7NB9fmApeb4/iVIuIIKG2T+cX2I+Oi2A/3Lst5e89ZppmYzsP77BrjLzeGauN487mDaUaE0/fsvgNqHZVHlS/MTpZf479Mq7mxXBwp7V/J5bPxze6r/XP9q7bjZ7ZP6ryx9u5sK45CXjxjvXI0Sr/6xAIkK0jie3ZAsDBh4kNtmEpOuOERLkgr4l+NfxQy8aEObn+tiBfVP3vI4IQp+sH2gwlQtdfMtiIB1i8jXSaGcBwWr4yg2Jpka5WtYQVsXtk63DoUH+A83ed4Hd+rfhhnvS0rjnA/WF/Wjs1ndM+y4noGFeHhro9yKq5rf80EulpPxW+Ye5x9xUvoozuL6IO6F/tX8yFbJzYntZe43mquDkob4P3RPPtUoTgbYzheZ1CilHEP29sYdyyGsvGPhhunsu/ujM3oor1gZ6MyjsoZ/Z7TRWhXzc/pkz1g8Ybnl8Uf83U0JjMdGvFQMX2LGOGfWftKDyuux7OU6Xps4+LBnTX8jJxZqXcqcFyQ2WJ5RK3NjE94pt3YcY3UeOwe0ywqBpidWd37XFD+q08MeEBZoYJ21MFT5I9tKge34o8rxDJUfJgFC+YORwYj7fs9JvQqJKWKU7SPY8X+rhCrxoYT/o4Uok0nHirxHN+PxkWWQFli6fezfTob4bnkxQRkRcSyhI2JJr4yf1jx7WIfx4jjzyZinE/Wbg9GuLJaLLLCpV/PBL2zrQo+3HdVJKEN9DX6UlkTVeSwWK2INbcXjDMUUGSz6xkXMZuVsW8R2d4oodrbsDioFAwsh2EuUHnuKLictTfnuHjJ9N7RUOtcaad8Ym3Zmerv1d4zLXXUGlSKSjz7Ln6jv8p/1s+t3zX1963C5a3KOrmCPdqI+S7m4RiHmMNQozlOQ1+UXxXdhfOvxLbzy9me5S52X61HlsOVXZWHqrZU3aTOoco/bk8y3fPcYR/UKJGRbXqWSDChqUIptkfghmKxxpKEsvMQ2DOOOriZIHNFRLzH7HWbSpxXyE/tDzu8jqxZUVMhXYwzliQqPvfrLKZYzB0RU7g/7EyN2DkDVDHS72VFCotZJ8LZmKqgdULViVn0Q/nIMBID14I6v+xaJsCdLWyHgtHZwzOPnFCZS7/u+o/mRhaf0Qf2nglhlzNHzn62N0qgO3uYP87CRR1OEznd09tgjoptext1TXGOE9rXwNHiumKLFQrZ+R1BVoyxfDDK8XEcxkVZERs/X3P9mQ7Cz0yjoI8uzqu+ML218BGqGr2ifVX+QTCtPaJjYh8WzxhzyhfFw8oHp6twLtWYy2qyET/Q5uh+OluqFlL7G9upGortP+6J0gAj6/EcUfrz3B3xwKhNZgJT9UO77HqEEqkVsGTO/GRjjV5zqLZX7Y4WRRmhd7i9ZPvAYqS692yPGVm7eFT+VPZ9Zg7Yl6FapLF+lXkqUf/y1Tm+wFPNXa1LFkeqT+UcVOHirfJe4Qgu2oujfVB5I17D9+5ztb+6pmzHebqclPnM5szusTlVcnE2lypcTh+xexYuOkIXdYzoFxVzOBbDQ/95bjbXrP3Rumjvn6pWa+o0S2y/Vxfh2Co/xDZHfIEy4/TMr8qcFGY4q/dZXybMv0y4w+WN1vwD4QrcGVHXnc6tjF89a9l4rp3Kx27dok8PocNcPcbadozoEtYmoqJr0B72x/m49mfG9JcJKxHONgGD0xFE/BftsnG7bXXgox3mf/+nghiJS8195NpeZGKkGsSjBMX2AA+OEyj9M9ubOCe31syf+BrHV+IYx2bjMoKL4zriVzHM5pSJHifI3DyVv3hWzkp4rEBW9/AVzzzGF3JKxlWxv9pvlVDxfTXJKy4a2e8YK6of414cz8VutJNdy/KBOmtufGaH7f1IAchiynGziw83Tuxf4X61H4qjo2+ZTVwnxzEjMXDrUOeanWfUS3gPc0rsw8ZSuoXZeWhca/zsjBwVY2qNVe7Ixna5Ctuwe/GzynVHrXemuV0+wPVhbdiryz/YN/q4cAfF8/iKOYtdd3HEzoLK7YzfqjpZ3VM5kMWJ03No12kKPHeu7qzqNzXuCBgPVPI5G99phMwG5hunx7G90lILH0f6V5+yTVAEwQLfJbU4FpIGjs/s9XHjK9p2RUUlOEaLoFlUktyM/dFiqb8qQorvXcJVIp+J22gTC0lXJLBEgO/ZuG7u6Ff0iYmZyjqxV9fH+cvm5ny5ZTgu6KhylUrKqghSMcr8YGO6MdA+mxfzVWFk3+N8HOdUz/YIKlzU7ePZdaKOrWu0z/YmjsPGUCIoE0aKf5jfDi7nYd50BVBVDLG8mHEn8/escEKTtevIYt7xPsa1ihvGRdfKBVkec30QR/h45Fyd7olnDfO/0x/KV4ylaF/FkNMV14LSRfE+88XVB5l2UefnmnF962D7El97G5ZLsJ0bY7RmqvCba48+snznYi2LGRV3rt3Iveg/a1/VRGxMtef9vdLFLI+h3lL+qroojuH0sNK/C6/D/uoT/roBwpGo2iiWjFyb/pm1V2OiP2w8NgeEE0SV+zNgNlUgZ2KiOl6HIt9sTVl7t/7MFo6TiaDss2vXURlH2asmKpyXsxfbVOK02u/lq/P9uoFDJcbifZfkKnZdTLg4OZKLMjCOrYiKvVw00kbFMGJ0TVk+yT6rfiwGKrnM+ab6qXhitqtgBaHyr3K96kdseyYuynKd27+Okbzgxo328d6Rv/p0NBdV7s9w0d5ffYq2r6GLGK9EeywG3PuXr76ya5/7Hld10agGUto/a++u743rs/zqU1Yf7cEM/7s8M2K/YqNSG6CfGZdk2rGKa9SII+NkNYLLM06PuNxW4W/HJUfG7i1h+lefWuPB6TaVvfZ2KtH0z/0fQ7ShxoxtX7yp/3c93nPjVgjG+ToDtd7q+ozfVT/c3jMyjGsaP7N9jtfjHrl9dr7GQ44+sliI/jBCitcYwWTrqxIB+hjvO1KPceqSCpvvQySKh0B2NlTMYn9caxV3s75l8cfuRT/cmXb+sXssVqriQfH8CBcxHyp+ow8sllmcszXFcdl5Zz6x+FD+Vfu4GMUYVIKHrad67z5XxJNDNZacb7cKNw+lNfq92C62YbYxjkbW8lprzbiowpsZD0T71WvVtnvh9EwcM3IQ9lPzRx3qzrxa8yMKG3f+R3OGsl3V1BkXnYVHjkIspJUeVlA6UeV/HNddj75FG9Ucpd67M8iQaeZ+D+sTtX5HP0io2lM6Vc0d56T6Kh7DNmgD20f/UKvgZ7e+Cx9H+mXCbBMZGeAmKgHIyB/tRZvsfQS7zuyhn67NU0OWJKtJlPVxrw5sr9U+xjFZ//650l8RtCu4ZlGZo0pmlXHVXEfXPisiX746x/9iZ8mjw/GT47IqN0VUBIOLFbTlbFyLm0ZsZ6Jo1FblfGdQ576Su6INV0y4HFcRNJVxKtymuKhyPyuQRjhMnanK+t46F339N71V/um+hYWFp4sz/ERN/82HjHfxemu5NlH5pTJOlusqdR/zKd5TPrN7WX2Q5cq9NVimd1TbrF3Ft4jqXrq9VzqnsnYjdeJzwq6fqMFFi5sTF5dtkAsQbBM32hUsrE9/3/9lc2H91ViZrWvDif4OdTgyoc1ssDVgbaNvat3Z4VTFjfLB7bVqU4mFPh6LZTVO9FG1YQQUx4ljsTVR54v5Fl9je7R3FuDaqH1X68c4BWPNxRUCfcB1R39UYsrON5sLm9csRngPY5ndr3KR444qFzH7Ga/juYt9kIPcPrnz5eaLOS6bV7ZO6EuMOyaqFPezcRWyPWZctLCwsLBwLJQWzIrhrF5j17Lcr9phfZfNxfmE11leq/RTOc/Vnsr2TO04u5bYrjK20kAj9bJ6yMRiJO6Jq+dG1u05o/RXn/DQYyHMhGEHCmC078QjCtQssCJQmI6IfmeT9b2GCM2KSgR7gKDIkn3G9tnDk4woVZJQfmE8sfmi/eyz6otjR7/YgxWcs4snVRCx9VVEVy2u2FxGzsgtQsVI5YFItJE9hIn9WVywhzTqTOCDAcWFzs+HwhHjVbgotmXrogRTxmtKbOLZxYdo7CEMs4E+YWywh0AZh8ex0U+XU5W9fl09rMn641rgGim+x7Xq76ticmFhYWFhDEpvsvyW6WBnFzk9tkOtguOrdvGe89HVcpizXQ5n11GLszFcbVNFtgfo51Go1nXxen+PeslpETWuW++IM9cte1H+MmEm8Fyx4QqnSnGkinxso/xwxFMVrNeCK1ywjXpogetb9T8T/eoz68PWMzvErHBgPqrxR+6xGFHvlR3nE/ZTc1b9joIixIhb/3UD92XCM1yU7UfGFRm/qLZOKLkzcW1Ux83a7eGifq21fI2ZDXb+0S7aGTmTiqvceCP3FFcxns9yX/ZgphK/am+y9cl8unUuWr/6tLBwDpzlV59ay2sup5OqOQnh8nSmx0dzUaVuG8mLrE+1BkKM1pR7dN6oBlNrXtm7eE2Npfrg9WvWQGeA00Wl76hpbfxpV/UBSqWIrhRNSsw636qHcBRKGLuHHbPjuOJh1EZW0GaEHW1UimNXoDBbM0kjS07VOVXHU9gzjrKh4pjN69aLo/6gZobslXipXGNjVpN6lhgzzjqCkypcsEc0ZHZbG5uHWk/8XImD0ZzAxsd5OP8qNl1fJ3Y69uZht4azud6NF6+tBzULCwtPCWd5UJNp54hKXdXhHj5kdqoPPUZyamyjHkZUMarfEWptK3prxOYsXG0Q/Yio1OD4ubLPlRruuT/IOew7al68+fqP/2dP2nq7fi0LbBW83V5vP1psK1/Z9b3BgusU/XDjzowTfWUHjM0Fr2XEgTbjmrJ+cWxXePT7lQNa2ZNKEqiMi+3wnopnF2Psff8c/VG2mK/qAdQZCY+tOVvL+D4TFcy2GtfFVFbgZslMjelsVlHhtiOFQXzf12aEi/CeWl+8F+H6MLiHC5X8pOJO8R/6guuENjH3sbGYbxXOwHuO77Lxon3GRWfjpIWFhYXHBuY4VhTHf5XciHqr8mBF6bLYT+kv9SCA5busvsD+SicyLanaZ3UCe1Vw9dJRWgzHwXiI1yt6M66100CsJlW6ZqGO9DtqqoJLiTgVlOoBCdtUdoiYaGf+qXbXKpSu1V4Fv3tooA4+2ydcu77WSpjjGjmBH9tn4j4mFmbLxQ/aw3FH11qtE4sp1zcrjph/bBzmk9vns8El3db8Q0V8H9vHe6qwVQJCxT4TMxiLjLMY5ymfq6g+IJqxp9ZwhovcdcYn7sGG4gD2Tz1Awvkwoct8chyFOZX526+pz4oPHRdV+IFxfvRNxTTOqZJjFxYWFhaOA9MOKre5+7GN+1zJE30cVbfFf24+LOdi/lYPm9xn95DF5cxMX2RgeiLrV9krtY74MKiSl/faymq9/sq0zMLHUfqJGnWQ2OFE0eYEpjsceF8FhDuM6h4Ts4qYXLAxVAur0aDE4jMjoRmhXCVnVQihr1HIK1JmhQFLAK7YZA8vYjtX9GZkjAkiiyFV/GX74AhMFZW4LiqWzwDFF4o7sG98jwlV8Rv2ibbYfsVX9p7FG0tUR/AL63NUImRzdHywl4vUgwx3vtV6Iw+pc1fNN3hdzd2tueqr+rAYVLk0E4RqfVQ7tIPrmInGhYWFhYXjwDQ0y5mZdqjkKMwNLkdivYB+Ou3E8r+r0Zjud7mJ+cD8droNMZrvMl2gfHXXK36xuszVlKqej7bUWAxMt7j5LSQPathBVg9ksJ8rIN1hZUKTHRa8jsUyCk5V8CuM3BsprDIbR4lbdbjY3sR1xrVTtt0hcw9AZtaOxYganxUliuiz8fF+dW+q7VSCYcWVsjtTuN8yGMmrhKcKcccDipswmTl+iGfZPWTA+TAfYhuGkQcg1UQ4GkPuvMdxM18VFyl7eFbUHqmzX+Fg3CclFtlnxrNOOGafnR9Z/Dt7aq1ZnEa7at/VHi8htrCwsHAsstqh8kAA9RRedznR1XDsvXsg4uwzn2M7leNV/5jDcH6VXKz8UeMozGr2aj0z8kBI1SKZH6jd2PjqAQ3W7guvo/znuVvT/8vJHsSw4GcHEItqJfxQdDMRHH1V8xkJhNECaERYKxuzD48y25mwZmTCHpDF9ll/1dbtMwNbQ1d4seIttsniLdrC9zgO2q08AIifXeEW72ckxvZuNN6fMqpcxNqzh3X4AMxxU9wfxkWxH+OzihCI19U5zArgSlFe4aPRmHEChbV1c1H7pM6WWleVJ5h/I8KMnWPms2rHuMdxYhXq/DvfsX/Gc4zPlFivjruwsLCwcAycvldtmb5n79W1qJMquYzpLVXPsRoyqwcwJ7G6dFT3uzXKkK1HVb/hvWquVfUD6h+2B6p2UzV3HAttuDqZaeqFj5D+ee7WdPGiCg4G3DB1GLE9GwMFriqOmW0MKnXvWsB5z46pDl6laFJFaXzv9lbtm9ur6J8jDezjxlFkoGyxtc9ss/lmhTab/1HkU1ljbPvizU+3L16+etN/3SD+eW62h5V47X3wnouLapy6flliQl/YuHvBzp6a9xH8lPnSmn/Ak53TjpEchH0q+Sw7v4xvHG8oVGKExdRoDnZzwbbZnEfmHtvcOhetv/q0sHAOnO2vPrWW/6dGpnkUnF5Gu9X8VM3BFRuZD9VXNQaD0kh4fUZLVftk7bLYUHVZluPZtZG9qlx/Ttj157lb48VKVtioYIj3RoIgs+sKjUofnOe1CqZo/1q21ViuyIzXM7Ed21X3TBUd2d6iv66QUoTJMDs3BVfQV8Z0drN9yxJVa+f689wdFY5xcAmmKnay9r2Ps5dx0SwekmNGkQkctQ6VfBOh4oW1GbnvHuowf5X9LE6UPxXxlb2P42bjsbYV0cb63ToXrQc1CwvnwBke1MT/xEJk9U5Vf7v21fyDtnBM1pf51a9VtF728KWyFnHcjseo39y1ij9uT/bo50yjzdp/btj157k7+sLGBWYb8vKV/24NvPfiTf8rGnHc2NcREI7JfFEPdtAv5ssRuPYhR18rBzmuR3/P1iSutSqoWCGaxUU2n+gbXsf3bG44T+a7K0ri2Mye6ofziPbQR7dHcU0r66V8uFW4c6o4hu1X/+weirDzg59VoozxhXwT2zNOPRKPte/Z2rX2+hnE9uzc4Fo6UcXWGq+zs67iBfsoPq1ykRpbnduRByuZnyxu3dpgW+cHmws7jwsLCwsLx0BxeOUhCctVKgc7O66v0t8sHyidW+2H7VVuY5od64Lqg5sjUK0h4jWnYdE2amVXs8VrquaJtpneZTZVTenmujDwoKY1HhTsIMS2yg4WM6xwZg90VIHEBKIqBpxQdjgyiKoPJirtHRn393FN2YOW+J4dpipUkRrHwH1k+6PIHomaxYqbW3zPivzoI7ZRSQLjmdl2D3PctTj3rI3CtR4EPAYqDzdYkVhNTllRi2NgMsexssI2CiwlQKoY2eeR+GPnY2R8N6eMi/r7kXWJ5xR5xnFRPPNM2MQ+aFvNg82B8UgmjhQfK/4ayS+Op9FfxctqvuzMLCwsLCwcA6ZhnEat2Ir21L1KjVDV4s4P1FfOrtJjrsZTDyOcfw6zOsw9sFA5vaKLVK2VaV6st1Tt3du6Wizq3DgO7s3SBxxDD2pY8dmaJwjspwKLFSxOZMfxI/CwOr9i24xoRu+xNiMPP1wByNpW/FGiWT30UIUmO+DON2YnE/tZnLkig83V7a8jGBZ3FduqT7Zmas5sXHbdJaQzIXIFS0SMA+J7dabY+jvucIi+Vc5UxZ67t6d/9y2zm3F4f61wIzt3Fa7unzPOiX2VbZZjsjxT4QQGFluMgzMhqx6ouDk6P5gd18/trZv7EmELCwsL14d7OKGKZ9QomWZm+cPlInYPx2I1hnqPqBb+Kte5dRpBlkddW9fOta3Ufkwr4H21dlUdjP0yvaZicOHjKH9HjSpsWEGpHhxkAj6OE1EpMNT4aDMrmLMHIu7wVouTil9u7UYLoereOFTJ7SGwd1xcRxfXcTzVNtqKfbF9Ze+VbeYLG1uNf+vfC4G/h53tw+gaO7ujNt3niv1sjmqM6JND5l+3UzkDlZiNyM6e4lg2LrN5LezhHNeXrXOE2494n9nL3mexlsWf89nl8Vvnov7lndWcwHijkkeYndi/I8szrbX2yc9+ftec33rvnTJv9XvKP4aMW6p5N77/0me+ZWKmd3j/3bdfm0cV19BFVf21Z85vvffOa7ZH91Xlxkq8KhvxOrbbM9/WzvkdNZUzpLgpoqqflC7q91T/kTPt4gjHxLHZ/Uy3uLi/BkY0wuj93qY1rZuytVC53PXJoDRIlivOiunvqHnx5us/Do7XX77iP8btxHa0ifZ6O7TjgHZxbCfwM/vKBvoYfcCgrhwi1ybeU0TJ1hX3pvd1h7big7v2ENgzrtrDaBfXL7ZRMRzXD9+z9tjOrW+MsWgT5+Hme+tQZ0BxR/ycCcLqmY1JZCSxqbbYHuMQfXVcGttHPnDzUiIK54lxhwKJjcH4EduqRI1+xs9qTlVOYHtRFRUjfbKzjW1ZTGGsqXZxPBefLt+NrAvGBuNC3Nuz8FCHO3fxOjsLzFb/h+upeKQSC+os7QHmnfjKNBjjjGjL+cbOg/KF8dIRyGLY5YER2xkeQn8xrYPjsZzA/Il7gvb7e5aL+j2nUY/e47OArXm8V4lf5HV2X+Vgx0dKa6DvrL2KDfaZQcWaesU4Y7UCG4O9dz6xsUb69vdZDRLt93vKRiWXKzg+Qq5W8VMd67nBPqhxJBvBkrUKPGezkmjVRquxmNDt7zMbTIzENopEXPGhiE+tAytelIBTSdOtz95kfw1BlN13bbNkVBGGGfm7RBYR40XtEY7JhLAiUTXffn2kgL0VqLPI1qZ/ZgJGrT07EyMJEN8jJ44mdPQD4wO5iF2PfrDYxUKHrVUcE0VPRbzgNXXGRmN2dA2r53+PHXefxU8mXFRB0+25NcU9rviOY2HeGRGrbPxbRzXeVe52fRkvKc5QXFf1dwTIM7i/6Cu2UXwT27L5xPGZVrrGXKOPqmBkPs6MgXB6mWFPPqn4E69na8D0vIPT+yoWFC8+Z7iaiZ29eL1fY+cr3mNnkY0Tx2D1k4olNj6bF2qVeE3lUrSBXIX3Mg2UIcuRbK1wHHdN6dNMJ8RXNRbr09tmfZ2eU2u9UEPpO2qqyYKJs44swVaINx5cRjQ4zgjYoVeJKbZTBJXZxySVFUbqoKG9ylqMCnAcl9lSbasHdKTwcW1HxFRWBLGYiJ/d3rtk5vaS9WGEyfrG+9l+3hoUx7j7o2sw0nZUxDOhpMZmxa0bY8Rm76NEcib2nCjE9ugnvuL5U364sapiwxUOR5ydansmoBhfRz9cHur9kAOYPRcP7NUVwipfM8F8Jh6KGCmiI1SOZjHqclS0gcUHtt0Lx0WZBqjwDcY6O4NKo6FPR84bbR8NLBgVb1bs7PVR6UZWyPZ2LA7YXqi8nfnu1uasvDIKPEPVttjH5SalN+O9ijZztV+Wr5Aj0G6mnXGOKo7jeGw+DNVzuzcvsjXJzo7yyeV3bK84u7+yOWGtouzFuTA/Fwq/+oTBkAlGPKy4gepQ9FdFGrGtE4kuIVTsZonEzT8bm/mBa4vB7exHgmJJkpGrW6OM6NU9tec4T7xWtX8tsCJIiU6VtFBIsFhxYhs/q3sVcleF3lmIz62jKxZGxAn2ZfuB58TZwLipFlLsLLJ59OvZmWexEdvguMhLbAwnRtBGJgCUH2ycEY4aTf6zZ+WIIgnP6owA7+/jHmRxpIohFuuxn5vHaEF2a6jkDWzvrrNzyvq6gsnpor2o6inlf4VzUf/s0UVHxtnevc1sZxxVzfl7gflEaVT0raKLWGy6PMrOlzoHzx1MB6jc2+G0f6bfs7Uf0UjxXFfqv8p1FXNKS+O4I3VcFcxW5exmZ59xH9p3tW1lXKUv43119t1cmMZUHP/cUfrVJ1ZguGTIBH28lhGHCir0BwMQE4ybkxKkSFgYpCqBIDEwm2w9lWjGtZwBChzmP+sToQqpSl+0oeDIuVqUZaSA1yvJq6OSrJBwlH+KSGM7luQyone+n4H41JpUC8lK7LNxsA9LSkpgMNEUryshw/YVbbC+rrBxYqRyVtg6uzV1hVk1ITsuwmusnfKpch4yLpo5UxV+qfCD6pOJXDcWs89ifS+fnIWLWvO8rc4+nkPUFi6PKVuVMx6/KHbPnFHXRB+xTfzH4krpEzZuJb9XNMoIHC9m53SP/Zm+R80741MWX6qty1kj4ypNdeRe3zKqsYD1SeyrNI/L/cxev1c5r0o/KD2h/EUtqOq1yDEuvzN/qrpBYTZWXS0Q56H2VdlwfjHOZvdcDLg97Ig21rn2SH+iJr629vpDBhQbrNBhiXwU7PBgMFUOlBIO+D6CHXgG1i4rWGI/bOfEC2uLfrC5V1ER/VW7o/cdyah+FbKP15WAcPvs4tjFHRPmlbgYLYpUYjoD8WFSUsIhvmfCPVsTdf6Vre4Pu5bFlSssnHBF3mDtlbjObLu5sXV3gseNjfsz4r+6Fu0qjJ4FxUUjfmdnOBOfiudUGxcXDMzm6D5ntkfyyVNHxtsdKGiVnZG1YeI8vkd906/t/es4Sosw3cF4ivnLxH5s53RRlhtH8qaC2jPFMZkGqZwp58dDFjO413HeTl8yHqrqIjwnijsyjn9uUBqH5RPUDq4mwWvRljp/GTexVxYHqOHYNTZ3nBuLK5yTyp0u9zocwT0Rca2rer6ii1ieYKhqMbyutIrKB8zewh12f0cNI/TWvMBXhJz54A4bBgsr1BRZMd+RyJzQwuBzCY5B+aruucSn1ig7KMynI4XQSHtHKDOHmO2HExqMqON7FWvqHvrBkiQTIxh/LjGq69k+3gqUQFTnS8VLlXfUuXN8gH65a8pf1g7fO3HFkm+MzayYwH6MMzKhhWOxPYhiUp0dNSf0O+OGkQIpa6vigd1XwhTbZ2c4fsb1csJH5Sy2f8q32A/9cDb7dSbCz4IZXRThOIC1VeOP2NkDFndKtzgfWRyj/f5Z2WdjXzu2KkUS4yK1T9hO2eqo6OQ9UOd4RBfhvrgc0F8rsYu56Ew8sheVGoOdl349ohJjI1zD9Ig6R4wHWF+s4WL/yBkxHhmnqDWbPWfoS+Wsq7ngZ3UuK/6oXOTyB1vLTEuy9WZjIies85yj9KtPFTGq+sb3VRGMCQODJbZnhB8PMyMxJSrRF5dEFEFkJKSEsgIjK7dWFX+r951/o2RWiRN3zxU7sW3mqyuKGLkwEkHC6q+qOBotmtTcULiyGEcbZyNATBoqcSjRwvbYxRQWJWiHJbGKGHLxquactVfFk+LdiphQ3BLbOx5Hu5Wiq8oJjs+ZPxlcLKi2VUFWHdeJGHWunc8oSmN/FsNqfR23xTmw+HMFxFmAa1SJI5dX0CYT1UxzKB58/923d88Px+zjuXyj2juOZDzA5qfOwKg2Yeg/geTOmdMayh+npdFulpecHzPIeCCOXdH0SnNX9kdxSbTx8tX+X+k7C1ge6sB87+KR5SI1XgbGW5W4VRoNzyLjy9gf+ZHlVrQXx8O5VOeb5Uq8xtZb8Vy87z4rO1luZjkHuVvZVP1w/hhjR3P3GbFdLhd588MPPrhUkpNClnD7Z/ae2ULSz/qx65iMRmxhu4gRUeHmxexX7mdimB3IKioFTCYuRsH2KPNNCUB1rRIHbB8cKvHs4lLZwfEzgYfvP/HGG1vq/BMGclF1XbP7FR5wZ1jxgjvHbGwWx3iuna9o3/VT/jCfKvZxLXAOChUfEZU2I+0quOaYjoMQGWdh2ziGs6f6Vvxw46s4+uLlq6fiotbGcsNeXcTWVtnonz/52c+PTRLw1nvvlPmvMsdRTh3hov55z5zZAwCmB0Z00RGclOmiPb/ixh7mZbGnYjGzc8T1F2/u/5W+r335SzfNRa3d8VFr+d4wfTGLkbrJjTejl5U2V/OMbdB/nIOb01F6An3e23ZEN43me6XRXP5hY2fYG49ngKvR0l99evnq9S96ixvy4k3+P3axrzsU+B6hgigTp24+MSiiP1mwxHu4JtEGa4t+Zu3QdvSd2cR1dvui/GdjdlvZIVLjjIKNkwkA9h6vKWEXX92eZYSk1rjiI7bF9ngvzin+i/eOTiqPDZwzXu/32PwxcTM+UrEf27hzxNpX5oOxqGzhvOJcGM/2tsx+Za7INbi2jG+wreISd1aya9WEfmT8q3gZHZP1VbmI2VVjY2wwmwpqD9FexkuZ7Y4ziLF45txZUlykztOMD4hMF8yiKvAZl1V1UWYz2lW8j3ZmwcafGeMoXcRw9FmaOa9K/+LnjMtVXs407MIdlKbF15ibGfdnsZrVH6wGrPJbtKG0htIzzBZ7jzHGdBS2czGs5sHex8+jvOzWkK25GjO2Z/Nm9pT9rCaq5Ea0eba65SiUvqOmA4uE/t4dGLbpsa0LakYorB22VYWZO3DVgMJrLLn0605MqYPq5or2cM2RdGIblVDVXJhfmchSUMShxsFXto4zwtZ9Ztdx3Ex0qQQYScglgtY4+WFhgO0YRhLkLUAVp0y0s/bxfiVJqngbFevq/GVFXrSd8Uj8jLxc8bG3yWIbfcm4CPthnzguGx/7sHPEbKprDLM8os6ss10Rwyge+zUlYON9JmhHxlLxg3NgPOTmxuyfCargwTVyceJimnHRSMxfIwdkRR4riPp1xWcxtkZ4NtNFM6jqoj4ee4/XqgWVs4W6yLXdA8fFjGuiH8ghKj4qusjp1zNyyV5UdC3bQxdrzC7GoRtXxQCzp3TuiJ51bVROxjmpusrZzNYQ7WW+jqKqJ7E2VOdT5XpsG23hfmF7x/84xsJHSH/1qTVdKODGx0OZLbgianXP2WSkw8ifBRpry4isgx10NbZLRqzdyFxHPqPvIwkO1+AMYjtbcxUvbi8j1B64va0mzGjH+Rg/v3jz06f4dYPWNBepe1VBUuW3DhUraq8cnynf3FnO/KjMT803+q38qviL19X+ODi+OQsXdVS5m3FyhkrcZWdFjV2Nvf7+DL+G2VqdNxhmNFKH2gO3X3t/RQR/9cnFnzuTo7Fa7cfOzp45Z999ojTEY2PvnLM8pXQN3q/qopEcePQet3auX31CVGumKp84OK5y99l4md6d1erO10xnzfhzJB9UdJBq49YiXlc6zc21esYz22wuzw1OF9kHNZ/bPnVxxBwxs0HuuoMKyplkXvHR2XHJyvldEVdxXtm6KOJ1gmKWTB5blBwx/mis9GvoQ7YnsS2OPytEM5HMrp3lQU3HDH9cIwEcJXRmxmTv+/joW5Vn4v04F0SlQB3hoiMxYzfrc5Svo+tZFU5ZQaP4H9tU4ASj87vfOyMXdYye+YrGYGON6pK931ETv7+kqouqPFyJR6X13Hoc/R01ceynoH8Yf++dc2X/OpSedeef9Y3+93tsrgx74/q5PKipFtiZnlL5pjV+flmuYn1cblJ+sHbZOlTmpPzMYjXi2noB2zhfsE2mFzoq6xJtoy2n9VxcXlM7P2VMf0cNW6wscNQBUgtfSbjRNtvEl6/yP6WNY8W2/Xq3Ecepimp1L47FfMP72YFh9lj/LEFWDoLaw4c4SFkczI6Pc1driXtfHZMJyrjmbO3ZWC5mMF5jnxgn2P6MqPIMosJhuLbsvkpAzKay4fxhPsQ9HuEitINxidwX+8fPLMZiO+TyaB9tuHPh5oK+4Ryr65H1wTkpO3vjTq2ny3dqfCUyqyIoi022ZyxXnpWLVNyzvFLVJNgP99AVMJnmOqJgUPwT7yuf8b3jD+wfx8T1xTGPmGcFKh/je4dMD7t+Kqb2zr/CqSwOVKGmgFyk+IxpWaeLnjucXmV5mbWtcgbaU/1UTGHNlsVeJQYc12Zcg3NSvBP7ubxWWb8K0A/ni+J/rNuwTlB5i2m1eC/LecoPFxMV/niOsA9qsgWfEWCu4InXXSHgxmTFk/KVBZo6EJlozfzGcWNft55MdOOBYe3UgRtJqm4NVd89RYwbe/T+aD81n0hmMT5a8+TsCDBCkRMjRUei+B79PxtcwaJelXBxtnHPVV/co9g/3o822TxYP9aOjecEk2rH7rFrbH0ZNykbGZePnvcZLjqKM0aEYIaYA1weUOe6st8s3uKeYs5UwirjlucotNh8lS5y50FxOOufaSDlyx4wTcRiJrZ1mgTtuPFGdNFR+W4khmd1UWZr5v5eVM54vB7XHHOSygNOZ2W+jMT+c0L1TLF+it9jH8c9ce9G4hzHVpqZ+eQ0ONPKqL2Uv8hd6Ae7z/yqXI/jo92sZsO2ro5A7YY5JuqACHWd3XMaW+0js7HONEfpO2pa40/jMhFRuVdtowhF+eD8ZJ9HBGd2D31TvjCf2bzU/LNiq+K7Gr8KXOtbQHVfK/sW7akYdmvszlC2z1n/iFv/Xoj+a5jVgmbkfLr7au9dvI+ee2a3ylFqHspelcfVfPCaGqvK+cp2xiXZ+jt7Va46mtPc+c/yaSUfVXKy2+8s51S4JuOnl69u/ztq8FfCW/OaJOOiClexdpV80a8f9R01GR9WNF73UXHRCJe4GD3iV5/26puH4JCIo7+jhiHT1P1a9Le6d5VcGdusX326q9OOKHJH9W92bQ9XON8q8VTxtTWtDTLNPZKfmf/MTpabq/ugxlNj4tzUvBGKiyqajrWJ7fbG8i1i+lefXKC4Qgn7v3yV/4RKbIP3cLzoQwQbp/fr/5Tf2FcFoToAOA9mSwWf8gHtss/oE1vDbJ+Y/1XsETHOH9cm83G0PaJCXnGfVZxkQhxjszKXOHZ23dm9Vai9zQpxFd94hnFf1R6xvWXnPr5iTLC5YPs4H7zPOJP5VuE19EOtE64345dKceqQnRtnx+29uh/vjfrqMGJPiSbFLQosr7LPeE0JJ3XNzW2Ub28ZUVcoLopt4ytrH9vgOWRnjeki9O/IvcCzn43j5lN5jf2cP8ynIzBiV2nPGag5V7TctaD0RMYpaIP1w3toy9lZ0IWvuob3Uesw26PXXBtWpyjfsH0W94yXUMNVuZP5jvZZTcjGY2MjH7r1Z9re8WL0x83P1QxsbOwf58R8U5pMjf9ctMMIhr6jJhPOTpxjAKvgy8Zjge98R7/YZ+yngjseerzPxFQ8rOowqCTHfO33lFhiB12toSPGyj7gmA8tzivFSvycJTDc29ZeF6XsPYIV0dEWWydG1sxPFg9M5KPtM4gadtbYtdb8mUdRgm3YWrMCgxUcCEzW8Xp8Vf3U/LCv4jAcm/EOcobjfBbP/X20o8QY+jsqlBjUGVOYOQsZ/6k2I6JD7SG+qthh3OLOB2vPzkXs63KL8pPN6daB867GG8snuGYsD4zE0YhmGwE7q2z/MUZVXnM5GfkIY17F1kMLfMfJ/fM1fRrRaSM2Wb6s6qKqLscxHRepdTwDlxwFl0dVHo73mW5lZ5aNWfEru1fJDe78s7ykeEJ9xnmrtWTr5dZf+amuMXsq9zNuVDl6dHw8j/G+ihO81/s6fq6u3XNG+hM1LEAYmWeHmhFBJdHMkD4LVBUolUDu17NCgM2fzZX5wuw68mIEww6X8knNYfSwjBZTM/1HCb0ydiVpMTssfjOfsB0rwHCMTPQxm/EeiuRbhxLryE+4poybVJKN93Bc9l6JA3XW1blHu6MFgBMUrA/jGSXMMwGIfuDZYHmiKmxG+BrPU4XfRrhf2XLiUV3P5qDa9WuOB/q1GVGk1s7lstgmiwU17q1BnZH+HjnZxUCWV6LdeA15w/l6BNRcMgGPbTA3IRhHMb5in53dx4KL/UwXZXtX5a8qMi7O+JjtuzsDLF+7Aq8a888NTPMwnTRzNkbO4YxtVcf0V5YTVd51uoahyp/MLzYXNrbLiSzfOv2iagX0lV2r6K3Yp9KG7RPmRrTJ8h7ObeF1lP88tyoUW6slSibYo53ZZFsR585n55vrj3aqBwFtVIiEJWQnmo86jN1WpVDBsSv2WXGFe3SNw6vGwTiqCmY1p36fkbMaW40X28d7Fd9evjrX90KoNa2cvawYbW1egCgfRsar8h8r0GfWISI7B6wN+q98c/4pfxAZFzFf8Zqz7Xw/Cmq/1F7gZ8cNrG0Wk+5+t5X1G42/W+ci9edwWxvTASwW2HvXt6K3Wtv/XR7x+0vYWBEuNyo/R/iQnQO2Ztf689x9jFEumtVcI32P+I4axU/ZPjlfXd5wXMLex3Z7v3vpLN9Rg9dGuCGrf1QMzOQNZrO3r9aFFa02qsGVj2wc55vyNcv7Ff1R0VkZd1R1jrOZjVPRKApVDXFGOF1UflCDyIigtVyYO1QOTSaKKoXZaEC44KyQQKUYGfWB3cvIZ6+YUBgpjtyYGVHssR397L5WYwrhElBVTFcEeraPeA3HP3Nx1DHDNVUuaq32wC5L3lVeqwqQ3m+GzyrJ1/XN4jxbIzUf5ssMHBdl9t253OtX5mt2zhWcYIyf3Rgj4lBdz2LjTFw0mjtGCijVBseo8N7eBzXvv/t26hODK0qUDaaZXC5V7Y56UDOqaRhGeaOiixj2PLh4/923X4vnUa2dfR7VRVlOXF8mnD84zrii0i9rl53Nip3WvMYa9bnKlXvaz5yP6lxd/CtOzfJzhqouchq2qo2zWvS5YfrLhFvTG5wlr36/sgloo0ouL1/xHwN3YjMGh5qDssN8dwKpB2O018dl/5QP0Q+0oezHz7FNJiKz+WZAfyvts+uzAsnFJtvzuH5sHiwuMoJUhKbaRHu4r2xsFuvV5HirUPsy0qcj2x92pmI/xSfVBI5nuV/DWHMcyfr16/gP58r4B9dE9XNcx2wxH9Gu+pzxLoPjoixmFP9U4mgEau1xf1w+qAij/hlzghKDyIMZPyobirtuHSpXZtrG3a/oItYO2+Kav3jz0/ZBSxWKk1gMs7VAn5QuqswT457lyqNQ1UWKQ5WN0TFn7MyOieuM+Y2df7YnI7pIaZnYXuWz5wy1b63N5c3YLst9e/Wm0k9VXys8oLiBxRvqE1UrsPfKNotvl0OjryxHx3tuzbJzwni7eqaYfsB5VNeN+bXwcaRfJpyJguphYtfUdSXu2UHCduw1+qCKW+VHJUmz8ZwvcQxFCipJqQPuBDEjwSr2JEMXGw91GF2BHN+72FLkiPfi5/6etVG+YdLJRD3aZHEwQr63ALb+TiywNWEJRJ2fbM8ywY7jVIF+s89sTNx7/Ifx5WIUOZfxD4tZTMhKkKj5ZLHPPjtEexU+qNqs+FPlwGw+I3YcF8U+bs3jfisdwMZxeeZMXJTFE7brYOcG77siS+1Dpov2/uQB2mPnEfeezcPFE9pj4zldhGNdE2rv3P2Oh/a1CuRJpxtVrsTrjuMquojZq8TPcwI7E5X1cfGqOCjer8T7jBaq5Fb3AILZq7R1vBvHVFor0/3VOGe6Po4TtWC8xuZbzRdMGzI/Vc1RmV8lNte55kh/oiYiO7wd6mAosq8EvDu8Svw48lD3XCA6G66QYAGaFY84jiMaJ8aZWM+S71FQhHPNsasFFN5DIejI0dljAkcVTipGXMxlUOt9FszEritI2TU2hosV107FUYTj1Sx+VZ/Mt/gZfVN2XTJnYyneY/aYX9X4rRR9mFtYm1mRm3GCatf9crmDrSnbJ8YtkYtQ3CkuYhwW/VfnQKEqkm8Z1RxTAVt3ld+VBlJ6ag9YLmc8meVfp4tiHDL+YrrMcdpRqK5hRRe3NubjQ+dwlTPY2XdaBW12GxVdxDCzds8JI3HCeNsV4/Ea20vHWfgex3M1Ad7H6yxPRbvIHeyaGjerx3obFZcs72I7V0+wc8fOjoLK32p+mSbF+TC7uAaou1weY1pk4SOU/+oTS/pMaLoD6gLNCeXsgFUPvbumRGdF7DDflajFA5gRZIcjxqoN9EVddz64z9lYFWIasY3tVZwweyzxxLaMTFQcKDJmwja2dXGlfHf7zojyTFB70t+zeSOHKZtKnDI4Hqvum9sjNz4WM+oe3mfx2dswAabOB2vvfGW8yObtkrey369XOI+NUeX6Kh+6+4pr+mfHVei7E9psnTOOwdyC47n5RW3gYuBsAgxjU8UvW89qvGJbll+ys9xf9/7qk9pDx1UuJvor85+N63K10kVHIeO66plx9/fqoiPmzeJKrSnmwKouUjzm+Iflgd7niF/pOwuq/FpZYxV3ldyk6jvnt+JK9MdxkJpPpplU7Cl72IbxkdM3eKaiPo334rWsrsn0k/MZfWJ7UdHQSou5dWRzWngdpV99cmLDibNM3ONYcQwMlkzcsmBRCRWvqcOgiimVuFlAIgnhOjjRxnxTYzAiHVkHNo+MbEeE9+hBPELUOPKP19Q+Y9yqOMySjPIN71fmpeKAnYcsvm4Jau3ZPbyOPNbvxbasP7bt7zPhHvtkiUkJB1eUsHngfcYxlWvMppoT81HFm0rglfVXhQDzhcHx34igZGM7UeuEo7Kn8kWFm9VaKlvx2iwct2TxdMtQZzBbS6aLquN0+4z3XEy3tv9LV7vdHi9sT1lOi684D/SV5TQcHz87XXQUsvns0UFH6aKj8rzSw3gv+oLXVFzGzyyXsbzn9vXFm58+9Ff6bhmV/XdnpZLDlR1WO0U4nsOx1NiK31xthbHGdBzaQs2o4hHbKM1TybfsLLA5RLvsNY6JfdjaYV/00cWAOp9Ku7Lx4/zYvi18hKFffWrt9YDJBHT87II52s82vJIYo91oT5GLClh2QJS4dSIG20W/VLt4Xa2LIyFlS33eI0AUcK4Vm9WEUx0XrzPyZLYVcatEFdsyf1wbFDpuroocnci5dbhE1d/HdXCimiVo9cpiRRUIzF/mk5ubS8xsvCzemE3GxYyLcB1wTBZ3lSSP88vmWI3jbB3iZ8bVyqbj0tkz5vg+K1IyHs1izcU3tmM+xD74Pr5mue0MYBpF8Yvrj58Vb0WoM3RN3s9yHIPiCYWReGE8e3S8Zevu/MpsjexZdp727ns1htVesj6ZLoltmEY8o5Z5LLgHAXjd5WAWh047YH/cb2UTx850sfIdfezvWVtXo2KsqzlkqGq8iraMr2yP3NyUBnR71/uxvIfar1K7KM5Y+DjKf/XJCXNVFDBhp+xGuA1nZB7HRR/wVQUW848dUOwb2+KaRN9V8FWCkhGCEm7MX9yHhzwI0Z9MRO1JytXiDtcCYxf9dCTIiD/zC9ef3Xfr4M6XOgNnIL5M6LMzXk3GmS11zlWRqvZ0JDEz7sT3TgQxqOSq1gD5xJ2FaoK9JhcpYcAKkIyLmE21fxU/XBuMKRxP5S8nzCpcxNqps5KJLvQ/rjHj2lsHyxkdjHuVAHbcg2B8FP0ZietZRO1R4T9cF9QuiktY/4joA+t/zTUYPd8Obr+xTaYl98LtaaaL2D30P35m9UKmbUZzzXOCqgkiWP5X7d0eVtYf9zKzp3xFWyzGnL6r5uzYlnEWa6e0mNKpI7yR9Y38h+ur8izTiajzmC21nswvl4PwHF+To8+G9EENBi8rOlg7JsRVonECEu1gUKn+7nq/p2yzeWbFkApgtV7xHvMrfmZtVPJTZIOJtCLaR+8rIJEpOyyRzI7pEgOLaSb8WP9qwmJz6ddd8YN9XSEQ26A/1xarDw12FrOkwISnO29qvbJ4ZFxXmY/yWV3LBEN/r8S04hsFx9Ou0Mr2BdvMxOpIe3fOKnt+lB+qj8pBjJMUD8T2iCy+WRuWG/G+8mn0LNwisvwbr7P8y1DJP7EtE+fV/DQKtsdOO8V28VosDvboomiT+Xgkqrpohgv26qIjix62Ry63sf3t/aMNnAvTQKhdsA8b90z65gioNWHcfuQ4+NAgGyPLDyq3zNrrdtBnNYbSYb2t4vn4irYVLzs/3GfkWHfe3Od+LZsz853Z6f3UOKxGyXh9YeBXn2aLSyYmWB+8lo3BEpwTqVlhoXzEtii4nKjOhPFI++g7jq8SuUqS6oBUyG4ETnhka7M3qVT2ENdAkW+0qQjWkY0agyU89DP6G8dSa1g9S7cMdhb7Z2yn2qprlTFx/bFIGS2g43tV2MQ27vwy0YR92bxG1oD56c4S26c4B2Y3gzvfI31xH931bA8VnHhia8D64zo73sj8VXHrciPuFwrFDCxHnQlqD5VmwTaZyI1Qoj/qAZXjZuF0kdNxjosQKo8qnYb2ovY8ElVdVNVySme4cfDcO127F1VtFq+p/uhfxrHsvRrvrPpmFEpfdLhcnO1lFbhPTvtje3YeFN84HaP0ebyP9zINzubJfMX+2B5jneXbjL8qa8K0aXaeld9VWxU+YHNjueOs+mAvyr/61IEHIl6P7ZkY6e2coET7bGOjCKneZ0K3QkpYHKlrzB+3FtkaIDJCcCSFNkfE2+jBcT5GqITBiI3dr5JGHA9jCuNAFUBIWFlBE/uMFHxVgnbEOFoE3hKU6FPzxz1gRUO0k42LCQdjyu1fRRBlvmWJmp0RZUPxM76Pa6zOARtHtY/czPypCk52LVs7BZUPMp/cOsfP7Aw6f+K5VQKzf86EN7Z3nDDC3W7NVO4+Cyr6h11na+C0CmuLdmM/lmucvRmw/BY/K//U3CMYr6o4zHi3qm9GMLKmjosc96pxKvx4BFiexLjCXMH6KG5EmxkXsbOGPj13sHPTWl4HsHYVnYL3snOh9Ec1v8bxXI6N9p0fTocrDaPmy/ozGyqe1flm+4h2cK6oTZnPKj+otWG85fbbrZHS7YpbFj7Cdrlc5M3PbZ+6sMC/JkmO2mbtqzZY4ChbqkBh1xGOPJyv6mBGn0fmpOa3F2pdqn2zfpU2e/1FqJh3e5+RGrOT+ajghA7z6xNvvLFZg08cyEVKXOzlD7WnFbuVs1Y5t+oMOw6J7Zk9Fa8V8eEEjbNT6afazcLxW8YvVZ4Z5btMGFaEslvXSlwq22rO1dhQdty9s3HRCEb6zHIR24e9fx0H/wxyJR/G6xFZ3hrhUZUPXrz56falz3zL9Hzfeu8dea+qQfbwW1UXof09c37/3bctFzGoPcfPKocpPeTiKWJvXH/ty1+6aS5q7Y6PWpurYyrnDW2wz5ndGVvsNc5z1q+sj7LToeJ11J9K+3gNx1fnKDtfFb1SrVNcfzbWCK89NzhdVHpQg6gIhWrBwQKr95kRQlVUiiZEdpiy62gjtq+SK9pypDZSbOC92eJppm+1QIr3HVlWRVSWvJhNF78jce+ux/tsbkogO8I/Q3HUWu2BglsT1k7ZrQgN1x9tVAVR1pfNJc4nE7cjY6Jd9crGc1yk1uQpwwkLVWhU7XaMCssRLhxpx1ARXJW8fgYuqpzLTGxj26oumsERD2qcpsjWYFQXORuK87DttR7UzGCPphrB3jlnuoi9V1BtMl3UUcl5e+bb2jke1Hz4wQd/poircEkVFb1ate3qG2enqtVUv9g+84Fdr+qpSn6u5Fqn53AcNceIWd6p7oXrXzn3qv1snrtlOF1kf/XJBZG71vu9fPXxH8nLCqfYJ9rp/bEtXmP31Pvolwq6yvydHy5QcdwYqGq9GFGwAxH79PVU68rmukdQqDkzzBRrTiy6vYxjxnXIhAZbI7b+uF/YRvmI82FngRVFeN0R+hmAa9qB82d7lNmqxl5so/bB9YmfXWES28wUaOwcuLMR+citC/IK+qXWNWtTEfujYP2qtpA7OzL+z66xcbI4RW5hPqIttKm4iLVTXBTvZ2MyLorzuXVU4lfFH2oip4tYf8wdLvbUPu4Fiz03VlUXqTYsX2I//HyN3DfLKaNxn3HotVDRRYqL2D91P15X+604BO0tfATFIVkOq9Y/Kp8rXyI/jWhtVw9WtVqmFzMwf5lNVZuyfIhazuUINobSZ7ENnjXlk/KDaT60zWxU5pNpmQUN+xM1H37wwQWDSz1ccIciCpGsSGFByuwrO6PtWV81HzUOPmjA9+rhghsj85ftC/MlG4v5tEfYjRAyflbvjwCzXSWISj8XA8yHeE31V3uq/HAkeIb/xe7vq3uZnUuHPVyUcV3VXzY/xSmj81TnviIwlD3V1vkax0Z/mJ8jqNh4KP5RNrO9ygoWBhcDI3uA16t+KJv98xcvX71pLlK6yGmBiKN0kTqz0a+OvT9RE3/CZEZbMThd5OZX0UWt7f/pkiPh+KSirau8tPdXnzpG8ojbH5W7IrLCzeWl9RM1H/+JmtY0B8T7irNdv1FUYqOPl9VIM1oKbWP7UW6p9qnqOJyf+6zGxj6VtYhQWjLL+Q5On2Z2slx4Zkz/RE0HBkL/V1nQfn9kA5A04vj9X7fX/+GY/Rrei5/xfRzL+YTziL644iCuHbOL9tnc1JyU325ODJmYmO3P7mckOoOR/WPrqOIG7at4cDGFnxWR4Vgs1tx+sDNz68CzgxzE9o2tG75340V7bC/j9Q6VuLE97gvjFLXX8VpsF+fG1iWOibGl+CqOEcdn77Ftxq9qjo77ol/4HlGJfRUTo2cG/WC867hcxQzuIZ4D3FfFbcpPdp/NpeIz3jur4HICucItsS/jeNZW7bGKD6WNZsH8ZJqH8Rr67jijt8Fx42c1J9fvIZBxkWqLa+K46CFyudPTqj3jUaWLGIcpjmH3zswtRyCrMapaU/Vn91mMZnmE+RljI8vzjGeU7di+6h8bT+klxbWoWZ3dSu3CfKn6w7Qf080Zp8e5sffu/LtYqubO54j0QQ0K/w4UC/F6vM9s9fvscMXXaBMPsSJqVTSx4MT3OC8mjNwBYW2yBIdzVPOJ64VElhVqTkApVIqMUajiUJFE5lPVT0XIjBgUWbD9ZHGE47GYYW2c/3hmWHwqv84kZli8IBe4BIOJxRUPCNx/VbiwPqx95DFmv7/H+aDvEUrsuzGUv9hX8QeuP/IUihDHRwrujKv9zeBsZgKxImYr4qZ/ruyHGpfdY+ch+uTynpqXaueEa2xzJh5qjWuH1vjaY8yPFh3dFuM69vmIXM38YxyIsYC81q8rGzNchH4pXXQERs7gCBdVNEZ17KPmq+KTaY44tltzFQfOVn/v8oPjzecOp0NG8mxvn8WXilOVE1TsO40dPyP/oMbAtkx7qXEyfZKtH+bXrJ/TccxvlsPj/J02zjDCN0pLYzumZ8+oB66N4T/PjddccFSKn0yEq0OohFLvN1pAYRGkxLoSIXHc7IA48lDzYb5WhAEeDCcW2ZjRBtpVyIhbjY+CrkIGlWSdxQPzTa0180OJZba3CNwXdk0lI2Z/hJhvEdn8WJJTidedOWyTFUAqWTIBED9nvObOIfqEc0bhy9qpuWf9kOdQPKk8wObArkVkPOe4SbVXNpW4UP6MIuMiFjsYg0qsxs/KptszBhefOCd1NrOzc2tAkazAzkTWJ7ZXtthn1vfIPFDRc04XRRtOW43oCqeLjoq3vVy0Zyy06c7XEXvNYqeqi9iao70RXRSh9uDMOmcPnFZ2uUHtIWur7GK8uNzj9pW9z/QY8y/TBmq+GOsuD6r77NX5Fm2xdXec7+ar1slpEaUt2X12ptk6qrxZzYvPGel31LDrKmAUEWDAO6KtknCWtJw4UIQwChSo6tCz607UZDbd3Nlc1KGoiK9RjNh0bSuHd4//lXWMfsR76lq2d8omAzs37D2bUxyjt7/176jp3wuRnXmXXBkPuLOo7KjxnV/dttq/rF1WoKm1GI0jx0VHrAm2wbk/FI7iqZG+yo5bJyecsZ3ad9YG7VXOC/MrE+OIs3BRf+84JdMhiMr9bku1V/xw1J/nzjiWXVdx5zjKFREVLnzx5u3+1Sen+zIc+VefEFmeUO/VPiNGYqm/X99Ro+u0jkouHsVemxXNrHJnRetVfXa1Id4f0UGVupLliIpdty44tuNN9I+tr9Ifap+cT9l+Mp+eE3b9ee7WxhayIhTVtcqhcPaVL9gfMXKYRvpUfav4He/h+BVRo8ZU65Ed8D0YFS0dbg9nfagQZYXsKqKEjaXGUPYR2Zr0e7f+BZ7IRdU1GeWiPoZqMzqOQ7WIc7GW9R/hXNcvE8zuXvexMsZDYpTXjuIe50Mlb0RfRnJaxkNZkYXjMvsqV8Y+Z3hQU43xjlGOqHKD6xdx1IOaOE5lr1kfhRGxXsnRe+ZceVBzNB+M6iLW9ug/z93Hai3n66oGjf5X9H60gz7tjeszPKj53Papi+LtDJV6Ql0f1UfseseMZq6OV5lrponYWYjI1l/NuXpP+VvxP+MUlt+ZH6yP01BOm6jx4pjPDbv+PDduiDoAGMi9X5Z84zXWB98zP3qfyoZj/0px1Nthn95G+ahsuYMTDx4eYHYw4mvcA5UcK37gOlcOOu6jw4ggwz5HCCMcE/2v2mAxoWIkXsc97evN1jGL0zgG83FE5D9luLXuiGuJ7RRHZGDnq39G+xmXsBhWfIbt1LhqDZywYJyLUNdxDZCvlK9sXhVuif5mZyHLL2wuI/EQz+lRYJytYq5DiSnGISwHq3ko0YTtnX3s29ucgYMi1B5E4Dlkgry3w/csn8azjf+YH0fmTOZrH0/lwkyPuftqjeJ8mS66dpzt0UXMTsSMLjpyvrj+qFUqcHomjsFyAfZnvIXvF3g+Vzm5Q9UK0Z6rw1g7xUUsruLY7uxmPqDfKte4nIdzYTyltDm7Xp2zqg0wvpmeY0D9iz65fuy9ssliTGluN35FYy7cYeivPimBliUkVuQwMIJnRYlK8BmBZ8IhzsuRhkoYsR8jrCxw1bhKwOD6VwSSK9jie1ZkOb8r+8vmlYn8EXsjGLUZCZSJxfge19jNhd1HocJeFYGyxHFW4sP1UEmuNf0w0e0V8pA6L+wz+oVtlCCI7VQyj7ZHRTR7r9pmXMTaMt6I11nMVvyu8MFs0q/40NtV19yJT2eTFSmsYEG+Z7FU2WMmrpnQd7zH5qzmfyYuUuc63q8I6/g54ximj3CvWL+9v8qjijC87/Ii2nF6JeNKlVdHOTFDJZdWdVF1vBGePPo8sRhyWpjtQcYdjB9YbKk8yDjyuYOdK3dGnB32GfdMnQPUJ+hfxhv9GosBdRZRDyvfHCfFdqwvtkOodUNtxPqpugHzvLOBvisNrOY2qoGUL8w2m1e/rvT2wsdR/tUnBRUEmXhXn11QVcZl7ZDcWQHE2vf76gDNIpt/lhgrbdR4ah2eEqpkm1139lvL1zradoIiXq/4wuKR+TN7Lthen+lXnxxGzyZb845Zzsr2NuNDvK4Km2oczaLCJ1Wfs3HcOUPM8Jbi8Rm7zkdnIxuTxURlL1X8VX0c2aMZ/9DOrXOR+r6sbI1GdBBDRTeoNnt/RaT/Wowat7/vOIp/svzneO+IX30a4Zpr6qnqeT7qe3myfFS9n/mrOBFtsnFaW7/61FrtO7MQe+qJEcxokT25xWkk1ibjz8w3pR+zGjK2ZdeVTTVf5WsG5qdaw5l5ZWfd2XluOORXn7J2relErURy/4cBiu37+/ga+7C2OFZvHz+j3xj4sQ++4rhoJ97H16zAY/NC33HOeBiyvagchtEDM9M+7kOH8n2G9EcQ45ERePSZ+Rvjw8VOfI1QIj/eZ/vOzhLaOQOcmFBt2Dly7VlMqv6Mv1wblnDVnsV2jl+V0EUuQn8YdzE7jouzdVU8iePieqhkjj44OE5hn0eKLHXWMt8qwhfXmuWDkfVkXBTfV+PcjYG+uRg7ExdVzgBqjw7F7/0eO3OuPRPZR663KiKwjToTLCZcPDtdqLhrlCMqGLFz1JhKYzhddK1x4xhsTOUTs6u4KH5W1/tYZ+KQa4CtH8sdlbOaaXPWF19H83CcQ5ZfcV4svynb8b3yKdMhaEPptv6e1RXsTKF+Qz9ZvmVrwvbP7Yd6n61B9ZrTTHF9juKzM6H8V59csYH3VRJh95kN9j6OqQigQuCZwGaCJ5tXhPI/s1fxT/lW8TPeY69sHtUDc8ThesgDqtZMiWC8psDWnp2ZLAbjeKovI3U19stX5/hLK9X9ynhj9OxhGzUeu898Uv45G+g/+hDheKKKEc5VHK/8rPRh86mCtT+Sz2a4KjuzFX5n9qqcpHIF+oC2q/xXPZOt3f6XCX/9N71l/8rKwsLCbeAMP1HTv0z4iLpoRitU7FbHrNaHo+ON6DJlR/nb4fS3s1PJx2qOFQ2Z5XUGpTvQprrn+lf6HBmDt4JD/upTa1wsIlTgYJuMUJxAdA8lMrLJhGdVMCtf4z23XlkwZoU4m8tIYVo9sOi/I4BRjBAH69taTgSuiM4KTYXKA4PYjo3tfK/u1QhZ33pxpLgoI/mRc+b6uGTnuMhBFbXoT2ZDJccZwZLZV3OPyJK186uKvQ9L2L0ZPyq+OLHkuAjbY7tKfspypZrzSB6N4zl0e7fORetBzcLCOXCGBzXqVzFHMao9sM+IDzOFuXsI48av1D+Zhqo8NHF+4xjZa2Ut0B9VD7LPESO1XFZPYbuK3szqqOeC3b/6hJ/7v76w+Nrf42cGvI4bGzccbeJmYh83LxyzWkjHucc+cex+L66VmzPzha099lX20Z47SAxsXOxf2dsqqg8s1PUsDlgsjiYVtB9jnsUdSwRoT50tHBPtYwyyfvH63v15ClBc5Na5f1YcU22Da8zGy2ISY1C1Y/sf+1YKZIw1FqvOpwqY34qLRjhK2cdrlTxQGSfe2/OQxuUexsFqP9G2sqnsqfhXXNjbOAGaxW3kIha3R+aKhYWFhYWPw+V8B1ZPsH6zeYqNh3nY5ZmYQ6r5P/bL/MLcx2o69ln5iz6r8ZmWZHky2sr2ktXmcW1Rw7J5qNpEzY2tA46JfVW/BY30J2pUEGeiV22WgupTtcUO7oi/2Bf9YrbwNevDxnB2s/FnwHzP8NDF/sh4bG3V52rcZu1n1t+tu/o8W3Ay3PoXeOKP96pz0691qLNUOb8RKvG5Pqp/xmnVGK367vyJPrH5KXvZWjJ7cTzm77W4qNLnCI7LzrXr42IUbY08VHGxgjaUTw4u96lzun6iZmFh4SngLD9R05p+WK/g8kIlZyh7I22q47haanTe2biulsRxWf5mbdQcKtoqXkM71Tmp+WXI5lexp+rfqkZ8Lpj+iRqECqr4yhDb4L9oG69hwLnxX77iX5zUr8VgYWOrwNhTrKNdNwbexyDuthjR4Zqhn2q9q9hbwIxCjcfWM64HK4yYLbVG/T2utYpVZg/XmhUqcQzcX7Uelb1jMens3iLceVBzdZyiwHiNxZ76jHGguAjHyPxx81Jt1FzQp5Gx3cOZGN/xcxxnRFwxXhzB7PpWfeqfGTdn/Z1QxHWMMe/8x7VnYMIza4O+9X4Y5/F6NsbCwsLCwj5grsB7VRv9n3t4oexXCneWN/G60huYB5Xuy2oiHFfpf1a7OQ3IfI3jM3uuFsYHOq7WjWM5Ler0LP5Df1y9rvY1zh/njX4vaAw9qOlgwpvd6/fVgWLiPtp0h5cFsApSHE8JXhXgbF5oX5FcVay6Iof1j2M5glR7MUKmR+CIBwaKgNUDCta/38P9YuTK4pb5EO1V9p61Qd/cPjHSRVT3+pbgErBaD1foungceYCgkmnFTvSpKngUF8X7sV3mAxsXuVL53l9RRDguyuK7Iob2oMpFbt4qH834qnKg4gm2dijuMuGuhKQSh7Gfy/FsDMeJCwsLCwv7wHJCpidY/lZ2Xc6raG82vrteueZ8Zg9FmD2sB1key3zJ9JrSM+rBCLZT++i0k5u/q436e6xrVXu2dk5jszpJ+bTwEdIHNUyo4X0l4JwNFJa4yS44mFBkr9E++8zIB685AdrvYzHIii4F7DcSwE6wKxsVv1z/Gbg9Pco+vs8KAlVcxPuVRMAKK0W+Cu6MKRKP81QPedD2LQPXVK1TVjCzQhfXiyVWVrTGNqwvjuv8csk6O49qHu4+s6fml6FqvzK2W+ejUOWiEWHp7LicOMLD7iENvsbYcTktE7+V2Hb9KmdyYWFhYWEeWABnvI+o5KusNqnmEjd2/+z0t/Jn9IFPpntYvo31K5tHpf6t+Ih2K31jzlcaR9XdbDxX5zjNnNXXrE5f8Egf1LiNxM8sqLE9K4Ii8CCwQMSAjH3Rr+xhEAYYtuuvTHCysfFBVLzG2jK/K/fUWke/4/WM+EZE+AiyBx4jfjj71YTUWvtY7GSFYuyj7OB9VcSz/pH4WFywGI++Yl9HoLcMtceOiyoPORBZTLI4YVyCbdw13GfFG9inMhYbw43F+E9xkeNQ9BPjVu2nS+xqbpXrGdwDkFEb3Q7jFiYuqw9SMHdm++P8QzvMRyf24n2Vz/DeWbhoYWFh4SkBc8uonnd1F9qr6vnMB1e7VfIF0xBZLYD5Fh8cxPlHPaR0CNrGV7cvah2zmo/NifVjdS7Of0S/Za/MFptHpiuWTngd6V99am2sOIn9Kg8nKgTDDiP2Y4KXFR84LvOHjYtzzUQ4js3mwOwqv5Uf0aY7JGwN8D5D5dDMPuRhBJMVGziu61sp5jLCcLHvbKlz4vZdnQWVALNC90zEh+e1tTxuVGGs2rvr2AYTp+I6d36RO1zBzuxiP3YNbSl+dffZuituZD7hnKOvlf0Y4St3nh2ys4T22JnMYofNRe2H4+sodtwao19OhEVRpwSmOm/Il47DzsBFCwsLC08NVe2cPbxgbbL+7HOW25lNlrdiG8xPTveqcZS/8XOldsDPmSZQub+qHZmPWS2oNDBbO1WTVOaKtplP7nPUJiM14HNB6Ttq8ICwIMkKKBRxTKQrUd7bZ++rQaIEs/Ip2nBjs0BjbVxhpIIV1zF7UODEM7OPa1FpN9IGfToCqrhiiYf1c8lBxSKSYAW4Z9l88LxV2vXruA6VcW8JbM7xeryG75ktBmcHBQLrlxWsqghXCXTUb5yDKpaZH2iDFe5uDXAuzl9sWxVHo8jin61LZg+5R4nKaNfZwvzDYkjxkcpV1TlinKpcweIG22XzPhMXLSwsLDwVsAcDqAf7e+wXXxUyPaJqJaVTs/HcQwimbbNCn+XaysMJVhOgvla1iLKFfmJNx/RipjnideYnW4/Yj2lTFk9s3ZhuVdfYZ1VXL9xh+MuEVaBVFpoFG2vjPrsCWvnLDhIeBPSR3VeFW7TpCq14nx0ktYZuvtnhHS32XJ8q1B5mRLpnHLeHri/uWezLiqc+Fhs7OwNq76MNFt+RICtnhhXWZ4E6x+rBAO6TSnxoUz1AYUmQjYX+unOIxXlFQKBIQOHD4hd9yvyKdnDc2LcSk+46riviyOLePWCYBeuPe1o5i2w9lXhjwgrXMeO/LLcxGyqeVU7Fs3YmLlpYWFh4qkDezXJQVtDvqRmYJqvke/Ye9Xm3idfQJtNTTOvhe/R9ZE2ZLabJUMtmujJeZ5oW6xjlj/rsfIv2Y39mU9XSbMwsLp4ztsvlIm9+bvvURRWIqnjNRO8eUVwVe0rwjowb22eFemzDxkWwdXLrp+ZdLRAqth1UUZu1Y8RxFLI1Vj6p+6o46p9VLDCoWMseEGRwe6vG7X2+ePnqlg7whPHhBx9c1Jxbez3WMqhCmrWr2qvyS4VPKrYzv90YGb/Fz7F99jBB+aDGwPvs8xG4Bhex3LfHt4yL1Pl2eU7tW4WLKuMpuBj6xBtv3DQXff03vaVF08LCws3ga1/+0k1zUWsfr9Naq+uKiOzh/IgWdu0ruWr2c7zG9IR6kDHqJ1svNa7qz7CnRo0+qRxf1X84RkRmW2mgEf01+3zg1uF0kX1QE4ujjplNzwSf2kBVjGdjY5sRP1gQjRY4bPxqYciQPfyYEerOrrvGcI2HMJm9kTGrRU6/FvvFdq59tp9qbbMiqvoAIiu2b704Ulw0umZZH1e8jhbNCBdHrH8mOpTdyhrg9cqDAueje492Mr9G4HhgLy8p8aH2xI2f8a66n/mUxXJElnPVPqp7zC76wtbj1h8arwc1CwvnwBke1DhtNFprYP+sTWs+b8T7aoxqLsJxY5+qtp/RX2q+eM3VDm78rEbLdESlLlDIdEemr51dta9KR1drqbPC1WiH/dWnuMhKDPbPLHijzf459sHrEbjBaqMrwhXn4A6uE/LxOs4x3mcB36+zeblgdoI/O5DZtT2Hh8WLi6vZ5KIQyQXXXO2d2kPWnq1tfM0InMU/QsVat8/WVPW5VbiYYEWqS6i4/mostTduP2bmgVzk/MseFGQ84fxXa6Hs4diK09QYThzM9h+xnYkcJxSz88XWhdljOQbzhYpDllcVB2HsOwEVx1HcxNaB8ZrK2wsLCwsL++HyudIo+FqF0pysDsw0W2yrcqOqA1HzZfN2c4n+9OsqL/fPak4ul7K6wOVX1R/bV/eSaRmnS1Tdno3Banw3p6NrvrNh6lefZhZV9XFCEQuvrL0at4MVNMwP5Xf2npEGWzc1T4dq4DNfqutzJDKiPEq4Z8SpxnJrFqH2jLVRn107BxXzqnBk/XqfW/9f7A8/+ODSWv3ho9szd35Ym2i/GjeuD/qHGI1Bxw2qPfrgYirj4wrPOH9wvAj1MGEED/GQoDpGJipdLGb5pbdlNkf2h/lZzb9sDfD+rXPR+omahYVz4Cw/UYPXVB1wVEHsNG9Wl1XuK11f9V3lPqefs/Vy9Sjrz8ZndrK8q3yoromyP1s7ZW2yGjnDc31gs+tXn5zhrHjpcIUFs5kVvaMCVI3hHpwwv+O9DnewqmSZHe6sUKk+JGBzcmSRFZOjxc/R9mbGrRbBGdG72Hdg42QPDSr9Ml9u/Vef+kPjCJcwsZ27V+Ui1abbdYlcnfsIV6Szz66Qdjbd3Fx8qzaIjH+q4mYvZmy6dZzxaWSdXO50a16JGzWOyqF7z0Vvx/y7dS5aD2oWFs6BMz6oGXkQUoXLEXvzQaVNNR9VcpnTG9kcXf7N2mb1pNMrFZ3r/GbjxXGcXs30SFWjOE2Vac7ngN3fUdNReZDB4B62MNszATgyZvbQJPrDxqwUMzjHbH6j5FkppFT7GWId6e/myGxkh71acLj+6kEM3ldtsK0jsirZsXkwf5St6lgv3rz9/8WOP1GzRzC4c+/iUtnqqJ6nLCFmXKT8rYqVSjscc0bYZf2q57wS8/G9W78RAVUZu9qWjaPij/mecYESR2xdnNBEP9x6ZfzpuPPWuehz26c+Jppmz0qWJ9H2Hpuf/Oznh+wg3nrvHenPzLnCNjP6RKH33zPn9999uzQGvh/1sb9vzRenLrfH1z1z7nvs7Fd4ZrRtds+1/9JnvmV2uq21cz6oWVhYuD0c9hM1rgBw1xjxMuxN0NFOHCcbl40/UvxhX3XPJV4m0vt1VZQpQTe6ji6hMl9xPLTjir9K8ZnNI1vPrIDNigonFlw/tBHnXsGogMHraryXr27/L61UuIghK4bUGd9TLKlxMp/wnuOgLPZH/M6Khuo5Y2M60a186VAxrc63mleFE7K5ok9qLDWu6ruXixQPVQomhWqxxNZwcZG+hvdbG/9PkD044sFFdu7YeWqt9r+/sX2EO5+xDcbnniI+zpchy8PRXzaXqubC9+xzvL5nzvFBjeONCv+MwvFYa3699sT1elCzsLDwFLDry4Qdqkm5fx4Rqi/ezL+U0gGTSSWhKKGMfkb/+r/el/mdCXC2LpUk3fvi2PEz8xc/Rx+iTTceQq1tJjiiT2ydcJ1HCjS0weLSxQSuH+vvrmEfPAd4L+6NEnyqaMQ5qn06CzJB3K8xfur9WdGO9yu8gXGStXH74rgg2sqEdG/H4ga5AeNQnROMLzyLbFzHc4yj1PzV2cFzg3ONtt0YeM+dJcal6K/yX0HtJ+41i9lKwex8Qx9xXmyN8T2Ozc7SGVHhHQYW02jjiAK4Y+9P1KC2c7HCPsdrzG5HpjWYrsMzcUS8Mb9Ybq+e7fiKPjJ+Yf3jZ6aTjzpjioswHzodlmkjpn3YPZerWtsf1wsLCwtPHUMPaqJgV/crNjpYwYHCviJWlFjIElx/zUQ8CmNM0uizKqxYMmYPT6K/TJhgocDas/VDH2YKiMzXrH+8xootLArZv94OwYpute64TmhHzQvjQM2VxRQTHCOCHtu7s3i00H8KYIW9Q5WP8CGEK/jZ2KO8hzZUEcziRj0siWMoLlKiXvEJ+q/41J1xNQ/0t7KeiovUfeV/pbhxe8q4SK2z4kvkOMd3Wcyzuahc7eKncp7YGKovrvkZgTl5Bqg7KnlpRhftQXaGmdbo7Rk/xVelCZ3WUNeYjzNw+ZnteeYru5fpImzDeItxxB6gFouo8Ca2x7aoi1jcVOZS0a4LCwsLZ0D6V5/6e0eKmFzUPfX+Gsjsj/iikmelD76qtswe66NE3QiwuBqdW8U+s8sKAycMYhtmv/dx6+TiL44ZP7tiD8dnfWbaVYpeFU9KPMe1OMP3QmTnqLU5Lsr6Z+e/Gn9qLIy9Cg+M+FdF9Ry6YqJjL3+4tajYZ+ds1H7GT+68Mj/QrppThYvQnvNP+cr6juScjN+Ur7fORe67+/bm5iPAxt37kwfuO1tUblLxV+Xx1nIN4XzY+2tAFX8rezziN9p0+Z5xyJG/+sR8iXCaBdugTTZOdo2Nvzeu168+LSwsPAVM/+pT9mABiTMTlSO2q9dH7bP7KtE4AR2vuUIFX5l9d1355ZJfhugT+sWuKX/wff/nipC+1piE3ZjKfwZmO77H+9FnFBXOJ5yPG0PtkSoKMWbwvDlxlMXlraLCF5X3DtXCip0Z5VPVnuMJttdqjBjLDuwMxLFcER7bqHM8EnsZh2bXmA12PtWcMi5S81JxwPg58ota494XOcvNn/Gti33FW4oXcUy2lsrfkf07E9QeHMHHFRvXXmNXlMc2LBYcz43YZ+2PzHfs3DEOZmce3zMOUrpI+cEenEQ7zOc9iLrIaSr0Ads4jkHbuJZOY6lxFxYWFs6G0q8+qSf5ikBZX3xfEanKjkp6aGsE6ol9tKXmpx4AxPfVQkvZZn4x/0cLJHzN/EbBgA86KsWLmkOlnVvHzP6oyM3iKhZesw/MUNThPfQJ7/XPSiSfTcyoM+kKXFYcjDzIcDHBHligX6OFB/ODzTsrmtG/TOyy9VHnDdu6td1TXDL/K1xdEfrRd3cGXTHB5pmtB/MX7YwUXThn5I7RhwYqxphfWU5Utm8ZuM9MF2X8k9lW10YecIzqgSpUEd5fMR+qHOdin8WQ48L+OrLWGaLGwXEZ/6AeiP2VPqrqmfgZ7R41X2cn00UIp5krfRR/OF20sLCwcEaUHtQ4YmbJp2oPk5JKzGxM91kJ3VnRomy5Yhn7ZwLfCXtWjKpE5oCiG+2r4s0VbazQYXvp9kj54+aBwscleVVAxfGyPWE+xHnOxOpoQTZq+2xQBXP8XHlAgjb7q3roE4HjsDV3DzEyP+Jn9EPxBHtIgW1UO/SvwkXRtyz+2bhxfq4YUfNkNlSBFG2wBzLqAQRbC9eerSOOUeEIZie2mVnnrD0bt/etxAL2UTgLJ7l8yNpVoTRQ1Y7KhUci4xbmk4tp9wAnm7vi66PiDB+GZLpIaSDkyOwBC9N4maat5rtsvjg/FtOOi1ROyrjm7NplYWFhYRblP8/NEk/1AYm6l12r3p/1qd9vTT+MyXyJfSuiSvmPdvrnkTWqFJtsviPrNroOmYhyYzghoASjElZuLDYG2mfi0o1Tucbm5ObN4p3dZ3M7w/dC4DUWA+qaixF1zjKM8l3FXvQ7s+fOIrPj4sXFXsQoNytU9yCbj5qHmi/aYVxb2YfKfjjbzI9sT9j7yp5h39m2bvyReHouXBTvOZ7APhknVbkl2tnz3SWtffw7arL87s6k8zHTFcqWGnPvn+ce1UXM13gt00kRI3o02t3znS39O2oq5z/DqAZSNlrzXLI3rtd31CwsLDwFuO+oGX5QEzGSyBicsMU2Tjw7u6OFWFVUqUTJ2sd7yh9lLxMwI4JI+cF8d/1ce+UjrgO2w7ZujSr7q+ankr4TU9iuUsw6Eabm69YD7WY20bYjgVuAEyPZeRjhpFGMPJBg/o7azQoVZtedgWx9Rnl3pm12VrN5zIzhrsd72M7Ns7pnzheEeiAS70efWH+2Jg7VImumoDs7F7U2/gA16+/OQSV/7y3gW/voy3Ur/mb3svid4UV276iHFmqsjtEco85MZl/5E3nhqC8TZmPjtUwLOY6raJiK3fWgZj2oWVg4A6a/TDiCJYjsIQveY/1Vwdz7xc8V26xPJhbc+Jho+n1WLMT2cT2yOeB1N88+XvQDkx/6EP1g+4T+szng2LFtHIftBxsD1y6+jgg1FA0snmYLKrTj2rD9yGxiu5HCjY2tbJ8ZGJMVTmE28L1av8ra93YuVjKfRuyhXXYO4jgqVpVN5gfzm3Gn8oWdp7gu6qFE9hBHcZ4ag9nOuAi5lM3J7QHrmz2kYeMov9Au41tnqxrj12hza8iKbqUDVN/eNitUMUazvLsXThcxKE1Q0UUV+9hW8cEeOD3V7+MZVe2VLmScHXOa462ZczqLTBdluq01/uBc6VA33nPRNgsLCwutDTyoQYwkUyaMmT0nIpWNrKjPireqIKoUNKMPW5zfrGhAQZBhdN4oDqI/7AFJ7MMeNGRFqisYR4qKTLSyObAHSPiqfGBCqvpABrFHXOF83dzPihh7KNRV/PX7eC3rG++jDSXE8X5/z8Z31xQXqZjGuHD+uHmifTy/ai7snKNt5RNbf/fwA8dle4H20UdVWLLPCjieK8yz9entM35RfquxR3N25vuIHyOa4Skjm1+V7yuFbcZFyta1i/ZK3sO27LPjj34f7TH715q/ewDBHobhe/dwJ9phfMBiya3VEVC5UOWceA19Y+vT27I+qv01Y3lhYWHhqWPoQU0kVpaAXVJRDxlYsqkkJ9U2XnNi2BW3TNQ6sR+hBIwT2llCRtvYziU2l/SxLUugWXGXFYNO0LHEzpJ3/MfsOzGEc1H75tYEx8ACFddYPSBQ+zQiTFScqHU4C+J6s31lxYAqHhUXMVuIjFNUX7bHjEuqfuK4agxs4+ajeI/5p2I/E/SZL2xMNh9VIMfzqrhopLDEay4nqHyoCrBqEY7zRN9YnLu8oObD5sTaqGuxP7Y7Exep2FMPDRCOl+IYuE+OA9wYR6Gii/A6s8HmGftUNYO7theKO2d0kdMtLJ9jXmMxhvnwCDh+jf5EPzHulfZEm7FNpmvPyCELCwsLVdgHNU6suaTN2o0m3yoU+Y/0U4W2EueVRFt9EKASHEuUbK3VQwPVvl93BSAT8lj8xgSt7DCo9criiAmUrAhkYndE+MUxWfEf28exYnygn0zkZ3NgNpgAdPM4AzIhmRULeDZHC2U2RhwrE/fol3sw4Xxj3FMR/cwH5Zd6sJH1i/OtcFH0M+MABlYgs8KI9cH7aj3Vww/G9SxXqMK0Oj/kWmYHC15XMCleUvym9nz0/JyFhyJUPqlyb5WLRtfu6LVWfNDv9Vd2luM9x5N4LdN213xoo84+O0uZLsoeRLBzpzgROX0mh2VzxvfoE3KNmle2Fpj3Mi151N4uLCws3BLsg5pMGGIb1781/YAgK3AqBZBrq16Vv1nBh4J4tsiIcEkIi/9qwkIBkAkf5QsmWhQnrB+2VbZwLbO4YoUOS/7MF/QVx2TrkAlJJlJxbVjR50Sa8sX5hcKvInxuCdnDAnadxb4rYBkcFzhhmxVwVY5hsdY/47/enolfNXbGha4dO2fqHt5nucWdw/7K5hk/u9jAtVTFI/ORnWv2cIPtG1sTnEfmgyt+2TrFV2czy2HI4dV8h+ftDBykUOEm1b41rYsUsv29Flw8qfw9UohHexUwXXRUvOE8WJ52uijTDZmmVPyueOKoWJjVRWr9kfOyfME4t6pbFxYWFs6I4V99Gk2ESsRgkRuvO5E3QtAuGUa7ai4jc2SFYCwGqvZUklf247WRoom1Y+LEJWtXTKJwYQLGJfc4RmW/cK2diMriigkjVVCxz27/8H38zIojJ1awf7VQu3XgHrvi3H2O1ysPLFyBOsJL7oFCjGM8K6zAY9yZcTSeU+aD649jMXGPDxgYb6kHDfheFQ44vrMR540PHuJ9tg7xHjtrqjhjY6lYqxZx0a7z3+VqVTip+aE9jDHHUW4/zgCVwzI+cDySnduM964Nt4+4FirXxnVz+iTaregixjczYP6ys8Y4g51zXAsVN2yujC+VvjoCVV3E2joeq/qHOghtsP1YWFhYOCuGfvWJJeARqELHFS14PyuUsiKZ2a3MKytesiJDwRV/zm7Vb1a4YUJlc1NFf2V90adM4LD9UMWd8g/nyu7hfHsbJoxQ/Kg5KqHCwISpK5pQoKkCzxVKyvdbRnZGK1DnzglmFV9ub7BfxgfYjj2IYHZVTEVfsocSjm8r6xNts5hk55udR+cfzpPtT/bAQHEQW1vGrYpvWVHj8pLysbqmyofMNxyD5ZVsjRjvsRjM/Lh1qBjP9tvtRxa/DBVdsgdZ/lN5TOkMtW7K95G4OSLGmL/RJ5Wr42eni0bmX3kwcdQ+R3tMF0V/nG/VvWO5QK1ttgcLCwsLZ0TpV5+yJFGFK6TZtYrgxL6ucHH9om31gEEVKRUxnz1EcIWTmoN7iKAKu9hvxGYUF6oAiXPO9o4Vm5V5Y1JXD0fUwwsnlFRbVhxi0alQiQtcD9WXFfu4F1j8Zv7dGqprncU4rpNaf7Z+TKRWi/nsTEe7bF7KDsaEilc2rjvzsQ1bK/dggPEO+qO4E/8xbmbjswID+yHcHqp1in7hWuM6KZ7CPWPrgXHA+DHbJzYeu89sxL4Mal7PAVVezXROa/o/o/C+2wd1/Sj+Z0W5y/NMJ7B4dfHOxnccenS+czwax3RcOKOfq5yPPhyBbH+Rv/trtnfMV9V/1KeFhYWFs2K7XC7y5ocffHBprZ5oKg8sqnD9Zu9Vx3JF1hHX8R4rlKK4RxGfzXHvPmTCmxWqlTFcwTIzfuYbGx/tskJIPejA/WLXsr5qDri3mdjM2uLefOKNNzZr8Imjc1HE6FmvPCCoCNPKWVdnZMRHtNWhbFaLINUvuz/iM/pd4dc9fmc8kHHWCFerszdq00HxZKWYUfzp9iO7nkGNjW1evPnp9sXLV0/BRSzGqjpgVhc5OM3QWmuf/Oznp22//+7bcrwR39Q15Xtrde7A9teYbwQ743u1Mtp392Obfu9Ln/mWdGyF9999O9UUzg+maSqoaBhla898W2vta1/+0k1zUWtcGy0sLNwWXI1W+o4aJT4yIt7zcICN5Qpm52sGl2RcUe7GjG0qa6XESTXpo60RH/H15av8W/2ZvT5utMf2LbZXvuIYuA+zD2+wb5ynilfmZ7TD9oyJTrXOsR37zMDiM7ZX928VFa5R11j8qX3OxsIYd+3cHqr+1WLc+c8+Ix+5ftjOrYvjeMcTzF5f17jGrHBzHMlsYpvK2rG+6lyqeaKd+MriI/ITrmuMC9ae+YwchRzj4hjbO+50mClibwVsPTJuwJhm991nvMbOCbO956EFG7vyoCGC5cHYB+Okco4rfu4BOyNOFzGuivdYOwamNyJnzHDQLNi5j2OPcIDiPMZ1rfFYGxl3YWFh4Qwof5mwKqbjPVbMsr7MLhN07MGB6+P8zQpX5bvzNSZy9lBCFV0sAVcK0djOCWw2HzV+nM+o8I7juLXL4oLNS4lPlryVSM1iAAtDNhe23ugvE6FZP7Y/TMRVHgigDVXg3TKy8+/isLqe6nyzeFXCVdmMn1l8oS2cR2bf8YI7H4oLGLJYU76pGFRFfCxKYlsU9xUw3lB+9vcVDo1+Os5i/KIKXFcEu7mMchHGuRpLrQveZ+ukzscZuMgh5n1cnxjX7Oz1tq35Bxgq76mcc/T8EBVdFPtnukg9HGAYielRqIcD7KFCRRex3J6tTaYRjjxPyCNxDriXjPsUP7OYrXKJi5eFhYWF54DSg5osMTDhwO4zu+wzCtqZJKyEYmUeVd+ZoGDFhxIdbL1comYCLCY+V6SjcIz3MyGvihwUn+y6A9sLTNpsfOUnjh/XJ95DH5QIYIXjyHoh2F6qAm5k/eKr2v8zgRWj7J7ri3DcpeJM+eAKpcgHTLQqX1Qcq4cC2I8J6SzesqJBIeNWVVC6da6MqYo/V0RkDxQct+M4jAfxVRXUak6ZTy6HKr9UX3auXDzF+yy/Oj/PBraWag/cnsT7CNUnO9ettfbWe+9MzErD7TNqHsdlGedgP/d+hjcUPvnZz5fPqzqXTPshF1R0E3I44yTFf3ug4jTmL2wfX2P7ODfcuzjnjKtxjKPjemFhYeGpofRXn1jx58gW+8dXFIFKCI8WnE70uPYVUcCuM/FcEdBxfExOSmRjH9aWPdRQe4LJTyXG2MYh21dmWxVIsT8rFpQAVH6rmFLzwM+qOEYf2KuL50rRVxFe7Bw6MXirUMW9E35MzGeFLYMrciuxzvpFu64Qdz6oOTh+UGMz/9S42ZpVY5e1U4VR5eEG43R21ipc5NYFzxXGoVqfjF8VDzMur/is/He5UhXeypYrmLOccKtwe8j2LbPD8qdr66DO+d7v8qiO15E9dGB9GR/2s8D0Uux3dGy9/+7bHxtbQXFU1MmMjxRfKL3XP+OY2H8vsnyA15zGUT6xdkzrsVe0d624XlhYWHgqSH+ihj2UYUmTJSZVvFYSX6VgYP3w2mgh4h6w4LydX05YVKAe3ji/WaKPgqmyhk5wuocLSoC4NVDJOXtAkxUTrtBC4TNaHOOaqvXIBDXbE3bO0JdqXLFYvXWo+Tp+cQ8sXPGDsTvKRyz+8ExXuEjt3ahorxZ6bNyRuTO+cWMroe84q8JF/VWJfOw/UpCoNnEc5AgWf6wgdbHG+NnFQZUvs0IY76uzlPHNTB58ioh71cHOHcuJsQ1rm513F8Ps/VEYzW/q7Cr+q77PdNE1wMaNvrmcEmMA46Z/dhqU6SJsc2Ser+hOFotqfBY3LlazfT0LhywsLCxUkf557uwBgSNyRCUZ989MtLhE5Ioz1s4VQcweS8DsfmZL+eWux3HVPSUGs/GxDyuQlMhQhXElgUc/mDjAQgfHZmsQ22HRowSiEtLoQ0WoMZ9xbLU2qoCL/asFAq7DGZAVMq3VHii4c8yus/PAxhrxHwW62ycVt8yumqOyh9fRZoQqxNF39IfxgcsP7Mww/9iYsZ06syon4F6o3OOKzCqvZ4WQs5UVOoyLGa+7IjPL36pYQv5TfW8dlZiucBG7N6MB0J9rFrKq8M4eLlQ4R71nZ0Jx51HxxXROxQeni5h9hkwXsbGP3nOl+Zi+UprfcQ1+dvzO7KkxFxYWFs6G6b/6hFCFZL9XtYPCkvmRFaDKD2ar4k+0yYoVZs8Vys43XCsmEFAAKXGfrSGzlwlKJj6wEFLFG+vn2qmCZkT8YXLHdpX9YQUftmdjsVhWexf9xT1lxT0rutz8zwIVv3ivcv6duIyfVZzHsfB+BDtflcLKPWRg8YntMpHPbCk/nJDGPWGclBX37LrbHzZm1pYBi4443yrXI+fF868KWec/ri8bP+P1Cu+r8dFHxn1svZ4bslzZmo/PkYcKrhiv6qIjgXmI5SV8333Mcqm67vRMtO3sjADPtDqrii+QD9gZx/wS/7F1RN+ude6q8eq0ttJBShe5vXM6amFhYeHM2C6Xi7z5ue1TF5ZQlCh04r2SUFwhsSchuf7sHhPGag1Gx8MxmIDBpFSF8w3tZePHNmhT9VX+OJ+cbeWz8mlkvSrFadbeFaxVwajWnMXcCNDeFy9f3YaNPCEwLmL7r9aSfY79GPbwjuMVNx67jzER31d4Jmvrzpk799m4sa1ajwpXuTGU4K/Eh/I5+p3Nj7Wv8J7jNbTLcgParxY3FbtqjGwc9RnH+8Qbb9w0F334wQeX1l4vwEdjfSTOo/0Z7P3z3O+/+7aMkz26SJ1T/Kxi12HPnPt8+3iIilYd1ZZ4P+NetiZ7vrMlfjGv0x1V7mK2Klwxcn3vd9R87ctfumkuau0jPlpYWLhdOF2U/uoTe42kmyUY9rkjI3mXDKt9szYqGaq5Zw8kmJ/xXxy3Jx2W0OO9yniZUIoJDsd386vsMa5hReCovmwsHBd9Z6+4duw984/NpVIE4dxxPnEdGVgcVM6Ri1XV5xaB662KSZx7toaOW1wRnPELO7+VgjfbL7bf6De+Ylv0Ic4T1zi2Rf8cPyk+w3GY31WwIoflKnwggfPH+bl1VVyk7rMckBVByHGM63BN45xxTvg+2sFXV3SzuWc+RTtu3rcI9cDAxRhrh20UN2RgfY9cb6UX3LlyfrKzoPRAv6fWvDLuCJDv2N45jnfaAHnDrSvacjnlqFxfOcPOD/zs4rm6l7heZ9E1CwsLCxlKP1EzAxTHLHGN2GbtMdEj+e8dc8QH5w/r6x4WjPjTbag1yPxnc2B22GuHEydqfmrd3BjVNavMHdfO3cP3GZzv7tpo2xGc5SdqWhv7n2gn7LHdNXHUGG5O6lpHVrRnfKU+Z/GacQ76oPhI+VA97xVudnlrhPsqttU6ujEY3P2KrdHxnP3Mr5evzvMTNVVU4+toRNtH/ESNsx+vdczqotH1UDaO/omayjwqGiXjRmZT8SLa2TPnt957R+owpoUyLsl0UMalbP2PnG9r6ydqFhYWngZ2/0TNizfHv7Ct98VXRLcVx8DXbAz23glnh5H7KqG8fMW/oBbn5QQxjoN7EO3iGGz9VGHEwNY1E1fYBu8pYZHZVm2V3bgnWRu00/+xwimOH/+hnxh7rpBU80d7ai3U2jBfbx1ZQVzpW7HdX9V5i+3wvcKIz5kPGZ+iTxX+jTGSFSF4RrAtciDjOFVYIWcyXmVw3IptcBz10ATPbwbWzuUKbOdiCtcc15j1iffiXrB2qlhS9tA3xjPIwS7X3SrcXBS/z+oixUcPtZ4sl7oC2nGCsl25znTRNfOb4gZ3TpivzC6eETU2jn8tZPot016odeLZR/51XMjsKC29sLCwcHYM/UTNTLK4VoLJ7LKE0ZH5g8lCiS41lmtTtel8q/hR7e8eGLCHC+7BA85PJW4cL/pUET6xXcUftMf6ZqIKbVf7s/EyuHWbLRzP9BM1s1APCPbCnfkOFZ9V20oAO18yzqmc2ap/MxjhIrzm7rl5KjguYtyerSnzL/MV7WS+KvvMzsgDmcocRhH73joXxf/BdrFZxZEaSflz9HfUZHonu4dtKhzkxmXz3vvTJR1VrTSqi5jtbM6qTb9+xE8RqbkxKB7p9xz/OG3oEPus76hZP1GzsHAG7P6JGvYZiTUjcmzjiJnde/Hm2FP1OG5///JV/tMFLBn3flkxUIFL0lWoOaB/+A/bxUTZ/cI96p9Zwo62MfFmPrriJ651HB/7O3HExmP+q/bMXpxrbM+uMV+y9UEf43hon8WkEq5HFQGPDbZflc8dR3KRsov3q+s/EpcqTpSoVjEyKp6VGFf30DfGRWiLcVgcg3FS5C52xlweY3NwnIFzw3OYjYVzc+MpjlBrE23Ea/iexRIbj+WD2CdbZ7R1Ji7qcLqAfZ6FOzczfDMzfhyDvce2yFEO7NxkPJvZ2APGQ8w3pn3cPeUr68u0j+LbI+bO8knFPvNvRhdVeZTpsIWFhYWzYtdP1DiyVAX0TALOxoptmL0qqbOHBqovziW2ZXaqqIwb58jWdHS82M8JD2yD/rgCI1ubmb2rrtXI/DJka6PGZjawH1tL9tn5pM7WrX8vxMxP92VtRs57tc0IRnipg61BhXdmfHac4sZ0fFEZS81Tjev8iT6gPxUuUjzPfFS+M6gz63xVNvB99KFSIFe5JWL2AcTLV+f6jhoVCzMcwmItQ/Vs7f3Jg/4dNaOclcV3bF/haheLeP3I72thfmY8V+HjER5l88X+R/wUkeOEChexe9H3jHOUTfZ5fUfN+omahYUzYNdP1IwUjNg3Iiail6/4N94rsCQXX+OYLNlXRWb3BX1VY/V2MwVRltTcuG6OTiCy992e649jsjYjD0viexRZap37P3aP7a8SOZUiSgH7KsFZmbPax24j3o/jsP2K56GyB7cInNcIF2G7eMazBwT4XnGRG08hK6Yxxlg8sFfmh1uvODfHDSzm1Xlm80QbbjzH8Qq4N+y94wXkfjW24vzouzuzyg9cv+wBCs4dr6u9cHGjuKrbUzHJfGJjn4GLWnudkyvzyh6kubWsnAfFRRU9kgHPhhuz+4XzcX6oM6f4hvFGxa8q2NmoarHY1vmhckqmEVj+P2KPYyxXdGTsp+zFfrgmLD7YnOOesxyxsLCwcGbYBzWt5f+z2d8jKgI7S4auUMoKplkSZwWRE0BKaLH28TVLYk6wVwsw1Z+Ng+NhwRF9VoJdFWBsvugLW5fYVxWMbF6qkHKFF46tijxmA2PSiUa1F5U1ietefXBwlIh7alB7ytalwj0qPljBrdqgbbZf0U9V/DD/K4IffWBx6+JDPUyoPgSINipFGfqQFS7u7FTPnHtgEsfK5uDGiXzI1p3xmeMgF5fMHzYHFQu4fnEO2I6tndIEKrefhYtwPZ0uwrhgdjrUGjkui/0qumsGTBfFseN7phsqGoXxAI6h4jPa6Pf3wJ1bdj36VNFF7MFERaexdo4fR5GdYdUH/cE1qegitp/xNc7zTFyysLCw4GB/9an/SN1RyY+hSriqIGUCacRuxZdZ2yiale/MLhMk7DNCCUYn8NCXbH5s7as21L6hSKsUU+xzts7ZOMw2zscVhm6/FZxQc32yOIj3b/0LPPuvPrF9n+GlPULP8cHIPWZz1K+Kzeq1rOCs2uqozHsUlXPP+rhzjf2zNWI20JcZjmB7EOH6sXEUKoXzSJ/nzkUql1WRxcpIXzf23i+azTDLffF+dh6ZLlJrt+fXvfqvPqHNUc1WtRGBesjFF/LRUV+grPxkWg19Hp2b48+Mp9aXCa9ffVpYOAPcrz6lD2qqDy/Utco9165a8M8iExcRmVCuiuhRO6PzGBFvyk5WkLhEG/tUxsP2SgBX+lWKI5b8mVBQ82f2Mzhhz+xU/B0Z/9aLo1Eucqjy1Mg5mrlXLV76+9bGRLyaj4t/VQiMcD1rN5ov4tjop+vL5o/zc23VvFUhObqWs/yh4GJV+ZXNa69Pys9ud3HRx9u3ti+u433Vf+93ecQHF46LKpqJzatS9CuoM3b0gyk2tzimesW+I7yg+jPfXr465q8+4RwdN1S546h2uL7rQc16ULOwcAZMf0dNtbCN13q/aEORcr+O9+N1tBlJ3CV/NYbymX2OY+ArJhOVlHH+Lkm5hxZubmweyp4bV61xViRGH+IaKF+Zf7F9tTiOvin7rG/cS5wjtsG5xNduX61DVjg5P7M4xZjDeZ0NbJ/ZGuyx3drr5z+2Uecb2yJmi3PFTYyLKrHG4j6LHzzTyjfE6N5knKm4KPaN3FQp/BRnuj1VaxbvOS5i51qtt/KB2WZ+RnusOGQ+VffMcTJ7rcb8LWBUFykbKs4yu4yLrs39Ks4xrpBX1JlEfnBx73I8tqmufwXdR3Vm8QzhK+vT7SrujnOJPjBtlXHVDFDzZnygYpXNyfFj7xffOz18Jj5ZWFhYcCj9eW6VaF3hEm1gOyYu8OFAvD8KRvjOZkb6WSHvElP8zOYYx8dEzPytimi1P/EzEyGsnSpYsK0TFdn6sDVk9tl8Yn/Whu1DjOlqodttqfWsFCV4llB8K9GmBJMrXHHuZwAKXFZoxvdqL1zR48atXldgxQ3zC9+7cZSoxc/qDGWFAhvH+Y121VzVWXIFWyb0WRHE+E7FhioKsnOpclhsy+wj17P1drHH1s/Ns7qfFc7H66xQdft561D6h+Vxp4vcdRY7VX0Ux8dfazkClfis8FYlV+O1kXFmoPIxjsuuZdovXnO6iI3P9LHTRSNQmqSSM9jZx7ZqzrE/jqVivX+u/ErewsLCwi0j/TLhDkXMLlFje5VgmH01vktO7P1Ioeb6qgcT7BrONf5Tc3NCX4EJHfTXFYTRLyUQVOKMdlSf7Dqzg+/ZnNhcMvt43RUslYI0K7L75z0iEgscXOsZIXmrYHNlBW4W92ir0p71Rx/i+6w4RhuxHdtzdcaVD9gP4yabK1vPDDim2hP0FwW+8o9dc3yQcdHInNAOjq0etGR8ruboCiRsr/xm80B+V/yU8QzbW/S3sj63ipGzk805yyNurEy7xDOw91dE0Lbyj80V42xEa6i853BE3kNfFZcxPnc8NarxcB8xF1RibASZLmLt4me1/2iTzV+dBbfWe3+lb2FhYeGpo/RlwgpO8GcPCPr71vj/msbP2KY6xkjx5eZQtYOCVL26Nuwezr9ayDC/RubtbLjC2CV3XM9Kv2x+aLv3zwrAqghxhSuOi+2ZnYp43yu8cE3P8L0QR9tUe8Fis3+O95k9dX+Gi9Q5ydpFZPFf5e/sTI/MA21U+lXHqoyv1iTb72o+UJyg5uDauYcxbt8zVDhG8bLjx4qds3PRUbqoNf8gZ+Qs7C1o408ujM7P5WTW7yhddO0vT1a+R7+cVnN8M6uLjvwyYRwvO/cZXzEbyo5qi1jfUbO+o2Zh4QyY/o6a1vz/GKuEjMnUCdHsqfqMMO99Zh/SVObH1oUlZ+aDS3DYL64RGweTO/OVrXNsE20xgZAlXzcHtvfYL77H+TKfcB9wvdW4OFe2tjg/ZpeNG8eJtpnQUf1iO3UOXHygD8z2GeEEHYthdWb6NQV1jjKb6vpocV3tq2KCxTquj/IxxjPjecaJah1YGzauE/AuL7kx1TlivrH7qoBSPFfxObZV5xa5iM0Z22BfVXSpHNXb4TwU1zpOU9duFU4LZBzCzly2Xziu0ibu/V4w/YH31RnCs6POUDz3GFsu3zq/9gL5rcKV6GOEWgOni/Ca0qh74MbBdk6fsjaouRwXubkxHxcWFhbOjPRBDRNirmDBvvG1o5JglEBn9lW//pkl7kqhw8arJmNVhDDhH8dUPqvinQnoKpgYd4Jc9VVrjPPt7XGOqph2iT76pZJ+vMbWB4sc9Tn+YyILxSXaVmvIfFb7yYpjNo6az1mQnSlEVpD3zy7GnR9oQ/XLHjxUUeWf2D7jIha/2ZnqbZA73EMDlkfYHFTsK7iiFcHOSvWcxHXC9cy4Bees2ldygCpYcU4qj0Vkdlhbtg7xOnt/Ni7C2I/XK33Za3V9RvTUNQr5WT+xb6Yx8DqLZaWLjog1pjkUR8axY1/GN/gZ/XVzRP4+8mwhd+B8Yjv8x2yxnDeqizA+ZnTuwsLCwi2j/B01KpGwYrsq+BxUYqr2i31VQmXFC+vHHmj0zy7xMDHH2rs5VKGKQJUoWcKP9zPxEK+pQk21j31wDm5+7uEDE4CZCIxjqhhg48f72J8VZwxqLZSQzaBi9axg50md545ZLmL9q3uL17PzrsbIijp2LuK5jOPjOcoKD3eWVVHEuMMVGzimKpLcGM5Xd17ZGBln4NrieCP8ptaf+cXyrDvrisPx/IzGc5YjM7/OCFcsVwrpUV2kcm2mv2aR8YXSOOq+81PlQ7yv2hwRd3iGs5zuOKSii50dhsgziodH4Pgm66O4S+2rQqZL8f3CwsLC2TH8ZcKYcPBhxGjyRrAHCapN/Kx8riRWtJsJahzTFV+qOMkSYlYAVYvOSvHGbKgHBq7AcQ+EsgchzDYbNxNwrE0WU5UHOio21D7EMVmhjH5Wi3uMC+bPcymU8GEBvsY2vV2/dpTQY3Gvzjz6zVD1UZ2LaiHhHjRUoPhJ+eHWQsWwKuzYGMwfLGZVTGTnj/nN7rP54JiO95gNx1mqQK1w5Iwfjotwz9w4Z0Qln1X7jYwR7zku2ovqHDJd5ngHuVv1xXke8ZCC+YKvTndivlfnF9uM5ALGgaz9LNi6ZraVtqryhWoX2yicWdssLCwsdJS+TJgVyVhIOzDxek2Srfin5sTsMJvVOTkbzN+OrF3mA9qcKZyYDVe4ZEWNmgeO69pk40Zka1EtguK1irhy7bLrqn923Y3z8tVX7BdV3QIUFylUzqNrN4KMbyo+V/lnj8/Oz1GuyeY2Op9uI2vToc7zXq5VflfbsjXJ1qhaZOI8Ku0rXJXdzzhnZJwzcNHXf9Nb68s7FxZOgPVlwgsLC08BThft+qtPFWRFRbUYmS2wKqJ6xG5FhFcfFI0UbuwhyOzDIVYYqWLfzakyzp6CEq9Fn9E/ZYfNA6EezGT+VR70jDywqRQ8cQ6VgrbjTH9p5ehzlT0wGB0jQ/Xcdl9m+8T7I9yqxqg+vFE2um+zD1GcTecnW6fKA53og1sL9AH7VK7jfNnnmQcnGRdV7LsxstzCcOtctB7ULCycA+tBzcLCwlPArr/65MCE2EzBq9oqsezsvniT//hmFJJMcCrBzfyt+sL8inZxDNYmjsHGZmAFiZonex0Zp9tk64yvOHfWJgL3CcW/2iP0KV7H+VWKkmhT+Yp9WeGSrauKd9UvzknFzdngCurW9EM5d75ZvFTHYPcrMYJjs8I/eyipivZszOyhgpqX4gwWe7i+OP7IQx63FziGe0ijYoCd195OrZXiO9Ynyy2Ki+I8WJtsH7P1VfuQPczpbeL158I/CwsLCwsLCwsPhaE/z43XmRCsim9s64oq5xMTlaOFnCuEsV3lwQN7OMIKdVcEMD/dXjChrIS7sp3tHRPnbJw4B1XQoD+swFJ79f9v71ySJLdhIFre+hTe+mZe+ii+nW/irb1SBAKTmQDVPeNSz3ubrpb4AUESRaRGPbW+8o0TqlRiqcoo8eM0CVKJ6yaR6bZXO5Sg0Oup8Z7syXdnOw8qUb8+X/WScLcV5tR8OL+rNk7Gk9pR5dW41ZpW7bn9NI1B+azvqTTO656Kydu4466leDHZ1X2gYu5WqHA2OpGm9nvdm4SnXuf6PX03br4/Unu93lePRQAAAADfk/jq0x+//Pbv6/XtQXx6wuae1FVcOVfPPfnrtmwSgW2fm7qn3KmffHWx8Xnvf5M8uYRG9Znam5LDKZmb5my7blwy5dZRGvu2vTROJya4+qqdaoez6+mvG6hYdKHm5bq+3Re9zp37d/pJc/hRW13Zkxj72SjxZrLpKq/snL5H0r5I8cldT+W232dqzj/C9J2wrZdi1sbWnyUW8eoTwNeAV58A4B349L9Rc/dAf5qIqL5OhYaP2JIO6dsDeS+/tXVK4qd+TxNUZXdlewiv5TeCTRrTNNcuyT0RrJLYtxmnKzcJNe5aSq6SUDbZ9/Tk6P8+jGzW7UlbaS1P9dLnbb8n8eHOODdx25Wf7l+/X2wFh152snuaHxff3dxu4pmyd7vHp5h4ghO2UpycRKHr/tNjEUINwNcAoQYA3oFP/xs1dwSRq95GGFF9qUTp+nw3iXAH79pXP5y6vlxiUW2cnhpfn5Udqp9eLtmSDtGqvvNt8sn1uY51K9KkxKv7Tfmw37/qKR+peUhzW8fSx9r7c+tV9TfZk2xT62nyy89En++T8upaYixLRgAABtxJREFUnYM7wrBjimF1Dae53uxvt4acr1Lc2AqX6ncnVl7tbtZxjzW97RTDnP0uFtUYMn2f9T6ndeXG1Me2XXMpFqmyrk217lKb07ydCkUAAAAAPztRqJkS/Fqufz5NlCY2icymP/dk1AkFru/tQfwkgdok3r1NJzjVtuqhXwkYtX8n/nSmJ6pOuFLX0vxN4slkoxJ90mc3xrTOJlGml1UiS/J7nTOXgCo/uvaeyHaf17k4FSjdvujcEWi289D3Z48zSkhJybOKRUpodf05un+ToJNscPurX0/7sO9TNfdOpHJ+qXW6eNJt7O33eemkmK186vrsbfb7UxztscjFlV6nj6OPJ9n7swrHAAAAAKesXn1yh85NsqzqXGzqbvqe7EgigbrvkpZJfFBJ+CTGbO5vRA83rgslDqi6fZxp7KrsJiE4sbtS2zhZk10Qcv1MSViqfyfZVeNLfffxp3rVpr/++Tv+s7onkP5GzR3cmtjUUW1sY9HU7kks2uynZHNlY/82FrlrtZ+p/a0fpzi0jV9uXyURZxtPVVuprBvPFBd7XbeW7jLFJ2WPsu3psYhXnwC+Brz6BADvwIdefboj0vQngu4pnKtXSU8l62d3CHV2Tgf0fv8qMx1S0+dJhFG2bkWalNBtE0d36K5jdwfy+rOXU7bWMnUOnJA3+U2NX9kxrRU1fjd3qq6zp/5061HZ6645+7b76mncET+un2pPuvlIvnJ7c4pFk//dOu/XeiyaBMEUh692T2NxEhRS/87Pbi2nNvv+Sjao2N1FBBf7U5vTeGu5Ol8qnk/jnfyefLiJx709FbOUrUmkqX53ZQEAAADAMwo1/ZDYk9aTxGbTl2J7mFV1XEKv+twkJ+7Q3xOH/ntKztXTWZdc9TEqO51Y5ASXq9zmEN1trtenhNUlpD1pdgJYT3b6NZUYKt+rxEl9Vmtfte/81Ot0myv9mhIBVHKaBKSeJD2daS+rspvE+yR5nPblJOjV65PA8nrNIo4SpFwyPfliikXdBidIKYGi4+JVup8EH9W/EnX6fUdfP85GZYeLfcpfk1iWRJPadrJTjUn11e3c7IvJL6kcAAAAAHjGV59SsucO4Qp3+L+bRLqkYtv/Z9BtOBmTS9Zr/U1CPyVXJ0nqJsHsiYKyRX12tqg6Tgg6sb+37eq4MU3Jo/uc6iRO25jKV/789ffH/08rm1iU7m32wUdjmev/R6Ji0RQrp+uncW3br+qj96NiYmVjn2sjxYsUn11dZdcmtqmxuLaSP04EqMmPJ/Zt7bw+8+oTALwDvPoEAO/Ah//Xp5MEZps8Tk8A01M99aSx1+lPmHu5dOB0T4p7H+5g6myupCej9fc+pnTQrn25J89qLMru/kS595Ho/u421/lJCYdLhtVYpqRrquNs7tR1q5KQ1IZqczN+ZdtG/DlJsJ9AGkdf769XTshT25u9ovpO9rl4Ns2js6G3WfeTW8tq3WzEm8k3/V6PWxvBw+31fi/FoxSnVXyrflOxyMVe12dvO9VVsd6hfKm+w5KdfcxpHO47Se2ntDbU/GziFgAAAAAc/vfc6QB9/T491ezX0qHSkZ74ObucndvEY+pLJfDJZpfUOB+7MSXb1HiSb6YDebWv+1AJWC5ZcqKG84WyKyVOKbndrNFpnKfJqrrnUOJf7yvNYxd3vlJitBEtKifCohMPUp8bW/qcq3lL62lKius9tS+m5FnZq+xxe9cl9V282ghSfW+m74c+pjr+JGD0ny4WKbucb3ud+vtW5HLt9HuTH7v/K8lexybGqe/Cq9wm5gEAAADAt6z+16cT+kFY3dvUP+3vpP3XK4sSd9tXT2WTqDDhEgOXJJ201a8re9U9V3eySbXhrimxwV1z8zP5Rt0/ETXSmnA2pkRPtZlEn1pnauf1ev7rBjUWTWtZMe2lO6g23bxt+tnuB9X3HdtOxrexTbWb/D7ti41v737HbGxN9d2ePIkL3Z5ukxLKXBl1X8WFKTY5ce4kNqo+q11Pj0W8+gTwNeDVJwB4B9K56NOEmk2yWMumJ6d3hY1U9/QQnq5Ph/CLJDhM/W+TvRNfbeyYyrhEyfnk4q6AkhJMJ2RMYtMkmCh7UxsngooTYyYm4SYlvq/X85Oj01i03SubvbUVBO5c28YGtw+24lSKTye2fWYsSna4Mk5gcPb2tjZi3Sb2TnZUu91ed7Fo8z2y9YsTxlTdbrtjEoWmePv0WIRQA/A1QKgBgHfgtlADAAAAAAAAAAA/jqO/UQMAAAAAAAAAAN8PhBoAAAAAAAAAgDcBoQYAAAAAAAAA4E1AqAEAAAAAAAAAeBMQagAAAAAAAAAA3gSEGgAAAAAAAACAN+E/wQOBY/875/QAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "\n", "# simulating G1', G2, deleting 25 vertices\n", "from graspologic.match import GraphMatch as GMP\n", "from graspologic.simulations import sbm_corr\n", "from graspologic.plot import heatmap\n", + "np.random.seed(1)\n", + "\n", "directed = False\n", "loops = False\n", "block_probs = [[0.9,0.4,0.3,0.2],\n", @@ -73,16 +101,16 @@ "G1p, G2 = sbm_corr(block_members,block_probs, rho, directed, loops)\n", "G1 = np.zeros((300,300))\n", "c = np.copy(G1p)\n", - "keep = [np.random.randint(100*i, 100*(i+1)-1, 75) for i in range(4)]\n", - "delete = [np.setdiff1d(np.arange(100*i, 100*(i+1)-1), keep[i]) for i in range(4)]\n", "\n", - "for i in range(5):\n", - " stepx = 100*i\n", - " c[75+stepx:stepx+100,:] = -1\n", - " c[:, 75+stepx:stepx+100] = -1\n", - " for j in range(5):\n", - " stepy = 100*j\n", - " G1[(75*i):(75*(i+1)),(75*j):(75*(j+1))] = G1p[stepx:(stepx+75),stepy:(stepy+75)]\n", + "step1 = np.arange(4) * 100 + 75\n", + "step2 = np.arange(5) * 75\n", + "step3 = np.arange(4) * 100\n", + "for i in range(len(step1)):\n", + " block1 = np.arange(step1[i], step1[i]+25)\n", + " c[block1,:] = -1\n", + " c[:, block1] = -1\n", + " for j in range(len(step3)):\n", + " G1[step2[i]:step2[i+1], step2[j]:step2[j+1]] = G1p[step3[i]: step1[i], step3[j]:step1[j]]\n", " \n", "topleft_G1 = np.zeros((400,400))\n", "topleft_G1[:300,:300] = G1\n", @@ -102,9 +130,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Match ratio of core vertices with naive padding: 0.15333333333333332\n", + "Match ratio of core vertices with adopted padding: 1.0\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "np.random.seed(1)\n", "\n", @@ -120,7 +167,13 @@ "\n", "fig, axs = plt.subplots(1, 2, figsize=(14, 7))\n", "heatmap(G1_naive, ax=axs[0], cbar=False, title=\"Naive Padding\")\n", - "heatmap(G1_adopted, ax=axs[1], cbar=False, title=\"Adopted Padding\")" + "heatmap(G1_adopted, ax=axs[1], cbar=False, title=\"Adopted Padding\")\n", + "\n", + "naive_matching = np.concatenate([gmp_naive.perm_inds_[x * 100 : (x * 100) + 75] for x in range(n_blocks)])\n", + "adopted_matching = np.concatenate([gmp_adopted.perm_inds_[x * 100 : (x * 100) + 75] for x in range(n_blocks)])\n", + "\n", + "print(f'Match ratio of core vertices with naive padding: {sum(naive_matching == np.arange(300))/300}')\n", + "print(f'Match ratio of core vertices with adopted padding: {sum(adopted_matching == np.arange(300))/300}')" ] }, { diff --git a/graspologic/match/gmp.py b/graspologic/match/gmp.py index b5bd179c8..1345764f7 100644 --- a/graspologic/match/gmp.py +++ b/graspologic/match/gmp.py @@ -177,6 +177,10 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): seeds_A = column_or_1d(seeds_A) seeds_B = column_or_1d(seeds_B) + # pads A and B according to section 2.5 of [2] + if A.shape[0] != B.shape[0]: + A, B = _adj_pad(A, B, self.padding) + if A.shape[0] != A.shape[1] or B.shape[0] != B.shape[1]: msg = "Adjacency matrix entries must be square" raise ValueError(msg) @@ -196,10 +200,6 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): msg = "Seed array entries must be less than or equal to n-1" raise ValueError(msg) - # pads A and B according to section 2.5 of [2] - if A.shape[0] != B.shape[0]: - A, B = _adj_pad(A, B, self.padding) - n = A.shape[0] # number of vertices in graphs n_seeds = seeds_A.shape[0] # number of seeds n_unseed = n - n_seeds From 93e684190338815cc7b5fc1f5a62ca339634218d Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin Date: Sun, 4 Oct 2020 10:34:12 -0400 Subject: [PATCH 030/140] fixes for build checks --- docs/tutorials/matching/padded_sgm.ipynb | 54 +++--------------------- tests/match/test_match.py | 2 +- 2 files changed, 7 insertions(+), 49 deletions(-) diff --git a/docs/tutorials/matching/padded_sgm.ipynb b/docs/tutorials/matching/padded_sgm.ipynb index 09b9f18c2..1f2940b0a 100644 --- a/docs/tutorials/matching/padded_sgm.ipynb +++ b/docs/tutorials/matching/padded_sgm.ipynb @@ -2,13 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# import graspologic\n", - "import sys\n", - "sys.path.insert(0,'/Users/asaadeldin/Downloads/Github/graspologic')\n", + "import graspologic\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt" @@ -56,30 +54,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# simulating G1', G2, deleting 25 vertices\n", "from graspologic.match import GraphMatch as GMP\n", @@ -130,28 +107,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Match ratio of core vertices with naive padding: 0.15333333333333332\n", - "Match ratio of core vertices with adopted padding: 1.0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "np.random.seed(1)\n", "\n", diff --git a/tests/match/test_match.py b/tests/match/test_match.py index 28fc28ce4..a99483619 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -108,7 +108,7 @@ def test_padding(self): np.random.seed(1) G1 = er_np(n=n, p=p) - G2 = G1[:48, :48] # remove two nodes + G2 = G1[: (n - 1), : (n - 1)] # remove two nodes gmp_adopted = GMP(padding="adopted") res = gmp_adopted.fit(G1, G2) From 927767bf6ed68290ef761f98e7af37dae1207a81 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin Date: Mon, 5 Oct 2020 11:29:33 -0400 Subject: [PATCH 031/140] addressing review --- .../{padded_sgm.ipynb => padded_gm.ipynb} | 21 ++++++++++++++----- graspologic/match/gmp.py | 7 +++++-- tests/match/test_match.py | 2 ++ 3 files changed, 23 insertions(+), 7 deletions(-) rename docs/tutorials/matching/{padded_sgm.ipynb => padded_gm.ipynb} (89%) diff --git a/docs/tutorials/matching/padded_sgm.ipynb b/docs/tutorials/matching/padded_gm.ipynb similarity index 89% rename from docs/tutorials/matching/padded_sgm.ipynb rename to docs/tutorials/matching/padded_gm.ipynb index 1f2940b0a..d954b4427 100644 --- a/docs/tutorials/matching/padded_sgm.ipynb +++ b/docs/tutorials/matching/padded_gm.ipynb @@ -40,9 +40,13 @@ "\\end{bmatrix}\\\\\n", "\\end{align*}\n", "\n", - "We realize $G_1$ from $G_1'$ by removing 25 nodes from each block of $G_1'$, yielding a 300 node graph.\n", + "We realize $G_1$ from $G_1'$ by removing 25 nodes from each block of $G_1'$, yielding a 300 node graph (example adapted from section 2.5 of [1]).\n", "\n", - "The goal of the matching in this case is to recover $G_1$ by matching the right most figure below and $G_2$. That is, we seek to recover the shared community structure common between two graphs of differing shapes." + "The goal of the matching in this case is to recover $G_1$ by matching the right most figure below and $G_2$. That is, we seek to recover the shared community structure common between two graphs of differing shapes.\n", + "\n", + "[1] \n", + "D. Fishkind, S. Adali, H. Patsolic, L. Meng, D. Singh, V. Lyzinski, C. Priebe,\n", + " \"Seeded graph matching\", Pattern Recognit. 87 (2019) 203–215" ] }, { @@ -114,7 +118,7 @@ "np.random.seed(1)\n", "\n", "gmp_naive = GMP(padding='naive')\n", - "seed1 = np.random.choice(np.arange(300),10)\n", + "seed1 = np.random.choice(np.arange(300),8)\n", "seed2 = [int(x/75)*25 + x for x in seed1]\n", "gmp_naive = gmp_naive.fit(G2, G1, seed2, seed1)\n", "G1_naive = topleft_G1[gmp_naive.perm_inds_][:, gmp_naive.perm_inds_]\n", @@ -130,8 +134,8 @@ "naive_matching = np.concatenate([gmp_naive.perm_inds_[x * 100 : (x * 100) + 75] for x in range(n_blocks)])\n", "adopted_matching = np.concatenate([gmp_adopted.perm_inds_[x * 100 : (x * 100) + 75] for x in range(n_blocks)])\n", "\n", - "print(f'Match ratio of core vertices with naive padding: {sum(naive_matching == np.arange(300))/300}')\n", - "print(f'Match ratio of core vertices with adopted padding: {sum(adopted_matching == np.arange(300))/300}')" + "print(f'Match ratio of nodes remaining in G1, with naive padding: {sum(naive_matching == np.arange(300))/300}')\n", + "print(f'Match ratio of nodes remaining in G1, with adopted padding: {sum(adopted_matching == np.arange(300))/300}')" ] }, { @@ -142,6 +146,13 @@ "\n", "On the other hand, we see that adopted padding preserves the common block structure between $G_1$ and $G_2$." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/graspologic/match/gmp.py b/graspologic/match/gmp.py index 1345764f7..dc4c97fdd 100644 --- a/graspologic/match/gmp.py +++ b/graspologic/match/gmp.py @@ -142,11 +142,14 @@ def __init__( else: msg = '"gmp" must be a boolean' raise TypeError(msg) - if padding in {"adopted", "naive"}: + if isinstance(padding, str) and padding in {"adopted", "naive"}: self.padding = padding - else: + elif isinstance(padding, str): msg = 'Invalid "padding" parameter string' raise ValueError(msg) + else: + msg = '"padding" parameter must be of type string' + raise TypeError(msg) def fit(self, A, B, seeds_A=[], seeds_B=[]): """ diff --git a/tests/match/test_match.py b/tests/match/test_match.py index a99483619..2fee2fe1a 100644 --- a/tests/match/test_match.py +++ b/tests/match/test_match.py @@ -32,6 +32,8 @@ def test_SGM_inputs(self): GMP(eps=-1) with pytest.raises(TypeError): GMP(gmp="hey") + with pytest.raises(TypeError): + GMP(padding=2) with pytest.raises(ValueError): GMP(padding="hey") with pytest.raises(ValueError): From f0cdd7cb814828a5f4323c3ad61a5f2879113604 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Date: Mon, 5 Oct 2020 11:43:40 -0400 Subject: [PATCH 032/140] Update tutorial.rst --- docs/tutorial.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 769eebfe2..104580b46 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -81,7 +81,7 @@ including an introduction to the module, and how to utilize the seeding feature. tutorials/matching/faq tutorials/matching/sgm - tutorials/matching/padded_sgm + tutorials/matching/padded_gm .. _subgraph_tutorials: From 54d28b94a2e4ccec40f1bc985cbc8b40ee57bc70 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 13 Oct 2020 07:25:41 -0700 Subject: [PATCH 033/140] Added an extra line to the README to trigger a new build --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 52b4fa282..b896a49a0 100644 --- a/README.md +++ b/README.md @@ -111,3 +111,4 @@ We appreciate detailed bug reports and feature requests (though we appreciate pu If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) > Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. + From dfc028302e2fd3c4233b56f3d6c3e0950a795a2b Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 13 Oct 2020 07:36:55 -0700 Subject: [PATCH 034/140] Removed an extra line in the README to trigger a new build --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index b896a49a0..52b4fa282 100644 --- a/README.md +++ b/README.md @@ -111,4 +111,3 @@ We appreciate detailed bug reports and feature requests (though we appreciate pu If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) > Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. - From 4f1eaea17c2613d508a81023666fdc21485adbd3 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Tue, 13 Oct 2020 14:26:49 -0400 Subject: [PATCH 035/140] Intersphinx woes (#530) * intersphinx woes * see if dwayne is right --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 2efd5f646..f1bffd7c2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -66,10 +66,10 @@ # -- sphinx.ext.intersphinx intersphinx_mapping = { - "numpy": ("https://docs.scipy.org/doc/numpy", None), + "numpy": ("https://numpy.org/doc/stable", None), "python": ("https://docs.python.org/3", None), "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), - "sklearn": ("http://scikit-learn.org/dev", None), + "sklearn": ("https://scikit-learn.org/dev", None), "hyppo": ("https://hyppo.neurodata.io", None), "matplotlib": ("https://matplotlib.org", None), } From d806a5c153952f46b676a2025641c559693b2a7a Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Date: Sun, 18 Oct 2020 12:15:37 -0400 Subject: [PATCH 036/140] Update gmp.py (#534) --- graspologic/match/gmp.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/graspologic/match/gmp.py b/graspologic/match/gmp.py index dc4c97fdd..afa7f8ee3 100644 --- a/graspologic/match/gmp.py +++ b/graspologic/match/gmp.py @@ -157,10 +157,10 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): Parameters ---------- - A : 2d-array, square, positive + A : 2d-array, square A square adjacency matrix - B : 2d-array, square, positive + B : 2d-array, square A square adjacency matrix seeds_A : 1d-array, shape (m , 1) where m <= number of nodes (default = []) @@ -330,11 +330,11 @@ def fit_predict(self, A, B, seeds_A=[], seeds_B=[]): Parameters ---------- - A : 2d-array, square, positive - A square, positive adjacency matrix + A : 2d-array, square + A square adjacency matrix - B : 2d-array, square, positive - A square, positive adjacency matrix + B : 2d-array, square + A square adjacency matrix seeds_A : 1d-array, shape (m , 1) where m <= number of nodes (default = []) An array where each entry is an index of a node in `A`. From c084ddf4502fb771fadf197acd7a5052b1736a99 Mon Sep 17 00:00:00 2001 From: Anshu Trivedi Date: Wed, 21 Oct 2020 22:49:19 +0530 Subject: [PATCH 037/140] fixes Move mug2vec to embed module #502 (#549) * fix:Move mug2vec from pipeline to embed #502 * fix:Change from ..embed to .embed #502 * fix:Remove pipeline #502 * fix:Add import mug2vec #502 * fix:Change pipeline to embed #502 * fix:Add .. autoclass:: mug2vec #502 * fix:Remove pipeline.rst #502 * fix:Remove pipeline from index.rst #502 * fix:Move mug2vec from pipeline to embed #502 * fix: removed support for import graspologic.pipeline #502 * fix:Move test_mug2vec to tests #502 --- docs/reference/embed.rst | 1 + docs/reference/index.rst | 1 - docs/reference/pipeline.rst | 9 --------- graspologic/__init__.py | 1 - graspologic/embed/__init__.py | 1 + graspologic/{pipeline => embed}/mug2vec.py | 3 ++- graspologic/pipeline/__init__.py | 6 ------ tests/{pipeline => }/test_mug2vec.py | 2 +- 8 files changed, 5 insertions(+), 19 deletions(-) delete mode 100644 docs/reference/pipeline.rst rename graspologic/{pipeline => embed}/mug2vec.py (98%) delete mode 100644 graspologic/pipeline/__init__.py rename tests/{pipeline => }/test_mug2vec.py (96%) diff --git a/docs/reference/embed.rst b/docs/reference/embed.rst index d800b87c1..5ba680b52 100644 --- a/docs/reference/embed.rst +++ b/docs/reference/embed.rst @@ -21,6 +21,7 @@ Multiple graph embedding .. autoclass:: OmnibusEmbed .. autoclass:: MultipleASE +.. autoclass:: mug2vec Dissimilarity graph embedding ----------------------------- diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 85c231372..7eee7444b 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -14,7 +14,6 @@ Reference plotting datasets utils - pipeline match subgraph align diff --git a/docs/reference/pipeline.rst b/docs/reference/pipeline.rst deleted file mode 100644 index 44af42aaa..000000000 --- a/docs/reference/pipeline.rst +++ /dev/null @@ -1,9 +0,0 @@ -Pipeline -======== - -.. currentmodule:: graspologic.pipeline - -Multigraph Embeddings ---------------------- - -.. autoclass:: mug2vec diff --git a/graspologic/__init__.py b/graspologic/__init__.py index bb133425a..654ac48ae 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -11,7 +11,6 @@ import graspologic.embed import graspologic.inference import graspologic.models -import graspologic.pipeline import graspologic.plot import graspologic.simulations import graspologic.subgraph diff --git a/graspologic/embed/__init__.py b/graspologic/embed/__init__.py index 217f4a612..08ecded5e 100644 --- a/graspologic/embed/__init__.py +++ b/graspologic/embed/__init__.py @@ -7,6 +7,7 @@ from .mds import ClassicalMDS from .omni import OmnibusEmbed from .svd import select_dimension, selectSVD +from .mug2vec import mug2vec __all__ = [ "ClassicalMDS", diff --git a/graspologic/pipeline/mug2vec.py b/graspologic/embed/mug2vec.py similarity index 98% rename from graspologic/pipeline/mug2vec.py rename to graspologic/embed/mug2vec.py index b45f396aa..d7594c3b5 100644 --- a/graspologic/pipeline/mug2vec.py +++ b/graspologic/embed/mug2vec.py @@ -4,7 +4,8 @@ import numpy as np from sklearn.base import BaseEstimator -from ..embed import ClassicalMDS, OmnibusEmbed +from .mds import ClassicalMDS +from .omni import OmnibusEmbed from ..utils import pass_to_ranks diff --git a/graspologic/pipeline/__init__.py b/graspologic/pipeline/__init__.py deleted file mode 100644 index 7c83ab19a..000000000 --- a/graspologic/pipeline/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Microsoft Corporation and contributors. -# Licensed under the MIT License. - -from .mug2vec import mug2vec - -__all__ = ["mug2vec"] diff --git a/tests/pipeline/test_mug2vec.py b/tests/test_mug2vec.py similarity index 96% rename from tests/pipeline/test_mug2vec.py rename to tests/test_mug2vec.py index 032d747aa..9a1b0dbe9 100644 --- a/tests/pipeline/test_mug2vec.py +++ b/tests/test_mug2vec.py @@ -6,7 +6,7 @@ from numpy.testing import assert_equal from graspologic.cluster import GaussianCluster -from graspologic.pipeline import mug2vec +from graspologic.embed import mug2vec from graspologic.simulations import sbm From 201844e410cdded6477a1d6766b946fd908f6edd Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 21 Oct 2020 07:52:28 -0700 Subject: [PATCH 038/140] Update issue templates I don't have any super strong feelings about the feature requests template. --- .github/ISSUE_TEMPLATE/bug_report.md | 33 +++++++++++++++++++++++ .github/ISSUE_TEMPLATE/feature_request.md | 20 ++++++++++++++ .github/ISSUE_TEMPLATE/usage-question.md | 10 +++++++ 3 files changed, 63 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md create mode 100644 .github/ISSUE_TEMPLATE/usage-question.md diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 000000000..3daa45e9f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,33 @@ +--- +name: Bug report +about: Bug reports help us improve! Thanks for submitting yours! +title: "[BUG] " +labels: bug +assignees: '' + +--- + +## Expected Behavior +Tell us what should happen + +## Actual Behavior +Tell us what happens instead + +## Example Code +Please see [How to create a Minimal, Reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) for some guidance on creating the best possible example of the problem +```python + +``` + +## Full Traceback +```pytb +Paste the full traceback in case there is an exception + +``` + +## Your Environment +* Python version: +* graspologic version: + +## Additional Details +Any other contextual information you might feel is important. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 000000000..bbcbbe7d6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/usage-question.md b/.github/ISSUE_TEMPLATE/usage-question.md new file mode 100644 index 000000000..a8836d37f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/usage-question.md @@ -0,0 +1,10 @@ +--- +name: Usage Question +about: Ask us a question about graspologic and graphs! +title: "[Question]" +labels: question +assignees: '' + +--- + +This is our forum for asking whatever network question you'd like! No need to feel shy - we're happy to talk about graphs! From 2cb55bda9a366ca1973bbaf81206f50d6c808ada Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 21 Oct 2020 11:02:48 -0400 Subject: [PATCH 039/140] Update feature_request.md --- .github/ISSUE_TEMPLATE/feature_request.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index bbcbbe7d6..a4b0db209 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -2,7 +2,7 @@ name: Feature request about: Suggest an idea for this project title: '' -labels: '' +labels: 'enhancement' assignees: '' --- @@ -16,5 +16,9 @@ A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. +**Provide any references for your feature request** +If your feature request is related to a published algorithm/idea, please provide links to +any relevant articles or webpages. + **Additional context** Add any other context or screenshots about the feature request here. From e238b47022b80a7836be8f81213fbb760c8c9ebb Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 21 Oct 2020 11:03:39 -0400 Subject: [PATCH 040/140] Update feature_request.md --- .github/ISSUE_TEMPLATE/feature_request.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index a4b0db209..e0e03fe15 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -16,7 +16,7 @@ A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. -**Provide any references for your feature request** +**Provide references (if applicable)** If your feature request is related to a published algorithm/idea, please provide links to any relevant articles or webpages. From 4d87424c6d26ff967d7a6dbbcd6b651edc6a9e1b Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 21 Oct 2020 17:28:52 -0400 Subject: [PATCH 041/140] Update feature_request.md --- .github/ISSUE_TEMPLATE/feature_request.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index e0e03fe15..60f7a46d2 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -7,18 +7,18 @@ assignees: '' --- -**Is your feature request related to a problem? Please describe.** +## Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] -**Describe the solution you'd like** +## Describe the solution you'd like A clear and concise description of what you want to happen. -**Describe alternatives you've considered** +## Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered. -**Provide references (if applicable)** +## Provide references (if applicable) If your feature request is related to a published algorithm/idea, please provide links to any relevant articles or webpages. -**Additional context** +## Additional context Add any other context or screenshots about the feature request here. From 37ed37131faeb66fe6050893c1ddc544e222b9be Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 21 Oct 2020 17:29:32 -0400 Subject: [PATCH 042/140] Update feature_request.md --- .github/ISSUE_TEMPLATE/feature_request.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index 60f7a46d2..ef7cb736f 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -2,7 +2,7 @@ name: Feature request about: Suggest an idea for this project title: '' -labels: 'enhancement' +labels: enhancement assignees: '' --- From 06c316ff3b738d781b1d166dbd753b0b22df3a3f Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Fri, 23 Oct 2020 11:15:46 -0700 Subject: [PATCH 043/140] Disabling blank issues --- .github/ISSUE_TEMPLATE.md | 25 ------------------------- .github/ISSUE_TEMPLATE/config.yml | 2 ++ .github/PULL_REQUEST_TEMPLATE.md | 18 ------------------ 3 files changed, 2 insertions(+), 43 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE.md create mode 100644 .github/ISSUE_TEMPLATE/config.yml delete mode 100644 .github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md deleted file mode 100644 index 5c385e57d..000000000 --- a/.github/ISSUE_TEMPLATE.md +++ /dev/null @@ -1,25 +0,0 @@ - - -## Expected Behavior -Tell us what should happen - -## Actual Behavior -Tell us what happens instead - -## Template Code -```graspologic -Paste the template code (ideally a minimal example) that causes the issue - -``` - -## Full Traceback -```pytb -Paste the full traceback in case there is an exception - -``` - -## Your Environment -* Python version: -* graspologic version: diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 000000000..99d680b0a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,2 @@ +blank_issues_enabled: false + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index 47ccd570a..000000000 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,18 +0,0 @@ - - -#### Reference Issues/PRs - - - -#### What does this implement/fix? Explain your changes. - - -#### Any other comments? From 6089bd4cf3536997d9065368943ad84fe63abcfc Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Thu, 22 Oct 2020 19:31:44 -0700 Subject: [PATCH 044/140] Moving (almost as-is) Node2Vec from topologic --- docs/reference/embed.rst | 1 + graspologic/embed/__init__.py | 2 + graspologic/embed/ase.py | 4 +- graspologic/embed/base.py | 4 +- graspologic/embed/lse.py | 4 +- graspologic/embed/n2v.py | 402 ++++++++++++++++++++++++++++++++++ setup.py | 9 +- tests/test_base_embed.py | 18 +- tests/test_n2v.py | 183 ++++++++++++++++ 9 files changed, 608 insertions(+), 19 deletions(-) create mode 100644 graspologic/embed/n2v.py create mode 100644 tests/test_n2v.py diff --git a/docs/reference/embed.rst b/docs/reference/embed.rst index 5ba680b52..22e9a821f 100644 --- a/docs/reference/embed.rst +++ b/docs/reference/embed.rst @@ -15,6 +15,7 @@ Single graph embedding .. autoclass:: AdjacencySpectralEmbed .. autoclass:: LaplacianSpectralEmbed +.. autofunction:: node2vec_embed Multiple graph embedding ------------------------ diff --git a/graspologic/embed/__init__.py b/graspologic/embed/__init__.py index 08ecded5e..33d010492 100644 --- a/graspologic/embed/__init__.py +++ b/graspologic/embed/__init__.py @@ -5,6 +5,7 @@ from .lse import LaplacianSpectralEmbed from .mase import MultipleASE from .mds import ClassicalMDS +from .n2v import node2vec_embed from .omni import OmnibusEmbed from .svd import select_dimension, selectSVD from .mug2vec import mug2vec @@ -15,6 +16,7 @@ "AdjacencySpectralEmbed", "LaplacianSpectralEmbed", "MultipleASE", + "node2vec_embed", "select_dimension", "selectSVD", ] diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index a9d3ee2c4..4978347ef 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -3,7 +3,7 @@ import warnings -from .base import BaseEmbed +from .base import BaseSpectralEmbed from ..utils import ( import_graph, is_fully_connected, @@ -13,7 +13,7 @@ ) -class AdjacencySpectralEmbed(BaseEmbed): +class AdjacencySpectralEmbed(BaseSpectralEmbed): r""" Class for computing the adjacency spectral embedding of a graph. diff --git a/graspologic/embed/base.py b/graspologic/embed/base.py index 44187d588..a253cdc92 100644 --- a/graspologic/embed/base.py +++ b/graspologic/embed/base.py @@ -10,7 +10,7 @@ from .svd import selectSVD -class BaseEmbed(BaseEstimator): +class BaseSpectralEmbed(BaseEstimator): """ A base class for embedding a graph. @@ -168,7 +168,7 @@ def fit_transform(self, graph, y=None): return self._fit_transform(graph) -class BaseEmbedMulti(BaseEmbed): +class BaseEmbedMulti(BaseSpectralEmbed): def __init__( self, n_components=None, diff --git a/graspologic/embed/lse.py b/graspologic/embed/lse.py index a07ab9dd6..ef02706a2 100644 --- a/graspologic/embed/lse.py +++ b/graspologic/embed/lse.py @@ -3,11 +3,11 @@ import warnings -from .base import BaseEmbed +from .base import BaseSpectralEmbed from ..utils import import_graph, to_laplace, is_fully_connected -class LaplacianSpectralEmbed(BaseEmbed): +class LaplacianSpectralEmbed(BaseSpectralEmbed): r""" Class for computing the laplacian spectral embedding of a graph. diff --git a/graspologic/embed/n2v.py b/graspologic/embed/n2v.py new file mode 100644 index 000000000..4bb0f26c6 --- /dev/null +++ b/graspologic/embed/n2v.py @@ -0,0 +1,402 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import logging +import math +import random +import time +from typing import Any, List, Optional, Tuple + +import networkx as nx +import numpy as np + + +def node2vec_embed( + graph: nx.Graph, + num_walks: int = 10, + walk_length: int = 80, + return_hyperparameter: int = 1, + inout_hyperparameter: int = 1, + dimensions: int = 128, + window_size: int = 10, + workers: int = 8, + iterations: int = 1, + interpolate_walk_lengths_by_node_degree: bool = True, +) -> Tuple[np.ndarray, np.ndarray]: + """ + Generates a node2vec embedding from a given graph. Will follow the word2vec algorithm to create the embedding. + + Parameters + ---------- + + graph: nx.Graph + A networkx graph. If the graph is unweighted, the weight of each edge will default to 1 + num_walks : int + Number of walks per source. Default is 10. + walk_length: int + Length of walk per source. Default is 80. + return_hyperparameter : int + Return hyperparameter (p). Default is 1. + inout_hyperparameter : int + Inout hyperparameter (q). Default is 1. + dimensions : int + Dimensionality of the word vectors. Default is 128. + window_size : int + Maximum distance between the current and predicted word within a sentence. Default is 10. + workers : int + Use these many worker threads to train the model. Default is 8. + iterations : int + Number of epochs in stochastic gradient descent (SGD) + interpolate_walk_lengths_by_node_degree : bool + Use a dynamic walk length that corresponds to each nodes + degree. If the node is in the bottom 20 percentile, default to a walk length of 1. If it is in the top 10 + percentile, use walk_length. If it is in the 20-80 percentiles, linearly interpolate between 1 and walk_length. + This will reduce lower degree nodes from biasing your resulting embedding. If a low degree node has the same + number of walks as a high degree node (which it will if this setting is not on), then the lower degree nodes + will take a smaller breadth of random walks when compared to the high degree nodes. This will result in your + lower degree walks dominating your higher degree nodes. + + Returns + Tuple[np.ndarray, np.ndarray] + A tuple containing a matrix, with each row index corresponding to the embedding for each node. The tuple + also contains a vector containing the corresponding vertex labels for each row in the matrix. + The matrix and vector are positionally correlated. + """ + node2vec_graph = _Node2VecGraph(graph, return_hyperparameter, inout_hyperparameter) + + logging.info( + f"Starting preprocessing of transition probabilities on graph with {str(len(graph.nodes()))} nodes and " + f"{str(len(graph.edges()))} edges" + ) + + start = time.time() + logging.info(f"Starting at time {str(start)}") + + node2vec_graph._preprocess_transition_probabilities() + + logging.info(f"Simulating walks on graph at time {str(time.time())}") + walks = node2vec_graph._simulate_walks( + num_walks, walk_length, interpolate_walk_lengths_by_node_degree + ) + + logging.info(f"Learning embeddings at time {str(time.time())}") + model = _learn_embeddings(walks, dimensions, window_size, workers, iterations) + + end = time.time() + logging.info( + f"Completed. Ending time is {str(end)} Elapsed time is {str(start - end)}" + ) + + return model.wv.vectors, model.wv.index2word + + +def _learn_embeddings( + walks: List[Any], dimensions: int, window_size: int, workers: int, iterations: int +): + """ + Learn embeddings by optimizing the skip-gram objective using SGD. + """ + from gensim.models import Word2Vec + + walks = [list(map(str, walk)) for walk in walks] + + # Documentation - https://radimrehurek.com/gensim/models/word2vec.html + model = Word2Vec( + walks, + size=dimensions, + window=window_size, + min_count=0, + sg=1, # Training algorithm: 1 for skip-gram; otherwise CBOW + workers=workers, + iter=iterations, + ) + + return model + + +class _Node2VecGraph: + """ + Temporary inner state object for constructing the random walks + + Parameters + ---------- + + graph: nx.Graph + A networkx graph + return_hyperparameter : float + Return hyperparameter + inout_hyperparameter : float + Inout hyperparameter + """ + + def __init__( + self, graph: nx.Graph, return_hyperparameter: float, inout_hyperparameter: float + ): + self.graph: nx.Graph = graph + self.is_directed = self.graph.is_directed() + self.p = return_hyperparameter + self.q = inout_hyperparameter + + def node2vec_walk( + self, walk_length: int, start_node: Any, degree_percentiles: Optional[np.ndarray] + ): + """ + Simulate a random walk starting from start node. + """ + graph = self.graph + alias_nodes = self.alias_nodes + alias_edges = self.alias_edges + + walk = [start_node] + + # Percentiles will be provided if we are using the 'interpolate_walk_lengths_by_node_degree' feature. + # the intent of the code is to default the bottom 20% of to a minimal walk length, default the top 10% to a + # maximum walk length, and interpolate the inner 70% linearly from min to max. + + # This is to avoid having your random walks be dominated by low degree nodes. If the low degree nodes have the + # same number of walks as the high degree nodes, the low degree nodes will take a smaller breadth of paths + # (due to their being less nodes to choose from) and will bias your resulting Word2Vec embedding + if degree_percentiles is not None: + degree = nx.degree(graph, start_node) + walk_length = self._get_walk_length_interpolated( + degree, degree_percentiles, walk_length + ) + + while len(walk) < walk_length: + current = walk[-1] + current_neighbors = sorted(graph.neighbors(current)) + + if len(current_neighbors) > 0: + if len(walk) == 1: + walk.append( + current_neighbors[ + _alias_draw( + alias_nodes[current][0], alias_nodes[current][1] + ) + ] + ) + else: + prev = walk[-2] + next = current_neighbors[ + _alias_draw( + alias_edges[(prev, current)][0], + alias_edges[(prev, current)][1], + ) + ] + walk.append(next) + else: + break + + return walk + + @staticmethod + def _get_walk_length_interpolated( + degree: int, percentiles: np.ndarray, max_walk_length: int + ): + """ + Given a node's degree, determine the length of a walk that should be used. If the degree is less than the + first element of the percentiles list, default the walk length to 1. Otherwise, if the degree is greater + than the last element of the list, default it to the max_walk_length. If it falls in the middle, do a linear + interpolation to decide the length of the walk. + """ + + new_walk_length = None + + for i, percentile in enumerate(percentiles): + # if we are below the first percentile in the list, default to a walk length of 1 + if i == 0 and degree < percentile: + return 1 + + # otherwise, find which bucket we are going to be in. + if degree <= percentile: + new_walk_length = max_walk_length * ((i * 0.1) + 0.2) + break + + # the degree is above the last percentile + if not new_walk_length: + new_walk_length = max_walk_length + + # a walk length of 0 is invalid but can happen depending on the percentiles used + if new_walk_length < 1: + new_walk_length = 1 + + return math.floor(new_walk_length) + + def _simulate_walks( + self, + num_walks: int, + walk_length: int, + interpolate_walk_lengths_by_node_degree: bool = False, + ): + """ + Repeatedly simulate random walks from each node. + """ + graph = self.graph + walks = [] + nodes = list(graph.nodes()) + + degree_percentiles: Optional[np.ndarray] = None + if interpolate_walk_lengths_by_node_degree: + degree_percentiles = np.percentile( + [degree for _, degree in graph.degree()], [x for x in range(20, 90, 10)] + ) + + for walk_iteration in range(num_walks): + logging.info( + "Walk iteration: " + str(walk_iteration + 1) + "/" + str(num_walks) + ) + random.shuffle(nodes) + for node in nodes: + walks.append( + self.node2vec_walk( + walk_length=walk_length, + start_node=node, + degree_percentiles=degree_percentiles, + ) + ) + + return walks + + def _get_alias_edge(self, source: Any, destination: Any): + """ + Get the alias edge setup lists for a given edge. + """ + graph = self.graph + p = self.p + q = self.q + + unnormalized_probs = [] + for destination_neighbor in sorted(graph.neighbors(destination)): + if destination_neighbor == source: + unnormalized_probs.append( + graph[destination][destination_neighbor].get("weight", 1) / p + ) + elif graph.has_edge(destination_neighbor, source): + unnormalized_probs.append( + graph[destination][destination_neighbor].get("weight", 1) + ) + else: + unnormalized_probs.append( + graph[destination][destination_neighbor].get("weight", 1) / q + ) + norm_const = sum(unnormalized_probs) + normalized_probs = [float(u_prob) / norm_const for u_prob in unnormalized_probs] + + return _alias_setup(normalized_probs) + + def _preprocess_transition_probabilities(self, weight_default: float = 1.0): + """ + Preprocessing of transition probabilities for guiding the random walks. + """ + graph = self.graph + is_directed = self.is_directed + + alias_nodes = {} + total_nodes = len(graph.nodes()) + bucket = 0 + current_node = 0 + quotient = int(total_nodes / 10) + + logging.info( + f"Beginning preprocessing of transition probabilities for {total_nodes} vertices" + ) + for node in graph.nodes(): + current_node += 1 + if current_node > bucket * quotient: + bucket += 1 + logging.info(f"Completed {current_node} / {total_nodes} vertices") + + unnormalized_probs = [ + graph[node][nbr].get("weight", weight_default) + for nbr in sorted(graph.neighbors(node)) + ] + norm_const = sum(unnormalized_probs) + normalized_probs = [ + float(u_prob) / norm_const for u_prob in unnormalized_probs + ] + alias_nodes[node] = _alias_setup(normalized_probs) + logging.info( + f"Completed preprocessing of transition probabilities for vertices" + ) + + alias_edges = {} + + total_edges = len(graph.edges()) + bucket = 0 + current_edge = 0 + quotient = int(total_edges / 10) + + logging.info( + f"Beginning preprocessing of transition probabilities for {total_edges} edges" + ) + if is_directed: + for edge in graph.edges(): + current_edge += 1 + if current_edge > bucket * quotient: + bucket += 1 + logging.info(f"Completed {current_edge} / {total_edges} edges") + + alias_edges[edge] = self._get_alias_edge(edge[0], edge[1]) + else: + for edge in graph.edges(): + current_edge += 1 + if current_edge > bucket * quotient: + bucket += 1 + logging.info(f"Completed {current_edge} / {total_edges} edges") + + alias_edges[edge] = self._get_alias_edge(edge[0], edge[1]) + alias_edges[(edge[1], edge[0])] = self._get_alias_edge(edge[1], edge[0]) + + logging.info(f"Completed preprocessing of transition probabilities for edges") + + self.alias_nodes = alias_nodes + self.alias_edges = alias_edges + + return + + +def _alias_setup(probabilities: List[float]): + """ + Compute utility lists for non-uniform sampling from discrete distributions. + Refer to + https://lips.cs.princeton.edu/the-alias-method-efficient-sampling-with-many-discrete-outcomes/ + for details + """ + number_of_outcomes = len(probabilities) + alias = np.zeros(number_of_outcomes) + sampled_probabilities = np.zeros(number_of_outcomes, dtype=np.int) + + smaller = [] + larger = [] + for i, prob in enumerate(probabilities): + alias[i] = number_of_outcomes * prob + if alias[i] < 1.0: + smaller.append(i) + else: + larger.append(i) + + while len(smaller) > 0 and len(larger) > 0: + small = smaller.pop() + large = larger.pop() + + sampled_probabilities[small] = large + alias[large] = alias[large] + alias[small] - 1.0 + if alias[large] < 1.0: + smaller.append(large) + else: + larger.append(large) + + return sampled_probabilities, alias + + +def _alias_draw(probabilities: List[float], alias: List[float]): + """ + Draw sample from a non-uniform discrete distribution using alias sampling. + """ + number_of_outcomes = len(probabilities) + random_index = int(np.floor(np.random.rand() * number_of_outcomes)) + + if np.random.rand() < alias[random_index]: + return random_index + else: + return probabilities[random_index] diff --git a/setup.py b/setup.py index f163650c3..8eeb5bc74 100644 --- a/setup.py +++ b/setup.py @@ -50,14 +50,15 @@ "version": [os.path.join("graspologic", "version", "version.txt")] }, install_requires=[ + "gensim", + "hyppo>=0.1.3", + "matplotlib>=3.0.0,<=3.3.0", "networkx>=2.1", "numpy>=1.8.1", + "POT>=0.7.0", + "seaborn>=0.9.0", "scikit-learn>=0.19.1", "scipy>=1.4.0", - "seaborn>=0.9.0", - "matplotlib>=3.0.0,<=3.3.0", - "hyppo>=0.1.3", - "POT>=0.7.0", ], extras_require={ "dev": [ diff --git a/tests/test_base_embed.py b/tests/test_base_embed.py index 761e2f665..3dd12fb36 100644 --- a/tests/test_base_embed.py +++ b/tests/test_base_embed.py @@ -6,7 +6,7 @@ import numpy as np import graspologic as gs -from graspologic.embed.base import BaseEmbed +from graspologic.embed.base import BaseSpectralEmbed from graspologic.simulations.simulations import er_nm, er_np @@ -20,7 +20,7 @@ def setup_class(cls): def test_baseembed_er(self): n_components = 4 - embed = BaseEmbed(n_components=n_components) + embed = BaseSpectralEmbed(n_components=n_components) n = 10 M = 20 A = er_nm(n, M) + 5 @@ -30,7 +30,7 @@ def test_baseembed_er(self): def test_baseembed_er_directed(self): n_components = 4 - embed = BaseEmbed(n_components=n_components) + embed = BaseSpectralEmbed(n_components=n_components) n = 10 M = 20 A = er_nm(n, M, directed=True) @@ -41,7 +41,7 @@ def test_baseembed_er_directed(self): def test_baseembed_er_directed_concat(self): n_components = 4 - embed = BaseEmbed(n_components=n_components, concat=True) + embed = BaseSpectralEmbed(n_components=n_components, concat=True) n = 10 M = 20 A = er_nm(n, M, directed=True) @@ -51,27 +51,27 @@ def test_baseembed_er_directed_concat(self): self.assertTrue(embed.latent_right_ is not None) def test_baseembed(self): - embed = BaseEmbed(n_components=None) + embed = BaseSpectralEmbed(n_components=None) n = 10 M = 20 A = er_nm(n, M) + 5 embed._reduce_dim(A) def test_algorithms(self): - embed = BaseEmbed(n_components=self.n, algorithm="full") + embed = BaseSpectralEmbed(n_components=self.n, algorithm="full") embed._reduce_dim(self.A) self.assertEqual(embed.latent_left_.shape, (self.n, self.n)) self.assertEqual(embed.latent_right_.shape, (self.n, self.n)) # When algoritm != 'full', cannot decompose to all dimensions - embed = BaseEmbed(n_components=self.n, algorithm="truncated") + embed = BaseSpectralEmbed(n_components=self.n, algorithm="truncated") with self.assertRaises(ValueError): embed._reduce_dim(self.A) - embed = BaseEmbed(n_components=self.n, algorithm="randomized") + embed = BaseSpectralEmbed(n_components=self.n, algorithm="randomized") with self.assertRaises(ValueError): embed._reduce_dim(self.A) def test_input_checks(self): with self.assertRaises(TypeError): - BaseEmbed(n_components=self.n, concat=42) + BaseSpectralEmbed(n_components=self.n, concat=42) diff --git a/tests/test_n2v.py b/tests/test_n2v.py new file mode 100644 index 000000000..db217708c --- /dev/null +++ b/tests/test_n2v.py @@ -0,0 +1,183 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +import networkx as nx +import numpy as np +import graspologic.embed.n2v as n2v +from graspologic.embed.n2v import _Node2VecGraph + + +class Node2VecEmbedTest(unittest.TestCase): + def test_node2vec_embedding_correct_shape_is_returned(self): + import io + + graph = nx.read_edgelist( + io.StringIO(_edge_list), nodetype=int, create_using=nx.DiGraph() + ) + + model = n2v.node2vec_embed(graph) + model_matrix: np.ndarray = model[0] + vocab_list = model[1] + self.assertIsNotNone(model) + self.assertIsNotNone(model[0]) + self.assertIsNotNone(model[1]) + + # model matrix should be 34 x 128 + self.assertEqual(model_matrix.shape[0], 34) + self.assertEqual(model_matrix.shape[1], 128) + + # vocab list should have exactly 34 elements + self.assertEqual(len(vocab_list), 34) + + def test_node2vec_embedding_florentine_graph_correct_shape_is_returned(self): + graph = nx.florentine_families_graph() + for s, t in graph.edges(): + graph.add_edge(s, t, weight=1) + + model = n2v.node2vec_embed(graph) + model_matrix: np.ndarray = model[0] + vocab_list = model[1] + self.assertIsNotNone(model) + self.assertIsNotNone(model[0]) + self.assertIsNotNone(model[1]) + + # model matrix should be 34 x 128 + self.assertEqual(model_matrix.shape[0], 15) + self.assertEqual(model_matrix.shape[1], 128) + + # vocab list should have exactly 34 elements + self.assertEqual(len(vocab_list), 15) + + def test_node2vec_embedding_barbell_graph_correct_shape_is_returned(self): + graph = nx.barbell_graph(25, 2) + for s, t in graph.edges(): + graph.add_edge(s, t, weight=1) + + model = n2v.node2vec_embed(graph) + model_matrix: np.ndarray = model[0] + vocab_list = model[1] + self.assertIsNotNone(model) + self.assertIsNotNone(model[0]) + self.assertIsNotNone(model[1]) + + # model matrix should be 34 x 128 + self.assertEqual(model_matrix.shape[0], 52) + self.assertEqual(model_matrix.shape[1], 128) + + # vocab list should have exactly 34 elements + self.assertEqual(len(vocab_list), 52) + + def test_get_walk_length_lower_defaults_to_1(self): + expected_walk_length = 1 + + g = _Node2VecGraph(nx.Graph(), 1, 1) + w = g._get_walk_length_interpolated( + degree=0, percentiles=[1, 2, 3, 4, 10, 100], max_walk_length=10 + ) + + self.assertEqual(w, expected_walk_length) + + def test_get_walk_length_higher_default_to_walk_length(self): + expected_walk_length = 100 + + g = _Node2VecGraph(nx.Graph(), 1, 1) + w = g._get_walk_length_interpolated( + degree=10, + percentiles=[2, 3, 4, 5, 6, 7, 8, 9], + max_walk_length=expected_walk_length, + ) + + self.assertEqual(w, expected_walk_length) + + def test_get_walk_length_in_middle_selects_interpolated_bucket(self): + expected_walk_length = 5 + + g = _Node2VecGraph(nx.Graph(), 1, 1) + w = g._get_walk_length_interpolated( + degree=5, percentiles=[2, 3, 4, 5, 6, 7, 8, 9], max_walk_length=10 + ) + + self.assertEqual(w, expected_walk_length) + + +_edge_list = """ +1 32 +1 22 +1 20 +1 18 +1 14 +1 13 +1 12 +1 11 +1 9 +1 8 +1 7 +1 6 +1 5 +1 4 +1 3 +1 2 +2 31 +2 22 +2 20 +2 18 +2 14 +2 8 +2 4 +2 3 +3 14 +3 9 +3 10 +3 33 +3 29 +3 28 +3 8 +3 4 +4 14 +4 13 +4 8 +5 11 +5 7 +6 17 +6 11 +6 7 +7 17 +9 34 +9 33 +9 33 +10 34 +14 34 +15 34 +15 33 +16 34 +16 33 +19 34 +19 33 +20 34 +21 34 +21 33 +23 34 +23 33 +24 30 +24 34 +24 33 +24 28 +24 26 +25 32 +25 28 +25 26 +26 32 +27 34 +27 30 +28 34 +29 34 +29 32 +30 34 +30 33 +31 34 +31 33 +32 34 +32 33 +33 34 +""" From 7cfffc60c205ed2ad5b15805196243843cc37206 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Fri, 23 Oct 2020 09:16:49 -0700 Subject: [PATCH 045/140] Addressing some of the PR feedback as well as adding preconditions/assertions --- graspologic/embed/n2v.py | 102 ++++++++++++++++++++++++++++++++++----- 1 file changed, 91 insertions(+), 11 deletions(-) diff --git a/graspologic/embed/n2v.py b/graspologic/embed/n2v.py index 4bb0f26c6..4e1d11069 100644 --- a/graspologic/embed/n2v.py +++ b/graspologic/embed/n2v.py @@ -5,18 +5,18 @@ import math import random import time -from typing import Any, List, Optional, Tuple +from typing import Any, List, Optional, Tuple, Union import networkx as nx import numpy as np def node2vec_embed( - graph: nx.Graph, + graph: Union[nx.Graph, nx.DiGraph], num_walks: int = 10, walk_length: int = 80, - return_hyperparameter: int = 1, - inout_hyperparameter: int = 1, + return_hyperparameter: float = 1.0, + inout_hyperparameter: float = 1.0, dimensions: int = 128, window_size: int = 10, workers: int = 8, @@ -29,16 +29,18 @@ def node2vec_embed( Parameters ---------- - graph: nx.Graph - A networkx graph. If the graph is unweighted, the weight of each edge will default to 1 + graph: Union[nx.Graph, nx.DiGraph] + A networkx graph or digraph. A multigraph should be turned into a non-multigraph so that the calling user + properly handles the multi-edges (i.e. aggregate weights or take last edge weight). + If the graph is unweighted, the weight of each edge will default to 1. num_walks : int Number of walks per source. Default is 10. walk_length: int Length of walk per source. Default is 80. - return_hyperparameter : int - Return hyperparameter (p). Default is 1. - inout_hyperparameter : int - Inout hyperparameter (q). Default is 1. + return_hyperparameter : float + Return hyperparameter (p). Default is 1.0 + inout_hyperparameter : float + Inout hyperparameter (q). Default is 1.0 dimensions : int Dimensionality of the word vectors. Default is 128. window_size : int @@ -57,11 +59,39 @@ def node2vec_embed( lower degree walks dominating your higher degree nodes. Returns + ------- Tuple[np.ndarray, np.ndarray] A tuple containing a matrix, with each row index corresponding to the embedding for each node. The tuple also contains a vector containing the corresponding vertex labels for each row in the matrix. The matrix and vector are positionally correlated. + + Notes + ----- + The original reference implementation of node2vec comes from Aditya Grover from + https://github.com/aditya-grover/node2vec/. + + Further details on the Alias Method used in this functionality can be found at + https://lips.cs.princeton.edu/the-alias-method-efficient-sampling-with-many-discrete-outcomes/ + + References + ---------- + .. [1] Aditya Grover and Jure Leskovec "node2vec: Scalable Feature Learning for Networks." + Knowledge Discovery and Data Mining, 2016. """ + + _preconditions( + graph, + num_walks, + walk_length, + return_hyperparameter, + inout_hyperparameter, + dimensions, + window_size, + workers, + iterations, + interpolate_walk_lengths_by_node_degree, + ) + node2vec_graph = _Node2VecGraph(graph, return_hyperparameter, inout_hyperparameter) logging.info( @@ -90,6 +120,53 @@ def node2vec_embed( return model.wv.vectors, model.wv.index2word +def _assert_is_positive_int(name: str, value: int): + if not isinstance(value, int): + raise TypeError(f"{name} must be an int") + if value <= 0: + raise ValueError(f"{name} must be > 0") + + +def _assert_is_nonnegative_float(name: str, value: float): + if not isinstance(value, float): + raise TypeError(f"{name} must be a float") + if value < 0.0: + raise ValueError(f"{name} must be >= 0.0") + + +def _preconditions( + graph: Union[nx.Graph, nx.DiGraph], + num_walks: int, + walk_length: int, + return_hyperparameter: float, + inout_hyperparameter: float, + dimensions: int, + window_size: int, + workers: int, + iterations: int, + interpolate_walk_lengths_by_node_degree: bool, +): + if not isinstance(graph, nx.Graph): + raise TypeError("graph must be a networkx Graph or DiGraph") + if graph.is_multigraph(): + raise ValueError( + "This function does not work on multigraphs - because there are two reasonable ways to treat a " + "multigraph with different behaviors, we insist that the caller create an appropriate Graph or " + "DiGraph that represents the manner in which they'd like the multigraph to be treated for the " + "purposes of this embedding" + ) + _assert_is_positive_int("num_walks", num_walks) + _assert_is_positive_int("walk_length", walk_length) + _assert_is_nonnegative_float("return_hyperparameter", return_hyperparameter) + _assert_is_nonnegative_float("inout_hyperparameter", inout_hyperparameter) + _assert_is_positive_int("dimensions", dimensions) + _assert_is_positive_int("window_size", window_size) + _assert_is_positive_int("workers", workers) + _assert_is_positive_int("iterations", iterations) + if not isinstance(interpolate_walk_lengths_by_node_degree, bool): + raise TypeError("interpolate_walk_lengths_by_node_degree must be a bool") + + def _learn_embeddings( walks: List[Any], dimensions: int, window_size: int, workers: int, iterations: int ): @@ -138,7 +215,10 @@ def __init__( self.q = inout_hyperparameter def node2vec_walk( - self, walk_length: int, start_node: Any, degree_percentiles: Optional[np.ndarray] + self, + walk_length: int, + start_node: Any, + degree_percentiles: Optional[np.ndarray], ): """ Simulate a random walk starting from start node. From f74c67a2583cd563b8e5e0c051be2049aaba349d Mon Sep 17 00:00:00 2001 From: tliu68 <54865879+tliu68@users.noreply.github.com> Date: Tue, 27 Oct 2020 19:33:55 +0800 Subject: [PATCH 046/140] Divisive Cluster (#398) * update HSBM code * Update HSBM.py * update predict function & label collection * Update cluster.rst * Rename rcluster.py to rclust.py * Update __init__.py * add anytree>=2.8.0 in setup.py * fix input checking * Create test_rclust * Rename test_rclust to test_rclust.py * add test of hierarchical 4-class * update _fit & address feedback * Update test_rclust.py * Update rclust.py * formatting * sort imports * docstring updates * graspy -> graspologic * remove embed_kws * return pred instead of store * minor tweaks * imports in test * simplify tests * fix test on aic * delta_criter defaults to 0 * test negative delta_criter * update relabel in fit_predict, disabled in predict * rename to DivisiveCluster * rename to DivisiveCluster * Update test_rclust.py * Update __init__.py * remove relabel * Update test_rclust.py * Rename rclust.py to divisive_cluster.py * Update __init__.py * Rename test_rclust.py to test_divisive_cluster.py * add explicit references to classes * minor doc changes * mention hsbm again * tiny typo * add intersphinx, alphabetize intersphinx * add a note about anytree Co-authored-by: Benjamin Pedigo --- docs/conf.py | 5 +- docs/reference/cluster.rst | 4 + graspologic/cluster/__init__.py | 3 +- graspologic/cluster/divisive_cluster.py | 283 ++++++++++++++++++++++++ setup.py | 5 +- tests/cluster/test_divisive_cluster.py | 264 ++++++++++++++++++++++ 6 files changed, 558 insertions(+), 6 deletions(-) create mode 100644 graspologic/cluster/divisive_cluster.py create mode 100644 tests/cluster/test_divisive_cluster.py diff --git a/docs/conf.py b/docs/conf.py index f1bffd7c2..b29fea7d8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -66,12 +66,13 @@ # -- sphinx.ext.intersphinx intersphinx_mapping = { + "anytree": ("https://anytree.readthedocs.io/en/latest/", None), + "hyppo": ("https://hyppo.neurodata.io", None), + "matplotlib": ("https://matplotlib.org", None), "numpy": ("https://numpy.org/doc/stable", None), "python": ("https://docs.python.org/3", None), "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), "sklearn": ("https://scikit-learn.org/dev", None), - "hyppo": ("https://hyppo.neurodata.io", None), - "matplotlib": ("https://matplotlib.org", None), } # -- sphinx options ---------------------------------------------------------- diff --git a/docs/reference/cluster.rst b/docs/reference/cluster.rst index ac3414351..c166c3b8c 100644 --- a/docs/reference/cluster.rst +++ b/docs/reference/cluster.rst @@ -13,3 +13,7 @@ Gaussian Mixture Models Clustering .. autoclass:: GaussianCluster .. autoclass:: AutoGMMCluster + +Hierarchical Clustering +---------------------------------- +.. autoclass:: DivisiveCluster diff --git a/graspologic/cluster/__init__.py b/graspologic/cluster/__init__.py index d368dce77..e75d5491d 100644 --- a/graspologic/cluster/__init__.py +++ b/graspologic/cluster/__init__.py @@ -4,5 +4,6 @@ from .gclust import GaussianCluster from .kclust import KMeansCluster from .autogmm import AutoGMMCluster +from .divisive_cluster import DivisiveCluster -__all__ = ["GaussianCluster", "KMeansCluster", "AutoGMMCluster"] +__all__ = ["GaussianCluster", "KMeansCluster", "AutoGMMCluster", "DivisiveCluster"] diff --git a/graspologic/cluster/divisive_cluster.py b/graspologic/cluster/divisive_cluster.py new file mode 100644 index 000000000..ab28c46f7 --- /dev/null +++ b/graspologic/cluster/divisive_cluster.py @@ -0,0 +1,283 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import numpy as np +from sklearn.base import BaseEstimator +from sklearn.utils import check_array +from sklearn.utils.validation import check_is_fitted + +from anytree import NodeMixin + +from .autogmm import AutoGMMCluster +from .kclust import KMeansCluster + + +def _check_common_inputs(min_components, max_components, cluster_kws): + if not isinstance(min_components, int): + raise TypeError("min_components must be an int") + elif min_components < 1: + raise ValueError("min_components must be > 0") + + if not isinstance(max_components, int): + raise TypeError("max_components must be an int") + elif max_components < 1: + raise ValueError("max_components must be > 0") + elif max_components < min_components: + raise ValueError("max_components must be >= min_components") + + if not isinstance(cluster_kws, dict): + raise TypeError("cluster_kws must be a dict") + + +class DivisiveCluster(NodeMixin, BaseEstimator): + """ + Recursively clusters data based on a chosen clustering algorithm. + This algorithm implements a "divisive" or "top-down" approach. + + Parameters + ---------- + cluster_method : str {"gmm", "kmeans"}, defaults to "gmm". + The underlying clustering method to apply. If "gmm" will use + :class:`~graspologic.cluster.AutoGMMCluster`. If "kmeans", will use + :class:`~graspologic.cluster.KMeansCluster`. + min_components : int, defaults to 1. + The minimum number of mixture components/clusters to consider + for the first split if "gmm" is selected as ``cluster_method``; + and is set to 1 for later splits. + If ``cluster_method`` is "kmeans", it is set to 2 for all splits. + max_components : int, defaults to 2. + The maximum number of mixture components/clusters to consider + at each split. + min_split : int, defaults to 1. + The minimum size of a cluster for it to be considered to be split again. + max_level : int, defaults to 4. + The maximum number of times to recursively cluster the data. + delta_criter : float, non-negative, defaults to 0. + The smallest difference between selection criterion values of a new + model and the current model that is required to accept the new model. + Applicable only if ``cluster_method`` is "gmm". + cluster_kws : dict, defaults to {} + Keyword arguments (except ``min_components`` and ``max_components``) for chosen + clustering method. + + Attributes + ---------- + model_ : GaussianMixture or KMeans object + Fitted clustering object based on which ``cluster_method`` was used. + + See Also + -------- + graspologic.cluster.AutoGMMCluster + graspologic.cluster.KMeansCluster + anytree.node.nodemixin.NodeMixin + + Notes + ----- + This class inherits from :class:`anytree.node.nodemixin.NodeMixin`, a lightweight + class for doing various simple operations on trees. + + This algorithm was strongly inspired by maggotcluster, a divisive + clustering algorithm in https://github.com/neurodata/maggot_models and the + algorithm for estimating a hierarchical stochastic block model presented in [2]_. + + References + ---------- + .. [1] Athey, T. L., & Vogelstein, J. T. (2019). + AutoGMM: Automatic Gaussian Mixture Modeling in Python. + arXiv preprint arXiv:1909.02688. + .. [2] Lyzinski, V., Tang, M., Athreya, A., Park, Y., & Priebe, C. E + (2016). Community detection and classification in hierarchical + stochastic blockmodels. IEEE Transactions on Network Science and + Engineering, 4(1), 13-26. + """ + + def __init__( + self, + cluster_method="gmm", + min_components=1, + max_components=2, + cluster_kws={}, + min_split=1, + max_level=4, + delta_criter=0, + ): + + _check_common_inputs(min_components, max_components, cluster_kws) + + if cluster_method not in ["gmm", "kmeans"]: + msg = "clustering method must be one of" + msg += "{gmm, kmeans}" + raise ValueError(msg) + + if delta_criter < 0: + raise ValueError("delta_criter must be non-negative") + + self.parent = None + self.min_components = min_components + self.max_components = max_components + self.cluster_method = cluster_method + self.cluster_kws = cluster_kws + self.min_split = min_split + self.max_level = max_level + self.delta_criter = delta_criter + + def fit(self, X): + """ + Fits clustering models to the data as well as resulting clusters + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + + Returns + ------- + self : object + Returns an instance of self. + """ + self.fit_predict(X) + return self + + def fit_predict(self, X): + """ + Fits clustering models to the data as well as resulting clusters + and using fitted models to predict a hierarchy of labels + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + + Returns + ------- + labels : array_label, shape (n_samples, n_levels) + """ + X = check_array(X, dtype=[np.float64, np.float32], ensure_min_samples=1) + + if self.max_components > X.shape[0]: + msg = "max_components must be >= n_samples, but max_components = " + msg += "{}, n_samples = {}".format(self.max_components, X.shape[0]) + raise ValueError(msg) + + labels = self._fit(X) + # delete the last column if predictions at the last level + # are all zero vectors + if (labels.shape[1] > 1) and (np.max(labels[:, -1]) == 0): + labels = labels[:, :-1] + + return labels + + def _cluster_and_decide(self, X): + if self.is_root: + min_components = self.min_components + else: + min_components = 1 + + if self.cluster_method == "gmm": + cluster = AutoGMMCluster( + min_components=min_components, + max_components=self.max_components, + **self.cluster_kws + ) + cluster.fit(X) + model = cluster.model_ + criter = cluster.criter_ + k = cluster.n_components_ + pred = cluster.predict(X) + + if self.delta_criter > 0: + single_cluster = AutoGMMCluster( + min_components=1, max_components=1, **self.cluster_kws + ) + single_cluster.fit(X) + criter_single_cluster = single_cluster.criter_ + + if k > 1: + # check whether the difference between the criterion + # of "split" and "not split" is greater than + # the threshold, delta_criter + if criter_single_cluster - criter < self.delta_criter: + pred = np.zeros((len(X), 1), dtype=int) + + elif self.cluster_method == "kmeans": + cluster = KMeansCluster( + max_clusters=self.max_components, **self.cluster_kws + ) + cluster.fit(X) + model = cluster.model_ + pred = cluster.predict(X) + + self.model_ = model + return pred + + def _fit(self, X): + pred = self._cluster_and_decide(X) + self.children = [] + + uni_labels = np.unique(pred) + labels = pred.reshape((-1, 1)).copy() + if len(uni_labels) > 1: + for ul in uni_labels: + inds = pred == ul + new_X = X[inds] + dc = DivisiveCluster( + cluster_method=self.cluster_method, + max_components=self.max_components, + min_split=self.min_split, + max_level=self.max_level, + cluster_kws=self.cluster_kws, + delta_criter=self.delta_criter, + ) + dc.parent = self + if ( + len(new_X) > self.max_components + and len(new_X) >= self.min_split + and self.depth + 1 < self.max_level + ): + child_labels = dc._fit(new_X) + while labels.shape[1] <= child_labels.shape[1]: + labels = np.column_stack( + (labels, np.zeros((len(X), 1), dtype=int)) + ) + labels[inds, 1 : child_labels.shape[1] + 1] = child_labels + + return labels + + def predict(self, X): + """ + Predicts a hierarchy of labels based on fitted models + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + + Returns + ------- + labels : array-like, shape (n_samples, n_levels) + """ + + check_is_fitted(self, ["model_"], all_or_any=all) + X = check_array(X, dtype=[np.float64, np.float32], ensure_min_samples=1) + + labels = self._predict_labels(X) + return labels + + def _predict_labels(self, X): + if not self.is_leaf: + pred_labels = np.zeros((len(X), self.height), dtype=int) + current_pred_labels = self.model_.predict(X) + pred_labels[:, 0] = current_pred_labels + for label in np.unique(current_pred_labels): + current_child = self.children[label] + if not current_child.is_leaf: + child_pred_labels = current_child._predict_labels( + X[current_pred_labels == label] + ) + pred_labels[ + current_pred_labels == label, 1 : child_pred_labels.shape[1] + 1 + ] = child_pred_labels + else: + # only for cases where root is a leaf cluster, i.e., + # only 1 cluster predicted at 1st level + if self.is_root: + pred_labels = np.zeros((len(X), 1), dtype=int) + + return pred_labels diff --git a/setup.py b/setup.py index 8eeb5bc74..c50e950b2 100644 --- a/setup.py +++ b/setup.py @@ -46,10 +46,9 @@ ], packages=find_packages(exclude=["tests", "tests.*", "tests/*"]), include_package_data=True, - package_data={ - "version": [os.path.join("graspologic", "version", "version.txt")] - }, + package_data={"version": [os.path.join("graspologic", "version", "version.txt")]}, install_requires=[ + "anytree>=2.8.0", "gensim", "hyppo>=0.1.3", "matplotlib>=3.0.0,<=3.3.0", diff --git a/tests/cluster/test_divisive_cluster.py b/tests/cluster/test_divisive_cluster.py new file mode 100644 index 000000000..6bfca955b --- /dev/null +++ b/tests/cluster/test_divisive_cluster.py @@ -0,0 +1,264 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import numpy as np +import pytest +from numpy.testing import assert_allclose, assert_array_less, assert_equal +from sklearn.exceptions import NotFittedError +from sklearn.metrics import adjusted_rand_score + +from graspologic.cluster import DivisiveCluster + + +def test_inputs(): + # Generate random data + X = np.random.normal(0, 1, size=(100, 3)) + + # min_components < 1 + with pytest.raises(ValueError): + dc = DivisiveCluster(min_components=0) + + # min_components not integer + with pytest.raises(TypeError): + dc = DivisiveCluster(min_components="1") + + # max_components < min_components + with pytest.raises(ValueError): + dc = DivisiveCluster(min_components=1, max_components=0) + + # max_components not integer + with pytest.raises(TypeError): + dc = DivisiveCluster(max_components="1") + + # cluster_method not in ['gmm', 'kmeans'] + with pytest.raises(ValueError): + dc = DivisiveCluster(cluster_method="graspologic") + + # delta_criter negative + with pytest.raises(ValueError): + dc = DivisiveCluster(delta_criter=-1) + + # cluster_kws not a dict + with pytest.raises(TypeError): + dc = DivisiveCluster(cluster_kws=0) + + # max_components > n_sample + with pytest.raises(ValueError): + dc = DivisiveCluster(max_components=101) + dc.fit(X) + + +def test_predict_without_fit(): + # Generate random data + X = np.random.normal(0, 1, size=(100, 3)) + + with pytest.raises(NotFittedError): + dc = DivisiveCluster(max_components=2) + dc.predict(X) + + +def test_predict_on_nonfitted_data_gmm(): + # Generate random data to fit on + np.random.seed(1) + n = 100 + d = 3 + X1 = np.random.normal(1, 0.1, size=(n, d)) + X2 = np.random.normal(2, 0.1, size=(n, d)) + X = np.vstack((X1, X2)) + y = np.repeat([0, 1], n) + + dc = DivisiveCluster(max_components=2) + pred1 = dc.fit_predict(X) + + # Generate random data to predict on + np.random.seed(2) + n = 50 + d = 3 + X1_new = np.random.normal(1, 0.1, size=(n, d)) + X2_new = np.random.normal(2, 0.1, size=(n, d)) + X_new = np.vstack((X1_new, X2_new)) + y_new = np.repeat([0, 1], n) + + pred2 = dc.predict(X_new) + + # Assert that both predictions have the same depth + assert_equal(pred1.shape[1], pred2.shape[1]) + + # Assert that both predictions represent a perfect clustering + # of 2 clusters + assert_equal(np.max(pred1) + 1, 2) + ari_1 = adjusted_rand_score(y, pred1[:, 0]) + assert_allclose(ari_1, 1) + + assert_equal(np.max(pred2) + 1, 2) + ari_2 = adjusted_rand_score(y_new, pred2[:, 0]) + assert_allclose(ari_2, 1) + + +def test_predict_on_nonfitted_data_kmeans(): + # Generate random data to fit on + np.random.seed(1) + n = 100 + d = 3 + X1 = np.random.normal(1, 0.1, size=(n, d)) + X2 = np.random.normal(2, 0.1, size=(n, d)) + X = np.vstack((X1, X2)) + y = np.repeat([0, 1], n) + + dc = DivisiveCluster(max_components=2, cluster_method="kmeans") + pred1 = dc.fit_predict(X) + + # Generate random data to predict on + np.random.seed(2) + n = 50 + d = 3 + X1_new = np.random.normal(1, 0.1, size=(n, d)) + X2_new = np.random.normal(2, 0.1, size=(n, d)) + X_new = np.vstack((X1_new, X2_new)) + y_new = np.repeat([0, 1], n) + + pred2 = dc.predict(X_new) + + # Assert that both predictions have the same depth + assert_equal(pred1.shape[1], pred2.shape[1]) + + # Assert that both predictions represent a perfect clustering + # of 2 clusters at 1st level + assert_equal(np.max(pred1[:, 0]) + 1, 2) + ari_1 = adjusted_rand_score(y, pred1[:, 0]) + assert_allclose(ari_1, 1) + + assert_equal(np.max(pred2[:, 0]) + 1, 2) + ari_2 = adjusted_rand_score(y_new, pred2[:, 0]) + assert_allclose(ari_2, 1) + + # Assert that predictions on new data have the same or fewer + # clusters than those on the fitted data at each level + for lvl in range(pred1.shape[1]): + n_cluster1 = np.max(pred1[:, lvl]) + 1 + n_cluster2 = np.max(pred2[:, lvl]) + 1 + assert_array_less(n_cluster2, n_cluster1 + 1) + + +# Easily separable hierarchical data with 2 levels +# of four gaussians +np.random.seed(1) +n = 100 +d = 3 + +X11 = np.random.normal(-5, 0.1, size=(n, d)) +X21 = np.random.normal(-2, 0.1, size=(n, d)) +X12 = np.random.normal(2, 0.1, size=(n, d)) +X22 = np.random.normal(5, 0.1, size=(n, d)) +X = np.vstack((X11, X21, X12, X22)) + +# true labels of 2 levels +y_lvl1 = np.repeat([0, 1], 2 * n) +y_lvl2 = np.repeat([0, 1, 2, 3], n) + + +def _test_hierarchical_four_class(**kws): + """ + Clustering above hierarchical data with gmm + """ + np.random.seed(1) + dc = DivisiveCluster(max_components=2, **kws) + pred = dc.fit_predict(X) + + # re-number "pred" so that each column represents + # a flat clustering at current level + for lvl in range(1, pred.shape[1]): + _, inds = np.unique(pred[:, : lvl + 1], axis=0, return_inverse=True) + pred[:, lvl] = inds + + # Assert that the 2-cluster model is the best at level 1 + assert_equal(np.max(pred[:, 0]) + 1, 2) + # Assert that the 4-cluster model is the best at level 1 + assert_equal(np.max(pred[:, 1]) + 1, 4) + + # Assert that we get perfect clustering at level 1 + ari_lvl1 = adjusted_rand_score(y_lvl1, pred[:, 0]) + assert_allclose(ari_lvl1, 1) + + # Assert that we get perfect clustering at level 2 + ari_lvl2 = adjusted_rand_score(y_lvl2, pred[:, 1]) + assert_allclose(ari_lvl2, 1) + + +def test_hierarchical_four_class_gmm(): + _test_hierarchical_four_class(cluster_method="gmm") + + +def test_hierarchical_four_class_aic(): + _test_hierarchical_four_class(cluster_kws=dict(selection_criteria="aic")) + + +def test_hierarchical_four_class_kmeans(): + _test_hierarchical_four_class(cluster_method="kmeans") + + +def test_hierarchical_six_class_delta_criter(): + """ + Clustering on less easily separable hierarchical data with 2 levels + of six gaussians + """ + + np.random.seed(1) + + n = 100 + d = 3 + + X11 = np.random.normal(-4, 0.8, size=(n, d)) + X21 = np.random.normal(-3, 0.8, size=(n, d)) + X31 = np.random.normal(-2, 0.8, size=(n, d)) + X12 = np.random.normal(2, 0.8, size=(n, d)) + X22 = np.random.normal(3, 0.8, size=(n, d)) + X32 = np.random.normal(4, 0.8, size=(n, d)) + X = np.vstack((X11, X21, X31, X12, X22, X32)) + + y_lvl1 = np.repeat([0, 1], 3 * n) + y_lvl2 = np.repeat([0, 1, 2, 3, 4, 5], n) + + # Perform clustering without setting delta_criter + dc = DivisiveCluster(max_components=2) + pred = dc.fit_predict(X) + + # re-number "pred" so that each column represents + # a flat clustering at current level + for lvl in range(1, pred.shape[1]): + _, inds = np.unique(pred[:, : lvl + 1], axis=0, return_inverse=True) + pred[:, lvl] = inds + + # Perform clustering while setting delta_criter + dc = DivisiveCluster(max_components=2, delta_criter=10) + pred_delta_criter = dc.fit_predict(X) + + # re-number "pred_delta_criter" so that each column represents + # a flat clustering at current level + for lvl in range(1, pred_delta_criter.shape[1]): + _, inds = np.unique( + pred_delta_criter[:, : lvl + 1], axis=0, return_inverse=True + ) + pred_delta_criter[:, lvl] = inds + + # Assert that pred has more levels than pred_delta_criter + assert_equal(pred.shape[1] - 1, pred_delta_criter.shape[1]) + + # Assert that both pred_delta_criter and pred represent + # perfect clustering at the first level + ari_lvl1 = adjusted_rand_score(y_lvl1, pred[:, 0]) + assert_allclose(ari_lvl1, 1) + ari_delta_criter_lvl1 = adjusted_rand_score(y_lvl1, pred_delta_criter[:, 0]) + assert_allclose(ari_delta_criter_lvl1, 1) + + # Assert that pred_delta_criter leads to a clustering as good as + # pred at the second level + ari_lvl2 = adjusted_rand_score(y_lvl2, pred[:, 1]) + ari_delta_criter_lvl2 = adjusted_rand_score(y_lvl2, pred_delta_criter[:, 1]) + assert_allclose(ari_delta_criter_lvl2, ari_lvl2) + + # Assert that pred suggests oversplitting at the last level (level 3) + # which leads to a worse clustering than the last level + # of pred_delta_criter (level 2) + ari_lvl3 = adjusted_rand_score(y_lvl2, pred[:, -1]) + assert_array_less(ari_lvl3, ari_delta_criter_lvl2) From f508636240c541399b8dd9d17907989fc7904dc3 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Date: Wed, 28 Oct 2020 08:13:51 -0400 Subject: [PATCH 047/140] Fix path issue, lose unused QAPLIB files (#535) * get rid of qaplib files, make instance chr12c directly in test file * Update test_match.py Co-authored-by: Benjamin Pedigo --- tests/match/qapdata/chr12c.dat | 27 - tests/match/qapdata/chr15a.dat | 35 - tests/match/qapdata/lipa20a.dat | 83 -- tests/match/qapdata/lipa20b.dat | 83 -- tests/match/qapdata/lipa30a.dat | 183 ---- tests/match/qapdata/lipa30b.dat | 184 ---- tests/match/qapdata/lipa40a.dat | 323 ------ tests/match/qapdata/lipa40b.dat | 324 ------ tests/match/qapdata/lipa50a.dat | 504 ---------- tests/match/qapdata/lipa50b.dat | 504 ---------- tests/match/qapdata/lipa60a.dat | 724 -------------- tests/match/qapdata/lipa60b.dat | 724 -------------- tests/match/qapdata/lipa70a.dat | 984 ------------------- tests/match/qapdata/lipa70b.dat | 984 ------------------- tests/match/qapdata/lipa80a.dat | 1284 ------------------------ tests/match/qapdata/lipa80b.dat | 1284 ------------------------ tests/match/qapdata/lipa90a.dat | 1624 ------------------------------- tests/match/qapdata/lipa90b.dat | 1624 ------------------------------- tests/{match => }/test_match.py | 52 +- 19 files changed, 39 insertions(+), 11495 deletions(-) delete mode 100644 tests/match/qapdata/chr12c.dat delete mode 100644 tests/match/qapdata/chr15a.dat delete mode 100644 tests/match/qapdata/lipa20a.dat delete mode 100644 tests/match/qapdata/lipa20b.dat delete mode 100644 tests/match/qapdata/lipa30a.dat delete mode 100644 tests/match/qapdata/lipa30b.dat delete mode 100644 tests/match/qapdata/lipa40a.dat delete mode 100644 tests/match/qapdata/lipa40b.dat delete mode 100644 tests/match/qapdata/lipa50a.dat delete mode 100644 tests/match/qapdata/lipa50b.dat delete mode 100644 tests/match/qapdata/lipa60a.dat delete mode 100644 tests/match/qapdata/lipa60b.dat delete mode 100644 tests/match/qapdata/lipa70a.dat delete mode 100644 tests/match/qapdata/lipa70b.dat delete mode 100644 tests/match/qapdata/lipa80a.dat delete mode 100644 tests/match/qapdata/lipa80b.dat delete mode 100644 tests/match/qapdata/lipa90a.dat delete mode 100644 tests/match/qapdata/lipa90b.dat rename tests/{match => }/test_match.py (69%) diff --git a/tests/match/qapdata/chr12c.dat b/tests/match/qapdata/chr12c.dat deleted file mode 100644 index 7bd56715c..000000000 --- a/tests/match/qapdata/chr12c.dat +++ /dev/null @@ -1,27 +0,0 @@ -12 - - 0 90 10 0 0 0 0 0 0 0 0 0 - 90 0 0 23 0 0 0 0 0 0 0 0 - 10 0 0 0 43 0 0 0 0 0 0 0 - 0 23 0 0 0 88 0 0 0 0 0 0 - 0 0 43 0 0 0 26 0 0 0 0 0 - 0 0 0 88 0 0 0 16 0 0 0 0 - 0 0 0 0 26 0 0 0 1 0 0 0 - 0 0 0 0 0 16 0 0 0 96 0 0 - 0 0 0 0 0 0 1 0 0 0 29 0 - 0 0 0 0 0 0 0 96 0 0 0 37 - 0 0 0 0 0 0 0 0 29 0 0 0 - 0 0 0 0 0 0 0 0 0 37 0 0 - - 0 36 54 26 59 72 9 34 79 17 46 95 - 36 0 73 35 90 58 30 78 35 44 79 36 - 54 73 0 21 10 97 58 66 69 61 54 63 - 26 35 21 0 93 12 46 40 37 48 68 85 - 59 90 10 93 0 64 5 29 76 16 5 76 - 72 58 97 12 64 0 96 55 38 54 0 34 - 9 30 58 46 5 96 0 83 35 11 56 37 - 34 78 66 40 29 55 83 0 44 12 15 80 - 79 35 69 37 76 38 35 44 0 64 39 33 - 17 44 61 48 16 54 11 12 64 0 70 86 - 46 79 54 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33 19 62 35 55 43 28 73 55 - 10 38 75 43 15 52 9 32 48 55 - 28 75 48 54 66 50 44 27 47 38 - 79 36 56 49 18 58 58 0 40 57 - 24 46 32 69 47 42 56 72 59 32 - 79 73 57 88 36 67 56 45 61 39 - 38 58 75 65 69 51 63 57 78 52 - 54 50 72 48 39 25 83 42 46 49 - 55 63 40 69 65 49 68 54 46 44 - 68 51 79 64 29 47 65 44 35 33 - 61 38 17 55 60 64 61 50 83 31 - 29 49 41 9 30 84 54 71 71 34 - 59 29 72 17 65 71 25 40 0 70 - 55 47 58 41 10 47 83 37 30 27 - 64 57 60 78 49 26 59 45 65 61 - 33 61 58 40 29 14 38 31 42 64 - 58 68 70 80 44 42 12 29 62 22 - 37 34 38 47 38 73 16 56 64 51 - 62 55 81 44 44 47 44 54 53 51 - 62 49 43 51 4 52 33 30 52 54 - 71 53 37 46 14 61 45 52 27 66 - 43 44 50 47 67 22 40 57 70 0 - diff --git a/tests/match/test_match.py b/tests/test_match.py similarity index 69% rename from tests/match/test_match.py rename to tests/test_match.py index 2fee2fe1a..34e41539e 100644 --- a/tests/match/test_match.py +++ b/tests/test_match.py @@ -61,21 +61,47 @@ def test_SGM_inputs(self): np.identity(3), np.identity(3), -1 * np.arange(2), -1 * np.arange(2) ) - def _get_AB(self, qap_prob): - with open("tests/match/qapdata/" + qap_prob + ".dat") as f: - f = [int(elem) for elem in f.read().split()] - - # adjusting - f = np.array(f[1:]) - n = int(math.sqrt(len(f) / 2)) - f = f.reshape(2 * n, n) - A = f[:n, :] - B = f[n:, :] - return A, B + def _get_AB(self): + # adjacency matrices from QAPLIB instance chr12c + # QAP problem is minimized with objective function value 11156 + + A = [ + [0, 90, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [90, 0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 0], + [10, 0, 0, 0, 43, 0, 0, 0, 0, 0, 0, 0], + [0, 23, 0, 0, 0, 88, 0, 0, 0, 0, 0, 0], + [0, 0, 43, 0, 0, 0, 26, 0, 0, 0, 0, 0], + [0, 0, 0, 88, 0, 0, 0, 16, 0, 0, 0, 0], + [0, 0, 0, 0, 26, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 16, 0, 0, 0, 96, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 29, 0], + [0, 0, 0, 0, 0, 0, 0, 96, 0, 0, 0, 37], + [0, 0, 0, 0, 0, 0, 0, 0, 29, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 0], + ] + B = [ + [0, 36, 54, 26, 59, 72, 9, 34, 79, 17, 46, 95], + [36, 0, 73, 35, 90, 58, 30, 78, 35, 44, 79, 36], + [54, 73, 0, 21, 10, 97, 58, 66, 69, 61, 54, 63], + [26, 35, 21, 0, 93, 12, 46, 40, 37, 48, 68, 85], + [59, 90, 10, 93, 0, 64, 5, 29, 76, 16, 5, 76], + [72, 58, 97, 12, 64, 0, 96, 55, 38, 54, 0, 34], + [9, 30, 58, 46, 5, 96, 0, 83, 35, 11, 56, 37], + [34, 78, 66, 40, 29, 55, 83, 0, 44, 12, 15, 80], + [79, 35, 69, 37, 76, 38, 35, 44, 0, 64, 39, 33], + [17, 44, 61, 48, 16, 54, 11, 12, 64, 0, 70, 86], + [46, 79, 54, 68, 5, 0, 56, 15, 39, 70, 0, 18], + [95, 36, 63, 85, 76, 34, 37, 80, 33, 86, 18, 0], + ] + A, B = np.array(A), np.array(B) + return A, B def test_barycenter_SGM(self): + # minimize such that we achieve some number close to the optimum, + # though strictly greater than or equal + # results vary due to random shuffle within GraphMatch - A, B = self._get_AB("chr12c") + A, B = self._get_AB() n = A.shape[0] pi = np.array([7, 5, 1, 3, 10, 4, 8, 6, 9, 11, 2, 12]) - [1] * n W1 = [4, 8, 10] @@ -91,7 +117,7 @@ def test_barycenter_SGM(self): assert 11156 == score def test_rand_SGM(self): - A, B = self._get_AB("chr12c") + A, B = self._get_AB() chr12c = self.rand.fit(A, B) score = chr12c.score_ assert 11156 <= score < 13500 From 17ca01b29b61a0038ed3d7ccf4a40f48dd6de7c8 Mon Sep 17 00:00:00 2001 From: Jaewon Chung Date: Mon, 2 Nov 2020 15:22:32 -0500 Subject: [PATCH 048/140] Add mypy.ini and mypy error fixes (#532) * Add mypy + fixes * Black? L100 was broken before. Co-authored-by: Benjamin Pedigo --- graspologic/plot/plot.py | 12 +++++------- graspologic/subgraph/__init__.py | 2 +- mypi.ini | 2 ++ 3 files changed, 8 insertions(+), 8 deletions(-) create mode 100644 mypi.ini diff --git a/graspologic/plot/plot.py b/graspologic/plot/plot.py index efd49ce72..ced3bb1c5 100644 --- a/graspologic/plot/plot.py +++ b/graspologic/plot/plot.py @@ -97,10 +97,8 @@ def _transform(arr, method): transformer = Binarizer().fit(arr) arr = transformer.transform(arr) else: - msg = "Transform must be one of {log, log10, binarize, zero-boost, simple-all, \ - simple-nonzero, not {}.".format( - method - ) + msg = f"Transform must be one of {{log, log10, binarize, zero-boost, \ + simple-all, simple-nonzero}}, not {method}." raise ValueError(msg) return arr @@ -152,7 +150,7 @@ def heatmap( ax=None, title_pad=None, sort_nodes=False, - **kwargs + **kwargs, ): r""" Plots a graph as a color-encoded matrix. @@ -314,7 +312,7 @@ def heatmap( ax=ax, vmin=vmin, vmax=vmax, - **kwargs + **kwargs, ) if title is not None: @@ -1112,7 +1110,7 @@ def _plot_brackets( ax.patch.set_alpha(0) ax.set_yticks([]) ax.set_xticks([]) - ax.tick_params(axis=axis, which=u"both", length=0, pad=7) + ax.tick_params(axis=axis, which="both", length=0, pad=7) for direction in ["left", "right", "bottom", "top"]: ax.spines[direction].set_visible(False) if axis == "x": diff --git a/graspologic/subgraph/__init__.py b/graspologic/subgraph/__init__.py index 4e839321b..264814e66 100644 --- a/graspologic/subgraph/__init__.py +++ b/graspologic/subgraph/__init__.py @@ -4,4 +4,4 @@ from .sg import SignalSubgraph -__all__ = [SignalSubgraph] +__all__ = ["SignalSubgraph"] diff --git a/mypi.ini b/mypi.ini new file mode 100644 index 000000000..1215375ed --- /dev/null +++ b/mypi.ini @@ -0,0 +1,2 @@ +[mypy] +ignore_missing_imports = True \ No newline at end of file From cd06cd46b2e5472140621cac3b4b91575278208d Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Tue, 3 Nov 2020 12:08:32 -0500 Subject: [PATCH 049/140] Update CODEOWNERS (#569) --- .github/CODEOWNERS | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 7fe892d95..26fc27789 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,15 +1,15 @@ # graspologic package -/graspologic/align @alyakin314 @bdpedigo -/graspologic/cluster @bdpedigo @j1c -/graspologic/datasets @bdpedigo -/graspologic/embed @j1c @bdpedigo -/graspologic/inference @alyakin314 @bdpedigo -/graspologic/match @asaadeldin11 @bdpedigo -/graspologic/models @bdpedigo @j1c -/graspologic/pipeline @j1c @bdpedigo -/graspologic/plot @j1c @bdpedigo -/graspologic/subgraph @j1c -/graspologic/utils/ @j1c @bdpedigo +# /graspologic/align @alyakin314 @bdpedigo +# /graspologic/cluster @bdpedigo @j1c +# /graspologic/datasets @bdpedigo +# /graspologic/embed @j1c @bdpedigo +# /graspologic/inference @alyakin314 @bdpedigo +# /graspologic/match @asaadeldin11 @bdpedigo +# /graspologic/models @bdpedigo @j1c +# /graspologic/pipeline @j1c @bdpedigo +# /graspologic/plot @j1c @bdpedigo +# /graspologic/subgraph @j1c +# /graspologic/utils/ @j1c @bdpedigo # tutorials From 5d3ed3282ce9b3cd939efe481b5ef099751e5759 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 4 Nov 2020 11:17:22 -0800 Subject: [PATCH 050/140] Preprocessing graph cuts (#572) * WIP: Draft of graph cuts. Still needs unit tests and type checking. * Adding the bulk of the unit tests from topologic * Used black to autoformat the code * Docs are still screwed up, I need a commit so I can revert back as I try things * a feeble attempt at networkx intersphinx * Fixed the docstrings for sphinx. * Formatting with black * Update graspologic/preprocessing/graph_cuts.py Co-authored-by: Benjamin Pedigo Co-authored-by: bdpedigo --- docs/conf.py | 1 + docs/reference/index.rst | 1 + docs/reference/preprocessing.rst | 43 +++ graspologic/__init__.py | 1 + graspologic/preprocessing/__init__.py | 23 ++ graspologic/preprocessing/graph_cuts.py | 400 ++++++++++++++++++++++ setup.py | 1 + tests/preprocessing/__init__.py | 0 tests/preprocessing/graph_cuts.py | 307 +++++++++++++++++ tests/test_data/actor_bipartite_graph.csv | 11 + tests/utils.py | 8 + 11 files changed, 796 insertions(+) create mode 100644 docs/reference/preprocessing.rst create mode 100644 graspologic/preprocessing/__init__.py create mode 100644 graspologic/preprocessing/graph_cuts.py create mode 100644 tests/preprocessing/__init__.py create mode 100644 tests/preprocessing/graph_cuts.py create mode 100644 tests/test_data/actor_bipartite_graph.csv create mode 100644 tests/utils.py diff --git a/docs/conf.py b/docs/conf.py index b29fea7d8..5d8c5bbf2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -69,6 +69,7 @@ "anytree": ("https://anytree.readthedocs.io/en/latest/", None), "hyppo": ("https://hyppo.neurodata.io", None), "matplotlib": ("https://matplotlib.org", None), + "networkx": ("https://networkx.org/documentation/stable", None), "numpy": ("https://numpy.org/doc/stable", None), "python": ("https://docs.python.org/3", None), "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 7eee7444b..dce08a611 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -17,3 +17,4 @@ Reference match subgraph align + preprocessing diff --git a/docs/reference/preprocessing.rst b/docs/reference/preprocessing.rst new file mode 100644 index 000000000..a2e8a6543 --- /dev/null +++ b/docs/reference/preprocessing.rst @@ -0,0 +1,43 @@ +Preprocessing +============= + +.. currentmodule:: graspologic.preprocessing + +Graph Cuts +---------- + +Constants +^^^^^^^^^ +.. py:data:: LARGER_THAN_INCLUSIVE + +Cut any edge or node > the ``cut_threshold`` + +.. py:data:: LARGER_THAN_EXCLUSIVE + +Cut any edge or node >= the ``cut_threshold`` + +.. py:data:: SMALLER_THAN_INCLUSIVE + +Cut any edge or node < the ``cut_threshold`` + +.. py:data:: SMALLER_THAN_EXCLUSIVE + +Cut any edge or node <= the ``cut_threshold`` + +Classes +^^^^^^^ +.. autoclass:: DefinedHistogram + +Functions +^^^^^^^^^ +.. autofunction:: cut_edges_by_weight + +.. autofunction:: cut_vertices_by_betweenness_centrality + +.. autofunction:: cut_vertices_by_degree_centrality + +.. autofunction:: histogram_betweenness_centrality + +.. autofunction:: histogram_degree_centrality + +.. autofunction:: histogram_edge_weight diff --git a/graspologic/__init__.py b/graspologic/__init__.py index 654ac48ae..b46653a09 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -11,6 +11,7 @@ import graspologic.embed import graspologic.inference import graspologic.models +import graspologic.preprocessing import graspologic.plot import graspologic.simulations import graspologic.subgraph diff --git a/graspologic/preprocessing/__init__.py b/graspologic/preprocessing/__init__.py new file mode 100644 index 000000000..94dcf67e2 --- /dev/null +++ b/graspologic/preprocessing/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +from .graph_cuts import ( + DefinedHistogram, + histogram_betweenness_centrality, + histogram_degree_centrality, + histogram_edge_weight, + cut_edges_by_weight, + cut_vertices_by_betweenness_centrality, + cut_vertices_by_degree_centrality, +) + + +__all__ = [ + "DefinedHistogram", + "histogram_betweenness_centrality", + "histogram_degree_centrality", + "histogram_edge_weight", + "cut_edges_by_weight", + "cut_vertices_by_betweenness_centrality", + "cut_vertices_by_degree_centrality", +] diff --git a/graspologic/preprocessing/graph_cuts.py b/graspologic/preprocessing/graph_cuts.py new file mode 100644 index 000000000..625229c7a --- /dev/null +++ b/graspologic/preprocessing/graph_cuts.py @@ -0,0 +1,400 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import logging +import random +from typing import Any, Callable, List, NamedTuple, Optional, Tuple, Union +import networkx as nx +import numpy as np + + +LARGER_THAN_INCLUSIVE = "larger_than_inclusive" +LARGER_THAN_EXCLUSIVE = "larger_than_exclusive" +SMALLER_THAN_INCLUSIVE = "smaller_than_inclusive" +SMALLER_THAN_EXCLUSIVE = "smaller_than_exclusive" + + +def _filter_function_for_make_cuts( + cut_threshold: Union[int, float], + cut_process: str, +) -> Callable[[Tuple[Any, Union[int, float]]], bool]: + filter_functions = { + LARGER_THAN_EXCLUSIVE: lambda x: x[1] > cut_threshold, + LARGER_THAN_INCLUSIVE: lambda x: x[1] >= cut_threshold, + SMALLER_THAN_EXCLUSIVE: lambda x: x[1] < cut_threshold, + SMALLER_THAN_INCLUSIVE: lambda x: x[1] <= cut_threshold, + } + if cut_process not in filter_functions: + raise ValueError(f"Provided cut_process '{cut_process}' is not a valid value") + return filter_functions[cut_process] + + +class DefinedHistogram(NamedTuple): + """ + Contains the histogram and the edges of the bins in the histogram. + The bin_edges will have a length 1 greater than the histogram, as it defines the minimal and maximal edges as well + as each edge in between. + """ + + histogram: np.ndarray + bin_edges: np.ndarray + + +def histogram_edge_weight( + graph: Union[nx.Graph, nx.DiGraph], + bin_directive: Union[int, List[Union[float, int]], np.ndarray, str] = 10, + weight_attribute: str = "weight", +) -> DefinedHistogram: + """ + Generates a histogram of the edge weights of the provided graph. Histogram function is fundamentally proxied + through to numpy's `histogram` function, and bin selection follows :func:`numpy.histogram` processes. + + Parameters + ---------- + + graph : nx.Graph + The graph. No changes will be made to it. + bin_directive : Union[int, List[Union[float, int]], numpy.ndarray, str] + Is passed directly through to numpy's "histogram" (and thus, "histogram_bin_edges") functions. + + See: :func:`numpy.histogram_bin_edges` + + In short: if an int is provided, we use ``bin_directive`` number of equal range bins. + + If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer + + Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be float or int. + weight_attribute : str + The weight attribute name in the data dictionary. Default is `weight`. + + Returns + ------- + DefinedHistogram + A named tuple that contains the histogram and the bin_edges used in the histogram + + Notes + ----- + Edges without a `weight_attribute` field will be excluded from this histogram. Enable logging to view any + messages about edges without weights. + """ + logger = logging.getLogger(__name__) + edge_weights: List[Union[int, float, None]] = [ + weight for _, _, weight in graph.edges(data=weight_attribute) + ] + none_weights: List[None] = [weight for weight in edge_weights if weight is None] + actual_weights: List[Union[int, float]] = [ + weight for weight in edge_weights if weight is not None + ] + + if len(none_weights) != 0: + logger.warning( + f"Graph contains {len(none_weights)} edges with no {weight_attribute}." + + f" Histogram excludes these values." + ) + + histogram, bin_edges = np.histogram(actual_weights, bin_directive) + + return DefinedHistogram(histogram=histogram, bin_edges=bin_edges) + + +def cut_edges_by_weight( + graph: Union[nx.Graph, nx.DiGraph], + cut_threshold: Union[int, float], + cut_process: str, + weight_attribute: str = "weight", + prune_isolates: bool = False, +) -> Union[nx.Graph, nx.DiGraph]: + """ + Thresholds edges (removing them from the graph and returning a copy) by weight. + + Parameters + ---------- + graph : Union[nx.Graph, nx.DiGraph] + The graph that will be copied and pruned. + cut_threshold : Union[int, float] + The threshold for making cuts based on weight. + cut_process : str + Describes how we should make the cut; cut all edges larger or smaller than the cut_threshold, and whether + exclusive or inclusive. Allowed values are + + - ``larger_than_inclusive`` + - ``larger_than_exclusive`` + - ``smaller_than_inclusive`` + - ``smaller_than_exclusive`` + weight_attribute : str + The weight attribute name in the edge's data dictionary. Default is `weight`. + prune_isolates : bool + If true, remove any vertex that no longer has an edge. Note that this only prunes vertices which have edges to + be pruned; any isolate vertex prior to any edge cut will be retained. + + Returns + ------- + Union[nx.Graph, nx.DiGraph] + Pruned copy of the same type of graph provided + + Notes + ----- + Edges without a `weight_attribute` field will be excluded from these cuts. Enable logging to view any messages + about edges without weights. + """ + filter_by = _filter_function_for_make_cuts(cut_threshold, cut_process) + if not isinstance(cut_threshold, int) and not isinstance(cut_threshold, float): + raise TypeError( + f"cut_threshold must be of type int or float; you provided: {type(cut_threshold)}" + ) + + logger = logging.getLogger(__name__) + graph_copy = graph.copy() + edge_weights: List[Tuple[Tuple[Any, Any], Union[int, float, None]]] = [ + ((source, target), weight) + for source, target, weight in graph.edges(data=weight_attribute) + ] + none_weights: List[Tuple[Tuple[Any, Any], None]] = [ + (edge, weight) for edge, weight in edge_weights if weight is None + ] + actual_weights: List[Tuple[Tuple[Any, Any], Union[int, float]]] = [ + (edge, weight) for edge, weight in edge_weights if weight is not None + ] + + if len(none_weights) != 0: + logger.warning( + f"Graph contains {len(none_weights)} edges with no {weight_attribute}." + + f"Ignoring these when cutting by weight" + ) + + edges_to_cut = [x for x in actual_weights if filter_by(x)] + for edge, weight in edges_to_cut: + source, target = edge + if ( + source in graph_copy + and target in graph_copy + and target in graph_copy[source] + ): + graph_copy.remove_edge(source, target) + if prune_isolates: + if len(graph_copy[source]) == 0: + graph_copy.remove_node(source) + if len(graph_copy[target]) == 0: + graph_copy.remove_node(target) + + return graph_copy + + +def histogram_degree_centrality( + graph: Union[nx.Graph, nx.DiGraph], + bin_directive: Union[int, List[Union[float, int]], np.ndarray, str] = 10, +) -> DefinedHistogram: + """ + Generates a histogram of the vertex degree centrality of the provided graph. Histogram function is fundamentally + proxied through to numpy's `histogram` function, and bin selection follows :func:`numpy.histogram` processes. + + Parameters + ---------- + + graph : Union[nx.Graph, nx.DiGraph] + The graph. No changes will be made to it. + bin_directive : Union[int, List[Union[float, int]], numpy.ndarray, str] + Is passed directly through to numpy's "histogram" (and thus, "histogram_bin_edges") functions. + + See: :func:`numpy.histogram_bin_edges` + + In short: if an int is provided, we use ``bin_directive`` number of equal range bins. + + If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer + + Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be float or int. + + Returns + ------- + DefinedHistogram + A named tuple that contains the histogram and the bin_edges used in the histogram + """ + + degree_centrality_dict = nx.degree_centrality(graph) + histogram, bin_edges = np.histogram( + list(degree_centrality_dict.values()), bin_directive + ) + return DefinedHistogram(histogram=histogram, bin_edges=bin_edges) + + +def cut_vertices_by_degree_centrality( + graph: Union[nx.Graph, nx.DiGraph], + cut_threshold: Union[int, float], + cut_process: str, +) -> Union[nx.Graph, nx.DiGraph]: + """ + Given a graph and a cut_threshold and a cut_process, return a copy of the graph with the vertices outside of the + cut_threshold. + + Parameters + ---------- + graph : Union[nx.Graph, nx.DiGraph] + The graph that will be copied and pruned. + cut_threshold : Union[int, float] + The threshold for making cuts based on weight. + cut_process : str + Describes how we should make the cut; cut all edges larger or smaller than the cut_threshold, and whether + exclusive or inclusive. Allowed values are + + - ``larger_than_inclusive`` + - ``larger_than_exclusive`` + - ``smaller_than_inclusive`` + - ``smaller_than_exclusive`` + + Returns + ------- + Union[nx.Graph, nx.DiGraph] + Pruned copy of the same type of graph provided + """ + graph_copy = graph.copy() + degree_centrality_dict = nx.degree_centrality(graph_copy) + filter_by = _filter_function_for_make_cuts(cut_threshold, cut_process) + vertices_to_cut = list(filter(filter_by, degree_centrality_dict.items())) + for vertex, degree_centrality in vertices_to_cut: + graph_copy.remove_node(vertex) + + return graph_copy + + +def histogram_betweenness_centrality( + graph: Union[nx.Graph, nx.DiGraph], + bin_directive: Union[int, List[Union[float, int]], np.ndarray, str] = 10, + num_random_samples: Optional[int] = None, + normalized: bool = True, + weight_attribute: Optional[str] = "weight", + include_endpoints: bool = False, + random_seed: Optional[Union[int, random.Random, np.random.RandomState]] = None, +) -> DefinedHistogram: + """ + Generates a histogram of the vertex betweenness centrality of the provided graph. Histogram function is + fundamentally proxied through to numpy's `histogram` function, and bin selection follows :func:`numpy.histogram` + processes. + + The betweenness centrality calculation can take advantage of networkx' implementation of randomized sampling by + providing num_random_samples (or ``k``, in networkx betweenness_centrality nomenclature). + + Parameters + ---------- + graph : Union[nx.Graph, nx.DiGraph] + The graph. No changes will be made to it. + bin_directive : Union[int, List[Union[float, int]], numpy.ndarray, str] + Is passed directly through to numpy's "histogram" (and thus, "histogram_bin_edges") functions. + + See: :func:`numpy.histogram_bin_edges` + + In short: if an int is provided, we use ``bin_directive`` number of equal range bins. + + If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer + + Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be float or int. + num_random_samples : Optional[int] + Use num_random_samples for vertex samples to *estimate* betweeness. num_random_samples should be <= + len(graph.nodes). The larger num_random_samples is, the better the approximation. Default is ``None``. + normalized : bool + If True the betweenness values are normalized by :math:`2/((n-1)(n-2))` for undirected graphs, and + :math:`1/((n-1)(n-2))` for directed graphs where n is the number of vertices in the graph. Default is ``True`` + weight_attribute : Optional[str] + If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight. + Default is ``weight`` + include_endpoints : bool + If True include the endpoints in the shortest path counts. Default is ``False`` + random_seed : Optional[Union[int, random.Random, np.random.RandomState]] + Random seed or preconfigured random instance to be used for selecting random samples. Only used if + num_random_samples is set. None will generate a new random state. Specifying a random state will provide + consistent results between runs. + + Returns + ------- + DefinedHistogram + A named tuple that contains the histogram and the bin_edges used in the histogram + + See Also + -------- + networkx.algorithms.centrality.betweenness_centrality + """ + + betweenness_centrality_dict = nx.betweenness_centrality( + G=graph, + k=num_random_samples, + normalized=normalized, + weight=weight_attribute, + endpoints=include_endpoints, + seed=random_seed, + ) + histogram, bin_edges = np.histogram( + list(betweenness_centrality_dict.values()), bin_directive + ) + return DefinedHistogram(histogram=histogram, bin_edges=bin_edges) + + +def cut_vertices_by_betweenness_centrality( + graph: Union[nx.Graph, nx.DiGraph], + cut_threshold: Union[int, float], + cut_process: str, + num_random_samples: Optional[int] = None, + normalized: bool = True, + weight_attribute: Optional[str] = "weight", + include_endpoints: bool = False, + random_seed: Optional[Union[int, random.Random, np.random.RandomState]] = None, +) -> Union[nx.Graph, nx.DiGraph]: + """ + Given a graph and a cut_threshold and a cut_process, return a copy of the graph with the vertices outside of the + cut_threshold. + + The betweenness centrality calculation can take advantage of networkx' implementation of randomized sampling + by providing num_random_samples (or k, in networkx betweenness_centrality nomenclature). + + Parameters + ---------- + graph : Union[nx.Graph, nx.DiGraph] + The graph that will be copied and pruned. + cut_threshold : Union[int, float] + The threshold for making cuts based on weight. + cut_process : str + Describes how we should make the cut; cut all edges larger or smaller than the cut_threshold, and whether + exclusive or inclusive. Allowed values are + + - ``larger_than_inclusive`` + - ``larger_than_exclusive`` + - ``smaller_than_inclusive`` + - ``smaller_than_exclusive`` + num_random_samples : Optional[int] + Use num_random_samples for vertex samples to *estimate* betweenness. num_random_samples should be <= + len(graph.nodes). The larger num_random_samples is, the better the approximation. Default is ``None``. + normalized : bool + If True the betweenness values are normalized by :math:`2/((n-1)(n-2))` for undirected graphs, and + :math:`1/((n-1)(n-2))` for directed graphs where n is the number of vertices in the graph. Default is ``True`` + weight_attribute : Optional[str] + If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight. + Default is ``weight`` + include_endpoints : bool + If True include the endpoints in the shortest path counts. Default is ``False`` + random_seed : Optional[Union[int, random.Random, np.random.RandomState]] + Random seed or preconfigured random instance to be used for selecting random samples. Only used if + num_random_samples is set. None will generate a new random state. Specifying a random state will provide + consistent results between runs. + + Returns + ------- + Union[nx.Graph, nx.DiGraph] + Pruned copy of the same type of graph provided + + See Also + -------- + networkx.algorithms.centrality.betweenness_centrality + """ + graph_copy = graph.copy() + betweenness_centrality_dict = nx.betweenness_centrality( + G=graph, + k=num_random_samples, + normalized=normalized, + weight=weight_attribute, + endpoints=include_endpoints, + seed=random_seed, + ) + filter_by = _filter_function_for_make_cuts(cut_threshold, cut_process) + vertices_to_cut = list(filter(filter_by, betweenness_centrality_dict.items())) + for vertex, degree_centrality in vertices_to_cut: + graph_copy.remove_node(vertex) + + return graph_copy diff --git a/setup.py b/setup.py index c50e950b2..b1fa371b2 100644 --- a/setup.py +++ b/setup.py @@ -73,6 +73,7 @@ "sphinx", "sphinxcontrib-rawfiles", "sphinx-rtd-theme", + "testfixtures", ] }, ) diff --git a/tests/preprocessing/__init__.py b/tests/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/preprocessing/graph_cuts.py b/tests/preprocessing/graph_cuts.py new file mode 100644 index 000000000..c11b47a10 --- /dev/null +++ b/tests/preprocessing/graph_cuts.py @@ -0,0 +1,307 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +import numpy as np +import networkx as nx + +from graspologic.preprocessing import graph_cuts +from ..utils import data_file + +from testfixtures import LogCapture + + +def _get_florentine_graph(): + graph = nx.florentine_families_graph() + + for s, t in graph.edges(): + graph.add_edge(s, t, weight=1) + + return graph + + +def _get_actor_graph(): + graph = nx.Graph() + with open(data_file("actor_bipartite_graph.csv")) as source_iterator: + next(source_iterator) # skip the header + for line in source_iterator: + source, target, _ = line.strip().split(",") + graph.add_edge(source, target, weight=1) + return graph + + +def _get_toy_graph(): + graph = nx.Graph() + graph.add_edge(1, 2, weight=3) + graph.add_edge(4, 5, weight=3) + graph.add_edge("a", "b", weight=5) + graph.add_edge("nick", "dwayne", weight=7) + return graph + + +class TestBetweennessCentralityHistogram(unittest.TestCase): + def test_histogram_by_bin_count(self): + graph = _get_florentine_graph() + defined_histogram = graph_cuts.histogram_betweenness_centrality(graph, 10) + self.assertEqual(10, len(defined_histogram.histogram)) + self.assertEqual(11, len(defined_histogram.bin_edges)) + + def test_histogram_by_edge_bins(self): + graph = _get_florentine_graph() + defined_histogram = graph_cuts.histogram_betweenness_centrality( + graph, [0.0, 0.03, 2.0] + ) + self.assertEqual(2, len(defined_histogram.histogram)) + self.assertEqual(3, len(defined_histogram.bin_edges)) + np.testing.assert_array_equal( + np.array([0.0, 0.03, 2.0], dtype=np.dtype(float)), + defined_histogram.bin_edges, + ) + + def test_histogram_by_auto(self): + graph = _get_florentine_graph() + defined_histogram = graph_cuts.histogram_betweenness_centrality(graph, "auto") + self.assertEqual(6, len(defined_histogram.histogram)) + self.assertEqual(7, len(defined_histogram.bin_edges)) + + +class TestBetweennessCentralityCut(unittest.TestCase): + def test_cut_none(self): + graph = _get_florentine_graph() + expected_graph_nodes = 14 + expected_graph_edges = 14 + + result = graph_cuts.cut_vertices_by_betweenness_centrality( + graph, 0.4, graph_cuts.LARGER_THAN_EXCLUSIVE + ) + self.assertEqual(expected_graph_edges, len(result.edges)) + self.assertEqual(expected_graph_nodes, len(result.nodes)) + + def test_cut_all(self): + graph = _get_florentine_graph() + + result = graph_cuts.cut_vertices_by_betweenness_centrality( + graph, 0.5, graph_cuts.SMALLER_THAN_INCLUSIVE + ) + self.assertEqual(0, len(result.edges)) + self.assertEqual(1, len(result.nodes)) + + def test_cut_less_than_inclusive(self): + graph = _get_florentine_graph() + + result = graph_cuts.cut_vertices_by_betweenness_centrality( + graph, 0.0166525071464909, graph_cuts.SMALLER_THAN_INCLUSIVE + ) + self.assertEqual(11, len(result.nodes)) + + def test_cut_less_than_exclusive(self): + graph = _get_florentine_graph() + + result = graph_cuts.cut_vertices_by_betweenness_centrality( + graph, 0.017, graph_cuts.SMALLER_THAN_EXCLUSIVE + ) + self.assertEqual(11, len(result.nodes)) + + def test_cut_greater_than_inclusive(self): + graph = _get_florentine_graph() + + result = graph_cuts.cut_vertices_by_betweenness_centrality( + graph, 0.01665250714649088, graph_cuts.LARGER_THAN_INCLUSIVE + ) + self.assertEqual(4, len(result.nodes)) + + def test_cut_greater_than_exclusive(self): + graph = _get_florentine_graph() + + result = graph_cuts.cut_vertices_by_betweenness_centrality( + graph, 0.017, graph_cuts.LARGER_THAN_EXCLUSIVE + ) + self.assertEqual(4, len(result.nodes)) + + +class TestDegreeCentralityHistogram(unittest.TestCase): + def test_histogram_by_bin_count(self): + graph = _get_actor_graph() + defined_histogram = graph_cuts.histogram_degree_centrality(graph, 10) + self.assertEqual(10, len(defined_histogram.histogram)) + self.assertEqual(11, len(defined_histogram.bin_edges)) + + def test_histogram_by_edge_bins(self): + graph = _get_actor_graph() + defined_histogram = graph_cuts.histogram_degree_centrality( + graph, [0.0, 0.03, 2.0] + ) + self.assertEqual(2, len(defined_histogram.histogram)) + self.assertEqual(3, len(defined_histogram.bin_edges)) + np.testing.assert_array_equal( + np.array([0.0, 0.03, 2.0], dtype=np.dtype(float)), + defined_histogram.bin_edges, + ) + + def test_histogram_by_auto(self): + graph = _get_actor_graph() + defined_histogram = graph_cuts.histogram_degree_centrality(graph, "auto") + self.assertEqual(5, len(defined_histogram.histogram)) + self.assertEqual(6, len(defined_histogram.bin_edges)) + + +class TestDegreeCentralityCut(unittest.TestCase): + def test_cut_none(self): + graph = _get_actor_graph() + expected_graph_nodes = len(graph.nodes) + expected_graph_edges = len(graph.edges) + + result = graph_cuts.cut_vertices_by_degree_centrality( + graph, 0.5, graph_cuts.LARGER_THAN_EXCLUSIVE + ) + self.assertEqual(expected_graph_edges, len(result.edges)) + self.assertEqual(expected_graph_nodes, len(result.nodes)) + + def test_cut_all(self): + graph = _get_actor_graph() + + result = graph_cuts.cut_vertices_by_degree_centrality( + graph, 0.5, graph_cuts.SMALLER_THAN_INCLUSIVE + ) + self.assertEqual(0, len(result.edges)) + self.assertEqual(0, len(result.nodes)) + + def test_cut_less_than_inclusive(self): + graph = _get_actor_graph() + + result = graph_cuts.cut_vertices_by_degree_centrality( + graph, 0.01665, graph_cuts.SMALLER_THAN_INCLUSIVE + ) + self.assertEqual(11, len(result.nodes)) + + def test_cut_less_than_exclusive(self): + graph = _get_actor_graph() + + result = graph_cuts.cut_vertices_by_degree_centrality( + graph, 0.01666666666666668, graph_cuts.SMALLER_THAN_EXCLUSIVE + ) + self.assertEqual(11, len(result.nodes)) + + def test_cut_greater_than_inclusive(self): + graph = _get_actor_graph() + + result = graph_cuts.cut_vertices_by_degree_centrality( + graph, 0.139280, graph_cuts.LARGER_THAN_INCLUSIVE + ) + self.assertEqual(6, len(result.nodes)) + + def test_cut_greater_than_exclusive(self): + graph = _get_actor_graph() + + result = graph_cuts.cut_vertices_by_degree_centrality( + graph, 0.1392857142857144, graph_cuts.LARGER_THAN_EXCLUSIVE + ) + self.assertEqual(6, len(result.nodes)) + + +class TestEdgeWeights(unittest.TestCase): + def test_histogram_from_graph(self): + with LogCapture() as log_capture: + graph = nx.Graph() + graph.add_edge(1, 2, weight=3) + graph.add_edge(4, 5, weight=3) + graph.add_edge("a", "b", weight=5) + graph.add_edge("nick", "dwayne") + expected = graph_cuts.DefinedHistogram( + histogram=np.array([2, 1]), bin_edges=np.array([3, 4, 5]) + ) + result = graph_cuts.histogram_edge_weight(graph, 2) + np.testing.assert_array_equal(expected.histogram, result.histogram) + np.testing.assert_array_equal(expected.bin_edges, result.bin_edges) + + # check logger is logging things correctly since it is an important part of this function + # by proxy this also checks that edges_by_weight is called + log_capture.check( + ( + "graspologic.preprocessing.graph_cuts", + "WARNING", + "Graph contains 1 edges with no weight. Histogram excludes these values.", + ) + ) + + def test_make_cuts_larger_than_inclusive(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 5, graph_cuts.LARGER_THAN_INCLUSIVE, prune_isolates=True + ) + self.assertEqual(2, len(updated_graph.edges)) + self.assertEqual(4, len(updated_graph.nodes)) + self.assertEqual(3, updated_graph[1][2]["weight"]) + self.assertEqual(3, updated_graph[5][4]["weight"]) + + def test_make_cuts_larger_than_exclusive(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 5, graph_cuts.LARGER_THAN_EXCLUSIVE, prune_isolates=True + ) + self.assertEqual(3, len(updated_graph.edges)) + self.assertEqual(6, len(updated_graph.nodes)) + self.assertEqual(3, updated_graph[1][2]["weight"]) + self.assertEqual(3, updated_graph[5][4]["weight"]) + self.assertEqual(5, updated_graph["b"]["a"]["weight"]) + + def test_make_cuts_smaller_than_inclusive(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 5, graph_cuts.SMALLER_THAN_INCLUSIVE, prune_isolates=True + ) + self.assertEqual(1, len(updated_graph.edges)) + self.assertEqual(2, len(updated_graph.nodes)) + self.assertEqual(7, updated_graph["nick"]["dwayne"]["weight"]) + + def test_make_cuts_smaller_than_exclusive(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 5, graph_cuts.SMALLER_THAN_EXCLUSIVE, prune_isolates=True + ) + self.assertEqual(2, len(updated_graph.edges)) + self.assertEqual(4, len(updated_graph.nodes)) + self.assertEqual(5, updated_graph["a"]["b"]["weight"]) + self.assertEqual(7, updated_graph["nick"]["dwayne"]["weight"]) + + def test_make_cuts_smaller_than_exclusive_no_prune_isolates(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 5, graph_cuts.SMALLER_THAN_EXCLUSIVE + ) + self.assertEqual(2, len(updated_graph.edges)) + self.assertEqual(8, len(updated_graph.nodes)) + self.assertEqual(5, updated_graph["a"]["b"]["weight"]) + self.assertEqual(7, updated_graph["nick"]["dwayne"]["weight"]) + self.assertIn(1, updated_graph) + self.assertIn(2, updated_graph) + self.assertIn(4, updated_graph) + self.assertIn(5, updated_graph) + + def test_cut_all(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 7, graph_cuts.SMALLER_THAN_INCLUSIVE, prune_isolates=True + ) + self.assertEqual(0, len(updated_graph.edges)) + self.assertEqual(0, len(updated_graph.nodes)) + + def test_cut_none(self): + graph = _get_toy_graph() + + updated_graph = graph_cuts.cut_edges_by_weight( + graph, 7, graph_cuts.LARGER_THAN_EXCLUSIVE, prune_isolates=True + ) + self.assertEqual(4, len(updated_graph.edges)) + self.assertEqual(8, len(updated_graph.nodes)) + + def test_broken_make_cuts(self): + graph = _get_toy_graph() + with self.assertRaises(ValueError): + graph_cuts.cut_edges_by_weight(graph, 5, None) diff --git a/tests/test_data/actor_bipartite_graph.csv b/tests/test_data/actor_bipartite_graph.csv new file mode 100644 index 000000000..259d65d7e --- /dev/null +++ b/tests/test_data/actor_bipartite_graph.csv @@ -0,0 +1,11 @@ +Person,Movie,Role +Tom Hanks,Apollo 13,Cast +Bill Paxton,Apollo 13,Cast +Kevin Bacon,Apollo 13,Cast +Kathleen Quinlan,Apollo 13,Cast +Kevin Bacon,Planes Trains & Automobiles,Cast +Steve Martin,Planes Trains & Automobiles,Cast +John Candy,Planes Trains & Automobiles,Cast +Tom Hanks,Mamma Mia! Here We Go Again,Executive Producer +Tom Hanks,Forrest Gump,Cast +Sally Field,Forrest Gump,Cast diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 000000000..da06dcaa9 --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,8 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import os + + +def data_file(filename): + return os.path.join(os.path.dirname(__file__), "test_data", filename) From 4e50960386296c6593010e2233df50994f051c0b Mon Sep 17 00:00:00 2001 From: Paul Adkisson Date: Fri, 6 Nov 2020 18:40:11 -0500 Subject: [PATCH 051/140] Standardize docs (#525) * Standardized first half of tutorials * updating repo to match upstream * Suppressing graph messages up to latent_position_test * finished standardizing tutorials * standardize simulations * second standardize of simulations * standardize models * standardize embed * standardize cluster * standardize inference * standardize plot * standardize datasets * standardize utility * standardize pipeline * standardize matching * standardize subgraph * standardize align * blackened repo * fixing the version.txt file * responding to feedback * Correcting version to 3.7.0 * Changing to _ = to suppress graph summary output * Blackened repo * Final check of netlify build * correcting minor typos * correcting one last typo Co-authored-by: Benjamin Pedigo --- .../embedding/AdjacencySpectralEmbed.ipynb | 10 ++--- docs/tutorials/embedding/Omnibus.ipynb | 10 ++--- .../inference/latent_distribution_test.ipynb | 10 ++--- .../inference/latent_position_test.ipynb | 16 ++++---- docs/tutorials/matching/faq.ipynb | 8 ++-- docs/tutorials/matching/padded_gm.ipynb | 11 +---- docs/tutorials/matching/sgm.ipynb | 6 +-- docs/tutorials/models/models.ipynb | 26 ++++++------ docs/tutorials/simulations/corr.ipynb | 11 +---- docs/tutorials/simulations/erdos_renyi.ipynb | 6 +-- docs/tutorials/simulations/rdpg.ipynb | 10 ++--- docs/tutorials/simulations/rdpg_corr.ipynb | 6 +-- docs/tutorials/simulations/sbm.ipynb | 8 ++-- docs/tutorials/subgraph/subgraph.ipynb | 8 ++-- graspologic/align/base.py | 32 +++++++-------- graspologic/align/orthogonal_procrustes.py | 32 +++++++-------- graspologic/align/seedless_procrustes.py | 40 +++++++++---------- graspologic/align/sign_flips.py | 20 +++++----- graspologic/cluster/autogmm.py | 12 +++--- graspologic/cluster/gclust.py | 14 +++---- graspologic/cluster/kclust.py | 4 +- graspologic/datasets/base.py | 2 +- graspologic/embed/ase.py | 8 ++-- graspologic/embed/lse.py | 12 +++--- graspologic/embed/mase.py | 10 ++--- graspologic/embed/mds.py | 14 +++---- graspologic/embed/mug2vec.py | 14 +++---- graspologic/embed/n2v.py | 2 +- graspologic/embed/omni.py | 6 +-- graspologic/embed/svd.py | 20 +++++----- .../inference/latent_distribution_test.py | 34 +++++++++------- graspologic/inference/latent_position_test.py | 2 +- graspologic/match/gmp.py | 26 ++++++------ graspologic/match/skp.py | 12 +++--- graspologic/models/base.py | 4 +- graspologic/models/rdpg.py | 2 +- graspologic/models/sbm.py | 10 ++--- graspologic/plot/plot.py | 34 ++++++++-------- graspologic/simulations/rdpg_corr.py | 4 +- graspologic/simulations/simulations.py | 36 ++++++++--------- graspologic/simulations/simulations_corr.py | 6 +-- graspologic/subgraph/sg.py | 16 +++++--- graspologic/utils/utils.py | 18 ++++----- 43 files changed, 292 insertions(+), 300 deletions(-) diff --git a/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb b/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb index bb67801f5..43c573c35 100644 --- a/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb +++ b/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb @@ -46,14 +46,14 @@ "undirected_sbm = sbm(2 * [n_verts], P)\n", "heatmap(undirected_sbm, title='2-block SBM (undirected)', inner_hier_labels=labels_sbm)\n", "directed_sbm = sbm(2 * [n_verts], P, directed=True)\n", - "heatmap(directed_sbm, title='2-block SBM (directed)', inner_hier_labels=labels_sbm)" + "_ = heatmap(directed_sbm, title='2-block SBM (directed)', inner_hier_labels=labels_sbm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can use the AdjacencySpectralEmbed class to embed the adjacency matrix as follows. If no parameters are given to the AdjacencySpectralEmbed class, it will automatically choose the number of dimensions to embed into." + "We can use the ``AdjacencySpectralEmbed`` class to embed the adjacency matrix as follows. If no parameters are given to the ``AdjacencySpectralEmbed`` class, it will automatically choose the number of dimensions to embed into." ] }, { @@ -64,7 +64,7 @@ "source": [ "ase = AdjacencySpectralEmbed()\n", "Xhat = ase.fit_transform(undirected_sbm)\n", - "pairplot(Xhat, title='SBM adjacency spectral embedding')" + "_ = pairplot(Xhat, title='SBM adjacency spectral embedding')" ] }, { @@ -83,7 +83,7 @@ "ase = AdjacencySpectralEmbed()\n", "Xhat, Yhat = ase.fit_transform(directed_sbm)\n", "pairplot(Xhat, title='SBM adjacency spectral embedding \"out\"')\n", - "pairplot(Yhat, title='SBM adjacency spectral embedding \"in\"')" + "_ = pairplot(Yhat, title='SBM adjacency spectral embedding \"in\"')" ] }, { @@ -101,7 +101,7 @@ "source": [ "ase = AdjacencySpectralEmbed(n_components=2, algorithm='truncated')\n", "Xhat = ase.fit_transform(undirected_sbm)\n", - "pairplot(Xhat, title='2-component embedding', height=4)" + "_ = pairplot(Xhat, title='2-component embedding', height=4)" ] }, { diff --git a/docs/tutorials/embedding/Omnibus.ipynb b/docs/tutorials/embedding/Omnibus.ipynb index 5730525e4..0581b9e40 100644 --- a/docs/tutorials/embedding/Omnibus.ipynb +++ b/docs/tutorials/embedding/Omnibus.ipynb @@ -32,11 +32,11 @@ "\n", "\\begin{align*}\n", "P_1 = \n", - "\\begin{bmatrix}0.2 & 0.1\\\\\n", + "\\begin{bmatrix}0.3 & 0.1\\\\\n", "0.1 & 0.7\n", "\\end{bmatrix},~\n", - "P_2 = \\begin{bmatrix}0.2 & 0.1\\\\\n", - "0.1 & 0.2\n", + "P_2 = \\begin{bmatrix}0.3 & 0.1\\\\\n", + "0.1 & 0.3\n", "\\end{bmatrix}\n", "\\end{align*}\n", "\n", @@ -82,7 +82,7 @@ "from graspologic.plot import heatmap\n", "\n", "heatmap(G1, figsize=(7, 7), title='Visualization of Graph 1')\n", - "heatmap(G2, figsize=(7, 7), title='Visualization of Graph 2')" + "_ = heatmap(G2, figsize=(7, 7), title='Visualization of Graph 2')" ] }, { @@ -152,7 +152,7 @@ "for i in range(50):\n", " ax.plot([Xhat1[i, 0], Xhat2[i, 0]], [Xhat1[i, 1], Xhat2[i, 1]], 'black', alpha = 0.15)\n", " \n", - "ax.set_title('Latent Positions from Omnibus Embedding', fontsize=20)" + "_ = ax.set_title('Latent Positions from Omnibus Embedding', fontsize=20)" ] } ], diff --git a/docs/tutorials/inference/latent_distribution_test.ipynb b/docs/tutorials/inference/latent_distribution_test.ipynb index 152c4acc3..88ca1075b 100644 --- a/docs/tutorials/inference/latent_distribution_test.ipynb +++ b/docs/tutorials/inference/latent_distribution_test.ipynb @@ -50,7 +50,7 @@ "A1 = sbm(csize, P)\n", "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", "heatmap(A1, title='2-block SBM adjacency matrix')\n", - "pairplot(X1, title='2-block adjacency spectral embedding', height=4.5)" + "_ = pairplot(X1, title='2-block adjacency spectral embedding', height=4.5)" ] }, { @@ -72,7 +72,7 @@ "A1 = sbm(csize, P)\n", "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", "heatmap(A2, title='2-block SBM adjacency matrix')\n", - "pairplot(X2, title='2-block adjacency spectral embedding', height=4.5)" + "_ = pairplot(X2, title='2-block adjacency spectral embedding', height=4.5)" ] }, { @@ -144,7 +144,7 @@ "ax[1].hist(ldt_mgc.null_distribution_, 50)\n", "ax[1].axvline(ldt_mgc.sample_T_statistic_, color='r')\n", "ax[1].set_title(\"MGC: P-value = {}\".format(ldt_mgc.p_value_), fontsize=20)\n", - "plt.show();" + "_ = plt.show()" ] }, { @@ -175,7 +175,7 @@ "A3 = sbm(csize, P2)\n", "heatmap(A3, title='2-block SBM adjacency matrix A3')\n", "X3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n", - "pairplot(X3, title='2-block adjacency spectral embedding A3', height=4.5)" + "_ = pairplot(X3, title='2-block adjacency spectral embedding A3', height=4.5)" ] }, { @@ -231,7 +231,7 @@ "ax[1].hist(ldt_mgc.null_distribution_, 50)\n", "ax[1].axvline(ldt_mgc.sample_T_statistic_, color='r')\n", "ax[1].set_title(\"MGC: P-value = {}\".format(ldt_mgc.p_value_), fontsize=20)\n", - "plt.show();" + "_ = plt.show()" ] } ], diff --git a/docs/tutorials/inference/latent_position_test.ipynb b/docs/tutorials/inference/latent_position_test.ipynb index 9aa3ff3a1..39dd1cdef 100644 --- a/docs/tutorials/inference/latent_position_test.ipynb +++ b/docs/tutorials/inference/latent_position_test.ipynb @@ -50,18 +50,18 @@ "A = sbm(csize, P)\n", "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n", "heatmap(A, title='4-block SBM adjacency matrix')\n", - "pairplot(X, title='4-block adjacency spectral embedding')" + "_ = pairplot(X, title='4-block adjacency spectral embedding')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. ASE(A) recovers an estimate of the **latent positions** of $A $. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n", + "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. $\\text{ASE}(A)$ recovers an estimate of the **latent positions** of $A$. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n", "\n", - "For an adjacency matrix $A \\in \\mathbb{R}^{n x n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n x n}$\n", + "For an adjacency matrix $A \\in \\mathbb{R}^{n \\times n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n \\times n}$\n", "\n", - "$P = XX^T$, where $X \\in \\mathbb{R}^{n x d} $ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n", + "$P = XX^T$, where $X \\in \\mathbb{R}^{n \\times d}$ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n", "\n", "ASE is one way to obtain an estimate of the latent positions of a graph, $\\hat{X}$\n", "\n", @@ -73,7 +73,7 @@ "metadata": {}, "source": [ "## Sample new RDPGs from this latent position\n", - "Given the estimate of X, we now sample two new RDPGs from the same latent position above" + "Given the estimate of $X$, we now sample two new RDPGs from the same latent position above" ] }, { @@ -97,7 +97,7 @@ "heatmap(A1, title='Sampled RDPG 1 adjacency matrix')\n", "heatmap(A2, title='Sampled RDPG 2 adjacency matrix')\n", "pairplot(Xhat1, title='Sampled RDPG 1 adjacency spectral embedding')\n", - "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')" + "_ = pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')" ] }, { @@ -120,7 +120,7 @@ "\n", "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n", "\n", - "Note that LatentPositionTest.fit() may take several minutes" + "Note that ``LatentPositionTest.fit()`` may take several minutes" ] }, { @@ -177,7 +177,7 @@ "heatmap(A3, title='Sampled RDPG 3 adjacency matrix')\n", "heatmap(A4, title='Sampled RDPG 4 (distorted) adjacency matrix')\n", "pairplot(Xhat3, title='Sampled RDPG 3 adjacency spectral embedding')\n", - "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')" + "_ = pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')" ] }, { diff --git a/docs/tutorials/matching/faq.ipynb b/docs/tutorials/matching/faq.ipynb index b28658954..b7b44e408 100644 --- a/docs/tutorials/matching/faq.ipynb +++ b/docs/tutorials/matching/faq.ipynb @@ -26,7 +26,7 @@ "\n", "\\begin{equation}\n", "\\begin{aligned}\n", - "\\min & {\\;-trace(APB^T P^T)}\\\\\n", + "\\min & {\\;-\\text{trace}(APB^T P^T)}\\\\\n", "\\text{s.t. } & {\\;P \\: \\epsilon \\: \\mathcal{P}} \\\\\n", "\\end{aligned}\n", "\\end{equation}\n", @@ -44,7 +44,7 @@ "\n", "\\begin{equation}\n", "\\begin{aligned}\n", - "\\min & {\\; trace(APB^T P^T)}\\\\\n", + "\\min & {\\; \\text{trace}(APB^T P^T)}\\\\\n", "\\text{s.t. } & {\\;P \\: \\epsilon \\: \\mathcal{P}} \\\\\n", "\\end{aligned}\n", "\\end{equation}\n", @@ -110,7 +110,7 @@ "source": [ "from graspologic.plot import heatmap\n", "heatmap(G1, cbar=False, title = 'G1 [ER-NP(50, 0.3) Simulation]')\n", - "heatmap(G2, cbar=False, title = 'G2 [G1 Randomly Shuffled]')" + "_ = heatmap(G2, cbar=False, title = 'G2 [G1 Randomly Shuffled]')" ] }, { @@ -141,7 +141,7 @@ "outputs": [], "source": [ "heatmap(G1, cbar=False, title = 'G1[ER-NP(50, 0.3) Simulation]')\n", - "heatmap(G2, cbar=False, title = 'G2[ER-NP(50, 0.3) Randomly Shuffled] unshuffled')" + "_ = heatmap(G2, cbar=False, title = 'G2[ER-NP(50, 0.3) Randomly Shuffled] unshuffled')" ] } ], diff --git a/docs/tutorials/matching/padded_gm.ipynb b/docs/tutorials/matching/padded_gm.ipynb index d954b4427..ef7145864 100644 --- a/docs/tutorials/matching/padded_gm.ipynb +++ b/docs/tutorials/matching/padded_gm.ipynb @@ -99,7 +99,7 @@ "heatmap(G1p, ax=axs[0], cbar=False, title=\"G1'\")\n", "heatmap(G2, ax=axs[1], cbar=False, title=\"G2\")\n", "heatmap(c, ax=axs[2], cbar=False, title=\"G1\")\n", - "heatmap(topleft_G1, ax=axs[3], cbar=False, title=\"G1 (to top left corner)\")" + "_ = heatmap(topleft_G1, ax=axs[3], cbar=False, title=\"G1 (to top left corner)\")" ] }, { @@ -146,13 +146,6 @@ "\n", "On the other hand, we see that adopted padding preserves the common block structure between $G_1$ and $G_2$." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -171,7 +164,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/docs/tutorials/matching/sgm.ipynb b/docs/tutorials/matching/sgm.ipynb index 04dad7750..f8c662228 100644 --- a/docs/tutorials/matching/sgm.ipynb +++ b/docs/tutorials/matching/sgm.ipynb @@ -77,7 +77,7 @@ "fig, axs = plt.subplots(1, 3, figsize=(10, 5))\n", "heatmap(A1, ax=axs[0], cbar=False, title=\"Graph 1\")\n", "heatmap(A2, ax=axs[1], cbar=False, title=\"Graph 2\")\n", - "heatmap(A1 - A2, ax=axs[2], cbar=False, title=\"Diff (G1 - G2)\")" + "_ = heatmap(A1 - A2, ax=axs[2], cbar=False, title=\"Diff (G1 - G2)\")" ] }, { @@ -116,7 +116,7 @@ "fig, axs = plt.subplots(1, 3, figsize=(10, 5))\n", "heatmap(A1, ax=axs[0], cbar=False, title=\"Graph 1\")\n", "heatmap(A2_shuffle, ax=axs[1], cbar=False, title=\"Graph 2 shuffled\")\n", - "heatmap(A1 - A2_shuffle, ax=axs[2], cbar=False, title=\"Diff (G1 - G2 shuffled)\")\n" + "_ = heatmap(A1 - A2_shuffle, ax=axs[2], cbar=False, title=\"Diff (G1 - G2 shuffled)\")" ] }, { @@ -181,7 +181,7 @@ "heatmap(A1 - A2_unshuffle, ax=axs[2], cbar=False, title=\"Diff (G1 - G2 unshuffled)\")\n", "\n", "match_ratio = 1-(np.count_nonzero(abs(sgm.perm_inds_-node_unshuffle_input))/n_verts)\n", - "print(\"Match Ratio with 10 seeds: \", match_ratio)\n" + "print(\"Match Ratio with 10 seeds: \", match_ratio)" ] }, { diff --git a/docs/tutorials/models/models.ipynb b/docs/tutorials/models/models.ipynb index 4f5708556..89d8064d1 100644 --- a/docs/tutorials/models/models.ipynb +++ b/docs/tutorials/models/models.ipynb @@ -47,11 +47,11 @@ "\n", "adj, labels = load_drosophila_right(return_labels=True)\n", "adj = binarize(adj)\n", - "heatmap(adj,\n", + "_ = heatmap(adj,\n", " inner_hier_labels=labels,\n", " title='Drosophila right MB',\n", " font_scale=1.5,\n", - " sort_nodes=True);" + " sort_nodes=True)" ] }, { @@ -115,11 +115,11 @@ " title=\"ER probability matrix\",\n", " vmin=0, vmax=1, \n", " sort_nodes=True)\n", - "heatmap(er.sample()[0],\n", + "_ = heatmap(er.sample()[0],\n", " inner_hier_labels=labels,\n", " font_scale=1.5,\n", " title=\"ER sample\",\n", - " sort_nodes=True);" + " sort_nodes=True)" ] }, { @@ -154,11 +154,11 @@ " font_scale=1.5,\n", " title=\"DCER probability matrix\", \n", " sort_nodes=True);\n", - "heatmap(dcer.sample()[0],\n", + "_ = heatmap(dcer.sample()[0],\n", " inner_hier_labels=labels,\n", " font_scale=1.5,\n", " title=\"DCER sample\", \n", - " sort_nodes=True);" + " sort_nodes=True)" ] }, { @@ -191,11 +191,11 @@ " font_scale=1.5,\n", " title=\"SBM probability matrix\", \n", " sort_nodes=True)\n", - "heatmap(sbme.sample()[0],\n", + "_ = heatmap(sbme.sample()[0],\n", " inner_hier_labels=labels,\n", " font_scale=1.5,\n", " title=\"SBM sample\", \n", - " sort_nodes=True);" + " sort_nodes=True)" ] }, { @@ -230,11 +230,11 @@ " vmin=0,\n", " vmax=1,\n", " sort_nodes=True)\n", - "heatmap(dcsbme.sample()[0],\n", + "_ = heatmap(dcsbme.sample()[0],\n", " inner_hier_labels=labels,\n", " title=\"DCSBM sample\",\n", " font_scale=1.5,\n", - " sort_nodes=True);" + " sort_nodes=True)" ] }, { @@ -271,11 +271,11 @@ " title=\"RDPG probability matrix\",\n", " sort_nodes=True\n", " )\n", - "heatmap(rdpge.sample()[0],\n", + "_ = heatmap(rdpge.sample()[0],\n", " inner_hier_labels=labels,\n", " font_scale=1.5,\n", " title=\"RDPG sample\",\n", - " sort_nodes=True);" + " sort_nodes=True)" ] }, { @@ -336,7 +336,7 @@ "# add colorbar\n", "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", "sm.set_array(dcsbme.p_mat_)\n", - "fig.colorbar(sm,\n", + "_ = fig.colorbar(sm,\n", " ax=axs,\n", " orientation=\"horizontal\",\n", " pad=0.04,\n", diff --git a/docs/tutorials/simulations/corr.ipynb b/docs/tutorials/simulations/corr.ipynb index 81c946ccd..28b7f515f 100644 --- a/docs/tutorials/simulations/corr.ipynb +++ b/docs/tutorials/simulations/corr.ipynb @@ -82,7 +82,7 @@ "source": [ "from graspologic.plot import heatmap\n", "heatmap(G1, title = 'Correlated ER Simulation Graph 1')\n", - "heatmap(G2, title = 'Correlated ER Simulation Graph 2')" + "_ = heatmap(G2, title = 'Correlated ER Simulation Graph 2')" ] }, { @@ -147,15 +147,8 @@ "outputs": [], "source": [ "heatmap(G1, title = 'Correlated SBM Simulation Graph 1')\n", - "heatmap(G2, title = 'Correlated SBM Simulation Graph 2')" + "_ = heatmap(G2, title = 'Correlated SBM Simulation Graph 2')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/tutorials/simulations/erdos_renyi.ipynb b/docs/tutorials/simulations/erdos_renyi.ipynb index 0d8d66f4e..d751327b5 100644 --- a/docs/tutorials/simulations/erdos_renyi.ipynb +++ b/docs/tutorials/simulations/erdos_renyi.ipynb @@ -65,7 +65,7 @@ "from graspologic.plot import heatmap\n", "\n", "heatmap(G1, title = 'ER-NP(50, 0.3) Simulation')\n", - "heatmap(G2, title = 'ER-NM(50, 250) Simulation')" + "_ = heatmap(G2, title = 'ER-NM(50, 250) Simulation')" ] }, { @@ -74,7 +74,7 @@ "source": [ "## Weighted ER Graphs\n", "\n", - "Both *er_np* and *er_nm* functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, we provide a probability distribution function with corresponding keyword arguments for the distribution function.\n", + "Both ``er_np()`` and ``er_nm()`` functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, we provide a probability distribution function with corresponding keyword arguments for the distribution function.\n", "\n", "Below we sample $G_1 \\sim ER_{NP}(50, 0.2)$ where the weights are distributed normally with $\\mu = 0,~\\sigma^2 = 1$." ] @@ -104,7 +104,7 @@ "metadata": {}, "outputs": [], "source": [ - "heatmap(G1, title = 'ER-NP(50, 0.2) with N(0,1) Weights Simulation')" + "_ = heatmap(G1, title = 'ER-NP(50, 0.2) with N(0,1) Weights Simulation')" ] } ], diff --git a/docs/tutorials/simulations/rdpg.ipynb b/docs/tutorials/simulations/rdpg.ipynb index 109d567de..f04cc90fc 100644 --- a/docs/tutorials/simulations/rdpg.ipynb +++ b/docs/tutorials/simulations/rdpg.ipynb @@ -25,9 +25,7 @@ "source": [ "RDPG is a latent position generative model, in which the probability of an edge existing between pairs of vertices is determined by the dot product of the associated latent position vectors. In other words, given $X \\in \\mathbb{R}^{n\\times d}$, where $n$ is the number of vertices and $d$ is the dimensionality of each vector, the probability matrix $P$ is given by:\n", "\n", - "\\begin{align*}\n", - "P = XX^T\n", - "\\end{align*}\n", + "$$ P = X X^T $$\n", "\n", "Both ER and SBM models can be formulated as a RDPG. Below, we sample $ER_{NP}(100, 0.5)$ using RDPG formulation. In this case, we set $X \\in \\mathbb{R}^{100\\times 2}$ where all the values in $X$ is 0.5. This results in $P$ matrix where all the probabilities are also 0.5." ] @@ -71,7 +69,7 @@ "source": [ "from graspologic.plot import heatmap\n", "\n", - "heatmap(A, title='ER_NP(100, 0.5) Using RDPG')" + "_ = heatmap(A, title='ER_NP(100, 0.5) Using RDPG')" ] }, { @@ -109,7 +107,7 @@ "source": [ "X = np.array([[0.5, 0.2, 0.2]] * 50 + [[0.1, 0.1, 0.1]] * 50)\n", "A_rdpg = rdpg(X, loops=False)\n", - "heatmap(A_rdpg, title='2-block SBM as RDPG')" + "_ = heatmap(A_rdpg, title='2-block SBM as RDPG')" ] }, { @@ -131,7 +129,7 @@ "p = [[0.33, 0.09], [0.09, 0.03]]\n", "\n", "A_sbm = sbm(n, p)\n", - "heatmap(A_sbm, title = 'SBM Simulation')" + "_ = heatmap(A_sbm, title = 'SBM Simulation')" ] } ], diff --git a/docs/tutorials/simulations/rdpg_corr.ipynb b/docs/tutorials/simulations/rdpg_corr.ipynb index c12651a7e..e28bff3e0 100644 --- a/docs/tutorials/simulations/rdpg_corr.ipynb +++ b/docs/tutorials/simulations/rdpg_corr.ipynb @@ -29,7 +29,7 @@ "\n", "Here, we want to generate a pair of graphs with the same latent positions but with correlation between edges. \n", "\n", - "There are several parameters in this function: \"X\" and \"Y\" are the input matrices (latent positions) which are used to generate the probability matrix; \"r\" is the correlation between the graph pair, which should be (-1,1) (note that not all values of r may be possible for a given set of latent positions).\n", + "There are several parameters in this function: $X$ and $Y$ are the input matrices (latent positions) which are used to generate the probability matrix; $r$ is the correlation between the graph pair, which should be (-1,1) (note that not all values of r may be possible for a given set of latent positions).\n", "\n", "Below, we sample a RDPG graph pair (undirected and no self-loops), G1 and G2, with the following parameters:\n", "\\begin{align*}\n", @@ -66,7 +66,7 @@ "metadata": {}, "source": [ "## Visualize the graphs using heatmap\n", - "Here, we define \"difference rate\" to be the number of edges between the two graphs which are not the same (exist or not exist) out of all potential edges (roughly $n^2$)" + "Here, we define *difference rate* to be the number of edges between the two graphs which are not the same (exist or not exist) out of all potential edges (roughly $n^2$)" ] }, { @@ -265,7 +265,7 @@ "x_list = np.linspace(-0.5,0.9,15)\n", "plt.plot(x_list,rlist,'o-')\n", "plt.xlabel(\"Correlation\")\n", - "plt.ylabel('Difference rate')" + "_ = plt.ylabel('Difference rate')" ] }, { diff --git a/docs/tutorials/simulations/sbm.ipynb b/docs/tutorials/simulations/sbm.ipynb index 08fea27b1..88fca1c89 100644 --- a/docs/tutorials/simulations/sbm.ipynb +++ b/docs/tutorials/simulations/sbm.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Unlike [Erdos-Renyi (ER) models](./erdos_renyi.ipynb), a Stochastic Block Model (SBM) produces graphs containing communities: disjoint subgraphs characterized by differing edge probabilities for vertices within and between communities[1](https://en.wikipedia.org/wiki/Stochastic_block_model).\n", + "Unlike [Erdos-Renyi (ER) models](./erdos_renyi.ipynb), a Stochastic Block Model (SBM) produces graphs containing communities: disjoint subgraphs characterized by differing edge probabilities for vertices within and between communities [(1)](https://en.wikipedia.org/wiki/Stochastic_block_model).\n", "\n", "SBM is parametrized by the number of vertices in each community $n$, and a block probability matrix $P \\in \\mathbb{R}^{n x n}$ where each element specifies the probability of an edge in a particular block. One can think of SBM as a collection of ER graphs where each block corresponds to an ER graph.\n", "\n", @@ -72,7 +72,7 @@ "source": [ "from graspologic.plot import heatmap\n", "\n", - "heatmap(G, title ='SBM Simulation')" + "_ = heatmap(G, title ='SBM Simulation')" ] }, { @@ -81,7 +81,7 @@ "source": [ "## Weighted SBM Graphs\n", "\n", - "Similar to ER simulations, *sbm* functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, you can either:\n", + "Similar to ER simulations, ``sbm()`` functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, you can either:\n", "\n", "1. Provide a *single* probability distribution function with corresponding keyword arguments for the distribution function. All weights will be sampled using the same function.\n", "2. Provide a probability distribution function with corresponding keyword arguments for each block.\n", @@ -139,7 +139,7 @@ "metadata": {}, "outputs": [], "source": [ - "heatmap(G, title='Weighted SBM Simulation')" + "_ = heatmap(G, title='Weighted SBM Simulation')" ] } ], diff --git a/docs/tutorials/subgraph/subgraph.ipynb b/docs/tutorials/subgraph/subgraph.ipynb index 91a6f133d..ec141f0e5 100644 --- a/docs/tutorials/subgraph/subgraph.ipynb +++ b/docs/tutorials/subgraph/subgraph.ipynb @@ -101,7 +101,7 @@ "\n", "fig, ax = plt.subplots(ncols=2, figsize=(10, 5), constrained_layout=True)\n", "heatmap(expected, ax=ax[0], cbar=False, title=\"Expected Signal-Subgraph\")\n", - "heatmap(estimatesigsub, ax=ax[1], cbar=False, title=\"Estimated Signal-Subgraph\")" + "_ = heatmap(estimatesigsub, ax=ax[1], cbar=False, title=\"Estimated Signal-Subgraph\")" ] }, { @@ -151,7 +151,7 @@ "\n", "fig, ax = plt.subplots(ncols=2, figsize=(10, 5), constrained_layout=True)\n", "heatmap(expected, ax=ax[0], cbar=False, title=\"Expected Signal-Subgraph\")\n", - "heatmap(estimatesigsub, ax=ax[1], cbar=False, title=\"Estimated Signal-Subgraph\")" + "_ = heatmap(estimatesigsub, ax=ax[1], cbar=False, title=\"Estimated Signal-Subgraph\")" ] }, { @@ -196,7 +196,7 @@ "\n", "fig, ax = plt.subplots(ncols=2, figsize=(10, 5), constrained_layout=True)\n", "heatmap(expected, ax=ax[0], cbar=False, title=\"Expected Signal-Subgraph\")\n", - "heatmap(estimatesigsub, ax=ax[1], cbar=False, title=\"Estimated Signal-Subgraph\")" + "_ = heatmap(estimatesigsub, ax=ax[1], cbar=False, title=\"Estimated Signal-Subgraph\")" ] }, { @@ -223,7 +223,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/graspologic/align/base.py b/graspologic/align/base.py index 78481ff7b..b9db25d5f 100644 --- a/graspologic/align/base.py +++ b/graspologic/align/base.py @@ -15,7 +15,7 @@ class BaseAlign(BaseEstimator): Attributes ---------- Q_ : array, size (d, d) - Final orthogonal matrix, used to modify `X` passed to transform + Final orthogonal matrix, used to modify ``X`` passed to transform """ @@ -47,17 +47,17 @@ def _check_datasets(self, X, Y): @abstractmethod def fit(self, X, Y): """ - Uses the two datasets to learn the matrix `self.Q_` that aligns the + Uses the two datasets to learn the matrix :attr:`~graspologic.align.BaseAlign.Q_` that aligns the first dataset with the second. Parameters ---------- X : np.ndarray, shape (n, d) - Dataset to be mapped to `Y`, must have same number of dimensions - (axis 1) as `Y`. + Dataset to be mapped to ``Y``, must have same number of dimensions + (axis 1) as ``Y``. Y : np.ndarray, shape (m, d) - Target dataset, must have same number of dimensions (axis 1) as `X`. + Target dataset, must have same number of dimensions (axis 1) as ``X``. Returns ------- @@ -67,21 +67,21 @@ def fit(self, X, Y): def transform(self, X): """ - Transforms the dataset `X` using the learned matrix `self.Q_`. This may - be the same as the first dataset as in .fit(), or a new dataset. + Transforms the dataset ``X`` using the learned matrix :attr:`~graspologic.align.BaseAlign.Q_`. This may + be the same as the first dataset as in :func:`~graspologic.align.BaseAlign.fit`, or a new dataset. For example, additional samples from the same dataset. Parameters ---------- X : np.ndarray, shape(m, d) Dataset to be transformed, must have same number of dimensions - (axis 1) as `X` and `Y` that were passed to fit. + (axis 1) as ``X`` and ``Y`` that were passed to fit. Returns ------- X_prime : np.ndarray, shape (n, d) First dataset of vectors, aligned to second. Equal to - `X` @ `self.Q_`. + ``X`` @ :attr:`~graspologic.align.BaseAlign.Q_`. """ if not isinstance(X, np.ndarray): msg = f"Dataset is a {type(X)}, not an np.ndarray! " @@ -99,24 +99,24 @@ def transform(self, X): def fit_transform(self, X, Y): """ - Uses the two datasets to learn the matrix `self.Q_` that aligns the - first dataset with the second. Then, transforms the first dataset `X` - using the learned matrix `self.Q_`. + Uses the two datasets to learn the matrix :attr:`~graspologic.align.BaseAlign.Q_` that aligns the + first dataset with the second. Then, transforms the first dataset ``X`` + using the learned matrix :attr:`~graspologic.align.BaseAlign.Q_`. Parameters ---------- X : np.ndarray, shape (n, d) - Dataset to be mapped to `Y`, must have same number of dimensions - (axis 1) as `Y`. + Dataset to be mapped to ``Y``, must have same number of dimensions + (axis 1) as ``Y``. Y : np.ndarray, shape (m, d) - Target dataset, must have same number of dimensions (axis 1) as `X`. + Target dataset, must have same number of dimensions (axis 1) as ``X``. Returns ------- X_prime : np.ndarray, shape (n, d) First dataset of vectors, aligned to second. Equal to - `X` @ `self.Q_`. + ``X`` @ :attr:`~graspologic.align.BaseAlign.Q_`. """ self.fit(X, Y) return self.transform(X) diff --git a/graspologic/align/orthogonal_procrustes.py b/graspologic/align/orthogonal_procrustes.py index 3dc7a3980..a5a162e7a 100644 --- a/graspologic/align/orthogonal_procrustes.py +++ b/graspologic/align/orthogonal_procrustes.py @@ -10,22 +10,22 @@ class OrthogonalProcrustes(BaseAlign): """ Computes the matrix solution of the classical orthogonal Procrustes [1]_ - problem, which is that given two matrices `X` and `Y` of equal shape - (n, d), find an orthogonal matrix `self.Q_` that most closely maps `X` to - `Y`. Subsequently, uses that matrix to transform either the original `X`, + problem, which is that given two matrices ``X`` and ``Y`` of equal shape + (n, d), find an orthogonal matrix that most closely maps ``X`` to + ``Y``. Subsequently, uses that matrix to transform either the original ``X``, or a different dataset in the same space. Note that when used to match two datasets, this method unlike :class:`~graspologic.align.SeedlessProcrustes`, not only requires that the datasets have the same number of entries, but also that there is some correspondence between the entries. In graph embeddings, this usually - corresponds to the assumption that the vertex i in graph X has the same - latent position as the vertex i in graph Y. + corresponds to the assumption that the vertex :math:`i` in graph ``X`` has the same + latent position as the vertex :math:`i` in graph ``Y``. Attributes ---------- Q_ : array, size (d, d) - Final orthogonal matrix, used to modify `X`. + Final orthogonal matrix, used to modify ``X``. score_ : float Final value of the objective function: :math:`|| X Q - Y ||_F` @@ -47,7 +47,7 @@ class OrthogonalProcrustes(BaseAlign): details, including the proof of the closed-form solution see [1]_. Implementation-wise, this class is a wrapper of the - `scipy.linalg.orthogonal_procrustes`, which itself uses an algorithm + :func:`scipy.linalg.orthogonal_procrustes`, which itself uses an algorithm described in find the optimal solution algorithm [2]_. """ @@ -60,16 +60,16 @@ def __init__( def fit(self, X, Y): """ - Uses the two datasets to learn the matrix `self.Q_` that aligns the + Uses the two datasets to learn the matrix :attr:`~graspologic.align.OrthogonalProcrustes.Q_` that aligns the first dataset with the second. Parameters ---------- X : np.ndarray, shape (n, d) - Dataset to be mapped to `Y`, must have the same shape as `Y`. + Dataset to be mapped to ``Y``, must have the same shape as ``Y``. Y : np.ndarray, shape (m, d) - Target dataset, must have the same shape as `X`. + Target dataset, must have the same shape as ``X``. Returns @@ -96,22 +96,22 @@ def fit(self, X, Y): def fit_transform(self, X, Y): """ - Uses the two datasets to learn the matrix `self.Q_` that aligns the - first dataset with the second. Then, transforms the first dataset `X` - using the learned matrix `self.Q_`. + Uses the two datasets to learn the matrix :attr:`~graspologic.align.OrthogonalProcrustes.Q_` that aligns the + first dataset with the second. Then, transforms the first dataset ``X`` + using the learned matrix :attr:`~graspologic.align.OrthogonalProcrustes.Q_`. Parameters ---------- X : np.ndarray, shape (n, d) - Dataset to be mapped to `Y`, must have the same shape as `Y`. + Dataset to be mapped to ``Y``, must have the same shape as ``Y``. Y : np.ndarray, shape (m, d) - Target dataset, must have the same shape as `X`. + Target dataset, must have the same shape as ``X``. Returns ------- X_prime : np.ndarray, shape (n, d) First dataset of vectors, aligned to second. Equal to - `X` @ `self.Q_`. + ``X`` @ :attr:`~graspologic.align.BaseAlign.Q_`. """ return super().fit_transform(X, Y) diff --git a/graspologic/align/seedless_procrustes.py b/graspologic/align/seedless_procrustes.py index ddfbbb671..f7a8d6651 100644 --- a/graspologic/align/seedless_procrustes.py +++ b/graspologic/align/seedless_procrustes.py @@ -59,30 +59,30 @@ class SeedlessProcrustes(BaseAlign): except this is a heuristic that can save time, but can sometimes yield suboptimal results. - 'custom' - Expects either an initial guess for `self.Q_` or an initial guess - for `self.P_`, but not both. See `initial_Q` and `initial_P`, - respectively. If neither is provided, initializes `initial_Q` to an + Expects either an initial guess for :attr:`Q_` or an initial guess + for :attr:`P_`, but not both. See ``initial_Q`` and ``initial_P``, + respectively. If neither is provided, initializes ``initial_Q`` to an identity with an appropriate number of dimensions. initial_Q : np.ndarray, shape (d, d) or None, optional (default=None) - An initial guess for the alignment matrix, `self.Q_`, if such exists. - Only one of `initial_Q`, `initial_P` can be provided at the same time, - and only if `init` argument is set to 'custom'. If None, and - `initial_P` is also None - initializes `initial_Q` to identity matrix. + An initial guess for the alignment matrix, :attr:`Q_`, if such exists. + Only one of ``initial_Q``, ``initial_P`` can be provided at the same time, + and only if ``init`` argument is set to 'custom'. If None, and + ``initial_P`` is also None - initializes ``initial_Q`` to identity matrix. Must be an orthogonal matrix, if provided. initial_P : np.ndarray, shape (n, m) or None, optional (default=None) - Initial guess for the optimal transport matrix, `self.P_`, if such - exists. Only one of `initial_Q`, `initial_P` can be provided at the - same time, and only if `init` argument is set to 'custom'. If None, and - `initial_Q` is also None - initializes `initial_Q` to identity matrix. + Initial guess for the optimal transport matrix, :attr:`P_`, if such + exists. Only one of ``initial_Q``, ``initial_P`` can be provided at the + same time, and only if ``init`` argument is set to 'custom'. If None, and + ``initial_Q`` is also None - initializes ``initial_Q`` to identity matrix. Must be a soft assignment matrix if provided (rows sum up to 1/n, cols sum up to 1/m.) Attributes ---------- Q_ : array, size (d, d) - Final orthogonal matrix, used to modify `X`. + Final orthogonal matrix, used to modify ``X``. P_ : array, size (n, m) where n and m are the sizes of two datasets Final matrix of optimal transports, represent soft matching weights @@ -95,10 +95,10 @@ class SeedlessProcrustes(BaseAlign): selected_initial_Q_ : array, size (d, d) Initial orthogonal matrix which was used as the initialization. - If `init` was set to `2d` or `sign_flips`, then it is the adaptively + If ``init`` was set to '2d' or 'sign_flips', then it is the adaptively selected matrix. - If `init` was set to custom, and `initial_Q` was provided, then equal - to that. If it was not provided, but `initial_P` was, then it is the + If ``init`` was set to 'custom', and ``initial_Q`` was provided, then equal + to that. If it was not provided, but ``initial_P`` was, then it is the matrix after the first procrustes performed. If neither was provided, then it is the identity matrix. @@ -142,9 +142,9 @@ class SeedlessProcrustes(BaseAlign): solving an optimal transport problem via Sinkhorn algorithm, whereas obtaining an orthogonal alignment matrix :math:`Q_{i+1} | P_{i}` ("M-step") is done via regular orthogonal procurstes. These alternating steps are - performed until `iterative_num_reps` is reached. + performed until ``iterative_num_reps`` is reached. - For more on how the initial guess can be performed, see `init`. + For more on how the initial guess can be performed, see ``init``. """ @@ -309,11 +309,11 @@ def fit(self, X, Y): Parameters ---------- X : np.ndarray, shape (n, d) - Dataset to be mapped to `Y`, must have same number of dimensions - (axis 1) as `Y`. + Dataset to be mapped to ``Y``, must have same number of dimensions + (axis 1) as ``Y``. Y : np.ndarray, shape (m, d) - Target dataset, must have same number of dimensions (axis 1) as `X`. + Target dataset, must have same number of dimensions (axis 1) as ``X``. Returns ------- diff --git a/graspologic/align/sign_flips.py b/graspologic/align/sign_flips.py index 3b95203b4..d66523617 100644 --- a/graspologic/align/sign_flips.py +++ b/graspologic/align/sign_flips.py @@ -8,9 +8,9 @@ class SignFlips(BaseAlign): """ - Flips the signs of all entries in one dataset, `X` along some of the + Flips the signs of all entries in one dataset, ``X`` along some of the dimensions. In particular, it does so in a way that brings this dataset to - the same orthant as the second dataset, `Y`, according to some criterion, + the same orthant as the second dataset, ``Y``, according to some criterion, computed along each dimension. The two critera currently available are the median and the maximum (in magnitude) value along each dimension. @@ -32,7 +32,7 @@ class SignFlips(BaseAlign): Attributes ---------- Q_ : array, size (d, d) - Final orthogonal matrix, used to modify `X`. + Final orthogonal matrix, used to modify ``X``. """ @@ -70,22 +70,22 @@ def max_criterion(X): def fit(self, X, Y): """ - Uses the two datasets to learn the matrix `self.Q_` that aligns the + Uses the two datasets to learn the matrix :attr:`~graspologic.align.SignFlips.Q_` that aligns the first dataset with the second. - In sign flips, `self.Q_` is an diagonal orthogonal matrices (i.e. a + In sign flips, :attr:`~graspologic.align.SignFlips.Q_` is an diagonal orthogonal matrices (i.e. a matrix with 1 or -1 in each entry on diagonal and 0 everywhere else) - picked such that all dimensions of `X` @ `self.Q_` and `Y` are in the - same orthant using some critera (median or max magnitude). + picked such that all dimensions of ``X`` @ :attr:`~graspologic.align.SignFlips.Q_` + and ``Y`` are in the same orthant using some critera (median or max magnitude). Parameters ---------- X : np.ndarray, shape (n, d) - Dataset to be mapped to `Y`, must have same number of dimensions - (axis 1) as `Y`. + Dataset to be mapped to ``Y``, must have same number of dimensions + (axis 1) as ``Y``. Y : np.ndarray, shape (m, d) - Target dataset, must have same number of dimensions (axis 1) as `X`. + Target dataset, must have same number of dimensions (axis 1) as ``X``. Returns ------- diff --git a/graspologic/cluster/autogmm.py b/graspologic/cluster/autogmm.py index 797fe296c..ae9108a1d 100644 --- a/graspologic/cluster/autogmm.py +++ b/graspologic/cluster/autogmm.py @@ -32,11 +32,11 @@ class AutoGMMCluster(BaseCluster): ---------- min_components : int, default=2. The minimum number of mixture components to consider (unless - max_components=None, in which case this is the maximum number of - components to consider). If max_components is not None, min_components - must be less than or equal to max_components. - If label_init is given, min_components must match number of unique labels - in label_init. + ``max_components`` is None, in which case this is the maximum number of + components to consider). If ``max_components`` is not None, ``min_components`` + must be less than or equal to ``max_components``. + If ``label_init`` is given, min_components must match number of unique labels + in ``label_init``. max_components : int or None, default=10. The maximum number of mixture components to consider. Must be greater @@ -113,7 +113,7 @@ class AutoGMMCluster(BaseCluster): label_init : array-like, shape (n_samples,), optional (default=None) List of labels for samples if available. Used to initialize the model. - If provided, min_components and max_components must match the number of + If provided, min_components and ``max_components`` must match the number of unique labels given here. max_iter : int, optional (default = 100). diff --git a/graspologic/cluster/gclust.py b/graspologic/cluster/gclust.py index f3f6969ec..f7c3e0409 100644 --- a/graspologic/cluster/gclust.py +++ b/graspologic/cluster/gclust.py @@ -17,13 +17,13 @@ class GaussianCluster(BaseCluster): Representation of a Gaussian mixture model probability distribution. This class allows to estimate the parameters of a Gaussian mixture distribution. It computes all possible models from one component to - max_components. The best model is given by the lowest BIC score. + ``max_components``. The best model is given by the lowest BIC score. Parameters ---------- min_components : int, default=2. The minimum number of mixture components to consider (unless - ``max_components=None``, in which case this is the maximum number of + ``max_components`` is None, in which case this is the maximum number of components to consider). If ``max_componens`` is not None, ``min_components`` must be less than or equal to ``max_components``. @@ -73,7 +73,7 @@ class GaussianCluster(BaseCluster): random_state : int, RandomState instance or None, optional (default=None) If int, ``random_state`` is the seed used by the random number generator; - If RandomState instance, random_state is the random number generator; + If RandomState instance, ``random_state`` is the random number generator; If None, the random number generator is the RandomState instance used by ``np.random``. @@ -89,12 +89,12 @@ class GaussianCluster(BaseCluster): and optimal covariance structure. bic_ : pandas.DataFrame A pandas DataFrame of BIC values computed for all possible number of clusters - given by range(min_components, max_components + 1) and all covariance - structures given by covariance_type. + given by ``range(min_components, max_components + 1)`` and all covariance + structures given by :attr:`covariance_type`. ari_ : pandas.DataFrame Only computed when y is given. Pandas Dataframe containing ARI values computed - for all possible number of clusters given by ``r``ange(min_components, - max_components)`` and all covariance structures given by covariance_type. + for all possible number of clusters given by ``range(min_components, + max_components)`` and all covariance structures given by :attr:`covariance_type`. """ def __init__( diff --git a/graspologic/cluster/kclust.py b/graspologic/cluster/kclust.py index d183f1c2c..5c2ae59e9 100644 --- a/graspologic/cluster/kclust.py +++ b/graspologic/cluster/kclust.py @@ -22,9 +22,9 @@ class KMeansCluster(BaseCluster): random_state : int, RandomState instance or None, optional (default=None) If int, ``random_state`` is the seed used by the random number generator; - If RandomState instance, random_state is the random number generator; + If RandomState instance, ``random_state`` is the random number generator; If None, the random number generator is the RandomState instance used - by `np.random`. + by ``np.random``. Attributes ---------- diff --git a/graspologic/datasets/base.py b/graspologic/datasets/base.py index 0ec87c297..977da5ccf 100644 --- a/graspologic/datasets/base.py +++ b/graspologic/datasets/base.py @@ -72,7 +72,7 @@ def load_drosophila_right(return_labels=False): graph : np.ndarray Adjacency matrix of the connectome labels : np.ndarray - Only returned if `return_labels` is true. Array of + Only returned if ``return_labels`` is true. Array of string labels for each cell (vertex) References diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index 4978347ef..f94c21c55 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -28,12 +28,12 @@ class AdjacencySpectralEmbed(BaseSpectralEmbed): ---------- n_components : int or None, default = None Desired dimensionality of output data. If "full", - n_components must be <= min(X.shape). Otherwise, n_components must be - < min(X.shape). If None, then optimal dimensions will be chosen by + ``n_components`` must be ``<= min(X.shape)``. Otherwise, ``n_components`` must be + ``< min(X.shape)``. If None, then optimal dimensions will be chosen by :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 - If ``n_components=None``, then compute the optimal embedding dimension using + If ``n_components`` is None, then compute the optimal embedding dimension using :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional @@ -72,7 +72,7 @@ class AdjacencySpectralEmbed(BaseSpectralEmbed): Attributes ---------- n_features_in_: int - Number of features passed to the fit method. + Number of features passed to the :func:`~graspologic.embed.AdjacencySpectralEmbed.fit` method. latent_left_ : array, shape (n_samples, n_components) Estimated left latent positions of the graph. latent_right_ : array, shape (n_samples, n_components), or None diff --git a/graspologic/embed/lse.py b/graspologic/embed/lse.py index ef02706a2..39618f169 100644 --- a/graspologic/embed/lse.py +++ b/graspologic/embed/lse.py @@ -25,12 +25,12 @@ class LaplacianSpectralEmbed(BaseSpectralEmbed): n_components : int or None, default = None Desired dimensionality of output data. If "full", - n_components must be <= min(X.shape). Otherwise, n_components must be - < min(X.shape). If None, then optimal dimensions will be chosen by + ``n_components`` must be ``<= min(X.shape)``. Otherwise, ``n_components`` must be + ``< min(X.shape)``. If None, then optimal dimensions will be chosen by :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 - If ``n_components=None``, then compute the optimal embedding dimension using + If ``n_components`` is None, then compute the optimal embedding dimension using :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional @@ -58,7 +58,7 @@ class LaplacianSpectralEmbed(BaseSpectralEmbed): regularizer: int, float or None, optional (default=None) Constant to be added to the diagonal of degree matrix. If None, average node degree is added. If int or float, must be >= 0. Only used when - ``form`` == 'R-DAD'. + ``form`` is 'R-DAD'. concat : bool, optional (default False) If graph is directed, whether to concatenate left and right (out and in) latent positions along axis 1. @@ -67,7 +67,7 @@ class LaplacianSpectralEmbed(BaseSpectralEmbed): Attributes ---------- n_features_in_: int - Number of features passed to the fit method. + Number of features passed to the :func:`~graspologic.embed.LaplacianSpectralEmbed.fit` method. latent_left_ : array, shape (n_samples, n_components) Estimated left latent positions of the graph. @@ -142,7 +142,7 @@ def fit(self, graph, y=None): Parameters ---------- graph : array_like or networkx.Graph - Input graph to embed. see graspologic.utils.import_graph + Input graph to embed. see :func:`~graspologic.utils.import_graph` Returns ------- diff --git a/graspologic/embed/mase.py b/graspologic/embed/mase.py index a9d7018c1..8828f0894 100644 --- a/graspologic/embed/mase.py +++ b/graspologic/embed/mase.py @@ -31,12 +31,12 @@ class MultipleASE(BaseEmbedMulti): ---------- n_components : int or None, default = None Desired dimensionality of output data. If "full", - n_components must be <= min(X.shape). Otherwise, n_components must be - < min(X.shape). If None, then optimal dimensions will be chosen by + ``n_components`` must be ``<= min(X.shape)``. Otherwise, ``n_components`` must be + ``< min(X.shape)``. If None, then optimal dimensions will be chosen by :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 - If ``n_components=None``, then compute the optimal embedding dimension using + If ``n_components`` is None, then compute the optimal embedding dimension using :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional @@ -90,8 +90,8 @@ class MultipleASE(BaseEmbedMulti): Notes ----- - When an input graph is directed, `n_components` of `latent_left_` may not be equal - to `n_components` of `latent_right_`. + When an input graph is directed, ``n_components`` of :attr:`latent_left_` may not be equal + to ``n_components`` of :attr:`latent_right_`. """ def __init__( diff --git a/graspologic/embed/mds.py b/graspologic/embed/mds.py index fded0deac..32c46f722 100644 --- a/graspologic/embed/mds.py +++ b/graspologic/embed/mds.py @@ -39,11 +39,11 @@ class ClassicalMDS(BaseEstimator): ---------- n_components : int, or None (default=None) Number of components to keep. If None, then it will run - ``select_dimension`` to find the optimal embedding dimension. + :func:`~graspologic.embed.select_dimension` to find the optimal embedding dimension. n_elbows : int, or None (default=2) - If ``n_components=None``, then compute the optimal embedding dimension using - `:func:`~graspologic.embed.select_dimension`. Otherwise, ignored. + If ``n_components`` is None, then compute the optimal embedding dimension using + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. dissimilarity : 'euclidean' | 'precomputed', optional, default: 'euclidean' Dissimilarity measure to use: @@ -51,17 +51,17 @@ class ClassicalMDS(BaseEstimator): - 'euclidean' Pairwise Euclidean distances between points in the dataset. - 'precomputed' - Pre-computed dissimilarities are passed directly to ``fit`` and - ``fit_transform``. + Pre-computed dissimilarities are passed directly to :func:`~graspologic.embed.ClassicalMDS.fit` and + :func:`~graspologic.embed.ClassicalMDS.fit_transform`. Attributes ---------- n_components_ : int - Equals the parameter n_components. If input n_components was None, + Equals the parameter ``n_components``. If input ``n_components`` was None, then equals the optimal embedding dimension. n_features_in_: int - Number of features passed to the fit method. + Number of features passed to the :func:`~graspologic.embed.ClassicalMDS.fit` method. components_ : array, shape (n_components, n_features) Principal axes in feature space. diff --git a/graspologic/embed/mug2vec.py b/graspologic/embed/mug2vec.py index d7594c3b5..8f784770a 100644 --- a/graspologic/embed/mug2vec.py +++ b/graspologic/embed/mug2vec.py @@ -53,22 +53,22 @@ class mug2vec(BaseEstimator): omnibus_components, cmds_components : int or None, default = None Desired dimensionality of output data. If "full", - n_components must be <= min(X.shape). Otherwise, n_components must be - < min(X.shape). If None, then optimal dimensions will be chosen by - ``select_dimension`` using ``n_elbows`` argument. + ``n_components`` must be ``<= min(X.shape)``. Otherwise, ``n_components`` must be + ``< min(X.shape)``. If None, then optimal dimensions will be chosen by + :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. omnibus_n_elbows, cmds_n_elbows: int, optional, default: 2 - If ``n_components=None``, then compute the optimal embedding dimension using - ``select_dimension``. Otherwise, ignored. + If ``n_components`` is None, then compute the optimal embedding dimension using + :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. Attributes ---------- omnibus_n_components_ : int - Equals the parameter n_components. If input n_components was None, + Equals the parameter ``n_components``. If input ``n_components`` was None, then equals the optimal embedding dimension. cmds_n_components_ : int - Equals the parameter n_components. If input n_components was None, + Equals the parameter ``n_components``. If input ``n_components`` was None, then equals the optimal embedding dimension. embeddings_ : array, shape (n_components, n_features) diff --git a/graspologic/embed/n2v.py b/graspologic/embed/n2v.py index 4e1d11069..dccdbe4bf 100644 --- a/graspologic/embed/n2v.py +++ b/graspologic/embed/n2v.py @@ -52,7 +52,7 @@ def node2vec_embed( interpolate_walk_lengths_by_node_degree : bool Use a dynamic walk length that corresponds to each nodes degree. If the node is in the bottom 20 percentile, default to a walk length of 1. If it is in the top 10 - percentile, use walk_length. If it is in the 20-80 percentiles, linearly interpolate between 1 and walk_length. + percentile, use ``walk_length``. If it is in the 20-80 percentiles, linearly interpolate between 1 and ``walk_length``. This will reduce lower degree nodes from biasing your resulting embedding. If a low degree node has the same number of walks as a high degree node (which it will if this setting is not on), then the lower degree nodes will take a smaller breadth of random walks when compared to the high degree nodes. This will result in your diff --git a/graspologic/embed/omni.py b/graspologic/embed/omni.py index 3e059dbc1..9832467df 100644 --- a/graspologic/embed/omni.py +++ b/graspologic/embed/omni.py @@ -57,12 +57,12 @@ class OmnibusEmbed(BaseEmbedMulti): ---------- n_components : int or None, default = None Desired dimensionality of output data. If "full", - n_components must be <= min(X.shape). Otherwise, n_components must be - < min(X.shape). If None, then optimal dimensions will be chosen by + ``n_components`` must be ``<= min(X.shape)``. Otherwise, ``n_components`` must be + ``< min(X.shape)``. If None, then optimal dimensions will be chosen by :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 - If ``n_components=None``, then compute the optimal embedding dimension using + If ``n_components`` is None, then compute the optimal embedding dimension using :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional diff --git a/graspologic/embed/svd.py b/graspologic/embed/svd.py index cf66a2d12..58ce1d6e8 100644 --- a/graspologic/embed/svd.py +++ b/graspologic/embed/svd.py @@ -63,12 +63,12 @@ def select_dimension( (n_samples, n_features). n_components : int, optional, default: None. Number of components to embed. If None, ``n_components = - floor(log2(min(n_samples, n_features)))``. Ignored if X is 1d-array. + floor(log2(min(n_samples, n_features)))``. Ignored if ``X`` is 1d-array. n_elbows : int, optional, default: 2. - Number of likelihood elbows to return. Must be > 1. + Number of likelihood elbows to return. Must be ``> 1``. threshold : float, int, optional, default: None - If given, only consider the singular values that are > threshold. Must - be >= 0. + If given, only consider the singular values that are ``> threshold``. Must + be ``>= 0``. return_likelihoods : bool, optional, default: False If True, returns the all likelihoods associated with each elbow. @@ -76,13 +76,13 @@ def select_dimension( ------- elbows : list Elbows indicate subsequent optimal embedding dimensions. Number of - elbows may be less than n_elbows if there are not enough singular + elbows may be less than ``n_elbows`` if there are not enough singular values. sing_vals : list The singular values associated with each elbow. likelihoods : list of array-like Array of likelihoods of the corresponding to each elbow. Only returned - if `return_likelihoods` is True. + if ``return_likelihoods`` is True. References ---------- @@ -176,7 +176,7 @@ def selectSVD(X, n_components=None, n_elbows=2, algorithm="randomized", n_iter=5 SciPy's wrapper for ARPACK, while truncated SVD is performed using either SciPy's wrapper for LAPACK or Sklearn's implementation of randomized SVD. - It also performs optimal dimensionality selectiong using Zhu & Godsie algorithm + It also performs optimal dimensionality selection using Zhu & Godsie algorithm if number of target dimension is not specified. Parameters @@ -185,11 +185,11 @@ def selectSVD(X, n_components=None, n_elbows=2, algorithm="randomized", n_iter=5 The data to perform svd on. n_components : int or None, default = None Desired dimensionality of output data. If "full", - n_components must be <= min(X.shape). Otherwise, n_components must be - < min(X.shape). If None, then optimal dimensions will be chosen by + ``n_components`` must be ``<= min(X.shape)``. Otherwise, ``n_components`` must be + ``< min(X.shape)``. If None, then optimal dimensions will be chosen by :func:`~graspologic.embed.select_dimension` using ``n_elbows`` argument. n_elbows : int, optional, default: 2 - If ``n_components=None``, then compute the optimal embedding dimension using + If ``n_components`` is None, then compute the optimal embedding dimension using :func:`~graspologic.embed.select_dimension`. Otherwise, ignored. algorithm : {'randomized' (default), 'full', 'truncated'}, optional SVD solver to use: diff --git a/graspologic/inference/latent_distribution_test.py b/graspologic/inference/latent_distribution_test.py index 72885a1b7..1b07d8420 100644 --- a/graspologic/inference/latent_distribution_test.py +++ b/graspologic/inference/latent_distribution_test.py @@ -50,9 +50,9 @@ class LatentDistributionTest(BaseInference): If a callable, then it should behave similarly to either :func:`sklearn.metrics.pairwise_distances` or to :func:`sklearn.metrics.pairwise.pairwise_kernels`. - If a string, then it should be either one of the keys in either - `sklearn.metrics.pairwise.PAIRED_DISTANCES` or in - `sklearn.metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS`, or "gaussian", + If a string, then it should be either one of the keys in + :py:attr:`sklearn.metrics.pairwise.PAIRED_DISTANCES` one of the keys in + :py:attr:`sklearn.metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS`, or "gaussian", which will use a gaussian kernel with an adaptively selected bandwidth. It is recommended to use kernels (e.g. "gaussian") with kernel-based hsic test and distances (e.g. "euclidean") with all other tests. @@ -61,7 +61,7 @@ class LatentDistributionTest(BaseInference): Number of embedding dimensions. If None, the optimal embedding dimensions are found by the Zhu and Godsi algorithm. See :func:`~graspologic.embed.selectSVD` for more information. - This argument is ignored if `input_graph` is False. + This argument is ignored if ``input_graph`` is False. n_bootstraps : int (default=200) Number of bootstrap iterations for the backend hypothesis test. @@ -85,7 +85,7 @@ class LatentDistributionTest(BaseInference): pooled : bool (default=False) Ignored whenever the two graphs have the same number of vertices or - `size_correction` is set to False. In order to correct the adjacency + ``size_correction`` is set to False. In order to correct the adjacency spectral embedding used in the test, it is needed to estimate the variance for each of the latent position estimates in the larger graph, which requires to compute different sample moments. These moments can @@ -106,7 +106,7 @@ class LatentDistributionTest(BaseInference): - 'sign_flips' A simple heuristic that flips the signs of one of the embeddings, if the medians of the two embeddings in that dimension differ from - each other. See :class:`~graspologic.align.SignFlips` for more + each other. See :class:`graspologic.align.SignFlips` for more information on this procedure. In the limit, this is guaranteed to lead to a valid test, as long as matrix :math:`X^T X`, where :math:`X` is the latent positions does not have repeated non-zero @@ -117,7 +117,7 @@ class LatentDistributionTest(BaseInference): procedure is slower than sign flips, but is guaranteed to yield a valid test in the limit, and also makes the test more valid in some finite sample cases, in which the eigenvalues are very close to - each other. See `~graspologic.align.SignFlips` for more information + each other. See :class:`graspologic.align.SignFlips` for more information on the procedure. - None Do not use any alignment technique. This is strongly not @@ -125,26 +125,30 @@ class LatentDistributionTest(BaseInference): align_kws : dict Keyword arguments for the aligner of choice, either - `~graspologic.align.SignFlips` or - `~graspologic.align.SeedlessProcrustes`, depending on the align_type. + :class:`graspologic.align.SignFlips` or + :class:`graspologic.align.SeedlessProcrustes`, depending on the ``align_type``. See respective classes for more information. input_graph : bool (default=True) Flag whether to expect two full graphs, or the embeddings. - True - .fit and .fit_predict() expect graphs, either as NetworkX graph objects - or as adjacency matrices, provided as ndarrays of size (n, n) and - (m, m). They will be embedded using adjacency spectral embeddings. + :func:`~graspologic.inference.LatentDistributionTest.fit` and + :func:`~graspologic.inference.LatentDistributionTest.fit_predict` + expect graphs, either as NetworkX graph objects or as adjacency matrices, + provided as ndarrays of size (n, n) and (m, m). + They will be embedded using adjacency spectral embeddings. - False - .fit() and .fit_predict() expect adjacency spectral embeddings of - the graphs, they must be ndarrays of size (n, d) and (m, d), where + :func:`~graspologic.inference.LatentDistributionTest.fit` and + :func:`~graspologic.inference.LatentDistributionTest.fit_predict` + expect adjacency spectral embeddings of the graphs, + they must be ndarrays of size (n, d) and (m, d), where d must be same. n_components attribute is ignored in this case. Attributes ---------- metric_func_ : callable - A callable associated with the specified metric. See `metric`. + A callable associated with the specified metric. See ``metric``. null_distribution_ : ndarray, shape (n_bootstraps, ) The distribution of T statistics generated under the null. diff --git a/graspologic/inference/latent_position_test.py b/graspologic/inference/latent_position_test.py index 03c49a9c1..bb93b6149 100644 --- a/graspologic/inference/latent_position_test.py +++ b/graspologic/inference/latent_position_test.py @@ -74,7 +74,7 @@ class LatentPositionTest(BaseInference): The p value estimated from the null distributions from sample 1 and sample 2. p_value_ : float - The overall p value from the test; this is the max of ``p_value_1_`` and ``p_value_2_`` + The overall p value from the test; this is the max of :attr:`p_value_1_` and :attr:`p_value_2_` See also -------- diff --git a/graspologic/match/gmp.py b/graspologic/match/gmp.py index afa7f8ee3..b4fd540c8 100644 --- a/graspologic/match/gmp.py +++ b/graspologic/match/gmp.py @@ -29,14 +29,14 @@ class GraphMatch(BaseEstimator): n_init : int, positive (default = 1) Number of random initializations of the starting permutation matrix that - the FAQ algorithm will undergo. n_init automatically set to 1 if - init_method = 'barycenter' + the FAQ algorithm will undergo. ``n_init`` automatically set to 1 if + ``init_method`` = 'barycenter' init_method : string (default = 'barycenter') The initial position chosen "barycenter" : the non-informative “flat doubly stochastic matrix,” - :math:`J=1*1^T /n` , i.e the barycenter of the feasible region + :math:`J=1 \\times 1^T /n` , i.e the barycenter of the feasible region "rand" : some random point near :math:`J, (J+K)/2`, where K is some random doubly stochastic matrix @@ -51,7 +51,7 @@ class GraphMatch(BaseEstimator): eps : float (default = 0.1) A positive, threshold stopping criteria such that FW continues to iterate - while Frobenius norm of :math:`(P_{i}-P_{i+1}) > eps` + while Frobenius norm of :math:`(P_{i}-P_{i+1}) > \\text{eps}` gmp : bool (default = True) Gives users the option to solve QAP rather than the Graph Matching Problem @@ -73,14 +73,14 @@ class GraphMatch(BaseEstimator): perm_inds_ : array, size (n,) where n is the number of vertices in the fitted graphs. The indices of the optimal permutation (with the fixed seeds given) on the nodes of B, - to best minimize the objective function :math:`f(P) = trace(A^T PBP^T )`. + to best minimize the objective function :math:`f(P) = \\text{trace}(A^T PBP^T )`. score_ : float The objective function value of for the optimal permutation found. n_iter_ : int - Number of Frank-Wolfe iterations run. If `n_init` > 1, `n_iter_` reflects the number of + Number of Frank-Wolfe iterations run. If ``n_init > 1``, :attr:`n_iter_` reflects the number of iterations performed at the initialization returned. @@ -164,12 +164,12 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): A square adjacency matrix seeds_A : 1d-array, shape (m , 1) where m <= number of nodes (default = []) - An array where each entry is an index of a node in `A`. + An array where each entry is an index of a node in ``A``. seeds_B : 1d-array, shape (m , 1) where m <= number of nodes (default = []) An array where each entry is an index of a node in `B` The elements of - `seeds_A` and `seeds_B` are vertices which are known to be matched, that is, - `seeds_A[i]` is matched to vertex `seeds_B[i]`. + ``seeds_A`` and ``seeds_B`` are vertices which are known to be matched, that is, + ``seeds_A[i]`` is matched to vertex ``seeds_B[i]``. Returns ------- @@ -337,12 +337,12 @@ def fit_predict(self, A, B, seeds_A=[], seeds_B=[]): A square adjacency matrix seeds_A : 1d-array, shape (m , 1) where m <= number of nodes (default = []) - An array where each entry is an index of a node in `A`. + An array where each entry is an index of a node in ``A``. seeds_B : 1d-array, shape (m , 1) where m <= number of nodes (default = []) - An array where each entry is an index of a node in `B` The elements of - `seeds_A` and `seeds_B` are vertices which are known to be matched, that is, - `seeds_A[i]` is matched to vertex `seeds_B[i]`. + An array where each entry is an index of a node in ``B`` The elements of + ``seeds_A`` and ``seeds_B`` are vertices which are known to be matched, that is, + ``seeds_A[i]`` is matched to vertex ``seeds_B[i]``. Returns ------- diff --git a/graspologic/match/skp.py b/graspologic/match/skp.py index 61ece1e76..2d83190a1 100644 --- a/graspologic/match/skp.py +++ b/graspologic/match/skp.py @@ -37,10 +37,10 @@ class SinkhornKnopp: """ Sinkhorn Knopp Algorithm - Takes a non-negative square matrix P, where P =/= 0 + Takes a non-negative square matrix :math:`P`, where :math:`P \\neq 0` and iterates through Sinkhorn Knopp's algorithm - to convert P to a doubly stochastic matrix. - Guaranteed convergence if P has total support [1]: + to convert :math:`P` to a doubly stochastic matrix. + Guaranteed convergence if :math:`P` has total support [1]: Parameters ---------- @@ -51,7 +51,7 @@ class SinkhornKnopp: Metric used to compute the stopping condition, which occurs if all the row and column sums are within epsilon of 1. This should be a very small value. - Epsilon must be between 0 and 1. + ``epsilon`` must be between 0 and 1. Attributes ---------- @@ -71,11 +71,11 @@ class SinkhornKnopp: _D1 : 2d-array Diagonal matrix obtained after a stopping condition was met - so that _D1.dot(P).dot(_D2) is close to doubly stochastic. + so that ``_D1.dot(P).dot(_D2)`` is close to doubly stochastic. _D2 : 2d-array Diagonal matrix obtained after a stopping condition was met - so that _D1.dot(P).dot(_D2) is close to doubly stochastic. + so that ``_D1.dot(P).dot(_D2)`` is close to doubly stochastic. References diff --git a/graspologic/models/base.py b/graspologic/models/base.py index ab8b366db..b0b253a32 100644 --- a/graspologic/models/base.py +++ b/graspologic/models/base.py @@ -93,7 +93,7 @@ def score_samples(self, graph, clip=None): Parameters ---------- graph : np.ndarray - Input graph. Must be same shape as model's ``p_mat_`` attribute + Input graph. Must be same shape as model's :attr:`p_mat_` attribute clip : scalar or None, optional (default=None) Values for which to clip probability matrix, entries less than c or more @@ -153,7 +153,7 @@ def score(self, graph): Parameters ---------- graph : np.ndarray - Input graph. Must be same shape as model's ``p_mat_`` attribute + Input graph. Must be same shape as model's :attr:`p_mat_` attribute Returns ------- diff --git a/graspologic/models/rdpg.py b/graspologic/models/rdpg.py index 61a4601ce..90c8d29ec 100644 --- a/graspologic/models/rdpg.py +++ b/graspologic/models/rdpg.py @@ -56,7 +56,7 @@ class RDPGEstimator(BaseGraphEstimator): The fit latent positions for the RDPG model. If a tuple, then the graph that was input to fit was directed, and the first and second elements of the tuple are the left and right latent positions, respectively. The left and right latent - positions will both be of shape (n_verts, n_components). If `latent_` is an + positions will both be of shape (n_verts, n_components). If :attr:`latent_` is an array, then the graph that was input to fit was undirected and the left and right latent positions are the same. diff --git a/graspologic/models/sbm.py b/graspologic/models/sbm.py index 1204fdac5..3579e987b 100644 --- a/graspologic/models/sbm.py +++ b/graspologic/models/sbm.py @@ -73,7 +73,7 @@ class SBMEstimator(BaseGraphEstimator): n_components : int, optional (default=None) Desired dimensionality of embedding for clustering to find communities. ``n_components`` must be ``< min(X.shape)``. If None, then optimal dimensions - will be chosen by :func:`~graspologic.embed.select_dimension``. + will be chosen by :func:`~graspologic.embed.select_dimension`. min_comm : int, optional (default=1) The minimum number of communities (blocks) to consider. @@ -100,7 +100,7 @@ class SBMEstimator(BaseGraphEstimator): vertex_assignments_ : np.ndarray, shape (n_verts) A vector of integer labels corresponding to the predicted block that each node - belongs to if ``y`` was not passed during the call to ``fit``. + belongs to if ``y`` was not passed during the call to :func:`~graspologic.models.SBMEstimator.fit`. block_weights_ : np.ndarray, shape (n_blocks) Contains the proportion of nodes that belong to each block in the fit model. @@ -265,7 +265,7 @@ class DCSBMEstimator(BaseGraphEstimator): n_components : int, optional (default=None) Desired dimensionality of embedding for clustering to find communities. ``n_components`` must be ``< min(X.shape)``. If None, then optimal dimensions - will be chosen by :func:`~graspologic.embed.select_dimension``. + will be chosen by :func:`~graspologic.embed.select_dimension`. min_comm : int, optional (default=1) The minimum number of communities (blocks) to consider. @@ -291,14 +291,14 @@ class DCSBMEstimator(BaseGraphEstimator): sampled. degree_corrections_ : np.ndarray, shape (n_verts, 1) or (n_verts, 2) - Degree correction vector(s) :math:`\theta`. If `degree_directed` parameter was + Degree correction vector(s) :math:`\theta`. If ``degree_directed`` parameter was False, then will be of shape (n_verts, 1) and element :math:`i` represents the degree correction for node :math:`i`. Otherwise, the first column contains out degree corrections and the second column contains in degree corrections. vertex_assignments_ : np.ndarray, shape (n_verts) A vector of integer labels corresponding to the predicted block that each node - belongs to if ``y`` was not passed during the call to ``fit``. + belongs to if ``y`` was not passed during the call to :func:`~graspologic.models.DCSBMEstimator.fit`. block_weights_ : np.ndarray, shape (n_blocks) Contains the proportion of nodes that belong to each block in the fit model. diff --git a/graspologic/plot/plot.py b/graspologic/plot/plot.py index ced3bb1c5..bf1be2aee 100644 --- a/graspologic/plot/plot.py +++ b/graspologic/plot/plot.py @@ -155,8 +155,8 @@ def heatmap( r""" Plots a graph as a color-encoded matrix. - Nodes can be grouped by providing `inner_hier_labels` or both - `inner_hier_labels` and `outer_hier_labels`. Nodes can also + Nodes can be grouped by providing ``inner_hier_labels`` or both + ``inner_hier_labels`` and ``outer_hier_labels``. Nodes can also be sorted by the degree from largest to smallest degree nodes. The nodes will be sorted within each group if labels are also provided. @@ -235,12 +235,12 @@ def heatmap( title_pad : int, float or None, optional (default=None) Custom padding to use for the distance of the title from the heatmap. Autoscales - if ``None`` + if None sort_nodes : boolean, optional (default=False) Whether or not to sort the nodes of the graph by the sum of edge weights (degree for an unweighted graph). If ``inner_hier_labels`` is passed and - ``sort_nodes`` is ``True``, will sort nodes this way within block. + ``sort_nodes`` is True, will sort nodes this way within block. **kwargs : dict, optional additional plotting arguments passed to Seaborn's ``heatmap`` @@ -402,7 +402,7 @@ def gridplot( elements. palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should - be values in the hue variable. + be values in the ``hue`` variable. For acceptable string arguments, see the palette options at :doc:`Choosing Colormaps in Matplotlib `. alpha : float [0, 1], default : 0.7 @@ -423,11 +423,11 @@ def gridplot( ``outer_hier_labels``. title_pad : int, float or None, optional (default=None) Custom padding to use for the distance of the title from the heatmap. Autoscales - if ``None`` + if None sort_nodes : boolean, optional (default=False) Whether or not to sort the nodes of the graph by the sum of edge weights (degree for an unweighted graph). If ``inner_hier_labels`` is passed and - ``sort_nodes`` is ``True``, will sort nodes this way within block. + ``sort_nodes`` is True, will sort nodes this way within block. """ _check_common_inputs( height=height, @@ -532,12 +532,12 @@ def pairplot( r""" Plot pairwise relationships in a dataset. - By default, this function will create a grid of Axes such that each dimension + By default, this function will create a grid of axes such that each dimension in data will by shared in the y-axis across a single row and in the x-axis across a single column. - The off-diagonal Axes show the pairwise relationships displayed as scatterplot. - The diagonal Axes show the univariate distribution of the data for that + The off-diagonal axes show the pairwise relationships displayed as scatterplot. + The diagonal axes show the univariate distribution of the data for that dimension displayed as either a histogram or kernel density estimates (KDEs). Read more in the :ref:`tutorials ` @@ -547,9 +547,9 @@ def pairplot( X : array-like, shape (n_samples, n_features) Input data. labels : array-like or list, shape (n_samples), optional - Labels that correspond to each sample in X. + Labels that correspond to each sample in ``X``. col_names : array-like or list, shape (n_features), optional - Names or labels for each feature in X. If not provided, the default + Names or labels for each feature in ``X``. If not provided, the default will be `Dimension 1, Dimension 2, etc`. title : str, optional, default: None Title of plot. @@ -567,7 +567,7 @@ def pairplot( elements. palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should - be values in the hue variable. + be values in the ``hue`` variable. For acceptable string arguments, see the palette options at :doc:`Choosing Colormaps in Matplotlib `. alpha : float, optional, default: 0.7 @@ -732,7 +732,7 @@ def degreeplot( ---------- X : np.ndarray (2D) input graph - labels : 1d np.ndarray or list, same length as dimensions of X + labels : 1d np.ndarray or list, same length as dimensions of ``X`` Labels for different categories of graph nodes direction : string, ('out', 'in') Whether to plot out degree or in degree for a directed graph @@ -745,7 +745,7 @@ def degreeplot( elements. palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should - be values in the hue variable. + be values in the ``hue`` variable. For acceptable string arguments, see the palette options at :doc:`Choosing Colormaps in Matplotlib `. figsize : tuple of length 2, default (10, 5) @@ -801,7 +801,7 @@ def edgeplot( ---------- X : np.ndarray (2D) Input graph - labels : 1d np.ndarray or list, same length as dimensions of X + labels : 1d np.ndarray or list, same length as dimensions of ``X`` Labels for different categories of graph nodes nonzero : boolean, default: False Whether to restrict the edgeplot to only the non-zero edges @@ -814,7 +814,7 @@ def edgeplot( elements. palette : str, dict, optional, default: 'Set1' Set of colors for mapping the ``hue`` variable. If a dict, keys should - be values in the hue variable. + be values in the ``hue`` variable. For acceptable string arguments, see the palette options at :doc:`Choosing Colormaps in Matplotlib `. figsize : tuple of length 2, default (10, 5) diff --git a/graspologic/simulations/rdpg_corr.py b/graspologic/simulations/rdpg_corr.py index d561c4233..a1a33efc7 100644 --- a/graspologic/simulations/rdpg_corr.py +++ b/graspologic/simulations/rdpg_corr.py @@ -13,7 +13,7 @@ def rdpg_corr(X, Y, r, rescale=False, directed=False, loops=False): :math:`P = XX^T`. If X, Y :math:`\in\mathbb{R}^{n\times d}` is given, then :math:`P = XY^T`. These operations correspond to the dot products between a set of latent positions, so each row in X or Y represents the latent positions in - :math:`\mathbb{R}^{d}` for a single vertex in the random graph + :math:`\mathbb{R}^{d}` for a single vertex in the random graph. Note that this function may also rescale or clip the resulting P matrix to get probabilities between 0 and 1, or remove loops. A binary random graph is then sampled from the P matrix described @@ -33,7 +33,7 @@ def rdpg_corr(X, Y, r, rescale=False, directed=False, loops=False): The value of the correlation between the same vertices in two graphs. rescale: boolean, optional (default=True) - when rescale is True, will subtract the minimum value in + when ``rescale`` is True, will subtract the minimum value in P (if it is below 0) and divide by the maximum (if it is above 1) to ensure that P has entries between 0 and 1. If False, elements of P outside of [0, 1] will be clipped. diff --git a/graspologic/simulations/simulations.py b/graspologic/simulations/simulations.py index fa4e8f542..d8da44f16 100644 --- a/graspologic/simulations/simulations.py +++ b/graspologic/simulations/simulations.py @@ -102,7 +102,7 @@ def er_np(n, p, directed=False, loops=False, wt=1, wtargs=None, dc=None, dc_kws= to weight function ``wt``. dc: function or array-like, shape (n_vertices) - `dc` is used to generate a degree-corrected Erdos Renyi Model in + ``dc`` is used to generate a degree-corrected Erdos Renyi Model in which each node in the graph has a parameter to specify its expected degree relative to other nodes. @@ -116,8 +116,8 @@ def er_np(n, p, directed=False, loops=False, wt=1, wtargs=None, dc=None, dc_kws= vertex is the node's relative expected degree. dc_kws: dictionary - Ignored if `dc` is none or array of scalar. - If `dc` is a function, `dc_kws` corresponds to its named arguments. + Ignored if ``dc`` is none or array of scalar. + If ``dc`` is a function, ``dc_kws`` corresponds to its named arguments. If not specified, in either case all functions will assume their default parameters. @@ -325,9 +325,9 @@ def sbm( Number of vertices in each community. Communities are assigned n[0], n[1], ... p: array-like, shape (n_communities, n_communities) - Probability of an edge between each of the communities, where p[i, j] indicates - the probability of a connection between edges in communities [i, j]. - 0 < p[i, j] < 1 for all i, j. + Probability of an edge between each of the communities, where ``p[i, j]`` indicates + the probability of a connection between edges in communities ``[i, j]``. + ``0 < p[i, j] < 1`` for all ``i, j``. directed: boolean, optional (default=False) If False, output adjacency matrix will be symmetric. Otherwise, output adjacency @@ -338,21 +338,21 @@ def sbm( are sampled in the diagonal. wt: object or array-like, shape (n_communities, n_communities) - if Wt is an object, a weight function to use globally over + if ``wt`` is an object, a weight function to use globally over the sbm for assigning weights. 1 indicates to produce a binary - graph. If Wt is an array-like, a weight function for each of - the edge communities. Wt[i, j] corresponds to the weight function + graph. If ``wt`` is an array-like, a weight function for each of + the edge communities. ``wt[i, j]`` corresponds to the weight function between communities i and j. If the entry is a function, should - accept an argument for size. An entry of Wt[i, j] = 1 will produce a + accept an argument for size. An entry of ``wt[i, j] = 1`` will produce a binary subgraph over the i, j community. wtargs: dictionary or array-like, shape (n_communities, n_communities) - if Wt is an object, Wtargs corresponds to the trailing arguments - to pass to the weight function. If Wt is an array-like, Wtargs[i, j] - corresponds to trailing arguments to pass to Wt[i, j]. + if ``wt`` is an object, ``wtargs`` corresponds to the trailing arguments + to pass to the weight function. If Wt is an array-like, ``wtargs[i, j]`` + corresponds to trailing arguments to pass to ``wt[i, j]``. dc: function or array-like, shape (n_vertices) or (n_communities), optional - `dc` is used to generate a degree-corrected stochastic block model [1] in + ``dc`` is used to generate a degree-corrected stochastic block model [1] in which each node in the graph has a parameter to specify its expected degree relative to other nodes within its community. @@ -369,9 +369,9 @@ def sbm( vertex is the node's relative expected degree within its community. dc_kws: dictionary or array-like, shape (n_communities), optional - Ignored if `dc` is none or array of scalar. - If `dc` is a function, `dc_kws` corresponds to its named arguments. - If `dc` is an array-like of functions, `dc_kws` should be an array-like, shape + Ignored if ``dc`` is none or array of scalar. + If ``dc`` is a function, ``dc_kws`` corresponds to its named arguments. + If ``dc`` is an array-like of functions, ``dc_kws`` should be an array-like, shape (n_communities), of dictionary. Each dictionary is the named arguments for the corresponding function for that community. If not specified, in either case all functions will assume their default @@ -644,7 +644,7 @@ def rdpg(X, Y=None, rescale=False, directed=False, loops=False, wt=1, wtargs=Non right latent position from which to generate a P matrix rescale: boolean, optional (default=False) - when rescale is True, will subtract the minimum value in + when ``rescale`` is True, will subtract the minimum value in P (if it is below 0) and divide by the maximum (if it is above 1) to ensure that P has entries between 0 and 1. If False, elements of P outside of [0, 1] will be clipped diff --git a/graspologic/simulations/simulations_corr.py b/graspologic/simulations/simulations_corr.py index d2f2a44bd..1c9690831 100644 --- a/graspologic/simulations/simulations_corr.py +++ b/graspologic/simulations/simulations_corr.py @@ -217,9 +217,9 @@ def sbm_corr(n, p, r, directed=False, loops=False): Number of vertices in each community. Communities are assigned n[0], n[1], ... p: array-like, shape (n_communities, n_communities) - Probability of an edge between each of the communities, where p[i, j] indicates - the probability of a connection between edges in communities [i, j]. - 0 < p[i, j] < 1 for all i, j. + Probability of an edge between each of the communities, where ``p[i, j]`` indicates + the probability of a connection between edges in communities ``[i, j]``. + ``0 < p[i, j] < 1`` for all ``i, j``. r: float Probability of the correlation between the same vertices in two graphs. diff --git a/graspologic/subgraph/sg.py b/graspologic/subgraph/sg.py index f3b482c16..ca608dc8e 100644 --- a/graspologic/subgraph/sg.py +++ b/graspologic/subgraph/sg.py @@ -24,7 +24,7 @@ class SignalSubgraph: contmat_: array-like, shape (n_vertices, n_vertices, 2, 2) An array that stores the 2-by-2 contingency matrix for each point in the graph samples. sigsub_: tuple, shape (2, n_edges) - A tuple of a row index array and column index array, where n_edges is the size of the signal-subgraph determined by *constraints*. + A tuple of a row index array and column index array, where n_edges is the size of the signal-subgraph determined by ``constraints``. mask_: array-like, shape (n_vertices, n_vertices) An array of boolean values. Entries are true for edges that are in the signal subgraph. @@ -59,8 +59,10 @@ def fit(self, graphs, labels, constraints): constraints: int or vector The constraints that will be imposed onto the estimated signal-subgraph. - If *constraints* is an int, *constraints* is the number of edges in the signal-subgraph. - If *constraints* is a vector, the first element of *constraints* is the number of edges in the signal-subgraph, and the second element of *constraints* is the number of vertices that the signal-subgraph must be incident to. + If ``constraints`` is an int, ``constraints`` is the number of edges in the signal-subgraph. + If ``constraints`` is a vector, the first element of ``constraints`` is the number of edges + in the signal-subgraph, and the second element of ``constraints`` + is the number of vertices that the signal-subgraph must be incident to. Returns ------- @@ -161,7 +163,7 @@ def fit(self, graphs, labels, constraints): def fit_transform(self, graphs, labels, constraints): """ - A function to return the indices of the signal-subgraph. If *return_mask* is True, also returns a mask for the signal-subgraph. + A function to return the indices of the signal-subgraph. If ``return_mask`` is True, also returns a mask for the signal-subgraph. Parameters ---------- @@ -172,8 +174,10 @@ def fit_transform(self, graphs, labels, constraints): constraints: int or vector The constraints that will be imposed onto the estimated signal-subgraph. - If *constraints* is an int, *constraints* is the number of edges in the signal-subgraph. - If *constraints* is a vector, the first element of *constraints* is the number of edges in the signal-subgraph, and the second element of *constraints* is the number of vertices that the signal-subgraph must be incident to. + If ``constraints`` is an int, ``constraints`` is the number of edges in the signal-subgraph. + If ``constraints`` is a vector, the first element of ``constraints`` is the number of edges + in the signal-subgraph, and the second element of ``constraints`` + is the number of vertices that the signal-subgraph must be incident to. Returns ------- diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index ea31b1721..c014d1fec 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -92,7 +92,7 @@ def import_edgelist( Convert node data from strings to specified type. return_vertices : bool, default=False, optional - Returns the union of all ind + Returns the union of all individual edgelists. Returns ------- @@ -101,7 +101,7 @@ def import_edgelist( an array is returned. vertices : array-like, shape (n_vertices, ) - If ``return_vertices`` == True, then returns an array of all vertices that were + If ``return_vertices``` is True, then returns an array of all vertices that were included in the output graphs. """ # p = Path(path) @@ -249,7 +249,7 @@ def to_laplace(graph, form="DAD", regularizer=None): matrix of degrees of each node raised to the -1/2 power, I is the identity matrix, and A is the adjacency matrix. - R-DAD is regularized Laplacian: where :math:`D_t = D + regularizer*I`. + R-DAD is regularized Laplacian: where :math:`D_t = D + regularizer \times I`. Parameters ---------- @@ -260,16 +260,16 @@ def to_laplace(graph, form="DAD", regularizer=None): form: {'I-DAD' (default), 'DAD', 'R-DAD'}, string, optional - 'I-DAD' - Computes :math:`L = I - D_i*A*D_i` + Computes :math:`L = I - D_i A D_i` - 'DAD' - Computes :math:`L = D_o*A*D_i` + Computes :math:`L = D_o A D_i` - 'R-DAD' - Computes :math:`L = D_o^r*A*D_i^r` - where :math:`D_o^r = D_o + regularizer * I` and likewise for :math:`D_i` + Computes :math:`L = D_o^r A D_i^r` + where :math:`D_o^r = D_o + regularizer \times I` and likewise for :math:`D_i` regularizer: int, float or None, optional (default=None) Constant to add to the degree vector(s). If None, average node degree is added. - If int or float, must be >= 0. Only used when ``form`` == 'R-DAD'. + If int or float, must be >= 0. Only used when ``form`` is 'R-DAD'. Returns ------- @@ -350,7 +350,7 @@ def is_fully_connected(graph): Checks whether the input graph is fully connected in the undirected case or weakly connected in the directed case. - Connected means one can get from any vertex u to vertex v by traversing + Connected means one can get from any vertex :math:`u` to vertex :math:`v` by traversing the graph. For a directed graph, weakly connected means that the graph is connected after it is converted to an unweighted graph (ignore the direction of each edge) From 8a8a22713b59748d33a58999066a0ddad6d8ce66 Mon Sep 17 00:00:00 2001 From: Jon McLean <4429525+jonmclean@users.noreply.github.com> Date: Thu, 12 Nov 2020 10:09:01 -0800 Subject: [PATCH 052/140] Updating joblib version to fix #582 (#583) Updating joblib version to fix #582 --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index b1fa371b2..cc5d59121 100644 --- a/setup.py +++ b/setup.py @@ -51,6 +51,7 @@ "anytree>=2.8.0", "gensim", "hyppo>=0.1.3", + "joblib>=0.17.0", # Older versions of joblib cause issue #806. Transitive dependency of hyppo. "matplotlib>=3.0.0,<=3.3.0", "networkx>=2.1", "numpy>=1.8.1", From 8f71afd43756c362273a00e95718a6bc8888a9db Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Date: Fri, 13 Nov 2020 12:57:49 -0500 Subject: [PATCH 053/140] Temporarily have scipy quadratic_assignment live in graspologic (#542) * wraps scipy code, gets rid of skp function * Update match.rst * remove mentions of skp file, init_method -> init * Update gmp.py Co-authored-by: Benjamin Pedigo --- docs/reference/match.rst | 4 - graspologic/match/__init__.py | 3 +- graspologic/match/gmp.py | 192 ++------- graspologic/match/qap.py | 713 ++++++++++++++++++++++++++++++++++ graspologic/match/skp.py | 185 --------- tests/test_match.py | 40 +- 6 files changed, 754 insertions(+), 383 deletions(-) create mode 100644 graspologic/match/qap.py delete mode 100644 graspologic/match/skp.py diff --git a/docs/reference/match.rst b/docs/reference/match.rst index 3521f6dcc..efe93021c 100644 --- a/docs/reference/match.rst +++ b/docs/reference/match.rst @@ -6,7 +6,3 @@ Matching Graph Matching -------------------- .. autoclass:: GraphMatch - -Sinkhorn-Knopp Algorithm ------------------------- -.. autoclass:: SinkhornKnopp diff --git a/graspologic/match/__init__.py b/graspologic/match/__init__.py index 110de14c2..4ba1646aa 100644 --- a/graspologic/match/__init__.py +++ b/graspologic/match/__init__.py @@ -2,6 +2,5 @@ # Licensed under the MIT License. from .gmp import GraphMatch -from .skp import SinkhornKnopp -__all__ = ["GraphMatch", "SinkhornKnopp"] +__all__ = ["GraphMatch"] diff --git a/graspologic/match/gmp.py b/graspologic/match/gmp.py index b4fd540c8..98ab3d6d4 100644 --- a/graspologic/match/gmp.py +++ b/graspologic/match/gmp.py @@ -8,7 +8,7 @@ from sklearn.base import BaseEstimator from sklearn.utils import check_array from sklearn.utils import column_or_1d -from .skp import SinkhornKnopp +from .qap import quadratic_assignment class GraphMatch(BaseEstimator): @@ -32,14 +32,18 @@ class GraphMatch(BaseEstimator): the FAQ algorithm will undergo. ``n_init`` automatically set to 1 if ``init_method`` = 'barycenter' - init_method : string (default = 'barycenter') + init : string (default = 'barycenter') or 2d-array The initial position chosen + If 2d-array, `init` must be :math:`m' x m'`, where :math:`m' = n - m`, + and it must be doubly stochastic: each of its rows and columns must + sum to 1. + "barycenter" : the non-informative “flat doubly stochastic matrix,” :math:`J=1 \\times 1^T /n` , i.e the barycenter of the feasible region - "rand" : some random point near :math:`J, (J+K)/2`, where K is some random doubly - stochastic matrix + "rand" : some random point near :math:`J, (J+K)/2`, where K is some random + doubly stochastic matrix max_iter : int, positive (default = 30) Integer specifying the max number of Franke-Wolfe iterations. @@ -101,24 +105,29 @@ class GraphMatch(BaseEstimator): def __init__( self, n_init=1, - init_method="barycenter", + init="barycenter", max_iter=30, shuffle_input=True, eps=0.1, gmp=True, padding="adopted", ): - if type(n_init) is int and n_init > 0: self.n_init = n_init else: msg = '"n_init" must be a positive integer' raise TypeError(msg) - if init_method == "rand": - self.init_method = "rand" - elif init_method == "barycenter": - self.init_method = "barycenter" - self.n_init = 1 + if type(n_init) is int and n_init > 0: + self.n_init = n_init + else: + msg = '"n_init" must be a positive integer' + raise TypeError(msg) + if init == "rand": + self.init = "randomized" + elif init == "barycenter": + self.init = "barycenter" + elif not isinstance(init, str): + self.init = init else: msg = 'Invalid "init_method" parameter string' raise ValueError(msg) @@ -179,148 +188,29 @@ def fit(self, A, B, seeds_A=[], seeds_B=[]): B = check_array(B, copy=True, ensure_2d=True) seeds_A = column_or_1d(seeds_A) seeds_B = column_or_1d(seeds_B) + partial_match = np.column_stack((seeds_A, seeds_B)) # pads A and B according to section 2.5 of [2] if A.shape[0] != B.shape[0]: A, B = _adj_pad(A, B, self.padding) - if A.shape[0] != A.shape[1] or B.shape[0] != B.shape[1]: - msg = "Adjacency matrix entries must be square" - raise ValueError(msg) - elif seeds_A.shape[0] != seeds_B.shape[0]: - msg = "Seed arrays must be of equal size" - raise ValueError(msg) - elif seeds_A.shape[0] > A.shape[0]: - msg = "There cannot be more seeds than there are nodes" - raise ValueError(msg) - elif not (seeds_A >= 0).all() or not (seeds_B >= 0).all(): - msg = "Seed array entries must be greater than or equal to zero" - raise ValueError(msg) - elif ( - not (seeds_A <= (A.shape[0] - 1)).all() - or not (seeds_B <= (A.shape[0] - 1)).all() - ): - msg = "Seed array entries must be less than or equal to n-1" - raise ValueError(msg) - - n = A.shape[0] # number of vertices in graphs - n_seeds = seeds_A.shape[0] # number of seeds - n_unseed = n - n_seeds - - score = math.inf - perm_inds = np.zeros(n) - - obj_func_scalar = 1 - if self.gmp: - obj_func_scalar = -1 - score = 0 - - seeds_B_c = np.setdiff1d(range(n), seeds_B) - if self.shuffle_input: - seeds_B_c = np.random.permutation(seeds_B_c) - # shuffle_input to avoid results from inputs that were already matched - - seeds_A_c = np.setdiff1d(range(n), seeds_A) - permutation_A = np.concatenate([seeds_A, seeds_A_c], axis=None).astype(int) - permutation_B = np.concatenate([seeds_B, seeds_B_c], axis=None).astype(int) - A = A[np.ix_(permutation_A, permutation_A)] - B = B[np.ix_(permutation_B, permutation_B)] - - # definitions according to Seeded Graph Matching [2]. - A11 = A[:n_seeds, :n_seeds] - A12 = A[:n_seeds, n_seeds:] - A21 = A[n_seeds:, :n_seeds] - A22 = A[n_seeds:, n_seeds:] - B11 = B[:n_seeds, :n_seeds] - B12 = B[:n_seeds, n_seeds:] - B21 = B[n_seeds:, :n_seeds] - B22 = B[n_seeds:, n_seeds:] - A11T = np.transpose(A11) - A12T = np.transpose(A12) - A22T = np.transpose(A22) - B21T = np.transpose(B21) - B22T = np.transpose(B22) - - for i in range(self.n_init): - # setting initialization matrix - if self.init_method == "rand": - sk = SinkhornKnopp() - K = np.random.rand( - n_unseed, n_unseed - ) # generate a nxn matrix where each entry is a random integer [0,1] - for i in range(10): # perform 10 iterations of Sinkhorn balancing - K = sk.fit(K) - J = np.ones((n_unseed, n_unseed)) / float( - n_unseed - ) # initialize J, a doubly stochastic barycenter - P = (K + J) / 2 - elif self.init_method == "barycenter": - P = np.ones((n_unseed, n_unseed)) / float(n_unseed) - - const_sum = A21 @ np.transpose(B21) + np.transpose(A12) @ B12 - grad_P = math.inf # gradient of P - n_iter = 0 # number of FW iterations - - # OPTIMIZATION WHILE LOOP BEGINS - while grad_P > self.eps and n_iter < self.max_iter: - - delta_f = ( - const_sum + A22 @ P @ B22T + A22T @ P @ B22 - ) # computing the gradient of f(P) = -tr(APB^tP^t) - rows, cols = linear_sum_assignment( - obj_func_scalar * delta_f - ) # run hungarian algorithm on gradient(f(P)) - Q = np.zeros((n_unseed, n_unseed)) - Q[rows, cols] = 1 # initialize search direction matrix Q - - def f(x): # computing the original optimization function - return obj_func_scalar * ( - np.trace(A11T @ B11) - + np.trace(np.transpose(x * P + (1 - x) * Q) @ A21 @ B21T) - + np.trace(np.transpose(x * P + (1 - x) * Q) @ A12T @ B12) - + np.trace( - A22T - @ (x * P + (1 - x) * Q) - @ B22 - @ np.transpose(x * P + (1 - x) * Q) - ) - ) - - alpha = minimize_scalar( - f, bounds=(0, 1), method="bounded" - ).x # computing the step size - P_i1 = alpha * P + (1 - alpha) * Q # Update P - grad_P = np.linalg.norm(P - P_i1) - P = P_i1 - n_iter += 1 - # end of FW optimization loop - - row, col = linear_sum_assignment( - -P - ) # Project onto the set of permutation matrices - perm_inds_new = np.concatenate( - (np.arange(n_seeds), np.array([x + n_seeds for x in col])) - ) - - score_new = np.trace( - np.transpose(A) @ B[np.ix_(perm_inds_new, perm_inds_new)] - ) # computing objective function value - - if obj_func_scalar * score_new < obj_func_scalar * score: # minimizing - score = score_new - perm_inds = np.zeros(n, dtype=int) - perm_inds[permutation_A] = permutation_B[perm_inds_new] - best_n_iter = n_iter - - permutation_A_unshuffle = _unshuffle(permutation_A, n) - A = A[np.ix_(permutation_A_unshuffle, permutation_A_unshuffle)] - permutation_B_unshuffle = _unshuffle(permutation_B, n) - B = B[np.ix_(permutation_B_unshuffle, permutation_B_unshuffle)] - score = np.trace(np.transpose(A) @ B[np.ix_(perm_inds, perm_inds)]) - - self.perm_inds_ = perm_inds # permutation indices - self.score_ = score # objective function value - self.n_iter_ = best_n_iter + options = { + "maximize": self.gmp, + "partial_match": partial_match, + "P0": self.init, + "shuffle_input": self.shuffle_input, + "maxiter": self.max_iter, + "tol": self.eps, + } + + res = min( + [quadratic_assignment(A, B, options=options) for i in range(self.n_init)], + key=lambda x: x.fun, + ) + + self.perm_inds_ = res.col_ind # permutation indices + self.score_ = res.fun # objective function value + self.n_iter_ = res.nit return self def fit_predict(self, A, B, seeds_A=[], seeds_B=[]): @@ -372,9 +262,3 @@ def pad(X, n): B = pad(B, n) return A, B - - -def _unshuffle(array, n): - unshuffle = np.array(range(n)) - unshuffle[array] = np.array(range(n)) - return unshuffle diff --git a/graspologic/match/qap.py b/graspologic/match/qap.py new file mode 100644 index 000000000..453e7d89d --- /dev/null +++ b/graspologic/match/qap.py @@ -0,0 +1,713 @@ +# adapted from scipy.optimze.quadratic_assignment() +# will live here temporalily until this function is officially released +# original code can be found here +# https://github.com/scipy/scipy/blob/master/scipy/optimize/_qap.py + +import numpy as np +import operator +from scipy.optimize import linear_sum_assignment, OptimizeResult + +from scipy._lib._util import check_random_state +import itertools + + +def quadratic_assignment(A, B, method="faq", options=None): + r""" + Approximates solution to the quadratic assignment problem and + the graph matching problem. + Quadratic assignment solves problems of the following form: + .. math:: + \min_P & \ {\ \text{trace}(A^T P B P^T)}\\ + \mbox{s.t. } & {P \ \epsilon \ \mathcal{P}}\\ + where :math:`\mathcal{P}` is the set of all permutation matrices, + and :math:`A` and :math:`B` are square matrices. + Graph matching tries to *maximize* the same objective function. + This algorithm can be thought of as finding the alignment of the + nodes of two graphs that minimizes the number of induced edge + disagreements, or, in the case of weighted graphs, the sum of squared + edge weight differences. + Note that the quadratic assignment problem is NP-hard, is not + known to be solvable in polynomial time, and is computationally + intractable. Therefore, the results given are approximations, + not guaranteed to be exact solutions. + Parameters + ---------- + A : 2d-array, square + The square matrix :math:`A` in the objective function above. + B : 2d-array, square + The square matrix :math:`B` in the objective function above. + method : str in {'faq', '2opt'} (default: 'faq') + The algorithm used to solve the problem. + :ref:`'faq' ` (default) and + :ref:`'2opt' ` are available. + options : dict, optional + A dictionary of solver options. All solvers support the following: + partial_match : 2d-array of integers, optional, (default = None) + Allows the user to fix part of the matching between the two + matrices. In the literature, a partial match is also + known as a "seed" [2]_. + Each row of `partial_match` specifies the indices of a pair of + corresponding nodes, that is, node ``partial_match[i, 0]`` of `A` + is matched to node ``partial_match[i, 1]`` of `B`. Accordingly, + ``partial_match`` is an array of size ``(m , 2)``, where ``m`` is + not greater than the number of nodes. + maximize : bool (default = False) + Setting `maximize` to ``True`` solves the Graph Matching Problem + (GMP) rather than the Quadratic Assingnment Problem (QAP). + rng : {None, int, `~np.random.RandomState`, `~np.random.Generator`} + This parameter defines the object to use for drawing random + variates. + If `rng` is ``None`` the `~np.random.RandomState` singleton is + used. + If `rng` is an int, a new ``RandomState`` instance is used, + seeded with `rng`. + If `rng` is already a ``RandomState`` or ``Generator`` + instance, then that object is used. + Default is None. + For method-specific options, see + :func:`show_options('quadratic_assignment') `. + Returns + ------- + res : OptimizeResult + A :class:`scipy.optimize.OptimizeResult` containing the following + fields. + col_ind : 1-D array + An array of column indices corresponding with the best + permutation of the nodes of `B` found. + fun : float + The corresponding value of the objective function. + nit : int + The number of iterations performed during optimization. + Notes + ----- + The default method :ref:`'faq' ` uses the Fast + Approximate QAP algorithm [1]_; it is typically offers the best + combination of speed and accuracy. + Method :ref:`'2opt' ` can be computationally expensive, + but may be a useful alternative, or it can be used to refine the solution + returned by another method. + References + ---------- + .. [1] J.T. Vogelstein, J.M. Conroy, V. Lyzinski, L.J. Podrazik, + S.G. Kratzer, E.T. Harley, D.E. Fishkind, R.J. Vogelstein, and + C.E. Priebe, "Fast approximate quadratic programming for graph + matching," PLOS one, vol. 10, no. 4, p. e0121002, 2015, + :doi:`10.1371/journal.pone.0121002` + .. [2] D. Fishkind, S. Adali, H. Patsolic, L. Meng, D. Singh, V. Lyzinski, + C. Priebe, "Seeded graph matching", Pattern Recognit. 87 (2019): + 203-215, :doi:`10.1016/j.patcog.2018.09.014` + .. [3] "2-opt," Wikipedia. + https://en.wikipedia.org/wiki/2-opt + Examples + -------- + >>> import numpy as np + >>> from scipy.optimize import quadratic_assignment + >>> A = np.array([[0, 80, 150, 170], [80, 0, 130, 100], + ... [150, 130, 0, 120], [170, 100, 120, 0]]) + >>> B = np.array([[0, 5, 2, 7], [0, 0, 3, 8], + ... [0, 0, 0, 3], [0, 0, 0, 0]]) + >>> res = quadratic_assignment(A, B) + >>> print(res) + col_ind: array([0, 3, 2, 1]) + fun: 3260 + nit: 9 + The see the relationship between the returned ``col_ind`` and ``fun``, + use ``col_ind`` to form the best permutation matrix found, then evaluate + the objective function :math:`f(P) = trace(A^T P B P^T )`. + >>> n = A.shape[0] + >>> perm = res['col_ind'] + >>> P = np.eye(n, dtype=int)[perm] + >>> fun = np.trace(A.T @ P @ B @ P.T) + >>> print(fun) + 3260 + Alternatively, to avoid constructing the permutation matrix explicitly, + directly permute the rows and columns of the distance matrix. + >>> fun = np.trace(A.T @ B[perm][:, perm]) + >>> print(fun) + 3260 + Although not guaranteed in general, ``quadratic_assignment`` happens to + have found the globally optimal solution. + >>> from itertools import permutations + >>> perm_opt, fun_opt = None, np.inf + >>> for perm in permutations([0, 1, 2, 3]): + ... perm = np.array(perm) + ... fun = np.trace(A.T @ B[perm][:, perm]) + ... if fun < fun_opt: + ... fun_opt, perm_opt = fun, perm + >>> print(np.array_equal(perm_opt, res['col_ind'])) + True + Here is an example for which the default method, + :ref:`'faq' `, does not find the global optimum. + >>> A = np.array([[0, 5, 8, 6], [5, 0, 5, 1], + ... [8, 5, 0, 2], [6, 1, 2, 0]]) + >>> B = np.array([[0, 1, 8, 4], [1, 0, 5, 2], + ... [8, 5, 0, 5], [4, 2, 5, 0]]) + >>> res = quadratic_assignment(A, B) + >>> print(res) + col_ind: array([1, 0, 3, 2]) + fun: 178 + nit: 13 + If accuracy is important, consider using :ref:`'2opt' ` + to refine the solution. + >>> guess = np.array([np.arange(A.shape[0]), res.col_ind]).T + >>> res = quadratic_assignment(A, B, method="2opt", + ... options = {'partial_guess': guess}) + >>> print(res) + col_ind: array([1, 2, 3, 0]) + fun: 176 + nit: 17 + """ + + if options is None: + options = {} + + method = method.lower() + methods = {"faq": _quadratic_assignment_faq, "2opt": _quadratic_assignment_2opt} + if method not in methods: + raise ValueError(f"method {method} must be in {methods}.") + res = methods[method](A, B, **options) + return res + + +def _calc_score(A, B, perm): + # equivalent to objective function but avoids matmul + return np.sum(A * B[perm][:, perm]) + + +def _common_input_validation(A, B, partial_match): + A = np.atleast_2d(A) + B = np.atleast_2d(B) + + if partial_match is None: + partial_match = np.array([[], []]).T + partial_match = np.atleast_2d(partial_match).astype(int) + + msg = None + if A.shape[0] != A.shape[1]: + msg = "`A` must be square" + elif B.shape[0] != B.shape[1]: + msg = "`B` must be square" + elif A.ndim != 2 or B.ndim != 2: + msg = "`A` and `B` must have exactly two dimensions" + elif A.shape != B.shape: + msg = "`A` and `B` matrices must be of equal size" + elif partial_match.shape[0] > A.shape[0]: + msg = "`partial_match` can have only as many seeds as there are nodes" + elif partial_match.shape[1] != 2: + msg = "`partial_match` must have two columns" + elif partial_match.ndim != 2: + msg = "`partial_match` must have exactly two dimensions" + elif (partial_match < 0).any(): + msg = "`partial_match` must contain only positive indices" + elif (partial_match >= len(A)).any(): + msg = "`partial_match` entries must be less than number of nodes" + elif not len(set(partial_match[:, 0])) == len(partial_match[:, 0]) or not len( + set(partial_match[:, 1]) + ) == len(partial_match[:, 1]): + msg = "`partial_match` column entries must be unique" + + if msg is not None: + raise ValueError(msg) + + return A, B, partial_match + + +def _quadratic_assignment_faq( + A, + B, + maximize=False, + partial_match=None, + rng=None, + P0="barycenter", + shuffle_input=False, + maxiter=30, + tol=0.03, +): + r""" + Solve the quadratic assignment problem (approximately). + This function solves the Quadratic Assignment Problem (QAP) and the + Graph Matching Problem (GMP) using the Fast Approximate QAP Algorithm + (FAQ) [1]_. + Quadratic assignment solves problems of the following form: + .. math:: + \min_P & \ {\ \text{trace}(A^T P B P^T)}\\ + \mbox{s.t. } & {P \ \epsilon \ \mathcal{P}}\\ + where :math:`\mathcal{P}` is the set of all permutation matrices, + and :math:`A` and :math:`B` are square matrices. + Graph matching tries to *maximize* the same objective function. + This algorithm can be thought of as finding the alignment of the + nodes of two graphs that minimizes the number of induced edge + disagreements, or, in the case of weighted graphs, the sum of squared + edge weight differences. + Note that the quadratic assignment problem is NP-hard, is not + known to be solvable in polynomial time, and is computationally + intractable. Therefore, the results given are approximations, + not guaranteed to be exact solutions. + Parameters + ---------- + A : 2d-array, square + The square matrix :math:`A` in the objective function above. + B : 2d-array, square + The square matrix :math:`B` in the objective function above. + method : str in {'faq', '2opt'} (default: 'faq') + The algorithm used to solve the problem. This is the method-specific + documentation for 'faq'. + :ref:`'2opt' ` is also available. + Options + ------- + maximize : bool (default = False) + Setting `maximize` to ``True`` solves the Graph Matching Problem (GMP) + rather than the Quadratic Assingnment Problem (QAP). This is + accomplished through trivial negation of the objective function. + rng : {None, int, `~np.random.RandomState`, `~np.random.Generator`} + This parameter defines the object to use for drawing random + variates. + If `rng` is ``None`` the `~np.random.RandomState` singleton is + used. + If `rng` is an int, a new ``RandomState`` instance is used, + seeded with `rng`. + If `rng` is already a ``RandomState`` or ``Generator`` + instance, then that object is used. + Default is None. + partial_match : 2d-array of integers, optional, (default = None) + Allows the user to fix part of the matching between the two + matrices. In the literature, a partial match is also known as a + "seed". + Each row of `partial_match` specifies the indices of a pair of + corresponding nodes, that is, node ``partial_match[i, 0]`` of `A` is + matched to node ``partial_match[i, 1]`` of `B`. Accordingly, + ``partial_match`` is an array of size ``(m , 2)``, where ``m`` is + not greater than the number of nodes, :math:`n`. + P0 : 2d-array, "barycenter", or "randomized" (default = "barycenter") + The initial (guess) permutation matrix or search "position" + `P0`. + `P0` need not be a proper permutation matrix; + however, it must be :math:`m' x m'`, where :math:`m' = n - m`, + and it must be doubly stochastic: each of its rows and columns must + sum to 1. + If unspecified or ``"barycenter"``, the non-informative "flat + doubly stochastic matrix" :math:`J = 1*1^T/m'`, where :math:`1` is + a :math:`m' \times 1` array of ones, is used. This is the "barycenter" + of the search space of doubly-stochastic matrices. + If ``"randomized"``, the algorithm will start from the + randomized initial search position :math:`P_0 = (J + K)/2`, + where :math:`J` is the "barycenter" and :math:`K` is a random + doubly stochastic matrix. + shuffle_input : bool (default = False) + To avoid artificially high or low matching due to inherent + sorting of input matrices, gives users the option + to shuffle the nodes. Results are then unshuffled so that the + returned results correspond with the node order of inputs. + Shuffling may cause the algorithm to be non-deterministic, + unless a random seed is set or an `rng` option is provided. + maxiter : int, positive (default = 30) + Integer specifying the max number of Franke-Wolfe iterations performed. + tol : float (default = 0.03) + A threshold for the stopping criterion. Franke-Wolfe + iteration terminates when the change in search position between + iterations is sufficiently small, that is, when the relative Frobenius + norm, :math:`\frac{||P_{i}-P_{i+1}||_F}{\sqrt{len(P_{i})}} \leq tol`, + where :math:`i` is the iteration number. + Returns + ------- + res : OptimizeResult + A :class:`scipy.optimize.OptimizeResult` containing the following + fields. + col_ind : 1-D array + An array of column indices corresponding with the best + permutation of the nodes of `B` found. + fun : float + The corresponding value of the objective function. + nit : int + The number of Franke-Wolfe iterations performed. + Notes + ----- + The algorithm may be sensitive to the initial permutation matrix (or + search "position") due to the possibility of several local minima + within the feasible region. A barycenter initialization is more likely to + result in a better solution than a single random initialization. However, + ``quadratic_assignment`` calling several times with different random + initializations may result in a better optimum at the cost of longer + total execution time. + Examples + -------- + As mentioned above, a barycenter initialization often results in a better + solution than a single random initialization. + >>> np.random.seed(0) + >>> n = 15 + >>> A = np.random.rand(n, n) + >>> B = np.random.rand(n, n) + >>> res = quadratic_assignment(A, B) # FAQ is default method + >>> print(res.fun) + 46.871483385480545 # may vary + >>> options = {"P0": "randomized"} # use randomized initialization + >>> res = quadratic_assignment(A, B, options=options) + >>> print(res.fun) + 47.224831071310625 # may vary + However, consider running from several randomized initializations and + keeping the best result. + >>> res = min([quadratic_assignment(A, B, options=options) + ... for i in range(30)], key=lambda x: x.fun) + >>> print(res.fun) + 46.671852533681516 # may vary + The '2-opt' method can be used to further refine the results. + >>> options = {"partial_guess": np.array([np.arange(n), res.col_ind]).T} + >>> res = quadratic_assignment(A, B, method="2opt", options=options) + >>> print(res.fun) + 46.47160735721583 # may vary + References + ---------- + .. [1] J.T. Vogelstein, J.M. Conroy, V. Lyzinski, L.J. Podrazik, + S.G. Kratzer, E.T. Harley, D.E. Fishkind, R.J. Vogelstein, and + C.E. Priebe, "Fast approximate quadratic programming for graph + matching," PLOS one, vol. 10, no. 4, p. e0121002, 2015, + :doi:`10.1371/journal.pone.0121002` + .. [2] D. Fishkind, S. Adali, H. Patsolic, L. Meng, D. Singh, V. Lyzinski, + C. Priebe, "Seeded graph matching", Pattern Recognit. 87 (2019): + 203-215, :doi:`10.1016/j.patcog.2018.09.014` + """ + + maxiter = operator.index(maxiter) + + # ValueError check + A, B, partial_match = _common_input_validation(A, B, partial_match) + + msg = None + if isinstance(P0, str) and P0 not in {"barycenter", "randomized"}: + msg = "Invalid 'P0' parameter string" + elif maxiter <= 0: + msg = "'maxiter' must be a positive integer" + elif tol <= 0: + msg = "'tol' must be a positive float" + if msg is not None: + raise ValueError(msg) + + rng = check_random_state(rng) + n = A.shape[0] # number of vertices in graphs + n_seeds = partial_match.shape[0] # number of seeds + n_unseed = n - n_seeds + + # check outlier cases + if n == 0 or partial_match.shape[0] == n: + score = _calc_score(A, B, partial_match[:, 1]) + res = {"col_ind": partial_match[:, 1], "fun": score, "nit": 0} + return OptimizeResult(res) + + obj_func_scalar = 1 + if maximize: + obj_func_scalar = -1 + + nonseed_B = np.setdiff1d(range(n), partial_match[:, 1]) + if shuffle_input: + nonseed_B = rng.permutation(nonseed_B) + # shuffle_input to avoid results from inputs that were already matched + + nonseed_A = np.setdiff1d(range(n), partial_match[:, 0]) + perm_A = np.concatenate([partial_match[:, 0], nonseed_A]) + perm_B = np.concatenate([partial_match[:, 1], nonseed_B]) + + # definitions according to Seeded Graph Matching [2]. + A11, A12, A21, A22 = _split_matrix(A[perm_A][:, perm_A], n_seeds) + B11, B12, B21, B22 = _split_matrix(B[perm_B][:, perm_B], n_seeds) + + # [1] Algorithm 1 Line 1 - choose initialization + if isinstance(P0, str): + # initialize J, a doubly stochastic barycenter + J = np.ones((n_unseed, n_unseed)) / n_unseed + if P0 == "barycenter": + P = J + elif P0 == "randomized": + # generate a nxn matrix where each entry is a random number [0, 1] + # would use rand, but Generators don't have it + # would use random, but old mtrand.RandomStates don't have it + K = rng.uniform(size=(n_unseed, n_unseed)) + # Sinkhorn balancing + K = _doubly_stochastic(K) + P = J * 0.5 + K * 0.5 + else: + P0 = np.atleast_2d(P0) + _check_init_input(P0, n_unseed) + P = P0 + + const_sum = A21 @ B21.T + A12.T @ B12 + + # [1] Algorithm 1 Line 2 - loop while stopping criteria not met + for n_iter in range(1, maxiter + 1): + # [1] Algorithm 1 Line 3 - compute the gradient of f(P) = -tr(APB^tP^t) + grad_fp = const_sum + A22 @ P @ B22.T + A22.T @ P @ B22 + # [1] Algorithm 1 Line 4 - get direction Q by solving Eq. 8 + _, cols = linear_sum_assignment(grad_fp, maximize=maximize) + Q = np.eye(n_unseed)[cols] + + # [1] Algorithm 1 Line 5 - compute the step size + # Noting that e.g. trace(Ax) = trace(A)*x, expand and re-collect + # terms as ax**2 + bx + c. c does not affect location of minimum + # and can be ignored. Also, note that trace(A@B) = (A.T*B).sum(); + # apply where possible for efficiency. + R = P - Q + b21 = ((R.T @ A21) * B21).sum() + b12 = ((R.T @ A12.T) * B12.T).sum() + AR22 = A22.T @ R + BR22 = B22 @ R.T + b22a = (AR22 * B22.T[cols]).sum() + b22b = (A22 * BR22[cols]).sum() + a = (AR22.T * BR22).sum() + b = b21 + b12 + b22a + b22b + # critical point of ax^2 + bx + c is at x = -d/(2*e) + # if a * obj_func_scalar > 0, it is a minimum + # if minimum is not in [0, 1], only endpoints need to be considered + if a * obj_func_scalar > 0 and 0 <= -b / (2 * a) <= 1: + alpha = -b / (2 * a) + else: + alpha = np.argmin([0, (b + a) * obj_func_scalar]) + + # [1] Algorithm 1 Line 6 - Update P + P_i1 = alpha * P + (1 - alpha) * Q + if np.linalg.norm(P - P_i1) / np.sqrt(n_unseed) < tol: + P = P_i1 + break + P = P_i1 + # [1] Algorithm 1 Line 7 - end main loop + + # [1] Algorithm 1 Line 8 - project onto the set of permutation matrices + _, col = linear_sum_assignment(-P) + perm = np.concatenate((np.arange(n_seeds), col + n_seeds)) + + unshuffled_perm = np.zeros(n, dtype=int) + unshuffled_perm[perm_A] = perm_B[perm] + + score = _calc_score(A, B, unshuffled_perm) + + res = {"col_ind": unshuffled_perm, "fun": score, "nit": n_iter} + + return OptimizeResult(res) + + +def _check_init_input(P0, n): + row_sum = np.sum(P0, axis=0) + col_sum = np.sum(P0, axis=1) + tol = 1e-3 + msg = None + if P0.shape != (n, n): + msg = "`P0` matrix must have shape m' x m', where m'=n-m" + elif ( + (~np.isclose(row_sum, 1, atol=tol)).any() + or (~np.isclose(col_sum, 1, atol=tol)).any() + or (P0 < 0).any() + ): + msg = "`P0` matrix must be doubly stochastic" + if msg is not None: + raise ValueError(msg) + + +def _split_matrix(X, n): + # definitions according to Seeded Graph Matching [2]. + upper, lower = X[:n], X[n:] + return upper[:, :n], upper[:, n:], lower[:, :n], lower[:, n:] + + +def _doubly_stochastic(P, tol=1e-3): + # Adapted from @btaba implementation + # https://github.com/btaba/sinkhorn_knopp + # of Sinkhorn-Knopp algorithm + # https://projecteuclid.org/euclid.pjm/1102992505 + + max_iter = 1000 + c = 1 / P.sum(axis=0) + r = 1 / (P @ c) + P_eps = P + + for it in range(max_iter): + if (np.abs(P_eps.sum(axis=1) - 1) < tol).all() and ( + np.abs(P_eps.sum(axis=0) - 1) < tol + ).all(): + # All column/row sums ~= 1 within threshold + break + + c = 1 / (r @ P) + r = 1 / (P @ c) + P_eps = r[:, None] * P * c + + return P_eps + + +def _quadratic_assignment_2opt( + A, + B, + maximize=False, + partial_match=None, + rng=None, + partial_guess=None, + **unknown_options, +): + r""" + Solve the quadratic assignment problem (approximately). + This function solves the Quadratic Assignment Problem (QAP) and the + Graph Matching Problem (GMP) using the 2-opt algorithm [3]_. + Quadratic assignment solves problems of the following form: + .. math:: + \min_P & \ {\ \text{trace}(A^T P B P^T)}\\ + \mbox{s.t. } & {P \ \epsilon \ \mathcal{P}}\\ + where :math:`\mathcal{P}` is the set of all permutation matrices, + and :math:`A` and :math:`B` are square matrices. + Graph matching tries to *maximize* the same objective function. + This algorithm can be thought of as finding the alignment of the + nodes of two graphs that minimizes the number of induced edge + disagreements, or, in the case of weighted graphs, the sum of squared + edge weight differences. + Note that the quadratic assignment problem is NP-hard, is not + known to be solvable in polynomial time, and is computationally + intractable. Therefore, the results given are approximations, + not guaranteed to be exact solutions. + Parameters + ---------- + A : 2d-array, square + The square matrix :math:`A` in the objective function above. + B : 2d-array, square + The square matrix :math:`B` in the objective function above. + method : str in {'faq', '2opt'} (default: 'faq') + The algorithm used to solve the problem. This is the method-specific + documentation for '2opt'. + :ref:`'faq' ` is also available. + Options + ------- + maximize : bool (default = False) + Setting `maximize` to ``True`` solves the Graph Matching Problem (GMP) + rather than the Quadratic Assingnment Problem (QAP). + rng : {None, int, `~np.random.RandomState`, `~np.random.Generator`} + This parameter defines the object to use for drawing random + variates. + If `rng` is ``None`` the `~np.random.RandomState` singleton is + used. + If `rng` is an int, a new ``RandomState`` instance is used, + seeded with `rng`. + If `rng` is already a ``RandomState`` or ``Generator`` + instance, then that object is used. + Default is None. + partial_match : 2d-array of integers, optional, (default = None) + Allows the user to fix part of the matching between the two + matrices. In the literature, a partial match is also known as a + "seed". + Each row of `partial_match` specifies the indices of a pair of + corresponding nodes, that is, node ``partial_match[i, 0]`` of `A` is + matched to node ``partial_match[i, 1]`` of `B`. Accordingly, + ``partial_match`` is an array of size ``(m , 2)``, where ``m`` is + not greater than the number of nodes. + partial_guess : 2d-array of integers, optional, (default = None) + Allows the user to provide a guess for the matching between the two + matrices. Unlike `partial_match`, `partial_guess` does not fix the + indices; they are still free to be optimized. + Each row of `partial_guess` specifies the indices of a pair of + corresponding nodes, that is, node ``partial_guess[i, 0]`` of `A` is + matched to node ``partial_guess[i, 1]`` of `B`. Accordingly, + ``partial_guess`` is an array of size ``(m , 2)``, where ``m`` is + less than or equal to the number of nodes. + Returns + ------- + res : OptimizeResult + A :class:`scipy.optimize.OptimizeResult` containing the following + fields. + col_ind : 1-D array + An array of column indices corresponding with the best + permutation of the nodes of `B` found. + fun : float + The corresponding value of the objective function. + nit : int + The number of iterations performed during optimization. + Notes + ----- + This is a greedy algorithm that works similarly to bubble sort: beginning + with an initial permutation, it iteratively swaps pairs of indices to + improve the objective function until no such improvements are possible. + References + ---------- + .. [3] "2-opt," Wikipedia. + https://en.wikipedia.org/wiki/2-opt + """ + + _check_unknown_options(unknown_options) + rng = check_random_state(rng) + A, B, partial_match = _common_input_validation(A, B, partial_match) + + N = len(A) + # check outlier cases + if N == 0 or partial_match.shape[0] == N: + score = _calc_score(A, B, partial_match[:, 1]) + res = {"col_ind": partial_match[:, 1], "fun": score, "nit": 0} + return OptimizeResult(res) + + if partial_guess is None: + partial_guess = np.array([[], []]).T + partial_guess = np.atleast_2d(partial_guess).astype(int) + + msg = None + if partial_guess.shape[0] > A.shape[0]: + msg = "`partial_guess` can have only as " "many entries as there are nodes" + elif partial_guess.shape[1] != 2: + msg = "`partial_guess` must have two columns" + elif partial_guess.ndim != 2: + msg = "`partial_guess` must have exactly two dimensions" + elif (partial_guess < 0).any(): + msg = "`partial_guess` must contain only positive indices" + elif (partial_guess >= len(A)).any(): + msg = "`partial_guess` entries must be less than number of nodes" + elif not len(set(partial_guess[:, 0])) == len(partial_guess[:, 0]) or not len( + set(partial_guess[:, 1]) + ) == len(partial_guess[:, 1]): + msg = "`partial_guess` column entries must be unique" + if msg is not None: + raise ValueError(msg) + + fixed_rows = None + if partial_match.size or partial_guess.size: + # use partial_match and partial_guess for initial permutation, + # but randomly permute the rest. + guess_rows = np.zeros(N, dtype=bool) + guess_cols = np.zeros(N, dtype=bool) + fixed_rows = np.zeros(N, dtype=bool) + fixed_cols = np.zeros(N, dtype=bool) + perm = np.zeros(N, dtype=int) + + rg, cg = partial_guess.T + guess_rows[rg] = True + guess_cols[cg] = True + perm[guess_rows] = cg + + # match overrides guess + rf, cf = partial_match.T + fixed_rows[rf] = True + fixed_cols[cf] = True + perm[fixed_rows] = cf + + random_rows = ~fixed_rows & ~guess_rows + random_cols = ~fixed_cols & ~guess_cols + perm[random_rows] = rng.permutation(np.arange(N)[random_cols]) + else: + perm = rng.permutation(np.arange(N)) + + best_score = _calc_score(A, B, perm) + + i_free = np.arange(N) + if fixed_rows is not None: + i_free = i_free[~fixed_rows] + + better = operator.gt if maximize else operator.lt + n_iter = 0 + done = False + while not done: + # equivalent to nested for loops i in range(N), j in range(i, N) + for i, j in itertools.combinations_with_replacement(i_free, 2): + n_iter += 1 + perm[i], perm[j] = perm[j], perm[i] + score = _calc_score(A, B, perm) + if better(score, best_score): + best_score = score + break + # faster to swap back than to create a new list every time + perm[i], perm[j] = perm[j], perm[i] + else: # no swaps made + done = True + + res = {"col_ind": perm, "fun": best_score, "nit": n_iter} + + return OptimizeResult(res) diff --git a/graspologic/match/skp.py b/graspologic/match/skp.py deleted file mode 100644 index 2d83190a1..000000000 --- a/graspologic/match/skp.py +++ /dev/null @@ -1,185 +0,0 @@ -# Copyright (c) Microsoft Corporation and contributors. -# Licensed under the MIT License. - -# Title: sinkhorn_knopp Source Code -# Author: Tabanpour, B -# Date: 2018 -# Code version: 0.2 -# Availability: https://pypi.org/project/sinkhorn_knopp/ -# -# The MIT License -# -# Copyright (c) 2016 Baruch Tabanpour -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. - - -import warnings -import numpy as np - - -class SinkhornKnopp: - """ - Sinkhorn Knopp Algorithm - Takes a non-negative square matrix :math:`P`, where :math:`P \\neq 0` - and iterates through Sinkhorn Knopp's algorithm - to convert :math:`P` to a doubly stochastic matrix. - Guaranteed convergence if :math:`P` has total support [1]: - - Parameters - ---------- - max_iter : int, default=1000 - The maximum number of iterations. - - epsilon : float, default=1e-3 - Metric used to compute the stopping condition, - which occurs if all the row and column sums are - within epsilon of 1. This should be a very small value. - ``epsilon`` must be between 0 and 1. - - Attributes - ---------- - _max_iter : int, default=1000 - User defined parameter. See above. - - _epsilon : float, default=1e-3 - User defined paramter. See above. - - _stopping_condition: string - Either "max_iter", "epsilon", or None, which is a - description of why the algorithm stopped iterating. - - _iterations : int - The number of iterations elapsed during the algorithm's - run-time. - - _D1 : 2d-array - Diagonal matrix obtained after a stopping condition was met - so that ``_D1.dot(P).dot(_D2)`` is close to doubly stochastic. - - _D2 : 2d-array - Diagonal matrix obtained after a stopping condition was met - so that ``_D1.dot(P).dot(_D2)`` is close to doubly stochastic. - - - References - ---------- - .. [1] Sinkhorn, Richard & Knopp, Paul. (1967). "Concerning nonnegative - matrices and doubly stochastic matrices," Pacific Journal of - Mathematics. 21. 10.2140/pjm.1967.21.343. - """ - - def __init__(self, max_iter=1000, epsilon=1e-3): - if type(max_iter) is int or type(max_iter) is float: - if max_iter > 0: - self._max_iter = int(max_iter) - else: - msg = "max_iter must be greater than 0" - raise ValueError(msg) - else: - msg = "max_iter is not of type int or float" - raise TypeError(msg) - - if type(epsilon) is int or type(epsilon) is float: - if epsilon > 0 and epsilon < 1: - self._epsilon = int(epsilon) - else: - msg = "epsilon must be between 0 and 1 exclusively" - raise ValueError(msg) - else: - msg = "epsilon is not of type int or float" - raise TypeError(msg) - - self._stopping_condition = None - self._iterations = 0 - self._D1 = np.ones(1) - self._D2 = np.ones(1) - - def fit(self, P): - """ - Fit the diagonal matrices in Sinkhorn Knopp's algorithm - - Parameters - ---------- - P : 2d array-like - Must be a square non-negative 2d array-like object, that - is convertible to a numpy array. The matrix must not be - equal to 0 and it must have total support for the algorithm - to converge. - - Returns - ------- - P_eps : A double stochastic matrix. - """ - P = np.asarray(P) - assert np.all(P >= 0) - assert P.ndim == 2 - assert P.shape[0] == P.shape[1] - - N = P.shape[0] - max_thresh = 1 + self._epsilon - min_thresh = 1 - self._epsilon - - # Initialize r and c, the diagonals of D1 and D2 - # and warn if the matrix does not have support. - r = np.ones((N, 1)) - pdotr = P.T.dot(r) - total_support_warning_str = ( - "Matrix P must have total support. " "See documentation" - ) - if not np.all(pdotr != 0): - warnings.warn(total_support_warning_str, UserWarning) - - c = 1 / pdotr - pdotc = P.dot(c) - if not np.all(pdotc != 0): - warnings.warn(total_support_warning_str, UserWarning) - - r = 1 / pdotc - del pdotr, pdotc - - P_eps = np.copy(P) - while ( - np.any(np.sum(P_eps, axis=1) < min_thresh) - or np.any(np.sum(P_eps, axis=1) > max_thresh) - or np.any(np.sum(P_eps, axis=0) < min_thresh) - or np.any(np.sum(P_eps, axis=0) > max_thresh) - ): - - c = 1 / P.T.dot(r) - r = 1 / P.dot(c) - - self._D1 = np.diag(np.squeeze(r)) - self._D2 = np.diag(np.squeeze(c)) - P_eps = self._D1.dot(P).dot(self._D2) - - self._iterations += 1 - - if self._iterations >= self._max_iter: - self._stopping_condition = "max_iter" - break - - if not self._stopping_condition: - self._stopping_condition = "epsilon" - - self._D1 = np.diag(np.squeeze(r)) - self._D2 = np.diag(np.squeeze(c)) - P_eps = self._D1.dot(P).dot(self._D2) - - return P_eps diff --git a/tests/test_match.py b/tests/test_match.py index 34e41539e..6fea96234 100644 --- a/tests/test_match.py +++ b/tests/test_match.py @@ -6,7 +6,6 @@ import math import random from graspologic.match import GraphMatch as GMP -from graspologic.match import SinkhornKnopp as SK from graspologic.simulations import er_np np.random.seed(0) @@ -16,14 +15,14 @@ class TestGMP: @classmethod def setup_class(cls): cls.barycenter = GMP(gmp=False) - cls.rand = GMP(n_init=100, init_method="rand", gmp=False) + cls.rand = GMP(n_init=100, init="rand", gmp=False) cls.barygm = GMP(gmp=True) def test_SGM_inputs(self): with pytest.raises(TypeError): GMP(n_init=-1.5) with pytest.raises(ValueError): - GMP(init_method="random") + GMP(init="random") with pytest.raises(TypeError): GMP(max_iter=-1.5) with pytest.raises(TypeError): @@ -141,38 +140,3 @@ def test_padding(self): res = gmp_adopted.fit(G1, G2) assert 1.0 == (sum(res.perm_inds_ == np.arange(n)) / n) - - -class TestSinkhornKnopp: - @classmethod - def test_SK_inputs(self): - with pytest.raises(TypeError): - SK(max_iter=True) - with pytest.raises(ValueError): - SK(max_iter=-1) - with pytest.raises(TypeError): - SK(epsilon=True) - with pytest.raises(ValueError): - SK(epsilon=2) - - def test_SK(self): - - # Epsilon = 1e-3 - sk = SK() - P = np.asarray([[1, 2], [3, 4]]) - n = P.shape[0] - Pt = sk.fit(P) - - f = np.concatenate((np.sum(Pt, axis=0), np.sum(Pt, axis=1)), axis=None) - f1 = [round(x, 5) for x in f] - assert (f1 == np.ones(2 * n)).all() - - # Epsilon = 1e-8 - sk = SK(epsilon=1e-8) - P = np.asarray([[1.4, 0.2, 4], [3, 4, 0.7], [0.4, 6, 1]]) - n = P.shape[0] - Pt = sk.fit(P) - - f = np.concatenate((np.sum(Pt, axis=0), np.sum(Pt, axis=1)), axis=None) - f1 = [round(x, 5) for x in f] - assert (f1 == np.ones(2 * n)).all() From 610a666bfb07d78f1fcfd4e158d54c27d0166a77 Mon Sep 17 00:00:00 2001 From: Paul Adkisson Date: Sun, 15 Nov 2020 13:46:28 -0500 Subject: [PATCH 054/140] singular_values_ attribute for MultipleASE class (#559) * Added singular_values_ attribute * responding to comments Co-authored-by: Benjamin Pedigo --- graspologic/embed/mase.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/graspologic/embed/mase.py b/graspologic/embed/mase.py index 8828f0894..d14cc4f9e 100644 --- a/graspologic/embed/mase.py +++ b/graspologic/embed/mase.py @@ -87,6 +87,13 @@ class MultipleASE(BaseEmbedMulti): scores_ : array, shape (n_samples, n_components, n_components) Estimated :math:`\hat{R}` matrices for each input graph. + singular_values_ : array, shape (n_components) OR length 2 tuple of arrays + If input graph is undirected, equal to the singular values of the concatenated + adjacency spectral embeddings. If input graph is directed, :attr:`singular_values_` + is a tuple of length 2, where :attr:`singular_values_[0]` corresponds to + the singular values of the concatenated left adjacency spectral embeddings, + and :attr:`singular_values_[1]` corresponds to + the singular values of the concatenated right adjacency spectral embeddings. Notes ----- @@ -166,7 +173,7 @@ def _reduce_dim(self, graphs): # Second SVD for vertices # The notation is slightly different than the paper - Uhat, _, _ = selectSVD( + Uhat, sing_vals_left, _ = selectSVD( Us, n_components=self.n_components, n_elbows=self.n_elbows, @@ -174,14 +181,14 @@ def _reduce_dim(self, graphs): n_iter=self.n_iter, ) - Vhat, _, _ = selectSVD( + Vhat, sing_vals_right, _ = selectSVD( Vs, n_components=self.n_components, n_elbows=self.n_elbows, algorithm=self.algorithm, n_iter=self.n_iter, ) - return Uhat, Vhat + return Uhat, Vhat, sing_vals_left, sing_vals_right def fit(self, graphs, y=None): """ @@ -209,14 +216,16 @@ def fit(self, graphs, y=None): graphs = self._diag_aug(graphs) # embed - Uhat, Vhat = self._reduce_dim(graphs) + Uhat, Vhat, sing_vals_left, sing_vals_right = self._reduce_dim(graphs) self.latent_left_ = Uhat if not undirected: self.latent_right_ = Vhat self.scores_ = Uhat.T @ graphs @ Vhat + self.singular_values_ = (sing_vals_left, sing_vals_right) else: self.latent_right_ = None self.scores_ = Uhat.T @ graphs @ Uhat + self.singular_values_ = sing_vals_left return self From 7e89ccd0669c3f471f344559c6a44eb382dc00f2 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Mon, 16 Nov 2020 17:12:33 -0500 Subject: [PATCH 055/140] Add doc status shield/link (#522) * Update README.md * make shield link to docs too * switch to readthedocs --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 52b4fa282..a3a12e5f0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) [![Downloads shield](https://img.shields.io/pypi/dm/graspologic.svg)](https://pypi.org/project/graspologic/) +[![Docs shield](https://img.shields.io/readthedocs/graspologic)](https://graspologic.readthedocs.io/) ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) [![codecov](https://codecov.io/gh/neurodata/graspy/branch/master/graph/badge.svg)](https://codecov.io/gh/neurodata/graspy) [![DOI](https://zenodo.org/badge/147768493.svg)](https://zenodo.org/badge/latestdoi/147768493) From 41fe592d57fa92c311d4cec8d0a06044aeb2bb3b Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Mon, 16 Nov 2020 17:50:26 -0500 Subject: [PATCH 056/140] Add function to remap categorical labelings (#529) * add relabel function * add tests * add to docs * clarify in docs * update docs * add arbitrary label type support * reblack * sort imports * update docs for dwayne feedback * add param documentation * add simple typing * add typing on output * update type checking * remove comment * fix docs * Update graspologic/utils/utils.py Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> --- docs/reference/utils.rst | 4 +- graspologic/utils/__init__.py | 2 + graspologic/utils/utils.py | 93 ++++++++++++++++++++++++++++++++++- tests/test_utils.py | 57 +++++++++++++++++++-- 4 files changed, 151 insertions(+), 5 deletions(-) diff --git a/docs/reference/utils.rst b/docs/reference/utils.rst index 0b628a98e..fabb0b248 100644 --- a/docs/reference/utils.rst +++ b/docs/reference/utils.rst @@ -34,5 +34,7 @@ IO .. autofunction:: import_edgelist +Other +----- - +.. autofunction:: remap_labels \ No newline at end of file diff --git a/graspologic/utils/__init__.py b/graspologic/utils/__init__.py index cddfcf8cb..1a8e79997 100644 --- a/graspologic/utils/__init__.py +++ b/graspologic/utils/__init__.py @@ -19,6 +19,7 @@ binarize, cartprod, fit_plug_in_variance_estimator, + remap_labels, ) from .ptr import pass_to_ranks @@ -41,4 +42,5 @@ "cartprod", "pass_to_ranks", "fit_plug_in_variance_estimator", + "remap_labels", ] diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index c014d1fec..8d50fe635 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -5,10 +5,15 @@ from collections import Iterable from functools import reduce from pathlib import Path +from typing import List, Union import networkx as nx import numpy as np -from sklearn.utils import check_array +import pandas as pd +from scipy.optimize import linear_sum_assignment +from sklearn.metrics import confusion_matrix +from sklearn.utils import check_array, check_consistent_length, column_or_1d +from sklearn.utils.multiclass import type_of_target, unique_labels def import_graph(graph, copy=True): @@ -701,3 +706,89 @@ def plug_in_variance_estimator(x): return covariances return plug_in_variance_estimator + + +def remap_labels( + y_true: Union[List, np.ndarray, pd.Series], + y_pred: Union[List, np.ndarray, pd.Series], + return_map: bool = False, +) -> np.ndarray: + """ + Remaps a categorical labeling (such as one predicted by a clustering algorithm) to + match the labels used by another similar labeling. + + Given two :math:`n`-length vectors describing a categorical labeling of :math:`n` + samples, this method reorders the labels of the second vector (`y_pred`) so that as + many samples as possible from the two label vectors are in the same category. + + + Parameters + ---------- + y_true : array-like of shape (n_samples,) + Ground truth labels, or, labels to map to. + y_pred : array-like of shape (n_samples,) + Labels to remap to match the categorical labeling of `y_true`. The categorical + labeling of `y_pred` will be preserved exactly, but the labels used to + denote the categories will be changed to best match the categories used in + `y_true`. + return_map : bool, optional + Whether to return a dictionary where the keys are the original category labels + from `y_pred` and the values are the new category labels that they were mapped + to. + + Returns + ------- + remapped_y_pred : np.ndarray of shape (n_samples,) + Same categorical labeling as that of `y_pred`, but with the category labels + permuted to best match those of `y_true`. + label_map : dict + Mapping from the original labels of `y_pred` to the new labels which best + resemble those of `y_true`. Only returned if `return_map` was True. + + Examples + -------- + >>> y_true = np.array([0,0,1,1,2,2]) + >>> y_pred = np.array([2,2,1,1,0,0]) + >>> remap_labels(y_true, y_pred) + array([0, 0, 1, 1, 2, 2]) + + Notes + ----- + This method will work well when the label vectors describe a somewhat similar + categorization of the data (as measured by metrics such as + :func:`sklearn.metrics.adjusted_rand_score`, for example). When the categorizations + are not similar, the remapping may not make sense (as such a remapping does not + exist). + + For example, consider when one category in `y_true` is exactly split in half into + two categories in `y_pred`. If this is the case, it is impossible to say which of + the categories in `y_pred` match that original category from `y_true`. + """ + check_consistent_length(y_true, y_pred) + true_type = type_of_target(y_true) + pred_type = type_of_target(y_pred) + + valid_target_types = {"binary", "multiclass"} + if (true_type not in valid_target_types) or (pred_type not in valid_target_types): + msg = "Elements of `y_true` and `y_pred` must represent a valid binary or " + msg += "multiclass labeling, see " + msg += "https://scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.type_of_target.html" + msg += " for more information." + raise ValueError(msg) + + y_true = column_or_1d(y_true) + y_pred = column_or_1d(y_pred) + + if not isinstance(return_map, bool): + raise TypeError("return_map must be of type bool.") + + labels = unique_labels(y_true, y_pred) + confusion_mat = confusion_matrix(y_true, y_pred, labels=labels) + row_inds, col_inds = linear_sum_assignment(confusion_mat, maximize=True) + label_map = dict(zip(labels[col_inds], labels[row_inds])) + + remapped_y_pred = np.vectorize(label_map.get)(y_pred) + if return_map: + return remapped_y_pred, label_map + else: + return remapped_y_pred diff --git a/tests/test_utils.py b/tests/test_utils.py index 5af2e8949..0ee7cf6c6 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,12 +2,16 @@ # Licensed under the MIT License. import unittest -import numpy as np -import networkx as nx -from graspologic.utils import utils as gus from math import sqrt + +import networkx as nx +import numpy as np +import pytest from numpy.testing import assert_equal +from graspologic.utils import remap_labels +from graspologic.utils import utils as gus + class TestInput(unittest.TestCase): @classmethod @@ -412,3 +416,50 @@ def test_binarize(): g2 = gus.binarize(g) g2_expected = np.ones_like(g) assert_equal(g2, g2_expected) + + +class TestRemapLabels(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.y_true = np.array([0, 0, 1, 1, 2, 2]) + cls.y_pred = np.array([1, 1, 2, 2, 0, 0]) + + def test_proper_relabeling(self): + y_pred_remapped, label_map = remap_labels( + self.y_true, self.y_pred, return_map=True + ) + assert_equal(y_pred_remapped, self.y_true) + for key, val in label_map.items(): + if key == 0: + assert val == 2 + elif key == 2: + assert val == 1 + elif key == 1: + assert val == 0 + + def test_imperfect_relabeling(self): + y_true = [0, 0, 0, 1, 1, 1, 2, 2, 2] + # swap 0 and 1, make the 3rd label wrong. + y_pred = [1, 1, 0, 0, 0, 0, 2, 2, 2] + y_pred_remapped = remap_labels(y_true, y_pred) + assert_equal(y_pred_remapped, [0, 0, 1, 1, 1, 1, 2, 2, 2]) + + def test_string_relabeling(self): + y_true = ["ant", "ant", "cat", "cat", "bird", "bird"] + y_pred = ["bird", "bird", "cat", "cat", "ant", "ant"] + y_pred_remapped = remap_labels(y_true, y_pred) + assert_equal(y_true, y_pred_remapped) + + def test_inputs(self): + with pytest.raises(ValueError): + # handled by sklearn confusion matrix + remap_labels(self.y_true[1:], self.y_pred) + + with pytest.raises(TypeError): + remap_labels(8, self.y_pred) + + with pytest.raises(TypeError): + remap_labels(self.y_pred, self.y_true, return_map="hi") + + with pytest.raises(ValueError): + remap_labels(self.y_pred, ["ant", "ant", "cat", "cat", "bird", "bird"]) From 3cc5525cdece6c4aeb561aaa2c83c9816cf35eab Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 18 Nov 2020 10:52:59 -0500 Subject: [PATCH 057/140] Create pull_request_template.md (#588) * Create pull_request_template.md * Rename pull_request_template.md to PULL_REQUEST_TEMPLATE.md --- .github/PULL_REQUEST_TEMPLATE.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 .github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 000000000..398624ced --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,16 @@ + + +#### Reference Issues/PRs + + +#### What does this implement/fix? Briefly explain your changes. + +#### Any other comments? From 8aa9daaad08d01cb2778c9a96ba7aa70e17034f0 Mon Sep 17 00:00:00 2001 From: Haoyin Xu Date: Wed, 18 Nov 2020 17:43:12 -0500 Subject: [PATCH 058/140] Add pypi badge and update codecov badge (#587) Co-authored-by: Benjamin Pedigo --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index a3a12e5f0..4812bf2a3 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,14 @@ # graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) +[![PyPI version](https://img.shields.io/pypi/v/graspologic.svg)](https://pypi.org/project/graspologic/) [![Downloads shield](https://img.shields.io/pypi/dm/graspologic.svg)](https://pypi.org/project/graspologic/) [![Docs shield](https://img.shields.io/readthedocs/graspologic)](https://graspologic.readthedocs.io/) ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) -[![codecov](https://codecov.io/gh/neurodata/graspy/branch/master/graph/badge.svg)](https://codecov.io/gh/neurodata/graspy) +[![codecov](https://codecov.io/gh/microsoft/graspologic/branch/dev/graph/badge.svg)](https://codecov.io/gh/microsoft/graspologic) [![DOI](https://zenodo.org/badge/147768493.svg)](https://zenodo.org/badge/latestdoi/147768493) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + ## `graspologic` is a package for graph statistical algorithms. - [Overview](#overview) From 7c6940d2fdc0f1cb8fe3f55dda1cd89a8d26d4bc Mon Sep 17 00:00:00 2001 From: Haoyin Xu Date: Fri, 20 Nov 2020 12:48:40 -0500 Subject: [PATCH 059/140] Fix code block & typos (#591) I especially like calling us out in the PR comments. Well played! --- CONTRIBUTING.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 37b27dcfa..f6231bfcc 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -47,11 +47,13 @@ follow these guidelines! This will make it a lot faster for us to respond to you - Please include your **operating system type and version number**, as well as your **Python and graspologic versions**. This information - can be found by running the following code snippet:: + can be found by running the following code snippet: + ```python import platform; print(platform.platform()) import sys; print(f"Python {sys.version}") import graspologic; print(f"graspologic {graspologic.__version__}") + ``` - Please ensure all **code snippets and error messages are formatted in appropriate code blocks**. See @@ -138,7 +140,7 @@ formatted and indented. Please read it and follow it. ## Docstring Guidelines -Properly formatted docstrings is required for documentation generation by Sphinx. The graspologic package closely +Properly formatted docstrings are required for documentation generation by Sphinx. The graspologic package closely follows the numpydoc guidelines. Please read and follow the [numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html#overview) guidelines. Refer to the [example.py](https://numpydoc.readthedocs.io/en/latest/example.html#example) provided by numpydoc. From 99955786dfae7538aebfa3fd1edfb058ee46bcf9 Mon Sep 17 00:00:00 2001 From: Paul Adkisson Date: Mon, 23 Nov 2020 08:46:57 -0500 Subject: [PATCH 060/140] Adding MASE Tutorial (#491) * Adding MASE Tutorial * Updated explanation and specified n_components * Correcting version to 3.7.0 * Updating to graspologic * Added 'read more in the tutorials' message to MASE reference docs * Fixing version.txt * Fixing version.txt * Fix Jupyter Notebook Version to 3.7 * Re-blackened repo * Responding to feedback * Cleared cell output * Fixing Errors * responding to minor comments * Added clustering and score matrix visualization * Added Visualization function * changed color palette and added remap_labels function * removing excess score matrices section * switched to built-in remap_labels function Co-authored-by: Benjamin Pedigo --- docs/tutorial.rst | 1 + docs/tutorials/embedding/MASE.ipynb | 380 +++++++++++++++++++++++++ docs/tutorials/embedding/Omnibus.ipynb | 2 +- graspologic/embed/mase.py | 2 + 4 files changed, 384 insertions(+), 1 deletion(-) create mode 100644 docs/tutorials/embedding/MASE.ipynb diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 104580b46..467f55518 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -41,6 +41,7 @@ Inference on random graphs depends on low-dimensional Euclidean representation o tutorials/embedding/AdjacencySpectralEmbed tutorials/embedding/Omnibus + tutorials/embedding/MASE .. _inference_tutorials: diff --git a/docs/tutorials/embedding/MASE.ipynb b/docs/tutorials/embedding/MASE.ipynb new file mode 100644 index 000000000..149c26051 --- /dev/null +++ b/docs/tutorials/embedding/MASE.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple Adjacency Spectral Embedding (MASE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Multiple Adjacency Spectral Embedding (MASE) is an extension of Adjacency Spectral Embedding (see ASE [tutorial](https://graspy.neurodata.io/tutorials/embedding/adjacencyspectralembed)) for an arbitrary number of graphs. Once graphs are embedded, the low-dimensional Euclidean representation can be used to visualize the latent positions of vertices, perform inference, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Replicating Omnibus Embedding Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to demonstrate how to use the ``MultipleASE`` class, we will use a simple example: two 2-block stochastic block models (SBMs) with different block probabilities. Indeed, we will use the same models as in the omnibus [tutorial](https://graspy.neurodata.io/tutorials/embedding/omnibus) to facilitate direct comparison. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate two different graphs using stochastic block models (SBM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We sample two 2-block SBMs (undirected, no self-loops) with 50 vertices, each block containing 25 vertices ($n = [25, 25]$), and with the following block connectivity matrices:\n", + "\n", + "\\begin{align*}\n", + "B_1 = \n", + "\\begin{bmatrix}0.3 & 0.1\\\\\n", + "0.1 & 0.7\n", + "\\end{bmatrix},~\n", + "B_2 = \\begin{bmatrix}0.3 & 0.1\\\\\n", + "0.1 & 0.3\n", + "\\end{bmatrix}\n", + "\\end{align*}\n", + "\n", + "The only difference between the two is the within-block probability for the second block. We sample $G_1 \\sim \\text{SBM}(n, B_1)$ and $G_2 \\sim \\text{SBM}(n, B_2)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.simulations import sbm\n", + "\n", + "n = [25, 25]\n", + "B1 = [[0.3, 0.1],\n", + " [0.1, 0.7]]\n", + "B2 = [[0.3, 0.1],\n", + " [0.1, 0.3]]\n", + "\n", + "np.random.seed(8)\n", + "G1 = sbm(n, B1)\n", + "G2 = sbm(n, B2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the graphs using heatmap\n", + "\n", + "We visualize the sampled graphs using the ``heatmap()`` function. ``heatmap()`` will plot the adjacency matrix, where the colors represent the weight of each edge. In this case, we have binary graphs so the values will be either 0 or 1. \n", + "\n", + "We see that there is clear block structure to the graphs. Furthermore, the lower right quarter of $G_1$, representing the within-group connections for the second group, is more dense than that of $G_2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.plot import heatmap\n", + "\n", + "heatmap(G1, figsize=(7,7), title=\"Visualization of Graph 1\", cbar=False)\n", + "_ = heatmap(G2, figsize=(7,7), title=\"Visualization of Graph 2\", cbar=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embed the two graphs using MASE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as ASE fits a single input graph to the Random Dot Product Graph (RDPG) model, MASE fits many input graphs to the common subspace independent-edge (COSIE) model, which is an extension of the RDPG model to multiple graphs. The COSIE model assumes that all of the graphs have important shared properties, but can also have meaningful differences. As a result, COSIE decomposes the set of graphs into a set of shared latent positions $V$ that describe similarities, and a set of score matrices $R^{(i)}$ that describe how each individual graph is different. Mathematically, for the adjacency matrix of an undirected graph $A^{(i)}$,\n", + "$$A^{(i)} = VR^{(i)}V^T$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, 2 clusters can be faithfully represented in 2 dimensions, so we specify the number of components as 2. Then, the ``fit_transform()`` method returns the estimated latent positions $\\hat{V}$, which we can see has 50 entries corresponding to the 50 vertices embedded in 2 dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.embed import MultipleASE as MASE\n", + "\n", + "embedder = MASE(n_components=2)\n", + "V_hat = embedder.fit_transform([G1, G2])\n", + "print(V_hat.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Common Latent Positions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the two graphs have clear block structures, with higher within-block probabilities than between-block, we see two distinct \"clusters\" when we visualize the shared latent positions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def vis_latent(V, title, predicted=False, pred_labels=None):\n", + " fig, ax = plt.subplots(figsize=(10,10))\n", + " if predicted:\n", + " ax.scatter(V_hat[pred_labels==0, 0], V_hat[pred_labels==0, 1], c=\"blue\", label=\"Predicted Block 1\")\n", + " ax.scatter(V_hat[pred_labels==1, 0], V_hat[pred_labels==1, 1], c=\"red\", label=\"Predicted Block 2\")\n", + " else:\n", + " ax.scatter(V[:25, 0], V[:25, 1], c=\"blue\", label=\"Block 1\")\n", + " ax.scatter(V[25:, 0], V[25:, 1], c=\"red\", label=\"Block 2\")\n", + " \n", + " ax.legend(prop={'size':20})\n", + " ax.set_xticklabels([])\n", + " ax.set_yticklabels([])\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " _ = ax.set_title(title, fontsize=20)\n", + "\n", + "vis_latent(V_hat, \"Common Latent Positions from MASE Embedding\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recover Original Communities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply a simple clustering algorithm (kmeans) to recover the original communities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Cluster V_hat\n", + "from graspologic.cluster import KMeansCluster\n", + "clusterer = KMeansCluster()\n", + "pred_labels = clusterer.fit_predict(V_hat)\n", + "\n", + "#Remap labels\n", + "from graspologic.utils import remap_labels\n", + "true_labels = [0]*25 + [1]*25\n", + "remapped_labels = remap_labels(true_labels, pred_labels)\n", + "\n", + "#Visualize latent positions\n", + "vis_latent(V_hat, \"Predicted Communities from Common Latent Positions\", True, remapped_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even with only one graph sampled from each SBM, we can perfectly recover the two communities!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sampling More Graphs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can recover a more accurate representation of the two SBM populations with more sample graphs. Let's try with 100 graphs sampled from each." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G1 = [sbm(n, B1) for i in range(100)]\n", + "G2 = [sbm(n, B2) for i in range(100)]\n", + "\n", + "V_hat = embedder.fit_transform(G1 + G2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing Common Latent Subspace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the clustering is even more distinct, as we would expect with more samples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vis_latent(V_hat, \"Common Latent Subspace from MASE Embedding with Many Sampled Graphs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Score Matrices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have more samples, we can apply the same process to visualize the individual score matrices. Note, since the score matrices are symmetric, $2 \\times 2$ matrices, we only need a 3-dimensional vector to represent each. In this case, we can clearly see two distinct clusters: one for the score matrices from the first SBM population another for the score matrices from the second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.plot import pairplot\n", + "\n", + "#Extract Score Matrices (in vector form)\n", + "R_1_hat = np.reshape(embedder.scores_[:100], (100, 4))\n", + "R_2_hat = np.reshape(embedder.scores_[100:], (100, 4))\n", + "\n", + "#Omitting redundant entries due to symmetry\n", + "R_1_hat = np.delete(R_1_hat, 1, axis=1)\n", + "R_2_hat = np.delete(R_2_hat, 1, axis=1)\n", + "\n", + "#Combining into a single array\n", + "R_hat = np.concatenate((R_1_hat, R_2_hat), axis=0)\n", + "\n", + "#Visualize\n", + "labels = [\"SBM 1\"]*100 + [\"SBM 2\"]*100\n", + "_ = pairplot(R_hat, labels, title=\"Score Matrices from MASE Embedding\",\n", + " palette={\"green\", \"purple\"}, legend_name=\"Population\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recover Original SBM Membership" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we can apply a simple clustering algorithm (kmeans) to recover from which SBM population each graph was generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Cluster R_hat\n", + "pred_labels = clusterer.fit_predict(R_hat)\n", + "true_labels = [0]*100 + [1]*100\n", + "remapped_labels = remap_labels(true_labels, pred_labels)\n", + "pred_sbm = []\n", + "for label in remapped_labels:\n", + " if label == 0:\n", + " pred_sbm.append(\"SBM 1\")\n", + " else:\n", + " pred_sbm.append(\"SBM 2\")\n", + "\n", + "#Visualize Results\n", + "_ = pairplot(R_hat, pred_sbm, title=\"Predicted SBM Membership from Score Matrices\",\n", + " palette={\"green\", \"purple\"}, legend_name=\"Population\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this method we can perfectly recover the original SBM population to which each graph belongs!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/embedding/Omnibus.ipynb b/docs/tutorials/embedding/Omnibus.ipynb index 0581b9e40..ba93fef5c 100644 --- a/docs/tutorials/embedding/Omnibus.ipynb +++ b/docs/tutorials/embedding/Omnibus.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Embedding Multiple Graphs\n", + "# Omnibus Embedding for Multiple Graphs\n", "\n", "This demo shows you how to simultaneously embed two graphs using omnibus embedding from two graphs sampled from different stochastic block models (SBM). We will also compare the results to that of adjacency spectral embedding, and show why it is useful to embed the graphs simultaneously." ] diff --git a/graspologic/embed/mase.py b/graspologic/embed/mase.py index d14cc4f9e..17e7f27e4 100644 --- a/graspologic/embed/mase.py +++ b/graspologic/embed/mase.py @@ -27,6 +27,8 @@ class MultipleASE(BaseEmbedMulti): :math:`R^{(i)}` can be assymetric and non-square, but all graphs still share a common latent position matrices :math:`U` and :math:`V`. + Read more in the :ref:`tutorials ` + Parameters ---------- n_components : int or None, default = None From e889d43148888270cd460c050d5c0fc1db57e6bf Mon Sep 17 00:00:00 2001 From: Alex Loftus Date: Mon, 23 Nov 2020 09:14:59 -0500 Subject: [PATCH 061/140] Out of sample ASE (#518) * add oos * y=None needs to be in fit for sklearn compliance * graspy > graspologic * old oos branch did some weird stuff * more changes to get up-to-date with upstream * clearer documentation * clearer documentation * rename 'predict' to 'transform' * fix bug in weighted check * change name back to predict * rough draft for OOS added to ASE tutorial * add directed tutorial * add remove_vertex utility function, fix ase documentation * better docstring on remove_vertex, remove weighted restriction on ase * improve remove_vertices * much more efficient and simple remove_vertices * lock numpy version to current, np.delete behavior changed in older versions * inline documentation and examples * updated tutorial * ase currently not a valid sklearn estimator, so removing for now * janky hack to deal with my local kernel in tutorial * fix random typo * explanation * add input checks * wording in ValueError * never saved merge conflict fix * rearranged code, had to repeat some code for efficiency, dont like that but cant think of a good solution * cleaner organization now * clarifying comments * ASE no longer satisfies estimator requirements with current implementation * fix inconsistent leading whitespace issue from test-coverage check? * add tests for remove_vertices * add integer index testing * fix bug in predict code * basic tests for spectral embed * fix test_utils * see if this fixes issue with test coverage * see if this fixes test coverage issue * cleaner syntax * chec max dimension of input * even cleaner syntax * even cleaner-er syntax * better syntax in remove_vertices * update inline documentation * exception tests * exception tests on ase * better examples, index check * add positional-only and keyword-only arguments (does this work with older python versions?) * Revert "add positional-only and keyword-only arguments (does this work with older python versions?)" jkjfsadf This reverts commit 3ec8f15e8f7b4b0e20f278cd8bda39272efe146d. * minor wording changes in docstrings * remove estimator check * revert numpy version change * use masks instead of delete for remove_vertices * cleaner syntax * dtype in np.ones for clarity * clean up masking code * make pinv variable private * add directed section to tutorial * add separate out-of-sample embedding tutorial * add new tutorial to rst * change parameter name on remove_vertices * move references, beefier docstring in utils * revert version.txt * indentation on docstring * fix title level * change name to Transform * change predict to transform anywhere, deal with original matrix passed to transform * fix tutorial * diag_aug set False by default * add test to make sure centers are in the correct place * correct embed positions (verified with test), change to docstring * diag_aug True by default * simplify plotting code in tutorial * fix diag_aug check * check diag_aug in new tests * take out diag_aug checks * some name changes to tests * merge conflicts * requested changes * line lengths and variable markings * phrasing * fixed bug that would have popped up in edge case * begin attempting to debug deploy preview * 'fix' edge case issue * assert warning * added note * line lengths in remove_vertices * remove comment in binarize * add remove_vertices to reference docs * add debugging line for deploy preview * more lines of debugging code * take out sphinx rst for now * I diddly do da done did it * unecessary param in plot function * something broke deploy preview, maybe this fixes * fix deploy issue * tight layout on plot, rename 'left' to 'out' * same as before, but with notebook saved * return plot in plotting code * delete unused line * black * think we can just raise an error here, i dont think theres a case where it makes sense for this to go through * update test * black on utils * Update graspologic/embed/ase.py Co-authored-by: Benjamin Pedigo Co-authored-by: Benjamin Pedigo --- docs/tutorial.rst | 1 + .../embedding/AdjacencySpectralEmbed.ipynb | 75 +++- .../embedding/OutOfSampleEmbed.ipynb | 343 ++++++++++++++++++ graspologic/embed/ase.py | 101 ++++++ graspologic/utils/__init__.py | 2 + graspologic/utils/utils.py | 83 +++++ tests/test_sklearn.py | 1 - tests/test_spectral_embed.py | 131 ++++++- tests/test_utils.py | 88 +++++ 9 files changed, 809 insertions(+), 16 deletions(-) create mode 100644 docs/tutorials/embedding/OutOfSampleEmbed.ipynb diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 467f55518..e3d12ea71 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -40,6 +40,7 @@ Inference on random graphs depends on low-dimensional Euclidean representation o :maxdepth: 1 tutorials/embedding/AdjacencySpectralEmbed + tutorials/embedding/OutOfSampleEmbed tutorials/embedding/Omnibus tutorials/embedding/MASE diff --git a/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb b/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb index 43c573c35..32c94ce93 100644 --- a/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb +++ b/docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb @@ -16,13 +16,19 @@ "outputs": [], "source": [ "import numpy as np\n", - "np.random.seed(8889)\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "from sklearn.metrics import adjusted_rand_score\n", "from sklearn.cluster import KMeans\n", + "\n", "from graspologic.embed import AdjacencySpectralEmbed\n", "from graspologic.simulations import sbm\n", "from graspologic.plot import heatmap, pairplot\n", "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "np.random.seed(8889)\n", "%matplotlib inline" ] }, @@ -30,8 +36,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using ASE on simple stochastic block model (SBM) graphs \n", - "ASE is a method for estimating the latent positions of a network modeled as a Random Dot Product Graph (RDPG). This embedding is both a form of dimensionality reduction for a graph, and a way of fitting a generative model to graph data. " + "## Data Generation\n", + "ASE is a method for estimating the latent positions of a network modeled as a Random Dot Product Graph (RDPG). This embedding is both a form of dimensionality reduction for a graph and a way of fitting a generative model to graph data. We first generate two 2-block SBMs: one directed, and one undirected." ] }, { @@ -40,20 +46,35 @@ "metadata": {}, "outputs": [], "source": [ + "# Define parameters\n", "n_verts = 100\n", "labels_sbm = n_verts * [0] + n_verts * [1]\n", - "P = np.array([[0.8, 0.2], [0.2, 0.8]])\n", + "P = np.array([[0.8, 0.2], \n", + " [0.2, 0.8]])\n", + "\n", + "# Generate SBMs from parameters\n", "undirected_sbm = sbm(2 * [n_verts], P)\n", - "heatmap(undirected_sbm, title='2-block SBM (undirected)', inner_hier_labels=labels_sbm)\n", "directed_sbm = sbm(2 * [n_verts], P, directed=True)\n", - "_ = heatmap(directed_sbm, title='2-block SBM (directed)', inner_hier_labels=labels_sbm)" + "\n", + "# Plot both SBMs\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n", + "heatmap(undirected_sbm, title='2-block SBM (undirected)', inner_hier_labels=labels_sbm, ax=axes[0])\n", + "heatmap(directed_sbm, title='2-block SBM (directed)', inner_hier_labels=labels_sbm, ax=axes[1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embedding: Undirected Case" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can use the ``AdjacencySpectralEmbed`` class to embed the adjacency matrix as follows. If no parameters are given to the ``AdjacencySpectralEmbed`` class, it will automatically choose the number of dimensions to embed into." + "We now use the AdjacencySpectralEmbed class to embed the adjacency matrix into lower-dimensional space. \n", + "If no parameters are given to the AdjacencySpectralEmbed class, it will automatically choose the number of dimensions to embed into." ] }, { @@ -62,11 +83,21 @@ "metadata": {}, "outputs": [], "source": [ + "# instantiate an ASE object\n", "ase = AdjacencySpectralEmbed()\n", + "\n", + "# call its fit_transform method to generate latent positions\n", "Xhat = ase.fit_transform(undirected_sbm)\n", "_ = pairplot(Xhat, title='SBM adjacency spectral embedding')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embedding: Directed Case" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -80,8 +111,11 @@ "metadata": {}, "outputs": [], "source": [ + "# Transform in directed case\n", "ase = AdjacencySpectralEmbed()\n", "Xhat, Yhat = ase.fit_transform(directed_sbm)\n", + "\n", + "# Plot both embeddings\n", "pairplot(Xhat, title='SBM adjacency spectral embedding \"out\"')\n", "_ = pairplot(Yhat, title='SBM adjacency spectral embedding \"in\"')" ] @@ -90,7 +124,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "One can also specify the parameters for embedding, for example, by manually inputting the number of embedded dimensions and changing the SVD solver used to compute the embedding." + "## Dimension specification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can also specify the parameters for embedding. \n", + "Here, we specify the number of embedded dimensions and change the SVD solver used to compute the embedding." ] }, { @@ -99,9 +141,12 @@ "metadata": {}, "outputs": [], "source": [ + "# fit and transform\n", "ase = AdjacencySpectralEmbed(n_components=2, algorithm='truncated')\n", "Xhat = ase.fit_transform(undirected_sbm)\n", - "_ = pairplot(Xhat, title='2-component embedding', height=4)" + "\n", + "# plot\n", + "pairplot(Xhat, title='2-component embedding', height=4)" ] }, { @@ -120,10 +165,10 @@ "source": [ "palette = {'Right':(0,0.7,0.2),\n", " 'Wrong':(0.8,0.1,0.1)}\n", + "\n", "for insularity in np.linspace(0.5, 0.625, 4):\n", " P = np.array([[insularity, 1-insularity], [1-insularity, insularity]])\n", " sampled_sbm = sbm(2 * [n_verts], P)\n", - " heatmap(sampled_sbm, title='Insularity: {}'.format(str(insularity)[:5]), inner_hier_labels=labels_sbm)\n", " Xhat = AdjacencySpectralEmbed(n_components=2).fit_transform(sampled_sbm)\n", " labels_kmeans = KMeans(n_clusters=2).fit_predict(Xhat)\n", " ari = adjusted_rand_score(labels_sbm, labels_kmeans)\n", @@ -137,18 +182,20 @@ " error_label = np.array(error_label)\n", " error_label[error] = 'Wrong'\n", " \n", + " heatmap(sampled_sbm, title=f'Insularity: {str(insularity)[:5]}', \n", + " inner_hier_labels=labels_sbm)\n", " pairplot(Xhat,\n", " labels=labels_kmeans,\n", - " title='KMeans on embedding, ARI: {}'.format(str(ari)[:5]),\n", + " title=f'KMeans on embedding, ARI: {str(ari)[:5]}',\n", " legend_name='Predicted label',\n", " height=3.5,\n", - " palette='muted')\n", + " palette='muted',)\n", " pairplot(Xhat,\n", " labels=error_label,\n", - " title='Error from KMeans, Error rate: {}'.format(str(error_rate)),\n", + " title=f'Error from KMeans, Error rate: {str(error_rate)}',\n", " legend_name='Error label',\n", " height=3.5,\n", - " palette=palette)" + " palette=palette,)" ] } ], diff --git a/docs/tutorials/embedding/OutOfSampleEmbed.ipynb b/docs/tutorials/embedding/OutOfSampleEmbed.ipynb new file mode 100644 index 000000000..5d7ca9e11 --- /dev/null +++ b/docs/tutorials/embedding/OutOfSampleEmbed.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Out-of-Sample (OOS) Embedding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we've embedded the nodes of a graph into Euclidean space using Adjacency Spectral Embedding (ASE). \n", + "Then, suppose we gain access to new nodes not seen in the original graph. We sometimes wish to determine their latent positions without the computationally-expensive task of re-embedding an entirely new adjacency matrix.\n", + "\n", + "More formally, suppose we have computed the embedding $\\hat{X} \\in \\textbf{R}^{n \\times d}$ from some adjacency matrix $A \\in \\textbf{R}^{n \\times n}$. \n", + "Suppose we then obtain some new vertex with adjacency vector $w \\in \\textbf{R}^n$ or new vertices with \"adjacency\" matrix $W \\in \\textbf{R}^{m \\times n}$, with $m$ the number of new vertices. We wish to estimate the latent positions for these new vertices.\n", + "\n", + "Here, an \"adjacency vector\" $w$ is a vector with $n$ elements, $n$ being the number of in-sample vertices, and a 1 in the $i_{th}$ position if the out-of-sample vertex has an edge with in-sample vertex $i$ in the unweighted case.\n", + "\n", + "$W \\in \\textbf{R}^{m \\times n}$ is a matrix with each row being an adjacency vector, for $m$ out-of-sample vertices.\n", + "\n", + "We can obtain this estimation with ASE's `transform` method. \n", + "Running through the Adjacency Spectral Embedding tutorial is recommended prior to this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from numpy.random import normal, poisson\n", + "\n", + "from graspologic.simulations import sbm\n", + "from graspologic.embed import AdjacencySpectralEmbed as ASE\n", + "from graspologic.plot import heatmap, pairplot\n", + "from graspologic.utils import remove_vertices\n", + "\n", + "np.random.seed(9002)\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Undirected out-of-sample prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we embed an undirected two-block stochastic block model with ASE. We then use its transform method to find an out-of-sample prediction for both a single vertex and multiple vertices. \n", + "\n", + "We begin by generating data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate parameters\n", + "nodes_per_community = 100\n", + "P = np.array([[0.8, 0.2],\n", + " [0.2, 0.8]])\n", + "\n", + "# Generate a directed and undirected Stochastic Block Model (SBM)\n", + "undirected, labels = sbm(2*[nodes_per_community], P, return_labels=True)\n", + "labels = list(labels)\n", + "\n", + "# Grab out-of-sample vertices\n", + "oos_idx = 0\n", + "oos_labels = labels.pop(oos_idx)\n", + "A, a = remove_vertices(undirected, indices=oos_idx, return_removed=True)\n", + "\n", + "# plot our SBMs\n", + "heatmap(A, title=f'2-block SBM (undirected), shape {A.shape}', inner_hier_labels=labels);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Embedding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then generate an embedding with ASE, and we use its `transform` method to determine our best estimate for the latent position of the out-of-sample vertex." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate an embedding with ASE\n", + "ase = ASE(n_components=2)\n", + "X_hat = ase.fit_transform(A)\n", + "\n", + "# predicted latent positions\n", + "w = ase.transform(a)\n", + "w" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting out-of-sample embedding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we plot the original latent positions as well as the out-of-sample vertices. Note that the out-of-sample vertices are near their expected latent positions despite not having been run through the original embedding. \n", + "In this plot, the stars are the out-of-sample latent positions, and the dots are the in-sample latent positions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_oos(X_hat, oos_vertices, labels, oos_labels, title):\n", + " # Plot the in-sample latent positions\n", + " plot = pairplot(X_hat, labels=labels, title=title)\n", + "\n", + " # generate out-of-sample dataframe\n", + " oos_vertices = np.atleast_2d(oos_vertices)\n", + " data = {'Type': oos_labels, \n", + " 'Dimension 1': oos_vertices[:, 0], \n", + " 'Dimension 2': oos_vertices[:, 1]}\n", + " oos_df = pd.DataFrame(data=data)\n", + " \n", + " # update plot with out-of-sample latent positions,\n", + " # plotting out-of-sample latent positions as stars\n", + " plot.data = oos_df\n", + " plot.hue_vals = oos_df[\"Type\"]\n", + " plot.map_offdiag(sns.scatterplot, s=500, marker=\"*\", edgecolor=\"black\")\n", + " plot.tight_layout()\n", + " return plot\n", + "\n", + " \n", + "plot_oos(X_hat, w, labels=labels, oos_labels=[0], title=\"Out-of-Sample Embeddings (2-block SBM)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Passing in multiple out-of-sample vertices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can pass a 2d numpy array into `transform`. The rows are the out-of-sample vertices, and the columns are their edges to the in-sample vertices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Grab out-of-sample vertices\n", + "labels = [0]*nodes_per_community + [1]*nodes_per_community\n", + "oos_idx = [0, -1]\n", + "oos_labels = [labels.pop(i) for i in oos_idx]\n", + "A, a = remove_vertices(undirected, indices=oos_idx, return_removed=True)\n", + "\n", + "# our out-of-sample array is m x n\n", + "print(f\"a is {type(a)} with shape {a.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate an embedding with ASE\n", + "ase = ASE(n_components=2)\n", + "X_hat = ase.fit_transform(A)\n", + "\n", + "# predicted latent positions\n", + "w = ase.transform(a)\n", + "print(f\"The out-of-sample prediction output has dimensions {w.shape}\\n\")\n", + "\n", + "plot_oos(X_hat, w, labels, oos_labels=oos_labels,\n", + " title=\"Out-of-Sample Embeddings (2-block SBM)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Directed out-of-sample prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not all graphs are undirected. When finding out-of-sample latent positions for directed graphs, $A \\in \\textbf{R}^{n \\times n}$ is not symmetric. $A_{i,j}$ represents the edge from node $i$ to node $j$, whereas $A_{j, i}$ represents the edge from node $j$ to node $i$.\n", + "\n", + "To account for this, we pass a tuple (out_oos, in_oos) into the `transform` method. It then outputs a tuple of (out_latent_prediction, in_latent_prediction). \n", + "Here, \"out\" means \"edges from out-of-sample vertices to in-sample vertices\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# a is a tuple of (out_oos, in_oos)\n", + "directed = sbm(2*[nodes_per_community], P, directed=True)\n", + "oos_idx = [0, -1]\n", + "A, a = remove_vertices(directed, indices=oos_idx, return_removed=True)\n", + "heatmap(directed);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit a directed graph\n", + "X_hat, Y_hat = ase.fit_transform(A)\n", + "\n", + "# predicted latent positions\n", + "w = ase.transform(a)\n", + "print(f\"output of `ase.transform(a)` is {type(w)}\", \"\\n\")\n", + "print(f\"out latent positions: \\n{w[0]}\\n\")\n", + "print(f\"in latent positions: \\n{w[1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting directed latent predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_oos(X_hat, w[0], labels, oos_labels=oos_labels, title=\"Out Latent Predictions\")\n", + "plot_oos(Y_hat, w[1], labels, oos_labels=oos_labels, title=\"In Latent Predictions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weighted out-of-sample prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Weighted graphs work as well. Here, we generate a directed, weighted graph and estimate the latent positions for multiple out-of-sample vertices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a weighted graph and out-of-sample vertices\n", + "wt = [[normal, poisson],\n", + " [poisson, normal]]\n", + "wtargs = [[dict(loc=3, scale=1), dict(lam=5)],\n", + " [dict(lam=5), dict(loc=3, scale=1)]]\n", + "weighted = sbm(2*[nodes_per_community], P, wt=wt, wtargs=wtargs, directed=True)\n", + "oos_idx = [0, -1]\n", + "A, a = remove_vertices(weighted, indices=oos_idx, return_removed=True)\n", + "heatmap(A)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Embed and transform\n", + "X_hat, Y_hat = ase.fit_transform(A)\n", + "w = ase.transform(a)\n", + "\n", + "# Plot\n", + "plot_oos(X_hat, w[0], labels, oos_labels=oos_labels, title=\"Out Latent Predictions\")\n", + "plot_oos(Y_hat, w[1],labels, oos_labels=oos_labels, title=\"In Latent Predictions\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5-final" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index f94c21c55..3ed507055 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -2,6 +2,9 @@ # Licensed under the MIT License. import warnings +import numpy as np +from sklearn.utils.validation import check_is_fitted +import networkx as nx from .base import BaseSpectralEmbed from ..utils import ( @@ -103,6 +106,10 @@ class AdjacencySpectralEmbed(BaseSpectralEmbed): .. [1] Sussman, D.L., Tang, M., Fishkind, D.E., Priebe, C.E. "A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," Journal of the American Statistical Association, Vol. 107(499), 2012 + + .. [2] Levin, K., Roosta-Khorasani, F., Mahoney, M. W., & Priebe, C. E. (2018). + Out-of-sample extension of graph adjacency spectral embedding. PMLR: Proceedings + of Machine Learning Research, 80, 2975-2984. """ def __init__( @@ -127,6 +134,7 @@ def __init__( if not isinstance(diag_aug, bool): raise TypeError("`diag_aug` must be of type bool") self.diag_aug = diag_aug + self.is_fitted_ = False def fit(self, graph, y=None): """ @@ -137,6 +145,8 @@ def fit(self, graph, y=None): graph : array_like or networkx.Graph Input graph to embed. + y: Ignored + Returns ------- self : object @@ -158,4 +168,95 @@ def fit(self, graph, y=None): self.n_features_in_ = len(A) self._reduce_dim(A) + + # for out-of-sample + inv_eigs = np.diag(1 / self.singular_values_) + self._pinv_left = self.latent_left_ @ inv_eigs + if self.latent_right_ is not None: + self._pinv_right = self.latent_right_ @ inv_eigs + + self.is_fitted_ = True return self + + def transform(self, X): + """ + Obtain latent positions from an adjacency matrix or matrix of out-of-sample + vertices. For more details on transforming out-of-sample vertices, see the + :ref:`tutorials `. For mathematical background, see [2]. + + Parameters + ---------- + X : array_like or tuple, original shape or (n_oos_vertices, n_vertices). + + The original fitted matrix ("graph" in fit) or new out-of-sample data. + If ``X`` is the original fitted matrix, returns a matrix close to + ``self.fit_transform(X)``. + + If ``X`` is an out-of-sample matrix, n_oos_vertices is the number + of new vertices, and n_vertices is the number of vertices in the + original graph. If tuple, graph is directed and ``X[0]`` contains + edges from out-of-sample vertices to in-sample vertices. + + Returns + ------- + array_like or tuple, shape (n_oos_vertices, n_components) + or (n_vertices, n_components). + + Array of latent positions. Transforms the fitted matrix if it was passed + in. + + If ``X`` is an array or tuple containing adjacency vectors corresponding to + new nodes, returns the estimated latent positions for the new out-of-sample + adjacency vectors. + If undirected, returns array. + If directed, returns ``(X_out, X_in)``, where ``X_out`` contains + latent positions corresponding to nodes with edges from out-of-sample + vertices to in-sample vertices. + + Notes + ----- + If the matrix was diagonally augmented (e.g., ``self.diag_aug`` was True), ``fit`` + followed by ``transform`` will produce a slightly different matrix than + ``fit_transform``. + + To get the original embedding, using ``fit_transform`` is recommended. In the + directed case, if A is the original in-sample adjacency matrix, the tuple + (A.T, A) will need to be passed to ``transform`` if you do not wish to use + ``fit_transform``. + """ + + # checks + check_is_fitted(self, "is_fitted_") + if isinstance(X, nx.classes.graph.Graph): + X = import_graph(X) + directed = self.latent_right_ is not None + + # correct types? + if directed and not isinstance(X, tuple): + if X.shape[0] == X.shape[1]: # in case original matrix was passed + msg = """A square matrix A was passed to ``transform`` in the directed case. + If this was the original in-sample matrix, either use ``fit_transform`` + or pass a tuple (A.T, A). If this was an out-of-sample matrix, directed + graphs require a tuple (X_out, X_in).""" + raise TypeError(msg) + else: + msg = "Directed graphs require a tuple (X_out, X_in) for out-of-sample transforms." + raise TypeError(msg) + if not directed and not isinstance(X, np.ndarray): + raise TypeError("Undirected graphs require array input") + + # correct shape in y? + latent_rows = self.latent_left_.shape[0] + _X = X[0] if directed else X + X_cols = _X.shape[-1] + if _X.ndim > 2: + raise ValueError("out-of-sample vertex must be 1d or 2d") + if latent_rows != X_cols: + msg = "out-of-sample vertex must be shape (n_oos_vertices, n_vertices)" + raise ValueError(msg) + + # workhorse code + if not directed: + return X @ self._pinv_left + elif directed: + return X[1] @ self._pinv_right, X[0] @ self._pinv_left diff --git a/graspologic/utils/__init__.py b/graspologic/utils/__init__.py index 1a8e79997..93556bff1 100644 --- a/graspologic/utils/__init__.py +++ b/graspologic/utils/__init__.py @@ -19,6 +19,7 @@ binarize, cartprod, fit_plug_in_variance_estimator, + remove_vertices, remap_labels, ) from .ptr import pass_to_ranks @@ -42,5 +43,6 @@ "cartprod", "pass_to_ranks", "fit_plug_in_variance_estimator", + "remove_vertices", "remap_labels", ] diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index 8d50fe635..c9448f008 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -708,6 +708,89 @@ def plug_in_variance_estimator(x): return plug_in_variance_estimator +def remove_vertices(graph, indices, return_removed=False): + """ + Remove a subgraph of adjacency vectors from an adjacency matrix, giving back the + truncated matrix and optionally the removed subgraph. Here, an adjacency vector + is the set of edge weights for a particular vertex. + + Parameters + ---------- + graph: networkx.Graph or array-like, shape (n, n) + The adjacency matrix for some graph. + indices: int or array-like, length m + Index/indices of the adjacency vector(s) to be removed. + return_removed: bool, by default False (optional) + Whether to return the tuple ``(A, V)``, + where ``A`` is the truncated adjacency matrix, + ``V`` is an array representing the removed subgraph. + + Returns + ------- + truncated_graph: np.ndarray + The truncated matrix. + This is a copy of `graph` of shape (k, k), with ``k=n-m``, without the ``m`` + adjacency vectors given by `indices`. + + removed_subgraph: np.ndarray or tuple, shape (m, k) (optional) + Array of removed adjacency vectors without edges to each other. + If directed, this is a tuple ``(V_1, V_2)``, + with ``V_1`` being an array of adjacency vectors from the removed subgraph to the truncated graph, + and ``V_2`` being an array of adjacency vectors from the truncated graph to the removed subgraph. + + Examples + -------- + # Undirected + >>> A = np.array([[0, 1, 2], + [1, 0, 3], + [2, 3, 0]]) + >>> remove_vertices(A, 0) + array([[0., 3.], + [3., 0.]])) + >>> remove_vertices(A, 0, return_removed=True) + (array([[0., 3.], + [3., 0.]]), + array([1., 2.])) + + # Directed + >>> B = np.array([[0, 1, 2, 3], + [4, 0, 5, 6], + [7, 8, 0, 9], + [10, 11, 12, 0]]) + >>> remove_vertices(B, 0, return_removed=True) + (array([[ 0., 5., 6.], + [ 8., 0., 9.], + [11., 12., 0.]]), + (array([ 4., 7., 10.]), array([1., 2., 3.]))) + >>> remove_vertices(B, [0, -1], return_removed=True) + (array([[0., 5.], + [8., 0.]]), + (array([[4., 7.], + [6., 9.]]), + array([[ 1., 2.], + [11., 12.]]))) + """ + graph = import_graph(graph) + if isinstance(indices, list) and len(indices) >= len(graph): + raise IndexError("You must pass in fewer vertex indices than vertices.") + directed = not is_almost_symmetric(graph) + + # truncate graph + mask = np.ones(graph.shape[0], dtype=bool) + mask[indices] = 0 + A = graph[mask, :][:, mask] + + if return_removed: + rows = graph[mask] + vertices = rows[:, indices].T + if directed: + cols = graph[:, mask] + vertices_right = cols[indices] + return A, (vertices, vertices_right) + return A, vertices + return A + + def remap_labels( y_true: Union[List, np.ndarray, pd.Series], y_pred: Union[List, np.ndarray, pd.Series], diff --git a/tests/test_sklearn.py b/tests/test_sklearn.py index 00dd7e203..17a0b8172 100644 --- a/tests/test_sklearn.py +++ b/tests/test_sklearn.py @@ -6,6 +6,5 @@ from sklearn.utils.estimator_checks import check_estimator -check_estimator(graspologic.embed.AdjacencySpectralEmbed) check_estimator(graspologic.embed.LaplacianSpectralEmbed) check_estimator(graspologic.embed.ClassicalMDS) diff --git a/tests/test_spectral_embed.py b/tests/test_spectral_embed.py index 58d7bc2d0..ed7be854c 100644 --- a/tests/test_spectral_embed.py +++ b/tests/test_spectral_embed.py @@ -2,12 +2,21 @@ # Licensed under the MIT License. import unittest +import pytest import numpy as np +import pandas as pd +from numpy.random import poisson, normal +from numpy.testing import assert_equal +import networkx as nx from graspologic.embed.ase import AdjacencySpectralEmbed from graspologic.embed.lse import LaplacianSpectralEmbed from graspologic.simulations.simulations import er_np, er_nm, sbm +from graspologic.utils import remove_vertices, is_symmetric from sklearn.cluster import KMeans -from sklearn.metrics import adjusted_rand_score +from sklearn.metrics import adjusted_rand_score, pairwise_distances +from sklearn.base import clone + +np.random.seed(9002) def _kmeans_comparison(data, labels, n_clusters): @@ -105,6 +114,23 @@ def _test_sbm_er_binary(self, method, P, directed=False, *args, **kwargs): class TestAdjacencySpectralEmbed(unittest.TestCase): + def setUp(self): + np.random.seed(9001) + n = [10, 10] + p = np.array([[0.9, 0.1], [0.1, 0.9]]) + wt = [[normal, poisson], [poisson, normal]] + wtargs = [ + [dict(loc=3, scale=1), dict(lam=5)], + [dict(lam=5), dict(loc=3, scale=1)], + ] + self.testgraphs = dict( + Guw=sbm(n=n, p=p), + Gw=sbm(n=n, p=p, wt=wt, wtargs=wtargs), + Guwd=sbm(n=n, p=p, directed=True), + Gwd=sbm(n=n, p=p, wt=wt, wtargs=wtargs, directed=True), + ) + self.ase = AdjacencySpectralEmbed(n_components=2) + def test_output_dim(self): _test_output_dim(self, AdjacencySpectralEmbed) @@ -130,6 +156,109 @@ def test_input_checks(self): ase = AdjacencySpectralEmbed(diag_aug="over 9000") ase.fit() + def test_transform_closeto_fit_transform(self): + atol = 0.15 + for diag_aug in [True, False]: + for g, A in self.testgraphs.items(): + ase = AdjacencySpectralEmbed(n_components=2, diag_aug=diag_aug) + ase.fit(A) + Y = ase.fit_transform(A) + if isinstance(Y, np.ndarray): + X = ase.transform(A) + self.assertTrue(np.allclose(X, Y, atol=atol)) + elif isinstance(Y, tuple): + with self.assertRaises(TypeError): + X = ase.transform(A) + X = ase.transform((A.T, A)) + self.assertTrue(np.allclose(X[0], Y[0], atol=atol)) + self.assertTrue(np.allclose(X[1], Y[1], atol=atol)) + else: + raise TypeError + + def test_transform_networkx(self): + G = nx.grid_2d_graph(5, 5) + ase = AdjacencySpectralEmbed(n_components=2) + ase.fit(G) + ase.transform(G) + + def test_transform_correct_types(self): + ase = AdjacencySpectralEmbed(n_components=2) + for graph in self.testgraphs.values(): + A, a = remove_vertices(graph, 1, return_removed=True) + ase.fit(A) + directed = ase.latent_right_ is not None + weighted = not np.array_equal(A, A.astype(bool)) + w = ase.transform(a) + if directed: + self.assertIsInstance(w, tuple) + self.assertIsInstance(w[0], np.ndarray) + self.assertIsInstance(w[1], np.ndarray) + elif not directed: + self.assertIsInstance(w, np.ndarray) + self.assertEqual(np.atleast_2d(w).shape[1], 2) + + def test_directed_vertex_direction(self): + M = self.testgraphs["Guwd"] + oos_idx = 0 + A, a = remove_vertices(M, indices=oos_idx, return_removed=True) + assert_equal(np.delete(M[:, 0], oos_idx), a[0]) + + def test_directed_correct_latent_positions(self): + # setup + ase = AdjacencySpectralEmbed(n_components=3) + P = np.array([[0.9, 0.1, 0.1], [0.3, 0.6, 0.1], [0.1, 0.5, 0.6]]) + M, labels = sbm([200, 200, 200], P, directed=True, return_labels=True) + + # one node from each community + oos_idx = np.nonzero(np.r_[1, np.diff(labels)[:-1]])[0] + labels = list(labels) + oos_labels = [labels.pop(i) for i in oos_idx] + + # Grab out-of-sample, fit, transform + A, a = remove_vertices(M, indices=oos_idx, return_removed=True) + latent_left, latent_right = ase.fit_transform(A) + oos_left, oos_right = ase.transform(a) + + # separate into communities + for i, latent in enumerate([latent_left, latent_right]): + left = i == 0 + df = pd.DataFrame( + { + "Type": labels, + "Dimension 1": latent[:, 0], + "Dimension 2": latent[:, 1], + "Dimension 3": latent[:, 2], + } + ) + # make sure that oos vertices are closer to their true community averages than other community averages + means = df.groupby("Type").mean() + if left: + avg_dist_within = np.diag(pairwise_distances(means, oos_left)) + avg_dist_between = np.diag(pairwise_distances(means, oos_right)) + self.assertTrue(all(avg_dist_within < avg_dist_between)) + elif not left: + avg_dist_within = np.diag(pairwise_distances(means, oos_right)) + avg_dist_between = np.diag(pairwise_distances(means, oos_left)) + self.assertTrue(all(avg_dist_within < avg_dist_between)) + + def test_exceptions(self): + ase = clone(self.ase) + + with pytest.raises(Exception): + ase.fit(self.testgraphs["Gwd"]) + ase.transform("9001") + + with pytest.raises(Exception): + Guwd = self.testgraphs["Guwd"] + ase.fit(Guwd) + ase.transform(np.ones(len(Guwd))) + + with pytest.raises(ValueError): + A, a = remove_vertices(self.testgraphs["Gw"], [0, 1], return_removed=True) + a = a.T + ase.fit(A) + ase.transform(a) + class TestLaplacianSpectralEmbed(unittest.TestCase): def test_output_dim(self): diff --git a/tests/test_utils.py b/tests/test_utils.py index 0ee7cf6c6..fa9f7d6aa 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,6 +2,10 @@ # Licensed under the MIT License. import unittest +import pytest +import numpy as np +import networkx as nx +from graspologic.utils import utils as gus from math import sqrt import networkx as nx @@ -418,6 +422,90 @@ def test_binarize(): assert_equal(g2, g2_expected) +class TestRemoveVertices(unittest.TestCase): + def setUp(self): + self.directed = np.array( + [ + [0, 2, 3, 4, 5], + [6, 0, 8, 9, 10], + [11, 12, 0, 14, 15], + [16, 17, 18, 0, 20], + [21, 22, 23, 24, 0], + ] + ) + self.undirected = np.array( + [ + [0, 6, 11, 16, 21], + [6, 0, 12, 17, 22], + [11, 12, 0, 18, 23], + [16, 17, 18, 0, 24], + [21, 22, 23, 24, 0], + ] + ) + + def test_undirected(self): + # with list index + indices = [0, -1, 1] + for idx in [indices, np.array(indices)]: + A, a = gus.remove_vertices(self.undirected, idx, return_removed=True) + self.assertIsInstance(a, np.ndarray) + assert_equal(A, np.array([[0, 18], [18, 0]])) + assert_equal(a, np.array([[11, 16], [23, 24], [12, 17]])) + self.assertTrue(gus.is_almost_symmetric(A)) + + # with integer index + indices = 0 + A, a = gus.remove_vertices(self.undirected, indices, return_removed=True) + assert_equal(A, self.undirected[1:, 1:]) + assert_equal(a, np.array([6, 11, 16, 21])) + self.assertTrue(gus.is_almost_symmetric(A)) + assert_equal( + gus.remove_vertices(self.undirected, 0), + gus.remove_vertices(self.undirected, [0]), + ) + + def test_directed(self): + # with list index + idx1 = [0, -1, 1] + idx2 = np.array(idx1) + for indices in [idx1, idx2]: + A, a = gus.remove_vertices(self.directed, indices, return_removed=True) + self.assertIsInstance(a, tuple) + self.assertIsInstance(a[0], np.ndarray) + self.assertIsInstance(a[1], np.ndarray) + assert_equal(A, np.array([[0, 14], [18, 0]])) + assert_equal(a[0], np.array([[11, 16], [15, 20], [12, 17]])) + assert_equal(a[1], np.array([[3, 4], [23, 24], [8, 9]])) + + # with integer index + idx = 0 + A, a = gus.remove_vertices(self.directed, idx, return_removed=True) + assert_equal(A, gus.remove_vertices(self.directed, idx)) + self.assertIsInstance(a, tuple) + self.assertIsInstance(a[0], np.ndarray) + self.assertIsInstance(a[1], np.ndarray) + assert_equal(A, self.directed[1:, 1:]) + assert_equal(a[0], np.array([6, 11, 16, 21])) + assert_equal(a[1], np.array([2, 3, 4, 5])) + + def test_exceptions(self): + # ensure proper errors are thrown when invalid inputs are passed. + with pytest.raises(TypeError): + gus.remove_vertices(9001, 0) + + with pytest.raises(ValueError): + nonsquare = np.vstack((self.directed, self.directed)) + gus.remove_vertices(nonsquare, 0) + + with pytest.raises(IndexError): + indices = np.arange(len(self.directed) + 1) + gus.remove_vertices(self.directed, indices) + + with pytest.raises(IndexError): + idx = len(self.directed) + 1 + gus.remove_vertices(self.directed, indices) + + class TestRemapLabels(unittest.TestCase): @classmethod def setUpClass(cls): From 388863190667263edc8607972b09223eaec857ac Mon Sep 17 00:00:00 2001 From: Alex Loftus Date: Mon, 23 Nov 2020 17:36:36 -0500 Subject: [PATCH 062/140] add titles to heatmaps (#593) --- docs/tutorials/embedding/OutOfSampleEmbed.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/embedding/OutOfSampleEmbed.ipynb b/docs/tutorials/embedding/OutOfSampleEmbed.ipynb index 5d7ca9e11..2d2b6e1e6 100644 --- a/docs/tutorials/embedding/OutOfSampleEmbed.ipynb +++ b/docs/tutorials/embedding/OutOfSampleEmbed.ipynb @@ -236,7 +236,7 @@ "directed = sbm(2*[nodes_per_community], P, directed=True)\n", "oos_idx = [0, -1]\n", "A, a = remove_vertices(directed, indices=oos_idx, return_removed=True)\n", - "heatmap(directed);" + "heatmap(directed, title=f'2-block SBM (directed), shape {A.shape}');" ] }, { @@ -300,7 +300,7 @@ "weighted = sbm(2*[nodes_per_community], P, wt=wt, wtargs=wtargs, directed=True)\n", "oos_idx = [0, -1]\n", "A, a = remove_vertices(weighted, indices=oos_idx, return_removed=True)\n", - "heatmap(A)" + "heatmap(A, title=f'2-block SBM (directed, weighted), shape {A.shape}')" ] }, { @@ -335,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5-final" + "version": "3.8.5" } }, "nbformat": 4, From 83d8c9e4cbc6a6f4cdf9ae3b20566c414174566b Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 30 Nov 2020 15:03:30 -0800 Subject: [PATCH 063/140] Leiden and modularity (#578) * Committed initial leiden wrapper from graspologic_native and modularity code from topologic. Next step is to figure out a good documentation plan and write tests, as well as get some early draft on input types (should we support np.ndarray or csr sparse matrices? Any other matrices? If so, should we write a 'get me a graph out of this' utility function like import_graph? If so, what does that look like? * Adding some documentation and increasing the superset of supported graph sources for leiden * I have done things today in this API that I swore I'd never do and I'm not sure my hands will ever be clean * We now have unit tests and docs * Someone pointed out that this link didn't work, and I needed to get netlify to try a build again * Addressing some PR feedback --- CONTRIBUTING.md | 2 +- docs/reference/index.rst | 1 + docs/reference/partition.rst | 20 + docs/reference/utils.rst | 4 +- graspologic/__init__.py | 1 + graspologic/partition/__init__.py | 14 + graspologic/partition/leiden.py | 444 + graspologic/partition/modularity.py | 164 + graspologic/utils/__init__.py | 2 + graspologic/utils/utils.py | 198 +- setup.py | 1 + tests/partition/test_leiden.py | 175 + tests/partition/test_modularity.py | 70 + tests/test_data/large-graph-partitions.csv | 7183 + tests/test_data/large-graph.csv | 151981 ++++++++++++++++++ tests/test_utils.py | 130 +- 16 files changed, 160379 insertions(+), 11 deletions(-) create mode 100644 docs/reference/partition.rst create mode 100644 graspologic/partition/__init__.py create mode 100644 graspologic/partition/leiden.py create mode 100644 graspologic/partition/modularity.py create mode 100644 tests/partition/test_leiden.py create mode 100644 tests/partition/test_modularity.py create mode 100644 tests/test_data/large-graph-partitions.csv create mode 100644 tests/test_data/large-graph.csv diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f6231bfcc..533b99d45 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -103,7 +103,7 @@ branch. Steps: We recommended that your contribution complies with the following rules before you submit a pull request: -- Follow the [coding-guidelines](guidelines). +- Follow the [coding-guidelines](#guidelines). - Give your pull request a helpful title that summarizes what your contribution does. - Link your pull request to the issue (see: [closing keywords](https://docs.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue) diff --git a/docs/reference/index.rst b/docs/reference/index.rst index dce08a611..705be27a9 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -18,3 +18,4 @@ Reference subgraph align preprocessing + partition diff --git a/docs/reference/partition.rst b/docs/reference/partition.rst new file mode 100644 index 000000000..5ff627eb2 --- /dev/null +++ b/docs/reference/partition.rst @@ -0,0 +1,20 @@ +Partition +========= + +.. currentmodule:: graspologic.partition + +Modularity and Component Modularity +----------------------------------- + +.. autofunction:: modularity + +.. autofunction:: modularity_components + +Leiden and Hierarchical Leiden +------------------------------ + +.. autofunction:: leiden + +.. autoclass:: HierarchicalCluster + +.. autofunction:: hierarchical_leiden diff --git a/docs/reference/utils.rst b/docs/reference/utils.rst index fabb0b248..2fcde74d7 100644 --- a/docs/reference/utils.rst +++ b/docs/reference/utils.rst @@ -37,4 +37,6 @@ IO Other ----- -.. autofunction:: remap_labels \ No newline at end of file +.. autofunction:: remap_labels + +.. autofunction:: to_weighted_edge_list \ No newline at end of file diff --git a/graspologic/__init__.py b/graspologic/__init__.py index b46653a09..8935600d8 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -11,6 +11,7 @@ import graspologic.embed import graspologic.inference import graspologic.models +import graspologic.partition import graspologic.preprocessing import graspologic.plot import graspologic.simulations diff --git a/graspologic/partition/__init__.py b/graspologic/partition/__init__.py new file mode 100644 index 000000000..7c6cdd107 --- /dev/null +++ b/graspologic/partition/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +from .modularity import modularity, modularity_components +from .leiden import HierarchicalCluster, hierarchical_leiden, leiden + + +__all__ = [ + "HierarchicalCluster", + "hierarchical_leiden", + "leiden", + "modularity", + "modularity_components", +] diff --git a/graspologic/partition/leiden.py b/graspologic/partition/leiden.py new file mode 100644 index 000000000..ca2548c49 --- /dev/null +++ b/graspologic/partition/leiden.py @@ -0,0 +1,444 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union +import networkx as nx +import numpy as np +import scipy +from .. import utils + +import graspologic_native as gn + + +def _validate_and_build_edge_list( + graph: Union[ + List[Tuple[Any, Any, Union[int, float]]], + nx.Graph, + np.ndarray, + scipy.sparse.csr.csr_matrix, + ], + is_weighted: Optional[bool], + weight_attribute: str, + check_directed: bool, + weight_default: float, +) -> List[Tuple[str, str, float]]: + if isinstance(graph, (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)): + raise TypeError("directed or multigraphs are not supported in these functions") + if ( + isinstance(graph, (np.ndarray, scipy.sparse.csr.csr_matrix)) + and check_directed is True + and not utils.is_almost_symmetric(graph) + ): + raise ValueError( + "leiden only supports undirected graphs and the adjacency matrix provided " + "was found to be directed" + ) + + return utils.to_weighted_edge_list( + graph=graph, + weight_attribute=weight_attribute, + weight_default=weight_default, + is_weighted=is_weighted, + is_directed=False, + ) + + +def _validate_common_arguments( + starting_communities: Optional[Dict[str, int]] = None, + extra_forced_iterations: int = 0, + resolution: float = 1.0, + randomness: float = 0.001, + use_modularity: bool = True, + random_seed: Optional[int] = None, + is_weighted: Optional[bool] = None, + weight_default: float = 1.0, + check_directed: bool = True, +): + if starting_communities is not None and not isinstance(starting_communities, dict): + raise TypeError("starting_communities must be a dictionary") + if not isinstance(extra_forced_iterations, int): + raise TypeError("iterations must be an int") + if not isinstance(resolution, (int, float)): + raise TypeError("resolution must be a float") + if not isinstance(randomness, (int, float)): + raise TypeError("randomness must be a float") + if not isinstance(use_modularity, bool): + raise TypeError("use_modularity must be a bool") + if random_seed is not None and not isinstance(random_seed, int): + raise TypeError("random_seed must either be an int or None") + if is_weighted is not None and not isinstance(is_weighted, bool): + raise TypeError("is_weighted must either be a bool or None") + if not isinstance(weight_default, (int, float)): + raise TypeError("weight_default must be a float") + if not isinstance(check_directed, bool): + raise TypeError("check_directed must be a bool") + + if extra_forced_iterations < 0: + raise ValueError("iterations must be a non negative integer") + if resolution <= 0: + raise ValueError("resolution must be a positive float") + if randomness <= 0: + raise ValueError("randomness must be a positive float") + if random_seed is not None and random_seed <= 0: + raise ValueError( + "random_seed must be a positive integer (the native PRNG implementation is" + " an unsigned 64 bit integer)" + ) + + +def leiden( + graph: Union[ + List[Tuple[Any, Any, Union[int, float]]], + nx.Graph, + np.ndarray, + scipy.sparse.csr.csr_matrix, + ], + starting_communities: Optional[Dict[str, int]] = None, + extra_forced_iterations: int = 0, + resolution: float = 1.0, + randomness: float = 0.001, + use_modularity: bool = True, + random_seed: Optional[int] = None, + weight_attribute: str = "weight", + is_weighted: Optional[bool] = None, + weight_default: float = 1.0, + check_directed: bool = True, +) -> Dict[str, int]: + """ + Leiden is a global network partitioning algorithm. Given a graph, it will iterate + through the network node by node, and test for an improvement in our quality + maximization function by speculatively joining partitions of each neighboring node. + + This process continues until no moves are made that increases the partitioning + quality. + + Parameters + ---------- + graph : Union[List[Tuple[Any, Any, Union[int, float]]], nx.Graph, np.ndarray, scipy.sparse.csr.csr_matrix] + A graph representation, whether a weighted edge list referencing an undirected + graph, an undirected networkx graph, or an undirected adjacency matrix in either + numpy.ndarray or scipy.sparse.csr.csr_matrix form. + starting_communities : Optional[Dict[str, int]] + Default is ``None``. An optional community mapping dictionary that contains the + string representation of the node and the community it belongs to. Note this map + must contain precisely the same nodes as the graph and every node must have a + positive community id. This mapping forms the starting point of Leiden + clustering and can be very useful in saving the state of a previous partition + schema from a previous graph and then adjusting the graph based on new temporal + data (additions, removals, weight changes, connectivity changes, etc). New nodes + should get their own unique community positive integer, but the original + partition can be very useful to speed up future runs of leiden. If no community + map is provided, the default behavior is to create a node community identity + map, where every node is in their own community. + extra_forced_iterations : int + Default is ``0``. Leiden will run until a maximum quality score has been found + for the node clustering and no nodes are moved to a new cluster in another + iteration. As there is an element of randomness to the Leiden algorithm, it is + sometimes useful to set ``extra_forced_iterations`` to a number larger than 0 + where the entire process is forced to attempt further refinement. + resolution : float + Default is ``1.0``. Higher resolution values lead to more communities and lower + resolution values leads to fewer communities. Must be greater than 0. + randomness : float + Default is ``0.001``. The larger the randomness value, the more exploration of + the partition space is possible. This is a major difference from the Louvain + algorithm, which is purely greedy in the partition exploration. + use_modularity : bool + Default is ``True``. If ``False``, will use a Constant Potts Model (CPM). + random_seed : Optional[int] + Default is ``None``. Can provide an optional seed to the PRNG used in Leiden for + deterministic output. + weight_attribute : str + Default is ``weight``. Only used when creating a weighed edge list of tuples + when the source graph is a networkx graph. This attribute corresponds to the + edge data dict key. + is_weighted : Optional[bool] + Default is ``None``. Only used when creating a weighted edge list of tuples + when the source graph is an adjacency matrix. The + :func:`graspologic.utils.to_weighted_edge_list` function will scan these + matrices and attempt to determine whether it is weighted or not. This flag can + short circuit this test and the values in the adjacency matrix will be treated + as weights. + weight_default : float + Default is ``1.0``. If the graph is a networkx graph and the graph does not have + a fully weighted sequence of edges, this default will be used. If the adjacency + matrix is found or specified to be unweighted, this weight_default will be used + for every edge. + check_directed : bool + Default is ``True``. If the graph is an adjacency matrix, we will attempt to + ascertain whether it is directed or undirected. As our leiden implementation is + only known to work with an undirected graph, this function will raise an error + if it is found to be a directed graph. If you know it is undirected and wish to + avoid this scan, you can set this value to ``False`` and only the lower triangle + of the adjacency matrix will be used to generate the weighted edge list. + + Returns + ------- + Dict[str, int] + The results of running leiden over the provided graph, a dictionary containing + mappings of node -> community id. The keys in the dictionary are the string + representations of the nodes. + + Raises + ------ + ValueError + TypeError + + See Also + -------- + graspologic.utils.to_weighted_edge_list + + References + ---------- + .. [1] Traag, V.A.; Waltman, L.; Van, Eck N.J. "From Louvain to Leiden: + guaranteeing well-connected communities", Scientific Reports, Vol. 9, 2019 + .. [2] https://github.com/microsoft/graspologic-native + + Notes + ----- + This function is implemented in the `graspologic-native` Python module, a module + written in Rust for Python. + + """ + _validate_common_arguments( + starting_communities, + extra_forced_iterations, + resolution, + randomness, + use_modularity, + random_seed, + is_weighted, + weight_default, + check_directed, + ) + graph = _validate_and_build_edge_list( + graph, is_weighted, weight_attribute, check_directed, weight_default + ) + + _improved, _modularity, partitions = gn.leiden( + edges=graph, + starting_communities=starting_communities, + resolution=resolution, + randomness=randomness, + iterations=extra_forced_iterations + 1, + use_modularity=use_modularity, + seed=random_seed, + ) + + return partitions + + +class HierarchicalCluster(NamedTuple): + node: str + cluster: int + parent_cluster: Optional[int] + level: int + is_final_cluster: bool + + @classmethod + def from_native( + cls, native_cluster: gn.HierarchicalCluster + ) -> "HierarchicalCluster": + if not isinstance(native_cluster, gn.HierarchicalCluster): + raise TypeError( + "This class method is only valid for graspologic_native.HierarchicalCluster" + ) + return cls( + node=native_cluster.node, + cluster=native_cluster.cluster, + parent_cluster=native_cluster.parent_cluster, + level=native_cluster.level, + is_final_cluster=native_cluster.is_final_cluster, + ) + + @staticmethod + def final_hierarchical_clustering( + hierarchical_clusters: List[ + Union["HierarchicalCluster", gn.HierarchicalCluster] + ], + ) -> Dict[str, int]: + if not isinstance(hierarchical_clusters, list): + raise TypeError( + "This static method requires a list of hierarchical clusters" + ) + final_clusters = ( + cluster for cluster in hierarchical_clusters if cluster.is_final_cluster + ) + return {cluster.node: cluster.cluster for cluster in final_clusters} + + +def hierarchical_leiden( + graph: Union[List[Tuple[str, str, float]], nx.Graph], + max_cluster_size: int = 1000, + starting_communities: Optional[Dict[str, int]] = None, + extra_forced_iterations: int = 0, + resolution: float = 1.0, + randomness: float = 0.001, + use_modularity: bool = True, + random_seed: Optional[int] = None, + weight_attribute: str = "weight", + is_weighted: Optional[bool] = None, + weight_default: float = 1.0, + check_directed: bool = True, +) -> List[HierarchicalCluster]: + """ + + Leiden is a global network partitioning algorithm. Given a graph, it will iterate + through the network node by node, and test for an improvement in our quality + maximization function by speculatively joining partitions of each neighboring node. + + This process continues until no moves are made that increases the partitioning + quality. + + Unlike the function :func:`graspologic.partition.leiden`, this function does not + stop after maximization has been achieved. On some large graphs, it's useful to + identify particularly large communities whose membership counts exceed + ``max_cluster_size`` and induce a subnetwork solely out of that community. This + subnetwork is then treated as a wholly separate entity, leiden is run over it, and + the new, smaller communities are then mapped into the original community map space. + + The results also differ substantially; the returned List[HierarchicalCluster] is + more of a log of state at each level. All HierarchicalClusters at level 0 should be + considered to be the results of running :func:`graspologic.partition.leiden`. Every + community whose membership is greater than ``max_cluster_size`` will then + also have entries where level == 1, and so on until no communities are greater in + population than ``max_cluster_size`` OR we are unable to break them down any + further. + + Once a node's membership registration in a community cannot be changed any further, + it is marked with the flag + ``graspologic.partition.HierarchicalCluster.is_final_cluster = 1``. + + Parameters + ---------- + graph : Union[List[Tuple[Any, Any, Union[int, float]]], nx.Graph, np.ndarray, scipy.sparse.csr.csr_matrix] + A graph representation, whether a weighted edge list referencing an undirected + graph, an undirected networkx graph, or an undirected adjacency matrix in + either numpy.ndarray or scipy.sparse.csr.csr_matrix form. + max_cluster_size : int + Default is ``1000``. Any partition or cluster with + membership >= ``max_cluster_size`` will be isolated into a subnetwork. This + subnetwork will be used for a new leiden global partition mapping, which will + then be remapped back into the global space after completion. Once all + clusters with membership >= ``max_cluster_size`` have been completed, the level + increases and the partition scheme is scanned again for any new clusters with + membership >= ``max_cluster_size`` and the process continues until every + cluster's membership is < ``max_cluster_size`` or if they cannot be broken into + more than one new community. + starting_communities : Optional[Dict[str, int]] + Default is ``None``. An optional community mapping dictionary that contains the + string representation of the node and the community it belongs to. Note this + map must contain precisely the same nodes as the graph and every node must + have a positive community id. This mapping forms the starting point of Leiden + clustering and can be very useful in saving the state of a previous partition + schema from a previous graph and then adjusting the graph based on new temporal + data (additions, removals, weight changes, connectivity changes, etc). New + nodes should get their own unique community positive integer, but the original + partition can be very useful to speed up future runs of leiden. If no community + map is provided, the default behavior is to create a node community identity + map, where every node is in their own community. + extra_forced_iterations : int + Default is ``0``. Leiden will run until a maximum quality score has been found + for the node clustering and no nodes are moved to a new cluster in another + iteration. As there is an element of randomness to the Leiden algorithm, it is + sometimes useful to set ``extra_forced_iterations`` to a number larger than 0 + where the entire process is forced to attempt further refinement. + resolution : float + Default is ``1.0``. Higher resolution values lead to more communities and lower + resolution values leads to fewer communities. Must be greater than 0. + randomness : float + Default is ``0.001``. The larger the randomness value, the more exploration of + the partition space is possible. This is a major difference from the Louvain + algorithm, which is purely greedy in the partition exploration. + use_modularity : bool + Default is ``True``. If ``False``, will use a Constant Potts Model (CPM). + random_seed : Optional[int] + Default is ``None``. Can provide an optional seed to the PRNG used in Leiden + for deterministic output. + weight_attribute : str + Default is ``weight``. Only used when creating a weighed edge list of tuples + when the source graph is a networkx graph. This attribute corresponds to the + edge data dict key. + is_weighted : Optional[bool] + Default is ``None``. Only used when creating a weighted edge list of tuples + when the source graph is an adjacency matrix. The + :func:`graspologic.utils.to_weighted_edge_list` function will scan these + matrices and attempt to determine whether it is weighted or not. This flag can + short circuit this test and the values in the adjacency matrix will be treated + as weights. + weight_default : float + Default is ``1.0``. If the graph is a networkx graph and the graph does not + have a fully weighted sequence of edges, this default will be used. If the + adjacency matrix is found or specified to be unweighted, this weight_default + will be used for every edge. + check_directed : bool + Default is ``True``. If the graph is an adjacency matrix, we will attempt to + ascertain whether it is directed or undirected. As our leiden implementation is + only known to work with an undirected graph, this function will raise an error + if it is found to be a directed graph. If you know it is undirected and wish to + avoid this scan, you can set this value to ``False`` and only the lower triangle + of the adjacency matrix will be used to generate the weighted edge list. + + Returns + ------- + List[HierarchicalCluster] + The results of running hierarchical leiden over the provided graph, a list of + HierarchicalClusters identifying the state of every node and cluster at each + level. The function + :func:`graspologic.partition.HierarchicalCluster.final_hierarchical_clustering` + can be used to create a dictionary mapping of node -> cluster ID + + Raises + ------ + ValueError + TypeError + + See Also + -------- + graspologic.utils.to_weighted_edge_list + + References + ---------- + .. [1] Traag, V.A.; Waltman, L.; Van, Eck N.J. "From Louvain to Leiden: + guaranteeing well-connected communities",Scientific Reports, Vol. 9, 2019 + .. [2] https://github.com/microsoft/graspologic-native + + Notes + ----- + This function is implemented in the `graspologic-native` Python module, a module + written in Rust for Python. + + """ + _validate_common_arguments( + starting_communities, + extra_forced_iterations, + resolution, + randomness, + use_modularity, + random_seed, + is_weighted, + weight_default, + check_directed, + ) + if not isinstance(max_cluster_size, int): + raise TypeError("max_cluster_size must be an int") + if max_cluster_size <= 0: + raise ValueError("max_cluster_size must be a positive int") + + graph = _validate_and_build_edge_list( + graph, is_weighted, weight_attribute, check_directed, weight_default + ) + hierarchical_clusters_native = gn.hierarchical_leiden( + edges=graph, + starting_communities=starting_communities, + resolution=resolution, + randomness=randomness, + iterations=extra_forced_iterations + 1, + use_modularity=use_modularity, + max_cluster_size=max_cluster_size, + seed=random_seed, + ) + + return [ + HierarchicalCluster.from_native(entry) for entry in hierarchical_clusters_native + ] diff --git a/graspologic/partition/modularity.py b/graspologic/partition/modularity.py new file mode 100644 index 000000000..1a36f16ae --- /dev/null +++ b/graspologic/partition/modularity.py @@ -0,0 +1,164 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import math +import networkx as nx +from collections import defaultdict +from typing import Any, Dict + + +def _modularity_component( + intra_community_degree: float, + total_community_degree: float, + network_degree_sum: float, + resolution: float, +) -> float: + community_degree_ratio = math.pow(total_community_degree, 2.0) / ( + 2.0 * network_degree_sum + ) + return (intra_community_degree - resolution * community_degree_ratio) / ( + 2.0 * network_degree_sum + ) + + +def _assertions( + graph: nx.Graph, + partitions: Dict[Any, int], + weight_attribute: str, + resolution: float, +): + if not isinstance(graph, nx.Graph): + raise TypeError("graph must be a networkx undirected graph") + if graph.is_directed(): + raise ValueError("The graph must be an undirected graph") + if graph.is_multigraph(): + raise ValueError( + "Multigraphs must be provided in the form of a non multigraph." + ) + if not nx.is_weighted(graph, weight=weight_attribute): + raise ValueError( + f"weight_attribute {weight_attribute} not found on every edge in the provided graph" + ) + if not isinstance(partitions, dict): + raise TypeError("partitions must be a dictionary") + if not isinstance(resolution, float): + raise TypeError("resolution must be a float") + + +def modularity( + graph: nx.Graph, + partitions: Dict[Any, int], + weight_attribute: str = "weight", + resolution: float = 1.0, +) -> float: + """ + Given an undirected graph and a dictionary of vertices to community ids, calculate + the modularity. + + Parameters + ---------- + graph : nx.Graph + An undirected graph + partitions : Dict[Any, int] + A dictionary representing a community partitioning scheme with the keys being + the vertex and the value being a community id. + weight_attribute : str + The edge data attribute on the graph that contains a float weight for the edge. + resolution : float + The resolution to use when calculating the modularity. + + Returns + ------- + Dict[int, float] + A dictionary of the community id to the modularity component of that community + + Raises + ------ + TypeError + If ``graph`` is not a networkx Graph or + If ``partitions`` is not a dictionary or + If ``resolution`` is not a float + ValueError + If ``graph`` is unweighted + If ``graph`` is directed + If ``graph`` is a multigraph + + References + ---------- + .. [1] https://en.wikipedia.org/wiki/Modularity_(networks) + """ + _assertions(graph, partitions, weight_attribute, resolution) + + components = modularity_components(graph, partitions, weight_attribute, resolution) + + return sum(components.values()) + + +def modularity_components( + graph: nx.Graph, + partitions: Dict[Any, int], + weight_attribute: str = "weight", + resolution: float = 1.0, +) -> Dict[int, float]: + """ + Given an undirected, weighted graph and a community partition dictionary, + calculates a modularity quantum for each community ID. The sum of these quanta + is the modularity of the graph and partitions provided. + + Parameters + ---------- + graph : nx.Graph + An undirected graph + partitions : Dict[Any, int] + A dictionary representing a community partitioning scheme with the keys being + the vertex and the value being a community id. + weight_attribute : str + The edge data attribute on the graph that contains a float weight for the edge. + resolution : float + The resolution to use when calculating the modularity. + + Returns + ------- + Dict[int, float] + A dictionary of the community id to the modularity component of that community + + Raises + ------ + TypeError + If ``graph`` is not a networkx Graph or + If ``partitions`` is not a dictionary or + If ``resolution`` is not a float + ValueError + If ``graph`` is unweighted + If ``graph`` is directed + If ``graph`` is a multigraph + """ + _assertions(graph, partitions, weight_attribute, resolution) + + total_edge_weight = 0.0 + + communities = set(partitions.values()) + + degree_sums_within_community: Dict[int, float] = defaultdict(lambda: 0.0) + degree_sums_for_community: Dict[int, float] = defaultdict(lambda: 0.0) + for vertex, neighbor_vertex, weight in graph.edges(data=weight_attribute): + vertex_community = partitions[vertex] + neighbor_community = partitions[neighbor_vertex] + if vertex_community == neighbor_community: + if vertex == neighbor_vertex: + degree_sums_within_community[vertex_community] += weight + else: + degree_sums_within_community[vertex_community] += weight * 2.0 + degree_sums_for_community[vertex_community] += weight + degree_sums_for_community[neighbor_community] += weight + total_edge_weight += weight + + return { + comm: _modularity_component( + degree_sums_within_community[comm], + degree_sums_for_community[comm], + total_edge_weight, + resolution, + ) + for comm in communities + } diff --git a/graspologic/utils/__init__.py b/graspologic/utils/__init__.py index 93556bff1..9ddeaf610 100644 --- a/graspologic/utils/__init__.py +++ b/graspologic/utils/__init__.py @@ -21,6 +21,7 @@ fit_plug_in_variance_estimator, remove_vertices, remap_labels, + to_weighted_edge_list, ) from .ptr import pass_to_ranks @@ -45,4 +46,5 @@ "fit_plug_in_variance_estimator", "remove_vertices", "remap_labels", + "to_weighted_edge_list", ] diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index c9448f008..c186720a5 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -5,10 +5,12 @@ from collections import Iterable from functools import reduce from pathlib import Path -from typing import List, Union +from typing import Any, List, Optional, Tuple, Union + import networkx as nx import numpy as np +import scipy import pandas as pd from scipy.optimize import linear_sum_assignment from sklearn.metrics import confusion_matrix @@ -157,19 +159,67 @@ def import_edgelist( def is_symmetric(X): - return np.array_equal(X, X.T) + return abs(X - X.T).sum() == 0 def is_loopless(X): return not np.any(np.diag(X) != 0) -def is_unweighted(X): - return ((X == 0) | (X == 1)).all() +def is_unweighted( + graph: Union[ + np.ndarray, + scipy.sparse.csr.csr_matrix, + nx.Graph, + nx.DiGraph, + nx.MultiGraph, + nx.MultiDiGraph, + ], + weight_attribute: Any = "weight", +): + """ + Attempts to determine if the provided graph is weighted. + + Parameters + ---------- + graph : Union[np.ndarray, scipy.sparse.csr.csr_matrix, nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDigraph] + The graph to test for weightedness. If a networkx graph, we can just ask it directly by querying the weight + attribute specified on every edge. It's possible an individual edge can be weighted but the full graph is not. + If an adjacency matrix defined by a numpy.ndarray or scipy.sparse.csr.csr_matrix, we check every value; if + they are only 0 and 1, we claim the graph is unweighted. + weight_attribute : Any + Default is ``weight``. Only used for networkx, and used on the edge data dictionary as a key to look up the + weight. + + Returns + ------- + bool + True if unweighted, False if weighted + + Raises + ------ + TypeError + If the provided graph is not a numpy.ndarray, scipy.sparse.csr.csr_matrix, or nx.Graph + """ + if isinstance(graph, np.ndarray): + return ((graph == 0) | (graph == 1)).all() + elif isinstance(graph, scipy.sparse.csr.csr_matrix): + # brute force. if anyone has a better way, please PR + rows, columns = graph.nonzero() + for i in range(0, len(rows)): + if graph[rows[i], columns[i]] != 1 or graph[rows[i], columns[i]] != 0: + return False + return True + elif isinstance(graph, nx.Graph): + return nx.is_weighted(graph, weight=weight_attribute) + else: + raise TypeError( + "This function only works on numpy.ndarray or scipy.sparse.csr.csr_matrix instances" + ) def is_almost_symmetric(X, atol=1e-15): - return np.allclose(X, X.T, atol=atol) + return abs(X - X.T).max() <= atol def symmetrize(graph, method="avg"): @@ -875,3 +925,141 @@ def remap_labels( return remapped_y_pred, label_map else: return remapped_y_pred + + +def to_weighted_edge_list( + graph: Union[ + List[Tuple[Any, Any, Union[float, int]]], + nx.Graph, + nx.DiGraph, + nx.MultiGraph, + nx.MultiDiGraph, + np.ndarray, + scipy.sparse.csr.csr_matrix, + ], + is_directed: Optional[bool] = None, + is_weighted: Optional[bool] = None, + weight_attribute: Any = "weight", + weight_default: Optional[float] = None, +) -> List[Tuple[str, str, float]]: + """ + Creates a weighted edge list with string representations of the nodes and float + weights. + + Parameters + ---------- + graph : Union[List[Tuple[Any, Any, Union[float, int]]], nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph, np.ndarray, scipy.sparse.csr.csr_matrix] + A representation of a graph either as a list of tuples, a networkx graph, or an + adjacency matrix from numpy or scipy.sparse.csr.csr_matrix + is_directed : Optional[bool] + Default is ``None``. Ignored if an edge tuple list or networkx graph is + provided, but can be used to short circuit an exhaustive matrix symmetry check + for numpy and scipy matrices. + is_weighted : Optional[bool] + Default is ``None``. Ignored if an edge tuple list or networkx graph is + provided, but can be used to short circuit an exhaustive matrix weight check for + numpy and scipy matrices. + weight_attribute : Any + Default is the string ``weight``. Used to retrieve the weight from networkx + edge attribute dictionary. + weight_default : Optional[float] + Default is ``None``. If given an unweighted graph, will use as a default weight + to output in the weighted edge list. + + Raises + ------ + TypeError + If the graph is not a type we support + ValueError + If an adjacency matrix shape is not :math:`n x n` + If the weight attribute on a networkx graph is not a float + If the adjacency matrix is unweighted and a default weight is not set + + Returns + ------- + List[Tuple[str, str, float]] + A list of edges for the type of graph. Undirected graphs will only contain a + single entry for an edge between any two nodes. + """ + # if we're already an edge list of str, str, float, return it + if weight_default is not None and not isinstance(weight_default, (float, int)): + raise TypeError("weight default must be a float or int") + + if isinstance(graph, list): + if len(graph) == 0: + return graph + if not isinstance(graph[0], tuple) or len(graph[0]) != 3: + raise TypeError( + "If the provided graph is a list, it must be a list of tuples with 3 " + "values in the form of Tuple[Any, Any, Union[int, float]], you provided" + f"{type(graph[0])}, {repr(graph[0])}" + ) + return [ + (str(source), str(target), float(weight)) + for source, target, weight in graph + ] + + if isinstance(graph, nx.Graph): + # will catch all networkx graph types + try: + return [ + ( + str(source), + str(target), + float(data.get(weight_attribute, weight_default)), + ) + for source, target, data in graph.edges(data=True) + ] + except TypeError: + # None is returned for the weight if it doesn't exist and a weight_default + # is not set, which results in a TypeError when you call float(None) + raise ValueError( + f"The networkx graph provided did not contain a {weight_attribute} that" + " could be cast to float in one of the edges" + ) + + if isinstance(graph, (np.ndarray, scipy.sparse.csr.csr_matrix)): + shape = graph.shape + if len(shape) != 2 or shape[0] != shape[1]: + raise ValueError( + "graphs of type np.ndarray or csr.sparse.csr.csr_matrix should be " + "adjacency matrices with n x n shape" + ) + + if is_weighted is None: + is_weighted = not is_unweighted(graph) + + if not is_weighted and weight_default is None: + raise ValueError( + "the adjacency matrix provided is not weighted and a default weight has" + " not been set" + ) + + if is_directed is None: + is_directed = not is_almost_symmetric(graph) + + edges = [] + if isinstance(graph, np.ndarray): + for i in range(0, shape[0]): + start = i if not is_directed else 0 + for j in range(start, shape[1]): + weight = graph[i][j] + if weight != 0: + if not is_weighted and weight == 1: + weight = weight_default + edges.append((str(i), str(j), float(weight))) + else: + edges = [] + rows, columns = graph.nonzero() + for i in range(0, len(rows)): + row = rows[i] + column = columns[i] + if is_directed or (not is_directed and row <= column): + edges.append((str(row), str(column), float(graph[row, column]))) + + return edges + + raise TypeError( + f"The type of graph provided {type(graph)} is not a list of 3-tuples, networkx " + f"graph, numpy.ndarray, or scipy.sparse.csr.csr_matrix" + ) diff --git a/setup.py b/setup.py index cc5d59121..759a5b6e2 100644 --- a/setup.py +++ b/setup.py @@ -50,6 +50,7 @@ install_requires=[ "anytree>=2.8.0", "gensim", + "graspologic-native", "hyppo>=0.1.3", "joblib>=0.17.0", # Older versions of joblib cause issue #806. Transitive dependency of hyppo. "matplotlib>=3.0.0,<=3.3.0", diff --git a/tests/partition/test_leiden.py b/tests/partition/test_leiden.py new file mode 100644 index 000000000..fdc17425b --- /dev/null +++ b/tests/partition/test_leiden.py @@ -0,0 +1,175 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from typing import List, Tuple + +import unittest +import networkx as nx + +from graspologic.partition import HierarchicalCluster, hierarchical_leiden, leiden + +from tests.utils import data_file + + +class TestHierarchicalCluster(unittest.TestCase): + def test_from_native(self): + with self.assertRaises(TypeError): + HierarchicalCluster.from_native("foo") + + # note: it is impossible to create a native instance of a HierarchicalCluster. We will + # test from_native indirectly through calling graspologic.partition.hierarchical_leiden() + + def test_final_hierarchical_clustering(self): + with self.assertRaises(TypeError): + HierarchicalCluster.final_hierarchical_clustering("I am not a list") + + hierarchical_clusters = [ + HierarchicalCluster("1", 0, None, 0, False), + HierarchicalCluster("2", 0, None, 0, False), + HierarchicalCluster("3", 0, None, 0, False), + HierarchicalCluster("4", 1, None, 0, True), + HierarchicalCluster("5", 1, None, 0, True), + HierarchicalCluster("1", 2, 0, 1, True), + HierarchicalCluster("2", 2, 0, 1, True), + HierarchicalCluster("3", 3, 0, 1, True), + ] + + expected = { + "1": 2, + "2": 2, + "3": 3, + "4": 1, + "5": 1, + } + self.assertEqual( + expected, + HierarchicalCluster.final_hierarchical_clustering(hierarchical_clusters), + ) + + +def _create_edge_list() -> List[Tuple[str, str, float]]: + edges = [] + with open(data_file("large-graph.csv"), "r") as edges_io: + for line in edges_io: + source, target, weight = line.strip().split(",") + edges.append((source, target, float(weight))) + return edges + + +class TestLeiden(unittest.TestCase): + def test_correct_types(self): + # both leiden and hierarchical_leiden require the same types and mostly the same value range restrictions + good_args = { + "starting_communities": {"1": 2}, + "extra_forced_iterations": 0, + "resolution": 1.0, + "randomness": 0.001, + "use_modularity": True, + "random_seed": None, + "is_weighted": True, + "weight_default": 1.0, + "check_directed": True, + } + + graph = nx.Graph() + graph.add_edge("1", "2", weight=3.0) + graph.add_edge("2", "3", weight=4.0) + + leiden(graph=graph, **good_args) + with self.assertRaises(TypeError): + args = good_args.copy() + args["starting_communities"] = 123 + leiden(graph=graph, **args) + + args = good_args.copy() + args["starting_communities"] = None + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["extra_forced_iterations"] = 1234.003 + leiden(graph=graph, **args) + + with self.assertRaises(ValueError): + args = good_args.copy() + args["extra_forced_iterations"] = -4003 + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["resolution"] = "leiden" + leiden(graph=graph, **args) + + with self.assertRaises(ValueError): + args = good_args.copy() + args["resolution"] = 0 + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["randomness"] = "leiden" + leiden(graph=graph, **args) + + with self.assertRaises(ValueError): + args = good_args.copy() + args["randomness"] = 0 + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["use_modularity"] = 1234 + leiden(graph=graph, **args) + + args = good_args.copy() + args["random_seed"] = 1234 + leiden(graph=graph, **args) + args["random_seed"] = None + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["random_seed"] = "leiden" + leiden(graph=graph, **args) + + with self.assertRaises(ValueError): + args = good_args.copy() + args["random_seed"] = -1 + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["is_weighted"] = "leiden" + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["weight_default"] = "leiden" + leiden(graph=graph, **args) + + with self.assertRaises(TypeError): + args = good_args.copy() + args["check_directed"] = "leiden" + leiden(graph=graph, **args) + + # one extra parameter hierarchical needs + with self.assertRaises(TypeError): + args = good_args.copy() + args["max_cluster_size"] = "leiden" + hierarchical_leiden(graph=graph, **args) + + with self.assertRaises(ValueError): + args = good_args.copy() + args["max_cluster_size"] = 0 + hierarchical_leiden(graph=graph, **args) + + def test_hierarchical(self): + # most of leiden is tested in unit / integration tests in graspologic-native. + # All we're trying to test through these unit tests are the python conversions + # prior to calling, so type and value validation and that we got a result + edges = _create_edge_list() + results = hierarchical_leiden(edges, random_seed=1234) + + total_nodes = len([item for item in results if item.level == 0]) + + partitions = HierarchicalCluster.final_hierarchical_clustering(results) + self.assertEqual(total_nodes, len(partitions)) diff --git a/tests/partition/test_modularity.py b/tests/partition/test_modularity.py new file mode 100644 index 000000000..5a587903e --- /dev/null +++ b/tests/partition/test_modularity.py @@ -0,0 +1,70 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import networkx as nx +import numpy as np +from graspologic.partition import modularity, modularity_components +import unittest +from typing import Dict + +from tests.utils import data_file + + +def _modularity_graph() -> nx.Graph: + graph = nx.Graph() + graph.add_edge("a", "b", weight=4.0) + graph.add_edge("b", "c", weight=3.0) + graph.add_edge("e", "f", weight=5.0) + + return graph + + +_PARTITIONS: Dict[str, int] = {"a": 0, "b": 0, "c": 0, "e": 1, "f": 1} + + +class TestModularity(unittest.TestCase): + def test_modularity(self): + graph = _modularity_graph() # links = 12.0 + partition = _PARTITIONS # in community degree for -> 0: 14, 1: 10, community degree -> 0:14, 1:10 + # modularity component for partition 0: (14.0 / (2.0 * 12.0)) - (1.0 * ((14.0 / (2 * 12.0)) ** 2.0)) + # (cont): 0.5833333333333334 - 0.34027777777777785 = 0.24305555555555552 + # modularity component for partition 1: (10.0 / (2.0 * 12.0)) - (1.0 * ((10.0 / (2 * 12.0)) ** 2.0)) + # (cont): 0.4166666666666667 - 0.17361111111111113 = 0.24305555555555555 + modularity_value = modularity(graph, partition) + + np.testing.assert_almost_equal(0.48611111111111105, modularity_value) + + def test_modularity_components(self): + graph = nx.Graph() + with open(data_file("large-graph.csv"), "r") as edge_list_io: + for line in edge_list_io: + source, target, weight = line.strip().split(",") + previous_weight = graph.get_edge_data(source, target, {"weight": 0})[ + "weight" + ] + weight = float(weight) + previous_weight + graph.add_edge(source, target, weight=weight) + + partitions = {} + with open(data_file("large-graph-partitions.csv"), "r") as communities_io: + for line in communities_io: + vertex, comm = line.strip().split(",") + partitions[vertex] = int(comm) + + partition_count = max(partitions.values()) + + graph.add_node("disconnected_node") + partitions["disconnected_node"] = partition_count + 1 + + components = modularity_components(graph, partitions) + + # from python-louvain modularity function + community_modularity = 0.8008595783563607 + total_modularity = sum(components.values()) + + self.assertSetEqual(set(components.keys()), set(partitions.values())) + 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a/tests/test_utils.py b/tests/test_utils.py index fa9f7d6aa..fe1dc6ccd 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,14 +2,11 @@ # Licensed under the MIT License. import unittest -import pytest -import numpy as np -import networkx as nx -from graspologic.utils import utils as gus from math import sqrt import networkx as nx import numpy as np +import scipy import pytest from numpy.testing import assert_equal @@ -551,3 +548,128 @@ def test_inputs(self): with pytest.raises(ValueError): remap_labels(self.y_pred, ["ant", "ant", "cat", "cat", "bird", "bird"]) + + +def add_edges_to_graph(graph: nx.Graph) -> nx.Graph: + graph.add_edge("nick", "dwayne", weight=1.0) + graph.add_edge("nick", "dwayne", weight=3.0) + graph.add_edge("dwayne", "nick", weight=2.2) + graph.add_edge("dwayne", "ben", weight=4.2) + graph.add_edge("ben", "dwayne", weight=0.001) + return graph + + +class TestToWeightedEdgeList(unittest.TestCase): + def test_empty_edge_list(self): + edges = [] + self.assertEqual([], gus.to_weighted_edge_list(edges)) + + def test_assert_wrong_types_in_tuples(self): + edges = [(True, 4, "sandwich")] + with self.assertRaises(ValueError): + gus.to_weighted_edge_list(edges) + edges = [(True, False, 3.2)] + self.assertEqual([("True", "False", 3.2)], gus.to_weighted_edge_list(edges)) + + def test_empty_nx(self): + self.assertEqual([], gus.to_weighted_edge_list(nx.Graph())) + self.assertEqual([], gus.to_weighted_edge_list(nx.DiGraph())) + self.assertEqual([], gus.to_weighted_edge_list(nx.MultiGraph())) + self.assertEqual([], gus.to_weighted_edge_list(nx.MultiDiGraph())) + + def test_valid_nx(self): + graph = add_edges_to_graph(nx.Graph()) + expected = [("nick", "dwayne", 2.2), ("dwayne", "ben", 0.001)] + self.assertEqual(expected, gus.to_weighted_edge_list(graph)) + + graph = add_edges_to_graph(nx.DiGraph()) + expected = [ + ("nick", "dwayne", 3.0), + ("dwayne", "nick", 2.2), + ("dwayne", "ben", 4.2), + ("ben", "dwayne", 0.001), + ] + self.assertEqual(expected, gus.to_weighted_edge_list(graph)) + + graph = add_edges_to_graph(nx.MultiGraph()) + expected = [ + ("nick", "dwayne", 1.0), + ("nick", "dwayne", 3.0), + ("nick", "dwayne", 2.2), + ("dwayne", "ben", 4.2), + ("dwayne", "ben", 0.001), + ] + self.assertEqual(expected, gus.to_weighted_edge_list(graph)) + + graph = add_edges_to_graph(nx.MultiDiGraph()) + expected = [ + ("nick", "dwayne", 1.0), + ("nick", "dwayne", 3.0), + ("dwayne", "nick", 2.2), + ("dwayne", "ben", 4.2), + ("ben", "dwayne", 0.001), + ] + self.assertEqual(expected, gus.to_weighted_edge_list(graph)) + + def test_unweighted_nx(self): + graph = nx.Graph() + graph.add_edge("dwayne", "nick") + graph.add_edge("nick", "ben") + + with self.assertRaises(ValueError): + gus.to_weighted_edge_list(graph) + + self.assertEqual( + [("dwayne", "nick", 3.33333), ("nick", "ben", 3.33333)], + gus.to_weighted_edge_list(graph, weight_default=3.33333), + ) + + graph.add_edge("salad", "sandwich", weight=100) + + self.assertEqual( + [ + ("dwayne", "nick", 3.33333), + ("nick", "ben", 3.33333), + ("salad", "sandwich", 100), + ], + gus.to_weighted_edge_list(graph, weight_default=3.33333), + ) + + def test_matrices(self): + graph = add_edges_to_graph(nx.Graph()) + di_graph = add_edges_to_graph(nx.DiGraph()) + + dense_undirected = nx.to_numpy_array(graph) + dense_directed = nx.to_numpy_array(di_graph) + + sparse_undirected = nx.to_scipy_sparse_matrix(graph) + sparse_directed = nx.to_scipy_sparse_matrix(di_graph) + + expected = [("0", "1", 2.2), ("1", "2", 0.001)] + self.assertEqual(expected, gus.to_weighted_edge_list(dense_undirected)) + self.assertEqual(expected, gus.to_weighted_edge_list(sparse_undirected)) + + expected = [ + ("0", "1", 3.0), + ("1", "0", 2.2), + ("1", "2", 4.2), + ("2", "1", 0.001), + ] + self.assertEqual(expected, gus.to_weighted_edge_list(dense_directed)) + self.assertEqual(expected, gus.to_weighted_edge_list(sparse_directed)) + + def test_empty_adj_matrices(self): + dense = np.array([]) + with self.assertRaises(ValueError): + gus.to_weighted_edge_list(dense) + + sparse = scipy.sparse.csr_matrix([]) + with self.assertRaises(ValueError): + gus.to_weighted_edge_list(sparse) + + def test_misshapen_matrices(self): + data = [[3, 2, 0], [2, 0, 1]] # this is utter gibberish + with self.assertRaises(ValueError): + gus.to_weighted_edge_list(np.array(data)) + with self.assertRaises(ValueError): + gus.to_weighted_edge_list(scipy.sparse.csr_matrix(data)) From 3aa1f17fb24377c79f844b1054ec79aaf5b8a7a2 Mon Sep 17 00:00:00 2001 From: Casey Weiner <54880846+CaseyWeiner@users.noreply.github.com> Date: Sat, 5 Dec 2020 11:36:21 -0500 Subject: [PATCH 064/140] Clustering Tutorials (#516) * Final drafts of clustering tutorial notebooks * clear nb outputs * Clear kernelspec * graspy -> graspologic * import data from github * Minor edits, fix netlify error * Modify nbs for inc simplicity, lower runtime * peer review changes * Office hours + Paul feedback * Edit kclust wording * Ben and Paul edits * Fixed plot labels * Fix colormap * Inc plot titles, edit plot func * Get rid of warning * Fix colormap bug, code spacing * Junk code * Pedigo feedback pt. 1 * Intersphinx test * Pedigo edits part 2 * Use new remap_labels function * Ben edits * Tweaks * fix typo * clarify estimation Co-authored-by: Benjamin Pedigo --- docs/tutorial.rst | 13 ++ docs/tutorials/clustering/autogmm.ipynb | 169 ++++++++++++++++++++++++ docs/tutorials/clustering/kclust.ipynb | 163 +++++++++++++++++++++++ 3 files changed, 345 insertions(+) create mode 100644 docs/tutorials/clustering/autogmm.ipynb create mode 100644 docs/tutorials/clustering/kclust.ipynb diff --git a/docs/tutorial.rst b/docs/tutorial.rst index e3d12ea71..b21f5e772 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -30,6 +30,19 @@ stochastic block model, and random dot product graph (RDPG). tutorials/simulations/corr tutorials/simulations/rdpg_corr +.. _cluster_tutorials: + +Clustering +========== +The following tutorials explain how to cluster vertex or graph embeddings with two +clustering algorithms, as well as the advantages of these to comparable implementations. + +.. toctree:: + :maxdepth: 1 + + tutorials/clustering/autogmm + tutorials/clustering/kclust + .. _embed_tutorials: Embedding diff --git a/docs/tutorials/clustering/autogmm.ipynb b/docs/tutorials/clustering/autogmm.ipynb new file mode 100644 index 000000000..33fc96e62 --- /dev/null +++ b/docs/tutorials/clustering/autogmm.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Automatic Gaussian Mixture Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "sns.set(font_scale=1.75)\n", + "sns.set_style(\"white\")\n", + "\n", + "import random\n", + "np.random.seed(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clustering is a foundational data analysis task, where members of the data set are sorted into groups or \"clusters\" according to measured similarities between the objects.\n", + "\n", + "The Automatic Gaussian Mixture Model (AutoGMM) is a wrapper of [Sklearn's Gaussian Mixture class](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture). Different combinations of agglomeration, GMM, and cluster numbers are used in the algorithm, and the clustering with the best selection criterion, either Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC), is provided to the user.\n", + "\n", + "Let's use AutoGMM on synthetic data and compare it to the existing Sklearn implementation." + ] + }, + { + "source": [ + "## Using AutoGMM on Synthetic Data" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Synthetic data\n", + "\n", + "# Dim 1\n", + "class_1 = np.random.randn(150, 1)\n", + "class_2 = 2 + np.random.randn(150, 1)\n", + "dim_1 = np.vstack((class_1, class_2))\n", + "\n", + "# Dim 2\n", + "class_1 = np.random.randn(150, 1)\n", + "class_2 = 2 + np.random.randn(150, 1)\n", + "dim_2 = np.vstack((class_1, class_2))\n", + "\n", + "X = np.hstack((dim_1, dim_2))\n", + "\n", + "# Labels\n", + "label_1 = np.zeros((150, 1))\n", + "label_2 = 1 + label_1\n", + "\n", + "c = np.vstack((label_1, label_2)).reshape(300,)\n", + "\n", + "# Plotting Function for Clustering\n", + "def plot(title, c_hat, X):\n", + " plt.figure(figsize=(10, 10))\n", + " n_components = int(np.max(c_hat) + 1)\n", + " palette = sns.color_palette(\"deep\")[:n_components]\n", + " fig = sns.scatterplot(x=X[:,0], y=X[:,1], hue=c_hat, legend=None, palette=palette)\n", + " fig.set(xticks=[], yticks=[], title=title)\n", + " plt.show()\n", + "\n", + "plot('True Clustering', c, X)" + ] + }, + { + "source": [ + "In the existing Sklearn implementation, one has to choose model parameters apriori, including the number of components. If parameters are input that don't match the data well, clustering performance can suffer. Performance can be measured by ARI, a metric ranging from 0 to 1. An ARI score of 1 indicates the estimated clusters are identical to the true clusters." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import mixture\n", + "from sklearn.metrics import confusion_matrix\n", + "from scipy.optimize import linear_sum_assignment\n", + "from sklearn.metrics import adjusted_rand_score\n", + "\n", + "from graspologic.utils import remap_labels\n", + "\n", + "# Say user provides inaccurate estimate of number of components\n", + "gmm_ = mixture.GaussianMixture(3)\n", + "c_hat_gmm = gmm_.fit_predict(X)\n", + "\n", + "# Remap Predicted labels\n", + "c_hat_gmm = remap_labels(c, c_hat_gmm)\n", + "\n", + "plot('Sklearn Clustering', c_hat_gmm, X)\n", + "\n", + "# ARI Score\n", + "print(\"ARI Score for Model: %.2f\" % adjusted_rand_score(c, c_hat_gmm))" + ] + }, + { + "source": [ + "Our method expands upon the existing Sklearn framework by allowing the user to automatically estimate the best hyperparameters for a Gaussian mixture model. In particular, the ideal `n_components_` is estimated by AutoGMM from a range of possible values given by the user. AutoGMM also sweeps over multiple covariance structures." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.cluster.autogmm import AutoGMMCluster\n", + "\n", + "# Fit AutoGMM model\n", + "autogmm_ = AutoGMMCluster(min_components=1, max_components=10)\n", + "c_hat_autogmm = autogmm_.fit_predict(X)\n", + "\n", + "c_hat_autogmm = remap_labels(c, c_hat_autogmm)\n", + "\n", + "plot('AutoGMM Clustering', c_hat_autogmm, X)\n", + "\n", + "print(\"ARI Score for Model: %.2f\" % adjusted_rand_score(c, c_hat_autogmm))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "version": "3.7.9-final" + }, + "orig_nbformat": 2, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/clustering/kclust.ipynb b/docs/tutorials/clustering/kclust.ipynb new file mode 100644 index 000000000..5823dcb04 --- /dev/null +++ b/docs/tutorials/clustering/kclust.ipynb @@ -0,0 +1,163 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# K-Means Clustering" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "sns.set(font_scale=1.75)\n", + "sns.set_style(\"white\")\n", + "\n", + "import random\n", + "np.random.seed(10)" + ] + }, + { + "source": [ + "K-Means Clustering in graspologic is a wrapper of [Sklearn's KMeans class](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html). Our algorithm finds the optimal kmeans clustering model by iterating over a range of values and creating a model with the lowest possible silhouette score, as defined in Sklearn [here](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html).\n", + "\n", + "Let's use K-Means Clustering on synthetic data and compare it to the existing Sklearn implementation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "## Using K Means on Synthetic Data" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Synthetic data\n", + "\n", + "# Dim 1\n", + "class_1 = np.random.randn(150, 1)\n", + "class_2 = 2 + np.random.randn(150, 1)\n", + "dim_1 = np.vstack((class_1, class_2))\n", + "\n", + "# Dim 2\n", + "class_1 = np.random.randn(150, 1)\n", + "class_2 = 2 + np.random.randn(150, 1)\n", + "dim_2 = np.vstack((class_1, class_2))\n", + "\n", + "X = np.hstack((dim_1, dim_2))\n", + "\n", + "# Labels\n", + "label_1 = np.zeros((150, 1))\n", + "label_2 = 1 + label_1\n", + "\n", + "c = np.vstack((label_1, label_2)).reshape(300,)\n", + "\n", + "# Plotting Function for Clustering\n", + "def plot(title, c_hat, X):\n", + " plt.figure(figsize=(10, 10))\n", + " n_components = int(np.max(c_hat) + 1)\n", + " palette = sns.color_palette(\"deep\")[:n_components]\n", + " fig = sns.scatterplot(x=X[:,0], y=X[:,1], hue=c_hat, legend=None, palette=palette)\n", + " fig.set(xticks=[], yticks=[], title=title)\n", + " plt.show()\n", + "\n", + "plot('True Clustering', c, X)" + ] + }, + { + "source": [ + "In the existing implementation of KMeans clustering in Sklearn, one has to choose parameters of the model, including number of components, apriori. If parameters are input that don't match the data well, clustering performance can suffer. Performance can be measured by ARI, a metric ranging from 0 to 1. An ARI score of 1 indicates the estimated clusters are identical to the true clusters." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import confusion_matrix\n", + "from scipy.optimize import linear_sum_assignment\n", + "from sklearn.metrics import adjusted_rand_score\n", + "\n", + "from graspologic.utils import remap_labels\n", + "\n", + "# Say user provides inaccurate estimate of number of components\n", + "kmeans_ = KMeans(3)\n", + "c_hat_kmeans = kmeans_.fit_predict(X)\n", + "\n", + "# Remap Predicted labels\n", + "c_hat_kmeans = remap_labels(c, c_hat_kmeans)\n", + "\n", + "plot('Sklearn Clustering', c_hat_kmeans, X)\n", + "\n", + "# ARI Score\n", + "print(\"ARI Score for Model: %.2f\" % adjusted_rand_score(c, c_hat_kmeans))" + ] + }, + { + "source": [ + "Our method expands upon the existing Sklearn framework by allowing the user to automatically estimate the best hyperparameters for a k-means clustering model. The ideal `n_clusters_`, less than the max value provided by the user, is found." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.cluster.kclust import KMeansCluster\n", + "\n", + "# Fit model\n", + "kclust_ = KMeansCluster(max_clusters=10)\n", + "c_hat_kclust = kclust_.fit_predict(X)\n", + "\n", + "c_hat_kclust = remap_labels(c, c_hat_kclust)\n", + "\n", + "plot('KClust Clustering', c_hat_kclust, X)\n", + "\n", + "print(\"ARI Score for Model: %.2f\" % adjusted_rand_score(c, c_hat_kclust))" + ] + } + ] +} From f01eecc88a94c84fa41b6df7df947cf9f1cbb30f Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Mon, 7 Dec 2020 15:05:06 -0500 Subject: [PATCH 065/140] Update documentation location to RTD (#600) --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 4812bf2a3..59b69a701 100644 --- a/README.md +++ b/README.md @@ -28,9 +28,9 @@ noting the missed titles so we can address all of them. A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. # Documentation -The official documentation with usage is at https://graspy.neurodata.io/ +The official documentation with usage is at https://graspologic.readthedocs.io/en/latest/ -Please visit the [tutorial section](https://graspy.neurodata.io/tutorial.html) in the official website for more in depth usage. +Please visit the [tutorial section](https://graspologic.readthedocs.io/en/latest/tutorial.html) in the official website for more in depth usage. # System Requirements ## Hardware requirements From 77df7ba7c4b4e0c22ab211dd983f33ddbe2a2a7b Mon Sep 17 00:00:00 2001 From: dfrancisco1998 <40680427+dfrancisco1998@users.noreply.github.com> Date: Tue, 8 Dec 2020 10:27:49 -0600 Subject: [PATCH 066/140] Pairplot with gmm functionality fixing #382 (#541) * Update .travis.yml to incorporate python 3.8 * pairplot with gmm v1 * fixes in test with import of gmm * adding in pairplot import to test_plot.py * formatting of test_plot.py * added in color palette but nwo only using one color for ellipses at all times * added input for palette and formated code with black * tutorial with error, will fix * first try at fixing formatting errors * fixing double quote issues for formatting * fixing test format * fixing doc error in pairplot with gmm docstring * commenting last two lines of test_tdpg_corr.py to pass tests * format changes for test_rdpg.py commenting out and test-coverage fixes * added in tutorial as well as made colors iterable, need to fix matching of colors * tutorial fixes and color matching fixed * fix minor issue * fix minor issue * fix small bug * fix small bug * trying to fix color mapping * fix indexing into color error * fix formatting error * fix formatting error * color matching with ellipse and scatter * will fix private later * fixes to tests and tutorials * fixes to tests * fixing method calls * fixes to tests * making blocks names match in testing * fixed closeness of means issue in color matching * fixed import error * made plot_ellipse private and fixed up notebook * fix import error with private plot_ellipse * fix import error with private plot_ellipse * small fix * trying to fix import errors of private function * to get test coverage to pass * to get test coverage to pass * cluster palette is maping ints to colors * fixed comments according to @tliu68 * need to fix * remove graspy/plot * fixing warning for handles and labels error generated from creating legend * this fixes the warning, I will link why we justs ignore when responding to Alex's comment * fixed phrasing and removed superfluous latex matrices stuff * fixed formatting and n_components variable renaming * added comments for readability * fixing formatting * clearing cell output so netifly renders correctly * dim to dimensions * figsize not hardcoded * remove title if not given * kde arg * histplot update * better handle legend handles and labels, include comment explaining * add comment explaining tick mark removal of histplot * add reference for _plot_ellipse * changed k to i * use check_X_y to validate X and labels * labels explained further in comment * small comment fix * change to _plot_ellipse_and_data * fix with error * TypeError is ValueError because using check_X_y * citation fixed * fixed function name * titel docstring description fixed * change back to distplot to be pacakge compatible * refactor code to make cleaner approach to X with 2 features * some formatting in notebook, formatting in plot.py, added comments * forgot to push plot.py... * formatting * clearer setting in _plot_ellipse_and_data * formatting * link citation instead... srry * legend moved center right * same palette differnt colors in mismatch * starting dimensions at 1 * add reaosn why this is useful in tutorial description * title change! * Update graspologic/plot/plot.py Co-authored-by: Benjamin Pedigo * Update graspologic/plot/plot.py Co-authored-by: Benjamin Pedigo * changed default to Set1 and fxed conditonals * only look at first handle and label set to make code simpler * remove the superfluous sklearn import * forgot to add the new legend part before returning * added in sns import * formatting * addeding in see also section for AutoGMM and GMM * tried to fix that weird formatting thing * tried to fix that weird formatting thing * tried to fix that weird formatting thing * tried to fix that weird formatting thing * add comment about GMM * more formatting * formatting * formatting * typos in GMM comment and title render fix * remove last two comments of tests * ax1, ax2, changed * adding legend name argument * stop overlap of plot with legend * added histplot instead of distplot * make seborn .11 in setup.py * make seborn .11 in setup.py * formatting * added kde_kws * formatting * histplot_kws * formatting * sort imports * remove defaults * doc fixes, try intersphinx * returns Co-authored-by: Benjamin Pedigo --- docs/conf.py | 1 + docs/reference/plotting.rst | 1 + docs/tutorial.rst | 21 +- docs/tutorials/plotting/pairplot.ipynb | 9 +- .../plotting/pairplot_with_gmm.ipynb | 147 ++++++++++ graspologic/plot/__init__.py | 20 +- graspologic/plot/plot.py | 260 +++++++++++++++++- setup.py | 2 +- tests/test_plot.py | 41 ++- tests/test_rdpg_corr.py | 2 - 10 files changed, 483 insertions(+), 21 deletions(-) create mode 100644 docs/tutorials/plotting/pairplot_with_gmm.ipynb diff --git a/docs/conf.py b/docs/conf.py index 5d8c5bbf2..8dad8a591 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -73,6 +73,7 @@ "numpy": ("https://numpy.org/doc/stable", None), "python": ("https://docs.python.org/3", None), "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), + "seaborn": ("https://seaborn.pydata.org", None), "sklearn": ("https://scikit-learn.org/dev", None), } diff --git a/docs/reference/plotting.rst b/docs/reference/plotting.rst index 019ed4b07..2f7b43957 100644 --- a/docs/reference/plotting.rst +++ b/docs/reference/plotting.rst @@ -15,6 +15,7 @@ Gridplot Pairplot -------- .. autofunction:: pairplot +.. autofunction:: pairplot_with_gmm Degreeplot ---------- diff --git a/docs/tutorial.rst b/docs/tutorial.rst index b21f5e772..266bd89b4 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -11,19 +11,19 @@ stochastic block model (SBM), degree-corrected SBM, and random dot product graph .. toctree:: :maxdepth: 1 - + tutorials/models/models .. _simulations_tutorials: Simulations =========== -The following tutorials demonstrate how to easily sample random graphs from graph models such as the Erdos-Renyi model, +The following tutorials demonstrate how to easily sample random graphs from graph models such as the Erdos-Renyi model, stochastic block model, and random dot product graph (RDPG). .. toctree:: :maxdepth: 1 - + tutorials/simulations/erdos_renyi tutorials/simulations/sbm tutorials/simulations/rdpg @@ -39,7 +39,7 @@ clustering algorithms, as well as the advantages of these to comparable implemen .. toctree:: :maxdepth: 1 - + tutorials/clustering/autogmm tutorials/clustering/kclust @@ -51,18 +51,18 @@ Inference on random graphs depends on low-dimensional Euclidean representation o .. toctree:: :maxdepth: 1 - + tutorials/embedding/AdjacencySpectralEmbed tutorials/embedding/OutOfSampleEmbed tutorials/embedding/Omnibus tutorials/embedding/MASE - -.. _inference_tutorials: + +.. _inference_tutorials: Inference =========================== -Statistical testing on graphs requires specialized methodology in order to account -for the fact that the edges and nodes of a graph are dependent on one another. Below +Statistical testing on graphs requires specialized methodology in order to account +for the fact that the edges and nodes of a graph are dependent on one another. Below are tutorials for robust statistical hypothesis testing on multiple graphs. .. toctree:: @@ -71,7 +71,7 @@ are tutorials for robust statistical hypothesis testing on multiple graphs. tutorials/inference/latent_position_test tutorials/inference/latent_distribution_test -.. _plot_tutorials: +.. _plot_tutorials: Plotting ======== @@ -83,6 +83,7 @@ The following tutorials present ways to visualize the graphs, such as its adjace tutorials/plotting/heatmaps tutorials/plotting/gridplot tutorials/plotting/pairplot + tutorials/plotting/pairplot_with_gmm .. _matching_tutorials: diff --git a/docs/tutorials/plotting/pairplot.ipynb b/docs/tutorials/plotting/pairplot.ipynb index cbfb1330a..ec74b2dbe 100644 --- a/docs/tutorials/plotting/pairplot.ipynb +++ b/docs/tutorials/plotting/pairplot.ipynb @@ -101,6 +101,13 @@ "\n", "plot = pairplot(X, labels)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -119,7 +126,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/tutorials/plotting/pairplot_with_gmm.ipynb b/docs/tutorials/plotting/pairplot_with_gmm.ipynb new file mode 100644 index 000000000..56194893c --- /dev/null +++ b/docs/tutorials/plotting/pairplot_with_gmm.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pairplot with GMM: Visualizing High Dimensional Data and Clustering\n", + "\n", + "This example provides how to visualize high dimensional data using the pairplot_with_gmm function. This function is specifcally useful because if we would like to cluster or learn about the geometry of high dimensional data, then we can call this function and utilize its ability to do pairwise comparisons across every dimension of that data as well as GMM clustering with ellipses plotted from the GMM covarariances. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import graspologic\n", + "\n", + "import numpy as np\n", + "import seaborn as sns\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate a binary graph using stochastic block model\n", + "The 3-block model is defined as below in the next cell. \n", + "\n", + "Thus, the first 50 vertices belong to block 1, the second 50 vertices belong to block 2, and the last 50 vertices belong to block 3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.simulations import sbm\n", + "\n", + "n_communities = [50, 50, 50]\n", + "p = [[0.5, 0.1, 0.05], \n", + " [0.1, 0.4, 0.15], \n", + " [0.05, 0.15, 0.3],]\n", + "\n", + "np.random.seed(2)\n", + "A = sbm(n_communities, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embed using adjacency spectral embedding to obtain lower dimensional representation of the graph\n", + "\n", + "The embedding dimension is automatically chosen. It should embed to 3 dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.embed import AdjacencySpectralEmbed\n", + "\n", + "ase = AdjacencySpectralEmbed()\n", + "X = ase.fit_transform(A)\n", + "\n", + "print(X.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use pairplot_with_gmm to plot the embedded data with ellipses\n", + "\n", + "First we generate labels that correspond to blocks. We pass the labels along with the data for to create a pairplot with the ellipses estimated by the GMM over each compnonent of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from graspologic.plot import pairplot_with_gmm\n", + "from sklearn.mixture import GaussianMixture\n", + "gmm = GaussianMixture(n_components=3, covariance_type='full').fit(X)\n", + "graph = pairplot_with_gmm(X, gmm, labels = None, cluster_palette = None, label_palette = None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to label your ellipses a specific color you can do the following. Please note that since we are assigning cluster assignment numbers (in this case 0, 1, 2) that the following plot will not necessarily have cluster colors matching those of the underlying scatter points. This is due to the fact that GMM does not label clusters in any particular order hence causing a permutations of colors. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from sklearn.mixture import GaussianMixture\n", + "gmm = GaussianMixture(n_components=3, covariance_type='full').fit(X)\n", + "labels = ['Block 1'] * 50 + ['Block 2'] * 50 + ['Block 3'] * 50\n", + "#we need 2*n_components to get two differnt colors for labels and clusters but from same palette\n", + "color = sns.color_palette(\"Set1\", 2*3)\n", + "cluster_palette = {0: color[0], 1:color[1], 2: color[2]}\n", + "label_palette = {\"Block 1\": color[3], 'Block 2':color[4], 'Block 3': color[5]}\n", + "graph = pairplot_with_gmm(X, gmm, labels = labels, cluster_palette = cluster_palette, label_palette = label_palette)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/graspologic/plot/__init__.py b/graspologic/plot/__init__.py index 64fc631c7..7ff08c096 100644 --- a/graspologic/plot/__init__.py +++ b/graspologic/plot/__init__.py @@ -4,6 +4,22 @@ import sys import matplotlib as mpl -from .plot import heatmap, gridplot, pairplot, degreeplot, edgeplot, screeplot +from .plot import ( + heatmap, + gridplot, + pairplot, + pairplot_with_gmm, + degreeplot, + edgeplot, + screeplot, +) -__all__ = ["heatmap", "gridplot", "pairplot", "degreeplot", "edgeplot", "screeplot"] +__all__ = [ + "heatmap", + "gridplot", + "pairplot", + "pairplot_with_gmm", + "degreeplot", + "edgeplot", + "screeplot", +] diff --git a/graspologic/plot/plot.py b/graspologic/plot/plot.py index bf1be2aee..d118f0e20 100644 --- a/graspologic/plot/plot.py +++ b/graspologic/plot/plot.py @@ -1,14 +1,16 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. +import matplotlib as mpl import matplotlib.pyplot as plt -from matplotlib.colors import Colormap import numpy as np import pandas as pd import seaborn as sns +from matplotlib.colors import Colormap from mpl_toolkits.axes_grid1 import make_axes_locatable -from sklearn.utils import check_array, check_consistent_length +from scipy import linalg from sklearn.preprocessing import Binarizer +from sklearn.utils import check_array, check_consistent_length, check_X_y from ..embed import selectSVD from ..utils import import_graph, pass_to_ranks @@ -671,6 +673,258 @@ def pairplot( return pairs +def _plot_ellipse_and_data( + data, X, j, k, means, covariances, ax, label_palette, cluster_palette, alpha +): + r""" + plot_ellipse makes a scatter plot from the two dimensions j,k where j + corresponds to x-axis + and k corresponds to the y-axis onto the axis that is ax. plot_ellipse then + applies a gmm ellipse onto the scatterplot + using the data from Y_(which is stored in data["clusters"]), + means, covariances. + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + Input data. + j : int + column index of feature of interest from X which will be the x-axis data. + k : int + column index of feature of interest from X which will be the y-axis data. + means : array-like, shape (n_components, n_features) + Estimated means from gmm + covariances : array-like, shape (with 'full') (n_components, n_features, n_features) + estimated covariances from gmm + ax : axis object + The location where plot_ellipse will plot + label_palette : dict, optional, default: dictionary using 'Set1' + cluster_palette : dict, optional, default: dictionary using 'Set1' + alpha : float, optional, default: 0.7 + Opacity value of plotter markers between 0 and 1 + + References + ---------- + .. [1] https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py. + """ + sns.scatterplot( + data=data, x=X[:, j], y=X[:, k], ax=ax, hue="labels", palette=label_palette + ) + for i, (mean, covar) in enumerate(zip(means, covariances)): + v, w = linalg.eigh(covar) + v = 2.0 * np.sqrt(2.0) * np.sqrt(v) + u = w[0] / linalg.norm(w[0]) + # Plot an ellipse to show the Gaussian component + angle = np.arctan(u[1] / u[0]) + angle = 180.0 * angle / np.pi + ell = mpl.patches.Ellipse( + [mean[j], mean[k]], + v[0], + v[1], + 180.0 + angle, + color=cluster_palette[i], + ) + ell.set_clip_box(ax.bbox) + ell.set_alpha(alpha) + ax.add_artist(ell) + # removes tick marks from off diagonal graphs + ax.set(xticks=[], yticks=[], xlabel=k, ylabel=k) + ax.legend().remove() + + +def pairplot_with_gmm( + X, + gmm, + labels=None, + cluster_palette="Set1", + label_palette="Set1", + title=None, + legend_name=None, + context="talk", + font_scale=1, + alpha=0.7, + figsize=(12, 12), + histplot_kws={}, +): + r""" + Plot pairwise relationships in a dataset, also showing a clustering predicted by + a Gaussian mixture model. + + By default, this function will create a grid of axes such that each dimension + in data will by shared in the y-axis across a single row and in the x-axis + across a single column. + + The off-diagonal axes show the pairwise relationships displayed as scatterplot. + The diagonal axes show the univariate distribution of the data for that + dimension displayed as either a histogram or kernel density estimates (KDEs). + + Read more in the :ref:`tutorials ` + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + Input data. + gmm: GaussianMixture object + A fit :class:`sklearn.mixture.GaussianMixture` object. + Gaussian mixture models (GMMs) are probabilistic models for representing data + based on normally distributed subpopulations, GMM clusters each data point into + a corresponding subpopulation. + labels : array-like or list, shape (n_samples), optional + Labels that correspond to each sample in ``X``. + If labels are not passed in then labels are predicted by ``gmm``. + label_palette : str or dict, optional, default: 'Set1' + Palette used to color points if ``labels`` are passed in. + cluster_palette : str or dict, optional, default: 'Set1' + Palette used to color GMM ellipses (and points if no ``labels`` are passed). + title : string, default: "" + Title of the plot. + legend_name : string, default: None + Name to put above the legend. + If ``None``, will be "Cluster" if no custom ``labels`` are passed, and "" + otherwise. + context : None, or one of {talk (default), paper, notebook, poster} + Seaborn plotting context + font_scale : float, optional, default: 1 + Separate scaling factor to independently scale the size of the font + elements. + alpha : float, optional, default: 0.7 + Opacity value of plotter markers between 0 and 1 + figsize : tuple + The size of the 2d subplots configuration + histplot_kws : dict, default: {} + Keyword arguments passed down to :func:`seaborn.histplot` + + Returns + ------- + fig : matplotlib Figure + axes : np.ndarray + Array of matplotlib Axes + + See Also + -------- + graspologic.plot.pairplot + graspologic.cluster.AutoGMMCluster + sklearn.mixture.GaussianMixture + """ + # Handle X and labels + if labels is not None: + check_X_y(X, labels) + # if custom labels pass sets default + if legend_name is None: + legend_name = "" + else: + # sets default if no custom labels passed + legend_name = "Cluster" + # Handle gmm + if gmm is None: + msg = "You must input a sklearn.mixture.GaussianMixture" + raise NameError(msg) + Y_, means, covariances = gmm.predict(X), gmm.means_, gmm.covariances_ + data = pd.DataFrame(data=X) + n_components = gmm.n_components + # setting up the data DataFrame + if labels is None: + lab_names = [i for i in range(n_components)] + data["labels"] = np.asarray([lab_names[Y_[i]] for i in range(Y_.shape[0])]) + else: + data["labels"] = labels + data["clusters"] = Y_ + # labels are given we must check whether input is correct + if labels is not None: + if isinstance(label_palette, str): + colors = sns.color_palette(label_palette, n_components) + label_palette = dict(zip(np.unique(np.asarray(labels)), colors)) + elif not isinstance(label_palette, dict): + msg = "When giving labels must supply palette in string or dictionary" + raise ValueError(msg) + if isinstance(cluster_palette, str): + colors = sns.color_palette(cluster_palette, n_components) + cluster_palette = dict(zip(np.unique(Y_), colors)) + elif not isinstance(label_palette, dict): + msg = "When giving labels must supply palette in string or dictionary" + raise ValueError(msg) + else: + # no labels given we go to default. + colors = sns.color_palette(cluster_palette, n_components) + labels = np.unique(Y_) + cluster_palette = dict(zip(np.unique(labels), colors)) + label_palette = dict(zip(np.unique(np.asarray(labels)), colors)) + + with sns.plotting_context(context=context, font_scale=font_scale): + dimensions = X.shape[1] + # we only want 1 scatter plot for 2 features + if X.shape[1] == 2: + dimensions = 1 + fig, axes = plt.subplots(dimensions, dimensions, figsize=figsize, squeeze=False) + # this will allow for uniform iteration whether axes was 2d or 1d + axes = axes.flatten() + for i in range(dimensions): + for j in range(dimensions): + if i == j and X.shape[1] > 2: + # take care of the histplot on diagonal + for t, lab in zip([i for i in range(X.shape[1])], label_palette): + sns.histplot( + X[Y_ == t, i], + ax=axes[dimensions * i + j], + color=label_palette[lab], + **histplot_kws, + ) + # this removes the tick marks from the histplot + axes[dimensions * i + j].set_xticks([]) + axes[dimensions * i + j].set_yticks([]) + else: + # take care off off-diagonal scatterplots + dim1, dim2 = j, i + # with only a scatter plot we must make sure we plot + # the first and second feature of X + if X.shape[1] == 2: + dim1, dim2 = 0, 1 + _plot_ellipse_and_data( + data, + X, + dim1, + dim2, + means, + covariances, + axes[dimensions * i + j], + label_palette, + cluster_palette, + alpha=alpha, + ) + # formatting + if title: + pairplot.suptitle(title) + for i in range(dimensions): + for j in range(dimensions): + if X.shape[1] == 2: + axes[dimensions * i + j].set_ylabel("Dimension " + str(1)) + axes[dimensions * i + j].set_xlabel("Dimension " + str(2)) + else: + axes[dimensions * i + j].set_ylabel("Dimension " + str(i + 1)) + axes[dimensions * i + j].set_xlabel("Dimension " + str(j + 1)) + + for ax in axes.flat: + ax.label_outer() + ax.spines["right"].set_visible(False) + ax.spines["top"].set_visible(False) + # set up the legend correctly by only getting handles(colored dot) + # and label corresponding to unique pairs + if X.shape[1] == 2: + handles, labels = axes[0].get_legend_handles_labels() + else: + handles, labels = axes[1].get_legend_handles_labels() + fig.legend( + handles, + labels, + loc="center right", + title=legend_name, + ) + # allows for the legend to not overlap with plots while also keeping + # legend in frame + fig.subplots_adjust(right=0.85) + return fig, axes + + def _distplot( data, labels=None, @@ -702,7 +956,7 @@ def _distplot( plt.legend() else: if data.min() != data.max(): - sns.distplot(data, hist=False, kde_kws=plt_kws) + sns.histplot(data, hist=False, kde_kws=plt_kws) else: ax.axvline(data[0]) diff --git a/setup.py b/setup.py index 759a5b6e2..744e3b1be 100644 --- a/setup.py +++ b/setup.py @@ -57,7 +57,7 @@ "networkx>=2.1", "numpy>=1.8.1", "POT>=0.7.0", - "seaborn>=0.9.0", + "seaborn>= 0.11.0", "scikit-learn>=0.19.1", "scipy>=1.4.0", ], diff --git a/tests/test_plot.py b/tests/test_plot.py index c793b3104..eca0013c4 100644 --- a/tests/test_plot.py +++ b/tests/test_plot.py @@ -3,8 +3,14 @@ import numpy as np import pytest - -from graspologic.plot.plot import heatmap, gridplot, pairplot, _sort_inds +from sklearn.mixture import GaussianMixture +from graspologic.plot.plot import ( + heatmap, + gridplot, + pairplot, + _sort_inds, + pairplot_with_gmm, +) from graspologic.simulations.simulations import er_np, sbm @@ -194,6 +200,37 @@ def test_pairplot_outputs(): ) +def test_pairplot_with_gmm_inputs(): + X = np.random.rand(15, 3) + gmm = GaussianMixture(n_components=3, covariance_type="full").fit(X) + labels = ["A"] * 5 + ["B"] * 5 + ["C"] * 5 + # test data + with pytest.raises(ValueError): + pairplot_with_gmm(X="test", gmm=gmm) + + with pytest.raises(ValueError): + pairplot_with_gmm(X=X, gmm=gmm, labels=["A"]) + + with pytest.raises(NameError): + pairplot_with_gmm(X, gmm=None) + + +def test_pairplot_with_gmm_outputs(): + X = np.random.rand(15, 3) + gmm = GaussianMixture(n_components=3, covariance_type="full").fit(X) + labels = ["A"] * 5 + ["B"] * 5 + ["C"] * 5 + cluster_palette = {0: "red", 1: "blue", 2: "green"} + label_palette = {"A": "red", "B": "blue", "C": "green"} + fig = pairplot_with_gmm(X, gmm) + fig = pairplot_with_gmm( + X, + gmm, + labels=labels, + cluster_palette=cluster_palette, + label_palette=label_palette, + ) + + def test_sort_inds(): B = np.array( [ diff --git a/tests/test_rdpg_corr.py b/tests/test_rdpg_corr.py index 16095d596..d9b1adf50 100644 --- a/tests/test_rdpg_corr.py +++ b/tests/test_rdpg_corr.py @@ -79,5 +79,3 @@ def test_p_is_close(self): graphs2 = np.stack(graphs2) np.testing.assert_allclose(np.mean(graphs1, axis=0), P, atol=0.1) np.testing.assert_allclose(np.mean(graphs2, axis=0), P, atol=0.1) - np.testing.assert_allclose(np.mean(np.mean(graphs1, axis=0) - P), 0, atol=1e-4) - np.testing.assert_allclose(np.mean(np.mean(graphs2, axis=0) - P), 0, atol=1e-4) From 4afdebc0f70fb621bc061fbf452a9951ea9de6cf Mon Sep 17 00:00:00 2001 From: Jingyan230 <69659070+Jingyan230@users.noreply.github.com> Date: Wed, 9 Dec 2020 23:02:58 +0800 Subject: [PATCH 067/140] Create a simple tutorial for `Aligning` (#573) * Add aligning tutorial * modified tutorial.rst * Correcting the path of tutorial * Correct some errors * Separate the tutorials into classes * Try to solve the errors in other modules in .rst * update 2 version of aliging tutorial of comparing * Deleting seperate tutorials, Modification according to the suggestions * text changes * file exchange * file exchange * fix error * fix line length * change some texts * change in texts * title fix * Add more descriptions * Fix the problems listed by reviewers * Change the grid * many-many anton's changes * anton's many-many changes one more time * changed titles slightly * adding matrices displays after examples * flushed the notebook * text fix * link text fix * text fix * text fixed and expression added * text fix * text fix Co-authored-by: Benjamin Pedigo Co-authored-by: alyakin314 --- docs/tutorial.rst | 13 +- docs/tutorials/aligning/aligning.ipynb | 838 +++++++++++++++++++++++++ 2 files changed, 850 insertions(+), 1 deletion(-) create mode 100644 docs/tutorials/aligning/aligning.ipynb diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 266bd89b4..dccaffda2 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -75,7 +75,7 @@ are tutorials for robust statistical hypothesis testing on multiple graphs. Plotting ======== -The following tutorials present ways to visualize the graphs, such as its adjacency matrix, and graph embeddings. +The following tutorials present ways to visualize the graphs, such as its adjacency matrix, and graph embeddings. .. toctree:: :maxdepth: 1 @@ -109,3 +109,14 @@ The following tutorial demonstrates how to estimate the signal-subgraph of sampl :maxdepth: 1 tutorials/subgraph/subgraph + +.. _aligning_tutorials: + +Aligning +======== +The following tutorials shows how to align two seperate datasets with each other, for better comparison of the data. + +.. toctree:: + :maxdepth: 1 + + tutorials/aligning/aligning diff --git a/docs/tutorials/aligning/aligning.ipynb b/docs/tutorials/aligning/aligning.ipynb new file mode 100644 index 000000000..b87c30563 --- /dev/null +++ b/docs/tutorials/aligning/aligning.ipynb @@ -0,0 +1,838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aligning\n", + "`align` is a module that can be used to align two different datasets. In particular, all three currently existing classes, namely `SignFlips`, regular `OrthogonalProcrustes`, and `SeedlessProcrustes` are aimed at correcting an orthogonal transformation of the data, the exact form of which is unknwon. The motivation for this are orthogonal non-identifiabilities, which are common when dealing with various embedding methods, whether in statistical graphs or other domains. Noted that if two graphs are embedded using omnibus embedding - they don't need to be aligned." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that an orthogonal matrix is any matrix $Q$ such that $Q^T Q = Q Q^T = I$. For all alignment methods, the main idea is to infer an orthogonal matrix $Q$ from the input data sets $X$ and $Y$, and subsequently use $Q$ in order to align $X$ to $Y$, by transforming $X$ as $\\hat{X} = X Q$. The major differences among classes in `align` come from the settings in which they are applicable, as well as additional assumptions made on the matrix $Q$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from graspologic.plot import heatmap\n", + "from scipy.stats import special_ortho_group, ortho_group\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple rotation example\n", + "\n", + "First, let's build two simple datasets $X$ and $Y$, with all entries of $X$ coming from the $Uniform(0, 1)$ distribution, and $Y$ is a copy of $X$, and subsequently rotated around the origin, using the randomly generated matrix $Q$, displayed below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "np.random.seed(314)\n", + "X = np.random.uniform(0, 1, (15, 2))\n", + "Q = special_ortho_group.rvs(2)\n", + "Y = X @ Q" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.94924938, -0.31452442],\n", + " [ 0.31452442, -0.94924938]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "heatmap(Q, figsize=(4,4), vmin=-1, vmax=1)\n", + "Q" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"Simple rotation example\")\n", + "ax.scatter(X[:,0], X[:,1], label=r\"$X$\", marker='x')\n", + "ax.scatter(Y[:,0], Y[:,1], label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SignFlips\n", + "\n", + "`SignFlips` assumes that $Q$ is not only orthogonal, but also diagonal, which constraints to matrices with $\\pm 1$ along the diagonal and $0$ everywhere else. In essence, it flips the dataset along some axes such that the two datasets end up in the same orthant (generalized quadrant). Which quadrant the dataset is \"located\" in is defined by some criterion, which is the dimension-wise medians by default. If the dataset needs to flip along some dimension, the corresponding entry in $Q$ will be $-1$, and $1$ otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.align import SignFlips\n", + "aligner_SF = SignFlips() \n", + "X_prime_SF = aligner_SF.fit_transform(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since $X$ is entirely in the first quadrant, its dimension-wise medians in both dimensions are greater than $0$. Meanwhile $Y$ is predominantly in the third quadrant, so its dimension-wise medians are all smaller than $0$. Thus $X$ needs to be flipped both along the x-axis and y-axis in order for two datasets to arive to the same quandrant. So, the $Q_{SF}$ that the algorithm finds is" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1, 0],\n", + " [ 0, -1]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "heatmap(aligner_SF.Q_, figsize= (4,4), vmin=-1, vmax=1)\n", + "aligner_SF.Q_" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"Simple rotation example, Sign Flips\")\n", + "ax.scatter(X[:,0], X[:,1],\n", + " label=r\"$X$\", marker='x')\n", + "ax.scatter(Y[:,0], Y[:,1],\n", + " label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.scatter(X_prime_SF[:,0],\n", + " X_prime_SF[:,1],\n", + " label=r\"$X_{SF}$\", \n", + " marker='$F$')\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`SignFlips` correctly brings $X$ to the third quadrant, yet the alignment is imperfect, as it is a heuristic-based method. Note that it does not assume that there is any sort of matching between the entries of $X$ and $Y$. Indeed, the two datasets need not even be of the same size." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This class can also be used to bring the dataset to the first orthant, if one provides the identity matrix as the second dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OrthogonalProcrustes\n", + "\n", + "`OrthogonalProcrustes` obtains $Q_{OP}$ by solving the classic orthogonal procrustes problem. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Orthogonal Procrustes problem](https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem) is a matrix approximation problem in linear algebra, as one is asked to find a orthogonal matrix $Q$, which could most closely map the given matrix $X$ to another given matrix $Y$. Formally it seeks to find a matrix $Q_{OP}$ such that $|| X Q_{OP} - Y ||_F$ is minimized. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since, this formulation strongly relies on the correspondence between the rows of the given matrices, this class can only be applied when the entery datasets $X$ and $Y$ have the equal shape ($n$, $d$), and the $i^{th}$ entry in $X$ is assumed to be the same value as vertex $i^{th}$ entry of $Y$, up to some noise. In graph setting, this means that the latent positions of the two graphs are equivalent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`OrthogonalProcrustes` has a widely known closed form solution. Indeed, this class simply wraps *[scipy.linal.orthogonal_procrustes()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.orthogonal_procrustes.html)*. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.align import OrthogonalProcrustes\n", + "aligner_OP = OrthogonalProcrustes() \n", + "X_prime_OP = aligner_OP.fit_transform(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that that $Q_{OP}$ is a generic orthogonal matrix, not a diagonal one." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.94924938, -0.31452442],\n", + " [ 0.31452442, -0.94924938]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"Simple rotation example, OrthogonalProcrustes\")\n", + "ax.scatter(X[:,0], X[:,1], label=r\"$X$\", marker='x')\n", + "ax.scatter(Y[:,0], Y[:,1], label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.scatter(X_prime_OP[:,0],\n", + " X_prime_OP[:,1],\n", + " label=r\"$X_{OP}$\", \n", + " marker='$O$')\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, `OrthogonalProcrustes` obtains an exact solution when $X$ is a rotated copy of $Y$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SeedlessProcrustes\n", + "\n", + "`SeedlessProcrustes` is an algorithm that extends regular Procrustes to a case when there is no known correspondence between the entries. In fact, it can be used even when the number of entries between the two datasest is different." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Formally, if $X$ has $n$ entries, and $Y$ has $m$ entries, it tries to solve for an orthogonal matrix $Q_{SP}$, and an $n \\times m$ matrix $P$, with a special condition that rows sum to $1/m$, and columns sum to $1/n$, such that the quantity $|| X Q_{SP} - P Y ||_F$ is minimized." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unlike the regular procrustes, this does not have a known closed form solution. `SeedlessProcrustes` tries to solve this optimization problem via an iterative algorithm that alternates optimal transport and regular procrustes. See the Notes section of the `SeedlessProcrustes` [references](https://graspy.neurodata.io/reference/align.html#seedless-procrustes) for more details on this algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.align import SeedlessProcrustes\n", + "aligner_SP = SeedlessProcrustes() \n", + "X_prime_SP = aligner_SP.fit_transform(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.94472887, -0.32785266],\n", + " [ 0.32785266, -0.94472887]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"SeedlessProcrustes, Simple rotation example\")\n", + "ax.scatter(X[:,0], X[:,1], label=r\"$X$\", marker='x')\n", + "ax.scatter(Y[:,0], Y[:,1], label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.scatter(X_prime_SP[:,0],\n", + " X_prime_SP[:,1],\n", + " label=r\"$X_{SP}$\", \n", + " marker='$S$')\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison between SignFlips, OrthogonalProcrustes, SeedlessProcrustes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The outcomes of three classes in simple rotation example together are plotted on the same figure in order to allow for a simpler comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"Aligning, Simple rotation example\")\n", + "ax.scatter(X[:,0], X[:,1], label=r\"$X$\", marker='x')\n", + "ax.scatter(Y[:,0], Y[:,1], label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.scatter(X_prime_SF[:,0],\n", + " X_prime_SF[:,1], \n", + " label=r\"$X_{SF}$\",\n", + " marker='$F$')\n", + "ax.scatter(X_prime_OP[:,0],\n", + " X_prime_OP[:,1], \n", + " label=r\"$X_{OP}$\", marker='$o$')\n", + "ax.scatter(X_prime_SP[:,0],\n", + " X_prime_SP[:,1], \n", + " label=r\"$X_{SP}$\", marker='$S$')\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize = (16, 4))\n", + "heatmap(Q, title=r'$Q$', vmin=-1, vmax=1, ax=ax1)\n", + "heatmap(aligner_SF.Q_, title=r'$Q_{SF}$', vmin=-1, vmax=1, ax=ax2)\n", + "heatmap(aligner_OP.Q_, title=r'$Q_{OP}$', vmin=-1, vmax=1, ax=ax3)\n", + "heatmap(aligner_SP.Q_, title=r'$Q_{SP}$', vmin=-1, vmax=1, ax=ax4);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this simple example, `OrthogonalProcrustes` and `SeedlessProcrustes` align $X$ to $Y$ extremply well in terms of the Frobenius norm, whereas `SignFlips` can only do so well as to bring them to the same quadrant. In fact, when a dataset is a rotated copy of another - `OrthogonalProcrustes` aligns it up to a numerical epsilon precision." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple example\n", + "Difference in Frobenious norm after Sign Flips 1.047909310874878\n", + "Difference in Frobenious norm after Orthogonal Procrustes 5.800542824167306e-16\n", + "Difference in Frobenious norm after Seedless Procrustes 0.046291889999320164\n" + ] + } + ], + "source": [ + "norm_SignFlips = np.linalg.norm(X_prime_SF - Y)\n", + "norm_Orthogonal = np.linalg.norm(X_prime_OP - Y)\n", + "norm_Seedless = np.linalg.norm(X_prime_SP - Y)\n", + "print('Simple example')\n", + "print('Difference in Frobenious norm after Sign Flips ', norm_SignFlips)\n", + "print('Difference in Frobenious norm after Orthogonal Procrustes ', norm_Orthogonal)\n", + "print('Difference in Frobenious norm after Seedless Procrustes ', norm_Seedless)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shuffled Example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that unlike the other two methods, `OrthogonalProcrustes` heavily relies on a fact that the vertices of the two datasets are matched. Its performance will deteriorate even on this simple example if the vertices are shuffled, as shown in the following figure." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.utils import shuffle" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "X_shuffled = shuffle(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "aligner_SF_shuffled = SignFlips()\n", + "X_prime_SF_shuffled = aligner_SF_shuffled.fit_transform(X_shuffled, Y)\n", + "aligner_OP_shuffled = OrthogonalProcrustes()\n", + "X_prime_OP_shuffled = aligner_OP_shuffled.fit_transform(X_shuffled, Y)\n", + "aligner_SP_shuffled = SeedlessProcrustes()\n", + "X_prime_SP_shuffled = aligner_SP_shuffled.fit_transform(X_shuffled, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"Aligning, Shuffled example\")\n", + "ax.scatter(X_shuffled[:,0], X_shuffled[:,1], label=r\"$X$\", marker='x')\n", + "ax.scatter(Y[:,0], Y[:,1], label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.scatter(X_prime_SF_shuffled[:,0],\n", + " X_prime_SF_shuffled[:,1], \n", + " label=r\"$X_{SF}$\", marker='$F$')\n", + "ax.scatter(X_prime_OP_shuffled[:,0],\n", + " X_prime_OP_shuffled[:,1], \n", + " label=r\"$X_{OP}$\", marker='$o$')\n", + "ax.scatter(X_prime_SP_shuffled[:,0],\n", + " X_prime_SP_shuffled[:,1], \n", + " label=r\"$X_{SP}$\", marker='$S$')\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize = (16, 4))\n", + "heatmap(Q, title=r'$Q$', vmin=-1, vmax=1, ax=ax1)\n", + "heatmap(aligner_SF_shuffled.Q_, title=r'$Q_{SF}$', vmin=-1, vmax=1, ax=ax2)\n", + "heatmap(aligner_OP_shuffled.Q_, title=r'$Q_{OP}$', vmin=-1, vmax=1, ax=ax3)\n", + "heatmap(aligner_SP_shuffled.Q_, title=r'$Q_{SP}$', vmin=-1, vmax=1, ax=ax4);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this shuffled example, though the dataset $X_{shuffled}$ looks the same as dataset $X$ in the figure, the performance of `OthogonalProcrustes` is worse than the one in the simple rotation example above. This is because the value of $i^{th}$ entry in $X_{shuffled}$ is no longer corresponding with the $i^{th}$ entry of $Y$ after being shuffled from $X$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unmatched example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, `SeedlessProcrustes` can be applied to two datasets with different number of entries. Consider the following example: $X$ is the same as previous, but the new $Y$ is a *different, larger* dataset, generated from the *same distribution*, and then subsequently rotated." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "Y_unmatched = np.random.uniform(0, 1, (30, 2)) @ Q" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "aligner_SF_unmatched = SignFlips()\n", + "X_prime_SF_unmatched = aligner_SF_unmatched.fit_transform(X, Y_unmatched)\n", + "aligner_SP_unmatched = SeedlessProcrustes()\n", + "X_prime_SP_unmatched = aligner_SP_unmatched.fit_transform(X, Y_unmatched)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.set_title(\"Unmatched input example, Seedless Procrustes\")\n", + "ax.scatter(X[:,0], X[:,1], label=r\"$X$\", marker='x')\n", + "ax.scatter(Y_unmatched[:,0], Y_unmatched[:,1], label=r\"$Y$\", s=100, alpha=0.5)\n", + "ax.scatter(X_prime_SF_unmatched[:,0],\n", + " X_prime_SF_unmatched[:,1],\n", + " label=r\"$X_{SF}$\", \n", + " marker='$F$')\n", + "ax.scatter(X_prime_SP_unmatched[:,0],\n", + " X_prime_SP_unmatched[:,1],\n", + " label=r\"$X_{SP}$\", \n", + " marker='$S$')\n", + "ax.set(xlim=(-1.20, 1.20), ylim=(-1.20, 1.20))\n", + "ax.set_xticks([0])\n", + "ax.set_yticks([0])\n", + "ax.legend()\n", + "ax.grid();" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize = (12, 4))\n", + "heatmap(Q, title=r'$Q$', vmin=-1, vmax=1, ax=ax1)\n", + "heatmap(aligner_SF_unmatched.Q_, title=r'$Q_{SF}$', vmin=-1, vmax=1, ax=ax2)\n", + "heatmap(aligner_SP_unmatched.Q_, title=r'$Q_{SP}$', vmin=-1, vmax=1, ax=ax3);" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the dataset X: [0.58510607 0.48290266]\n", + "Mean of the dataset Y: [-0.4035269 -0.6424252]\n", + "Mean of the dataset X_{SF}: [-0.58510607 -0.48290266]\n", + "Mean of the dataset X_{SP}: [-0.38535747 -0.65348582]\n" + ] + } + ], + "source": [ + "print(\"Mean of the dataset X: \", np.mean(X, axis=0))\n", + "print(\"Mean of the dataset Y: \", np.mean(Y, axis=0))\n", + "print(\"Mean of the dataset X_{SF}: \", np.mean(X_prime_SF_unmatched, axis=0))\n", + "print(\"Mean of the dataset X_{SP}: \", np.mean(X_prime_SP_unmatched, axis=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`SeedlessProcrustes` is still able to align the distributions, while the `SignFlips` method is only a good heuristic. Note that `OthogonalProcrustes` is simply inapplicable in this context. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 02166af6f078cfb69e58420849b07d8af1b1b846 Mon Sep 17 00:00:00 2001 From: kareef928 <51966539+kareef928@users.noreply.github.com> Date: Thu, 10 Dec 2020 08:03:07 -0500 Subject: [PATCH 068/140] LPT/LDT implemented as a function (#547) * modify test * pull from upstream * test * revert * remove graspy folder * add lpt as function * test * docs tests tutorial * add ldt function * add random seed * fix seed * update * test and tutorial * updates * fix minor issues * fix conf.py * fix LDT test * ensure lpt/ldt from most recent version/debug tests * reference/format update * formatting * minor formatting * small formatting in lpt * fix ldt test * fix p val format * fix formatting * change name of files * inconsequential commit * revert * fix errors * resolve changes: * remove line * LPT tutorial fixes * LDT tutorial fixes * trigger unit and doc tests * revert * LPT tutorial render fix attempt * Inconsequential change * revert * address anton's comments * typo * small fix * small fix * format issue * inconsequential change * remove trailing underscores * don't expose difference_norm * add embedding tests back * import from a file directly, see if test passes * difference norm unexposed * remove fit in input-graph desc * black * remove extra white spaces * return namedtuples * misc_stats for ldt * docstring modifications * remove whitespace * modify tutorials * modify tests * return identity if no align_type * fix last name of namedtuple * ldt tutorial mods * lpt tutorial mods * add namedtuple test * check names for namedtuple * typo * revert unwanted changes in tutorials * change order of heatmap and pairplot * unwanted white spaces * unwanted whitespace * remove namedtuple test Co-authored-by: Kareef Ullah Co-authored-by: Benjamin Pedigo Co-authored-by: alyakin314 --- docs/reference/inference.rst | 4 +- .../inference/latent_distribution_test.ipynb | 79 +-- .../inference/latent_position_test.ipynb | 20 +- graspologic/inference/__init__.py | 6 +- graspologic/inference/base.py | 89 --- .../inference/latent_distribution_test.py | 571 ++++++++---------- graspologic/inference/latent_position_test.py | 272 ++++----- graspologic/version/version.txt | 1 - setup.py | 2 +- tests/test_latentdistributiontest.py | 311 +++++----- tests/test_latentpositiontest.py | 120 ++-- 11 files changed, 661 insertions(+), 814 deletions(-) delete mode 100644 graspologic/inference/base.py diff --git a/docs/reference/inference.rst b/docs/reference/inference.rst index 5e0792651..f73d41763 100644 --- a/docs/reference/inference.rst +++ b/docs/reference/inference.rst @@ -6,6 +6,6 @@ Inference Two-graph hypothesis testing ---------------------------- -.. autoclass:: LatentPositionTest +.. autofunction:: latent_position_test -.. autoclass:: LatentDistributionTest +.. autofunction:: latent_distribution_test diff --git a/docs/tutorials/inference/latent_distribution_test.ipynb b/docs/tutorials/inference/latent_distribution_test.ipynb index 88ca1075b..96e470944 100644 --- a/docs/tutorials/inference/latent_distribution_test.ipynb +++ b/docs/tutorials/inference/latent_distribution_test.ipynb @@ -19,7 +19,7 @@ "import matplotlib.pyplot as plt\n", "np.random.seed(8888)\n", "\n", - "from graspologic.inference import LatentDistributionTest\n", + "from graspologic.inference import latent_distribution_test\n", "from graspologic.embed import AdjacencySpectralEmbed\n", "from graspologic.simulations import sbm, rdpg\n", "from graspologic.utils import symmetrize\n", @@ -57,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We generate a second SBM in the same way." + "We generate a second SBM with a different number of vertices in each block, but with the same probability matrix." ] }, { @@ -66,11 +66,10 @@ "metadata": {}, "outputs": [], "source": [ - "A2 = sbm(csize, P)\n", + "csize_2 = [75] * 2\n", + "A2 = sbm(csize_2, P)\n", "X2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n", "\n", - "A1 = sbm(csize, P)\n", - "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", "heatmap(A2, title='2-block SBM adjacency matrix')\n", "_ = pairplot(X2, title='2-block adjacency spectral embedding', height=4.5)" ] @@ -82,11 +81,13 @@ "### Latent distribution test where null is true\n", "We want to know whether the latent positions of the two graphs above were generated from the same latent distribution. In other words, we are testing\n", "\n", - "$$ H_0:F_{X_1} = F_{X_2} R$$$$ H_\\alpha: F_{X_1} \\neq F_{X_2} R $$ \n", + "$$ H_0:F_{X_1} = F_{X_2} Q$$$$ H_\\alpha: F_{X_1} \\neq F_{X_2} Q $$ \n", "\n", - "The $R$ is an orthogonal rotation matrix present due to the orthogonal non-identifiability in the random dot product graphs.\n", + "The $Q$ is an orthogonal rotation matrix present due to the orthogonal non-identifiability in the random dot product graphs.\n", "\n", - "We know that in this case the graphs were actually generated from the same distribution, so the test should reject no more often than the significance level $\\alpha$, and on average the $p$-value should be high (fail to reject the null)" + "We know that in this case the graphs were actually generated from the same distribution, so the test should reject no more often than the significance level $\\alpha$, and on average the $p$-value should be high (fail to reject the null).\n", + "\n", + "In this tutorial, we will use the latent distribution test on unmatched graphs. This means there is not an alignment between each node of the two graphs." ] }, { @@ -106,8 +107,7 @@ "metadata": {}, "outputs": [], "source": [ - "ldt_dcorr = LatentDistributionTest(\"dcorr\", metric=\"euclidean\", n_bootstraps=100)\n", - "ldt_dcorr.fit(A1, A2)" + "ldt_dcorr = latent_distribution_test(A1, A2, test=\"dcorr\", metric=\"euclidean\", n_bootstraps=100)" ] }, { @@ -116,8 +116,7 @@ "metadata": {}, "outputs": [], "source": [ - "ldt_mgc = LatentDistributionTest(\"mgc\", metric=\"euclidean\", n_bootstraps=100)\n", - "ldt_mgc.fit(A1, A2)" + "ldt_mgc = latent_distribution_test(A1, A2, test=\"mgc\", metric=\"euclidean\", n_bootstraps=100)" ] }, { @@ -128,7 +127,7 @@ }, "outputs": [], "source": [ - "print(ldt_dcorr.p_value_, ldt_mgc.p_value_)" + "print(ldt_dcorr[0], ldt_mgc[0])" ] }, { @@ -138,13 +137,17 @@ "outputs": [], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(10, 6))\n", - "ax[0].hist(ldt_dcorr.null_distribution_, 50)\n", - "ax[0].axvline(ldt_dcorr.sample_T_statistic_, color='r')\n", - "ax[0].set_title(\"DCorr: P-value = {}\".format(ldt_dcorr.p_value_), fontsize=20)\n", - "ax[1].hist(ldt_mgc.null_distribution_, 50)\n", - "ax[1].axvline(ldt_mgc.sample_T_statistic_, color='r')\n", - "ax[1].set_title(\"MGC: P-value = {}\".format(ldt_mgc.p_value_), fontsize=20)\n", - "_ = plt.show()" + "ax[0].hist(ldt_dcorr[2]['null_distribution'], 50)\n", + "ax[0].axvline(ldt_dcorr[1], color='r')\n", + "ax[0].set_title(\"DCorr: P-value = {}\".format(ldt_dcorr[0]), fontsize=20)\n", + "ax[0].set_xlabel(\"test static\")\n", + "ax[0].set_ylabel(\"frequency\")\n", + "ax[1].hist(ldt_mgc[2]['null_distribution'], 50)\n", + "ax[1].axvline(ldt_mgc[1], color='r')\n", + "ax[1].set_title(\"MGC: P-value = {}\".format(ldt_mgc[0]), fontsize=20)\n", + "ax[1].set_xlabel(\"test static\")\n", + "ax[1].set_ylabel(\"frequency\")\n", + "plt.show();" ] }, { @@ -172,9 +175,13 @@ "P2 = np.array([[0.9, 0.4],\n", " [0.4, 0.9]])\n", "\n", - "A3 = sbm(csize, P2)\n", - "heatmap(A3, title='2-block SBM adjacency matrix A3')\n", + "A3 = sbm(csize_2, P2)\n", + "\n", + "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", "X3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n", + "heatmap(A1, title='2-block SBM adjacency matrix A1')\n", + "heatmap(A3, title='2-block SBM adjacency matrix A3')\n", + "pairplot(X1, title='2-block adjacency spectral embedding A1', height=4.5)\n", "_ = pairplot(X3, title='2-block adjacency spectral embedding A3', height=4.5)" ] }, @@ -193,8 +200,7 @@ "metadata": {}, "outputs": [], "source": [ - "ldt_dcorr = LatentDistributionTest(\"dcorr\", metric=\"euclidean\", n_bootstraps=100)\n", - "ldt_dcorr.fit(A1, A3)" + "ldt_dcorr = latent_distribution_test(A1, A3, test=\"dcorr\", metric=\"euclidean\", n_bootstraps=100)" ] }, { @@ -203,8 +209,7 @@ "metadata": {}, "outputs": [], "source": [ - "ldt_mgc = LatentDistributionTest(\"mgc\", metric=\"euclidean\", n_bootstraps=100)\n", - "ldt_mgc.fit(A1, A3)" + "ldt_mgc = latent_distribution_test(A1, A3, test=\"mgc\", metric=\"euclidean\", n_bootstraps=100)" ] }, { @@ -215,7 +220,7 @@ }, "outputs": [], "source": [ - "print(ldt_dcorr.p_value_, ldt_mgc.p_value_)" + "print(ldt_dcorr[0], ldt_mgc[0])" ] }, { @@ -225,13 +230,17 @@ "outputs": [], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(10, 6))\n", - "ax[0].hist(ldt_dcorr.null_distribution_, 50)\n", - "ax[0].axvline(ldt_dcorr.sample_T_statistic_, color='r')\n", - "ax[0].set_title(\"DCorr: P-value = {}\".format(ldt_dcorr.p_value_), fontsize=20)\n", - "ax[1].hist(ldt_mgc.null_distribution_, 50)\n", - "ax[1].axvline(ldt_mgc.sample_T_statistic_, color='r')\n", - "ax[1].set_title(\"MGC: P-value = {}\".format(ldt_mgc.p_value_), fontsize=20)\n", - "_ = plt.show()" + "ax[0].hist(ldt_dcorr[2]['null_distribution'], 50)\n", + "ax[0].axvline(ldt_dcorr[1], color='r')\n", + "ax[0].set_title(\"DCorr: P-value = {}\".format(ldt_dcorr[0]), fontsize=20)\n", + "ax[0].set_xlabel(\"test static\")\n", + "ax[0].set_ylabel(\"frequency\")\n", + "ax[1].hist(ldt_mgc[2]['null_distribution'], 50)\n", + "ax[1].axvline(ldt_mgc[1], color='r')\n", + "ax[1].set_title(\"MGC: P-value = {}\".format(ldt_mgc[0]), fontsize=20)\n", + "ax[1].set_xlabel(\"test static\")\n", + "ax[1].set_ylabel(\"frequency\")\n", + "plt.show();" ] } ], @@ -251,7 +260,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.4" } }, "nbformat": 4, diff --git a/docs/tutorials/inference/latent_position_test.ipynb b/docs/tutorials/inference/latent_position_test.ipynb index 39dd1cdef..b450ac20f 100644 --- a/docs/tutorials/inference/latent_position_test.ipynb +++ b/docs/tutorials/inference/latent_position_test.ipynb @@ -16,7 +16,7 @@ "import numpy as np\n", "np.random.seed(88889999)\n", "\n", - "from graspologic.inference import LatentPositionTest\n", + "from graspologic.inference import latent_position_test\n", "from graspologic.embed import AdjacencySpectralEmbed\n", "from graspologic.simulations import sbm, rdpg\n", "from graspologic.utils import symmetrize\n", @@ -57,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. $\\text{ASE}(A)$ recovers an estimate of the **latent positions** of $A$. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n", + "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. Below it, we see the **adjacency spectral embedding (ASE)** of this graph. $\\text{ASE}(A)$ recovers an estimate of the **latent positions** of $A$. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n", "\n", "For an adjacency matrix $A \\in \\mathbb{R}^{n \\times n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n \\times n}$\n", "\n", @@ -112,7 +112,7 @@ "metadata": {}, "source": [ "## Latent position test where null is true\n", - "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance. In this case, we will use the `LatentPositionTest` in `graspologic` because we know the true alignment between the vertices of the two graphs we are testing. In other words, node $i$ in graph 1 can be thought of as equivalent to node $i$ in graph 2 because of the way we generated these graphs. \n", + "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance. In this case, we will use the `latent_position_test` in `graspologic` because we know the true alignment between the vertices of the two graphs we are testing. In other words, node $i$ in graph 1 can be thought of as equivalent to node $i$ in graph 2 because of the way we generated these graphs. \n", "\n", "In other words, we are testing $$ H_0: X_1 = X_2 R$$$$ H_a: X_1 \\neq X_2 R$$\n", "\n", @@ -120,7 +120,7 @@ "\n", "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n", "\n", - "Note that ``LatentPositionTest.fit()`` may take several minutes" + "Note that latent_position_test() may take several minutes" ] }, { @@ -130,9 +130,8 @@ "outputs": [], "source": [ "# note: may want to use more bootstraps in practice, low number used here is for speed\n", - "lpt = LatentPositionTest(n_bootstraps=100, n_components=n_components)\n", - "lpt.fit(A1, A2)\n", - "print('p = {}'.format(lpt.p_value_))" + "lpt = latent_position_test(A1, A2, n_bootstraps=100, n_components=n_components)\n", + "print('p = {}'.format(lpt[0]))" ] }, { @@ -187,9 +186,8 @@ "outputs": [], "source": [ "# note: may want to use more bootstraps in practice, low number used here is for speed\n", - "lpt = LatentPositionTest(n_bootstraps=100, n_components=n_components)\n", - "lpt.fit(A3, A4)\n", - "print('p = {}'.format(lpt.p_value_))" + "lpt = latent_position_test(A3, A4, n_bootstraps=100, n_components=n_components)\n", + "print('p = {}'.format(lpt[0]))" ] } ], @@ -209,7 +207,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.4" } }, "nbformat": 4, diff --git a/graspologic/inference/__init__.py b/graspologic/inference/__init__.py index e279bc457..a1162337d 100644 --- a/graspologic/inference/__init__.py +++ b/graspologic/inference/__init__.py @@ -1,7 +1,7 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .latent_position_test import LatentPositionTest -from .latent_distribution_test import LatentDistributionTest +from .latent_position_test import latent_position_test +from .latent_distribution_test import latent_distribution_test -__all__ = ["LatentPositionTest", "LatentDistributionTest"] +__all__ = ["latent_position_test", "latent_distribution_test"] diff --git a/graspologic/inference/base.py b/graspologic/inference/base.py deleted file mode 100644 index a576e004d..000000000 --- a/graspologic/inference/base.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) Microsoft Corporation and contributors. -# Licensed under the MIT License. - -import numpy as np -from abc import abstractmethod -from sklearn.base import BaseEstimator - - -class BaseInference(BaseEstimator): - """ - Base class for inference tasks such as semiparametric latent position test - and nonparametric latent distribution test. - - Parameters - ---------- - n_components : None (default), or int - Number of embedding dimensions. If None, the optimal embedding - dimensions are chosen via the Zhu and Godsi algorithm. - """ - - def __init__(self, n_components=None): - if (not isinstance(n_components, (int, np.integer))) and ( - n_components is not None - ): - raise TypeError("n_components must be int or np.integer") - if n_components is not None and n_components <= 0: - raise ValueError( - "Cannot embed into {} dimensions, must be greater than 0".format( - n_components - ) - ) - self.n_components = n_components - - @abstractmethod - def _embed(self, A1, A2, n_componets): - """ - Computes the latent positions of input graphs - - Parameters - ---------- - A1 : np.ndarray, shape (n_vertices, n_vertices) - Adjacency matrix of the first graph - A2 : np.ndarray, shape (n_vertices, n_vertices) - Adjacency matrix of the second graph - - Returns - ------- - X1_hat : array-like, shape (n_vertices, n_components) - Estimated latent positions of the vertices in the first graph - X2_hat : array-like, shape(n_vertices, n_components) - Estimated latent positions of the vertices in the second graph - """ - - @abstractmethod - def fit(self, A1, A2): - """ - Compute the test statistic and the null distribution. - - Parameters - ---------- - A1, A2 : nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray - The two graphs to run a hypothesis test on. - If np.ndarray, shape must be ``(n_vertices, n_vertices)`` for both - graphs, where ``n_vertices`` is the same for both - - Returns - ------ - self - """ - pass - - def fit_predict(self, A1, A2): - """ - Fits the model and returns the p-value - - Parameters - ---------- - A1, A2 : nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray - The two graphs to run a hypothesis test on. - If np.ndarray, shape must be ``(n_vertices, n_vertices)`` for both - graphs, where ``n_vertices`` is the same for both - - Returns - ------ - p_value_ : float - The overall p value from the test - """ - self.fit(A1, A2) - return self.p_value_ diff --git a/graspologic/inference/latent_distribution_test.py b/graspologic/inference/latent_distribution_test.py index 1b07d8420..422d9b794 100644 --- a/graspologic/inference/latent_distribution_test.py +++ b/graspologic/inference/latent_distribution_test.py @@ -10,7 +10,6 @@ from ..utils import import_graph, fit_plug_in_variance_estimator from ..align import SignFlips from ..align import SeedlessProcrustes -from .base import BaseInference from sklearn.utils import check_array from sklearn.metrics import pairwise_distances from sklearn.metrics.pairwise import pairwise_kernels @@ -18,6 +17,7 @@ from sklearn.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS from hyppo.ksample import KSample from hyppo._utils import gaussian +from collections import namedtuple _VALID_DISTANCES = list(PAIRED_DISTANCES.keys()) _VALID_KERNELS = list(PAIRWISE_KERNEL_FUNCTIONS.keys()) @@ -26,8 +26,23 @@ _VALID_TESTS = ["cca", "dcorr", "hhg", "rv", "hsic", "mgc"] - -class LatentDistributionTest(BaseInference): +ldt_result = namedtuple("ldt_result", ("p_value", "sample_T_statistic", "misc_stats")) + + +def latent_distribution_test( + A1, + A2, + test="dcorr", + metric="euclidean", + n_components=None, + n_bootstraps=500, + workers=1, + size_correction=True, + pooled=False, + align_type="sign_flips", + align_kws={}, + input_graph=True, +): """Two-sample hypothesis test for the problem of determining whether two random dot product graphs have the same distributions of latent positions. @@ -39,6 +54,10 @@ class LatentDistributionTest(BaseInference): Parameters ---------- + A1, A2 : variable (see description of 'input_graph') + The two graphs, or their embeddings to run a hypothesis test on. + Expected variable type and shape depends on input_graph attribute + test : str (default="hsic") Backend hypothesis test to use, one of ["cca", "dcorr", "hhg", "rv", "hsic", "mgc"]. These tests are typically used for independence testing, but here they @@ -133,32 +152,34 @@ class LatentDistributionTest(BaseInference): Flag whether to expect two full graphs, or the embeddings. - True - :func:`~graspologic.inference.LatentDistributionTest.fit` and - :func:`~graspologic.inference.LatentDistributionTest.fit_predict` - expect graphs, either as NetworkX graph objects or as adjacency matrices, - provided as ndarrays of size (n, n) and (m, m). - They will be embedded using adjacency spectral embeddings. + This function expects graphs, either as NetworkX graph objects + or as adjacency matrices, provided as ndarrays of size (n, n) and + (m, m). They will be embedded using adjacency spectral embeddings. - False - :func:`~graspologic.inference.LatentDistributionTest.fit` and - :func:`~graspologic.inference.LatentDistributionTest.fit_predict` - expect adjacency spectral embeddings of the graphs, + This function expects adjacency spectral embeddings of the graphs, they must be ndarrays of size (n, d) and (m, d), where d must be same. n_components attribute is ignored in this case. - Attributes + Returns ---------- - metric_func_ : callable - A callable associated with the specified metric. See ``metric``. - - null_distribution_ : ndarray, shape (n_bootstraps, ) - The distribution of T statistics generated under the null. + p_value : float + The overall p value from the test. - sample_T_statistic_ : float + sample_T_statistic : float The observed difference between the embedded latent positions of the two input graphs. - p_value_ : float - The overall p value from the test. + misc_stats : dictionary + A collection of other statistics obtained from the latent position test + + - null_distribution : ndarray, shape (n_bootstraps,) + The distribution of T statistics generated under the null. + + - n_components : int + Number of embedding dimensions. + + - Q : array, size (d, d) + Final orthogonal matrix, used to modify ``X``. References ---------- @@ -176,317 +197,241 @@ class LatentDistributionTest(BaseInference): """ - def __init__( - self, - test="dcorr", - metric="euclidean", - n_components=None, - n_bootstraps=200, - workers=1, - size_correction=True, - pooled=False, - align_type="sign_flips", - align_kws={}, - input_graph=True, - ): - # check test argument - if not isinstance(test, str): - msg = "test must be a str, not {}".format(type(test)) - raise TypeError(msg) - elif test not in _VALID_TESTS: - msg = "Unknown test {}. Valid tests are {}".format(test, _VALID_TESTS) - raise ValueError(msg) - # metric argument is checked when metric_func_ is instantiated - # check n_components argument - if n_components is not None: - if not isinstance(n_components, int): - msg = "n_components must be an int, not {}.".format(type(n_components)) - raise TypeError(msg) - # check n_bootstraps argument - if not isinstance(n_bootstraps, int): - msg = "n_bootstraps must be an int, not {}".format(type(n_bootstraps)) - raise TypeError(msg) - elif n_bootstraps < 0: - msg = "{} is invalid number of bootstraps, must be non-negative" - raise ValueError(msg.format(n_bootstraps)) - # check workers argument - if not isinstance(workers, int): - msg = "workers must be an int, not {}".format(type(workers)) + # check test argument + if not isinstance(test, str): + msg = "test must be a str, not {}".format(type(test)) + raise TypeError(msg) + elif test not in _VALID_TESTS: + msg = "Unknown test {}. Valid tests are {}".format(test, _VALID_TESTS) + raise ValueError(msg) + # metric argument is checked when metric_func_ is instantiated + # check n_components argument + if n_components is not None: + if not isinstance(n_components, int): + msg = "n_components must be an int, not {}.".format(type(n_components)) raise TypeError(msg) - # check size_correction argument - if not isinstance(size_correction, bool): - msg = "size_correction must be a bool, not {}".format(type(size_correction)) - raise TypeError(msg) - # check pooled argument - if not isinstance(pooled, bool): - msg = "pooled must be a bool, not {}".format(type(pooled)) - raise TypeError(msg) - # check align_type argument - if (not isinstance(align_type, str)) and (align_type is not None): - msg = "align_type must be a string or None, not {}".format(type(align_type)) - raise TypeError(msg) - align_types_supported = ["sign_flips", "seedless_procrustes", None] - if align_type not in align_types_supported: - msg = "supported align types are {}".format(align_types_supported) - raise ValueError(msg) - # check align_kws argument - if not isinstance(align_kws, dict): - msg = "align_kws must be a dictionary of keyword arguments, not {}".format( - type(align_kws) + # check n_bootstraps argument + if not isinstance(n_bootstraps, int): + msg = "n_bootstraps must be an int, not {}".format(type(n_bootstraps)) + raise TypeError(msg) + elif n_bootstraps < 0: + msg = "{} is invalid number of bootstraps, must be non-negative" + raise ValueError(msg.format(n_bootstraps)) + # check workers argument + if not isinstance(workers, int): + msg = "workers must be an int, not {}".format(type(workers)) + raise TypeError(msg) + # check size_correction argument + if not isinstance(size_correction, bool): + msg = "size_correction must be a bool, not {}".format(type(size_correction)) + raise TypeError(msg) + # check pooled argument + if not isinstance(pooled, bool): + msg = "pooled must be a bool, not {}".format(type(pooled)) + raise TypeError(msg) + # check align_type argument + if (not isinstance(align_type, str)) and (align_type is not None): + msg = "align_type must be a string or None, not {}".format(type(align_type)) + raise TypeError(msg) + align_types_supported = ["sign_flips", "seedless_procrustes", None] + if align_type not in align_types_supported: + msg = "supported align types are {}".format(align_types_supported) + raise ValueError(msg) + # check align_kws argument + if not isinstance(align_kws, dict): + msg = "align_kws must be a dictionary of keyword arguments, not {}".format( + type(align_kws) + ) + raise TypeError(msg) + # check input_graph argument + if not isinstance(input_graph, bool): + msg = "input_graph must be a bool, not {}".format(type(input_graph)) + raise TypeError(msg) + + if input_graph: + A1 = import_graph(A1) + A2 = import_graph(A2) + + X1_hat, X2_hat = _embed(A1, A2, n_components) + else: + # check for nx objects, since they are castable to arrays, + # but we don't want that + if not isinstance(A1, np.ndarray): + msg = ( + f"Embedding of the first graph is of type {type(A1)}, not " + "np.ndarray. If input_graph is False, the inputs need to be " + "adjacency spectral embeddings, with shapes (n, d) and " + "(m, d), passed as np.ndarrays." ) raise TypeError(msg) - # check input_graph argument - if not isinstance(input_graph, bool): - msg = "input_graph must be a bool, not {}".format(type(input_graph)) + if not isinstance(A2, np.ndarray): + msg = ( + f"Embedding of the second graph is of type {type(A2)}, not an " + "array. If input_graph is False, the inputs need to be " + "adjacency spectral embeddings, with shapes (n, d) and " + "(m, d), passed as np.ndarrays." + ) raise TypeError(msg) - super().__init__(n_components=n_components) - - self.test = test - self.metric = metric - self.metric_func_ = self._instantiate_metric_func(metric, test) - self.n_bootstraps = n_bootstraps - self.workers = workers - self.size_correction = size_correction - self.pooled = pooled - self.input_graph = input_graph - self.align_type = align_type - self.align_kws = align_kws - - def _instantiate_metric_func(self, metric, test): - # check metric argument - if not isinstance(metric, str) and not callable(metric): - msg = "Metric must be str or callable, not {}".format(type(metric)) - raise TypeError(msg) - elif metric not in _VALID_METRICS and not callable(metric): - msg = "Unknown metric {}. Valid metrics are {}, or a callable".format( - metric, _VALID_METRICS + if A1.ndim != 2: + msg = ( + "Embedding array of the first graph does not have two dimensions. " + "If input_graph is False, the inputs need to be adjacency " + "spectral embeddings, with shapes (n, d) and (m, d)" ) raise ValueError(msg) - if callable(metric): - metric_func = metric - else: - if metric in _VALID_DISTANCES: - if test == "hsic": - msg = ( - f"{test} is a kernel-based test, but {metric} " - "is a distance. results may not be optimal. it is " - "recomended to use either a different test or one of " - f"the kernels: {_VALID_KERNELS} as a metric." - ) - warnings.warn(msg, UserWarning) - - def metric_func(X, Y=None, metric=metric, workers=None): - return pairwise_distances(X, Y, metric=metric, n_jobs=workers) - - elif metric == "gaussian": - if test != "hsic": - msg = ( - f"{test} is a distance-based test, but {metric} " - "is a kernel. results may not be optimal. it is " - "recomended to use either a hisc as a test or one of " - f"the distances: {_VALID_DISTANCES} as a metric." - ) - warnings.warn(msg, UserWarning) - metric_func = gaussian - else: - if test != "hsic": - msg = ( - f"{test} is a distance-based test, but {metric} " - "is a kernel. results may not be optimal. it is " - "recomended to use either a hisc as a test or one of " - f"the distances: {_VALID_DISTANCES} as a metric." - ) - warnings.warn(msg, UserWarning) - - def metric_func(X, Y=None, metric=metric, workers=None): - return pairwise_kernels(X, Y, metric=metric, n_jobs=workers) - - return metric_func - - def _embed(self, A1, A2): - if self.n_components is None: - num_dims1 = select_dimension(A1)[0][-1] - num_dims2 = select_dimension(A2)[0][-1] - self.n_components = max(num_dims1, num_dims2) - - ase = AdjacencySpectralEmbed(n_components=self.n_components) - X1_hat = ase.fit_transform(A1) - X2_hat = ase.fit_transform(A2) - - if isinstance(X1_hat, tuple) and isinstance(X2_hat, tuple): - X1_hat = np.concatenate(X1_hat, axis=-1) - X2_hat = np.concatenate(X2_hat, axis=-1) - elif isinstance(X1_hat, tuple) ^ isinstance(X2_hat, tuple): + if A2.ndim != 2: msg = ( - "input graphs do not have same directedness. " - "consider symmetrizing the directed graph." + "Embedding array of the second graph does not have two dimensions. " + "If input_graph is False, the inputs need to be adjacency " + "spectral embeddings, with shapes (n, d) and (m, d)" + ) + raise ValueError(msg) + if A1.shape[1] != A2.shape[1]: + msg = ( + "Two input embeddings have different number of components. " + "If input_graph is False, the inputs need to be adjacency " + "spectral embeddings, with shapes (n, d) and (m, d)" ) raise ValueError(msg) - return X1_hat, X2_hat - - def _sample_modified_ase(self, X, Y, pooled=False): - N, M = len(X), len(Y) - - # return if graphs are same order, else else ensure X the larger graph. - if N == M: - return X, Y - elif M > N: - reverse_order = True - X, Y = Y, X - N, M = M, N - else: - reverse_order = False - - # estimate the central limit theorem variance - if pooled: - two_samples = np.concatenate([X, Y], axis=0) - get_sigma = fit_plug_in_variance_estimator(two_samples) - else: - get_sigma = fit_plug_in_variance_estimator(X) - X_sigmas = get_sigma(X) * (N - M) / (N * M) - - # increase the variance of X by sampling from the asy dist - X_sampled = np.zeros(X.shape) - # TODO may be parallelized, but requires keeping track of random state - for i in range(N): - X_sampled[i, :] = X[i, :] + stats.multivariate_normal.rvs(cov=X_sigmas[i]) - - # return the embeddings in the appropriate order - return (Y, X_sampled) if reverse_order else (X_sampled, Y) - - def fit(self, A1, A2): - """ - Fits the test to the two input graphs - - Parameters - ---------- - A1, A2 : variable (see description) - The two graphs, or their embeddings to run a hypothesis test on. - Expected variable type and shape depends on input_graph attribute: - - - input_graph=True - expects two unembedded graphs either as NetworkX graph objects, or as - two np.ndarrays, representing the adjacency matrices. In this - case will be embedded using adjacency spectral embedding. - - input_graph-False - expects two already embedded graphs. In this case they must be - arrays of shape (n, d) and (m, d), where d, the number of - components, must be shared. - - Note that regardless of how the graphs are passed, they need not - have the same number of vertices. - - Returns - ------- - self - - """ - if self.input_graph: - A1 = import_graph(A1) - A2 = import_graph(A2) - - X1_hat, X2_hat = self._embed(A1, A2) - else: - # check for nx objects, since they are castable to arrays, - # but we don't want that - if not isinstance(A1, np.ndarray): - msg = ( - f"Embedding of the first graph is of type {type(A1)}, not " - "np.ndarray. If input_graph is False, the inputs need to be " - "adjacency spectral embeddings, with shapes (n, d) and " - "(m, d), passed as np.ndarrays." - ) - raise TypeError(msg) - if not isinstance(A2, np.ndarray): + # checking for inf values + X1_hat = check_array(A1) + X2_hat = check_array(A2) + + if align_type == "sign_flips": + aligner = SignFlips(**align_kws) + X1_hat = aligner.fit_transform(X1_hat, X2_hat) + Q = aligner.Q_ + elif align_type == "seedless_procrustes": + aligner = SeedlessProcrustes(**align_kws) + X1_hat = aligner.fit_transform(X1_hat, X2_hat) + Q = aligner.Q_ + else: + Q = np.identity(X1_hat.shape[0]) + + if size_correction: + X1_hat, X2_hat = _sample_modified_ase(X1_hat, X2_hat, pooled=pooled) + + metric_func_ = _instantiate_metric_func(metric=metric, test=test) + test_obj = KSample(test, compute_distance=metric_func_) + + data = test_obj.test(X1_hat, X2_hat, reps=n_bootstraps, workers=workers, auto=False) + + null_distribution = test_obj.indep_test.null_dist + + misc_stats = { + "null_distribution": null_distribution, + "n_components": n_components, + "Q": Q, + } + sample_T_statistic = data[0] + p_value = data[1] + + return ldt_result(p_value, sample_T_statistic, misc_stats) + + +def _instantiate_metric_func(metric, test): + # check metric argument + if not isinstance(metric, str) and not callable(metric): + msg = "Metric must be str or callable, not {}".format(type(metric)) + raise TypeError(msg) + elif metric not in _VALID_METRICS and not callable(metric): + msg = "Unknown metric {}. Valid metrics are {}, or a callable".format( + metric, _VALID_METRICS + ) + raise ValueError(msg) + if callable(metric): + metric_func = metric + else: + if metric in _VALID_DISTANCES: + if test == "hsic": msg = ( - f"Embedding of the second graph is of type {type(A2)}, not an " - "array. If input_graph is False, the inputs need to be " - "adjacency spectral embeddings, with shapes (n, d) and " - "(m, d), passed as np.ndarrays." + f"{test} is a kernel-based test, but {metric} " + "is a distance. results may not be optimal. it is " + "recomended to use either a different test or one of " + f"the kernels: {_VALID_KERNELS} as a metric." ) - raise TypeError(msg) + warnings.warn(msg, UserWarning) - if A1.ndim != 2: - msg = ( - "Embedding array of the first graph does not have two dimensions. " - "If input_graph is False, the inputs need to be adjacency " - "spectral embeddings, with shapes (n, d) and (m, d)" - ) - raise ValueError(msg) - if A2.ndim != 2: + def metric_func(X, Y=None, metric=metric, workers=None): + return pairwise_distances(X, Y, metric=metric, n_jobs=workers) + + elif metric == "gaussian": + if test != "hsic": msg = ( - "Embedding array of the second graph does not have two dimensions. " - "If input_graph is False, the inputs need to be adjacency " - "spectral embeddings, with shapes (n, d) and (m, d)" + f"{test} is a distance-based test, but {metric} " + "is a kernel. results may not be optimal. it is " + "recomended to use either a hisc as a test or one of " + f"the distances: {_VALID_DISTANCES} as a metric." ) - raise ValueError(msg) - if A1.shape[1] != A2.shape[1]: + warnings.warn(msg, UserWarning) + metric_func = gaussian + else: + if test != "hsic": msg = ( - "Two input embeddings have different number of components. " - "If input_graph is False, the inputs need to be adjacency " - "spectral embeddings, with shapes (n, d) and (m, d)" + f"{test} is a distance-based test, but {metric} " + "is a kernel. results may not be optimal. it is " + "recomended to use either a hisc as a test or one of " + f"the distances: {_VALID_DISTANCES} as a metric." ) - raise ValueError(msg) - - # checking for inf values - X1_hat = check_array(A1) - X2_hat = check_array(A2) - - if self.align_type == "sign_flips": - aligner = SignFlips(**self.align_kws) - X1_hat = aligner.fit_transform(X1_hat, X2_hat) - elif self.align_type == "seedless_procrustes": - aligner = SeedlessProcrustes(**self.align_kws) - X1_hat = aligner.fit_transform(X1_hat, X2_hat) - - if self.size_correction: - X1_hat, X2_hat = self._sample_modified_ase( - X1_hat, X2_hat, pooled=self.pooled - ) + warnings.warn(msg, UserWarning) - self.metric_func_ = self._instantiate_metric_func(self.metric, self.test) - test_obj = KSample(self.test, compute_distance=self.metric_func_) + def metric_func(X, Y=None, metric=metric, workers=None): + return pairwise_kernels(X, Y, metric=metric, n_jobs=workers) - data = test_obj.test( - X1_hat, X2_hat, reps=self.n_bootstraps, workers=self.workers, auto=False - ) + return metric_func + + +def _embed(A1, A2, n_components): + if n_components is None: + num_dims1 = select_dimension(A1)[0][-1] + num_dims2 = select_dimension(A2)[0][-1] + n_components = max(num_dims1, num_dims2) - self.null_distribution_ = test_obj.indep_test.null_dist - self.sample_T_statistic_ = data[0] - self.p_value_ = data[1] - - return self - - def fit_predict(self, A1, A2): - """ - Fits the test to the two input graphs and returns the p-value - - Parameters - ---------- - A1, A2 : variable (see description) - The two graphs, or their embeddings to run a hypothesis test on. - Expected variable type and shape depends on input_graph attribute: - - - input_graph=True - expects two unembedded graphs either as NetworkX graph objects, or as - two np.ndarrays, representing the adjacency matrices. In this - case will be embedded using adjacency spectral embedding. - - input_graph-False - expects two already embedded graphs. In this case they must be - arrays of shape (n, d) and (m, d), where d, the number of - components, must be shared. - - Note that regardless of how the graphs are passed, they need not to - have the same number of vertices. - - - Returns - ------- - p_value_ : float - The overall p value from the test - """ - # abstract method overwritten in order to have a custom doc string - self.fit(A1, A2) - return self.p_value_ + ase = AdjacencySpectralEmbed(n_components=n_components) + X1_hat = ase.fit_transform(A1) + X2_hat = ase.fit_transform(A2) + + if isinstance(X1_hat, tuple) and isinstance(X2_hat, tuple): + X1_hat = np.concatenate(X1_hat, axis=-1) + X2_hat = np.concatenate(X2_hat, axis=-1) + elif isinstance(X1_hat, tuple) ^ isinstance(X2_hat, tuple): + msg = ( + "input graphs do not have same directedness. " + "consider symmetrizing the directed graph." + ) + raise ValueError(msg) + + return X1_hat, X2_hat + + +def _sample_modified_ase(X, Y, pooled=False): + N, M = len(X), len(Y) + + # return if graphs are same order, else ensure X the larger graph. + if N == M: + return X, Y + elif M > N: + reverse_order = True + X, Y = Y, X + N, M = M, N + else: + reverse_order = False + + # estimate the central limit theorem variance + if pooled: + two_samples = np.concatenate([X, Y], axis=0) + get_sigma = fit_plug_in_variance_estimator(two_samples) + else: + get_sigma = fit_plug_in_variance_estimator(X) + X_sigmas = get_sigma(X) * (N - M) / (N * M) + + # increase the variance of X by sampling from the asy dist + X_sampled = np.zeros(X.shape) + # TODO may be parallelized, but requires keeping track of random state + for i in range(N): + X_sampled[i, :] = X[i, :] + stats.multivariate_normal.rvs(cov=X_sigmas[i]) + + # return the embeddings in the appropriate order + return (Y, X_sampled) if reverse_order else (X_sampled, Y) diff --git a/graspologic/inference/latent_position_test.py b/graspologic/inference/latent_position_test.py index bb93b6149..ca41084a4 100644 --- a/graspologic/inference/latent_position_test.py +++ b/graspologic/inference/latent_position_test.py @@ -8,10 +8,14 @@ from ..embed import AdjacencySpectralEmbed, OmnibusEmbed, select_dimension from ..simulations import rdpg from ..utils import import_graph, is_symmetric -from .base import BaseInference +from collections import namedtuple +lpt_result = namedtuple("lpt_result", ("p_value", "sample_T_statistic", "misc_stats")) -class LatentPositionTest(BaseInference): + +def latent_position_test( + A1, A2, embedding="ase", n_components=None, test_case="rotation", n_bootstraps=500 +): r""" Two-sample hypothesis test for the problem of determining whether two random dot product graphs have the same latent positions. @@ -25,6 +29,11 @@ class LatentPositionTest(BaseInference): Parameters ---------- + A1, A2 : nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray + The two graphs to run a hypothesis test on. + If np.ndarray, shape must be ``(n_vertices, n_vertices)`` for both graphs, + where ``n_vertices`` is the same for both + embedding : string, { 'ase' (default), 'omnibus'} String describing the embedding method to use: @@ -58,23 +67,26 @@ class LatentPositionTest(BaseInference): n_bootstraps : int, optional (default 500) Number of bootstrap simulations to run to generate the null distribution - Attributes + Returns ---------- - null_distribution_1_, null_distribution_2_ : np.ndarray (n_bootstraps,) - The distribution of T statistics generated under the null, using the first and - and second input graph, respectively. The latent positions of each sample graph - are used independently to sample random dot product graphs, so two null - distributions are generated + p_value : float + The overall p value from the test; this is the max of 'p_value_1' and 'p_value_2' - sample_T_statistic_ : float + sample_T_statistic : float The observed difference between the embedded positions of the two input graphs after an alignment (the type of alignment depends on ``test_case``) - p_value_1_, p_value_2_ : float - The p value estimated from the null distributions from sample 1 and sample 2. + misc_stats : dictionary + A collection of other statistics obtained from the latent position test + + - 'p_value_1', 'p_value_2' : float + The p value estimate from the null distributions from sample 1 and sample 2 - p_value_ : float - The overall p value from the test; this is the max of :attr:`p_value_1_` and :attr:`p_value_2_` + - 'null_distribution_1', 'null_distribution_2' : np.ndarray (n_bootstraps,) + The distribution of T statistics generated under the null, using the first and + and second input graph, respectively. The latent positions of each sample graph + are used independently to sample random dot product graphs, so two null + distributions are generated See also -------- @@ -89,129 +101,113 @@ class LatentPositionTest(BaseInference): Journal of Computational and Graphical Statistics, Vol. 26(2), 2017 """ - def __init__( - self, embedding="ase", n_components=None, n_bootstraps=500, test_case="rotation" - ): - if type(embedding) is not str: - raise TypeError("embedding must be str") - if type(n_bootstraps) is not int: - raise TypeError() - if type(test_case) is not str: - raise TypeError() - if n_bootstraps < 1: - raise ValueError( - "{} is invalid number of bootstraps, must be greater than 1".format( - n_bootstraps - ) - ) - if embedding not in ["ase", "omnibus"]: - raise ValueError("{} is not a valid embedding method.".format(embedding)) - if test_case not in ["rotation", "scalar-rotation", "diagonal-rotation"]: - raise ValueError( - "test_case must be one of 'rotation', 'scalar-rotation'," - + "'diagonal-rotation'" - ) - - super().__init__(n_components=n_components) - - self.embedding = embedding - self.n_bootstraps = n_bootstraps - self.test_case = test_case - # paper uses these always, but could be kwargs eventually. need to test - self.rescale = False - self.loops = False - - def _bootstrap(self, X_hat): - t_bootstrap = np.zeros(self.n_bootstraps) - for i in range(self.n_bootstraps): - A1_simulated = rdpg(X_hat, rescale=self.rescale, loops=self.loops) - A2_simulated = rdpg(X_hat, rescale=self.rescale, loops=self.loops) - X1_hat_simulated, X2_hat_simulated = self._embed( - A1_simulated, A2_simulated, check_lcc=False + if type(embedding) is not str: + raise TypeError("embedding must be str") + if type(n_bootstraps) is not int: + raise TypeError() + if type(test_case) is not str: + raise TypeError() + if n_bootstraps < 1: + raise ValueError( + "{} is invalid number of bootstraps, must be greater than 1".format( + n_bootstraps ) - t_bootstrap[i] = self._difference_norm(X1_hat_simulated, X2_hat_simulated) - return t_bootstrap - - def _difference_norm(self, X1, X2): - if self.embedding in ["ase"]: - if self.test_case == "rotation": - pass - elif self.test_case == "scalar-rotation": - X1 = X1 / np.linalg.norm(X1, ord="fro") - X2 = X2 / np.linalg.norm(X2, ord="fro") - elif self.test_case == "diagonal-rotation": - normX1 = np.sum(X1 ** 2, axis=1) - normX2 = np.sum(X2 ** 2, axis=1) - normX1[normX1 <= 1e-15] = 1 - normX2[normX2 <= 1e-15] = 1 - X1 = X1 / np.sqrt(normX1[:, None]) - X2 = X2 / np.sqrt(normX2[:, None]) - aligner = OrthogonalProcrustes() - X1 = aligner.fit_transform(X1, X2) - return np.linalg.norm(X1 - X2) - - def _embed(self, A1, A2, check_lcc=True): - if self.embedding == "ase": - X1_hat = AdjacencySpectralEmbed( - n_components=self.n_components, check_lcc=check_lcc - ).fit_transform(A1) - X2_hat = AdjacencySpectralEmbed( - n_components=self.n_components, check_lcc=check_lcc - ).fit_transform(A2) - elif self.embedding == "omnibus": - X_hat_compound = OmnibusEmbed( - n_components=self.n_components, check_lcc=check_lcc - ).fit_transform((A1, A2)) - X1_hat = X_hat_compound[0] - X2_hat = X_hat_compound[1] - return (X1_hat, X2_hat) - - def fit(self, A1, A2): - """ - Fits the test to the two input graphs - - Parameters - ---------- - A1, A2 : nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray - The two graphs to run a hypothesis test on. - If np.ndarray, shape must be ``(n_vertices, n_vertices)`` for both graphs, - where ``n_vertices`` is the same for both - - Returns - ------- - self - """ - A1 = import_graph(A1) - A2 = import_graph(A2) - if not is_symmetric(A1) or not is_symmetric(A2): - raise NotImplementedError() # TODO asymmetric case - if A1.shape != A2.shape: - raise ValueError("Input matrices do not have matching dimensions") - if self.n_components is None: - # get the last elbow from ZG for each and take the maximum - num_dims1 = select_dimension(A1)[0][-1] - num_dims2 = select_dimension(A2)[0][-1] - self.n_components = max(num_dims1, num_dims2) - X_hats = self._embed(A1, A2) - sample_T_statistic = self._difference_norm(X_hats[0], X_hats[1]) - null_distribution_1 = self._bootstrap(X_hats[0]) - null_distribution_2 = self._bootstrap(X_hats[1]) - - # uisng exact mc p-values (see, for example, Phipson and Smyth, 2010) - p_value_1 = ( - len(null_distribution_1[null_distribution_1 >= sample_T_statistic]) + 1 - ) / (self.n_bootstraps + 1) - p_value_2 = ( - len(null_distribution_2[null_distribution_2 >= sample_T_statistic]) + 1 - ) / (self.n_bootstraps + 1) - - p_value = max(p_value_1, p_value_2) - - self.null_distribution_1_ = null_distribution_1 - self.null_distribution_2_ = null_distribution_2 - self.sample_T_statistic_ = sample_T_statistic - self.p_value_1_ = p_value_1 - self.p_value_2_ = p_value_2 - self.p_value_ = p_value - - return self + ) + if embedding not in ["ase", "omnibus"]: + raise ValueError("{} is not a valid embedding method.".format(embedding)) + if test_case not in ["rotation", "scalar-rotation", "diagonal-rotation"]: + raise ValueError( + "test_case must be one of 'rotation', 'scalar-rotation'," + + "'diagonal-rotation'" + ) + + A1 = import_graph(A1) + A2 = import_graph(A2) + if not is_symmetric(A1) or not is_symmetric(A2): + raise NotImplementedError() # TODO asymmetric case + if A1.shape != A2.shape: + raise ValueError("Input matrices do not have matching dimensions") + if n_components is None: + # get the last elbow from ZG for each and take the maximum + num_dims1 = select_dimension(A1)[0][-1] + num_dims2 = select_dimension(A2)[0][-1] + n_components = max(num_dims1, num_dims2) + X_hats = _embed(A1, A2, embedding, n_components) + sample_T_statistic = _difference_norm(X_hats[0], X_hats[1], embedding, test_case) + null_distribution_1 = _bootstrap( + X_hats[0], embedding, n_components, n_bootstraps, test_case + ) + null_distribution_2 = _bootstrap( + X_hats[1], embedding, n_components, n_bootstraps, test_case + ) + + # using exact mc p-values (see, for example, Phipson and Smyth, 2010) + p_value_1 = ( + len(null_distribution_1[null_distribution_1 >= sample_T_statistic]) + 1 + ) / (n_bootstraps + 1) + p_value_2 = ( + len(null_distribution_2[null_distribution_2 >= sample_T_statistic]) + 1 + ) / (n_bootstraps + 1) + + p_value = max(p_value_1, p_value_2) + + misc_stats = { + "null_distribution_1": null_distribution_1, + "null_distribution_2_": null_distribution_2, + "p_value_1": p_value_1, + "p_value_2": p_value_2, + } + + return lpt_result(p_value, sample_T_statistic, misc_stats) + + +def _bootstrap( + X_hat, embedding, n_components, n_bootstraps, test_case, rescale=False, loops=False +): + t_bootstrap = np.zeros(n_bootstraps) + for i in range(n_bootstraps): + A1_simulated = rdpg(X_hat, rescale=rescale, loops=loops) + A2_simulated = rdpg(X_hat, rescale=rescale, loops=loops) + X1_hat_simulated, X2_hat_simulated = _embed( + A1_simulated, A2_simulated, embedding, n_components, check_lcc=False + ) + t_bootstrap[i] = _difference_norm( + X1_hat_simulated, X2_hat_simulated, embedding, test_case + ) + return t_bootstrap + + +def _difference_norm(X1, X2, embedding, test_case): + if embedding in ["ase"]: + if test_case == "rotation": + pass + elif test_case == "scalar-rotation": + X1 = X1 / np.linalg.norm(X1, ord="fro") + X2 = X2 / np.linalg.norm(X2, ord="fro") + elif test_case == "diagonal-rotation": + normX1 = np.sum(X1 ** 2, axis=1) + normX2 = np.sum(X2 ** 2, axis=1) + normX1[normX1 <= 1e-15] = 1 + normX2[normX2 <= 1e-15] = 1 + X1 = X1 / np.sqrt(normX1[:, None]) + X2 = X2 / np.sqrt(normX2[:, None]) + aligner = OrthogonalProcrustes() + X1 = aligner.fit_transform(X1, X2) + return np.linalg.norm(X1 - X2) + + +def _embed(A1, A2, embedding, n_components, check_lcc=True): + if embedding == "ase": + X1_hat = AdjacencySpectralEmbed( + n_components=n_components, check_lcc=check_lcc + ).fit_transform(A1) + X2_hat = AdjacencySpectralEmbed( + n_components=n_components, check_lcc=check_lcc + ).fit_transform(A2) + elif embedding == "omnibus": + X_hat_compound = OmnibusEmbed( + n_components=n_components, check_lcc=check_lcc + ).fit_transform((A1, A2)) + X1_hat = X_hat_compound[0] + X2_hat = X_hat_compound[1] + return (X1_hat, X2_hat) diff --git a/graspologic/version/version.txt b/graspologic/version/version.txt index 8b1378917..e69de29bb 100644 --- a/graspologic/version/version.txt +++ b/graspologic/version/version.txt @@ -1 +0,0 @@ - diff --git a/setup.py b/setup.py index 744e3b1be..1fd878dac 100644 --- a/setup.py +++ b/setup.py @@ -52,7 +52,7 @@ "gensim", "graspologic-native", "hyppo>=0.1.3", - "joblib>=0.17.0", # Older versions of joblib cause issue #806. Transitive dependency of hyppo. + "joblib>=0.17.0", # Older versions of joblib cause issue #806. Transitive dependency of hyppo. "matplotlib>=3.0.0,<=3.3.0", "networkx>=2.1", "numpy>=1.8.1", diff --git a/tests/test_latentdistributiontest.py b/tests/test_latentdistributiontest.py index dd95e8aa5..d653399c3 100644 --- a/tests/test_latentdistributiontest.py +++ b/tests/test_latentdistributiontest.py @@ -8,96 +8,90 @@ from sklearn.metrics import pairwise_distances from graspologic.embed import AdjacencySpectralEmbed -from graspologic.inference import LatentDistributionTest +from graspologic.inference import latent_distribution_test from graspologic.simulations import er_np, sbm class TestLatentDistributionTest(unittest.TestCase): @classmethod - def setUpClass(cls): - np.random.seed(123456) - cls.tests = {"dcorr": "euclidean", "hsic": "gaussian", "mgc": "euclidean"} - cls.A1 = er_np(20, 0.3) - cls.A2 = er_np(20, 0.3) - - def test_fit_ase_works(self): - for test in self.tests.keys(): - ldt = LatentDistributionTest(test, self.tests[test], n_bootstraps=10) - assert ldt.fit(self.A1, self.A2) is ldt - - def test_fit_predict_ase_works(self): - for test in self.tests: - ldt = LatentDistributionTest(test, self.tests[test], n_bootstraps=10) - p = ldt.fit_predict(self.A1, self.A2) - assert float(p) <= 1 and float(p) >= 0 + def test_ase_works(self): + np.random.seed(888) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + tests = {"dcorr": "euclidean", "hsic": "gaussian", "mgc": "euclidean"} + for test in tests.keys(): + ldt = latent_distribution_test(A1, A2, test, tests[test], n_bootstraps=10) def test_workers(self): - ldt = LatentDistributionTest("dcorr", "euclidean", n_bootstraps=4, workers=4) - ldt.fit(self.A1, self.A2) + np.random.seed(888) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + ldt = latent_distribution_test( + A1, A2, "dcorr", "euclidean", n_bootstraps=4, workers=4 + ) def test_callable_metric(self): + np.random.seed(888) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + def metric_func(X, Y=None, workers=None): return pairwise_distances(X, metric="euclidean") * 0.5 - ldt = LatentDistributionTest("dcorr", metric_func, n_bootstraps=10) - ldt.fit(self.A1, self.A2) + ldt = latent_distribution_test(A1, A2, "dcorr", metric_func, n_bootstraps=10) def test_bad_kwargs(self): + np.random.seed(888) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + # check test argument - with self.assertRaises(TypeError): - LatentDistributionTest(test=0) - with self.assertRaises(ValueError): - LatentDistributionTest(test="foo") + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, test=0) + with pytest.raises(ValueError): + latent_distribution_test(A1, A2, test="foo") # check metric argument - with self.assertRaises(TypeError): - LatentDistributionTest(metric=0) - with self.assertRaises(ValueError): - LatentDistributionTest(metric="some_kind_of_kernel") - # check metric argument modified - with self.assertRaises(TypeError): - ldt = LatentDistributionTest() - ldt.metric = 0 - ldt.fit(self.A1, self.A2) - with self.assertRaises(ValueError): - ldt = LatentDistributionTest() - ldt.metric = "some_kind_of_kernel" - ldt.fit(self.A1, self.A2) + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, metric=0) + with pytest.raises(ValueError): + latent_distribution_test(A1, A2, metric="some_kind_of_kernel") # check n_components argument - with self.assertRaises(TypeError): - LatentDistributionTest(n_components=0.5) - with self.assertRaises(ValueError): - LatentDistributionTest(n_components=-100) + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, n_components=0.5) + with pytest.raises(ValueError): + latent_distribution_test(A1, A2, n_components=-100) # check n_bootstraps argument - with self.assertRaises(TypeError): - LatentDistributionTest(n_bootstraps=0.5) - with self.assertRaises(ValueError): - LatentDistributionTest(n_bootstraps=-100) + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, n_bootstraps=0.5) + with pytest.raises(ValueError): + latent_distribution_test(A1, A2, n_bootstraps=-100) # check workers argument - with self.assertRaises(TypeError): - LatentDistributionTest(workers=0.5) + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, workers=0.5) # check size_correction argument - with self.assertRaises(TypeError): - LatentDistributionTest(size_correction=0) + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, size_correction=0) # check pooled argument - with self.assertRaises(TypeError): - LatentDistributionTest(pooled=0) + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, pooled=0) # check align_type argument - with self.assertRaises(ValueError): - LatentDistributionTest(align_type="foo") - with self.assertRaises(TypeError): - LatentDistributionTest(align_type={"not a": "string"}) + with pytest.raises(ValueError): + latent_distribution_test(A1, A2, align_type="foo") + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, align_type={"not a": "string"}) # check align_kws argument - with self.assertRaises(TypeError): - LatentDistributionTest(align_kws="foo") + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, align_kws="foo") # check input_graph argument - with self.assertRaises(TypeError): - LatentDistributionTest(input_graph="hello") + with pytest.raises(TypeError): + latent_distribution_test(A1, A2, input_graph="hello") def test_n_bootstraps(self): - for test in self.tests.keys(): - ldt = LatentDistributionTest(test, self.tests[test], n_bootstraps=123) - ldt.fit(self.A1, self.A2) - self.assertEqual(ldt.null_distribution_.shape[0], 123) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + + ldt = latent_distribution_test(A1, A2, n_bootstraps=123) + assert ldt[2]["null_distribution"].shape[0] == 123 def test_passing_networkx(self): np.random.seed(123) @@ -107,17 +101,13 @@ def test_passing_networkx(self): A2_nx = nx.from_numpy_matrix(A2) # check passing nx, when exepect embeddings with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(A1_nx, A2) + latent_distribution_test(A1_nx, A1, input_graph=False) with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(A1, A2_nx) + latent_distribution_test(A1, A2_nx, input_graph=False) with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(A1_nx, A2_nx) + latent_distribution_test(A1_nx, A2_nx, input_graph=False) # check that the appropriate input works - ldt = LatentDistributionTest(input_graph=True) - ldt.fit_predict(A1_nx, A2_nx) + latent_distribution_test(A1_nx, A2_nx, input_graph=True) def test_passing_embeddings(self): np.random.seed(123) @@ -131,48 +121,36 @@ def test_passing_embeddings(self): X3 = ase_3.fit_transform(A2) # check embeddings having weird ndim with self.assertRaises(ValueError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1, X2.reshape(-1, 1, 1)) + latent_distribution_test(X1, X2.reshape(-1, 1, 1), input_graph=False) with self.assertRaises(ValueError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1.reshape(-1, 1, 1), X2) + latent_distribution_test(X1.reshape(-1, 1, 1), X2, input_graph=False) # check embeddings having mismatching number of components with self.assertRaises(ValueError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1, X3) + latent_distribution_test(X1, X3, input_graph=False) with self.assertRaises(ValueError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X3, X1) + latent_distribution_test(X3, X1, input_graph=False) # check passing weird stuff as input (caught by us) with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict("hello there", X1) + latent_distribution_test("hello there", X1, input_graph=False) with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1, "hello there") + latent_distribution_test(X1, "hello there", input_graph=False) with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict({"hello": "there"}, X1) + latent_distribution_test({"hello": "there"}, X1, input_graph=False) with self.assertRaises(TypeError): - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1, {"hello": "there"}) + latent_distribution_test(X1, {"hello": "there"}, input_graph=False) # check passing infinite in input (caught by check_array) with self.assertRaises(ValueError): X1_w_inf = X1.copy() X1_w_inf[1, 1] = np.inf - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1_w_inf, X2) + latent_distribution_test(X1_w_inf, X2, input_graph=False) # check that the appropriate input works - ldt = LatentDistributionTest(input_graph=False) - ldt.fit_predict(X1, X2) + latent_distribution_test(X1, X2, input_graph=False) def test_pooled(self): np.random.seed(123) A1 = er_np(20, 0.3) A2 = er_np(100, 0.3) - - ldt = LatentDistributionTest(pooled=True) - ldt.fit(A1, A2) + latent_distribution_test(A1, A2, pooled=True) def test_distances_and_kernels(self): np.random.seed(123) @@ -187,25 +165,23 @@ def test_distances_and_kernels(self): # ldt = LatentDistributionTest("hsic", "rbf") # ldt.fit(A1, A2) # assert len(record) == 0 - for test in self.tests.keys(): - ldt = LatentDistributionTest(test, self.tests[test]) - ldt.fit(A1, A2) - ldt = LatentDistributionTest("hsic", "rbf") - ldt.fit(A1, A2) + latent_distribution_test(A1, A2, test="hsic", metric="rbf") # some invalid combinations of test and metric with pytest.warns(UserWarning): - ldt = LatentDistributionTest("hsic", "euclidean") + latent_distribution_test(A1, A2, "hsic", "euclidean") with pytest.warns(UserWarning): - ldt = LatentDistributionTest("dcorr", "gaussian") + latent_distribution_test(A1, A2, "dcorr", "gaussian") with pytest.warns(UserWarning): - ldt = LatentDistributionTest("dcorr", "rbf") + latent_distribution_test(A1, A2, "dcorr", "rbf") def test_bad_matrix_inputs(self): - ldt = LatentDistributionTest("dcorr") + np.random.seed(1234556) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) bad_matrix = [[1, 2]] with self.assertRaises(TypeError): - ldt.fit(bad_matrix, self.A2) + latent_distribution_test(bad_matrix, A2, test="dcorr") def test_directed_inputs(self): np.random.seed(2) @@ -214,36 +190,27 @@ def test_directed_inputs(self): C = er_np(100, 0.3, directed=False) # two directed graphs is okay - ldt = LatentDistributionTest() - ldt.fit(A, B) + latent_distribution_test(A, B) # an undirected and a direced graph is not okay with self.assertRaises(ValueError): - ldt.fit(A, C) + latent_distribution_test(A, C) with self.assertRaises(ValueError): - ldt.fit(C, B) + latent_distribution_test(C, B) def test_SBM_dcorr(self): - for test in self.tests.keys(): - np.random.seed(12345678) - B1 = np.array([[0.5, 0.2], [0.2, 0.5]]) - - B2 = np.array([[0.7, 0.2], [0.2, 0.7]]) - b_size = 200 - A1 = sbm(2 * [b_size], B1) - A2 = sbm(2 * [b_size], B1) - A3 = sbm(2 * [b_size], B2) - - ldt_null = LatentDistributionTest( - test, self.tests[test], n_components=2, n_bootstraps=100 - ) - ldt_alt = LatentDistributionTest( - test, self.tests[test], n_components=2, n_bootstraps=100 - ) - p_null = ldt_null.fit_predict(A1, A2) - p_alt = ldt_alt.fit_predict(A1, A3) - self.assertTrue(p_null > 0.05) - self.assertTrue(p_alt <= 0.05) + np.random.seed(12345678) + B1 = np.array([[0.5, 0.2], [0.2, 0.5]]) + + B2 = np.array([[0.7, 0.2], [0.2, 0.7]]) + b_size = 200 + A1 = sbm(2 * [b_size], B1) + A2 = sbm(2 * [b_size], B1) + A3 = sbm(2 * [b_size], B2) + ldt_null = latent_distribution_test(A1, A2) + ldt_alt = latent_distribution_test(A1, A3) + self.assertTrue(ldt_null[0] > 0.05) + self.assertTrue(ldt_alt[0] <= 0.05) def test_different_sizes_null(self): np.random.seed(314) @@ -251,23 +218,37 @@ def test_different_sizes_null(self): A1 = er_np(100, 0.8) A2 = er_np(1000, 0.8) - ldt_not_corrected = LatentDistributionTest( - "hsic", "gaussian", n_components=2, n_bootstraps=100, size_correction=False + ldt_not_corrected = latent_distribution_test( + A1, + A2, + test="hsic", + metric="gaussian", + n_components=2, + n_bootstraps=100, + size_correction=False, ) - ldt_corrected_1 = LatentDistributionTest( - "hsic", "gaussian", n_components=2, n_bootstraps=100, size_correction=True + ldt_corrected_1 = latent_distribution_test( + A1, + A2, + test="hsic", + metric="gaussian", + n_components=2, + n_bootstraps=100, + size_correction=True, ) - ldt_corrected_2 = LatentDistributionTest( - "hsic", "gaussian", n_components=2, n_bootstraps=100, size_correction=True + ldt_corrected_2 = latent_distribution_test( + A2, + A1, + test="hsic", + metric="gaussian", + n_components=2, + n_bootstraps=100, + size_correction=True, ) - p_not_corrected = ldt_not_corrected.fit_predict(A1, A2) - p_corrected_1 = ldt_corrected_1.fit_predict(A1, A2) - p_corrected_2 = ldt_corrected_2.fit_predict(A2, A1) - - self.assertTrue(p_not_corrected <= 0.05) - self.assertTrue(p_corrected_1 > 0.05) - self.assertTrue(p_corrected_2 > 0.05) + self.assertTrue(ldt_not_corrected[0] <= 0.05) + self.assertTrue(ldt_corrected_1[0] > 0.05) + self.assertTrue(ldt_corrected_2[0] > 0.05) def test_different_sizes_null(self): np.random.seed(314) @@ -275,18 +256,27 @@ def test_different_sizes_null(self): A1 = er_np(100, 0.8) A2 = er_np(1000, 0.7) - ldt_corrected_1 = LatentDistributionTest( - "hsic", "gaussian", n_components=2, n_bootstraps=100, size_correction=True + ldt_corrected_1 = latent_distribution_test( + A1, + A2, + test="hsic", + metric="gaussian", + n_components=2, + n_bootstraps=100, + size_correction=True, ) - ldt_corrected_2 = LatentDistributionTest( - "hsic", "gaussian", n_components=2, n_bootstraps=100, size_correction=True + ldt_corrected_2 = latent_distribution_test( + A2, + A1, + test="hsic", + metric="gaussian", + n_components=2, + n_bootstraps=100, + size_correction=True, ) - p_corrected_1 = ldt_corrected_1.fit_predict(A1, A2) - p_corrected_2 = ldt_corrected_2.fit_predict(A2, A1) - - self.assertTrue(p_corrected_1 <= 0.05) - self.assertTrue(p_corrected_2 <= 0.05) + self.assertTrue(ldt_corrected_1[0] <= 0.05) + self.assertTrue(ldt_corrected_2[0] <= 0.05) def test_different_aligners(self): np.random.seed(314) @@ -298,22 +288,23 @@ def test_different_aligners(self): X2 = ase_2.fit_transform(A2) X2 = -X2 - ldt_1 = LatentDistributionTest(input_graph=False, align_type=None) - p_val_1 = ldt_1.fit_predict(X1, X2) - self.assertTrue(p_val_1 < 0.05) + ldt_1 = latent_distribution_test(X1, X2, input_graph=False, align_type=None) + self.assertTrue(ldt_1[0] < 0.05) - ldt_2 = LatentDistributionTest(input_graph=False, align_type="sign_flips") - p_val_2 = ldt_2.fit_predict(X1, X2) - self.assertTrue(p_val_2 >= 0.05) + ldt_2 = latent_distribution_test( + X1, X2, input_graph=False, align_type="sign_flips" + ) + self.assertTrue(ldt_2[0] >= 0.05) # also checking that kws are passed through - ldt_3 = LatentDistributionTest( + ldt_3 = latent_distribution_test( + X1, + X2, input_graph=False, align_type="seedless_procrustes", align_kws={"init": "sign_flips"}, ) - p_val_3 = ldt_3.fit_predict(X1, X2) - self.assertTrue(p_val_3 >= 0.05) + self.assertTrue(ldt_3[0] >= 0.05) if __name__ == "__main__": diff --git a/tests/test_latentpositiontest.py b/tests/test_latentpositiontest.py index bc76ddea5..ef2e7d60b 100644 --- a/tests/test_latentpositiontest.py +++ b/tests/test_latentpositiontest.py @@ -1,70 +1,70 @@ -# Copyright (c) Microsoft Corporation and contributors. -# Licensed under the MIT License. - import unittest +import pytest import numpy as np -from graspologic.inference import LatentPositionTest + +from graspologic.inference import latent_position_test +from graspologic.inference.latent_position_test import _difference_norm from graspologic.simulations import er_np, sbm class TestLatentPositionTest(unittest.TestCase): @classmethod - def setUpClass(cls): + def test_ase_works(self): np.random.seed(1234556) - cls.A1 = er_np(20, 0.3) - cls.A2 = er_np(20, 0.3) - - def test_fit_ase_works(self): - spt = LatentPositionTest() - assert spt.fit(self.A1, self.A2) is spt + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + lpt = latent_position_test(A1, A2) - def test_fit_omni_works(self): - spt = LatentPositionTest(embedding="omnibus") - assert spt.fit(self.A1, self.A2) is spt - - def test_fit_predict_ase_works(self): - spt = LatentPositionTest() - p = spt.fit_predict(self.A1, self.A2) - assert float(p) <= 1 and float(p) >= 0 + def test_omni_works(self): + np.random.seed(1234556) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + lpt = latent_position_test(A1, A2, embedding="omnibus") def test_bad_kwargs(self): - with self.assertRaises(ValueError): - LatentPositionTest(n_components=-100) - with self.assertRaises(ValueError): - LatentPositionTest(n_components=-100) - with self.assertRaises(ValueError): - LatentPositionTest(test_case="oops") - with self.assertRaises(ValueError): - LatentPositionTest(n_bootstraps=-100) - with self.assertRaises(ValueError): - LatentPositionTest(embedding="oops") - with self.assertRaises(TypeError): - LatentPositionTest(n_bootstraps=0.5) - with self.assertRaises(TypeError): - LatentPositionTest(n_components=0.5) - with self.assertRaises(TypeError): - LatentPositionTest(embedding=6) - with self.assertRaises(TypeError): - LatentPositionTest(test_case=6) + np.random.seed(1234556) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + + with pytest.raises(ValueError): + latent_position_test(A1, A2, n_components=-100) + with pytest.raises(ValueError): + latent_position_test(A1, A2, test_case="oops") + with pytest.raises(ValueError): + latent_position_test(A1, A2, n_bootstraps=-100) + with pytest.raises(ValueError): + latent_position_test(A1, A2, embedding="oops") + with pytest.raises(TypeError): + latent_position_test(A1, A2, n_bootstraps=0.5) + with pytest.raises(TypeError): + latent_position_test(A1, A2, n_components=0.5) + with pytest.raises(TypeError): + latent_position_test(A1, A2, embedding=6) + with pytest.raises(TypeError): + latent_position_test(A1, A2, test_case=6) def test_n_bootstraps(self): - spt = LatentPositionTest(n_bootstraps=234, n_components=None) - spt.fit(self.A1, self.A2) - self.assertEqual(spt.null_distribution_1_.shape[0], 234) + np.random.seed(1234556) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) + + lpt = latent_position_test(A1, A2, n_bootstraps=234, n_components=None) + assert lpt[2]["null_distribution_1"].shape[0] == 234 def test_bad_matrix_inputs(self): - spt = LatentPositionTest() - A1 = self.A1.copy() + np.random.seed(1234556) + A1 = er_np(20, 0.3) + A2 = er_np(20, 0.3) A1[2, 0] = 1 # make asymmetric - with self.assertRaises(NotImplementedError): # TODO : remove when we implement - spt.fit(A1, self.A2) + with pytest.raises(NotImplementedError): # TODO : remove when we implement + latent_position_test(A1, A2) bad_matrix = [[1, 2]] - with self.assertRaises(TypeError): - spt.fit(bad_matrix, self.A2) + with pytest.raises(TypeError): + latent_position_test(bad_matrix, A2) - with self.assertRaises(ValueError): - spt.fit(self.A1[:2, :2], self.A2) + with pytest.raises(ValueError): + latent_position_test(A1[:2, :2], A2) def test_rotation_norm(self): # two triangles rotated by 90 degrees @@ -72,8 +72,7 @@ def test_rotation_norm(self): rotation = np.array([[0, 1], [-1, 0]]) points2 = np.dot(points1, rotation) - spt = LatentPositionTest(embedding="ase", test_case="rotation") - n = spt._difference_norm(points1, points2) + n = _difference_norm(points1, points2, embedding="ase", test_case="rotation") self.assertAlmostEqual(n, 0) def test_diagonal_rotation_norm(self): @@ -86,8 +85,9 @@ def test_diagonal_rotation_norm(self): diagonal = np.array([[2, 0, 0], [0, 3, 0], [0, 0, 2]]) points2 = np.dot(diagonal, points2) - spt = LatentPositionTest(embedding="ase", test_case="diagonal-rotation") - n = spt._difference_norm(points1, points2) + n = _difference_norm( + points1, points2, embedding="ase", test_case="diagonal-rotation" + ) self.assertAlmostEqual(n, 0) def test_scalar_rotation_norm(self): @@ -99,26 +99,24 @@ def test_scalar_rotation_norm(self): # scaled points2 = 2 * points2 - spt = LatentPositionTest(embedding="ase", test_case="scalar-rotation") - n = spt._difference_norm(points1, points2) + n = _difference_norm( + points1, points2, embedding="ase", test_case="scalar-rotation" + ) self.assertAlmostEqual(n, 0) def test_SBM_epsilon(self): np.random.seed(12345678) B1 = np.array([[0.5, 0.2], [0.2, 0.5]]) - B2 = np.array([[0.7, 0.2], [0.2, 0.7]]) b_size = 200 A1 = sbm(2 * [b_size], B1) A2 = sbm(2 * [b_size], B1) A3 = sbm(2 * [b_size], B2) - spt_null = LatentPositionTest(n_components=2, n_bootstraps=100) - spt_alt = LatentPositionTest(n_components=2, n_bootstraps=100) - p_null = spt_null.fit_predict(A1, A2) - p_alt = spt_alt.fit_predict(A1, A3) - self.assertTrue(p_null > 0.05) - self.assertTrue(p_alt <= 0.05) + lpt_null = latent_position_test(A1, A2, n_components=2, n_bootstraps=100) + lpt_alt = latent_position_test(A1, A3, n_components=2, n_bootstraps=100) + self.assertTrue(lpt_null[0] > 0.05) + self.assertTrue(lpt_alt[0] <= 0.05) if __name__ == "__main__": From e955d0d81cb41e6c3abff8aaf386b03aa97f8e55 Mon Sep 17 00:00:00 2001 From: Alex Loftus Date: Fri, 11 Dec 2020 12:28:15 -0500 Subject: [PATCH 069/140] better comments in OOS tutorial (#605) --- .../embedding/OutOfSampleEmbed.ipynb | 32 ++++++++++++------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/docs/tutorials/embedding/OutOfSampleEmbed.ipynb b/docs/tutorials/embedding/OutOfSampleEmbed.ipynb index 2d2b6e1e6..9de6e5417 100644 --- a/docs/tutorials/embedding/OutOfSampleEmbed.ipynb +++ b/docs/tutorials/embedding/OutOfSampleEmbed.ipynb @@ -75,16 +75,16 @@ "P = np.array([[0.8, 0.2],\n", " [0.2, 0.8]])\n", "\n", - "# Generate a directed and undirected Stochastic Block Model (SBM)\n", - "undirected, labels = sbm(2*[nodes_per_community], P, return_labels=True)\n", - "labels = list(labels)\n", + "# Generate an undirected Stochastic Block Model (SBM)\n", + "undirected, labels_ = sbm(2*[nodes_per_community], P, return_labels=True)\n", + "labels = list(labels_)\n", "\n", "# Grab out-of-sample vertices\n", "oos_idx = 0\n", "oos_labels = labels.pop(oos_idx)\n", "A, a = remove_vertices(undirected, indices=oos_idx, return_removed=True)\n", "\n", - "# plot our SBMs\n", + "# plot our SBM\n", "heatmap(A, title=f'2-block SBM (undirected), shape {A.shape}', inner_hier_labels=labels);" ] }, @@ -142,7 +142,7 @@ " # Plot the in-sample latent positions\n", " plot = pairplot(X_hat, labels=labels, title=title)\n", "\n", - " # generate out-of-sample dataframe\n", + " # generate an out-of-sample dataframe\n", " oos_vertices = np.atleast_2d(oos_vertices)\n", " data = {'Type': oos_labels, \n", " 'Dimension 1': oos_vertices[:, 0], \n", @@ -153,11 +153,12 @@ " # plotting out-of-sample latent positions as stars\n", " plot.data = oos_df\n", " plot.hue_vals = oos_df[\"Type\"]\n", - " plot.map_offdiag(sns.scatterplot, s=500, marker=\"*\", edgecolor=\"black\")\n", + " plot.map_offdiag(sns.scatterplot, s=500,\n", + " marker=\"*\", edgecolor=\"black\")\n", " plot.tight_layout()\n", " return plot\n", "\n", - " \n", + "# Plot all latent positions\n", "plot_oos(X_hat, w, labels=labels, oos_labels=[0], title=\"Out-of-Sample Embeddings (2-block SBM)\");" ] }, @@ -182,7 +183,7 @@ "outputs": [], "source": [ "# Grab out-of-sample vertices\n", - "labels = [0]*nodes_per_community + [1]*nodes_per_community\n", + "labels = list(labels_)\n", "oos_idx = [0, -1]\n", "oos_labels = [labels.pop(i) for i in oos_idx]\n", "A, a = remove_vertices(undirected, indices=oos_idx, return_removed=True)\n", @@ -205,6 +206,7 @@ "w = ase.transform(a)\n", "print(f\"The out-of-sample prediction output has dimensions {w.shape}\\n\")\n", "\n", + "# Plot all latent positions\n", "plot_oos(X_hat, w, labels, oos_labels=oos_labels,\n", " title=\"Out-of-Sample Embeddings (2-block SBM)\");" ] @@ -232,10 +234,14 @@ "metadata": {}, "outputs": [], "source": [ - "# a is a tuple of (out_oos, in_oos)\n", + "# Generate a directed SBM\n", "directed = sbm(2*[nodes_per_community], P, directed=True)\n", "oos_idx = [0, -1]\n", + "\n", + "# a is a tuple of (out_oos, in_oos)\n", "A, a = remove_vertices(directed, indices=oos_idx, return_removed=True)\n", + "\n", + "# Plot the new adjacency matrix\n", "heatmap(directed, title=f'2-block SBM (directed), shape {A.shape}');" ] }, @@ -245,7 +251,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Fit a directed graph\n", + "# Fit our directed graph\n", "X_hat, Y_hat = ase.fit_transform(A)\n", "\n", "# predicted latent positions\n", @@ -292,14 +298,18 @@ "metadata": {}, "outputs": [], "source": [ - "# Generate a weighted graph and out-of-sample vertices\n", + "# Generate a weighted, directed SBM\n", "wt = [[normal, poisson],\n", " [poisson, normal]]\n", "wtargs = [[dict(loc=3, scale=1), dict(lam=5)],\n", " [dict(lam=5), dict(loc=3, scale=1)]]\n", "weighted = sbm(2*[nodes_per_community], P, wt=wt, wtargs=wtargs, directed=True)\n", + "\n", + "# Generate out-of-sample vertices\n", "oos_idx = [0, -1]\n", "A, a = remove_vertices(weighted, indices=oos_idx, return_removed=True)\n", + "\n", + "# Plot our weighted, directed SBM\n", "heatmap(A, title=f'2-block SBM (directed, weighted), shape {A.shape}')" ] }, From 9fbbe001f8e034a4fa043d62f84a1fdf5e0f6026 Mon Sep 17 00:00:00 2001 From: Daniel Borders <32661399+dtborders@users.noreply.github.com> Date: Fri, 18 Dec 2020 15:00:00 -0600 Subject: [PATCH 070/140] Implement Vertex Nomination Via Seeded Graph Matching (#562) * initial commit * added probability matrix as an output * updated vn code * added docstrings * added rst file for doc generation * edited rst file * added __init__.py file * added tutorial notebook * modified RST file * removed extra file * fixed most of spencers comments * reformated code * formatting * formatting * formatting * formatting * fixes build doc fail * docs update * added tests * update to tutorial and docs * black reformatting * added ref to toxtree * updated input checking and tests * removed unessisary cells from tutorial notebook * updated fit and fit_predict to match sklearns conventions * formatting * minor bug change * removed unessiary params * fixed small name error * modified test to use diffrent voi * fixed formatting issues * changed nomination list output format * tutorial update * inconsequential change to get tests to run * inconsequential edit * fixed naming error * Revert "inconsequential edit" This reverts commit f75867b7d0f720b2ba85b2659b4568d998b995d5. * added back unshuffle * changed arg name * reverted back to before it was failing tests * reverting back to before changes to make sure it still works * small import change * updated code to work with new graph matching structure * reformatting * updated docstrings * black reformatting * updated message for when voi is not in subgraph of A[seedsA] to include more info * black reformatting * removed unnessisary file * fixed ali's comments * black reformatting * fixed test error * updated test argument names * changed restarts to n_inits * minor docs changes * added comments to explain code and made some structural/var name modifications to make code clearer * updated tuturial for new fit format * minor tutorial error * documentation updates * changed typechecking from type to isinstance * doc update * removed np._ix * removed unessisary array casting * addressed ali's comments * fixed tests * removed unnessiary import * swwitched testing to er_np instead of hard_coded graphs * addressed ali's comments * black reformatting * minor changes to docs, and small error solving * added options for user to change GMP settings from nomination * updated tutorial * modified how user passes args to graph match * fixed a test * fixed minor bug * updated tests * fixed test * removed unessisary code from tutorail * added tests to check if seeds is a list * changed default to true * added ability to pass arrays in as seeds * test * made shuffle_input default to true * fixed small error * black reformatting * updated GraphMatch docs to include probability_matrix * . * reverted matching algs to unchanged versions * reverted matching algs to unchanged versions * addressed alis comments * fixed minor error in ego * small doc change * fixed ali's comments * fixed ali's comments * fixed ali's comments * updated docs * updated docs * added np for nom list creatioin * fixed ali's comments * fixed ali's comments * added blank notes section * reformatted docs * docs change * updated error messages to include quotes around params * changed a var name * changed tutoral folder name to Nomination * updated docs * updated max line length in docstring to 88 * small docstring error * small docstring error * small docstring error * small docstring error * finally fixed it * Update docs/tutorial.rst Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> * Update docs/tutorials/nominate/vertex_nomination_via_SGM.ipynb Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> * Update docs/tutorials/nominate/vertex_nomination_via_SGM.ipynb Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> * added a line * Fix class name * add header Co-authored-by: Daniel Borders Co-authored-by: Benjamin Pedigo Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> --- docs/reference/index.rst | 1 + docs/reference/nominate.rst | 8 + docs/tutorial.rst | 12 + .../nominate/vertex_nomination_via_SGM.ipynb | 131 ++++++ graspologic/nominate/VNviaSGM.py | 422 ++++++++++++++++++ graspologic/nominate/__init__.py | 6 + tests/test_vertex_nomination_via_SGM.py | 111 +++++ 7 files changed, 691 insertions(+) create mode 100644 docs/reference/nominate.rst create mode 100644 docs/tutorials/nominate/vertex_nomination_via_SGM.ipynb create mode 100644 graspologic/nominate/VNviaSGM.py create mode 100644 graspologic/nominate/__init__.py create mode 100644 tests/test_vertex_nomination_via_SGM.py diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 705be27a9..ea3d49ffc 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -15,6 +15,7 @@ Reference datasets utils match + nominate subgraph align preprocessing diff --git a/docs/reference/nominate.rst b/docs/reference/nominate.rst new file mode 100644 index 000000000..0640929b9 --- /dev/null +++ b/docs/reference/nominate.rst @@ -0,0 +1,8 @@ +Nomination +========== + +.. currentmodule:: graspologic.nominate + +Vertex Nomination via SGM +------------------------- +.. autoclass:: VNviaSGM \ No newline at end of file diff --git a/docs/tutorial.rst b/docs/tutorial.rst index dccaffda2..07072a587 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -110,6 +110,18 @@ The following tutorial demonstrates how to estimate the signal-subgraph of sampl tutorials/subgraph/subgraph +.. _nomination_tutorials: + +Nomination +========== +The following tutorial demonstrates how to use vertex nomination via seeded graph matching. + +.. toctree:: + :maxdepth: 1 + + tutorials/nominate/vertex_nomination_via_SGM + + .. _aligning_tutorials: Aligning diff --git a/docs/tutorials/nominate/vertex_nomination_via_SGM.ipynb b/docs/tutorials/nominate/vertex_nomination_via_SGM.ipynb new file mode 100644 index 000000000..5b5de08a6 --- /dev/null +++ b/docs/tutorials/nominate/vertex_nomination_via_SGM.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nomination via SGM\n", + "\n", + "This class implements Vertex Nomination via Seeded Graph Matching (VNviaSGM) with the algorithm described in [1].\n", + "\n", + "Given two graphs $G_1$ and $G_2$ with associated adjacency matrices $A$ and $B$, VNviaSGM proposes a nomination list of potential matches in graph $G_2$ to a vertex of interest $voi \\in G_1$ with associated probabilities. \n", + "\n", + "Let $A_L(a)$ be the induced subgraph derived from $A$, and centered about vertex $a \\in A$ with a maximum distance from $a$ of $L$. VNviaSGM first finds $A_L(voi)$, and if no seeds are in this subgraph, the algorithm stops early and returns a nomination list of None.\n", + "\n", + "Define $S_A \\subset A_L(voi)$ to be the seed vertices in the subgraph centered around the voi, with associated seeds from graph $B$ ($S_B$). \n", + "\n", + "Two subgraphs are then generated around $S_A$ for graph $A$, as well as around the associated seeds $S_B$ for graph $B$.\n", + "\n", + "Specifically, define $SG_1 = \\underset{s_A \\in S_A}{\\bigcup} A_L(s_A)$ and $SG_2 = \\underset{s_B \\in S_B}{\\bigcup} B_L(s_B)$\n", + "\n", + "These subgraphs ($SG_1$ and $SG_2$) are matched using SGM over several random initializations, resulting in probabilities corresponding to the proportion in which a node in $B$ is matching to the voi. See [Graph Matching Algorithm Reference](https://graspologic.readthedocs.io/en/latest/reference/match.html#graph-matching) for more details. \n", + "\n", + "\n", + "\n", + "[1] Patsolic, HG, Park, Y, Lyzinski, V, Priebe, CE. Vertex nomination via seeded graph matching. Stat Anal Data Min: The ASA Data Sci Journal. 2020; 13: 229– 244. https://doi.org/10.1002/sam.11454\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.nominate import VNviaSGM\n", + "from graspologic.simulations import er_np\n", + "from graspologic.plot import heatmap\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "np.set_printoptions(suppress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters\n", + "n = 50\n", + "p = 0.3\n", + "num_seeds = 4\n", + "\n", + "voi = 5 # choose a vertex of interest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(2)\n", + "G1 = er_np(n=n, p=p)\n", + "node_shuffle_input = np.random.permutation(n)\n", + "\n", + "G2 = G1[np.ix_(node_shuffle_input, node_shuffle_input)]\n", + "\n", + "heatmap(G1, title = \"Origional ER Graph (unshuffled)\")\n", + "heatmap(G2, title = \"Shuffled ER graph\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kklst= [(xx, yy) for xx, yy in zip(node_shuffle_input, np.arange(len(node_shuffle_input)))]\n", + "kklst.sort(key=lambda x:x[0])\n", + "print(\"Association voi in G1 to vertex in G2 =\", kklst[voi])\n", + "kklst = np.array(kklst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The algorithm produces an $n \\times 2$ nomination list, where n is the number of nominees. Each row has the following format (vertex $j \\in G_2$, probability that j matches voi). Note: the output is sorted with the largest probability coming first in the output list. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "VNalg = VNviaSGM()\n", + "print(VNalg.fit_predict(G1, G2, voi, [kklst[0:num_seeds, 0], kklst[0:num_seeds, 1]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen, the actual correspondence is 5--37 and the model predicts that 5 (in graph $G_1$) matches with 37 (in graph $G_2$) with >90% confidence." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/graspologic/nominate/VNviaSGM.py b/graspologic/nominate/VNviaSGM.py new file mode 100644 index 000000000..15b67ecae --- /dev/null +++ b/graspologic/nominate/VNviaSGM.py @@ -0,0 +1,422 @@ +import numpy as np +from ..match import GraphMatch as GMP +from sklearn.base import BaseEstimator +import itertools +import warnings + + +class VNviaSGM(BaseEstimator): + """ + This class implements Vertex Nomination via Seeded Graph Matching (VNviaSGM) with + the algorithm described in [1]. + + Rather than providing a 1-1 matching for the vertices of two graphs, as in + :class:`~graspologic.match.GraphMatch`, VNviaSGM ranks the potential matches for a + vertex of interst (VOI) in one to graph to the vertices in another graph, based on + probability of matching. + + + Parameters + ---------- + order_voi_subgraph: int, positive (default = 1) + Order used to create induced subgraph on ``A`` about VOI where the max distance + between VOI and other nodes is ``order_voi_subgraph``. This induced subgraph + will be used to determine what seeds are used when the SGM algorithm is called. + If no seeds are in this subgraph about VOI, then a UserWarning is thrown, and + ``nomination_list_`` is None. + + order_seeds_subgraph: int, positive (default = 1) + Order used to create induced subgraphs on ``A`` and ``B``. These subgraphs + are centered about the seeds that were determined by the subgraph generated + by ``order_voi_subgraph``. These two subgraphs will be passed into the SGM + algorithm. + + n_init: int, positive (default = 100) + Number of random initializations of the seeded graph matching algorithm (SGM). + Increasing the number of restarts will make the probabilities returned more + precise. + + max_nominations: int (default = None) + Max number of nominations to include in the nomination list. If None is passed, + then all nominations computed will be returned. + + graph_match_kws : dict (default = {}) + Gives users the option to pass custom arguments to the graph matching + algorithm. Format should be {'arg_name': arg_value, ...}. See + :class:`~graspologic.match.GraphMatch` + + + Attributes + ---------- + n_seeds_: int + Number of seeds passed in `seedsA` that occured in the induced subgraph about + VOI + + nomination_list_: 2d-array + An array containing vertex nominations in the form nomination + list = [[j, p_val],...] where p_val is the probability that the VOI matches + to node j in graph B (sorted by descending probability) + + Notes + ----- + VNviaSGM generates an initial induced subgraph about the VOI to determine which + seeds are close enough to be used. If no seeds are close enough, then a warning + is thrown and ``nomination_list_`` is set to None. + + All the seeds that are close enough are then used to generate subgraphs in both + ``A`` and ``B``. These subgraphs are matched using several random initializations + of the seeded graph matching algorithm (SGM), and a nomination list is returned. + See :class:`~graspologic.match.GraphMatch` for SGM docs + + References + ---------- + .. [1] Patsolic, HG, Park, Y, Lyzinski, V, Priebe, CE. Vertex nomination via seeded + graph matching. Stat Anal Data Min: The ASA Data Sci Journal. 2020; 13: 229– + 244. https://doi.org/10.1002/sam.11454 + + """ + + def __init__( + self, + order_voi_subgraph=1, + order_seeds_subgraph=1, + n_init=100, + max_nominations=None, + graph_match_kws={}, + ): + if isinstance(order_voi_subgraph, int) and order_voi_subgraph > 0: + self.order_voi_subgraph = order_voi_subgraph + else: + msg = '"order_voi_subgraph" must be an integer > 0' + raise ValueError(msg) + if isinstance(order_seeds_subgraph, int) and order_seeds_subgraph > 0: + self.order_seeds_subgraph = order_seeds_subgraph + else: + msg = '"order_seeds_subgraph" must be an integer > 0' + raise ValueError(msg) + if isinstance(n_init, int) and n_init > 0: + self.n_init = n_init + else: + msg = '"n_init" must be an integer > 0' + raise ValueError(msg) + + if max_nominations is None: + self.max_nominations = max_nominations + elif isinstance(max_nominations, int) and max_nominations >= 1: + self.max_nominations = max_nominations + else: + msg = '"max_nominations" must be an integer >= 1' + raise ValueError(msg) + + # Error checking of these will be handled by GMP + if isinstance(graph_match_kws, dict): + self.graph_match_kws = graph_match_kws + else: + msg = '"graph_match_kws` must be type dict' + raise ValueError(msg) + + def fit(self, A, B, voi, seeds): + """ + Fits the model to two graphs. + + Parameters + ---------- + A: 2d-array, square + Adjacency matrix, the graph where ``voi`` is known + + B: 2d-array, square + Adjacency matrix, the graph where ``voi`` is not known + + voi: int + Vertex of interest + + seeds: list, 2d-array + List of length two, of form `[seedsA, seedsB]`. The elements of `seedsA` + and `seedsB` are vertex indices from ``A`` and ``B``, respectively, which + are known to be matched; that is, vertex `seedsA[i]` is matched to vertex + `seedsB[i]`. Note: `len(seedsA)==len(seedsB)`. + + Returns + ------- + self: An instance of self + """ + A = np.atleast_2d(A) + B = np.atleast_2d(B) + + if not isinstance(A, np.ndarray) or not isinstance(B, np.ndarray): + msg = '"A" and "B" must be type np.ndarray' + raise ValueError(msg) + elif A.ndim != 2 or B.ndim != 2: + msg = '"A" and "B" must be two-dimensional' + raise ValueError(msg) + elif A.shape[0] != A.shape[1] or B.shape[0] != B.shape[1]: + msg = '"A" and "B" must be square' + raise ValueError(msg) + + if not isinstance(voi, int): + msg = '"voi" must be an integer' + raise ValueError(msg) + elif voi < 0 or voi >= A.shape[0]: + msg = '"voi" must be in range[0, num_verts_A)' + raise ValueError(msg) + + if not (isinstance(seeds, list) or isinstance(seeds, np.ndarray)): + msg = '"seeds" must be a list' + raise ValueError(msg) + + if isinstance(seeds, list): + if len(seeds) != 2: + msg = 'seeds must be length two, with first element containing seeds \ + of "A" and the second containing seeds of "B"' + raise ValueError(msg) + + if not ( + isinstance(seeds[0], list) or isinstance(seeds[0], np.ndarray) + ) or not (isinstance(seeds[1], list) or isinstance(seeds[1], np.ndarray)): + msg = '"seeds" elements must be lists or arrays' + raise ValueError(msg) + + seedsA = np.array(seeds[0]) + seedsB = np.array(seeds[1]) + + else: + seeds = np.atleast_2d(seeds) + + if not isinstance(seeds, np.ndarray): + msg = '"seeds" be a list or 2d-array' + raise ValueError(msg) + if seeds.shape[1] != 2: + msg = '"seeds" must have a second dimension of two' + raise ValueError(msg) + + seedsA = seeds[:, 0] + seedsB = seeds[:, 1] + + if len(seedsA) != len(seedsB): + msg = "Must have the same number of seeds for each adjacency matrix" + raise ValueError(msg) + elif len(seedsA) == 0: + msg = 'len("seeds") must be at least one' + raise ValueError(msg) + elif not len(set(seedsA)) == len(seedsA) or not len(set(seedsB)) == len(seedsB): + msg = '"seeds" column entries must be unique' + raise ValueError(msg) + elif len(seedsA) > A.shape[0]: + msg = '"seeds" cant have more entries than its associated adjacency matrix' + raise ValueError(msg) + elif (seedsA >= A.shape[0]).any() or (seedsB >= B.shape[0]).any(): + msg = '"seeds" entries must be less than number of nodes in their graphs' + raise ValueError(msg) + + # get vertex reordering for Ax + # in the form (seedsA, voi, rest in order) + nsx1 = np.setdiff1d(np.arange(A.shape[0]), np.append(seedsA, voi)) + a_reord = np.append(np.append(seedsA, voi), nsx1) + + # get reordering for B in the form (seedsB, rest in numerical order) + nsx2 = np.setdiff1d(np.arange(B.shape[0]), seedsB) + b_reord = np.concatenate((seedsB, nsx2)) + + # Reorder the two graphs with our new vertices order + A_perm = A[a_reord][:, a_reord] + B_perm = B[b_reord][:, b_reord] + + # Record where the new seeds and voi locations are + # in our re-ordered graphs + seeds_reord = np.arange(len(seedsA)) + voi_reord = len(seedsA) + + # Determine what seeds are within a specified subgraph + # given by `self.order_voi_subgraph`. If there are no + # seeds in this subgraph, print a message and return None + subgraph_A_perm = _get_induced_subgraph_list( + A_perm, self.order_voi_subgraph, voi_reord, mindist=1 + ) + + close_seeds = np.intersect1d(subgraph_A_perm, seeds_reord) + + if len(close_seeds) <= 0: + warnings.warn( + 'Voi {} was not a member of the induced subgraph A[{}], \ + Try increasing "order_voi_subgraph"'.format( + voi, seedsA + ) + ) + self.n_seeds_ = None + self.nomination_list_ = None + return self + + # Generate the two induced subgraphs that will be used by the matching + # algorithm using the seeds that we identified in the previous step. + verts_A = _get_induced_subgraph_list( + A_perm, self.order_seeds_subgraph, close_seeds, mindist=0 + ) + + verts_B = _get_induced_subgraph_list( + B_perm, self.order_seeds_subgraph, close_seeds, mindist=0 + ) + + # Determine the final reordering for the graphs that include only + # the vertices found by the induced subgraphs in the previous step + # For graph A, its of the form (close_seeds, voi, rest in verts_A + # in num order). For graph B its of the form (close_seeds, rest in + # verts_B in num order) + permed_verts = np.append(close_seeds, voi_reord) + ind1 = np.append( + np.append(close_seeds, voi_reord), np.setdiff1d(verts_A, permed_verts) + ) + ind2 = np.concatenate((close_seeds, np.setdiff1d(verts_B, close_seeds))) + + # Generate adjacency matrices for the ordering found in the prev step + SG_1 = A_perm[ind1][:, ind1] + SG_2 = B_perm[ind2][:, ind2] + + # Record the number of seeds used because this may differ from the number + # of seeds passed. See the step where close_seeds was computed for an + # explanation + self.n_seeds_ = len(close_seeds) + seeds_fin = np.arange(self.n_seeds_) + + # Call the SGM algorithm using user set parameters and generate a prob + # vector for the voi. + sgm = GMP( + **self.graph_match_kws, + ) + + prob_vector = np.zeros((max(SG_1.shape[0], SG_2.shape[0]) - self.n_seeds_)) + + for ii in range(self.n_init): + sgm.fit(SG_1, SG_2, seeds_A=seeds_fin, seeds_B=seeds_fin) + prob_vector[sgm.perm_inds_[self.n_seeds_] - self.n_seeds_] += 1.0 + + prob_vector /= self.n_init + + # Get the original vertices names in the B graph to make the nom list + b_inds = b_reord[ind2] + + # Generate the nomination list. Note, the probability matrix does not + # include the seeds, so we must remove them from b_inds. Return a list + # sorted so it returns the vertex with the highest probability first. + nomination_list_ = np.dstack((b_inds[self.n_seeds_ :], prob_vector))[0] + nomination_list_ = nomination_list_[nomination_list_[:, 1].argsort()][::-1] + + if self.max_nominations is not None and self.max_nominations < len( + nomination_list_ + ): + nomination_list_ = nomination_list_[0 : self.max_nominations] + + self.nomination_list_ = nomination_list_ + + return self + + def fit_predict(self, A, B, voi, seeds): + """ + Fits model to two adjacency matrices and returns nomination list + + Parameters + ---------- + A: 2d-array, square + Adjacency matrix, the graph where ``voi`` is known + + B: 2d-array, square + Adjacency matrix, the graph where ``voi`` is not known + + voi: int + Vertex of interest + + seeds: list, 2d-array + List of length two, of form `[seedsA, seedsB]`. The elements of `seedsA` + and `seedsB` are vertex indices from ``A`` and ``B``, respectively, which + are known to be matched; that is, vertex `seedsA[i]` is matched to + vertex `seedsB[i]`. Note: `len(seedsA)==len(seedsB)`. + + Returns + ------- + nomination_list_ : 2d-array + The nomination list. + """ + self.fit(A, B, voi, seeds) + + return self.nomination_list_ + + +def _get_induced_subgraph(graph_adj_matrix, order, node, mindist=1): + """ + Generates a vertex list for the induced subgraph about a node with + max and min distance parameters. + + Parameters + ---------- + graph_adj_matrix: 2-d array + Adjacency matrix of interest. + + order: int + Distance to create the induced subgraph with. Max distance away from the node + to include in subgraph. + + node: int + The vertex to center the induced subgraph about. + + mindist: int (default = 1) + The minimum distance away from the node to include in the subgraph. + + Returns + ------- + induced_subgraph : list + The list containing all the vertices in the induced subgraph. + """ + # Note all nodes are zero based in this implementation, i.e the first node is 0 + dists = [[node]] + dists_conglom = [node] + for ii in range(1, order + 1): + clst = [] + for nn in dists[-1]: + clst.extend(list(np.where(graph_adj_matrix[nn] >= 1)[0])) + clst = np.unique(clst) + + cn_proc = np.setdiff1d(clst, dists_conglom) + + dists.append(cn_proc) + + dists_conglom = np.unique(np.append(dists_conglom, cn_proc)) + + ress = list(itertools.chain(*dists[mindist : order + 1])) + + return np.unique(ress) + + +def _get_induced_subgraph_list(graph_adj_matrix, order, node, mindist=1): + """ + Generates a vertex list for the induced subgraph about a node with + max and min distance parameters. + + Parameters + ---------- + graph_adj_matrix: 2-d array + Adjacency matrix of interest. + + order: int + Distance to create the induce subgraph with. Max distance away from the node + to include in subgraph. + + node: int or list + The list of vertices to center the induced subgraph about. + + mindist: int (default = 1) + The minimum distance away from the node to include in the subgraph. + + Returns + ------- + induced_subgraph : list + The list containing all the vertices in the induced subgraph. + """ + if isinstance(node, list) or isinstance(node, np.ndarray): + total_res = [] + for nn in node: + ego_res = _get_induced_subgraph( + graph_adj_matrix, order, nn, mindist=mindist + ) + total_res.extend(ego_res) + return np.unique(total_res) + else: + return _get_induced_subgraph(graph_adj_matrix, order, node) diff --git a/graspologic/nominate/__init__.py b/graspologic/nominate/__init__.py new file mode 100644 index 000000000..dc38ca4b6 --- /dev/null +++ b/graspologic/nominate/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +from .VNviaSGM import VNviaSGM + +__all__ = ["VNviaSGM"] diff --git a/tests/test_vertex_nomination_via_SGM.py b/tests/test_vertex_nomination_via_SGM.py new file mode 100644 index 000000000..2f109c659 --- /dev/null +++ b/tests/test_vertex_nomination_via_SGM.py @@ -0,0 +1,111 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import pytest +import numpy as np +from graspologic.nominate import VNviaSGM +from graspologic.simulations import er_np + +np.random.seed(1) + + +class TestVNviaSGM: + def test_VNviaSGM_inputs(self): + with pytest.raises(ValueError): + VNviaSGM(order_voi_subgraph=-1) + with pytest.raises(ValueError): + VNviaSGM(order_voi_subgraph=1.5) + with pytest.raises(ValueError): + VNviaSGM(order_seeds_subgraph=-1) + with pytest.raises(ValueError): + VNviaSGM(order_seeds_subgraph=1.5) + with pytest.raises(ValueError): + VNviaSGM(n_init=-1) + with pytest.raises(ValueError): + VNviaSGM(n_init=1.5) + with pytest.raises(ValueError): + VNviaSGM(max_nominations=0) + + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(3, 4), + np.random.randn(4, 4), + 0, + [np.arange(2), np.arange(2)], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(3, 4), + 0, + [np.arange(2), np.arange(2)], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + [np.arange(2), 1], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + np.random.randn(3, 3), + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + [np.arange(2), np.arange(3)], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + [np.arange(5), np.arange(5)], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + [[], []], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + [[1, 1], [1, 2]], + ) + with pytest.raises(ValueError): + VNviaSGM().fit( + np.random.randn(4, 4), + np.random.randn(4, 4), + 0, + [[1, 5], [1, 2]], + ) + + def test_vn_algorithm(self): + g1 = er_np(n=50, p=0.6) + node_shuffle = np.random.permutation(50) + + g2 = g1[np.ix_(node_shuffle, node_shuffle)] + + kklst = [(xx, yy) for xx, yy in zip(node_shuffle, np.arange(len(node_shuffle)))] + kklst.sort(key=lambda x: x[0]) + kklst = np.array(kklst) + + voi = 7 + nseeds = 6 + + vnsgm = VNviaSGM() + nomlst = vnsgm.fit_predict( + g1, g2, voi, [kklst[0:nseeds, 0], kklst[0:nseeds, 1]] + ) + + assert nomlst[0][0] == kklst[np.where(kklst[:, 0] == voi)[0][0], 1] From d60a86abc50633a6c0902c1a768816bb2e936541 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Tue, 22 Dec 2020 09:10:41 -0500 Subject: [PATCH 071/140] Change downloads count to cumulative (#607) Co-authored-by: Jaewon Chung --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 59b69a701..ecacee04c 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) [![PyPI version](https://img.shields.io/pypi/v/graspologic.svg)](https://pypi.org/project/graspologic/) -[![Downloads shield](https://img.shields.io/pypi/dm/graspologic.svg)](https://pypi.org/project/graspologic/) +[![Downloads shield](https://pepy.tech/badge/graspologic)](https://pepy.tech/project/graspologic) [![Docs shield](https://img.shields.io/readthedocs/graspologic)](https://graspologic.readthedocs.io/) ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) [![codecov](https://codecov.io/gh/microsoft/graspologic/branch/dev/graph/badge.svg)](https://codecov.io/gh/microsoft/graspologic) From 89232c6408a1c9013d04677ad2e235a632e8ab31 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 23 Dec 2020 14:58:36 -0500 Subject: [PATCH 072/140] Bug fixes for sklearn 0.24 compatibility (#619) * pass instance * make mug2vec task easier --- tests/test_mug2vec.py | 4 ++-- tests/test_sklearn.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_mug2vec.py b/tests/test_mug2vec.py index 9a1b0dbe9..da3a7ebed 100644 --- a/tests/test_mug2vec.py +++ b/tests/test_mug2vec.py @@ -13,8 +13,8 @@ def generate_data(): np.random.seed(1) - p1 = [[0.2, 0.1], [0.1, 0.2]] - p2 = [[0.1, 0.2], [0.2, 0.1]] + p1 = [[0.3, 0.1], [0.1, 0.3]] + p2 = [[0.1, 0.3], [0.3, 0.1]] n = [50, 50] g1 = [sbm(n, p1) for _ in range(20)] diff --git a/tests/test_sklearn.py b/tests/test_sklearn.py index 17a0b8172..06e8464fa 100644 --- a/tests/test_sklearn.py +++ b/tests/test_sklearn.py @@ -6,5 +6,5 @@ from sklearn.utils.estimator_checks import check_estimator -check_estimator(graspologic.embed.LaplacianSpectralEmbed) -check_estimator(graspologic.embed.ClassicalMDS) +check_estimator(graspologic.embed.LaplacianSpectralEmbed()) +check_estimator(graspologic.embed.ClassicalMDS()) From c84cf1997bc14954188fa92dc3fe476f793e5c24 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Wed, 30 Dec 2020 17:14:37 -0500 Subject: [PATCH 073/140] Add "Roles" to top of repo (#616) * add "Roles" page at top level * add a link to the contributing --- CONTRIBUTING.md | 3 ++- ROLES.md | 41 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+), 1 deletion(-) create mode 100644 ROLES.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 533b99d45..bdcaa87f3 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,8 @@ or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any addi # Issue Submission (Bug or Feature) We use GitHub issues to track all bugs and feature requests; feel free to open an issue if you have found a bug or wish -to see a feature implemented. +to see a feature implemented. Please also feel free to tag one of the core +contributors (see our [Roles page](https://github.com/microsoft/graspologic/blob/dev/ROLES.md)). In case you experience issues using this package, do not hesitate to submit a ticket to our [Issue Tracker](https://github.com/microsoft/graspologic/issues). You are also welcome to post feature requests or pull diff --git a/ROLES.md b/ROLES.md new file mode 100644 index 000000000..3c36d3c73 --- /dev/null +++ b/ROLES.md @@ -0,0 +1,41 @@ +# Roles and Areas of Responsibility + +The following is a non-exhaustive list of the primary contributors to `graspologic` and +their roles and areas of responsibilities. Please feel free to use this list to `@` +specific contributors in your issues or pull requests that seem to line up best with +your issue! + +## Core Contributors + +### Ali Saad-Eldin (@asaadeldin11) +Ali is a Masters Student at Johns Hopkins University. He contributes and reviews code +mostly for the `match` and `embed` modules. + +### Anton Alyakin (@alyakin314) +Anton is an Assistant Research Engineer at Johns Hopkins University. His primary +contributions to `graspologic` are within `align` and `inference` modules. Ask him +anything about those. + +### Benjamin Pedigo (@bdpedigo) +Ben is a PhD student at Johns Hopkins University in the NeuroData lab. Ask Ben about +network model fitting and sampling, clustering, and spectral embedding (`models`, +`simulations`, `cluster`, and `embed`, respectfully). Ben is also happy to hear how we +can improve our tutorials. + +### Carolyn Buractaon (@carolyncb) +Carolyn is a Technical Program Manager at Microsoft. Ask Carolyn about where the project +is going and how it’s organized. + +### Dwayne Pryce (@dwaynepryce) +Dwayne Pryce is a Software Engineer at Microsoft Research. His primary contributions to +`graspologic` are on the steering committee, quality of life utility functions, and +build and release processes. + +### Jaewon Chung (@j1c) +Jaewon is a PhD student at Johns Hopkins University. He is a maintainer and developer +for `graspologic`, and is responsible for reviewing code contributions, merging pull +requests, and making decisions on the `graspologic` API. + +### Nick Caurvina (@nyecarr) +Nick is a Software Engineer at Microsoft Research. Ask Nick about the network +embeddings and their application to business problems. \ No newline at end of file From 0a42debe72298a87dd4c1d3c9be961ff0370219f Mon Sep 17 00:00:00 2001 From: Saumitra Sharma <43502778+darkyed@users.noreply.github.com> Date: Mon, 4 Jan 2021 22:43:38 +0530 Subject: [PATCH 074/140] Resolved the deprecation warning in AutoGMM (#620) * resolved the deprecation warning * refactored the code around model.fit * revert version.txt Co-authored-by: Benjamin Pedigo --- graspologic/cluster/autogmm.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/graspologic/cluster/autogmm.py b/graspologic/cluster/autogmm.py index ae9108a1d..5e1b353eb 100644 --- a/graspologic/cluster/autogmm.py +++ b/graspologic/cluster/autogmm.py @@ -11,8 +11,9 @@ _estimate_gaussian_parameters, ) from sklearn.model_selection import ParameterGrid -from sklearn.utils._testing import ignore_warnings + from sklearn.exceptions import ConvergenceWarning + from joblib import Parallel, delayed import warnings @@ -375,9 +376,6 @@ def __init__( self.max_agglom_size = max_agglom_size self.n_jobs = n_jobs - # ignoring warning here because if convergence is not reached, the regularization - # is automatically increased - @ignore_warnings(category=ConvergenceWarning) def _fit_cluster(self, X, y, params): label_init = self.label_init if label_init is not None: @@ -418,7 +416,11 @@ def _fit_cluster(self, X, y, params): while gm_params["reg_covar"] <= 1 and criter == np.inf: model = GaussianMixture(**gm_params) try: - model.fit(X) + # ignoring warning here because if convergence is not reached, the regularization + # is automatically increased + with warnings.catch_warnings(): + warnings.simplefilter("ignore", ConvergenceWarning) + model.fit(X) predictions = model.predict(X) counts = [ sum(predictions == i) for i in range(gm_params["n_components"]) From f8d938114d6289e4bacc4525140fa713805f9869 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 4 Jan 2021 13:21:29 -0800 Subject: [PATCH 075/140] Automatic Layouts and Rendering (#611) * adding files These are not in a workable state. We need to change all relative references and probably the include/colors-100.json Add some tests back and formatting * tabs to spaces to follow guidelines * Running black formatter * updating imports Making everything work again. We still need to move more things around to make more sense in the context of graspologic * moving classes from layouts.layouts * Renaming and moving to make more sense * First pass. massive refactor, and still not done :( * Fixing the init, and by fixing I mean making it not break * Some updating to make it actually work * I think I introduced a bug, but we at least have colors now * adding some preliminary tests * updating references to node_id from nid * Updating some unit tests for the no overlap * Removing two manual tests. We don't really have a good place to put these. They'll still be in our history though, so we can pull them back out if we find a different place to put them or use them. * Stub docstrings for the moment * Added more docstrings. Need Bryan to tweak a few, specifically the parameters we pass in to t-SNE and UMAP, and how they will relate to the automatic layout process. I also am not super happy with the sequential colors docstring because I don't know how to say it best. * Getting closer on documentation! * Numpy style pydocs have issues with external links in the RST syntax (as in, using it as a defined reference vs. doing it inlined). I did not know that. I know that now. * updating docstrings * Adding reference * Last documentation changes * numpy-izing a for loop around our points. * This file was causing pytest-cov to bomb. I couldn't get it to ignore the file entirely, so here we are. * Renaming 'nominal_colors' to 'categorical_colors'. Also describing the edge colors that will be chosen in the docs. * Latest version of sklearn breaks our unit tests * We now work with sklearn again * cleaned up in response to bdpedigo * removing extra add of nodes * Adding main to run from cmdline * I managed to not remove overlaps. @bryantower caught it and I added it back in * Fixing a bunch of deprecation warnings from unittest - it's real easy to call assertEquals instead of assertEqual * How did 6 files that I didn't touch start failing black formatting that weren't doing it earlier? Am I taking crazy pills? * Adding docs * More black changes * There's a bit more to adding a subparser, it needs a destination or else it causes problems when you provide no argument at all Co-authored-by: Bryan Tower --- MANIFEST.in | 1 + README.md | 17 +- docs/cli.rst | 50 + docs/index.rst | 1 + docs/reference/index.rst | 18 +- docs/reference/layouts.rst | 22 + graspologic/layouts/__init__.py | 17 + graspologic/layouts/__main__.py | 316 ++++ graspologic/layouts/auto.py | 347 ++++ graspologic/layouts/classes.py | 20 + graspologic/layouts/colors.py | 171 ++ graspologic/layouts/include/colors-100.json | 1667 +++++++++++++++++ graspologic/layouts/nooverlap/__init__.py | 8 + graspologic/layouts/nooverlap/_grid.py | 198 ++ graspologic/layouts/nooverlap/_node.py | 24 + graspologic/layouts/nooverlap/_quad_node.py | 1095 +++++++++++ graspologic/layouts/nooverlap/_quad_tree.py | 71 + graspologic/layouts/nooverlap/nooverlap.py | 37 + graspologic/layouts/render.py | 318 ++++ graspologic/preprocessing/graph_cuts.py | 159 +- setup.py | 3 +- tests/layouts/__init__.py | 0 tests/layouts/nooverlap/__init__.py | 0 tests/layouts/nooverlap/test_grid.py | 88 + .../nooverlap/test_grid_cell_creation.py | 44 + tests/layouts/nooverlap/test_nooverlap.py | 286 +++ tests/layouts/nooverlap/test_overlap_check.py | 160 ++ tests/layouts/test_auto.py | 20 + 28 files changed, 5075 insertions(+), 83 deletions(-) create mode 100644 docs/cli.rst create mode 100644 docs/reference/layouts.rst create mode 100644 graspologic/layouts/__init__.py create mode 100644 graspologic/layouts/__main__.py create mode 100644 graspologic/layouts/auto.py create mode 100644 graspologic/layouts/classes.py create mode 100644 graspologic/layouts/colors.py create mode 100644 graspologic/layouts/include/colors-100.json create mode 100644 graspologic/layouts/nooverlap/__init__.py create mode 100644 graspologic/layouts/nooverlap/_grid.py create mode 100644 graspologic/layouts/nooverlap/_node.py create mode 100644 graspologic/layouts/nooverlap/_quad_node.py create mode 100644 graspologic/layouts/nooverlap/_quad_tree.py create mode 100644 graspologic/layouts/nooverlap/nooverlap.py create mode 100644 graspologic/layouts/render.py create mode 100644 tests/layouts/__init__.py create mode 100644 tests/layouts/nooverlap/__init__.py create mode 100644 tests/layouts/nooverlap/test_grid.py create mode 100644 tests/layouts/nooverlap/test_grid_cell_creation.py create mode 100644 tests/layouts/nooverlap/test_nooverlap.py create mode 100644 tests/layouts/nooverlap/test_overlap_check.py create mode 100644 tests/layouts/test_auto.py diff --git a/MANIFEST.in b/MANIFEST.in index b151080ed..0d1d6a65c 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,4 @@ recursive-include graspologic/datasets *.csv recursive-include graspologic/version version.txt +recursive-include graspologic/layouts/include colors-100.json exclude tests/* diff --git a/README.md b/README.md index ecacee04c..433a2f3dd 100644 --- a/README.md +++ b/README.md @@ -20,8 +20,8 @@ - [Issues](#issues) # Notice: `graspologic` is the merger project of `GraSPy` and `topologic` -We're actively merging these projects into one, but you may see some references to `graspy` or `topologic` from time to time in documentation and -issues. If you notice anything in the documentation referencing either graspy or topologic, please raise an issue (if one does not already exist) +We're actively merging these projects into one, but you may see some references to `graspy` or `topologic` from time to time in documentation and +issues. If you notice anything in the documentation referencing either graspy or topologic, please raise an issue (if one does not already exist) noting the missed titles so we can address all of them. # Overview @@ -34,7 +34,7 @@ Please visit the [tutorial section](https://graspologic.readthedocs.io/en/latest # System Requirements ## Hardware requirements -`graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. +`graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. ## Software requirements ### OS Requirements @@ -49,7 +49,7 @@ And across the following versions of Python: - 3.8 (x64) If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, -please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users +please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users if we have concrete examples of things not working! ### Python Dependencies @@ -63,6 +63,7 @@ POT seaborn scikit-learn scipy +umap-learn ``` Developers of `graspologic` will also have the following dependencies: @@ -99,10 +100,10 @@ python3 setup.py install ``` # Contributing -We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our -[issues](https://github.com/microsoft/graspologic/issues) page is full of places we could use help! -If you have an idea for an improvement not listed there, please -[make an issue](https://github.com/microsoft/graspologic/issues/new) first so you can discuss with the developers. +We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our +[issues](https://github.com/microsoft/graspologic/issues) page is full of places we could use help! +If you have an idea for an improvement not listed there, please +[make an issue](https://github.com/microsoft/graspologic/issues/new) first so you can discuss with the developers. # License This project is covered under the MIT License. diff --git a/docs/cli.rst b/docs/cli.rst new file mode 100644 index 000000000..9750a42d6 --- /dev/null +++ b/docs/cli.rst @@ -0,0 +1,50 @@ +CLI +=== + +In addition to the main library, there is also a CLI runnable module for automatically +generating layouts for graphs in an edge list. + +You can run this from the command line like so: + +.. code-block:: bash + + python -m graspologic.layouts --help + +Which should return something like: + +.. code-block:: none + + usage: python -m graspologic.layouts [-h] [--verbose VERBOSE] {n2vumap,n2vtsne,render} ... + + Runnable module that automatically generates a layout of a graph by a provided edge list + + positional arguments: + {n2vumap,n2vtsne,render} + n2vumap Auto layout using UMAP for dimensionality reduction + n2vtsne Auto layout using tSNE for dimensionality reduction + render Renders a graph via an input file + +Of those commands, you can then do: + +.. code-block:: bash + + python -m graspologic.layouts n2vumap --help + +Which will return something like: + +.. code-block:: none + + usage: python -m graspologic.layouts n2vumap [-h] --edge_list EDGE_LIST [--skip_header] [--image_file IMAGE_FILE] [--location_file LOCATION_FILE] [--max_edges MAX_EDGES] [--dpi DPI] + + optional arguments: + -h, --help show this help message and exit + --edge_list EDGE_LIST + edge list in csv file. must be source,target,weight. + --skip_header skip first line in csv file, corresponding to header. + --image_file IMAGE_FILE + output path and filename for generated image file. required if --location_file is omitted. + --location_file LOCATION_FILE + output path and filename for location file. required if --image_file is omitted. + --max_edges MAX_EDGES + maximum edges to keep during embedding. edges with low weights will be pruned to keep at most this many edges + --dpi DPI used with --image_file to render an image at this dpi diff --git a/docs/index.rst b/docs/index.rst index b5df5343b..dbab253fe 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -63,6 +63,7 @@ graspologic is a graph statistics package in python. install tutorial reference/index + cli contributing release license diff --git a/docs/reference/index.rst b/docs/reference/index.rst index ea3d49ffc..cdb8fc633 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -6,17 +6,19 @@ Reference .. toctree:: :maxdepth: 2 - simulations - models + align + cluster + datasets embed cluster inference - plotting - datasets - utils + layouts match + models nominate - subgraph - align - preprocessing partition + plotting + preprocessing + simulations + subgraph + utils diff --git a/docs/reference/layouts.rst b/docs/reference/layouts.rst new file mode 100644 index 000000000..b0e0ca633 --- /dev/null +++ b/docs/reference/layouts.rst @@ -0,0 +1,22 @@ +Layouts +======= +.. currentmodule:: graspologic.layouts + +NodePosition +------------ +.. autoclass:: NodePosition + +Automatic Graph Layout +---------------------- +.. autofunction:: layout_tsne +.. autofunction:: layout_umap + +Colors +------ +.. autofunction:: categorical_colors +.. autofunction:: sequential_colors + +Rendering +--------- +.. autofunction:: save_graph +.. autofunction:: show_graph diff --git a/graspologic/layouts/__init__.py b/graspologic/layouts/__init__.py new file mode 100644 index 000000000..83a626605 --- /dev/null +++ b/graspologic/layouts/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from .classes import NodePosition +from .colors import categorical_colors, sequential_colors +from .render import save_graph, show_graph +from .auto import layout_tsne, layout_umap + +__all__ = [ + "NodePosition", + "categorical_colors", + "sequential_colors", + "layout_tsne", + "layout_umap", + "save_graph", + "show_graph", +] diff --git a/graspologic/layouts/__main__.py b/graspologic/layouts/__main__.py new file mode 100644 index 000000000..c30540894 --- /dev/null +++ b/graspologic/layouts/__main__.py @@ -0,0 +1,316 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import argparse +import logging +from pathlib import Path +import sys +from typing import Any, Dict, List + +import networkx as nx + +from . import auto, NodePosition, render +from .colors import categorical_colors + + +def _graph_from_file( + path: str, + skip_header: bool = False, +) -> nx.Graph: + graph = nx.Graph() + with open(path, "r") as edge_io: + if skip_header is True: + next(edge_io) + for line in edge_io: + source, target, weight = line.strip().split(",") + weight = float(weight) + if graph.has_edge(source, target): + weight += graph[source][target]["weight"] + graph.add_edge(source, target, weight=weight) + return graph + + +def _ensure_output_dir(path: str): + Path(path).parent.mkdir(parents=True, exist_ok=True) + + +def _location(path: str, positions: List[NodePosition], colors: Dict[Any, str]): + with open(path, "w") as node_positions_out: + print("id,x,y,size,community,color", file=node_positions_out) + for position in positions: + print( + f"{position.node_id},{position.x},{position.y},{position.size}," + f"{position.community},{colors[position.node_id]}", + file=node_positions_out, + ) + + +def _output( + arguments: argparse.Namespace, graph: nx.Graph, positions: List[NodePosition] +): + partitions = {position.node_id: position.community for position in positions} + color_map = categorical_colors(partitions) + if arguments.image_file is not None: + render.save_graph(arguments.image_file, graph, positions, node_colors=color_map) + if arguments.location_file is not None: + _location(arguments.location_file, positions, color_map) + + +def _tsne(arguments: argparse.Namespace): + valid_args(arguments) + graph = _graph_from_file(arguments.edge_list, arguments.skip_header) + graph, positions = auto.layout_tsne( + graph, perplexity=30, n_iter=1000, max_edges=arguments.max_edges + ) + _output(arguments, graph, positions) + + +def _umap(arguments: argparse.Namespace): + valid_args(arguments) + graph = _graph_from_file(arguments.edge_list, arguments.skip_header) + graph, positions = auto.layout_umap(graph, max_edges=arguments.max_edges) + _output(arguments, graph, positions) + + +def _render(arguments: argparse.Namespace): + positions = [] + node_colors = {} + with open(arguments.location_file, "r") as location_io: + next(location_io) + for line in location_io: + node_id, x, y, size, community, color = line.strip().split(",") + positions.append( + NodePosition(node_id, float(x), float(y), float(size), int(community)) + ) + node_colors[node_id] = color + if arguments.edge_list is not None: + graph = _graph_from_file(arguments.edge_list, arguments.skip_header) + else: + graph = nx.Graph() + for position in positions: + graph.add_node(position.node_id) + + render.save_graph( + arguments.image_file, + graph, + positions, + node_colors=node_colors, + vertex_line_width=arguments.vertex_line_width, + vertex_alpha=arguments.vertex_alpha, + edge_line_width=arguments.edge_line_width, + edge_alpha=arguments.edge_alpha, + figure_height=arguments.figure_height, + figure_width=arguments.figure_width, + vertex_shape=arguments.vertex_shape, + arrows=arguments.arrows, + dpi=arguments.dpi, + light_background=arguments.light_background, + ) + + +def _common_edge_list_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser.add_argument( + "--edge_list", + help="edge list in csv file. must be source,target,weight.", + required=True, + ) + parser.add_argument( + "--skip_header", + help="skip first line in csv file, corresponding to header.", + action="store_true", + required=False, + default=False, + ) + parser.add_argument( + "--image_file", + help="output path and filename for generated image file. " + "required if --location_file is omitted.", + required=False, + default=None, + ) + parser.add_argument( + "--location_file", + help="output path and filename for location file. " + "required if --image_file is omitted.", + required=False, + default=None, + ) + parser.add_argument( + "--max_edges", + help="maximum edges to keep during embedding. edges with low weights will be " + "pruned to keep at most this many edges", + type=int, + required=False, + default=10000000, + ) + parser.add_argument( + "--dpi", + help="used with --image_file to render an image at this dpi", + type=int, + required=False, + default=500, + ) + return parser + + +def _parser() -> argparse.ArgumentParser: + root_parser = argparse.ArgumentParser( + prog="python -m graspologic.layouts", + description="Runnable module that automatically generates a layout of a graph " + "by a provided edge list", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + root_parser.add_argument( + "--verbose", + type=bool, + required=False, + default=False, + ) + + subparsers = root_parser.add_subparsers( + required=True, + dest="COMMAND", + help="auto layout via umap, tsne, or a pure render only mode", + ) + + n2vumap_parser = subparsers.add_parser( + "n2vumap", + help="Auto layout using UMAP for dimensionality reduction", + ) + _common_edge_list_args(n2vumap_parser) + n2vumap_parser.set_defaults(func=_umap) + n2vtsne_parser = subparsers.add_parser( + "n2vtsne", + help="Auto layout using tSNE for dimensionality reduction", + ) + _common_edge_list_args(n2vtsne_parser) + n2vtsne_parser.set_defaults(func=_tsne) + render_parser = subparsers.add_parser( + "render", + help="Renders a graph via an input file", + ) + render_parser.set_defaults(func=_render) + render_parser.add_argument( + "--edge_list", + help="edge list in csv file. must be source,target,weight.", + required=False, + default=None, + ) + render_parser.add_argument( + "--skip_header", + help="skip first line in csv file, corresponding to header.", + action="store_true", + required=False, + default=False, + ) + render_parser.add_argument( + "--location_file", + help="location file used for node positioning, partitioning, and coloring", + required=True, + default=None, + ) + render_parser.add_argument( + "--image_file", + help="output path and filename for generated image file. ", + required=True, + default=None, + ) + render_parser.add_argument( + "--dark_background", + dest="light_background", + action="store_false", + default=True, + ) + render_parser.add_argument( + "--vertex_alpha", + help="Alpha (transparency) of vertices in visualization (default 0.9)", + type=float, + required=False, + default=0.9, + ) + render_parser.add_argument( + "--vertex_line_width", + help="Line width of vertex outline (default 0.01)", + type=float, + required=False, + default=0.01, + ) + render_parser.add_argument( + "--edge_line_width", + help="Line width of edge (default 0.5)", + type=float, + required=False, + default=0.5, + ) + render_parser.add_argument( + "--edge_alpha", + help="Alpha (transparency) of edges in visualization (default 0.2)", + type=float, + required=False, + default=0.2, + ) + render_parser.add_argument( + "--figure_width", + help="Width of figure (default 15.0)", + type=float, + required=False, + default=15.0, + ) + render_parser.add_argument( + "--figure_height", + help="Height of figure (default 15.0)", + type=float, + required=False, + default=15.0, + ) + render_parser.add_argument( + "--vertex_shape", + help="Matplotlib Marker for the vertex shape.", + required=False, + default="o", + ) + render_parser.add_argument( + "--arrows", + dest="arrows", + action="store_true", + default=False, + ) + render_parser.add_argument( + "--dpi", + help="Set dpi for image", + type=int, + required=False, + default=500, + ) + return root_parser + + +def valid_args(args: argparse.Namespace): + if args.image_file is None and args.location_file is None: + print( + "error: --image_file or --location_file must be provided", file=sys.stderr + ) + exit(-1) + if args.location_file is not None: + _ensure_output_dir(args.location_file) + if args.image_file is not None: + _ensure_output_dir(args.image_file) + + +def main(): + parser = _parser() + + args = parser.parse_args() + + if args.verbose is True: + logging.basicConfig( + format="%(asctime)s:%(levelname)s:%(name)s, %(message)s", level=logging.INFO + ) + logger = logging.getLogger("graspologic.layouts") + + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/graspologic/layouts/auto.py b/graspologic/layouts/auto.py new file mode 100644 index 000000000..6e448419b --- /dev/null +++ b/graspologic/layouts/auto.py @@ -0,0 +1,347 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from typing import Any, Dict, List, Tuple + +import logging +import math +import time + +import networkx as nx +import numpy as np + +from sklearn.manifold import TSNE +import umap + +from .classes import NodePosition +from .nooverlap import remove_overlaps + +from ..embed import node2vec_embed +from ..partition import leiden +from ..preprocessing import cut_edges_by_weight, histogram_edge_weight + +logger = logging.getLogger(__name__) + + +# automatically generates a layout by running node2vec over the graph and down +# projecting the embedding into 2d space via umap or tsne +def layout_tsne( + graph: nx.Graph, + perplexity: int, + n_iter: int, + max_edges: int = 10000000, +) -> Tuple[nx.Graph, List[NodePosition]]: + """ + Automatic graph layout generation by creating a generalized node2vec embedding, + then using t-SNE for dimensionality reduction to 2d space. + + By default, this function automatically attempts to prune each graph to a maximum + of 10,000,000 edges by removing the lowest weight edges. This pruning is approximate + and will leave your graph with at most ``max_edges``, but is not guaranteed to be + precisely ``max_edges``. + + In addition to pruning edges by weight, this function also only operates over the + largest connected component in the graph. + + After dimensionality reduction, sizes are generated for each node based upon + their degree centrality, and these sizes and positions are further refined by an + overlap removal phase. Lastly, a global partitioning algorithm + (:func:`graspologic.partition.leiden`) is executed for the largest connected + component and the partition ID is included with each node position. + + Parameters + ---------- + graph : :class:`networkx.Graph` + The graph to generate a layout for. This graph may have edges pruned if the + count is too high and only the largest connected component will be used to + automatically generate a layout. + perplexity : int + The perplexity is related to the number of nearest neighbors that is used in + other manifold learning algorithms. Larger datasets usually require a larger + perplexity. Consider selecting a value between 4 and 100. Different values can + result in significanlty different results. + n_iter : int + Maximum number of iterations for the optimization. We have found in practice + that larger graphs require more iterations. We hope to eventually have more + guidance on the number of iterations based on the size of the graph and the + density of the edge connections. + max_edges : int + The maximum number of edges to use when generating the embedding. Default is + ``10000000``. The edges with the lowest weights will be pruned until at most + ``max_edges`` exist. Warning: this pruning is approximate and more edges than + are necessary may be pruned. Running in 32 bit enviornment you will most + likely need to reduce this number or you will out of memory. + + Returns + ------- + Tuple[nx.Graph, List[NodePosition]] + The largest connected component and a list of NodePositions for each node in + the largest connected component. The NodePosition object contains: + - node_id + - x coordinate + - y coordinate + - size + - community + + References + ---------- + .. [1] van der Maaten, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data Using + t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008. + """ + lcc_graph, tensors, labels = _node2vec_for_layout(graph, max_edges) + points = TSNE(perplexity=perplexity, n_iter=n_iter).fit_transform(tensors) + positions = _node_positions_from(lcc_graph, labels, points) + return lcc_graph, positions + + +def layout_umap( + graph: nx.Graph, + min_dist: float = 0.75, + n_neighbors: int = 25, + max_edges: int = 10000000, +) -> Tuple[nx.Graph, List[NodePosition]]: + """ + Automatic graph layout generation by creating a generalized node2vec embedding, + then using UMAP for dimensionality reduction to 2d space. + + By default, this function automatically attempts to prune each graph to a maximum + of 10,000,000 edges by removing the lowest weight edges. This pruning is approximate + and will leave your graph with at most ``max_edges``, but is not guaranteed to be + precisely ``max_edges``. + + In addition to pruning edges by weight, this function also only operates over the + largest connected component in the graph. + + After dimensionality reduction, sizes are generated for each node based upon + their degree centrality, and these sizes and positions are further refined by an + overlap removal phase. Lastly, a global partitioning algorithm + (:func:`graspologic.partition.leiden`) is executed for the largest connected + component and the partition ID is included with each node position. + + Parameters + ---------- + graph : :class:`networkx.Graph` + The graph to generate a layout for. This graph may have edges pruned if the + count is too high and only the largest connected component will be used to + automatically generate a layout. + min_dist : float + The effective minimum distance between embedded points. Default is ``0.75``. + Smaller values will result in a more clustered/clumped embedding where nearby + points on the manifold are drawn closer together, while larger values will + result on a more even dispersal of points. The value should be set relative to + the ``spread`` value, which determines the scale at which embedded points will + be spread out. + n_neighbors : int + The size of local neighborhood (in terms of number of neighboring sample points) + used for manifold approximation. Default is ``25``. Larger values result in + more global views of the manifold, while smaller values result in more local + data being preserved. + max_edges : int + The maximum number of edges to use when generating the embedding. Default is + ``10000000``. The edges with the lowest weights will be pruned until at most + ``max_edges`` exist. Warning: this pruning is approximate and more edges than + are necessary may be pruned. Running in 32 bit enviornment you will most + likely need to reduce this number or you will out of memory. + + Returns + ------- + Tuple[nx.Graph, List[NodePosition]] + The largest connected component and a list of NodePositions for each node in + the largest connected component. The NodePosition object contains: + - node_id + - x coordinate + - y coordinate + - size + - community + + References + ---------- + .. [1] McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection + for Dimension Reduction, ArXiv e-prints 1802.03426, 2018 + .. [2] Böhm, Jan Niklas; Berens, Philipp; Kobak, Dmitry. A Unifying Perspective + on Neighbor Embeddings along the Attraction-Repulsion Spectrum. ArXiv e-prints + 2007.08902v1, 17 Jul 2020. + """ + + lcc_graph, tensors, labels = _node2vec_for_layout(graph, max_edges) + points = umap.UMAP(min_dist=min_dist, n_neighbors=n_neighbors).fit_transform( + tensors + ) + positions = _node_positions_from(lcc_graph, labels, points) + return lcc_graph, positions + + +def _largest_connected_component(graph: nx.Graph) -> nx.Graph: + largest_component = max(nx.connected_components(graph), key=len) + return graph.subgraph(largest_component).copy() + + +def _approximate_prune(graph: nx.Graph, max_edges_to_keep: int = 1000000): + num_edges = len(graph.edges()) + logger.info(f"num edges: {num_edges}") + + if num_edges > max_edges_to_keep: + histogram, bins = histogram_edge_weight(graph, bin_directive=100) + counts = 0 + bin_edge_for_maximum_weight = bins[0] + for i, count in reversed(list(enumerate(histogram))): + counts += count + if counts >= max_edges_to_keep: + bin_edge_for_maximum_weight = bins[i + 1] + break + threshold = bins[bin_edge_for_maximum_weight] + graph = cut_edges_by_weight( + graph, cut_threshold=threshold, cut_process="smaller_than_inclusive" + ) + logger.debug(f"after cut num edges: {len(graph.edges())}") + + return graph + + +def _node2vec_for_layout( + graph: nx.Graph, + max_edges: int = 10000000, +) -> Tuple[nx.Graph, np.ndarray, np.ndarray]: + graph = _approximate_prune(graph, max_edges) + graph = _largest_connected_component(graph) + + start = time.time() + tensors, labels = node2vec_embed( + graph=graph, dimensions=128, num_walks=10, window_size=2, iterations=3 + ) + embedding_time = time.time() - start + logger.info(f"embedding completed in {embedding_time} seconds") + return graph, tensors, labels + + +def _node_positions_from( + graph: nx.Graph, + labels: np.ndarray, + down_projection_2d: np.ndarray, +) -> List[NodePosition]: + degree = graph.degree() + sizes = _compute_sizes(degree) + covered_area = _covered_size(sizes) + scaled_points = _scale_points(down_projection_2d, covered_area) + partitions = leiden(graph) + positions = [ + NodePosition( + node_id=key, + x=scaled_points[index][0], + y=scaled_points[index][1], + size=sizes[key], + community=partitions[key], + ) + for index, key in enumerate(labels) + ] + positions = remove_overlaps(positions) + return positions + + +def _find_min_max_degree( + degrees: nx.classes.reportviews.DegreeView, +) -> Tuple[float, float]: + min_degree = math.inf + max_degree = -math.inf + for _, degree in degrees: + min_degree = min(min_degree, degree) + max_degree = max(max_degree, degree) + return min_degree, max_degree + + +def _compute_sizes( + degrees: nx.classes.reportviews.DegreeView, + min_size: float = 5.0, + max_size: float = 150.0, +) -> Dict[Any, float]: + min_degree, max_degree = _find_min_max_degree(degrees) + sizes = {} + for node_id, degree in degrees: + if max_degree == min_degree: + size = min_size + else: + normalized = (degree - min_degree) / (max_degree - min_degree) + size = normalized * (max_size - min_size) + min_size + sizes[node_id] = size + return sizes + + +def _covered_size(sizes: Dict[Any, float]) -> float: + total = sum(math.pow(value, 2) * math.pi for value in sizes.values()) + return total + + +def _get_bounds(points: np.ndarray) -> Tuple[float, float, float, float]: + min_x, min_y = points.min(axis=0) + max_x, max_y = points.max(axis=0) + return min_x, min_y, max_x, max_y + + +def _center_points_on_origin(points: np.ndarray) -> np.ndarray: + min_x, min_y, max_x, max_y = _get_bounds(points) + x_range = max_x - min_x + y_range = max_y - min_y + center_x = x_range / 2 + min_x + center_y = y_range / 2 + min_y + points[:, 0] -= center_x + points[:, 1] -= center_y + return points + + +def _scale_to_unit_square(points: np.ndarray) -> np.ndarray: + _, _, max_x, max_y = _get_bounds(points) + points[:, 0] /= max_x + points[:, 1] /= max_y + return points + + +def _scale_to_new_bounds(points: np.ndarray, max_x: float, max_y: float) -> np.ndarray: + points[:, 0] *= max_x + points[:, 1] *= max_y + return points + + +def _new_bounds( + min_x: float, + min_y: float, + max_x: float, + max_y: float, + covered_area: float, + target_ratio: float, +) -> Tuple[float, float, float, float]: + range_x = max_x - min_x + mid_x = min_x + range_x / 2 + range_y = max_y - min_y + mid_y = min_y + range_y / 2 + range_ratio = range_x / range_y + + new_area = covered_area / target_ratio + new_range_y = math.sqrt(new_area / range_ratio) + new_range_x = new_area / new_range_y + new_min_x, new_min_y, new_max_x, new_max_y = ( + mid_x - new_range_x / 2, + mid_y - new_range_y / 2, + mid_x + new_range_x / 2, + mid_y + new_range_y / 2, + ) + + return new_min_x, new_min_y, new_max_x, new_max_y + + +def _scale_points( + points: np.ndarray, covered_area: float, target_ratio: float = 0.14 +) -> np.ndarray: + # through lots of experiments we have found 14% to be a good ratio + # of color to whitespace + moved_points = _center_points_on_origin(points) + min_x, min_y, max_x, max_y = _get_bounds(moved_points) + + _, _, new_max_x, new_max_y = _new_bounds( + min_x, min_y, max_x, max_y, covered_area, target_ratio + ) + + scaled_points = _scale_to_unit_square(moved_points) + + final_locs = _scale_to_new_bounds(scaled_points, new_max_x, new_max_y) + + return final_locs diff --git a/graspologic/layouts/classes.py b/graspologic/layouts/classes.py new file mode 100644 index 000000000..5f6bc9471 --- /dev/null +++ b/graspologic/layouts/classes.py @@ -0,0 +1,20 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from typing import NamedTuple + +__all__ = [ + "NodePosition", +] + + +class NodePosition(NamedTuple): + """ + Contains the node id, 2d coordinates, size, and community id for a node. + """ + + node_id: str + x: float + y: float + size: float + community: int diff --git a/graspologic/layouts/colors.py b/graspologic/layouts/colors.py new file mode 100644 index 000000000..10e0a9aa7 --- /dev/null +++ b/graspologic/layouts/colors.py @@ -0,0 +1,171 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import atexit +from itertools import cycle +import json +import math +import numpy as np +import os +from pathlib import Path +import pkg_resources +from sklearn.preprocessing import minmax_scale +from typing import Any, Dict, Optional, Tuple + + +__all__ = ["categorical_colors", "sequential_colors"] + + +def _load_thematic_json(path: Optional[str]) -> Tuple[Dict[Any, Any], Dict[Any, Any]]: + if path is not None and Path(path).is_file(): + colors_path = path + else: + atexit.register(pkg_resources.cleanup_resources) + include_path = pkg_resources.resource_filename(__package__, "include") + colors_path = os.path.join(include_path, "colors-100.json") + + with open(colors_path) as thematic_json_io: + thematic_json = json.load(thematic_json_io) + light = thematic_json["light"] + dark = thematic_json["dark"] + return light, dark + + +_CACHED_LIGHT, _CACHED_DARK = _load_thematic_json(None) + + +def _get_colors(light_background: bool, theme_path: Optional[str]) -> Dict[Any, Any]: + ( + light, + dark, + ) = _CACHED_LIGHT, _CACHED_DARK if theme_path is None else _load_thematic_json( + theme_path + ) + return light if light_background else dark + + +def categorical_colors( + partitions: Dict[Any, int], + light_background: bool = True, + theme_path: Optional[str] = None, +) -> Dict[Any, str]: + """ + Generates a node -> color mapping based on the partitions provided. + + The partitions are ordered by population descending, and a series of perceptually + balanced, complementary colors are chosen in sequence. + + If a theme_path is provided, it must contain a path to a json file generated by + `Thematic `_, otherwise it will use the theme + packaged with this library. + + Colors will be different when selecting for a light background vs. a dark + background, using the principles defined by + `Thematic `_. + + If more partitions than colors available (100) are selected, the colors will be + cycled through again. + + Parameters + ---------- + partitions : Dict[Any, int] + A dictionary of node ids to partition ids. + light_background : bool + Default is ``True``. Colors selected for a light background will be slightly + different in hue and saturation to complement a light or dark background. + theme_path : Optional[str] + A color scheme is provided with ``graspologic``, but if you wish to use your own + you can generate one with `Thematic `_ and + provide the path to it to override the bundled theme. + + Returns + ------- + Dict[Any, str] + Returns a dictionary of node id -> color based on the partitions provided. + + """ + color_scheme = _get_colors(light_background, theme_path) + partition_populations = {} + for node_id, partition in partitions.items(): + count = partition_populations.get(partition, 0) + 1 + partition_populations[partition] = count + + ordered_partitions = sorted( + partition_populations.items(), key=lambda x: x[1], reverse=True + ) + nominal_cycle = cycle(color_scheme["nominal"]) + colors_by_partitions = {} + for index, item in enumerate(ordered_partitions): + partition, _ = item + color = next(nominal_cycle) + colors_by_partitions[partition] = color + + colors_by_node = { + node_id: colors_by_partitions[partition] + for node_id, partition in partitions.items() + } + return colors_by_node + + +def sequential_colors( + node_and_value: Dict[Any, float], + light_background: bool = True, + use_log_scale: bool = False, + theme_path: Optional[str] = None, +) -> Dict[Any, str]: + """ + Generates a node -> color mapping where a color is chosen for the value as it + maps the value range into the sequential color space. + + If a theme_path is provided, it must contain a path to a json file generated by + `Thematic `_, otherwise it will use the theme + packaged with this library. + + Colors will be different when selecting for a light background vs. a dark + background, using the principles defined by + `Thematic `_. + + If more partitions than colors available (100) are selected, the colors will be + cycled through again. + + Parameters + ---------- + node_and_value : Dict[Any, float] + A node to value mapping. The value is a single entry in a continuous range, + which is then mapped into a sequential color space. + light_background : bool + Default is ``True``. Colors selected for a light background will be slightly + different in hue and saturation to complement a light or dark background. + use_log_scale : bool + Default is ``False``. + theme_path : Optional[str] + A color scheme is provided with ``graspologic``, but if you wish to use your own + you can generate one with `Thematic `_ and + provide the path to it to override the bundled theme. + + Returns + ------- + Dict[Any, str] + Returns a dictionary of node id -> color based on the original value + provided for the node as it relates to the total range of all values. + + """ + color_scheme = _get_colors(light_background, theme_path) + color_list = color_scheme["sequential"] + num_colors = len(color_list) + + keys, values = zip(*node_and_value.items()) + + if use_log_scale: + values = map(math.log, values) + + np_values = np.array(values).reshape(1, -1) + new_values = minmax_scale(np_values, feature_range=(0, num_colors - 1), axis=1) + node_colors = {} + for key_index, node_id in enumerate(keys): + index = int(new_values[0, key_index]) + + color = color_list[index] + node_colors[node_id] = color + + return node_colors diff --git a/graspologic/layouts/include/colors-100.json b/graspologic/layouts/include/colors-100.json new file mode 100644 index 000000000..ae44b9fe3 --- /dev/null +++ b/graspologic/layouts/include/colors-100.json @@ -0,0 +1,1667 @@ +{ + "name": "Default", + "params": { + "accentHue": 252, + "accentSaturation": 78, + "accentLuminance": 49, + "backgroundLevel": 95, + "backgroundHueShift": 50, + "nominalHueStep": 10 + }, + "light": { + "background": "#fdfeff", + "offsetBackground": "#ffffff", + "foreground": "#303030", + "accent": "#3f75bf", + "lowContrastAnnotation": "#c5c5c5", + "midContrastAnnotation": "#767676", + "highContrastAnnotation": "#303030", + "faintAnnotation": "#f1f1f1", + "sequential": [ + "#f5f5f5", + "#f3f4f4", + "#f2f2f2", + "#f1f1f1", + "#efeff0", + "#eeeeef", + "#ecedee", + "#ebebed", + "#e9eaec", + "#e8e9eb", + "#e6e7ea", + "#e5e6e9", + "#e3e4e8", + "#e2e3e7", + "#e0e2e6", + "#dfe0e5", + "#dddfe4", + "#dbdee4", + "#dadce3", + "#d8dbe2", + "#d7dae1", + "#d5d8e1", + "#d3d7e0", + "#d2d6e0", + "#d0d4df", + "#ced3de", + "#cdd2de", + "#cbd0dd", + "#c9cfdd", + "#c8cedd", + "#c6ccdc", + "#c4cbdc", + "#c2cadb", + "#c1c8db", + "#bfc7db", + "#bdc6db", + "#bbc4da", + "#b9c3da", + "#b8c2da", + "#b6c0da", + "#b4bfda", + "#b2beda", + "#b0bdda", + "#aebbda", + "#acbada", + "#aab9da", + "#a8b7da", + "#a6b6da", + "#a4b5da", + "#a2b3da", + "#a0b2da", + "#9eb1da", + "#9bb0da", + "#99aedb", + "#97addb", + "#95acdb", + "#93aadb", + "#90a9dc", + "#8ea8dc", + "#8ba7dc", + "#89a5dd", + "#86a4dd", + "#84a3dd", + "#81a2de", + "#7fa0de", + "#7c9fdf", + "#799edf", + "#769de0", + "#739be0", + "#709ae1", + "#6d99e2", + "#6a98e2", + "#6796e3", + "#6395e3", + "#5f94e4", + "#5c93e5", + "#5892e6", + "#5390e6", + "#4f8fe7", + "#4b8ee8", + "#488de7", + "#468be6", + "#438ae5", + "#4189e4", + "#3e88e3", + "#3c86e2", + "#3985e1", + "#3684e0", + "#3383df", + "#3082de", + "#2d80dd", + "#2a7fdc", + "#267edb", + "#237dda", + "#1f7cd9", + "#1b7ad8", + "#1679d7", + "#1078d6", + "#0877d5", + "#0076d4" + ], + "sequential2": [ + "#f5f5f5", + "#f3f4f3", + "#f1f2f2", + "#f0f1f0", + "#eef0ee", + "#ebefec", + "#e9eeea", + "#e7ede8", + "#e5ece6", + "#e2ebe3", + "#e0eae1", + "#dde9de", + "#dae8dc", + "#d7e7d9", + "#d4e7d6", + "#d1e6d3", + "#cde5d0", + "#cae5cd", + "#c7e4ca", + "#c4e3c8", + "#c2e2c6", + "#bfe1c3", + "#bde0c1", + "#bbdfbf", + "#b8debd", + "#b6ddbb", + "#b3dcb8", + "#b1dbb6", + "#afdab4", + "#acd9b2", + "#aad7b0", + "#a7d6ad", + "#a5d5ab", + "#a3d4a9", + "#a0d3a7", + "#9ed2a5", + "#9cd1a3", + "#9ad0a1", + "#97cf9f", + "#95ce9d", + "#93cd9b", + "#90cc98", + "#8ecb96", + "#8cc994", + "#8ac892", + "#87c790", + "#85c68e", + "#83c58c", + "#81c48a", + "#7fc388", + "#7cc286", + "#7ac084", + "#78bf82", + "#76be80", + "#74bd7e", + "#71bc7c", + "#6fbb7b", + "#6dba79", + "#6bb877", + "#69b775", + "#67b673", + "#64b571", + "#62b46f", + "#60b36d", + "#5eb16b", + "#5cb06a", + "#5aaf68", + "#58ae66", + "#55ad64", + "#53ac62", + "#51aa60", + "#4fa95f", + "#4da85d", + "#4ba75b", + "#48a659", + "#46a457", + "#44a356", + "#42a254", + "#40a152", + "#3da050", + "#3b9e4e", + "#399d4d", + "#379c4b", + "#349b49", + "#329a47", + "#309846", + "#2d9744", + "#2b9642", + "#289541", + "#26933f", + "#23923d", + "#20913b", + "#1d903a", + "#1a8f38", + "#178d36", + "#138c34", + "#0f8b33", + "#0b8a31", + "#05882f", + "#00872e" + ], + "diverging": [ + "#de0090", + "#e21394", + "#e61f97", + "#e9289b", + "#ed309e", + "#f037a1", + "#ee46a3", + "#ec52a4", + "#ea5da6", + "#e866a8", + "#e66faa", + "#e476ac", + "#e37dad", + "#e184af", + "#e08ab2", + "#df90b4", + "#de96b6", + "#dd9bb8", + "#dca0ba", + "#dca5bc", + "#dca9bf", + "#dbaec1", + "#dbb2c3", + "#dbb6c5", + "#dbbac8", + "#dcbeca", + "#dcc1cc", + "#ddc5ce", + "#ddc8d1", + "#deccd3", + "#dfcfd5", + "#e0d2d7", + "#e1d5d9", + "#e2d8dc", + "#e3dade", + "#e4dde0", + "#e6dfe2", + "#e7e2e4", + "#e8e4e6", + "#e9e6e7", + "#ebe8e9", + "#eceaeb", + "#edecec", + "#efedee", + "#f0efef", + "#f1f0f1", + "#f2f2f2", + "#f3f3f3", + "#f4f4f4", + "#f5f4f5", + "#f4f5f5", + "#f4f4f4", + "#f3f3f3", + "#f2f2f2", + "#f0f1f1", + "#efeff0", + "#edeeef", + "#ececed", + "#eaebec", + "#e8e9eb", + "#e6e7e9", + "#e4e5e8", + "#e1e3e7", + "#dfe1e5", + "#dddfe4", + "#dadce3", + "#d7dae2", + "#d4d8e1", + "#d1d5df", + "#ced3de", + "#cbd0dd", + "#c8cedd", + "#c4cbdc", + "#c1c8db", + "#bdc6db", + "#b9c3da", + "#b5c0da", + "#b1bdda", + "#adbada", + "#a8b7da", + "#a3b4da", + "#9eb1da", + "#99aedb", + "#94abdb", + "#8ea8dc", + "#88a5dd", + "#81a2de", + "#7a9edf", + "#739be1", + "#6a98e2", + "#6195e4", + "#5791e6", + "#4b8ee8", + "#448be5", + "#3d87e3", + "#3584e0", + "#2c80dd", + "#227dda", + "#1679d7", + "#0076d4" + ], + "diverging2": [ + "#007f9b", + "#12839e", + "#1e86a2", + "#288aa5", + "#308da8", + "#3790ab", + "#3e94af", + "#4597b2", + "#4b9ab5", + "#519eb8", + "#57a1ba", + "#5da4bd", + "#63a7c0", + "#68aac2", + "#6eadc5", + "#73b0c8", + "#78b3ca", + "#7eb6cc", + "#83b9cf", + "#88bcd1", + "#8dbed3", + "#92c1d5", + "#97c4d7", + "#9cc6d9", + "#a1c9da", + "#a8cbdb", + "#aecddb", + "#b4cfdc", + "#bad1dd", + "#bfd4de", + "#c4d6de", + "#c8d8df", + "#cddae1", + "#d1dce2", + "#d4dee3", + "#d8e0e4", + "#dbe2e5", + "#dee4e7", + "#e1e6e8", + "#e4e8e9", + "#e6e9eb", + "#e9ebec", + "#ebeced", + "#edeeef", + "#eeeff0", + "#f0f1f1", + "#f1f2f2", + "#f3f3f3", + "#f4f4f4", + "#f4f5f5", + "#f4f5f4", + "#f4f4f4", + "#f2f3f2", + "#f1f2f1", + "#eff1ef", + "#edf0ee", + "#ebefec", + "#e9eee9", + "#e6ece6", + "#e3ebe4", + "#dfeae0", + "#dbe9dd", + "#d7e7d9", + "#d2e6d4", + "#cce5cf", + "#c7e4ca", + "#c3e2c7", + "#bfe0c3", + "#badfbf", + "#b6ddba", + "#b1dbb6", + "#acd9b2", + "#a8d7ae", + "#a3d4a9", + "#9ed2a5", + "#99d0a0", + "#94cd9c", + "#8fcb97", + "#8ac993", + "#85c68e", + "#80c38a", + "#7bc185", + "#76be80", + "#70bb7c", + "#6bb877", + "#66b672", + "#60b36d", + "#5bb068", + "#55ac64", + "#4fa95f", + "#49a65a", + "#44a355", + "#3ea050", + "#379c4b", + "#319947", + "#2a9542", + "#22923d", + "#1a8e38", + "#0f8b33", + "#00872e" + ], + "nominalBold": [ + "#005fae", + "#006e23", + "#c00004", + "#026a69", + "#6d5e02", + "#aa03a2", + "#04667c", + "#506503", + "#b30474", + "#056a54", + "#855105", + "#8d0ada", + "#08628a", + "#386806", + "#b50755", + "#09685e", + "#765708", + "#9c0db4", + "#0b646e", + "#5d5e09", + "#a80c86", + "#0b6844", + "#94430a", + "#4434f7", + "#105e91", + "#21670b", + "#b20d3e", + "#0f645f", + "#6d570d", + "#9b13a2", + "#10616f", + "#545d0d", + "#a51178", + "#10644a", + "#86490f", + "#711be2", + "#135e79", + "#43600f", + "#a71260", + "#126254", + "#755010", + "#8b19b5", + "#145f63", + "#5e5711", + "#9b1787", + "#126337", + "#983512", + "#1c4fb9", + "#18578e", + "#126216", + "#a8132d", + "#165e5c", + "#645313", + "#931a92", + "#175b6b", + "#4c5913", + "#9c186b", + "#165f4a", + "#7a4815", + "#7321c4", + "#195875", + "#3b5b15", + "#9e1854", + "#175c52", + "#6b4d16", + "#841fa1", + "#19595f", + "#565316", + "#911b79", + "#175d3a", + "#883917", + "#2541c7", + "#1c5479", + "#2d5b16", + "#9a1943", + "#1a5953", + "#634d17", + "#852091", + "#1b5660", + "#4e5217", + "#8f1d6d", + "#195940", + "#7a3f18", + "#5428cc", + "#1c5467", + "#415418", + "#911c5a", + "#1a5749", + "#6a4619", + "#7424a1", + "#1c5456", + "#554d19", + "#841f79", + "#1a582e", + "#8d291a", + "#224b8f", + "#204d7e", + "#195721", + "#931a25", + "#1c5352" + ], + "nominal": [ + "#80acf7", + "#35c456", + "#f88c8d", + "#3dbcbc", + "#c2ab37", + "#f67eed", + "#40b6d8", + "#95b439", + "#f683ba", + "#3fbc9b", + "#e7973c", + "#c095f4", + "#47afea", + "#71b83c", + "#f582a1", + "#43b7a8", + "#ce9e3f", + "#db84f2", + "#46b1c0", + "#a6a840", + "#f278cc", + "#44b782", + "#f48553", + "#9d9af0", + "#52a6ef", + "#55b542", + "#f27c8e", + "#4ab0a9", + "#bd9d45", + "#e674ed", + "#4caabf", + "#98a545", + "#ee73bc", + "#4aaf8a", + "#e18847", + "#a88feb", + "#50a5cb", + "#7fa847", + "#ed73a3", + "#4dab97", + "#c79149", + "#c280e8", + "#4fa5ab", + "#a39b49", + "#e868cf", + "#4caa70", + "#ec765e", + "#8094e6", + "#5899e3", + "#4aa94d", + "#ea6f7a", + "#51a3a0", + "#ab944c", + "#e362e1", + "#549db2", + "#8a9b4c", + "#e566ad", + "#51a286", + "#c8844e", + "#a582e0", + "#5798bd", + "#739d4d", + "#e46692", + "#539d90", + "#b28a4f", + "#be71dd", + "#5498a0", + "#94914e", + "#de5dbf", + "#519d72", + "#d87151", + "#7f87da", + "#5990be", + "#62994f", + "#e0617f", + "#55978f", + "#a48851", + "#c763d6", + "#56929e", + "#888d50", + "#d85aad", + "#549678", + "#c07752", + "#8b7dd3", + "#588da5", + "#778e51", + "#d95894", + "#559181", + "#aa7e52", + "#a56fcf", + "#568d8f", + "#8e8451", + "#c55cb6", + "#539163", + "#d55f53", + "#6282cc", + "#5d82ba", + "#528f57", + "#d8555c", + "#578a89" + ], + "nominalMuted": [ + "#ced4e4", + "#abe2b2", + "#e5d0d0", + "#b0dddd", + "#e1d4ab", + "#e3cee1", + "#bed7e2", + "#c9d9aa", + "#e3cdd6", + "#afddcd", + "#e4cfc3", + "#d7d0e2", + "#c5d3e1", + "#badbaa", + "#e2ccd1", + "#b0dad3", + "#e3cfb5", + "#dbcde1", + "#b1d7de", + "#d2d3ab", + "#e0cad8", + "#addac1", + "#e1ccc7", + "#cfcfdf", + "#c6d0de", + "#afdaaa", + "#dfcacc", + "#b0d6d3", + "#decdac", + "#dcc9de", + "#b3d3dd", + "#cad1ab", + "#ddc8d3", + "#aed6c4", + "#dec9c1", + "#cfcbdc", + "#bccfdb", + "#bed3ab", + "#dcc7cd", + "#afd4ca", + "#ddc8b6", + "#d3c8db", + "#b0d1d4", + "#d0ccab", + "#dac4d5", + "#add4b8", + "#dbc6c4", + "#c7cada", + "#c2cad8", + "#abd4ab", + "#dac4c5", + "#b0cfce", + "#d5c8ac", + "#d8c3d8", + "#b1ccd7", + "#c3ccab", + "#d7c2cc", + "#aecfc2", + "#d9c4ba", + "#cac5d6", + "#b9c9d5", + "#b8cdab", + "#d6c1c7", + "#afcdc6", + "#d8c3ae", + "#cec2d5", + "#afcace", + "#c8c6ab", + "#d4bfcd", + "#adcdb8", + "#d5c1bd", + "#c3c4d3", + "#bac5d2", + "#b1cbab", + "#d3bfc2", + "#aec9c5", + "#d1c2ac", + "#cebfd2", + "#afc6cc", + "#c2c5ab", + "#d1bdc8", + "#adc9ba", + "#d2beb8", + "#c3c1d0", + "#b1c4cf", + "#b9c5aa", + "#d0bcc3", + "#adc6be", + "#d1bdaf", + "#c6bece", + "#aec4c5", + "#c5c0ab", + "#cdbaca", + "#acc6b1", + "#cebbba", + "#bbbfcd", + "#b8becb", + "#aac5ac", + "#cdbaba", + "#aec2c2" + ], + "greys": [ + "#f5f5f5", + "#f3f3f3", + "#f0f0f0", + "#eeeeee", + "#ececec", + "#eaeaea", + "#e8e8e8", + "#e5e5e5", + "#e3e3e3", + "#e1e1e1", + "#dfdfdf", + "#dddddd", + "#dadada", + "#d8d8d8", + "#d6d6d6", + "#d4d4d4", + "#d2d2d2", + "#d0d0d0", + "#cecece", + "#cbcbcb", + "#c9c9c9", + "#c7c7c7", + "#c5c5c5", + "#c3c3c3", + "#c1c1c1", + "#bfbfbf", + "#bcbcbc", + "#bababa", + "#b8b8b8", + "#b6b6b6", + "#b4b4b4", + "#b2b2b2", + "#b0b0b0", + "#aeaeae", + "#acacac", + "#aaaaaa", + "#a8a8a8", + "#a5a5a5", + "#a3a3a3", + "#a1a1a1", + "#9f9f9f", + "#9d9d9d", + "#9b9b9b", + "#999999", + "#979797", + "#959595", + "#939393", + "#919191", + "#8f8f8f", + "#8d8d8d", + "#8b8b8b", + "#898989", + "#878787", + "#858585", + "#838383", + "#818181", + "#7f7f7f", + "#7d7d7d", + "#7b7b7b", + "#797979", + "#777777", + "#757575", + "#737373", + "#717171", + "#707070", + "#6e6e6e", + "#6c6c6c", + "#6a6a6a", + "#686868", + "#666666", + "#646464", + "#626262", + "#606060", + "#5e5e5e", + "#5d5d5d", + "#5b5b5b", + "#595959", + "#575757", + "#555555", + "#535353", + "#525252", + "#505050", + "#4e4e4e", + "#4c4c4c", + "#4a4a4a", + "#494949", + "#474747", + "#454545", + "#434343", + "#414141", + "#404040", + "#3e3e3e", + "#3c3c3c", + "#3a3a3a", + "#393939", + "#373737", + "#353535", + "#343434", + "#323232", + "#303030" + ], + "warning": "#ff8c00", + "error": "#d13438" + }, + "dark": { + "background": "#00040d", + "offsetBackground": "#000000", + "foreground": "#f1f1f1", + "accent": "#3f75bf", + "lowContrastAnnotation": "#323232", + "midContrastAnnotation": "#858585", + "highContrastAnnotation": "#e2e2e2", + "faintAnnotation": "#1b1b1b", + "sequential": [ + "#1d1d1d", + "#1e1e1f", + "#1f1f20", + "#1f2021", + "#202122", + "#212123", + "#212224", + "#222326", + "#222427", + "#232528", + "#242529", + "#24262b", + "#25272c", + "#26282d", + "#26292f", + "#272930", + "#272a31", + "#282b33", + "#282c34", + "#292d35", + "#292e37", + "#2a2e38", + "#2b2f3a", + "#2b303b", + "#2c313c", + "#2c323e", + "#2d333f", + "#2d3341", + "#2e3442", + "#2e3544", + "#2f3645", + "#2f3747", + "#303849", + "#30384a", + "#30394c", + "#313a4d", + "#313b4f", + "#323c51", + "#323d52", + "#323e54", + "#333f56", + "#333f57", + "#344059", + "#34415b", + "#34425c", + "#34435e", + "#354460", + "#354562", + "#354663", + "#364665", + "#364767", + "#364869", + "#36496b", + "#364a6d", + "#374b6f", + "#374c70", + "#374d72", + "#374e74", + "#374f76", + "#375078", + "#37507a", + "#37517c", + "#37527e", + "#375380", + "#375482", + "#375584", + "#375686", + "#375788", + "#36588a", + "#36598d", + "#365a8f", + "#365b91", + "#355c93", + "#355c95", + "#355d97", + "#345e9a", + "#345f9c", + "#33609e", + "#3361a0", + "#3262a3", + "#3163a5", + "#3064a7", + "#2f65aa", + "#2f66ac", + "#2d67ae", + "#2c68b1", + "#2b69b3", + "#2a6ab6", + "#286bb8", + "#276cba", + "#256dbd", + "#236ebf", + "#216fc2", + "#1e70c4", + "#1b71c7", + "#1872ca", + "#1473cc", + "#0f74cf", + "#0875d1", + "#0076d4" + ], + "sequential2": [ + "#1d1d1d", + "#1e1e1e", + "#1f1f1f", + "#1f201f", + "#202120", + "#202220", + "#212321", + "#212422", + "#222422", + "#222523", + "#232623", + "#232724", + "#242824", + "#242925", + "#252a25", + "#252b26", + "#262c26", + "#262d27", + "#262e27", + "#272f28", + "#273028", + "#283129", + "#283129", + "#29322a", + "#29332a", + "#29342b", + "#2a352b", + "#2a362c", + "#2a372c", + "#2b382c", + "#2b392d", + "#2b3a2d", + "#2c3b2e", + "#2c3c2e", + "#2c3d2f", + "#2d3e2f", + "#2d3f2f", + "#2d4030", + "#2d4130", + "#2e4230", + "#2e4331", + "#2e4531", + "#2e4631", + "#2e4732", + "#2f4832", + "#2f4932", + "#2f4a33", + "#2f4b33", + "#2f4c33", + "#2f4d34", + "#2f4e34", + "#2f4f34", + "#2f5034", + "#2f5134", + "#305235", + "#305335", + "#2f5535", + "#2f5635", + "#2f5735", + "#2f5836", + "#2f5936", + "#2f5a36", + "#2f5b36", + "#2f5c36", + "#2f5e36", + "#2e5f36", + "#2e6036", + "#2e6136", + "#2e6236", + "#2d6336", + "#2d6436", + "#2d6636", + "#2c6736", + "#2c6836", + "#2b6936", + "#2b6a36", + "#2a6b36", + "#2a6d36", + "#296e36", + "#286f36", + "#287036", + "#277135", + "#267235", + "#257435", + "#247535", + "#237634", + "#227734", + "#217834", + "#1f7a33", + "#1e7b33", + "#1c7c33", + "#1b7d32", + "#197f32", + "#168031", + "#148131", + "#118230", + "#0e8430", + "#0a852f", + "#05862e", + "#00872e" + ], + "diverging": [ + "#de0090", + "#d7118c", + "#cf1a88", + "#c82184", + "#c12580", + "#ba297c", + "#b32b78", + "#ad2e74", + "#a62f71", + "#a0316d", + "#9a326a", + "#943366", + "#8f3363", + "#893460", + "#84345d", + "#7f345a", + "#7a3457", + "#753454", + "#703351", + "#6b334e", + "#67324c", + "#633249", + "#5e3147", + "#5a3144", + "#563042", + "#532f40", + "#4f2e3d", + "#4c2e3b", + "#482d39", + "#452c37", + "#422b35", + "#3f2a33", + "#3c2931", + "#392830", + "#37282e", + "#34272c", + "#32262b", + "#2f2529", + "#2d2428", + "#2b2327", + "#292325", + "#282224", + "#262123", + "#242122", + "#232021", + "#221f20", + "#201f1f", + "#1f1e1f", + "#1e1e1e", + "#1e1e1e", + "#1e1e1e", + "#1e1e1e", + "#1e1f1f", + "#1f1f20", + "#1f2021", + "#202122", + "#212224", + "#212225", + "#222326", + "#232428", + "#24262a", + "#25272c", + "#26282d", + "#26292f", + "#272a32", + "#282c34", + "#292d36", + "#2a2f39", + "#2b303b", + "#2c323e", + "#2d3341", + "#2e3544", + "#2f3747", + "#30384a", + "#313a4d", + "#323c51", + "#333e54", + "#334058", + "#34425c", + "#354460", + "#354664", + "#364868", + "#364a6d", + "#374c71", + "#374f76", + "#37517b", + "#375380", + "#375685", + "#36588b", + "#365a91", + "#355d96", + "#34609c", + "#3262a3", + "#3065a9", + "#2d67b0", + "#296ab6", + "#256dbd", + "#1e70c5", + "#1473cc", + "#0076d4" + ], + "diverging2": [ + "#007f9b", + "#117c96", + "#1a7891", + "#20758d", + "#257288", + "#286f84", + "#2b6c80", + "#2d697c", + "#2f6678", + "#306374", + "#326070", + "#325d6c", + "#335a69", + "#335865", + "#345562", + "#34535e", + "#34505b", + "#334e58", + "#334b55", + "#334952", + "#32474f", + "#32444c", + "#31424a", + "#304047", + "#303e44", + "#2f3c42", + "#2e3a3f", + "#2d383d", + "#2d363b", + "#2c3539", + "#2b3337", + "#2a3135", + "#293033", + "#282e31", + "#282d2f", + "#272b2d", + "#262a2c", + "#25282a", + "#242729", + "#232627", + "#232526", + "#222425", + "#212323", + "#212222", + "#202121", + "#1f2020", + "#1f1f20", + "#1e1f1f", + "#1e1e1e", + "#1e1e1e", + "#1e1e1e", + "#1e1e1e", + "#1e1f1e", + "#1f1f1f", + "#1f201f", + "#202120", + "#202220", + "#212321", + "#222422", + "#222523", + "#232623", + "#242824", + "#242925", + "#252b26", + "#262c26", + "#262e27", + "#272f28", + "#283129", + "#29332a", + "#29342b", + "#2a362c", + "#2b382c", + "#2b3a2d", + "#2c3c2e", + "#2d3e2f", + "#2d4030", + "#2e4331", + "#2e4531", + "#2f4732", + "#2f4a33", + "#2f4c33", + "#2f4f34", + "#2f5134", + "#305435", + "#2f5735", + "#2f5a36", + "#2f5c36", + "#2e5f36", + "#2e6236", + "#2d6536", + "#2c6836", + "#2a6c36", + "#286f36", + "#267235", + "#237535", + "#207934", + "#1c7c33", + "#168031", + "#0e8430", + "#00872e" + ], + "nominalBold": [ + "#005fae", + "#006e23", + "#c00004", + "#026a69", + "#6d5e02", + "#aa03a2", + "#04667c", + "#506503", + "#b30474", + "#056a54", + "#855105", + "#8d0ada", + "#08628a", + "#386806", + "#b50755", + "#09685e", + "#765708", + "#9c0db4", + "#0b646e", + "#5d5e09", + "#a80c86", + "#0b6844", + "#94430a", + "#4434f7", + "#105e91", + "#21670b", + "#b20d3e", + "#0f645f", + "#6d570d", + "#9b13a2", + "#10616f", + "#545d0d", + "#a51178", + "#10644a", + "#86490f", + "#711be2", + "#135e79", + "#43600f", + "#a71260", + "#126254", + "#755010", + "#8b19b5", + "#145f63", + "#5e5711", + "#9b1787", + "#126337", + "#983512", + "#1c4fb9", + "#18578e", + "#126216", + "#a8132d", + "#165e5c", + "#645313", + "#931a92", + "#175b6b", + "#4c5913", + "#9c186b", + "#165f4a", + "#7a4815", + "#7321c4", + "#195875", + "#3b5b15", + "#9e1854", + "#175c52", + "#6b4d16", + "#841fa1", + "#19595f", + "#565316", + "#911b79", + "#175d3a", + "#883917", + "#2541c7", + "#1c5479", + "#2d5b16", + "#9a1943", + "#1a5953", + "#634d17", + "#852091", + "#1b5660", + "#4e5217", + "#8f1d6d", + "#195940", + "#7a3f18", + "#5428cc", + "#1c5467", + "#415418", + "#911c5a", + "#1a5749", + "#6a4619", + "#7424a1", + "#1c5456", + "#554d19", + "#841f79", + "#1a582e", + "#8d291a", + "#224b8f", + "#204d7e", + "#195721", + "#931a25", + "#1c5352" + ], + "nominal": [ + "#80acf7", + "#35c456", + "#f88c8d", + "#3dbcbc", + "#c2ab37", + "#f67eed", + "#40b6d8", + "#95b439", + "#f683ba", + "#3fbc9b", + "#e7973c", + "#c095f4", + "#47afea", + "#71b83c", + "#f582a1", + "#43b7a8", + "#ce9e3f", + "#db84f2", + "#46b1c0", + "#a6a840", + "#f278cc", + "#44b782", + "#f48553", + "#9d9af0", + "#52a6ef", + "#55b542", + "#f27c8e", + "#4ab0a9", + "#bd9d45", + "#e674ed", + "#4caabf", + "#98a545", + "#ee73bc", + "#4aaf8a", + "#e18847", + "#a88feb", + "#50a5cb", + "#7fa847", + "#ed73a3", + "#4dab97", + "#c79149", + "#c280e8", + "#4fa5ab", + "#a39b49", + "#e868cf", + "#4caa70", + "#ec765e", + "#8094e6", + "#5899e3", + "#4aa94d", + "#ea6f7a", + "#51a3a0", + "#ab944c", + "#e362e1", + "#549db2", + "#8a9b4c", + "#e566ad", + "#51a286", + "#c8844e", + "#a582e0", + "#5798bd", + "#739d4d", + "#e46692", + "#539d90", + "#b28a4f", + "#be71dd", + "#5498a0", + "#94914e", + "#de5dbf", + "#519d72", + "#d87151", + "#7f87da", + "#5990be", + "#62994f", + "#e0617f", + "#55978f", + "#a48851", + "#c763d6", + "#56929e", + "#888d50", + "#d85aad", + "#549678", + "#c07752", + "#8b7dd3", + "#588da5", + "#778e51", + "#d95894", + "#559181", + "#aa7e52", + "#a56fcf", + "#568d8f", + "#8e8451", + "#c55cb6", + "#539163", + "#d55f53", + "#6282cc", + "#5d82ba", + "#528f57", + "#d8555c", + "#578a89" + ], + "nominalMuted": [ + "#ced4e4", + "#abe2b2", + "#e5d0d0", + "#b0dddd", + "#e1d4ab", + "#e3cee1", + "#bed7e2", + "#c9d9aa", + "#e3cdd6", + "#afddcd", + "#e4cfc3", + "#d7d0e2", + "#c5d3e1", + "#badbaa", + "#e2ccd1", + "#b0dad3", + "#e3cfb5", + "#dbcde1", + "#b1d7de", + "#d2d3ab", + "#e0cad8", + "#addac1", + "#e1ccc7", + "#cfcfdf", + "#c6d0de", + "#afdaaa", + "#dfcacc", + "#b0d6d3", + "#decdac", + "#dcc9de", + "#b3d3dd", + "#cad1ab", + "#ddc8d3", + "#aed6c4", + "#dec9c1", + "#cfcbdc", + "#bccfdb", + "#bed3ab", + "#dcc7cd", + "#afd4ca", + "#ddc8b6", + "#d3c8db", + "#b0d1d4", + "#d0ccab", + "#dac4d5", + "#add4b8", + "#dbc6c4", + "#c7cada", + "#c2cad8", + "#abd4ab", + "#dac4c5", + "#b0cfce", + "#d5c8ac", + "#d8c3d8", + "#b1ccd7", + "#c3ccab", + "#d7c2cc", + "#aecfc2", + "#d9c4ba", + "#cac5d6", + "#b9c9d5", + "#b8cdab", + "#d6c1c7", + "#afcdc6", + "#d8c3ae", + "#cec2d5", + "#afcace", + "#c8c6ab", + "#d4bfcd", + "#adcdb8", + "#d5c1bd", + "#c3c4d3", + "#bac5d2", + "#b1cbab", + "#d3bfc2", + "#aec9c5", + "#d1c2ac", + "#cebfd2", + "#afc6cc", + "#c2c5ab", + "#d1bdc8", + "#adc9ba", + "#d2beb8", + "#c3c1d0", + "#b1c4cf", + "#b9c5aa", + "#d0bcc3", + "#adc6be", + "#d1bdaf", + "#c6bece", + "#aec4c5", + "#c5c0ab", + "#cdbaca", + "#acc6b1", + "#cebbba", + "#bbbfcd", + "#b8becb", + "#aac5ac", + "#cdbaba", + "#aec2c2" + ], + "greys": [ + "#1d1d1d", + "#1f1f1f", + "#212121", + "#222222", + "#242424", + "#262626", + "#272727", + "#292929", + "#2b2b2b", + "#2c2c2c", + "#2e2e2e", + "#303030", + "#323232", + "#333333", + "#353535", + "#373737", + "#393939", + "#3a3a3a", + "#3c3c3c", + "#3e3e3e", + "#404040", + "#424242", + "#434343", + "#454545", + "#474747", + "#494949", + "#4b4b4b", + "#4d4d4d", + "#4e4e4e", + "#505050", + "#525252", + "#545454", + "#565656", + "#585858", + "#5a5a5a", + "#5c5c5c", + "#5e5e5e", + "#606060", + "#616161", + "#636363", + "#656565", + "#676767", + "#696969", + "#6b6b6b", + "#6d6d6d", + "#6f6f6f", + "#717171", + "#737373", + "#757575", + "#777777", + "#797979", + "#7b7b7b", + "#7d7d7d", + "#7f7f7f", + "#818181", + "#838383", + "#858585", + "#878787", + "#898989", + "#8c8c8c", + "#8e8e8e", + "#909090", + "#929292", + "#949494", + "#969696", + "#989898", + "#9a9a9a", + "#9c9c9c", + "#9e9e9e", + "#a1a1a1", + "#a3a3a3", + "#a5a5a5", + "#a7a7a7", + "#a9a9a9", + "#ababab", + "#adadad", + "#afafaf", + "#b2b2b2", + "#b4b4b4", + "#b6b6b6", + "#b8b8b8", + "#bababa", + "#bdbdbd", + "#bfbfbf", + "#c1c1c1", + "#c3c3c3", + "#c5c5c5", + "#c8c8c8", + "#cacaca", + "#cccccc", + "#cecece", + "#d0d0d0", + "#d3d3d3", + "#d5d5d5", + "#d7d7d7", + "#d9d9d9", + "#dcdcdc", + "#dedede", + "#e0e0e0", + "#e2e2e2" + ], + "warning": "#ff8c00", + "error": "#d13438" + } +} \ No newline at end of file diff --git a/graspologic/layouts/nooverlap/__init__.py b/graspologic/layouts/nooverlap/__init__.py new file mode 100644 index 000000000..d2954b6e7 --- /dev/null +++ b/graspologic/layouts/nooverlap/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + + +from graspologic.layouts.nooverlap.nooverlap import remove_overlaps + + +__all__ = ["remove_overlaps"] diff --git a/graspologic/layouts/nooverlap/_grid.py b/graspologic/layouts/nooverlap/_grid.py new file mode 100644 index 000000000..e496315a4 --- /dev/null +++ b/graspologic/layouts/nooverlap/_grid.py @@ -0,0 +1,198 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import math +from collections import defaultdict + + +class _GridBuckets: + """ + One thing to note, that right now this grid must have cells the same size or bigger + than the radius of the largest node in the graph. + """ + + def __init__(self, cell_size): + self.cell_size = cell_size + self.grid = defaultdict(list) + self.min_x = math.inf + self.min_y = math.inf + self.max_x = -math.inf + self.max_y = -math.inf + + def get_cell(self, x, y): + xval = x // self.cell_size + yval = y // self.cell_size + return xval * self.cell_size, yval * self.cell_size + + def get_grid_cells(self, x, y, node_size): + return self._get_grid_cells(x, y, node_size, False) + + def _get_grid_cells(self, x, y, node_size, update_max=True): + """ + Each node will be at least one cell but up to four cells. + It depends on the grid cell size and the size and location of the node + :param x: x location + :param y: y location + :param node_size: nodesize + :return: + """ + min_x = x - node_size + max_x = x + node_size + min_y = y - node_size + max_y = y + node_size + if update_max: + self.min_x = min(self.min_x, min_x) + self.min_y = min(self.min_y, min_y) + self.max_x = max(self.max_x, max_x) + self.max_y = max(self.max_y, max_y) + nw = self.get_cell(min_x, max_y) + ne = self.get_cell(max_x, max_y) + se = self.get_cell(max_x, min_y) + sw = self.get_cell(min_x, min_y) + return {nw, ne, se, sw} + + def add_node(self, node): + """ + Node to add must have an x, y, and size property + :param node: The node to add to the grid + :return: The node added + """ + cells = self._get_grid_cells(node.x, node.y, node.size) + for cell in cells: + self.grid[cell].append(node) + return node + + def add_node_once(self, node): + """ + Node to add must have an x, y, and size property + :param node: The node to add to the grid + :return: The node added + """ + cell = self.get_cell(node.x, node.y) + self.grid[cell].append(node) + return node + + def remove_node(self, node): + """ + Will throw an exception if the node is not in the grid. + :param node: node to remove + :return: the node removed + """ + cells = self._get_grid_cells(node.x, node.y, node.size) + for cell in cells: + self.grid[cell].remove(node) + return node + + def get_nodes_for_cell(self, cell): + return self.grid[cell] + + def get_potential_overlapping_nodes_by_node(self, node): + return self.get_potential_overlapping_nodes(node.x, node.y, node.size) + + def get_potential_overlapping_nodes(self, x, y, size): + cells = self._get_grid_cells(x, y, size, update_max=False) + nodes = set() + for c in cells: + nodes.update(self.grid[c]) + return nodes + + def _get_x_cells(self): + xrange = self.max_x - self.min_x + return xrange // self.cell_size + + def _get_y_cells(self): + yrange = self.max_y - self.min_y + return yrange // self.cell_size + + def num_cells(self): + x_cells = self._get_x_cells() + y_cells = self._get_y_cells() + return x_cells * y_cells + + def add_nodes(self, node_list, only_once=False): + for n in node_list: + if only_once: + self.add_node_once(n) + else: + self.add_node(n) + return self + + def max_in_cell(self): + max_in_cell = -1 + for c in self.grid: + max_in_cell = max(max_in_cell, len(self.grid[c])) + return max_in_cell + + def get_grid_cell_stats(self, with_zeros=True): + num_nodes_in_cell_to_count_of_cells = defaultdict(int) + for c in self.grid: + num_nodes_in_cell_to_count_of_cells[len(self.grid[c])] += 1 + if with_zeros: + number_of_cells = self.num_cells() + number_of_zeros = int(number_of_cells - len(self.grid)) + num_nodes_in_cell_to_count_of_cells[0] = number_of_zeros + return [ + (cell_count, node_count) + for cell_count, node_count in sorted( + num_nodes_in_cell_to_count_of_cells.items() + ) + ] + + def get_grid_bounds(self): + x_min, y_min = math.inf, math.inf + x_max, y_max = -math.inf, -math.inf + for x, y in self.grid: + x_min = min(x_min, x) + y_min = min(y_min, y) + x_max = max(x_max, x) + y_max = max(y_max, y) + return x_min, y_min, x_max, y_max + + def get_all_grid_cells(self): + rows = [] + x_min, y_min, x_max, y_max = self.get_grid_bounds() + cell_size = self.cell_size + # print (type(y_min), type(y_max), type(cell_size)) + y = y_min + while y <= y_max: + row = [] + x = x_min + while x <= x_max: + row.append(len(self.grid[(x, y)])) + x += cell_size + rows.insert(0, row) + y += cell_size + # for c in sorted(self.grid): + # print (c) + return rows + + def print_stats(self): + print( + f"cell size: {self.cell_size}, area: {self.cell_size*self.cell_size}, " + f"rows: {self._get_y_cells()}, cols: {self._get_x_cells()}" + ) + return None + + def _get_area(self, node_list): + area = 0.0 + for n in node_list: + area += n.size * n.size * 4 + return area + + def get_all_grid_cells_by_area(self): + rows = [] + x_min, y_min, x_max, y_max = self.get_grid_bounds() + cell_size = self.cell_size + # print (type(y_min), type(y_max), type(cell_size)) + y = y_min + while y <= y_max: + row = [] + x = x_min + while x <= x_max: + row.append(self._get_area(self.grid[(x, y)])) + x += cell_size + rows.insert(0, row) + y += cell_size + # for c in sorted(self.grid): + # print (c) + return rows diff --git a/graspologic/layouts/nooverlap/_node.py b/graspologic/layouts/nooverlap/_node.py new file mode 100644 index 000000000..f0ea544be --- /dev/null +++ b/graspologic/layouts/nooverlap/_node.py @@ -0,0 +1,24 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + + +class _Node: + def __init__(self, node_id, x, y, size, community=9999999, color=""): + self.node_id = node_id + self.x = float(x) + self.y = float(y) + self.original_x = self.x + self.original_y = self.y + self.size = float(size) + self.community = community + self.color = color + + def reset_original_position(self, newx, newy): + self.original_x = self.x = newx + self.original_y = self.y = newy + + def __eq__(self, other): + return self.node_id == other.node_id + + def __hash__(self): + return hash(self.node_id) diff --git a/graspologic/layouts/nooverlap/_quad_node.py b/graspologic/layouts/nooverlap/_quad_node.py new file mode 100644 index 000000000..d9c1afdc8 --- /dev/null +++ b/graspologic/layouts/nooverlap/_quad_node.py @@ -0,0 +1,1095 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from graspologic.layouts.classes import NodePosition +import math +import logging +from sklearn.preprocessing import normalize +import numpy as np + +from scipy.spatial import distance + +_EPSILON = 0.001 +logger = logging.getLogger(__name__) + + +def node_positions_overlap(n1: NodePosition, n2: NodePosition): + return is_overlap(n1.x, n1.y, n1.size, n2.x, n2.y, n2.size) + + +def is_overlap(x1, y1, s1, x2, y2, s2): + min_dist = s1 + s2 + if abs(x2 - x1) > min_dist or abs(y2 - y1) > min_dist: + # shortcut to reduce the amount of math required, the distance calc is more expensive + return False + d = distance.euclidean([x1, y1], [x2, y2]) + if d <= s1 + s2: + return True + return False + + +def is_overlapping_any_node_and_index(node, new_x, new_y, nodes, start, end): + overlapping = None + idx = 0 + for idx, n in enumerate(nodes[start:end]): + if n.node_id == node.node_id: + # don't check against self + continue + if is_overlap(n.x, n.y, n.size, new_x, new_y, node.size): + overlapping = n + break + return idx + start, overlapping + + +def is_overlapping_any_node_and_index_with_grid( + node, new_x, new_y, nodes, start, end, size, grid +): + overlapping = None + idx = 0 + potentially_over_lapping = grid.get_potential_overlapping_nodes(new_x, new_y, size) + for idx, n in enumerate(nodes[start:end]): + if n not in potentially_over_lapping: + continue + if n.node_id == node.node_id: + # don't check against self + continue + if is_overlap(n.x, n.y, n.size, new_x, new_y, node.size): + overlapping = n + break + return idx + start, overlapping + + +def stats_nodes(nodes): + max_x = -math.inf + min_x = math.inf + max_y = -math.inf + min_y = math.inf + max_size = -1 + for gnode in nodes: + max_x = max(gnode.x, max_x) + max_y = max(gnode.y, max_y) + min_x = min(gnode.x, min_x) + min_y = min(gnode.y, min_y) + max_size = max(gnode.size, max_size) + return min_x, min_y, max_x, max_y, max_size + + +def total_area(min_x, min_y, max_x, max_y): + return (max_x - min_x) * (max_y - min_y) + + +def move_point_on_line(a, b, ratio): + npa = np.array(a) + npb = np.array(b) + d = npb - npa + d_norm = normalize([d]) + new_point = b + d_norm[0] * ratio + return new_point + + +def scale_graph(g, scale_factor): + for _, n in g.items(): + n.x, n.y = move_point_on_line([0, 0], [n.x, n.y], scale_factor) + return g + + +def find_center(nodes): + num_nodes = len(nodes) + if num_nodes <= 0: + raise Exception("Zero nodes!") + + min_x, min_y, max_x, max_y, max_size = stats_nodes(nodes) + tot_area = total_area(min_x, min_y, max_x, max_y) + x, y = 0.0, 0.0 + # sum X and Y values then devide by number of nodes to get the average (x,y) or center + for n in nodes: + x += n.x + y += n.y + # need to fix x and y + x = x / num_nodes + y = y / num_nodes + return x, y, max_size + + +class _QuadNode: + """ + Represents a node in a quad tree. Each node has a list of nodes that are represented here + or in its children. + Each node knows its own depth in the tree. Each node will have less than max_nodes_per_quad + in the nodes list or it will have populated children. + Each node also has a point back up to its parent. The root node in a tree will have None for + its parent attribute. + Each node has an x and y that are the center point which is a weighted center. + Each node has min_x, min_y, max_x_, max_y that defines the area of this node + Each node also has four child nodes NW, NE, SE, and SW they represent the nodes that are + that direction from the center. + + Each node has a property indicating if has been laid out. + """ + + max_ratio = 0.95 + + def __init__(self, nodes, depth, max_nodes_per_quad, parent=None): + self.nodes = nodes + self.depth = depth + self.max_nodes_per_quad = max_nodes_per_quad + self.is_laid_out = False + self.parent = parent + self.find_center() + self.push_to_kids() + self.total_nodes_moved = 0 + self.not_first_choice = 0 + + def __lt__(self, other): + return self.x < other.x + + def child_list(self): + return [self.NW, self.NE, self.SW, self.SE] + + def get_total_cells(self, min_x, min_y, max_x, max_y, max_size): + side_size = max_size * 2.0 + self.x_range = max_x - min_x + self.y_range = max_y - min_y + number_of_x_cells = self.x_range // side_size + number_of_y_cells = self.y_range // side_size + total_cells = number_of_y_cells * number_of_x_cells + return total_cells + + def find_center(self): + if len(self.nodes) <= 0: + raise Exception("Invalid to create a quad node with zero nodes!") + + self.min_x, self.min_y, self.max_x, self.max_y, self.max_size = stats_nodes( + self.nodes + ) + self.circle_size = self.total_circle_size() + self.square_size = self.total_square_size() + tot_area = total_area(self.min_x, self.min_y, self.max_x, self.max_y) + if tot_area == 0: + tot_area = 0.001 + self.x, self.y = 0.0, 0.0 + self.total_cells = self.get_total_cells( + self.min_x, self.min_y, self.max_x, self.max_y, self.max_size + ) + if tot_area == 0: + self.sq_ratio = 1.0 + self.cir_ratio = 1.0 + else: + self.sq_ratio = self.square_size / tot_area + self.cir_ratio = self.circle_size / tot_area + + # sum X and Y values then devide by number of nodes to get the average (x,y) or center + for n in self.nodes: + self.x += n.x + self.y += n.y + # need to fix x and y + self.x = self.x / self.num_nodes() + self.y = self.y / self.num_nodes() + + # print ("depth: %d, size: %d, center %g, %g, min (%g,%g), max(%g,%g)" %(self.depth, len(self.nodes),self.x, self.y,min_x, min_y, max_x, max_y)) + + def push_to_kids(self): + if len(self.nodes) <= self.max_nodes_per_quad: + # if len == nodes then we already have the center and mass initialized because of + # the calculation done in the constructor + self.NW = None + self.NE = None + self.SW = None + self.SE = None + return + nw_nodes, sw_nodes, ne_nodes, se_nodes = [], [], [], [] + for node in self.nodes: + if node.y > self.y: + if node.x > self.x: + ne_nodes.append(node) + else: + nw_nodes.append(node) + else: + if node.x > self.x: + se_nodes.append(node) + else: + sw_nodes.append(node) + + self.NW = ( + _QuadNode(nw_nodes, self.depth + 1, self.max_nodes_per_quad, self) + if len(nw_nodes) > 0 + else None + ) + self.NE = ( + _QuadNode(ne_nodes, self.depth + 1, self.max_nodes_per_quad, self) + if len(ne_nodes) > 0 + else None + ) + self.SW = ( + _QuadNode(sw_nodes, self.depth + 1, self.max_nodes_per_quad, self) + if len(sw_nodes) > 0 + else None + ) + self.SE = ( + _QuadNode(se_nodes, self.depth + 1, self.max_nodes_per_quad, self) + if len(se_nodes) > 0 + else None + ) + + for quad in self.child_list(): + if quad: + quad.push_to_kids() + return + + def num_children(self): + total = 1 + for quad in self.child_list(): + if quad: + total += quad.num_children() + return total + + def num_nodes(self): + return len(self.nodes) + + def print_node(self, max_depth): + tot_area = total_area(self.min_x, self.min_y, self.max_x, self.max_y) + # print (tot_area, min_x, min_y, max_x, max_y) + # if tot_area == 0: + # for n in self.nodes: + # print(n.x, n.y) + # tot_area = 0.001 + tag = "ttttt" if self.circle_size / tot_area > self.max_ratio else "" + print( + "-" * self.depth, + "size: %d, (%g, %g), (%g, %g) cs: %g, tot: %g, ratio: %g, ss: %g, ratio: %g, tag: %s" + % ( + len(self.nodes), + self.min_x, + self.min_y, + self.max_x, + self.max_y, + self.circle_size, + tot_area, + self.circle_size / tot_area, + self.square_size, + self.square_size / tot_area, + tag, + ), + ) + if self.depth >= max_depth: + return + + for quad in self.child_list(): + if quad: + quad.print_node(max_depth) + + def total_circle_size(self): + total_size = 0 + for n in self.nodes: + total_size += n.size * n.size * math.pi + return total_size + + def total_square_size(self): + total_size = 0 + for n in self.nodes: + total_size += n.size * n.size * 2 * 2 + return total_size + + def get_stats_for_quad(self, max_depth, stats_list, magnification=10): + tot_area = total_area(self.min_x, self.min_y, self.max_x, self.max_y) + if tot_area == 0: + tot_area = 0.001 + tag = 5 if self.circle_size / tot_area > self.max_ratio else 0 + # print ('-'*self.depth, "size: %d, (%g, %g), (%g, %g) cs: %g, tot: %g, ratio: %g, ss: %g, ratio: %g, tag: %s" %(len(self.nodes), self.min_x, self.min_y, self.max_x, self.max_y, self.circle_size, tot_area, self.circle_size/tot_area, self.square_size, self.square_size/tot_area, tag)) + if self.depth >= max_depth: + stats_list.append( + [ + self.x, + self.y, + self.circle_size / tot_area * magnification, + tag, + self.square_size / tot_area * magnification, + ] + ) + return + + has_children = False + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + quad.get_stats_for_quad(max_depth, stats_list, magnification) + has_children = True + if not has_children: + stats_list.append( + [ + self.x, + self.y, + self.circle_size / tot_area * magnification, + tag, + self.square_size / tot_area * magnification, + ] + ) + return + + def find_grid_cell_and_center(self, min_x, min_y, max_size, x, y): + # zero the cordinates + side_size = max_size * 2.0 + zeroed_x = x - min_x + zeroed_y = y - min_y + x_cell = int(zeroed_x // side_size) + y_cell = int(zeroed_y // side_size) + return x_cell, y_cell, min_x + side_size * x_cell, min_y + side_size * y_cell + + def find_free_cell( + self, cells, x, y, num_x_cells, num_y_cells, min_x, min_y, max_size + ): + # zero the cordinates + square_size = max_size * 2 + new_x, new_y = x, y + distance_to_move = 1 + count = 0 + done = True + while (new_x, new_y) in cells or not done: + done = False + # go right + # print ('newx:', new_x+1, 'end x', new_x+distance_to_move+1) + for new_x in range(new_x + 1, new_x + distance_to_move + 1): + # print ("ffc-r (%g,%g) to_move %d" %(new_x, new_y, distance_to_move)) + if ( + not (new_x, new_y) in cells + and new_x < num_x_cells + and new_y < num_y_cells + and new_x >= 0 + and new_y >= 0 + ): + # print('breaking-r') + done = True + break + if done: + break + # go up + for new_y in range(new_y + 1, new_y + distance_to_move + 1): + # print ("ffc-u (%g,%g) to_move %d" %(new_x, new_y, distance_to_move)) + if ( + not (new_x, new_y) in cells + and new_x < num_x_cells + and new_y < num_y_cells + and new_x >= 0 + and new_y >= 0 + ): + # print('breaking-u') + done = True + break + distance_to_move += 1 + if done: + break + # go left + # print ('about to go left, %g, %g' %(new_x, new_y)) + for new_x in range(new_x - 1, new_x - distance_to_move - 1, -1): + # print ("ffc-l (%g,%g) to_move %d" %(new_x, new_y, distance_to_move)) + if ( + not (new_x, new_y) in cells + and new_x < num_x_cells + and new_y < num_y_cells + and new_x >= 0 + and new_y >= 0 + ): + # print('breaking-l') + done = True + break + if done: + break + # go down + for new_y in range(new_y - 1, new_y - distance_to_move - 1, -1): + # print ("ffc-d (%g,%g) to_move %d" %(new_x, new_y, distance_to_move)) + if ( + not (new_x, new_y) in cells + and new_x < num_x_cells + and new_y < num_y_cells + and new_x >= 0 + and new_y >= 0 + ): + # print('breaking-d') + done = True + break + distance_to_move += 1 + if done: + break + # keep going + # print ("ALL FULL", count) + count += 1 + + return new_x, new_y, min_x + square_size * new_x, min_y + square_size * new_y + + def layout_node_list(self, min_x, min_y, max_x, max_y, max_size, node_list): + """ + This method will layout the nodes in the current quad. If there are more nodes than cells an Exception + will be raised. + :param min_x: + :param min_y: + :param max_x: + :param max_y: + :param max_size: + :param node_list: + :return: + """ + # print ("layout_node_list %d", len(node_list)) + self._mark_laid_out() + nodes_by_size = sorted(node_list, key=lambda n: n.size, reverse=True) + largest_size = nodes_by_size[0].size + if largest_size != max_size: + raise Exception( + "This can not be!!! max: %g, largest: %g" % (max_size, largest_size) + ) + num_nodes = len(node_list) + + side_size = max_size * 2.0 + x_range = max_x - min_x + y_range = max_y - min_y + number_of_x_cells = x_range // side_size + number_of_y_cells = y_range // side_size + total_cells = number_of_y_cells * number_of_x_cells + + if num_nodes > total_cells: + raise Exception( + "Too many nodes per Cell for this quad! nodes: %d, cells: %d" + % (num_nodes, total_cells) + ) + + # print ("largest_size: %g, x_range: %g, y_range: %g, min(%g, %g), max(%g, %g), x_cells %d, y_cells %d, total_cells: %d, num nodes: %d" + # %(largest_size, self.x_range, self.y_range, min_x, min_y, max_x, max_y, self.number_of_x_cells, self.number_of_y_cells, self.total_cells, num_nodes)) + + number_overlapping = 0 + for idx, node_to_move in enumerate(nodes_by_size): + for placed_node in nodes_by_size[idx + 1 :]: + if is_overlap( + node_to_move.x, + node_to_move.y, + node_to_move.size, + placed_node.x, + placed_node.y, + placed_node.size, + ): + number_overlapping += 1 + # print ("Is OVERLAPPING!!!!, to_move: (%g,%g) %g, (%g,%g) %g" % (node_to_move.x, node_to_move.y, node_to_move.size, placed_node.x, placed_node.y, placed_node.size)) + break + if number_overlapping > 0: + break + if number_overlapping == 0: + return 0 + + cells = {} + for idx, node_to_move in enumerate(nodes_by_size): + # the first one does not need to move all the rest might need to move + ( + cell_x, + cell_y, + cell_center_x, + cell_center_y, + ) = self.find_grid_cell_and_center( + min_x, min_y, max_size, node_to_move.x, node_to_move.y + ) + found = (cell_x, cell_y) in cells + self.total_nodes_moved += 1 + if found: + # need to find an open cell and then move there. + # print ("Occupied Cell: [%d, %d], used: %s , center: (%g, %g)" % (cell_x, cell_y, str(found), cell_center_x, cell_center_y) ) + cell_x, cell_y, cell_center_x, cell_center_y = self.find_free_cell( + cells, + cell_x, + cell_y, + number_of_x_cells, + number_of_y_cells, + min_x, + min_y, + max_size, + ) + # print ("Found Cell: [%d, %d], used: %s , center: (%g, %g)" % (cell_x, cell_y, str(found), cell_center_x, cell_center_y) ) + self.not_first_choice += 1 + # Just a test to see how many move + # node_to_move.color = '#FF0004' + cells[(cell_x, cell_y)] = True + node_to_move.x = cell_center_x + node_to_move.y = cell_center_y + + return number_overlapping + + def _mark_laid_out(self): + """ + Recursively mark all children as being laid out already + :return: + """ + self.is_laid_out = True + for qn in self.child_list(): + if qn is not None: + qn._mark_laid_out() + + def get_new_bounds(self, min_x, min_y, max_x, max_y, max_size, nodes): + xrange = max_x - min_x + yrange = max_y - min_y + side_size = 2 * max_size + x_cells = xrange // side_size + y_cells = yrange // side_size + total_cells = y_cells * x_cells + cells_needed = len(nodes) + # print ('original_total cells %d, (%d, %d) needed: %d' %(total_cells, x_cells, y_cells, cells_needed)) + while total_cells < cells_needed: + x_cells += 1 + y_cells += 1 + total_cells = y_cells * x_cells + # print ('new_cells %d, (%d, %d) needed: %d' %(total_cells, x_cells, y_cells, cells_needed)) + new_xrange = x_cells * side_size + 2 + new_yrange = y_cells * side_size + 2 + # print("bounds", min_x, min_y, max_x, max_y, new_xrange, new_yrange, side_size, x_cells, y_cells, total_cells) + expanded_min_x = min_x - (new_xrange - xrange) / 2 + expanded_min_y = min_y - (new_yrange - yrange) / 2 + expanded_max_x = max_x + (new_xrange - xrange) / 2 + expanded_max_y = max_y + (new_yrange - yrange) / 2 + + ## now I have the number of cells we need to go back and expand the bounds + return expanded_min_x, expanded_min_y, expanded_max_x, expanded_max_y + + def layout_quad(self): + # print("layout_quad") + num_skipped = 0 + num_nodes = len(self.nodes) + if self.total_cells == 0: + nodes_per_cell = math.inf + else: + nodes_per_cell = num_nodes / self.total_cells + + if self.is_laid_out: + # print ("ALREADY LAID OUT!!, ratio: %g " %(nodes_per_cell)) + return num_skipped + + has_children = False + for quad in self.child_list(): + if quad: + has_children = True + if not has_children: + if num_nodes > self.total_cells: + logger.info( + "We don't fit! going up one level depth: %d, cells: %d, nodes %d, ratio: %g, max_size: %g" + % ( + self.depth, + self.total_cells, + num_nodes, + nodes_per_cell, + self.max_size, + ) + ) + parent = self.parent + # if parent is not None: + while parent is not None: + logger.info( + "parent: sq_ratio: %g, cir_ratio: %g, cells %d, nodes: %d current_level %d, max_size: %g" + % ( + parent.sq_ratio, + parent.cir_ratio, + parent.total_cells, + len(parent.nodes), + parent.depth, + parent.max_size, + ) + ) + if len(parent.nodes) > parent.total_cells: + # go up one more level + logger.info( + "A Quad at level %d does not have enough space to layout its nodes" + % (parent.depth) + ) + parent = parent.parent + else: + # min_x, min_y, max_x, max_y, max_size = stats_nodes(parent.nodes) + # for n in parent.nodes: + # n.color = '#FF0004' + overlapping = parent.layout_node_list( + parent.min_x, + parent.min_y, + parent.max_x, + parent.max_y, + parent.max_size, + parent.nodes, + ) + parent._do_contraction() + break + if parent is None: + # expand the canvas and try to lay it out. + root = self.get_top_quad_node() + ( + expanded_min_x, + expanded_min_y, + expanded_max_x, + expanded_max_y, + ) = self.get_new_bounds( + root.min_x, + root.min_y, + root.max_x, + root.max_y, + root.max_size, + root.nodes, + ) + overlapping = root.layout_node_list( + expanded_min_x, + expanded_min_y, + expanded_max_x, + expanded_max_y, + root.max_size, + root.nodes, + ) + # Just fixed this no longer need to throw the exception. + self._do_contraction_with_given_nodes(root.nodes) + # raise Exception('This root level does not have enough space to layout this graph') + # print ("quad too dense, nodes_per_cell: %g, nn: %d, level: %d" %(nodes_per_cell, len(self.nodes), self.depth)) + # print ("quad (%g, %g) (%g, %g) max_size %g, ss: %g, area: %g, ratio: %g" %(parent.min_x, parent.min_y, parent.max_x, parent.max_y, parent.max_size, square_size, tot_area, ratio)) + return 1 + else: + # we are at the bottom and we can fit everyone in here. + # print ("laying out quad (%g, %g) (%g, %g) max_size %g, ss: %g, area: %g, ratio: %g, nodes: %d, npc: %g" %(self.min_x, self.min_y, self.max_x, self.max_y, self.max_size, square_size, tot_area, ratio, len(self.nodes), nodes_per_cell)) + overlapping = self.layout_node_list( + self.min_x, + self.min_y, + self.max_x, + self.max_y, + self.max_size, + self.nodes, + ) + # print ("Should Fit") + self._do_contraction() + # print ("jiggled nodes, overlapping %d" %(overlapping)) + else: + for quad in self.child_list(): + if quad: + num_skipped += quad.layout_quad() + return num_skipped + + def quad_stats(self): + square_size = self.total_square_size() + tot_area = total_area(self.min_x, self.min_y, self.max_x, self.max_y) + if tot_area == 0: + tot_area = 0.01 + num_quad_no_kids = 0 + num_quad_to_dense = 0 + num_quad_fits = 0 + total_nodes_moved = self.total_nodes_moved + not_first_choice = self.not_first_choice + lowest_level = math.inf + max_nodes_in_grid = 0 + + has_children = False + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + no_kids, too_dense, fits, tnm, nfc, low_level, mng = quad.quad_stats() + num_quad_no_kids += no_kids + num_quad_to_dense += too_dense + num_quad_fits += fits + total_nodes_moved += tnm + not_first_choice += nfc + has_children = True + lowest_level = min(lowest_level, low_level) + max_nodes_in_grid = max(max_nodes_in_grid, mng) + + if not has_children: + num_quad_no_kids += 1 + if len(self.nodes) > self.total_cells: + if self.total_cells == 0: + nodes_to_cells = math.inf + else: + nodes_to_cells = len(self.nodes) / self.total_cells + # print ("too dense, nodes/cells: %g, nn: %d, cells: %d, level: %d" %(nodes_to_cells, len(self.nodes), self.total_cells, self.depth)) + num_quad_to_dense = 1 + lowest_level = math.inf + parent = self.parent + while parent is not None: + max_nodes_in_grid = parent.num_nodes() + lowest_level = parent.depth + if parent.num_nodes() > parent.total_cells: + # doesn't fit, go up one more level + parent = parent.parent + else: + break + else: + # we are at the bottom and we can fit everyone in here. + lowest_level = self.depth + max_nodes_in_grid = self.num_nodes() + num_quad_fits = 1 + return ( + num_quad_no_kids, + num_quad_to_dense, + num_quad_fits, + total_nodes_moved, + not_first_choice, + lowest_level, + max_nodes_in_grid, + ) + + def num_overlapping(self): + has_children = False + num_overlapping_nodes = 0 + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + overlapping = quad.num_overlapping() + num_overlapping_nodes += overlapping + has_children = True + + if not has_children: + for idx, node_a in enumerate(self.nodes): + for node_b in self.nodes[:idx]: + if is_overlap( + node_a.x, node_a.y, node_a.size, node_b.x, node_b.y, node_b.size + ): + num_overlapping_nodes += 1 + # print ("***Is OVERLAPPING!!!!, to_move: (%g,%g) %g, (%g,%g) %g" % (node_a.x, node_a.y, node_a.size, node_b.x, node_b.y, node_b.size)) + break + return num_overlapping_nodes + + def is_just_outside_box(self, minx, miny, maxx, maxy, maxsize, x, y, size): + dist = maxsize + size + if x > minx - dist and x < maxx + dist: + if y <= maxy + dist and y > miny - dist: + # Here we are inside the box with buffer around it. now we need to check we are in the area just outside + # of the original box + if y > maxy: + return True + elif y < miny: + return True + elif x > maxx: + return True + elif x < minx: + return True + else: + return False + else: + return False + + def get_top_quad_node(self): + tmp = self.parent + prev = self + while tmp is not None: + prev = tmp + tmp = tmp.parent + return prev + + def get_nodes_near_lines(self, all_nodes): + nodes_just_outside = [] + for n in all_nodes: + if self.is_just_outside_box( + self.min_x, + self.min_y, + self.max_x, + self.max_y, + self.max_size, + n.x, + n.y, + n.size, + ): + nodes_just_outside.append(n) + return nodes_just_outside + + def num_overlapping_across_quads(self, all_nodes): + has_children = False + num_overlapping_nodes = 0 + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + overlapping = quad.num_overlapping_across_quads(all_nodes) + num_overlapping_nodes += overlapping + has_children = True + + if not has_children: + nodes_to_check = self.get_nodes_near_lines(all_nodes) + for idx, node_a in enumerate(self.nodes): + # the first one does not need to move all the rest might need to move + for node_b in nodes_to_check: + if node_a.node_id == node_b.node_id: + continue + if is_overlap( + node_a.x, node_a.y, node_a.size, node_b.x, node_b.y, node_b.size + ): + num_overlapping_nodes += 1 + # print ("***Is OVERLAPPING!!!! across quads, to_move: (%g,%g) %g, (%g,%g) %g" % (node_a.x, node_a.y, node_a.size, node_b.x, node_b.y, node_b.size)) + break + return num_overlapping_nodes + + def get_density_list(self): + square_size = self.total_square_size() + tot_area = total_area(self.min_x, self.min_y, self.max_x, self.max_y) + if tot_area == 0: + tot_area = 0.001 + ratio = square_size / tot_area + + retval = [] + has_children = False + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + retval += quad.get_density_list() + has_children = True + + if not has_children: + if self.total_cells == 0: + nodes_to_cells = math.inf + else: + nodes_to_cells = len(self.nodes) / self.total_cells + return [(nodes_to_cells, ratio, self.total_cells, self)] + else: + return retval + + def get_overlapping_node_list(self, node, new_x, new_y, nodes): + overlapping_nodes = [] + for n in nodes: + if n.node_id == node.node_id: + # don't check against self + continue + if is_overlap(n.x, n.y, n.size, new_x, new_y, node.size): + overlapping_nodes.append(n) + return overlapping_nodes + + def is_overlapping_any_node(self, node, new_x, new_y, nodes): + overlapping = None + for n in nodes: + if n.node_id == node.node_id: + # don't check against self + continue + if is_overlap(n.x, n.y, n.size, new_x, new_y, node.size): + overlapping = n + break + return overlapping + + def is_between(self, x, one_end, other_end): + if x <= one_end: + return x >= other_end + else: + return x <= other_end + + ### I wanted to add a little thank you to the webiste: https://www.calculator.net/triangle-calculator.html + ### it helped me debug the issues I was having in the calculation of the overlaps. + def _do_contraction(self): + logger.info("contracting nodes:%d" % (len(self.nodes))) + node_list = self.nodes + nodes_by_size = sorted(node_list, key=lambda n: n.size, reverse=True) + num_nodes = len(node_list) + nodes_around_the_edge = self.get_nodes_near_lines( + self.get_top_quad_node().nodes + ) + num_nodes_around_edge = len(nodes_around_the_edge) + + cells = {} + for idx, node_to_move in enumerate(nodes_by_size): + # move to its original spot node_to_move.original_x, node_to_move.original_y + # then move it toward where it is until it does not overlap with anything already placed. + prev_x, prev_y = node_to_move.original_x, node_to_move.original_y + new_x, new_y = node_to_move.x, node_to_move.y + ov_idx = 0 + ov_idx, overlapping_node = is_overlapping_any_node_and_index( + node_to_move, + node_to_move.original_x, + node_to_move.original_y, + nodes_around_the_edge + nodes_by_size, + ov_idx, + idx + num_nodes_around_edge, + ) + if overlapping_node is None: + new_x, new_y = prev_x, prev_y + + if node_to_move.x == node_to_move.original_x: + # this is needed just in case the min x node is overlapping. + # then the orginal X is eual to the X where is moves and that give us a divide by zero + # when calculating the slope + # We wiggle it just a little bit to prevent an error + node_to_move.x += _EPSILON + + # print ("contracting: %d, node_to_move %s, overlapping: %s" % (idx, str(node_to_move.to_list()), overlapping_node)) + while ( + overlapping_node is not None + and node_to_move.x != node_to_move.original_x + ): + # slope doesn't change leave as original_xy + slope_ca = (node_to_move.y - node_to_move.original_y) / ( + node_to_move.x - node_to_move.original_x + ) + if node_to_move.node_id == overlapping_node.node_id: + raise Exception( + "They should not be the same node!! %s" % (node_to_move.node_id) + ) + if node_to_move.original_x == new_x: + new_x += _EPSILON + a = dist_original_to_over = distance.euclidean( + [node_to_move.original_x, node_to_move.original_y], + [overlapping_node.x, overlapping_node.y], + ) + b = dist_from_original_to_new = distance.euclidean( + [node_to_move.original_x, node_to_move.original_y], [new_x, new_y] + ) + c = dist_from_new_to_overlapping = distance.euclidean( + [new_x, new_y], [overlapping_node.x, overlapping_node.y] + ) + node_to_move.color = "#FF0004" # RED + overlapping_node.color = "#F1FD00" # Yellow + # print ("not None, a: %g, b: %g, c: %g" %(a, b, c), node_to_move.node_id, node_to_move.size, overlapping_node.size) + # print ("original(%g,%g), current(%g,%g), overlap(%g,%g)" %(node_to_move.original_x, node_to_move.original_y, new_x, new_y, overlapping_node.x, overlapping_node.y)) + angle_c = math.acos((a ** 2 + b ** 2 - c ** 2) / (2 * a * b)) + len_c_new = node_to_move.size + overlapping_node.size + _EPSILON + angle_a_new = math.asin(a * math.sin(angle_c) / len_c_new) + angle_b_new = 180 - math.degrees(angle_c) - math.degrees(angle_a_new) + new_len_b = ( + len_c_new * math.sin(math.radians(angle_b_new)) / math.sin(angle_c) + ) + # print ("slope: %g, angle c: %g, new angle a: %g, newlenC: %g, new angle a: %g, new lenB %g" %(slope_ca, math.degrees(angle_c), math.degrees(angle_a_new), len_c_new, math.degrees(angle_a_new), new_len_b)) + x_new_plus = node_to_move.original_x + math.sqrt( + new_len_b ** 2 / (1 + slope_ca ** 2) + ) + x_new_neg = node_to_move.original_x - math.sqrt( + new_len_b ** 2 / (1 + slope_ca ** 2) + ) + x_plus_diff = x_new_plus - new_x + x_neg_diff = x_new_neg - new_x + # print ("both outsize, plus diff: %g, minus diff: %g" %(x_plus_diff, x_neg_diff)) + if abs(x_plus_diff) < abs(x_neg_diff): + prev_x, prev_y = new_x, new_y + new_x = x_new_plus + new_y = prev_y - slope_ca * prev_x + slope_ca * x_new_plus + else: + prev_x, prev_y = new_x, new_y + new_x = x_new_neg + new_y = prev_y - slope_ca * prev_x + slope_ca * x_new_neg + # print ("before: idx: %d, node: %s" %(ov_idx, str(overlapping_node.to_list()) )) + ov_idx, overlapping_node = is_overlapping_any_node_and_index( + node_to_move, + new_x, + new_y, + nodes_around_the_edge + nodes_by_size, + 0, + idx + num_nodes_around_edge, + ) + # print ("after: idx: %d, node: %s" %(ov_idx, str(overlapping_node is not None) )) + node_to_move.x = new_x + node_to_move.y = new_y + + # if idx > 20: # only do a few in the first quad + # break + + return + + def _do_contraction_with_given_nodes(self, node_list): + logger.info("contracting nodes:%d" % (len(node_list))) + nodes_by_size = sorted(node_list, key=lambda n: n.size, reverse=True) + nodes_around_the_edge = [] + num_nodes_around_edge = len(nodes_around_the_edge) + + cells = {} + for idx, node_to_move in enumerate(nodes_by_size): + if idx % 100 == 0: + logger.info(f"processing {idx}") + # move to its original spot node_to_move.original_x, node_to_move.original_y + # then move it toward where it is until it does not overlap with anything already placed. + prev_x, prev_y = node_to_move.original_x, node_to_move.original_y + new_x, new_y = node_to_move.x, node_to_move.y + ov_idx = 0 + ov_idx, overlapping_node = is_overlapping_any_node_and_index( + node_to_move, + node_to_move.original_x, + node_to_move.original_y, + nodes_around_the_edge + nodes_by_size, + ov_idx, + idx + num_nodes_around_edge, + ) + if overlapping_node is None: + new_x, new_y = prev_x, prev_y + + if node_to_move.x == 0.0: + # this is needed just in case the min x node is overlapping. + # then the orginal X is equal to the X where is moves and that give us a divide by zero + # when calculating the slope + # We wiggle it just a little bit to make the math work + node_to_move.x += _EPSILON + + # print ("contracting: %d, node_to_move %s, overlapping: %s" % (idx, str(node_to_move.to_list()), overlapping_node)) + while ( + overlapping_node is not None + and node_to_move.x != node_to_move.original_x + ): + # slope doesn't change leave as original_xy + slope_ca = (node_to_move.y - node_to_move.original_y) / ( + node_to_move.x - node_to_move.original_x + ) + if node_to_move.node_id == overlapping_node.node_id: + raise Exception( + "They should not be the same node!! %s" % (node_to_move.node_id) + ) + if node_to_move.original_x == new_x: + new_x += _EPSILON + a = dist_original_to_over = distance.euclidean( + [node_to_move.original_x, node_to_move.original_y], + [overlapping_node.x, overlapping_node.y], + ) + b = dist_from_original_to_new = distance.euclidean( + [node_to_move.original_x, node_to_move.original_y], [new_x, new_y] + ) + c = dist_from_new_to_overlapping = distance.euclidean( + [new_x, new_y], [overlapping_node.x, overlapping_node.y] + ) + # print ("not None, a: %g, b: %g, c: %g" %(a, b, c), node_to_move.node_id, node_to_move.size, overlapping_node.size) + # print ("original(%g,%g), current(%g,%g), overlap(%g,%g)" %(node_to_move.original_x, node_to_move.original_y, new_x, new_y, overlapping_node.x, overlapping_node.y)) + denominator = 2 * a * b + if 0 == denominator: + denominator = 0.00000001 + value = (a ** 2 + b ** 2 - c ** 2) / denominator + if value > 1: + value = 0.999999 + elif value < -1: + value = -0.999999 + angle_c = math.acos(value) + len_c_new = node_to_move.size + overlapping_node.size + _EPSILON + angle_a_new = math.asin(a * math.sin(angle_c) / len_c_new) + angle_b_new = 180 - math.degrees(angle_c) - math.degrees(angle_a_new) + new_len_b = ( + len_c_new * math.sin(math.radians(angle_b_new)) / math.sin(angle_c) + ) + # print ("slope: %g, angle c: %g, new angle a: %g, newlenC: %g, new angle a: %g, new lenB %g" %(slope_ca, math.degrees(angle_c), math.degrees(angle_a_new), len_c_new, math.degrees(angle_a_new), new_len_b)) + x_new_plus = node_to_move.original_x + math.sqrt( + new_len_b ** 2 / (1 + slope_ca ** 2) + ) + x_new_neg = node_to_move.original_x - math.sqrt( + new_len_b ** 2 / (1 + slope_ca ** 2) + ) + x_plus_diff = x_new_plus - new_x + x_neg_diff = x_new_neg - new_x + # print ("both outsize, plus diff: %g, minus diff: %g" %(x_plus_diff, x_neg_diff)) + if abs(x_plus_diff) < abs(x_neg_diff): + prev_x, prev_y = new_x, new_y + new_x = x_new_plus + new_y = prev_y - slope_ca * prev_x + slope_ca * x_new_plus + else: + prev_x, prev_y = new_x, new_y + new_x = x_new_neg + new_y = prev_y - slope_ca * prev_x + slope_ca * x_new_neg + # print ("before: idx: %d, node: %s" %(ov_idx, str(overlapping_node.to_list()) )) + ov_idx, overlapping_node = is_overlapping_any_node_and_index( + node_to_move, + new_x, + new_y, + nodes_around_the_edge + nodes_by_size, + 0, + idx + num_nodes_around_edge, + ) + # print ("after: idx: %d, node: %s" %(ov_idx, str(overlapping_node is not None) )) + node_to_move.x = new_x + node_to_move.y = new_y + + # if idx > 20: # only do a few in the first quad + # break + + return + + def collect_nodes(self, all_nodes): + has_children = False + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + quad.collect_nodes(all_nodes) + has_children = True + + if not has_children: + return all_nodes.extend(self.nodes) + + def boxes_by_level(self, boxes): + has_children = False + for quad in [self.NW, self.NE, self.SW, self.SE]: + if quad: + quad.boxes_by_level(boxes) + has_children = True + + boxes.append((self.depth, self.min_x, self.min_y, self.max_x, self.max_y)) + return diff --git a/graspologic/layouts/nooverlap/_quad_tree.py b/graspologic/layouts/nooverlap/_quad_tree.py new file mode 100644 index 000000000..148ba2bc9 --- /dev/null +++ b/graspologic/layouts/nooverlap/_quad_tree.py @@ -0,0 +1,71 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from ._quad_node import _QuadNode + + +class _QuadTree: + # used to hold objects that have x, y, and mass property + # nodes = [] + + def __init__(self, nodes, max_nodes_per_quad): + self.nodes = nodes + self.root = _QuadNode(nodes, 0, max_nodes_per_quad, None) + + def dump_one_level_to_csv(self, level, filename, magnification): + stats = [] + self.root.get_stats_for_quad(level, stats, magnification) + # print(type(stats)) + # print(len(stats)) + with open(filename, "w", encoding="utf-8", newline="") as ofile: + writer = csv.writer(ofile) + for idx, row in enumerate(stats): + color = "blue" + if row[3] > 0: + color = "red" + writer.writerow([idx] + row[:3] + [0, color]) + + def get_quad_density_list(self): + density_list = self.root.get_density_list() + return sorted(density_list, reverse=True) + + def layout_graph(self): + return self.layout_dense_first() + + def tree_stats(self): + results = self.root.quad_stats() + return list(results) + [ + results[3] / len(self.nodes), + results[4] / len(self.nodes), + self.root.sq_ratio, + ] + + def collect_nodes(self): + ret_val = [] + self.root.collect_nodes(ret_val) + return ret_val + + def get_tree_node_bounds(self): + ret_val = [] + self.root.boxes_by_level(ret_val) + return ret_val + + def count_overlaps(self): + return self.root.num_overlapping() + + def count_overlaps_across_quads(self): + return self.root.num_overlapping_across_quads(self.root.nodes) + + def layout_dense_first(self, first_color=None): + den_list = list(self.get_quad_density_list()) + first = True + # count = 0 + for cell_density, density_ratio, cell_count, qn in den_list: + # print ('cell density', cell_density, 'sq_density', density_ratio, 'cell_count', cell_count) + qn.layout_quad() + if first: + if first_color is not None: + for n in qn.parent.nodes: + n.color = first_color #'#FF0004' + first = False + return self.nodes diff --git a/graspologic/layouts/nooverlap/nooverlap.py b/graspologic/layouts/nooverlap/nooverlap.py new file mode 100644 index 000000000..6c30cb279 --- /dev/null +++ b/graspologic/layouts/nooverlap/nooverlap.py @@ -0,0 +1,37 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import logging +import time +from typing import List + +from ._node import _Node +from ._quad_tree import _QuadTree +from .. import NodePosition + +logger = logging.getLogger(__name__) + + +def remove_overlaps(node_positions: List[NodePosition]): + start = time.time() + logger.info("removing overlaps") + local_nodes = [ + _Node(node.node_id, node.x, node.y, node.size, node.community) + for node in node_positions + ] + qt = _QuadTree(local_nodes, 50) + qt.layout_dense_first(first_color=None) + stop = time.time() + logger.info(f"removed overlap in {stop-start} seconds") + + new_positions = [ + NodePosition( + node_id=node.node_id, + x=node.x, + y=node.y, + size=node.size, + community=node.community, + ) + for node in local_nodes + ] + return new_positions diff --git a/graspologic/layouts/render.py b/graspologic/layouts/render.py new file mode 100644 index 000000000..f71826ef8 --- /dev/null +++ b/graspologic/layouts/render.py @@ -0,0 +1,318 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import networkx as nx +from typing import Any, Dict, List, Optional, Tuple +from graspologic.layouts.classes import NodePosition +import matplotlib.pyplot as plt + + +def _calculate_x_y_domain( + positions: List[NodePosition], +) -> Tuple[Tuple[float, float], Tuple[float, float]]: + """calculate the overall x/y domain, converting to a square + so we can have a consistent scale + """ + min_x = min_y = float("inf") + max_x = max_y = float("-inf") + for node_position in positions: + min_x = min(min_x, node_position.x - node_position.size) + max_x = max(max_x, node_position.x + node_position.size) + min_y = min(min_y, node_position.y - node_position.size) + max_y = max(max_y, node_position.y + node_position.size) + + x_delta = max_x - min_x + y_delta = max_y - min_y + max_delta = max(x_delta, y_delta) + + if max_delta == x_delta: + difference = (max_delta - y_delta) / 2 + min_y = min_y - difference + max_y = max_y + difference + elif max_delta == y_delta: + difference = (max_delta - x_delta) / 2 + min_x = min_x - difference + max_x = max_x + difference + + return (min_x, max_x), (min_y, max_y) + + +def _scale_value( + domain: Tuple[float, float], data_range: Tuple[float, float], value: float +) -> float: + return data_range[0] + (data_range[1] - data_range[0]) * ( + (value - domain[0]) / (domain[1] - domain[0]) + ) + + +def _scale_node_sizes_for_rendering( + sizes: List[float], + spatial_domain: Tuple[float, float], + spatial_range: Tuple[float, float], + dpi: float, +): + """scale the size again to match the rendered pixel range + we would expect this to be handled by the underlying viz framework, but it isn't, size is specified + as the bounding box in points of the rendered output, so we need to transform our size to match. + + There are 72 points per inch. Multiplying by 72 / dpi converts from pixels to points. + """ + spatial_domain = (0, spatial_domain[1] - spatial_domain[0]) + return list( + map( + lambda s: _scale_value(spatial_domain, spatial_range, s * 2 * 72.0 / dpi) + ** 2, + sizes, + ) + ) + + +def _draw_graph( + graph: nx.Graph, + positions: List[NodePosition], + node_colors: Dict[Any, str], + vertex_alpha: float, + edge_line_width: float, + edge_alpha: float, + figure_width: float, + figure_height: float, + vertex_line_width: float = 0.01, + vertex_shape: str = "o", + arrows: bool = False, + dpi: int = 100, +): + if len(positions) != len(graph.nodes()): + raise ValueError( + f"The number of positions provided {len(positions)} is not the same as the " + f"number of nodes in the graph {len(graph.nodes())}" + ) + for position in positions: + if position.node_id not in graph: + raise ValueError( + f"The node position provided for {position.node_id} references a node " + f"not found in our graph" + ) + + plt.rcParams["figure.dpi"] = dpi # TODO, test at different dpi + + plt.clf() + figure = plt.gcf() + ax = plt.gca() + ax.set_axis_off() + figure.set_size_inches(figure_width, figure_height) + window_extent_width = ax.get_window_extent().width + + x_domain, y_domain = _calculate_x_y_domain(positions) + + position_map = {position.node_id: position for position in positions} + node_positions = { + position.node_id: (position.x, position.y) for position in positions + } + + vertices = [] + vertex_sizes = [] + node_color_list = [] + edge_color_list = [] + + for node in graph.nodes(): + vertices.append(node) + vertex_sizes.append(position_map[node].size) + node_color_list.append(node_colors[node]) + + vertex_sizes = _scale_node_sizes_for_rendering( + vertex_sizes, x_domain, (0, window_extent_width), dpi + ) + + for source, target in graph.edges(): + edge_color_list.append(node_colors[source]) + + ax.set_xbound(x_domain) + ax.set_xlim(x_domain) + ax.set_ybound(y_domain) + ax.set_ylim(y_domain) + + nx.draw_networkx_edges( + graph, + pos=node_positions, + alpha=edge_alpha, + width=edge_line_width, + edge_color=edge_color_list, + arrows=arrows, + ax=ax, + ) + + nx.draw_networkx_nodes( + graph, + pos=node_positions, + nodelist=vertices, + node_color=node_color_list, + alpha=vertex_alpha, + linewidths=vertex_line_width, + node_size=vertex_sizes, + node_shape=vertex_shape, + ax=ax, + ) + + +def show_graph( + graph: nx.Graph, + positions: List[NodePosition], + node_colors: Dict[Any, str], + vertex_line_width: float = 0.01, + vertex_alpha: float = 0.55, + edge_line_width: float = 0.5, + edge_alpha: float = 0.02, + figure_width: float = 15.0, + figure_height: float = 15.0, + light_background: bool = True, + vertex_shape: str = "o", + arrows: bool = False, + dpi: int = 500, +): + """ + Renders and displays a graph. + + Attempts to display it via the platform-specific display library such as TkInter + + Edges will be displayed with the same color as the source node. + + Parameters + ---------- + graph : nx.Graph + The graph to be displayed. If the networkx Graph contains only nodes, no + edges will be displayed. + positions : List[:class:`graspologic.layouts.NodePosition`] + The positionsfor every node in the graph. + node_colors : Dict[Any, str] + A mapping of node id to colors. Must contain an entry for every node in the + graph. + vertex_line_width : float + Line width of vertex outline. Default is``0.01``. + vertex_alpha : float + Alpha (transparency) of vertices in visualization. Default is``0.55``. + edge_line_width : float + Line width of edge. Default is``0.5``. + edge_alpha : float + Alpha (transparency) of edges in visualization. Default is``0.02``. + figure_width : float + Width of figure. Default is ``15.0``. + figure_height : float + eight of figure. Default is``15.0``. + light_background : bool + Light background or dark background. Default is``True``. + vertex_shape : str + Matplotlib Marker for the vertex shape. See + `https://matplotlib.org/api/markers_api.html `_ + for a list of allowed values . Default is ``o`` (i.e: a circle) + arrows : bool + For directed graphs, if ``True``, draw arrow heads. Default is ``False`` + dpi : float + Dots per inch of the figure. Default is ``500``. + """ + ax = plt.gca() + if light_background: + facecolor = ax.get_facecolor() + else: + facecolor = "#030303" + + _draw_graph( + graph=graph, + positions=positions, + node_colors=node_colors, + vertex_line_width=vertex_line_width, + vertex_alpha=vertex_alpha, + edge_line_width=edge_line_width, + edge_alpha=edge_alpha, + figure_width=figure_width, + figure_height=figure_height, + vertex_shape=vertex_shape, + arrows=arrows, + dpi=dpi, + ) + plt.gcf().set_facecolor(facecolor) + plt.show() + plt.close("all") + + +def save_graph( + output_path: str, + graph: nx.Graph, + positions: List[NodePosition], + node_colors: Dict[Any, str], + vertex_line_width: float = 0.01, + vertex_alpha: float = 0.55, + edge_line_width: float = 0.5, + edge_alpha: float = 0.02, + figure_width: float = 15.0, + figure_height: float = 15.0, + light_background: bool = True, + vertex_shape: str = "o", + arrows: bool = False, + dpi: int = 100, +): + """ + Renders a graph to file. + + Edges will be displayed with the same color as the source node. + + Parameters + ---------- + output_path : str + The output path to write the rendered graph to. Suggested file extension is + ``.png``. + graph : nx.Graph + The graph to be displayed. If the networkx Graph contains only nodes, no + edges will be displayed. + positions : List[:class:`graspologic.layouts.NodePosition`] + The positionsfor every node in the graph. + node_colors : Dict[Any, str] + A mapping of node id to colors. Must contain an entry for every node in the + graph. + vertex_line_width : float + Line width of vertex outline. Default is``0.01``. + vertex_alpha : float + Alpha (transparency) of vertices in visualization. Default is``0.55``. + edge_line_width : float + Line width of edge. Default is``0.5``. + edge_alpha : float + Alpha (transparency) of edges in visualization. Default is``0.02``. + figure_width : float + Width of figure. Default is ``15.0``. + figure_height : float + eight of figure. Default is``15.0``. + light_background : bool + Light background or dark background. Default is``True``. + vertex_shape : str + Matplotlib Marker for the vertex shape. See + `https://matplotlib.org/api/markers_api.html `_ + for a list of allowed values . Default is ``o`` (i.e: a circle) + arrows : bool + For directed graphs, if ``True``, draw arrow heads. Default is ``False`` + dpi : float + Dots per inch of the figure. Default is ``100``. + + Returns + ------- + + """ + _draw_graph( + graph=graph, + positions=positions, + node_colors=node_colors, + vertex_line_width=vertex_line_width, + vertex_alpha=vertex_alpha, + edge_line_width=edge_line_width, + edge_alpha=edge_alpha, + figure_width=figure_width, + figure_height=figure_height, + vertex_shape=vertex_shape, + arrows=arrows, + dpi=dpi, + ) + ax = plt.gca() + if light_background: + facecolor = ax.get_facecolor() + else: + facecolor = "#030303" + plt.savefig(output_path, facecolor=facecolor) + plt.close("all") diff --git a/graspologic/preprocessing/graph_cuts.py b/graspologic/preprocessing/graph_cuts.py index 625229c7a..8a79ddefc 100644 --- a/graspologic/preprocessing/graph_cuts.py +++ b/graspologic/preprocessing/graph_cuts.py @@ -32,8 +32,8 @@ def _filter_function_for_make_cuts( class DefinedHistogram(NamedTuple): """ Contains the histogram and the edges of the bins in the histogram. - The bin_edges will have a length 1 greater than the histogram, as it defines the minimal and maximal edges as well - as each edge in between. + The bin_edges will have a length 1 greater than the histogram, as it defines the + minimal and maximal edges as well as each edge in between. """ histogram: np.ndarray @@ -46,8 +46,9 @@ def histogram_edge_weight( weight_attribute: str = "weight", ) -> DefinedHistogram: """ - Generates a histogram of the edge weights of the provided graph. Histogram function is fundamentally proxied - through to numpy's `histogram` function, and bin selection follows :func:`numpy.histogram` processes. + Generates a histogram of the edge weights of the provided graph. Histogram function + is fundamentally proxied through to numpy's `histogram` function, and bin selection + follows :func:`numpy.histogram` processes. Parameters ---------- @@ -55,27 +56,32 @@ def histogram_edge_weight( graph : nx.Graph The graph. No changes will be made to it. bin_directive : Union[int, List[Union[float, int]], numpy.ndarray, str] - Is passed directly through to numpy's "histogram" (and thus, "histogram_bin_edges") functions. + Is passed directly through to numpy's "histogram" (and thus, + "histogram_bin_edges") functions. See: :func:`numpy.histogram_bin_edges` - In short: if an int is provided, we use ``bin_directive`` number of equal range bins. + In short: if an int is provided, we use ``bin_directive`` number of equal range + bins. - If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer + If a sequence is provided, these bin edges will be used and can be sized to + whatever size you prefer - Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be float or int. + Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be + float or int. weight_attribute : str The weight attribute name in the data dictionary. Default is `weight`. Returns ------- - DefinedHistogram - A named tuple that contains the histogram and the bin_edges used in the histogram + :class:`DefinedHistogram` + A named tuple that contains the histogram and the bin_edges used in the + histogram Notes ----- - Edges without a `weight_attribute` field will be excluded from this histogram. Enable logging to view any - messages about edges without weights. + Edges without a `weight_attribute` field will be excluded from this histogram. + Enable logging to view any messages about edges without weights. """ logger = logging.getLogger(__name__) edge_weights: List[Union[int, float, None]] = [ @@ -114,8 +120,8 @@ def cut_edges_by_weight( cut_threshold : Union[int, float] The threshold for making cuts based on weight. cut_process : str - Describes how we should make the cut; cut all edges larger or smaller than the cut_threshold, and whether - exclusive or inclusive. Allowed values are + Describes how we should make the cut; cut all edges larger or smaller than the + cut_threshold, and whether exclusive or inclusive. Allowed values are - ``larger_than_inclusive`` - ``larger_than_exclusive`` @@ -124,8 +130,9 @@ def cut_edges_by_weight( weight_attribute : str The weight attribute name in the edge's data dictionary. Default is `weight`. prune_isolates : bool - If true, remove any vertex that no longer has an edge. Note that this only prunes vertices which have edges to - be pruned; any isolate vertex prior to any edge cut will be retained. + If true, remove any vertex that no longer has an edge. Note that this only + prunes vertices which have edges to be pruned; any isolate vertex prior to any + edge cut will be retained. Returns ------- @@ -134,13 +141,14 @@ def cut_edges_by_weight( Notes ----- - Edges without a `weight_attribute` field will be excluded from these cuts. Enable logging to view any messages - about edges without weights. + Edges without a `weight_attribute` field will be excluded from these cuts. Enable + logging to view any messages about edges without weights. """ filter_by = _filter_function_for_make_cuts(cut_threshold, cut_process) if not isinstance(cut_threshold, int) and not isinstance(cut_threshold, float): raise TypeError( - f"cut_threshold must be of type int or float; you provided: {type(cut_threshold)}" + f"cut_threshold must be of type int or float; you provided: " + f"{type(cut_threshold)}" ) logger = logging.getLogger(__name__) @@ -185,8 +193,9 @@ def histogram_degree_centrality( bin_directive: Union[int, List[Union[float, int]], np.ndarray, str] = 10, ) -> DefinedHistogram: """ - Generates a histogram of the vertex degree centrality of the provided graph. Histogram function is fundamentally - proxied through to numpy's `histogram` function, and bin selection follows :func:`numpy.histogram` processes. + Generates a histogram of the vertex degree centrality of the provided graph. + Histogram function is fundamentally proxied through to numpy's `histogram` function, + and bin selection follows :func:`numpy.histogram` processes. Parameters ---------- @@ -194,20 +203,25 @@ def histogram_degree_centrality( graph : Union[nx.Graph, nx.DiGraph] The graph. No changes will be made to it. bin_directive : Union[int, List[Union[float, int]], numpy.ndarray, str] - Is passed directly through to numpy's "histogram" (and thus, "histogram_bin_edges") functions. + Is passed directly through to numpy's "histogram" (and thus, + "histogram_bin_edges") functions. See: :func:`numpy.histogram_bin_edges` - In short: if an int is provided, we use ``bin_directive`` number of equal range bins. + In short: if an int is provided, we use ``bin_directive`` number of equal range + bins. - If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer + If a sequence is provided, these bin edges will be used and can be sized to + whatever size you prefer - Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be float or int. + Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be + float or int. Returns ------- - DefinedHistogram - A named tuple that contains the histogram and the bin_edges used in the histogram + :class:`DefinedHistogram` + A named tuple that contains the histogram and the bin_edges used in the + histogram """ degree_centrality_dict = nx.degree_centrality(graph) @@ -223,8 +237,8 @@ def cut_vertices_by_degree_centrality( cut_process: str, ) -> Union[nx.Graph, nx.DiGraph]: """ - Given a graph and a cut_threshold and a cut_process, return a copy of the graph with the vertices outside of the - cut_threshold. + Given a graph and a cut_threshold and a cut_process, return a copy of the graph + with the vertices outside of the cut_threshold. Parameters ---------- @@ -233,8 +247,8 @@ def cut_vertices_by_degree_centrality( cut_threshold : Union[int, float] The threshold for making cuts based on weight. cut_process : str - Describes how we should make the cut; cut all edges larger or smaller than the cut_threshold, and whether - exclusive or inclusive. Allowed values are + Describes how we should make the cut; cut all edges larger or smaller than the + cut_threshold, and whether exclusive or inclusive. Allowed values are - ``larger_than_inclusive`` - ``larger_than_exclusive`` @@ -266,47 +280,56 @@ def histogram_betweenness_centrality( random_seed: Optional[Union[int, random.Random, np.random.RandomState]] = None, ) -> DefinedHistogram: """ - Generates a histogram of the vertex betweenness centrality of the provided graph. Histogram function is - fundamentally proxied through to numpy's `histogram` function, and bin selection follows :func:`numpy.histogram` - processes. + Generates a histogram of the vertex betweenness centrality of the provided graph. + Histogram function is fundamentally proxied through to numpy's `histogram` function, + and bin selection follows :func:`numpy.histogram` processes. - The betweenness centrality calculation can take advantage of networkx' implementation of randomized sampling by - providing num_random_samples (or ``k``, in networkx betweenness_centrality nomenclature). + The betweenness centrality calculation can take advantage of networkx' + implementation of randomized sampling by providing num_random_samples (or ``k``, + in networkx betweenness_centrality nomenclature). Parameters ---------- graph : Union[nx.Graph, nx.DiGraph] The graph. No changes will be made to it. bin_directive : Union[int, List[Union[float, int]], numpy.ndarray, str] - Is passed directly through to numpy's "histogram" (and thus, "histogram_bin_edges") functions. + Is passed directly through to numpy's "histogram" (and thus, + "histogram_bin_edges") functions. See: :func:`numpy.histogram_bin_edges` - In short: if an int is provided, we use ``bin_directive`` number of equal range bins. + In short: if an int is provided, we use ``bin_directive`` number of equal + range bins. - If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer + If a sequence is provided, these bin edges will be used and can be sized to + whatever size you prefer - Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be float or int. + Note that the :class:`numpy.ndarray` should be ndim=1 and the values should be + float or int. num_random_samples : Optional[int] - Use num_random_samples for vertex samples to *estimate* betweeness. num_random_samples should be <= - len(graph.nodes). The larger num_random_samples is, the better the approximation. Default is ``None``. + Use num_random_samples for vertex samples to *estimate* betweeness. + num_random_samples should be <= len(graph.nodes). The larger num_random_samples + is, the better the approximation. Default is ``None``. normalized : bool - If True the betweenness values are normalized by :math:`2/((n-1)(n-2))` for undirected graphs, and - :math:`1/((n-1)(n-2))` for directed graphs where n is the number of vertices in the graph. Default is ``True`` + If True the betweenness values are normalized by :math:`2/((n-1)(n-2))` for + undirected graphs, and :math:`1/((n-1)(n-2))` for directed graphs where n is + the number of vertices in the graph. Default is ``True`` weight_attribute : Optional[str] - If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight. - Default is ``weight`` + If None, all edge weights are considered equal. Otherwise holds the name of the + edge attribute used as weight. Default is ``weight`` include_endpoints : bool If True include the endpoints in the shortest path counts. Default is ``False`` random_seed : Optional[Union[int, random.Random, np.random.RandomState]] - Random seed or preconfigured random instance to be used for selecting random samples. Only used if - num_random_samples is set. None will generate a new random state. Specifying a random state will provide - consistent results between runs. + Random seed or preconfigured random instance to be used for selecting random + samples. Only used if num_random_samples is set. None will generate a new + random state. Specifying a random state will provide consistent results between + runs. Returns ------- - DefinedHistogram - A named tuple that contains the histogram and the bin_edges used in the histogram + :class:`DefinedHistogram` + A named tuple that contains the histogram and the bin_edges used in the + histogram See Also -------- @@ -338,11 +361,12 @@ def cut_vertices_by_betweenness_centrality( random_seed: Optional[Union[int, random.Random, np.random.RandomState]] = None, ) -> Union[nx.Graph, nx.DiGraph]: """ - Given a graph and a cut_threshold and a cut_process, return a copy of the graph with the vertices outside of the - cut_threshold. + Given a graph and a cut_threshold and a cut_process, return a copy of the graph + with the vertices outside of the cut_threshold. - The betweenness centrality calculation can take advantage of networkx' implementation of randomized sampling - by providing num_random_samples (or k, in networkx betweenness_centrality nomenclature). + The betweenness centrality calculation can take advantage of networkx' + implementation of randomized sampling by providing num_random_samples (or k, in + networkx betweenness_centrality nomenclature). Parameters ---------- @@ -351,28 +375,31 @@ def cut_vertices_by_betweenness_centrality( cut_threshold : Union[int, float] The threshold for making cuts based on weight. cut_process : str - Describes how we should make the cut; cut all edges larger or smaller than the cut_threshold, and whether - exclusive or inclusive. Allowed values are + Describes how we should make the cut; cut all edges larger or smaller than the + cut_threshold, and whether exclusive or inclusive. Allowed values are - ``larger_than_inclusive`` - ``larger_than_exclusive`` - ``smaller_than_inclusive`` - ``smaller_than_exclusive`` num_random_samples : Optional[int] - Use num_random_samples for vertex samples to *estimate* betweenness. num_random_samples should be <= - len(graph.nodes). The larger num_random_samples is, the better the approximation. Default is ``None``. + Use num_random_samples for vertex samples to *estimate* betweenness. + num_random_samples should be <= len(graph.nodes). The larger num_random_samples + is, the better the approximation. Default is ``None``. normalized : bool - If True the betweenness values are normalized by :math:`2/((n-1)(n-2))` for undirected graphs, and - :math:`1/((n-1)(n-2))` for directed graphs where n is the number of vertices in the graph. Default is ``True`` + If True the betweenness values are normalized by :math:`2/((n-1)(n-2))` for + undirected graphs, and :math:`1/((n-1)(n-2))` for directed graphs where n is + the number of vertices in the graph. Default is ``True`` weight_attribute : Optional[str] - If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight. - Default is ``weight`` + If None, all edge weights are considered equal. Otherwise holds the name of the + edge attribute used as weight. Default is ``weight`` include_endpoints : bool If True include the endpoints in the shortest path counts. Default is ``False`` random_seed : Optional[Union[int, random.Random, np.random.RandomState]] - Random seed or preconfigured random instance to be used for selecting random samples. Only used if - num_random_samples is set. None will generate a new random state. Specifying a random state will provide - consistent results between runs. + Random seed or preconfigured random instance to be used for selecting random + samples. Only used if num_random_samples is set. None will generate a new + random state. Specifying a random state will provide consistent results between + runs. Returns ------- diff --git a/setup.py b/setup.py index 1fd878dac..2e161c3f5 100644 --- a/setup.py +++ b/setup.py @@ -49,7 +49,7 @@ package_data={"version": [os.path.join("graspologic", "version", "version.txt")]}, install_requires=[ "anytree>=2.8.0", - "gensim", + "gensim>=3.8.0,<=3.9.0", #methods signatures changed in the 4.0.0beta release "graspologic-native", "hyppo>=0.1.3", "joblib>=0.17.0", # Older versions of joblib cause issue #806. Transitive dependency of hyppo. @@ -60,6 +60,7 @@ "seaborn>= 0.11.0", "scikit-learn>=0.19.1", "scipy>=1.4.0", + 'umap-learn>=0.4.6', ], extras_require={ "dev": [ diff --git a/tests/layouts/__init__.py b/tests/layouts/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/layouts/nooverlap/__init__.py b/tests/layouts/nooverlap/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/layouts/nooverlap/test_grid.py b/tests/layouts/nooverlap/test_grid.py new file mode 100644 index 000000000..9f98652cd --- /dev/null +++ b/tests/layouts/nooverlap/test_grid.py @@ -0,0 +1,88 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +from graspologic.layouts.nooverlap._grid import _GridBuckets +from graspologic.layouts.nooverlap._node import _Node + + +class TestGrid(unittest.TestCase): + + # def setUp(self): + # self.g = _GridBuckets(10) + + def test_get_cell(self): + g = _GridBuckets(10) + cell = g.get_cell(0, 0) + self.assertTupleEqual((0, 0), cell) + + cell = g.get_cell(-1, -1) + self.assertTupleEqual((-10, -10), cell) + + cell = g.get_cell(11, 11) + self.assertTupleEqual((10, 10), cell) + + cell = g.get_cell(105, 87) + self.assertTupleEqual((100, 80), cell) + + cell = g.get_cell(-105, -87) + self.assertTupleEqual((-110, -90), cell) + + cell = g.get_cell(-105, 87) + self.assertTupleEqual((-110, 80), cell) + + cell = g.get_cell(105, -57) + self.assertTupleEqual((100, -60), cell) + + def test_get_grid_cells(self): + g = _GridBuckets(10) + cells = g._get_grid_cells(5, 12, 1) + self.assertSetEqual({(0, 10)}, cells) + + g2 = _GridBuckets(20) + cells = g2._get_grid_cells(5, 12, 10) + self.assertSetEqual({(-20, 20), (0, 20), (0, 0), (-20, 0)}, cells) + + g3 = _GridBuckets(20) + cells = g3._get_grid_cells(-5, -12, 10) + self.assertSetEqual({(-20, -20), (0, -20), (0, -40), (-20, -40)}, cells) + + def test_add_node(self): + g = _GridBuckets(10) + n0 = _Node(0, 1, 1, 10, 1, "blue") + n1 = _Node(1, 2, 1, 10, 1, "blue") + n2 = _Node(2, 40, -20, 10, 1, "blue") + + g.add_node(n0) + nodes = g.get_potential_overlapping_nodes_by_node(n0) + self.assertSetEqual(nodes, {n0}) + + g.add_node(n1) + nodes = g.get_potential_overlapping_nodes_by_node(n1) + self.assertSetEqual(nodes, {n0, n1}) + + g.add_node(n2) + nodes = g.get_potential_overlapping_nodes_by_node(n1) + self.assertSetEqual(nodes, {n0, n1}) + + def test_get_cell_stats(self): + g = _GridBuckets(10) + n0 = _Node(0, 1, 1, 10, 1, "blue") + n1 = _Node(1, 2, 1, 10, 1, "blue") + n2 = _Node(2, 40, -20, 10, 1, "blue") + n3 = _Node(3, -33, -33, 1, 1, "blue") + n4 = _Node(4, -193, 78, 1, 1, "blue") + g.add_node(n0) + g.add_node(n1) + g.add_node(n2) + g.add_node(n3) + g.add_node(n4) + stats = g.get_grid_cell_stats() + self.assertEqual(3, len(stats), "Correct size list") + self.assertEqual(254, stats[0][1], "empty cells") + self.assertEqual(6, stats[1][1], "one item in cell") + self.assertEqual(4, stats[2][1], "two items in cell") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/layouts/nooverlap/test_grid_cell_creation.py b/tests/layouts/nooverlap/test_grid_cell_creation.py new file mode 100644 index 000000000..6da45e6f2 --- /dev/null +++ b/tests/layouts/nooverlap/test_grid_cell_creation.py @@ -0,0 +1,44 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +from graspologic.layouts.nooverlap._quad_node import _QuadNode +from graspologic.layouts.nooverlap._node import _Node + + +class TestGridCellCreation(unittest.TestCase): + def setUp(self): + self.qn = _QuadNode( + [_Node(99, 3, 7, 2, 0, "red"), _Node(100, 2, 9, 3, 0, "blue")], 5, 50 + ) + + def test_grid_cell_center(self): + cell_x, cell_y, center_x, center_y = self.qn.find_grid_cell_and_center( + 0, 0, 10, 50, 50 + ) + self.assertEqual(cell_x, 2) + self.assertEqual(cell_y, 2) + self.assertEqual(center_x, 40) + self.assertEqual(center_y, 40) + + def test_grid_cell_center2(self): + cell_x, cell_y, center_x, center_y = self.qn.find_grid_cell_and_center( + 0, 0, 10, 50, 40 + ) + self.assertEqual(cell_x, 2) + self.assertEqual(cell_y, 2) + self.assertEqual(center_x, 40) + self.assertEqual(center_y, 40) + + def test_grid_cell_center3(self): + cell_x, cell_y, center_x, center_y = self.qn.find_grid_cell_and_center( + 3, 4, 10, 53, 44 + ) + self.assertEqual(cell_x, 2) + self.assertEqual(cell_y, 2) + self.assertEqual(center_x, 43) + self.assertEqual(center_y, 44) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/layouts/nooverlap/test_nooverlap.py b/tests/layouts/nooverlap/test_nooverlap.py new file mode 100644 index 000000000..7facfcc56 --- /dev/null +++ b/tests/layouts/nooverlap/test_nooverlap.py @@ -0,0 +1,286 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +from graspologic.layouts.nooverlap._quad_node import ( + move_point_on_line, + _QuadNode, + node_positions_overlap, +) +from graspologic.layouts.nooverlap._node import _Node +from graspologic.layouts.nooverlap.nooverlap import remove_overlaps +from graspologic.layouts import NodePosition + + +class TestNoOverlap(unittest.TestCase): + def setUp(self): + self.qn = _QuadNode( + [_Node(99, 3, 7, 2, 0, "red"), _Node(100, 2, 9, 3, 0, "blue")], 5, 50 + ) + cells = {} + self.x, self.y = (3, 3) + self.num_x, self.num_y = 5, 5 + self.min_x, self.min_y = 0, 0 + self.max_size = 1.5 + + def test_find_grid_cell_and_center(self): + x, y = 2, 5 + ( + cell_x, + cell_y, + cell_center_x, + cell_center_y, + ) = self.qn.find_grid_cell_and_center( + self.min_x, self.min_y, self.max_size, x, y + ) + self.assertEqual(0, cell_x) + self.assertEqual(1, cell_y) + self.assertEqual(0.0, cell_center_x) + self.assertEqual(3.0, cell_center_y) + + def test_move_point_on_line(self): + print(move_point_on_line([1, 1], [3, 3], 2.0)) + + def test_find_free_cell(self): + cells = {} + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (3, 3, 9, 9), "Failure") + + def test_find_free_cell_one_used(self): + cells = {} + cells[(3, 3)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (4, 3, 12, 9), "Failure") + + def test_find_free_cell_two_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (4, 4, 12, 12), "Failure") + + def test_find_free_cell_three_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (3, 4, 9, 12), "Failure") + + def test_find_free_cell_four_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (2, 4, 6, 12), "Failure") + + def test_find_free_cell_five_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + cells[(2, 4)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (2, 3, 6, 9), "Failure") + + def test_find_free_cell_six_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + cells[(2, 4)] = 999 + cells[(2, 3)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (2, 2, 6, 6), "Failure") + + def test_find_free_cell_seven_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + cells[(2, 4)] = 999 + cells[(2, 3)] = 999 + cells[(2, 2)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (3, 2, 9, 6), "Failure") + + def test_find_free_cell_nine_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + cells[(2, 4)] = 999 + cells[(2, 3)] = 999 + cells[(2, 2)] = 999 + cells[(3, 2)] = 999 + cells[(4, 2)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (1, 4, 3, 12), "Failure") + + # + def test_find_free_cell_16_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + cells[(2, 4)] = 999 + cells[(2, 3)] = 999 + cells[(2, 2)] = 999 + cells[(3, 2)] = 999 + cells[(4, 2)] = 999 + cells[(1, 4)] = 999 + cells[(1, 3)] = 999 + cells[(1, 2)] = 999 + cells[(1, 1)] = 999 + cells[(2, 1)] = 999 + cells[(3, 1)] = 999 + cells[(4, 1)] = 999 + result = self.qn.find_free_cell( + cells, + self.x, + self.y, + self.num_x, + self.num_y, + self.min_x, + self.min_y, + self.max_size, + ) + self.assertTupleEqual(result, (0, 4, 0, 12), "Failure") + + ## + def test_find_free_cell_20_used(self): + cells = {} + cells[(3, 3)] = 999 + cells[(4, 3)] = 999 + cells[(4, 4)] = 999 + cells[(3, 4)] = 999 + cells[(2, 4)] = 999 + cells[(2, 3)] = 999 + cells[(2, 2)] = 999 + cells[(3, 2)] = 999 + cells[(4, 2)] = 999 + cells[(1, 4)] = 999 + cells[(1, 3)] = 999 + cells[(1, 2)] = 999 + cells[(1, 1)] = 999 + cells[(2, 1)] = 999 + cells[(3, 1)] = 999 + cells[(4, 1)] = 999 + cells[(0, 4)] = 999 + cells[(0, 3)] = 999 + cells[(0, 2)] = 999 + cells[(0, 1)] = 999 + result = self.qn.find_free_cell( + cells, 0, 2, self.num_x, self.num_y, self.min_x, self.min_y, self.max_size + ) + self.assertTupleEqual(result, (0, 0, 0, 0), "Failure") + + def test_remove_overlap(self): + list_of_nodes = [ + NodePosition(node_id=99, x=3, y=7, size=2, community=0), + NodePosition(node_id=100, x=2, y=9, size=3, community=0), + NodePosition(node_id=101, x=2, y=9, size=3, community=0), + ] + + overlap_count = 0 + answer = remove_overlaps(list_of_nodes) + for idx, n1 in enumerate(answer): + print(idx, n1) + for idx2, n2 in enumerate(answer[idx + 1 :]): + print("****", idx2, "****", n2) + if node_positions_overlap(n1, n2): + overlap_count += 1 + print("overlap ****", idx2, "****", n2) + self.assertEqual(0, overlap_count, "We found overlaps") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/layouts/nooverlap/test_overlap_check.py b/tests/layouts/nooverlap/test_overlap_check.py new file mode 100644 index 000000000..e43c5e356 --- /dev/null +++ b/tests/layouts/nooverlap/test_overlap_check.py @@ -0,0 +1,160 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +from graspologic.layouts.nooverlap._node import _Node +from graspologic.layouts.nooverlap._quad_node import ( + _QuadNode, + is_overlap, + is_overlapping_any_node_and_index, +) + + +class TestOverlapCheck(unittest.TestCase): + def setUp(self): + self.qn = _QuadNode( + [_Node(99, 3, 7, 2, 0, "red"), _Node(100, 2, 9, 3, 0, "blue")], 5, 50 + ) + + def test_grid_is_overlap(self): + overlaps = is_overlap(0, 0, 5, 20, 20, 5) + self.assertFalse(overlaps) + overlaps = is_overlap(0, 0, 5, 10, 10, 5) + self.assertFalse(overlaps) + overlaps = is_overlap(0, 0, 5, 0, 10, 5) + self.assertTrue(overlaps) # barely touches + overlaps = is_overlap(0, 0, 5, 10, 0, 5) + self.assertTrue(overlaps) + overlaps = is_overlap(0, 0, 4.999, 10, 0, 5) + self.assertFalse(overlaps) + overlaps = is_overlap(2, 2, 1, 4, 4, 1) + self.assertFalse(overlaps) + + def test_overlap_check_list(self): + + to_check = _Node(0, 2, 2, 1, -1, "blue") + others = [ + _Node(1, 5, 5, 1, -1, "blue"), + _Node(2, 6, 6, 1, -1, "blue"), + _Node(3, 7, 7, 1, -1, "blue"), + ] + overlapping_node = self.qn.is_overlapping_any_node( + to_check, to_check.x, to_check.y, others + ) + self.assertIsNone(overlapping_node) + others += [_Node(4, 3, 3, 1, -1, "red")] + overlapping_node = self.qn.is_overlapping_any_node( + to_check, to_check.x, to_check.y, others + ) + self.assertIsNotNone(overlapping_node) + self.assertEqual(overlapping_node.node_id, 4) + + def test_is_overlapping_any_node_and_index(self): + to_check = _Node(0, 2, 2, 1, -1, "blue") + others = [ + _Node(1, 5, 5, 1, -1, "blue"), + _Node(2, 6, 6, 1, -1, "blue"), + _Node(3, 7, 7, 1, -1, "blue"), + ] + ov_idx, idx = 0, len(others) + ov_idx, overlapping_node = is_overlapping_any_node_and_index( + to_check, to_check.x, to_check.y, others, ov_idx, idx + ) + self.assertEqual(2, ov_idx) + + ov_idx, overlapping_node = is_overlapping_any_node_and_index( + to_check, to_check.x, to_check.y, others, 2, 3 + ) + self.assertEqual(2, ov_idx) + + def test_just_outside_box(self): + self.assertTrue(self.qn.is_just_outside_box(5, 5, 10, 10, 1, 6, 4.9, 1)) # down + self.assertTrue( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 4, 4, 1) + ) # down left + self.assertTrue(self.qn.is_just_outside_box(5, 5, 10, 10, 1, 4, 7.1, 1)) # left + self.assertTrue( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 11, 7.1, 1) + ) # right + self.assertTrue(self.qn.is_just_outside_box(5, 5, 10, 10, 1, 7.1, 11, 1)) # up + + # inside + self.assertFalse( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 6, 6, 1) + ) # inside + self.assertFalse( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 9, 9, 1) + ) # inside + + # way outside + self.assertFalse( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 2.5, 7.1, 1) + ) # far left + self.assertFalse( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 12.5, 7, 1) + ) # right + self.assertFalse( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 7.1, 15, 1) + ) # far up + self.assertFalse( + self.qn.is_just_outside_box(5, 5, 10, 10, 1, 7.1, -3, 1) + ) # far down + + # TESTING NEGATIVE + self.assertTrue( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -6, -11, 1) + ) # down + self.assertTrue( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -4, -4, 1) + ) # up right + self.assertTrue( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -11, -7.1, 1) + ) # left + self.assertTrue( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -4, -7.1, 1) + ) # right + self.assertTrue( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -7.1, -4, 1) + ) # up + + # inside + self.assertFalse( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -6, -6, 1) + ) # inside + self.assertFalse( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -9, -9, 1) + ) # inside + + # way outside + self.assertFalse( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -2.5, -7.1, 1) + ) # far right + self.assertFalse( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -12.5, -7, 1) + ) # left + self.assertFalse( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -7.1, -15, 1) + ) # far down + self.assertFalse( + self.qn.is_just_outside_box(-10, -10, -5, -5, 1, -7.1, -1, 1) + ) # far up + + def test_get_nodes_just_outside_box(self): + others = [ + _Node(0, 6, 4.9, 1, 7, "blue"), + _Node(1, 4, 4, 1, -1, "blue"), + _Node(2, 4, 7.1, 1, -1, "blue"), + _Node(3, 11, 7.1, 1, -1, "blue"), + _Node(4, 7.1, 11, 1, -1, "blue"), + _Node(5, 6, 6, 1, -1, "blue"), + _Node(6, 9, 9, 1, -1, "blue"), + _Node(7, 2.5, 7.1, 1, -1, "blue"), + _Node(8, 7.1, 15, 1, -1, "blue"), + _Node(9, 7.1, -3, 1, -1, "blue"), + ] + local_quad = _QuadNode( + [_Node(99, 5, 5, 1, 0, "red"), _Node(100, 10, 10, 1, 0, "blue")], 5, 50 + ) + just_outside = local_quad.get_nodes_near_lines(others) + + self.assertEqual(5, len(just_outside)) diff --git a/tests/layouts/test_auto.py b/tests/layouts/test_auto.py new file mode 100644 index 000000000..b7b4cc1fc --- /dev/null +++ b/tests/layouts/test_auto.py @@ -0,0 +1,20 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +import numpy +from graspologic.layouts.auto import _get_bounds + + +class TestAuto(unittest.TestCase): + def test_get_bounds(self): + y = numpy.array([(1, 2), (4, 5), (-1, -2), (10, -20)]) + minx, miny, maxx, maxy = _get_bounds(y) + self.assertEqual(-1, minx) + self.assertEqual(-20, miny) + self.assertEqual(10, maxx) + self.assertEqual(5, maxy) + + +if __name__ == "__main__": + unittest.main() From 617b5091dfe5a096a7d115d4501f46c08f6a983d Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 4 Jan 2021 16:11:53 -0800 Subject: [PATCH 076/140] Forgot to actually import `graspologic.layouts` (#624) Without importing it, it doesn't show up as a package for use. --- graspologic/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/graspologic/__init__.py b/graspologic/__init__.py index 8935600d8..202920437 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -10,6 +10,7 @@ import graspologic.datasets import graspologic.embed import graspologic.inference +import graspologic.layouts import graspologic.models import graspologic.partition import graspologic.preprocessing From b5334cc95176c101006b26aa075a5792f8f1d80c Mon Sep 17 00:00:00 2001 From: PerifanosPrometheus <69210497+PerifanosPrometheus@users.noreply.github.com> Date: Tue, 5 Jan 2021 10:54:50 -0600 Subject: [PATCH 077/140] Implemented mixed membership SBM sampling. (#561) * Implemented mixed membership SBM sampling. graspologic/simulations/simulations.py now contains the new function called MMsbm. tests/test_sims.py now contains the test for the new function. docs/tutorials/simulations/MMsbm.ipynb contains tutorial for new function * Update to PR. Should now fix pytest issues. * Adding docs to .rst files * Changes seed in Notebook tutorial for MMsbm * Fixes docs indentation issue * Fixes docs build error * Fixes formatting * Fixes typos in test_sims.py * Solves typos in docs and tutorial and formatting. * Fixes typos in tutorial and docs * Add mmsbm tutorial to sbm tutorial * Tries to fix rendering issue when moving mmsbm tutorial inside sbm tutorial * Tries to fix rendering issue caused by moving mmsbm tutorial notebook under sbm tutorial notebook * Tutorial improvement * Math formatting of docstrings, moves mmsbm tutorial on separate notebook * Fixes doc build * Add optional to labels, change rng to None * Code formatted docstrings * Simpligy code. Intersphinx. Remove colorbars. * Fixes code formatting * simplifies tutorial. Fixes tests check_array. * Fixes docs * Added typerror if alpha is not given as input to function * Should fix input tests * Add intersphinx cross-reference * adds np.nan to labels for diagonal in graph with no loops.Add intersphinx * Changes nan to -1. Simplifies P matrix creation * Add tests to check ER generation. * Fixes code error in test_ER_np * Adds quicker test * Fix formatting Co-authored-by: Benjamin Pedigo --- docs/reference/simulations.rst | 2 + docs/tutorial.rst | 1 + docs/tutorials/simulations/mmsbm.ipynb | 302 +++++++++++++++++++++++++ docs/tutorials/simulations/sbm.ipynb | 4 +- graspologic/simulations/__init__.py | 3 +- graspologic/simulations/simulations.py | 182 +++++++++++++++ tests/test_sims.py | 176 ++++++++++++++ 7 files changed, 667 insertions(+), 3 deletions(-) create mode 100644 docs/tutorials/simulations/mmsbm.ipynb diff --git a/docs/reference/simulations.rst b/docs/reference/simulations.rst index 484feeb99..2e82de0eb 100644 --- a/docs/reference/simulations.rst +++ b/docs/reference/simulations.rst @@ -20,3 +20,5 @@ Simulations .. autofunction:: rdpg_corr +.. autofunction:: mmsbm + diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 07072a587..727c49e62 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -26,6 +26,7 @@ stochastic block model, and random dot product graph (RDPG). tutorials/simulations/erdos_renyi tutorials/simulations/sbm + tutorials/simulations/mmsbm tutorials/simulations/rdpg tutorials/simulations/corr tutorials/simulations/rdpg_corr diff --git a/docs/tutorials/simulations/mmsbm.ipynb b/docs/tutorials/simulations/mmsbm.ipynb new file mode 100644 index 000000000..a1a32faf4 --- /dev/null +++ b/docs/tutorials/simulations/mmsbm.ipynb @@ -0,0 +1,302 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Mixed Membership Stochastic Blockmodel (MMSBM)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import graspologic\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "source": [ + "Unlike [Stochastic Block Models (SBM)](./sbm.ipynb), Mixed Membership Stochastic Blockmodels (MMSBM) allow nodes to pertain to multiple communities when interacting with other nodes.\n", + "\n", + "Given a network with $k$ communities, an MMSBM is parametrized by the number of nodes $n$ of the graph, a block connectivity matrix $B \\in \\mathbb{R}^{k x k}$ where each element specifies the probability of interaction between nodes based upon their community membership, and a vector $\\vec{\\alpha}$ which controls the mixed-membership allowed for each node.\n", + "\n", + "Below, we sample a two-block MMSBM with following parameters:\n", + "\n", + "\\begin{align*}\n", + "n &= 100\\\\\n", + "P &= \\begin{bmatrix} \n", + "0.7 & 0.2\\\\\n", + "0.2 & 0.05\n", + "\\end{bmatrix}\\\\\n", + "\\vec{\\alpha} &= \\begin{bmatrix} 0.1 & 0.1 \\end{bmatrix}\\\\\n", + "\\end{align*}" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.simulations import mmsbm\n", + "\n", + "rng = np.random.default_rng(123)\n", + "\n", + "n = 100\n", + "p = [[0.7, 0.2],\n", + " [0.2, 0.05]]\n", + "alpha = [0.1]*2\n", + "\n", + "G = mmsbm(n, p, alpha, rng = rng)" + ] + }, + { + "source": [ + "## Visualize the graph using heatmap" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.plot import heatmap\n", + "\n", + "heatmap(G, cbar= False, title ='MMSBM Simulation');" + ] + }, + { + "source": [ + "## Generating various types of graphs" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "Each node $p$ is assigned a membership vector $\\vec{\\pi}_p \\in \\mathbb{R}^{k}$ sampled from a [Dirichlet distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution):\n", + "\\begin{align*}\n", + "\\vec{\\pi}_p \\sim \\mbox{Dir}(\\vec{\\alpha})\\\\\n", + "\\end{align*}\n", + "\n", + "This vector indicates the probability of a node to interact with other nodes as if belonging to a certain community. For example, in a four communities graph, let:\n", + "\n", + "\\begin{align*}\n", + "\\vec{\\pi}_p &= \\begin{bmatrix} 1 & 0 & 0 & 0 \\end{bmatrix}\\\\\n", + "\\vec{\\pi}_q &= \\begin{bmatrix} 0.25 & 0.25 & 0.25 & 0.25 \\end{bmatrix}\\\\\n", + "\\end{align*}\n", + "\n", + "According to $\\vec{\\pi}_p$, node $p$ will always behave as if pertaining to the first community when interacting with other nodes, while node $q$ will interact 25% of the time as if belonging to community 1, 25% of the time as if belonging to community 2, etc.\n", + "\n", + "Therefore, controlling these membership vectors by varying the parameter $\\vec{\\alpha}$ allows generation of various types of graphs.\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "### Generating Erdos-Renyi (ER) graphs" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "To generate [Erdos-Renyi (ER)](./erdos_renyi.ipynb), one of the $\\vec{\\alpha}$ entries can be set to be much greater than the others.\n", + "\n", + "For example, consider the two graphs (undirected, no self-loops) parameterized by:\n", + "\n", + "\\begin{align*}\n", + "n &= 100\\\\\n", + "P &= \\begin{bmatrix} \n", + "0.8 & 0.2\\\\\n", + "0.2 & 0.5\n", + "\\end{bmatrix}\\\\\n", + "\\end{align*}\n", + "\n", + "With $\\vec{\\alpha}$ respectively equal to:\n", + "\n", + "\\begin{align*}\n", + "\\vec{\\alpha_1} &= \\begin{bmatrix} 100 & 1 \\end{bmatrix}\\\\\n", + "\\vec{\\alpha_2} &= \\begin{bmatrix} 1 & 100 \\end{bmatrix}\\\\\n", + "\\end{align*}\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.simulations import er_np\n", + "\n", + "n = 100\n", + "p = [[0.8, 0.2],\n", + " [0.2, 0.5]]\n", + "\n", + "alpha_1 = [100, 1]\n", + "G_1 = mmsbm(n, p, alpha_1, rng = rng)\n", + "\n", + "alpha_2 = [1, 100]\n", + "G_2 = mmsbm(n, p, alpha_2, rng = rng)" + ] + }, + { + "source": [ + "### Visualize the graphs using heatmap" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "heatmap(G_1, cbar= False, title ='MMSBM Simulation(alpha = [100, 1])');\n", + "heatmap(G_2, cbar= False, title ='MMSBM Simulation(alpha = [1, 100])');" + ] + }, + { + "source": [ + "### Differences with Stochastic Block Model (SBM) graphs" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "According to the [Stochastic Block Models (SBM)](./sbm.ipynb), each node in a graph can only pertain to one community. Therefore, one way to recreate such model with an MMSBM is to set all the entries of $\\vec{\\alpha}$ equal to the same value. The closer the value is to $0$ the more the simulated graphs will appear similar to an SBM, the closer the value is to $1$ the more uniform the graph will look, thus losing the community structure dictated by the SBM.\n", + "\n", + "To show this we sample a four-block SBM (undirected, no self-loops) graph with following parameters:\n", + "\\begin{align*}\n", + "n &= \\begin{bmatrix} 50 & 50 & 50 & 50 \\end{bmatrix}\\\\\n", + "B &=\\begin{bmatrix} 0.8 & 0.2 & 0.2 & 0.2 \\\\ 0.2 & 0.8 & 0.2 & 0.2 \\\\ 0.2 & 0.2 & 0.8 & 0.2 \\\\ 0.2 & 0.2 & 0.2 & 0.8 \\end{bmatrix}\n", + "\\end{align*}\n", + "\n", + "We also sample a four-block MMSBM with parameter $\\vec{\\alpha}$ such that:\n", + "\\begin{align*}\n", + "\\vec{\\alpha} &= \\begin{bmatrix} 0.01 & 0.01 & 0.01 & 0.01 \\end{bmatrix}\\\\\n", + "\\end{align*}\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.simulations import sbm\n", + "\n", + "n = [50] * 4\n", + "p = [[0.8,0.2,0.2,0.2],\n", + " [ 0.2,0.8,0.2,0.2],\n", + " [ 0.2,0.2,0.8,0.2],\n", + " [ 0.2,0.2,0.2,0.8]]\n", + "\n", + "G_sbm = sbm(n, p, directed=False, loops=False)\n", + "\n", + "n = 200\n", + "k = 4\n", + "alpha = [0.01]*k\n", + "\n", + "G_mmsbm = mmsbm(n, p, alpha= alpha, rng = rng)" + ] + }, + { + "source": [ + "### Plot SBM and MMSBM graphs using heatmap" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "heatmap(G_sbm, cbar= False, title ='SBM Simulation');\n", + "heatmap(G_mmsbm, cbar= False, title ='MMSBM Simulation(alpha = 0.01)');" + ] + }, + { + "source": [ + "As expected, the two graphs appear to be generated from four-block SBMs with connectivity matrix $B$. In the MMSBM case, the number of nodes assigned to each block is not specified which is why we can see a different number of nodes per block.\n", + "\n", + "Now let's try increasing the values of each entry of the concentration parameter $\\vec{\\alpha}$ first to $0.25$, then $0.5$ and finally to $1$." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alpha = [0.25]*k\n", + "print(\"First increase alpha to: \" + str(alpha))\n", + "\n", + "G_mmsbm_25 = mmsbm(n, p, alpha= alpha, rng = rng)\n", + "heatmap(G_mmsbm_25, cbar= False, title ='MMSBM Simulation(alpha = 0.25)');\n", + "\n", + "alpha = [0.5]*k\n", + "print(\"Then increase alpha to: \" + str(alpha))\n", + "\n", + "G_mmsbm_50 = mmsbm(n, p, alpha= alpha, rng = rng)\n", + "heatmap(G_mmsbm_50, cbar= False, title ='MMSBM Simulation(alpha = 0.5)');\n", + "\n", + "alpha = [1.0]*k\n", + "print(\"Finally increase alpha to: \" + str(alpha))\n", + "\n", + "G_mmsbm_100 = mmsbm(n, p, alpha= alpha, rng = rng)\n", + "heatmap(G_mmsbm_100, cbar= False, title ='MMSBM Simulation(alpha = 1.0)');" + ] + }, + { + "source": [ + "As expected, as we increase the value of $\\vec{\\alpha}$ we increase the mixed-membership allowed at each node so that the community structure is progressively lost." + ], + "cell_type": "markdown", + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/docs/tutorials/simulations/sbm.ipynb b/docs/tutorials/simulations/sbm.ipynb index 88fca1c89..926884bae 100644 --- a/docs/tutorials/simulations/sbm.ipynb +++ b/docs/tutorials/simulations/sbm.ipynb @@ -159,9 +159,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.3-final" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/graspologic/simulations/__init__.py b/graspologic/simulations/__init__.py index 93370577c..2b796a780 100644 --- a/graspologic/simulations/__init__.py +++ b/graspologic/simulations/__init__.py @@ -1,7 +1,7 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .simulations import sample_edges, er_np, er_nm, sbm, rdpg, p_from_latent +from .simulations import sample_edges, er_np, er_nm, sbm, rdpg, p_from_latent, mmsbm from .simulations_corr import sample_edges_corr, er_corr, sbm_corr from .rdpg_corr import rdpg_corr @@ -16,4 +16,5 @@ "er_corr", "sbm_corr", "rdpg_corr", + "mmsbm", ] diff --git a/graspologic/simulations/simulations.py b/graspologic/simulations/simulations.py index d8da44f16..74b11abc8 100644 --- a/graspologic/simulations/simulations.py +++ b/graspologic/simulations/simulations.py @@ -4,6 +4,7 @@ import numpy as np from ..utils import symmetrize, cartprod +from sklearn.utils import check_array, check_scalar import warnings @@ -795,3 +796,184 @@ def p_from_latent(X, Y=None, rescale=False, loops=True): P[P < 0] = 0 P[P > 1] = 1 return P + + +def mmsbm( + n, + p, + alpha=None, + rng=None, + directed=False, + loops=False, + return_labels=False, +): + r""" + Samples a graph from Mixed Membership Stochastic Block Model (MMSBM). + + MMSBM produces a graph given the specified block connectivity matrix B, + which indicates the probability of connection between nodes based upon + their community membership. + Each node is assigned a membership vector drawn from Dirichlet distribution + with parameter :math:`\vec{\alpha}`. The entries of this vector indicate the + probabilities for that node of pertaining to each community when interacting with + another node. Each node's membership is determined according to those probabilities. + Finally, interactions are sampled according to the assigned memberships. + + Read more in the :ref:`tutorials ` + + Parameters + ---------- + n: int + Number of vertices of the graph. + + p: array-like, shape (n_communities, n_communities) + Probability of an edge between each of the communities, where ``p[i, j]`` + indicates the probability of a connection between edges in communities + :math:`(i, j)`. + 0 < ``p[i, j]`` < 1 for all :math:`i, j`. + + alpha: array-like, shape (n_communities,) + Parameter alpha of the Dirichlet distribution used + to sample the mixed-membership vectors for each node. + ``alpha[i]`` > 0 for all :math:`i`. + + rng: numpy.random.Generator, optional (default = None) + :class:`numpy.random.Generator` object to sample from distributions. + If None, the random number generator is the Generator object constructed + by ``np.random.default_rng()``. + + directed: boolean, optional (default=False) + If False, output adjacency matrix will be symmetric. Otherwise, output adjacency + matrix will be asymmetric. + + loops: boolean, optional (default=False) + If False, no edges will be sampled in the diagonal. Otherwise, edges + are sampled in the diagonal. + + return_labels: boolean, optional (default=False) + If False, the only output is the adjacency matrix. + If True, output is a tuple. The first element of the tuple is the adjacency + matrix. The second element is a matrix in which the :math:`(i^{th}, j^{th})` + entry indicates the membership assigned to node i when interacting with node j. + Community 1 is labeled with a 0, community 2 with 1, etc. + -1 indicates that no community was assigned for that interaction. + + References + ---------- + .. [1] Airoldi, Edoardo, et al. “Mixed Membership Stochastic Blockmodels.” + Journal of Machine Learning Research, vol. 9, 2008, pp. 1981–2014. + + Returns + ------- + A: ndarray, shape (n, n) + Sampled adjacency matrix + labels: ndarray, shape (n, n), optional + Array containing the membership assigned to each node when interacting with + another node. + + Examples + -------- + >>> rng = np.random.default_rng(1) + >>> np.random.seed(1) + >>> n = 6 + >>> p = [[0.5, 0], [0, 1]] + + To sample a binary MMSBM in which very likely all nodes pertain to community two: + + >>> alpha = [0.05, 1000] + >>> mmsbm(n, p, alpha, rng = rng) + array([[0., 1., 1., 1., 1., 1.], + [1., 0., 1., 1., 1., 1.], + [1., 1., 0., 1., 1., 1.], + [1., 1., 1., 0., 1., 1.], + [1., 1., 1., 1., 0., 1.], + [1., 1., 1., 1., 1., 0.]]) + + To sample a binary MMSBM similar to 2-block SBM with connectivity matrix B: + + >>> rng = np.random.default_rng(1) + >>> np.random.seed(1) + >>> alpha = [0.05, 0.05] + >>> mmsbm(n, p, alpha, rng = rng) + array([[0., 1., 0., 0., 0., 0.], + [1., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 1., 1.], + [0., 0., 0., 1., 0., 1.], + [0., 0., 0., 1., 1., 0.]]) + + """ + + check_scalar(x=n, name="n", target_type=int, min_val=1) + + p = check_array(p, ensure_2d=True) + nx, ny = p.shape + if nx != ny: + msg = "p must be a square matrix, not {}".format(p.shape) + raise ValueError(msg) + if not np.issubdtype(p.dtype, np.number): + msg = "There are non-numeric elements in p" + raise ValueError(msg) + if np.any(p < 0) or np.any(p > 1): + msg = "Values in p must be in between 0 and 1." + raise ValueError(msg) + + alpha = check_array(alpha, ensure_2d=False, ensure_min_features=1) + if not np.issubdtype(alpha.dtype, np.number): + msg = "There are non-numeric elements in alpha" + raise ValueError(msg) + if np.any(alpha <= 0): + msg = "Alpha entries must be > 0." + raise ValueError(msg) + if alpha.shape != (len(p),): + msg = "alpha must be a list or np.array of shape {c}, not {w}.".format( + c=(len(p),), w=alpha.shape + ) + raise ValueError(msg) + + if not isinstance(rng, (np.random._generator.Generator)): + msg = "rng must be not {}.".format( + type(rng) + ) + raise TypeError(msg) + elif rng == None: + rng = np.random.default_rng() + + if type(loops) is not bool: + raise TypeError("loops is not of type bool.") + if type(directed) is not bool: + raise TypeError("directed is not of type bool.") + if type(return_labels) is not bool: + raise TypeError("return_labels is not of type bool.") + + if not directed: + if np.any(p != p.T): + raise ValueError("Specified undirected, but P is directed.") + + # Naming convention follows paper listed in references. + mm_vectors = rng.dirichlet(alpha, n) + + mm_vectors = np.array(sorted(mm_vectors, key=lambda x: np.argmax(x))) + + # labels:(n,n) matrix with all membership indicators for initiators and receivers + # instead of storing the indicator vector, argmax is directly computed + # check docstrings for more info. + labels = np.apply_along_axis( + lambda p_vector: np.argmax( + rng.multinomial(n=1, pvals=p_vector, size=n), axis=1 + ), + axis=1, + arr=mm_vectors, + ) + + P = p[(labels, labels.T)] + + A = sample_edges(P, directed=directed, loops=loops) + + if not loops: + np.fill_diagonal(labels, -1) + + if return_labels: + return (A, labels) + + return A diff --git a/tests/test_sims.py b/tests/test_sims.py index 6dc441928..54ce4e105 100644 --- a/tests/test_sims.py +++ b/tests/test_sims.py @@ -882,3 +882,179 @@ def test_kwarg_passing(self): g = rdpg(X, rescale=True, loops=False, directed=False) self.assertTrue(is_symmetric(g)) self.assertTrue(is_loopless(g)) + + +class Test_MMSBM(unittest.TestCase): + @classmethod + def setUpClass(cls): + # 120 vertex graph + cls.n = 120 + # define connectivity matrix B, two blocks + cls.p = np.vstack(([0.3, 0.2], [0.2, 0.9])) + # define alpha parameter + cls.alpha = 0.05 * np.ones(len(cls.p)) + # define seed + cls.seed = 12345 + # define random number generator + cls.rng = np.random.default_rng(cls.seed) + + def test_ER_np(self): + rng = np.random.default_rng(self.seed) + np.random.seed(self.seed) + + p = [[1, 0.4], [0.4, 0.7]] + + alpha = [1000, 0.05] + + n = 10 + # Produce 1000 graphs in which the majority of nodes are very likely assigned to + # community 1, thus approximate + # 1000 ER graphs with probability almost equal to 1. + graphs = [] + + for i in range(300): + graphs.append(mmsbm(n, p, alpha, directed=True, loops=True, rng=rng)) + graphs = np.stack(graphs) + + # check that probability of these graphs is actually almost equal to that of the + # first block which is 1. + self.assertAlmostEqual(np.mean(graphs), p[0][0], delta=0.01) + + alpha = [0.05, 1000] + # Produce 1000 graphs in which the majority of nodes are very likely assigned to + # community 2, thus approximate + # 1000 ER graphs with probability almost equal to 0.7. + graphs = [] + + for i in range(300): + graphs.append(mmsbm(n, p, alpha, directed=True, loops=True, rng=rng)) + graphs = np.stack(graphs) + + # check that probability of these graphs is actually almost equal to that of the + # second block which is 0.7. + self.assertAlmostEqual(np.mean(graphs), p[1][1], delta=0.01) + pass + + def test_labels(self): + rng = np.random.default_rng(self.seed) + np.random.seed(self.seed) + alpha = np.array([0.0001, 100]) # assign all nodes to second group(index 1) + A = mmsbm( + self.n, + self.p, + alpha, + rng=rng, + directed=False, + loops=False, + return_labels=True, + ) + labels = A[1] + expected_labels = np.ones((self.n, self.n)) + np.fill_diagonal(expected_labels, -1) + # since we expect all nodes on the off diagonal to pertain to community 2 + # we expect all values on the off diagonal to be 1 + # We expect nan on the diagonal as this graph has no loops + + # check that expected labels is the same as the labels output by function + self.assertTrue(np.allclose(labels, expected_labels)) + pass + + def test_loop_directed(self): + rng = np.random.default_rng(self.seed) + A = mmsbm(self.n, self.p, self.alpha, rng=rng, directed=True, loops=True) + + # check loops and directed + self.assertFalse(is_symmetric(A)) + self.assertFalse(is_loopless(A)) + + # check dimensions + self.assertTrue(A.shape == (self.n, self.n)) + pass + + def test_noloop_undirected(self): + rng = np.random.default_rng(self.seed) + # Test that when loops = False and directed = False the output is undirected + # and with no loops + A = mmsbm(self.n, self.p, self.alpha, rng=rng, directed=False, loops=False) + + # check loopless and undirected + self.assertTrue(is_symmetric(A)) + self.assertTrue(is_loopless(A)) + + # check dimensions + self.assertTrue(A.shape == (self.n, self.n)) + pass + + def test_bad_inputs(self): + with self.assertRaises(ValueError): + n = -1 # n must be greater than 0 + mmsbm(n, self.p, self.alpha, rng=self.rng) + + with self.assertRaises(TypeError): + n = "1" # n must be an integer + mmsbm(n, self.p, self.alpha, rng=self.rng) + + with self.assertRaises(TypeError): + n = 2.5 # n must be an integer + mmsbm(n, self.p, self.alpha, rng=self.rng) + + with self.assertRaises(TypeError): + n = ["1", 10] # n must be an integer + mmsbm(n, self.p, self.alpha, rng=self.rng) + + with self.assertRaises(ValueError): + p = 0.5 # p must be a 2d array, not scalar + mmsbm(self.n, p, self.alpha, rng=self.rng) + + with self.assertRaises(ValueError): + p = [[0.2, 0.1], ["str", 0.6]] # p must only contain mumeric elements + mmsbm(self.n, p, self.alpha, rng=self.rng) + + with self.assertRaises(ValueError): + p = [[5, 5], [4, 4]] # p must be between 0 and 1 + mmsbm(self.n, p, self.alpha, rng=self.rng) + + with self.assertRaises(TypeError): + alpha = 1 # alpha must be an array + mmsbm(self.n, self.p, alpha, rng=self.rng) + + with self.assertRaises(ValueError): + # test deafult alpha = None, should output ValueError + mmsbm(self.n, self.p, rng=self.rng) + + with self.assertRaises(ValueError): + alpha = ["str", 0.3] # alpha must only contain numeric elements + mmsbm(self.n, self.p, alpha, rng=self.rng) + + with self.assertRaises(ValueError): + alpha = [0.1, -2] # alpha entries must be > 0 + mmsbm(self.n, self.p, alpha, rng=self.rng) + + with self.assertRaises(ValueError): + alpha = [ + 0.1, + 0.5, + 0.7, + ] # dimension of p is 2x2 so alpha should be of length 2. + mmsbm(self.n, self.p, alpha, rng=self.rng) + + with self.assertRaises(TypeError): + rng = 2 # must be generator + mmsbm(self.n, self.p, self.alpha, rng=rng) + + with self.assertRaises(TypeError): + loops = 2 # loops must be a bool + mmsbm(self.n, self.p, self.alpha, loops=loops, rng=self.rng) + + with self.assertRaises(TypeError): + directed = 2 # directed must be a bool + mmsbm(self.n, self.p, self.alpha, directed=directed, rng=self.rng) + + with self.assertRaises(ValueError): + # specified undirected but provided directed B matrix + p = np.vstack(([0.6, 0.2], [0.3, 0.4])) + mmsbm(self.n, p, self.alpha, directed=False, rng=self.rng) + + with self.assertRaises(TypeError): + return_labels = 2 # return labels must be a bool + mmsbm(self.n, self.p, self.alpha, return_labels=return_labels, rng=self.rng) From a6b652b538c0b355102c1e35b286a333ef3740d4 Mon Sep 17 00:00:00 2001 From: spencer-loggia <36051767+spencer-loggia@users.noreply.github.com> Date: Tue, 5 Jan 2021 12:26:39 -0500 Subject: [PATCH 078/140] unattributed Spectral Vertex Nomination (#610) * initialize VN base branch * add VN base class * add spectral file * move work from sl/160, adds SpectralVN classes * expose baseEmbed, used for type checking * add module init * clustering based working * fixed nan proliferation error, was breaking wknn in some cases * Add initial tests * add initial tests * fix output shape bug simple knn * basic test improved, fixed bugs * prune for initial PR * simplified further, using sum-inverse weighting * fully vectorize, simplify * beautiful broadcasting, remove simple knn * reduce memory overhead * expand docstrings, comments; reformat * refactor tests, fix bugs in svn * remove unused import * add short tutorial for SVN, fix unattributed bug * clear tutorial outputs * remove redundant option, add ref * fix tutorial bug, add tutorial toctree * formatting changes * reformat * revise tutorial title * fix underline length * add nomination doc ref * reformat __init__ * fix name issue * refactor to detect wierd macOS/py36 issue * update docs, style fixes * fix fuction ref style * autoformat * Update docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb change head size Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> * respond to review. naming, style changes * fix tutorial * fix tutorial doc * replace cdist with sklearn nearest neighbors * allow access to NearestNeighbors distance metrics * fix docstring math Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> * change attr to attributes * refactor naming in test * fix typo * change embedding vs adj specification method, restructure type checks * reformat * better test refactor tests, add more type checks * tutorial needs changes to svn class * format * limit line lengths to 88 * better predict commenting, add return self to fit * more explaination of attributed in notebook * change plot style, more explaination * clarify some math * change some names * remove empty atr initialization, remove empty base * revise type checks for unattributed only * unattributed only, move nn params to init, do knn in predict * local black doesn't agree with remote test * minor fixes * refactor tests * reformat * revise tutorial * add description of n_neighbors * combine docs for SGM and SVN Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Co-authored-by: Benjamin Pedigo --- docs/reference/nominate.rst | 7 +- docs/tutorial.rst | 13 +- .../SpectralVertexNomination.ipynb | 213 ++++++++++++++ graspologic/embed/__init__.py | 3 + graspologic/nominate/__init__.py | 6 +- graspologic/nominate/spectralVN.py | 259 ++++++++++++++++++ tests/test_spectral_nomination.py | 168 ++++++++++++ 7 files changed, 661 insertions(+), 8 deletions(-) create mode 100644 docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb create mode 100644 graspologic/nominate/spectralVN.py create mode 100644 tests/test_spectral_nomination.py diff --git a/docs/reference/nominate.rst b/docs/reference/nominate.rst index 0640929b9..b08041287 100644 --- a/docs/reference/nominate.rst +++ b/docs/reference/nominate.rst @@ -3,6 +3,11 @@ Nomination .. currentmodule:: graspologic.nominate +Spectral Vertex Nomination +--------------------------------------- + +.. autoclass:: SpectralVertexNomination + Vertex Nomination via SGM ------------------------- -.. autoclass:: VNviaSGM \ No newline at end of file +.. autoclass:: VNviaSGM diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 727c49e62..3ded4da85 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -111,17 +111,17 @@ The following tutorial demonstrates how to estimate the signal-subgraph of sampl tutorials/subgraph/subgraph -.. _nomination_tutorials: +.. _vertex_nomination_tutorials: -Nomination -========== -The following tutorial demonstrates how to use vertex nomination via seeded graph matching. +Vertex Nomination +================= +The following tutorials demonstrate how to use unattributed single graph spectral vertex nomination or vertex nomination via seeded graph matching to find vertices that are related to a given vertex / set of vertices of interest. .. toctree:: :maxdepth: 1 - - tutorials/nominate/vertex_nomination_via_SGM + tutorials/vertex_nomination/SpectralVertexNomination + tutorials/nominate/vertex_nomination_via_SGM .. _aligning_tutorials: @@ -133,3 +133,4 @@ The following tutorials shows how to align two seperate datasets with each other :maxdepth: 1 tutorials/aligning/aligning + diff --git a/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb b/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb new file mode 100644 index 000000000..36877f475 --- /dev/null +++ b/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Spectral Vertex Nomination\n", + "\n", + "This demo shows how to use the Spectral Vertex Nomination (SVN) class. We will use SVN to nominate vertices in a Stochastic Block Model (SBM)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "from graspologic.nominate import SpectralVertexNomination\n", + "from graspologic.simulations import sbm\n", + "from graspologic.plot import heatmap\n", + "from matplotlib import pyplot as plt\n", + "\n", + "%matplotlib inline" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## What is Spectral Vertex Nomination?\n", + "\n", + "Given a graph $G=(V,E)$ and a subset of $V$ called $S$ (the \"seed\"), Single Graph Vertex Nomination is the problem of ranking all $V$ ordered by degree of relation to members of $S$.\n", + "Spectral Vertex Nomination (SVN) solves the Vertex Nomination problem by embedding a graph's adjacency matrix into a low dimensional euclidean space, then using distance based methods to establish relationship between vertices. (See embedding tutorials)\n", + "\n", + "SVN has the advantage of being extremely efficient on very large graphs, and requires very little prior knowledge of a graph's properties or structure. However, it does somewhat conflate the notion of community membership with vertex similarity.\n", + "This might not be appropriate for all vertex nomination use cases. Synthetic SBM data, with inherently defined communities, is therefore a good setting for demonstrating SVN." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# construct graph from sbm\n", + "n_verts = 100\n", + "p = np.array([[0.5, 0.35, 0.2],\n", + " [0.35, 0.6, 0.3],\n", + " [0.2, 0.3, 0.65]])\n", + "labels = np.array([0] * n_verts + [1] * n_verts + [2] * n_verts)\n", + "adj = np.array(sbm(3 * [n_verts], p), dtype=np.int)\n", + "fig = heatmap(adj, title='3-block SBM (undirected)', inner_hier_labels=labels)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "There are two cases of single graph vertex nomination. In the unattributed case, the seed $S$ is simply a list of any number of vertices in the graph $G$. For each seed vertex $s \\in S$, SVN will return a list of the other vertices in order of distance to $s$.\n", + "Any distance metric supported by sklearn's `NearestNeighbors` can be used, euclidean is default and is generally sufficient.\n", + "\n", + "The `n_neighbors` argument can be used to specify how many vertices should be nominated for each seed, otherwise all vertices will be ranked for each seed. Setting `n_neighbors` will improve runtime on large graphs and might also improve prediction accuracy in some cases." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "SpectralVertexNomination(n_neighbors=5)" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# instantiate a default SVN\n", + "svn = SpectralVertexNomination(n_neighbors=5)\n", + "# fit to the adjacency matrix\n", + "svn.fit(X=adj)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The predict method takes a numpy array of seed vertex indices as an argument. It generates the nomination list, and returns a tuple of the nomination list itself and the associated distance matrix.\n", + "\n", + "Here, we select a seed of 5 random vertices, and the top 5 vertices nominated for each seed are plotted along the first two dimensions of the embedded space." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# define the seed as a random sample of 5 vertices from V.\n", + "seed = np.random.choice(3 * n_verts, size=5, replace=False).astype(np.int)\n", + "nomination_matrix, distance_matrix = svn.predict(seed)\n", + "\n", + "color = ['red', 'lightgreen', 'gold', 'cyan', 'pink']\n", + "seed_color = ['firebrick', 'green', 'tan', 'darkblue', 'purple']\n", + "plt.figure(figsize=(12, 9))\n", + "for i in range(nomination_matrix.shape[1]):\n", + " plt.scatter(svn.embedding_[nomination_matrix[:, i], 0],\n", + " svn.embedding_[nomination_matrix[:, i], 1],\n", + " c=color[i])\n", + " plt.scatter(svn.embedding_[seed[i], 0],\n", + " svn.embedding_[seed[i], 1],\n", + " c=seed_color[i])\n", + " plt.xticks([], [])\n", + " plt.yticks([], [])\n", + " plt.title('Top 5 Nominees For Each Seed (black) in First Two Embeded Dimensions')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The attributed case is not currently handled by Graspologic, and may be added in the future." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/graspologic/embed/__init__.py b/graspologic/embed/__init__.py index 33d010492..d7d9646d3 100644 --- a/graspologic/embed/__init__.py +++ b/graspologic/embed/__init__.py @@ -8,8 +8,10 @@ from .n2v import node2vec_embed from .omni import OmnibusEmbed from .svd import select_dimension, selectSVD +from .base import BaseSpectralEmbed from .mug2vec import mug2vec + __all__ = [ "ClassicalMDS", "OmnibusEmbed", @@ -19,4 +21,5 @@ "node2vec_embed", "select_dimension", "selectSVD", + "BaseSpectralEmbed", ] diff --git a/graspologic/nominate/__init__.py b/graspologic/nominate/__init__.py index dc38ca4b6..8247a4aad 100644 --- a/graspologic/nominate/__init__.py +++ b/graspologic/nominate/__init__.py @@ -1,6 +1,10 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. +from .spectralVN import SpectralVertexNomination from .VNviaSGM import VNviaSGM -__all__ = ["VNviaSGM"] +__all__ = [ + "SpectralVertexNomination", + "VNviaSGM", +] diff --git a/graspologic/nominate/spectralVN.py b/graspologic/nominate/spectralVN.py new file mode 100644 index 000000000..8279974d2 --- /dev/null +++ b/graspologic/nominate/spectralVN.py @@ -0,0 +1,259 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +from typing import Union, Tuple +from ..embed import BaseSpectralEmbed +from ..embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed +import numpy as np +from sklearn.neighbors import NearestNeighbors +from sklearn.base import BaseEstimator + + +class SpectralVertexNomination(BaseEstimator): + """ + Class for spectral vertex nomination on a single graph. + + Given a graph :math:`G=(V,E)` and a subset of :math:`V` called :math:`S` + (the "seed"), Single Graph Vertex Nomination is the problem of ranking all :math:`V` + in order of relation to members of :math:`S`. Spectral Vertex Nomination solves + this problem by embedding :math:`G` into a low dimensional euclidean space + (:ref:`tutorials `), and then generating a nomination list by some + distance based algorithm. In the simple unattributed case, for each seed vertex + :math:`u`, the other vertices are ranked in order of euclidean distance from + :math:`u`. + + Parameters + ---------- + input_graph: bool, default = True + Flag whether to expect two full graphs, or the embeddings. + + - True + .fit and .fit_predict() expect graphs as adjacency matrix, provided as + ndarray of shape (n, n). They will be embedded using the specified + ``embedder``. + - False + .fit() and .fit_predict() expect an embedding of the graph, i.e. a ndarray + of size (n, d). + embedder: str or BaseEmbed, default = 'ASE' + May provide either a embed object or a string indicating which embedding method + to use, which may be either: + "ASE" for :py:class:`~graspologic.embed.AdjacencySpectralEmbed` or + "LSE" for :py:class:`~graspologic.embed.LaplacianSpectralEmbed`. + n_neighbors: int, default=None + The number of vertices to nominate for each seed. + metric : str, default = 'euclidean' + Distance metric to use when finding the nearest neighbors, all sklearn metrics + available. + metric_params : dict, default = None + Arguments for the sklearn `DistanceMetric` specified via `metric` parameter. + + Attributes + ---------- + nearest_neighbors_ : sklearn.neighbors.NearestNeighbors + A fit sklearn `NearestNeighbors` classifier used to find closest vertices to + each seed. + + References + ---------- + .. [1] Fishkind, D. E.; Lyzinski, V.; Pao, H.; Chen, L.; Priebe, C. E. Vertex + nomination schemes for membership prediction. Ann. Appl. Stat. 9 2015. + https://projecteuclid.org/euclid.aoas/1446488749 + + .. [2] Jordan Yoder, Li Chen, Henry Pao, Eric Bridgeford, Keith Levin, + Donniell E. Fishkind, Carey Priebe, Vince Lyzinski, Vertex nomination: The + canonical sampling and the extended spectral nomination schemes, Computational + Statistics & Data Analysis, Volume 145, 2020. + http://www.sciencedirect.com/science/article/pii/S0167947320300074 + + """ + + def __init__( + self, + input_graph: bool = True, + embedder: Union[str, BaseSpectralEmbed] = "ase", + n_neighbors=None, + metric: str = "euclidean", + metric_params: dict = None, + ): + super().__init__() + self.embedder = embedder + self.input_graph = input_graph + self.n_neighbors = n_neighbors + self.metric = metric + self.metric_params = metric_params + self._check_params() + + def _check_x(self, X: np.ndarray): + # check X + if not isinstance(X, np.ndarray): + raise TypeError("X must be of type np.ndarray.") + elif not np.issubdtype(X.dtype, np.number): + raise TypeError("Adjacency matrix or embedding should have numeric type") + elif np.ndim(X) != 2: + raise IndexError("Adjacency matrix or embedding must have dim 2") + elif not self.input_graph: + # embedding was provided + if X.shape[1] > X.shape[0]: + raise IndexError("Dim 1 of an embedding should be smaller than dim 0.") + if not np.issubdtype(X.dtype, np.float): + raise TypeError("Embedding should have type float") + elif not np.issubdtype(X.dtype, np.int): + raise TypeError("Adjacency matrix should have type int") + elif X.shape[0] != X.shape[1]: + raise IndexError("Adjacency Matrix should be square.") + + def _check_y(self, y: np.ndarray): + # check y + if not isinstance(y, np.ndarray): + raise TypeError("y must be of type np.ndarray") + elif not np.issubdtype(y.dtype, np.integer): + raise TypeError("y must have dtype int") + elif np.ndim(y) > 2 or (y.ndim == 2 and y.shape[1] > 1): + raise IndexError("y must have shape (n) or (n, 1).") + elif y.shape[0] > self.embedding_.shape[0]: + raise ValueError( + "the number of seeds must be less than the number of vertices" + ) + + def _check_params(self): + + if self.n_neighbors is not None and type(self.n_neighbors) is not int: + raise TypeError("k must be an integer") + elif self.n_neighbors is not None and self.n_neighbors <= 0: + raise ValueError("k must be greater than 0") + if type(self.metric) is not str: + raise TypeError("metric must be a string") + if self.metric_params is not None and type(self.metric_params) is not dict: + raise TypeError("metric_params must be a dictionary") + if type(self.input_graph) is not bool: + raise TypeError("input_graph_ must be of type bool.") + if self.input_graph: + if not isinstance(self.embedder, BaseSpectralEmbed) and not isinstance( + self.embedder, str + ): + raise TypeError( + "embedder must be either of type str or BaseSpectralEmbed" + ) + + def _embed(self, X: np.ndarray): + # Embed graph if embedding not provided + if self.input_graph: + if isinstance(self.embedder, BaseSpectralEmbed): + embedder = self.embedder + elif not isinstance(self.embedder, str): + raise TypeError("embedder must be type str or BaseSpectralEmbed") + elif self.embedder.lower() == "ase": + embedder = AdjacencySpectralEmbed() + elif self.embedder.lower() == "lse": + embedder = LaplacianSpectralEmbed() + else: + raise ValueError( + "Requested embedding method does not exist, if str is passed must " + "be either 'ASE' or 'LSE'." + ) + self.embedding_ = embedder.fit_transform(X) + else: + self.embedding_ = X + + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Constructs the embedding if not provided, then calculates the pairwise distance + from each seed to each vertex in graph. + + Parameters + ---------- + X : np.ndarray + - If `input_graph` is True + Expects a graph as an adjacency matrix, i.e. an ndarray of shape (n, n). + Will be embedded using the specified embedder. + - If `input_graph` is False + Expects an embedding of the graph, i.e. a ndarray of size (n, d). + y : NoneType + Included by sklearn convention. + + Returns + ------- + self : object + Returns an instance of self. + """ + # ensure y has correct shape. If unattributed (1d) + # add unique attribute to each seed vertex. + self._check_x(X) + self._embed(X) + if self.n_neighbors is None: + self.n_neighbors = X.shape[0] + self.nearest_neighbors_ = NearestNeighbors( + n_neighbors=self.n_neighbors, + metric=self.metric, + metric_params=self.metric_params, + ) + self.nearest_neighbors_.fit(self.embedding_) + return self + + def predict(self, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """ + Nominates vertices for each seed vertex. Methodology is distance based ranking. + + + Parameters + ---------- + y : np.ndarray + The indices of the seed vertices. Should be a dim 1 array with length less + than :math:`|V|`. + + Returns + ------- + Nomination List : np.ndarray + Shape is ``(number_vertices, number_vertices_in_seed)`` . Each + column is a seed vertex, and the rows of each + column are a list of vertex indexes from the original adjacency + matrix in order degree of match. + Distance Matrix : np.ndarray + The matrix of distances associated with each element of the + nomination list. + """ + self._check_y(y) + y = y.reshape(-1) + y_vec = self.embedding_[y.astype(np.int)] + if not hasattr(self, "nearest_neighbors_"): + raise ValueError("Fit must be called before predict.") + distance_matrix, nomination_list = self.nearest_neighbors_.kneighbors(y_vec) + # transpose for consistency with literature + return nomination_list.T.astype(np.int), distance_matrix.T + + def fit_predict( + self, + X: np.ndarray, + y: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """ + Calls this class' fit and then predict methods. + + Parameters + ---------- + X : np.ndarray + - If `input_graph` is True + Expects a graph as an adjacency matrix, i.e. an ndarray of shape (n, n). + Will be embedded using the specified embedder. + - If `input_graph` is False + Expects an embedding of the graph, i.e. a ndarray of size (n, d). + y : np.ndarray. + List of unattributed seed vertex indices. + + Returns + ------- + Nomination List : np.ndarray + Shape is ``(number_vertices, number_vertices_in_seed)`` . Each + column is a seed vertex, and the rows of each + column are a list of vertex indexes from the original adjacency + matrix in order degree of match. + Distance Matrix : np.ndarray + The matrix of distances associated with each element of the + nomination list. + """ + self.fit(X) + return self.predict(y) diff --git a/tests/test_spectral_nomination.py b/tests/test_spectral_nomination.py new file mode 100644 index 000000000..f0ee9342d --- /dev/null +++ b/tests/test_spectral_nomination.py @@ -0,0 +1,168 @@ +import pytest +import numpy as np +from graspologic.embed.ase import AdjacencySpectralEmbed +from graspologic.simulations.simulations import sbm +from graspologic.nominate import SpectralVertexNomination + +# global constants for tests +n_verts = 50 +p = np.array([[0.7, 0.25, 0.2], [0.25, 0.8, 0.3], [0.2, 0.3, 0.85]]) +labels = np.array([0] * n_verts + [1] * n_verts + [2] * n_verts) +adj = np.array(sbm(3 * [n_verts], p), dtype=np.int) +embeder = AdjacencySpectralEmbed() +pre_embeded = embeder.fit_transform(adj) + + +def _nominate(X, seed, nominator=None, k=None): + if nominator is None: + nominator = SpectralVertexNomination(n_neighbors=k) + nominator.fit(X) + n_verts = X.shape[0] + nom_list, dists = nominator.predict(seed) + assert nom_list.shape == (n_verts, seed.shape[0]) + assert dists.shape == (n_verts, seed.shape[0]) + return nom_list + + +def _test_seed_input_dimensions(): + with pytest.raises(IndexError): + _nominate(adj, np.zeros((5, 5, 5), dtype=np.int)) + + +def _test_seed_shape(): + with pytest.raises(IndexError): + _nominate(adj, np.zeros((1, 50), dtype=np.int)) + + +def _test_seed_input_array_dtype(): + with pytest.raises(TypeError): + _nominate(adj, np.random.random((10, 2))) + + +def _test_seed_input_type(): + with pytest.raises(TypeError): + _nominate(adj, [0] * 10) + + +def _test_X_input_type(): + with pytest.raises(TypeError): + _nominate([[0] * 10] * 10, np.zeros(3, dtype=np.int)) + + +def _test_X_array_dtype(): + with pytest.raises(TypeError): + _nominate(np.random.random((10, 10)), np.zeros(3, dtype=np.int)) + + +def _test_X_input_dimensions(): + with pytest.raises(IndexError): + _nominate(np.zeros((5, 5, 5), dtype=np.int), np.zeros(3, dtype=np.int)) + + +def _test_embedding_dimensions(): + # embedding should have less cols than rows. + svn = SpectralVertexNomination(input_graph=False) + with pytest.raises(IndexError): + _nominate( + np.zeros((10, 20), dtype=np.int), np.zeros(3, dtype=np.int), nominator=svn + ) + + +def _test_adjacency_shape(): + # adj matrix should be square + with pytest.raises(IndexError): + _nominate(np.zeros((3, 4), dtype=np.int), np.zeros(3, dtype=np.int)) + + +def _test_input_graph_bool_type(): + # input graph param has wrong type + with pytest.raises(TypeError): + svn = SpectralVertexNomination(input_graph=4) + + +def _test_k_type(): + # k of wrong type + with pytest.raises(TypeError): + _nominate(adj, np.zeros(3, dtype=np.int), k="hello world") + + +def _test_k_value(): + # k should be > 0 + with pytest.raises(ValueError): + _nominate(adj, np.zeros(3, dtype=np.int), k=0) + + +def _test_embedder_type(): + # embedder must be BaseSpectralEmbed or str + with pytest.raises(TypeError): + svn = SpectralVertexNomination(embedder=45) + + +def _test_embedder_value(): + with pytest.raises(ValueError): + svn = SpectralVertexNomination(embedder="hi") + _nominate(adj, np.zeros(3, dtype=int), nominator=svn) + + +class TestSpectralVertexNominatorOutputs: + def test_seed_inputs(self): + _test_seed_input_dimensions() + _test_seed_shape() + _test_seed_input_array_dtype() + _test_seed_input_type() + + def test_X_inputs(self): + _test_X_array_dtype() + _test_X_input_dimensions() + _test_X_input_type() + _test_embedding_dimensions() + _test_adjacency_shape() + + def _test_k(self): + _test_k_value() + _test_k_type() + + def test_constructor_inputs(self): + _test_embedder_value() + + def test_constructor_inputs1(self): + _test_embedder_type() + + def test_constructor_inputs2(self): + _test_input_graph_bool_type() + + @pytest.mark.parametrize( + "nominator", + [ + SpectralVertexNomination(embedder="ASE"), + SpectralVertexNomination(embedder="LSE"), + SpectralVertexNomination(embedder=embeder), + ], + ) + @pytest.mark.parametrize( + "seed", + [ + np.array([8]), + np.array([2, 6, 9, 15, 25]), + np.arange(n_verts - 1, dtype=np.int), + ], + ) + def test_basic_unattributed(self, nominator, seed): + """ + Runs two attributed seeds and two unattributed seeds with each nominator. + Ensures all options work. Should be fast. Nested parametrization tests all + combinations of listed parameters. + """ + _nominate(adj, seed, nominator) + + @pytest.mark.parametrize( + "seed", + [ + np.array([8]), + np.array([2, 6, 9, 15, 25]), + np.arange(n_verts - 1, dtype=np.int), + ], + ) + def test_pre_embedded(self, seed): + svn = SpectralVertexNomination(input_graph=False) + _nominate(pre_embeded, seed, nominator=svn) From bc5ea306a85904611fb3a81f2fec23acce4080cc Mon Sep 17 00:00:00 2001 From: Vivek Gopalakrishnan Date: Tue, 5 Jan 2021 23:22:41 -0500 Subject: [PATCH 079/140] Add Duke mouse dataset + tutorial notebook (#596) * Add connectomes * Add anatomical features * Add atlas information * Add BIDS spec description of subjects * Add edgelist files to MANIFEST * Remove index column * Remove dataset parameters * Add load_mice function to import dataset * Add unit test for load_mice * Add edge test tutorial * Fix typo in docstring * Add a dictionary of metadata * Sort participants by ID * Implement omni embeddings of mouse connectomes * Add more text description * Add MCC notebook * Add explanation for cMDS embedding * Replace list of vertex labels with num of vertices * Add nonparametric MANOVA testing for vertices * Add Holm--Bonferroni correction * Fix `Title underline too short` error * Change kernel so documentation can compile * Add `load_mice` to documentation * Reduce num permutations to avoid runtime limit * Account for ties in Bonferroni--Holm correction * Change kernel again * Further reduce number of permutations * Even further reduce number of permutations * Fix small typos * Fix latex rendering * Update version.txt * Update tutorial.rst Co-authored-by: Jaewon Chung --- MANIFEST.in | 1 + docs/reference/datasets.rst | 5 + docs/tutorial.rst | 12 +- docs/tutorials/connectomics/mcc.ipynb | 495 + graspologic/datasets/__init__.py | 4 +- graspologic/datasets/base.py | 83 + graspologic/datasets/mice/atlas.csv | 167 + graspologic/datasets/mice/blocks.csv | 15 + .../edgelists/sub-54776_ses-1_dti.edgelist | 36390 ++++++++++++++ .../edgelists/sub-54777_ses-1_dti.edgelist | 32737 +++++++++++++ .../edgelists/sub-54779_ses-1_dti.edgelist | 37612 +++++++++++++++ .../edgelists/sub-54781_ses-1_dti.edgelist | 40095 ++++++++++++++++ .../edgelists/sub-54790_ses-1_dti.edgelist | 38032 +++++++++++++++ .../edgelists/sub-54793_ses-1_dti.edgelist | 39280 +++++++++++++++ .../edgelists/sub-54794_ses-1_dti.edgelist | 39258 +++++++++++++++ .../edgelists/sub-54797_ses-1_dti.edgelist | 38206 +++++++++++++++ .../edgelists/sub-54811_ses-1_dti.edgelist | 33290 +++++++++++++ .../edgelists/sub-54813_ses-1_dti.edgelist | 32287 +++++++++++++ .../edgelists/sub-54815_ses-1_dti.edgelist | 32904 +++++++++++++ .../edgelists/sub-54817_ses-1_dti.edgelist | 33423 +++++++++++++ .../edgelists/sub-54821_ses-1_dti.edgelist | 36029 ++++++++++++++ .../edgelists/sub-54823_ses-1_dti.edgelist | 35687 ++++++++++++++ .../edgelists/sub-54829_ses-1_dti.edgelist | 39229 +++++++++++++++ .../edgelists/sub-54831_ses-1_dti.edgelist | 33613 +++++++++++++ .../edgelists/sub-54833_ses-1_dti.edgelist | 32365 +++++++++++++ .../edgelists/sub-54835_ses-1_dti.edgelist | 38116 +++++++++++++++ .../edgelists/sub-54842_ses-1_dti.edgelist | 36594 ++++++++++++++ .../edgelists/sub-54847_ses-1_dti.edgelist | 36810 ++++++++++++++ .../edgelists/sub-54849_ses-1_dti.edgelist | 33872 +++++++++++++ .../edgelists/sub-54851_ses-1_dti.edgelist | 31911 ++++++++++++ .../edgelists/sub-54853_ses-1_dti.edgelist | 35251 ++++++++++++++ .../edgelists/sub-54855_ses-1_dti.edgelist | 26802 +++++++++++ .../edgelists/sub-54864_ses-1_dti.edgelist | 35305 ++++++++++++++ .../edgelists/sub-54866_ses-1_dti.edgelist | 38801 +++++++++++++++ .../edgelists/sub-54868_ses-1_dti.edgelist | 38499 +++++++++++++++ .../edgelists/sub-54870_ses-1_dti.edgelist | 37602 +++++++++++++++ .../edgelists/sub-54883_ses-1_dti.edgelist | 37684 +++++++++++++++ .../edgelists/sub-54885_ses-1_dti.edgelist | 32609 +++++++++++++ .../edgelists/sub-54887_ses-1_dti.edgelist | 28460 +++++++++++ .../edgelists/sub-54890_ses-1_dti.edgelist | 37152 ++++++++++++++ graspologic/datasets/mice/features/54776.csv | 336 + graspologic/datasets/mice/features/54777.csv | 336 + graspologic/datasets/mice/features/54779.csv | 336 + graspologic/datasets/mice/features/54781.csv | 336 + graspologic/datasets/mice/features/54790.csv | 336 + graspologic/datasets/mice/features/54793.csv | 336 + graspologic/datasets/mice/features/54794.csv | 336 + graspologic/datasets/mice/features/54797.csv | 336 + graspologic/datasets/mice/features/54811.csv | 336 + graspologic/datasets/mice/features/54813.csv | 336 + graspologic/datasets/mice/features/54815.csv | 336 + graspologic/datasets/mice/features/54817.csv | 336 + graspologic/datasets/mice/features/54821.csv | 336 + graspologic/datasets/mice/features/54823.csv | 336 + graspologic/datasets/mice/features/54829.csv | 336 + graspologic/datasets/mice/features/54831.csv | 336 + graspologic/datasets/mice/features/54833.csv | 336 + graspologic/datasets/mice/features/54835.csv | 336 + graspologic/datasets/mice/features/54842.csv | 336 + graspologic/datasets/mice/features/54847.csv | 336 + graspologic/datasets/mice/features/54849.csv | 336 + graspologic/datasets/mice/features/54851.csv | 336 + graspologic/datasets/mice/features/54853.csv | 336 + graspologic/datasets/mice/features/54855.csv | 336 + graspologic/datasets/mice/features/54864.csv | 336 + graspologic/datasets/mice/features/54866.csv | 336 + graspologic/datasets/mice/features/54868.csv | 336 + graspologic/datasets/mice/features/54870.csv | 336 + graspologic/datasets/mice/features/54883.csv | 336 + graspologic/datasets/mice/features/54885.csv | 336 + graspologic/datasets/mice/features/54887.csv | 336 + graspologic/datasets/mice/features/54890.csv | 336 + graspologic/datasets/mice/participants.csv | 33 + graspologic/version/version.txt | 1 + tests/test_datasets.py | 4 + 75 files changed, 1147474 insertions(+), 3 deletions(-) create mode 100644 docs/tutorials/connectomics/mcc.ipynb create mode 100644 graspologic/datasets/mice/atlas.csv create mode 100644 graspologic/datasets/mice/blocks.csv create mode 100644 graspologic/datasets/mice/edgelists/sub-54776_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54777_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54779_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54781_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54790_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54793_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54794_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54797_ses-1_dti.edgelist create mode 100644 graspologic/datasets/mice/edgelists/sub-54811_ses-1_dti.edgelist create mode 100644 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graspologic/datasets/mice/features/54777.csv create mode 100755 graspologic/datasets/mice/features/54779.csv create mode 100755 graspologic/datasets/mice/features/54781.csv create mode 100755 graspologic/datasets/mice/features/54790.csv create mode 100755 graspologic/datasets/mice/features/54793.csv create mode 100755 graspologic/datasets/mice/features/54794.csv create mode 100755 graspologic/datasets/mice/features/54797.csv create mode 100755 graspologic/datasets/mice/features/54811.csv create mode 100755 graspologic/datasets/mice/features/54813.csv create mode 100755 graspologic/datasets/mice/features/54815.csv create mode 100755 graspologic/datasets/mice/features/54817.csv create mode 100755 graspologic/datasets/mice/features/54821.csv create mode 100755 graspologic/datasets/mice/features/54823.csv create mode 100755 graspologic/datasets/mice/features/54829.csv create mode 100755 graspologic/datasets/mice/features/54831.csv create mode 100755 graspologic/datasets/mice/features/54833.csv create mode 100755 graspologic/datasets/mice/features/54835.csv create mode 100755 graspologic/datasets/mice/features/54842.csv create mode 100755 graspologic/datasets/mice/features/54847.csv create mode 100755 graspologic/datasets/mice/features/54849.csv create mode 100755 graspologic/datasets/mice/features/54851.csv create mode 100755 graspologic/datasets/mice/features/54853.csv create mode 100755 graspologic/datasets/mice/features/54855.csv create mode 100755 graspologic/datasets/mice/features/54864.csv create mode 100755 graspologic/datasets/mice/features/54866.csv create mode 100755 graspologic/datasets/mice/features/54868.csv create mode 100755 graspologic/datasets/mice/features/54870.csv create mode 100755 graspologic/datasets/mice/features/54883.csv create mode 100755 graspologic/datasets/mice/features/54885.csv create mode 100755 graspologic/datasets/mice/features/54887.csv create mode 100755 graspologic/datasets/mice/features/54890.csv create mode 100644 graspologic/datasets/mice/participants.csv diff --git a/MANIFEST.in b/MANIFEST.in index 0d1d6a65c..7e15ce2eb 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,4 @@ +recursive-include graspologic/datasets *.edgelist recursive-include graspologic/datasets *.csv recursive-include graspologic/version version.txt recursive-include graspologic/layouts/include colors-100.json diff --git a/docs/reference/datasets.rst b/docs/reference/datasets.rst index 0f72e4113..288ecafd7 100644 --- a/docs/reference/datasets.rst +++ b/docs/reference/datasets.rst @@ -9,3 +9,8 @@ Drosophila larval mushroom body .. autofunction:: load_drosophila_left .. autofunction:: load_drosophila_right + +Duke mouse whole-brain connectomes +---------------------------------- + +.. autofunction:: load_mice \ No newline at end of file diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 3ded4da85..09af92a46 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -131,6 +131,16 @@ The following tutorials shows how to align two seperate datasets with each other .. toctree:: :maxdepth: 1 - + tutorials/aligning/aligning +.. _connectomics_tutorials: + +Connectomics +============ +The following tutorials demonstrate how to apply methods in this package to the analysis of connectomics datasets. + +.. toctree:: + :maxdepth: 1 + + tutorials/connectomics/mcc diff --git a/docs/tutorials/connectomics/mcc.ipynb b/docs/tutorials/connectomics/mcc.ipynb new file mode 100644 index 000000000..3db28cb14 --- /dev/null +++ b/docs/tutorials/connectomics/mcc.ipynb @@ -0,0 +1,495 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Methods for Multiscale Comparative Connectomics\n", + "\n", + "This demo shows you how to use methods in `graspologic` to analyze patterns\n", + "in brain connectivity in connectomics datasets. We specifically demonstrate\n", + "methods for identifying differences in edges and vertices across subjects. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "pd.options.mode.chained_assignment = None\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the Duke mouse brain dataset\n", + "\n", + "Dataset of 32 mouse connectomes derived from whole-brain diffusion\n", + "magnetic resonance imaging of four distinct mouse genotypes:\n", + "BTBR T+ Itpr3tf/J (BTBR), C57BL/6J(B6), CAST/EiJ (CAST), and DBA/2J (DBA2).\n", + "For each strain, connectomes were generated from eight age-matched mice\n", + "(N = 8 per strain), with a sex distribution of four males and four females.\n", + "Each connectome was parcellated using asymmetric Waxholm Space, yielding a\n", + "vertex set with a total of 332 regions of interest (ROIs) symmetrically\n", + "distributed across the left and right hemispheres. Within a given\n", + "hemisphere, there are seven superstructures consisting up multiple ROIs,\n", + "resulting in a total of 14 distinct communities in each connectome." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.datasets import load_mice" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the full mouse dataset\n", + "mice = load_mice()\n", + "\n", + "# Stack all adjacency matrices in a 3D numpy array\n", + "graphs = np.array(mice.graphs)\n", + "\n", + "# Get sample parameters\n", + "n_subjects = mice.meta[\"n_subjects\"]\n", + "n_vertices = mice.meta[\"n_vertices\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the set of graphs by genotype\n", + "btbr = graphs[mice.labels == \"BTBR\"]\n", + "b6 = graphs[mice.labels == \"B6\"]\n", + "cast = graphs[mice.labels == \"CAST\"]\n", + "dba2 = graphs[mice.labels == \"DBA2\"]\n", + "\n", + "connectomes = [btbr, b6, cast, dba2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identifying Signal Edges\n", + "\n", + "The simplest approach for comparing connectomes is to treat them as a _bag of edges_ without considering interactions between the edges.\n", + "Serially performing univariate statistical analyses at each edge enables the discovery of _signal edges_ whose neurological connectivity differs across categorical or dimensional phenotypes.\n", + "Here, we demonstrate the possibility of using Distance Correlation (`Dcorr`), a nonparametric universally consistent test, to detect changes in edges.\n", + "\n", + "In this model, we assume that each edge in the connectome is independently and\n", + "identically sampled from some distribution $F_i$, where $i$ represents the group\n", + "to which the given connectome belongs. In this setting the groups are the mouse\n", + "genotypes. `Dcorr` allows us to test the following hypothesis:\n", + "\n", + "\\begin{align*}\n", + "H_0:\\ &F_1 = F_2 = \\ldots F_k \\\\\n", + "H_A:\\ &\\exists \\ j \\neq j' \\text{ s.t. } F_j \\neq F_{j'}\n", + "\\end{align*}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from hyppo.ksample import KSample" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the connectomes in this dataset are undirected, we only need to do edge\n", + "comparisons on the upper triangle of the adjacency matrices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make iterator for traversing the upper triangle of the connectome\n", + "indices = zip(*np.triu_indices(n_vertices, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "edge_pvals = []\n", + "\n", + "for roi_i, roi_j in indices:\n", + " \n", + " # Get the (i,j)-th edge for each connectome\n", + " samples = [genotype[:, roi_i, roi_j] for genotype in connectomes]\n", + " \n", + " # Calculate the p-value for the (i,j)-th edge\n", + " try:\n", + " statistic, pvalue = KSample(\"Dcorr\").test(*samples, reps=10000, workers=-1)\n", + " except ValueError:\n", + " # A ValueError is thrown when any of the samples have equal edge \n", + " # weights (i.e. one of the inputs has 0 variance)\n", + " statistic = np.nan\n", + " pvalue = 1\n", + "\n", + " edge_pvals.append([roi_i, roi_j, statistic, pvalue])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Connectomes are a high-dimensional dataype.\n", + "Thus, statistical tests on components of the connectome (e.g. edge and vertices)\n", + "results in multiple comparisons.\n", + "We recommend correcting for multiple comparisons using the Holm-Bonferroni (HB)\n", + "corection.\n", + "\n", + "The algorithm is described below:\n", + "1. Sort the p-values from lowest-to-highest $P_1, P_2, \\dots, P_n$, where $n$ is the number of tests\n", + "2. Correct the p-value as $P_1(n), P_2(n-1), \\dots, P_n(1)$\n", + "3. If any corrected p-value is $>1$, replace with $1$\n", + "4. If the corrected p-value is less than a significance level $\\alpha$, reject" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the nested list to a dataframe\n", + "edge_pvals = pd.DataFrame(edge_pvals, columns=[\"ROI_1\", \"ROI_2\", \"stat\", \"pval\"])\n", + "\n", + "# Correct p-values using the Holm-Bonferroni correction\n", + "edge_pvals.sort_values(by=\"pval\", inplace=True, ignore_index=True)\n", + "pval_rank = edge_pvals[\"pval\"].rank(ascending=False, method=\"max\")\n", + "edge_pvals[\"holm_pval\"] = edge_pvals[\"pval\"].multiply(pval_rank)\n", + "edge_pvals[\"holm_pval\"] = edge_pvals[\"holm_pval\"].apply(\n", + " lambda pval: 1 if pval > 1 else pval\n", + ")\n", + "\n", + "# Test for significance using alpha=0.05\n", + "alpha = 0.05\n", + "edge_pvals[\"significant\"] = (edge_pvals[\"holm_pval\"] < alpha)\n", + "\n", + "# Get the top 10 strongest signal edges\n", + "edge_pvals_top = edge_pvals.head(10)\n", + "\n", + "# Replace ROI indices with actual names\n", + "def lookup_roi_name(index):\n", + " hemisphere = \"R\" if index // 166 else \"L\"\n", + " index = index % 166\n", + " roi_name = mice.atlas.query(f\"ROI == {index+1}\")[\"Structure\"].item()\n", + " return f\"{roi_name} ({hemisphere})\"\n", + "\n", + "edge_pvals_top[\"ROI_1\"] = edge_pvals_top[\"ROI_1\"].apply(lookup_roi_name)\n", + "edge_pvals_top[\"ROI_2\"] = edge_pvals_top[\"ROI_2\"].apply(lookup_roi_name)\n", + "\n", + "edge_pvals_top.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that none of the edges achieve significance at $\\alpha=0.05$ following \n", + "Holm-Bonferroni correction.\n", + "We might expect this, given that we are correcting for $N=54,946$ tests.\n", + "Instead of the magnitude, the **ranking of the p-values** can be used to determine signal edges." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identifying Signal Vertices\n", + "\n", + "A sample of connectomes can be jointly embedded in a low-dimensional Euclidean space using the omnibus embedding (`omni`).\n", + "A host of machine learning tasks can be accomplished with this joint embedded representation of the connectome, such as clustering or classification of vertices.\n", + "Here, we use the embedding to formulate a statistical test that can be used to identify vertices that are strongly associated with given phenotypes.\n", + "According to a Central Limit Theorem for `omni`, these latent positions are universally consistent and asymptotically normal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.embed import OmnibusEmbed\n", + "from graspologic.plot import pairplot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Jointly embed graphs using omnibus embedding\n", + "embedder = OmnibusEmbed()\n", + "omni_embedding = embedder.fit_transform(graphs)\n", + "print(omni_embedding.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each of the 32 mice, `omni` embeds each vertex in the connectome to a\n", + "latent position vector $x_i \\in \\mathbb{R}^5$.\n", + "We test for differences in the distribution of vertex latent positions using \n", + "the `MGC` independence test implemented in `hyppo`.\n", + "This test essentially acts as a nonparametric MANOVA test." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vertex_pvals = []\n", + "\n", + "for vertex_i in range(n_vertices):\n", + " \n", + " # Get the embedding of the i-th vertex for \n", + " samples = [\n", + " omni_embedding[mice.labels==genotype, vertex_i, :] \n", + " for genotype in np.unique(mice.labels)\n", + " ]\n", + " \n", + " # Calculate the p-value for the i-th vertex\n", + " statistic, pvalue, _ = KSample(\"MGC\").test(\n", + " *samples, reps=250, workers=-1\n", + " )\n", + " vertex_pvals.append([vertex_i, statistic, pvalue])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Important:** When running on data, make sure `reps` $\\geq \\frac{|V|}{\\alpha}$ where $|V|$ is the cardinality of the vertex set and $\\alpha$ is the significance level for your test.\n", + "Again, this has to do with p-value correction:\n", + "because `MGC` is a permutation test, you need many replications to ensure accurate computation of the p-value.\n", + "Because this notebook is primarily for demonstration purposes, we use a small value of `reps=250`. For this dataset, a more appropriate number of permutations would be around $10^6$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the nested list to a dataframe\n", + "vertex_pvals = pd.DataFrame(vertex_pvals, columns=[\"ROI\", \"stat\", \"pval\"])\n", + "\n", + "# Correct p-values using the Holm-Bonferroni correction\n", + "vertex_pvals.sort_values(by=\"pval\", inplace=True, ignore_index=True)\n", + "pval_rank = vertex_pvals[\"pval\"].rank(ascending=False, method=\"max\")\n", + "vertex_pvals[\"holm_pval\"] = vertex_pvals[\"pval\"].multiply(pval_rank)\n", + "vertex_pvals[\"holm_pval\"] = vertex_pvals[\"holm_pval\"].apply(\n", + " lambda pval: 1 if pval > 1 else pval\n", + ")\n", + "\n", + "# Test for significance using alpha=0.05\n", + "alpha = 0.05\n", + "vertex_pvals[\"significant\"] = (vertex_pvals[\"holm_pval\"] < alpha)\n", + "\n", + "# Get the top 10 strongest signal edges\n", + "vertex_pvals_top = vertex_pvals.head(10)\n", + "\n", + "# Replace ROI indices with actual names\n", + "vertex_pvals_top[\"ROI\"] = vertex_pvals_top[\"ROI\"].apply(lookup_roi_name)\n", + "\n", + "vertex_pvals_top.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use pairplots to visualize the embeddings of specific vertices.\n", + "Below are pairsplots of the corpus callosum from the left and right hemispheres." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "left = pairplot(\n", + " omni_embedding[:, 121 - 1, :3], \n", + " mice.labels, \n", + " palette=[\"#e7298a\", \"#1b9e77\", \"#d95f02\", \"#7570b3\"],\n", + " title=\"Left Corpus Callosum\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "right = pairplot(\n", + " omni_embedding[:, 121 + 166 - 1, :3], \n", + " mice.labels, \n", + " palette=[\"#e7298a\", \"#1b9e77\", \"#d95f02\", \"#7570b3\"],\n", + " title=\"Right Corpus Callosum\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Whole-brain Comparisons\n", + "\n", + "We can use the results of the omnibus embedding to perform whole-brain comparisons\n", + "across subjects from different phenotypes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.embed import ClassicalMDS\n", + "from graspologic.plot import heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two-dimensional representations of each connectome were obtained by using \n", + "Classical Multidimensional Scaling (`cmds`) to reduce the dimensionality \n", + "of the embeddings obtained by `omni`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Further reduce embedding dimensionality using cMDS\n", + "cmds = ClassicalMDS(2)\n", + "cmds_embedding = cmds.fit_transform(omni_embedding)\n", + "cmds_embedding = pd.DataFrame(cmds_embedding, columns=[\"Dimension 1\", \"Dimension 2\"])\n", + "cmds_embedding[\"Genotype\"] = mice.labels\n", + "\n", + "# Embedding with BTBR\n", + "sns.scatterplot(\n", + " x=\"Dimension 1\",\n", + " y=\"Dimension 2\",\n", + " hue=\"Genotype\",\n", + " data=cmds_embedding,\n", + " palette=[\"#e7298a\", \"#1b9e77\", \"#d95f02\", \"#7570b3\"],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the dissimilarity between subjects' connectomes using the cMDS embedding\n", + "dis = cmds.dissimilarity_matrix_\n", + "scaled_dissimilarity = dis / np.max(dis)\n", + "\n", + "heatmap(scaled_dissimilarity,\n", + " context=\"paper\",\n", + " inner_hier_labels=mice.labels,)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that mice from the same genotype are most similar to each other, as expected." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/graspologic/datasets/__init__.py b/graspologic/datasets/__init__.py index cfed50709..4a5fd2adf 100644 --- a/graspologic/datasets/__init__.py +++ b/graspologic/datasets/__init__.py @@ -1,6 +1,6 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .base import load_drosophila_left, load_drosophila_right +from .base import load_drosophila_left, load_drosophila_right, load_mice -__all__ = ["load_drosophila_left", "load_drosophila_right"] +__all__ = ["load_drosophila_left", "load_drosophila_right", "load_mice"] diff --git a/graspologic/datasets/base.py b/graspologic/datasets/base.py index 977da5ccf..37b71c0bd 100644 --- a/graspologic/datasets/base.py +++ b/graspologic/datasets/base.py @@ -2,7 +2,13 @@ # Licensed under the MIT License. from os.path import dirname, join +from pathlib import Path + import numpy as np +import pandas as pd +from sklearn.utils import Bunch + +from ..utils import import_edgelist def load_drosophila_left(return_labels=False): @@ -95,3 +101,80 @@ def load_drosophila_right(return_labels=False): return graph, labels else: return graph + + +def load_mice(): + """ + Load connectomes of mice from distinct genotypes. + + Dataset of 32 mouse connectomes derived from whole-brain diffusion + magnetic resonance imaging of four distinct mouse genotypes: + BTBR T+ Itpr3tf/J (BTBR), C57BL/6J(B6), CAST/EiJ (CAST), and DBA/2J (DBA2). + For each strain, connectomes were generated from eight age-matched mice + (N = 8 per strain), with a sex distribution of four males and four females. + Each connectome was parcellated using asymmetric Waxholm Space, yielding a + vertex set with a total of 332 regions of interest (ROIs) symmetrically + distributed across the left and right hemispheres. Within a given + hemisphere, there are seven superstructures consisting up multiple ROIs, + resulting in a total of 14 distinct communities in each connectome. + + Returns + ------- + data : :class:`~sklearn.utils.Bunch` + Dictionary-like object, with the following attributes. + + graphs : list of np.ndarray + List of adjacency matrices of the connectome + labels : np.ndarray + Array of string labels for each mouse (subject) + atlas : pd.DataFrame + DataFrame of information for each ROI + blocks : pd.DataFrame + DataFrame of block assignments for each ROI + features : pd.DataFrame + DataFrame of anatomical features for each ROI in each connectome + + References + ---------- + .. [1] Wang, N., Anderson, R. J., Ashbrook, D. G., Gopalakrishnan, V., + Park, Y., Priebe, C. E., ... & Johnson, G. A. (2020). Variability + and heritability of mouse brain structure: Microscopic MRI atlases + and connectomes for diverse strains. NeuroImage. + https://doi.org/10.1016/j.neuroimage.2020.117274 + """ + + data = Path(__file__).parent.joinpath("mice") + + # Load all connectomes and construct a dictionary of study metadata + graphs, vertices = import_edgelist(data.joinpath("edgelists"), return_vertices=True) + + n_vertices = len(vertices) + n_subjects = len(graphs) + meta = {"n_subjects": n_subjects, "n_vertices": n_vertices} + + # Read the participants file and get genotype labels + participants = pd.read_csv(data.joinpath("participants.csv")) + labels = participants["genotype"].values + + # Read the atlas and block information + atlas = pd.read_csv(data.joinpath("atlas.csv")) + blocks = pd.read_csv(data.joinpath("blocks.csv")) + + # Read features + tmp = [] + for fl in data.joinpath("features").glob("*" + "csv"): + subid = fl.stem + df = pd.read_csv(fl, skiprows=2) + df["participant_id"] = subid + tmp.append(df) + features = pd.concat(tmp, axis=0) + features = features.reset_index(drop=True) + + return Bunch( + graphs=graphs, + labels=labels, + atlas=atlas, + blocks=blocks, + features=features, + meta=meta, + ) diff --git a/graspologic/datasets/mice/atlas.csv b/graspologic/datasets/mice/atlas.csv new file mode 100644 index 000000000..20f6a6049 --- /dev/null +++ b/graspologic/datasets/mice/atlas.csv @@ -0,0 +1,167 @@ +ROI,Structure,Abbreviation,c_R,c_G,c_B,c_A,Level_1,Level_2,Level_3,Level_4,Macrostructure +1,Cingulate_Cortex_Area_24a,A24a,255,0,0,255,forebrain,secondary_prosencephalon,isocortex,cingulate_cortex,isocortex +2,Cingulate_Cortex_Area_24a_prime,A24aPrime,255,124,0,255,forebrain,secondary_prosencephalon,isocortex,cingulate_cortex,isocortex +3,Cingulate_Cortex_Area_24b,A24b,255,248,0,255,forebrain,secondary_prosencephalon,isocortex,cingulate_cortex,isocortex +4,Cingulate_Cortex_Area_24b_prime,A24bPrime,138,255,0,255,forebrain,secondary_prosencephalon,isocortex,cingulate_cortex,isocortex 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+35,Primary_Visual_Cortex,V1,0,38,255,255,forebrain,secondary_prosencephalon,isocortex,occipital_cortex,isocortex +36,Primary_Visual_Cortex_Binocular_Area,V1B,255,162,0,255,forebrain,secondary_prosencephalon,isocortex,occipital_cortex,isocortex +37,Primary_Visual_Cortex_Monocular_Area,V1M,190,0,255,255,forebrain,secondary_prosencephalon,isocortex,occipital_cortex,isocortex +38,Secondary_Visual_CortexLateral_Area,V2L,255,0,207,255,forebrain,secondary_prosencephalon,isocortex,occipital_cortex,isocortex +39,Secondary_Visual_Cortex_Mediolateral_Area,V2ML,255,0,93,255,forebrain,secondary_prosencephalon,isocortex,occipital_cortex,isocortex +40,Secondary_Visual_Cortex_Mediomedial_Area,V2MM,255,31,0,255,forebrain,secondary_prosencephalon,isocortex,occipital_cortex,isocortex +41,Ventral_Orbital_Cortex,VO,255,155,0,255,forebrain,secondary_prosencephalon,isocortex,frontal_cortex,isocortex +42,Caudomedial_Entorhinal_Cortex,CEnt,231,255,0,255,forebrain,secondary_prosencephalon,temporal_cortex,dorsal_pallium,pallium +43,Dorsal_Intermediate_Entorhinal_Cortex,DIEnt,107,255,0,255,forebrain,secondary_prosencephalon,temporal_cortex,dorsal_pallium,pallium +44,Dorsolateral_Entorhinal_Cortex,DLEnt,255,46,0,255,forebrain,secondary_prosencephalon,temporal_cortex,dorsal_pallium,pallium +45,Medial_Entorhinal_Cortex,MEnt,0,255,141,255,forebrain,secondary_prosencephalon,temporal_cortex,dorsal_pallium,pallium +46,Ventral_Intermediate_Entorhinal_Cortex,VIEnt,0,255,255,255,forebrain,secondary_prosencephalon,temporal_cortex,dorsal_pallium,pallium +47,Claustrum,Cl,0,141,255,255,forebrain,secondary_prosencephalon,lateral_pallium,,pallium +48,Dorsal_Claustrum,DCl,0,28,255,255,forebrain,secondary_prosencephalon,lateral_pallium,,pallium +49,Posterolateral_Cortical_Amygdaloid_Area,PLCo,86,0,255,255,forebrain,secondary_prosencephalon,lateral_pallium,subpallial_amygdala,pallium +50,Ventral_Claustrum,VCl,200,0,255,255,forebrain,secondary_prosencephalon,lateral_pallium,,pallium +51,Hippocampus,Hc,127,127,255,255,forebrain,secondary_prosencephalon,medial_pallium,,pallium +52,Dorsal_Tenia_Tecta,DTT,59,59,247,255,forebrain,secondary_prosencephalon,medial_pallium,,pallium +53,Ectorhinal_Cortex,Ect,255,0,0,255,forebrain,secondary_prosencephalon,temporal_cortex,medial_pallium,pallium +54,Parasubiculum,PaS,255,165,0,255,forebrain,secondary_prosencephalon,medial_pallium,,pallium +55,Perirhinal_Cortex,PRh,59,59,247,255,forebrain,secondary_prosencephalon,temporal_cortex,medial_pallium,pallium +56,Presubiculum,PrS,96,255,0,255,forebrain,secondary_prosencephalon,medial_pallium,,pallium +57,Piriform_Cortex,Pir,31,170,170,255,forebrain,secondary_prosencephalon,ventral_pallium,pallium,pallium +58,Amygdalopiriform_Transition_Area,APir,0,255,152,255,forebrain,secondary_prosencephalon,lateral_pallium,ventral_pallium,pallium +59,Hypothalamus,Hyp,0,245,255,255,forebrain,secondary_prosencephalon,rostral_secondary_prosencephalon,hypothalamus,pallium +60,Preoptic_Telencephalon,Preoptic_Telencephalon,0,131,255,255,forebrain,secondary_prosencephalon,medial_pallium,rostral_secondary_prosencephalon,pallium +61,Subthalamic_Nucleus,Sthal,0,17,255,255,forebrain,secondary_prosencephalon,hypothalamus,caudal_secondary_prosencephalon,pallium +62,Septum,SPT,96,0,255,255,forebrain,secondary_prosencephalon,subpallium,subpallial_septum,subpallium +63,Globus_Pallidus,GP,210,0,255,255,forebrain,secondary_prosencephalon,subpallium,pallidum,subpallium +64,Striatum,Cpu,214,0,204,255,forebrain,secondary_prosencephalon,subpallium,striatum,subpallium +65,Amygdala,Amy,255,0,72,255,forebrain,secondary_prosencephalon,subpallium,subpallial_amygdala,subpallium +66,Accumbens,Acb,59,59,247,255,forebrain,secondary_prosencephalon,subpallium,paraseptal_subpallium,subpallium +67,Bed_Nucleus_of_the_Stria_Terminalis,BNst,255,176,0,255,forebrain,secondary_prosencephalon,subpallium,subpallial_amygdala,subpallium +68,Ventral_Pallidum,Vpal,210,255,0,255,forebrain,secondary_prosencephalon,subpallium,pallidum,subpallium +69,Periaquaductal_Grey,PAG,86,255,0,255,forebrain,p1,diencephalon,pretectum,diencephalon +70,Anterior_Pretectal_Nucleus,APT,0,255,38,255,forebrain,p1,diencephalon,pretectum,diencephalon +71,Ventral_Tegmental_Area,VTA,151,21,225,255,forebrain,p1,diencephalon,tegmentum,diencephalon +72,Thalamus_Rest,Thalamus_Rest,0,234,255,255,forebrain,diencephalon,thalamus,p2,diencephalon +73,Ventral_Thalamic_Nuclei,VT,0,121,255,255,forebrain,diencephalon,thalamus,p2,diencephalon +74,Latero_Dorsal_Nucleus_of_Thalamus,LDNT,0,7,255,255,forebrain,diencephalon,thalamus,p2,diencephalon +75,Medial_Geniculate_Nucleus,MGN,107,0,255,255,forebrain,diencephalon,thalamus,p2,diencephalon +76,Lateral_Geniculate_Nucleus,LGN,221,0,255,255,forebrain,diencephalon,thalamus,p2,diencephalon +77,Zona_Incerta,ZI,255,0,176,255,forebrain,diencephalon,p3,prethalamus,diencephalon +78,Reticular_Nucleus_of_Thalamus,RTN,127,127,255,255,forebrain,diencephalon,thalamus,p3,diencephalon +79,Subbrachial_Nucleus_and_Peripeduncular_Nucleus,Sub_PP,0,214,42,255,forebrain,midbrain_tectum,diencephalon,p3,diencephalon +80,Deep_Mesencephalic_Nuclei,DpMe,255,186,0,255,midbrain,p1,combo,pretectal_tegmentum,midbrain +81,Superior_Colliculus,SC,200,255,0,255,midbrain,midbrain_tectum,m1,,midbrain +82,Inferior_Colliculus,IC,76,255,0,255,midbrain,midbrain_tectum,m1,,midbrain +83,Substantia_Nigra,SN,0,255,48,255,midbrain,m1,midbrain_tegmentum,,midbrain +84,Red_Nucleus_Parvicellular,RPC,0,255,172,255,forebrain,p1,pretectal_tegmentum,diencephalon,midbrain +85,Midbrain_Reticular_Nucleus,MRN,0,224,255,255,midbrain,midbrain_tectum,m1,,midbrain +86,Rostral_Linear_Nucleus,RI,0,110,255,255,midbrain,m1,midbrain_tegmentum,,midbrain +87,Cuneiform_Nucleus,Cn,3,0,255,255,midbrain,midbrain_tectum,m2,,midbrain +88,Precuneiform_Nucleus,PrCn,117,0,255,255,midbrain,midbrain_tectum,m2,,midbrain +89,Brain_Stem_Rest,Brain_Stem_Rest,0,58,255,255,hindbrain,combo,,,hindbrain +90,Interpeduncular_Nucleus,Iped,255,0,165,255,hindbrain,prepontine_hindbrain,pons,isthmus,hindbrain +91,Cerebellar_Cortex,1Cb-10Cb,220,24,24,255,hindbrain,prepontine_hindbrain,isthmus,cerebellum,hindbrain +92,Dentate_(Lateral)_Nucleus_of_Cerebellum,Den,255,72,0,255,hindbrain,prepontine_hindbrain,isthmus,cerebellum,hindbrain +93,Interposed_Nucleus_of_Cerebellum,Int,255,196,0,255,hindbrain,prepontine_hindbrain,isthmus,cerebellum,hindbrain +94,Fastigial_Medial_Dorsolateral_Nucleus_of_Cerebellum,FasDL,190,255,0,255,hindbrain,prepontine_hindbrain,isthmus,cerebellum,hindbrain +95,Fastigial_Medial_Nucleus_of_Cerebellum,FasMed,65,255,0,255,hindbrain,prepontine_hindbrain,isthmus,cerebellum,hindbrain +96,Ventral_Lateral_Lemniscus_Nucleus,Vll,0,255,59,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,isthmus,hindbrain +97,Parabrachial_Nucleus,ParB,0,255,183,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,isthmus,hindbrain +98,Parabrachial_Medial_Nucleus_and_Koelliker_Fuse_Nucleus,KF,0,214,255,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,isthmus,hindbrain +99,Parvicellular_Reticular_Nucleus_and_Principal_Sensory_Trigeminal_Nucleus,PCRt_Pr5,0,100,255,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,r1r2,hindbrain +100,Central_Gray,CG,14,0,255,255,hindbrain,prepontine_hindbrain,pons,r1,hindbrain +101,Pedunculotegmental_Medial_Paralemniscial_and_Supratrigemnial_Nuclei,Su5,128,0,255,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,r1,hindbrain +102,Motor_Root_of_Trigeminal_Nerve,m5,241,0,255,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,hindbrain +103,Trigeminal_Motor_Nucleus,5N,255,0,155,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,r2,hindbrain +104,Pontine_Reticular_Nucleus,PnC_PnO_PnV,235,229,121,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,r2,hindbrain +105,Raphe_Nucleus,Raphe_Nucleus,59,59,247,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,r1r2,hindbrain +106,Trigeminal_Sensory_Nucleus,Pr5,255,207,0,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,r1r2,hindbrain +107,Dorsal_Tegmentum,Dorsal_Tegmentum,55,255,0,255,hindbrain,prepontine_hindbrain,pons,r1r2,hindbrain +108,Tegmental_Nucleus,Tg,0,255,69,255,hindbrain,pontine_hindbrain,pons,r1,hindbrain +109,Cochlear_Nucleus,CoN,0,255,193,255,hindbrain,pontine_hindbrain,pons,r3,hindbrain +110,Pontine_Nucleus,Pn,0,203,255,255,hindbrain,pontine_hindbrain,pons,r3r4,hindbrain +111,Reticulotegmental_Nucleus_of_Pons,RTg,0,90,255,255,hindbrain,pontine_hindbrain,pons,r3r4,hindbrain +112,Olivary_Complex,Olivary_Complex,24,0,255,255,hindbrain,pontomedullary_hindbrain,pons,r5,hindbrain +113,Pontine_Reticular_Nucleus,PnRt,138,0,255,255,hindbrain,pontomedullary_hindbrain,pons,r5,hindbrain +114,Spinal_Trigeminal_Nucleus,Sp5,252,0,255,255,hindbrain,pontomedullary_hindbrain,pons,r9r6r7r8r10r3r4r5r11,hindbrain +115,Vestibular_Nuclei,Ve,255,0,145,255,hindbrain,pontomedullary_hindbrain,pons,r9r6r7r8r3r4r5,hindbrain +116,Gigantocellular_Reticular_Nucleus,Gi,255,0,31,255,hindbrain,r9r6r7r8r10,medullary_hindbrain,medulla,hindbrain +117,Cuneate_Nucleus,Cu,255,232,0,255,hindbrain,r9r10,medullary_hindbrain,medulla,white_matter +118,Anterior_Commisure,ac,255,104,0,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +119,Optic_Tracts,ot,169,255,0,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +120,Fimbria,fi,45,255,0,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +121,Corpus_Callosum,cc,0,255,79,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +122,Fornix,fx,255,162,0,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +123,Stria_Terminalis,st,0,193,255,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +124,Cingulum,cg,0,79,255,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +125,Lateral_Olfactory_Tract,lo,34,0,255,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +126,Ventral_Hippocampal_Commissure,vhc,148,0,255,255,white_matter_tracts,white_matter,forebrain_white_matter,prosencephalon_white_matter,white_matter +127,Internal_Capsule,ic,255,0,248,255,white_matter_tracts,white_matter,forebrain_white_matter,diencephalon_white_matter,white_matter +128,Fasciculus_Retroflexus,fr,255,0,134,255,white_matter_tracts,white_matter,forebrain_white_matter,diencephalon_white_matter,white_matter +129,Stria_Medularis,sm,31,148,58,255,white_matter_tracts,white_matter,forebrain_white_matter,diencephalon_white_matter,white_matter +130,Mamillothalamic_Tract,mt,255,103,0,255,white_matter_tracts,white_matter,forebrain_white_matter,diencephalon_white_matter,white_matter +131,Posterior_Commissure,pc,255,227,0,255,white_matter_tracts,white_matter,forebrain_white_matter,diencephalon_white_matter,white_matter +132,Brachium_of_Superior_Colliculus,bsc,159,255,0,255,white_matter_tracts,white_matter,midbrain_white_matter,colliculi_white_matter,white_matter +133,Cerebral_Peduncle,cp,34,255,0,255,white_matter_tracts,white_matter,midbrain_white_matter,other_midbrain_white_matter,white_matter +134,Lateral_Lemniscus,ll,0,255,90,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +135,Spinal_Trigeminal_Nerve,sp5,0,255,214,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +136,Pyramidal_Tract,py,0,183,255,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +137,Vestibulocochlear_Nerve,n8,0,69,255,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +138,Facial_Nerve,n7,235,223,18,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +139,Longitudinal_Fasciculus_of_Pons,lfp,159,0,255,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +140,Medial_Longitudinal_Fasciculus_and_Tectospinal_Tract,mlf_ts,255,0,238,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +141,Spinocerebellar_Tract,sc,121,235,148,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +142,Medial_Lemniscus,ml,127,127,255,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +143,Ventral_Spinocerebellar_Tract,vsc,255,114,0,255,white_matter_tracts,white_matter,hindbrain_white_matter,axial_hindbrain_white_matter,white_matter +144,Middle_Cerebellar_Peduncle,mcp,255,238,0,255,white_matter_tracts,white_matter,hindbrain_white_matter,cerebellar_white_matter,white_matter +145,Superior_Cerebellar_Peduncle,scp,148,255,0,255,white_matter_tracts,white_matter,hindbrain_white_matter,cerebellar_white_matter,white_matter +146,Inferior_Cerebellar_Peduncle,icp_oc_tz,24,255,0,255,white_matter_tracts,white_matter,hindbrain_white_matter,cerebellar_white_matter,white_matter +147,Cerebellar_White_Matter,cbw,0,232,255,255,white_matter_tracts,white_matter,hindbrain_white_matter,cerebellar_white_matter,white_matter +148,Lateral_Ventricle,LV,56,41,204,255,forebrain,ventricular_system,cerebrospinal_fluid,forebrain_CSF,white_matter +149,Cingulate_Cortex_Area_25,A25,41,204,103,255,forebrain,secondary_prosencephalon,isocortex,cingulate_cortex,white_matter +150,Dorsal_Acustic_Stria,das,41,116,204,255,white_matter_tracts,white_matter,pontine_hindbrain,axial_hindbrain_white_matter,white_matter +151,Postsubiculum,Post,204,108,41,255,forebrain,secondary_prosencephalon,medial_pallium,,white_matter +152,Ventricular_System_4thVentricle,4V,41,204,103,255,hindbrain,ventricular_system,cerebrospinal_fluid,hindbrain_CSF,white_matter +153,Microcellular_Tegmental_Nucleus,MiTg,41,116,204,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,isthmus,white_matter +154,Pretectal_Nucleus,Pretectal_Nucleus,41,204,103,255,forebrain,p1,diencephalon,pretectum,white_matter +155,Latero_Dorsal_Thalamic_Nucleus_Ventro_Lateral,LDVL,41,204,103,255,forebrain,diencephalon,thalamus,p2,white_matter +156,Latero_Posterior_Nuclei_of_Thalamus,LP,135,204,41,255,forebrain,diencephalon,thalamus,p2,white_matter +157,Anterior_Thalamic_Nuclei,Athal,41,116,204,255,forebrain,diencephalon,thalamus,p2,white_matter +158,Red_Nucleus_Magnocellular,RMC,41,204,63,255,midbrain,pretectal_tegmentum,m1,,white_matter +159,Pararubral_Nucleus,PaR,130,204,41,255,midbrain,p1,m1m2,midbrain_tegmentum,white_matter +160,Retro_Rubral_Field,Retro_Rubral_Field,41,204,103,255,hindbrain,prepontine_hindbrain,pontomedullary_hindbrain,isthmus,white_matter +161,CSF,CSF,135,204,41,255,ventricular_system,combo,CSF,,white_matter +162,Intermediate_Reticular_Nucleus,Intermediate_Reticular_Nucleus,41,116,204,255,hindbrain,pontine_hindbrain,pons,r1r2r9r6r7r8r10r3r4r5r11,white_matter +163,PHD_PaMP_Post_and_Lateral_Hypothalamus,PHD_PaMP_Post_and_Lateral_Hypothalamus,41,204,103,255,forebrain,secondary_prosencephalon,rostral_secondary_prosencephalon,hypothalamus,white_matter +164,Prerubral_Forel,Prerubral_Forel,135,204,41,255,forebrain,diencephalon,thalamus,p3p2,white_matter +165,PVG_of_Hypothalamus,PVG_of_Hypothalamus,41,116,204,255,forebrain,secondary_prosencephalon,rostral_secondary_prosencephalon,hypothalamus,white_matter +166,BLA_Basalateral_Amygdala,BLA_Basalateral_Amygdala,41,204,103,255,forebrain,secondary_prosencephalon,subpallium,subpallial_amygdala,white_matter \ No newline at end of file diff --git a/graspologic/datasets/mice/blocks.csv b/graspologic/datasets/mice/blocks.csv new file mode 100644 index 000000000..d053d655f --- /dev/null +++ b/graspologic/datasets/mice/blocks.csv @@ -0,0 +1,15 @@ +block,hemisphere,i,j +isocortex,L,0,41 +pallium,L,41,61 +subpallium,L,61,68 +diencephalon,L,68,79 +midbrain,L,79,88 +hindbrain,L,88,116 +white_matter,L,116,166 +isocortex,R,166,207 +pallium,R,207,227 +subpallium,R,227,234 +diencephalon,R,234,245 +midbrain,R,245,254 +hindbrain,R,254,282 +white_matter,R,282,332 diff --git a/graspologic/datasets/mice/edgelists/sub-54776_ses-1_dti.edgelist b/graspologic/datasets/mice/edgelists/sub-54776_ses-1_dti.edgelist new file mode 100644 index 000000000..c58c05467 --- /dev/null +++ b/graspologic/datasets/mice/edgelists/sub-54776_ses-1_dti.edgelist @@ -0,0 +1,36390 @@ +0 1 3735.0 +0 2 1060.0 +0 3 241.0 +0 4 2842.0 +0 5 3.0 +0 6 4977.0 +0 7 649.0 +0 8 3000.0 +0 9 47.0 +0 10 75.0 +0 11 59.0 +0 13 26.0 +0 14 311.0 +0 15 123.0 +0 16 61.0 +0 18 247.0 +0 19 1392.0 +0 20 1010.0 +0 23 14.0 +0 24 70.0 +0 26 64.0 +0 27 1.0 +0 28 43.0 +0 30 1.0 +0 31 19.0 +0 32 32.0 +0 33 148.0 +0 34 33.0 +0 35 79.0 +0 36 89.0 +0 37 182.0 +0 38 3.0 +0 39 4.0 +0 40 136.0 +0 41 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+1164,435,0.03963938,0.00027166,3114.91503906,0.00034515,0.00026623,0.00020361,0.26076087,0.00023492 +1165,388,0.03535650,0.00028101,3456.68017578,0.00032257,0.00027711,0.00024335,0.14609307,0.00026023 +1166,5677,0.51731669,0.00034567,2924.05468750,0.00041098,0.00033455,0.00029147,0.17621744,0.00031301 diff --git a/graspologic/datasets/mice/participants.csv b/graspologic/datasets/mice/participants.csv new file mode 100644 index 000000000..2b5c48dbd --- /dev/null +++ b/graspologic/datasets/mice/participants.csv @@ -0,0 +1,33 @@ +participant_id,genotype +sub-54776,DBA2 +sub-54777,DBA2 +sub-54779,DBA2 +sub-54781,DBA2 +sub-54790,B6 +sub-54793,B6 +sub-54794,B6 +sub-54797,B6 +sub-54811,BTBR +sub-54813,BTBR +sub-54815,BTBR +sub-54817,BTBR +sub-54821,CAST +sub-54823,CAST +sub-54829,DBA2 +sub-54831,DBA2 +sub-54833,DBA2 +sub-54835,DBA2 +sub-54842,CAST +sub-54847,CAST +sub-54849,BTBR +sub-54851,BTBR +sub-54853,BTBR +sub-54855,BTBR +sub-54864,B6 +sub-54866,B6 +sub-54868,B6 +sub-54870,B6 +sub-54883,CAST +sub-54885,CAST +sub-54887,CAST +sub-54890,CAST diff --git a/graspologic/version/version.txt b/graspologic/version/version.txt index e69de29bb..8b1378917 100644 --- a/graspologic/version/version.txt +++ b/graspologic/version/version.txt @@ -0,0 +1 @@ + diff --git a/tests/test_datasets.py b/tests/test_datasets.py index 475d3e107..550bbebd0 100644 --- a/tests/test_datasets.py +++ b/tests/test_datasets.py @@ -13,3 +13,7 @@ def test_drosphila_left(): def test_drosphila_right(): graph = load_drosophila_right() graph, labels = load_drosophila_right(return_labels=True) + + +def test_load_mice(): + data = load_mice() From e19451f69030da1186d676adc25d69f88a03af67 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Fri, 8 Jan 2021 09:15:21 -0800 Subject: [PATCH 080/140] This is a complete versioning system rework. (#629) * This is a complete versioning system rework. First, setuptools has pushed us toward using a `pyproject.toml` and `setup.cfg` in lieu of having a setuptools.setup() function call specify the package parameters. In addition, we have a much easier way of injecting our prerelease tags. We still technically call `setuptools.setup()`, but it knows to rely on pyproject.toml and setup.cfg to provide all the parameters, but also to use a commandline argument for any possible version appendix to use. However, this information is written to the sdist package, so the prerelease version numbers are maintained on publish. This is what we want. If for some reason we are trying to get the version from source code, not the sdist package, we still try to use the `setup.cfg` file, and we append a dev tag based on today's date and time. This is most common for the developers who work on graspologic itself. The thing this fixes is that using `pip install -r requirements.txt` no longer changes the source tree and include a then-materialized version number. This should avoid many of the accidental commits of `version.txt` that plagued and confused everyone in the past. IMPORTANT NOTE: We may need to do some minor tweaks to fix the build workflow stuff because some of it isn't testable until we're on dev or main. Fixes #554 * This file is necessary for netlify to work. Eventually we want to remote netlify and rely on RTD, but until I get that all done this file needs to stay. * Netlify doesn't support 3.8 apparently * Ran black formatting * This will at least unbreak the doc build on netlify, but it's unclear if it will give us the right level of detail re: version numbers in the generated documentation. I'm pretty sure it won't. * Error in workflow --- .github/build/set_version.py | 25 ----------- .github/workflows/build.yml | 11 ++--- .gitignore | 2 + MANIFEST.in | 1 - docs/conf.py | 8 +++- graspologic/__init__.py | 10 ++--- graspologic/utils/utils.py | 9 ++++ graspologic/version.py | 50 +++++++++++++++++++++ graspologic/version/__init__.py | 2 - graspologic/version/version.py | 36 --------------- graspologic/version/version.txt | 1 - pyproject.toml | 3 ++ setup.cfg | 60 +++++++++++++++++++++++++ setup.py | 80 +-------------------------------- 14 files changed, 141 insertions(+), 157 deletions(-) delete mode 100644 .github/build/set_version.py create mode 100644 graspologic/version.py delete mode 100644 graspologic/version/__init__.py delete mode 100644 graspologic/version/version.py delete mode 100644 graspologic/version/version.txt create mode 100644 pyproject.toml create mode 100644 setup.cfg diff --git a/.github/build/set_version.py b/.github/build/set_version.py deleted file mode 100644 index 7ef21e5e0..000000000 --- a/.github/build/set_version.py +++ /dev/null @@ -1,25 +0,0 @@ - -""" -Build script that will check some environment variables set by Github during build. -See: https://help.github.com/en/actions/configuring-and-managing-workflows/using-environment-variables#default-environment-variables -If the branch is master or dev, it will generate the appropriate content for version.txt. If it isn't, it returns nothing. Note that the version.txt file is set by bash, not by this file. -""" - -import os -from graspologic.version.version import __semver - -ref_key = "GITHUB_REF" -run_id_key = "GITHUB_RUN_ID" -prerelease_ref = "refs/heads/dev" -release_ref = "refs/heads/main" -publish_refs = [prerelease_ref, release_ref] - -environ = os.environ - -if ref_key in environ and run_id_key in environ: - ref = environ[ref_key] - run_id = environ[run_id_key] - if ref == prerelease_ref: - print(f"{__semver}.dev{run_id}") - elif ref == release_ref: - print(f"{__semver}") diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 6ae7b867a..e9cd11917 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -90,13 +90,14 @@ jobs: run: | python -m pip install --upgrade pip setuptools wheel twine pip install -r requirements.txt - - name: Materialize Fixed Version + - name: Build Release with setuptools + if: github.ref=='refs/head/main' run: | - python .github/build/set_version.py > graspologic/version/version.txt - cat graspologic/version/version.txt - - name: Build with setuptools + python setup.py sdist + - name: Build Prerelease with setuptools + if: github.ref=='refs/head/dev' run: | - python setup.py build sdist + python setup.py egg_info -b "dev$GITHUB_RUN_ID" sdist - name: Publish with twine env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} diff --git a/.gitignore b/.gitignore index 882f2aa9e..4d51b2b24 100644 --- a/.gitignore +++ b/.gitignore @@ -123,3 +123,5 @@ notebooks/bpedigo *.DS_Store .idea *.iml +pip-wheel-metadata + diff --git a/MANIFEST.in b/MANIFEST.in index 7e15ce2eb..3ee980048 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,4 @@ recursive-include graspologic/datasets *.edgelist recursive-include graspologic/datasets *.csv -recursive-include graspologic/version version.txt recursive-include graspologic/layouts/include colors-100.json exclude tests/* diff --git a/docs/conf.py b/docs/conf.py index 8dad8a591..86a5700f4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -29,8 +29,12 @@ import graspologic -version = graspologic.version.version.__semver -release = graspologic.version.version.version +release = graspologic.__version__ +try: + split = release.split(".") + version = f"{split[0]}.{split[1]}" +except BaseException: + version = release # -- Extension configuration ------------------------------------------------- extensions = [ diff --git a/graspologic/__init__.py b/graspologic/__init__.py index 202920437..ad156e719 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -1,10 +1,6 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -from .version.version import name, version as __version__ - -import warnings - import graspologic.align import graspologic.cluster import graspologic.datasets @@ -19,6 +15,6 @@ import graspologic.subgraph import graspologic.utils -warnings.filterwarnings("ignore", category=RuntimeWarning) -warnings.filterwarnings("ignore", category=PendingDeprecationWarning) -warnings.simplefilter("always", category=UserWarning) +from graspologic.version import __version + +__version__ = __version() diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index c186720a5..9c3002b34 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -1063,3 +1063,12 @@ def to_weighted_edge_list( f"The type of graph provided {type(graph)} is not a list of 3-tuples, networkx " f"graph, numpy.ndarray, or scipy.sparse.csr.csr_matrix" ) + + +def suppress_common_warnings(): + """ + Suppresses common warnings that occur when using graspologic. + """ + warnings.filterwarnings("ignore", category=RuntimeWarning) + warnings.filterwarnings("ignore", category=PendingDeprecationWarning) + warnings.simplefilter("always", category=UserWarning) diff --git a/graspologic/version.py b/graspologic/version.py new file mode 100644 index 000000000..aea8ecc5a --- /dev/null +++ b/graspologic/version.py @@ -0,0 +1,50 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import datetime +import pkg_resources +import configparser + +from typing import Optional + + +def __from_distribution() -> Optional[str]: + """ + This will be coming from our sdist's setup.cfg that we package up, and usually (or + always?) means it will mean the sdist has been installed in the python environment. + + This is the common case when you've installed graspologic from PyPI + """ + try: + return pkg_resources.get_distribution("graspologic").version + except BaseException: + return None + + +def __from_filesystem(setup_config: str = "setup.cfg") -> Optional[str]: + """ + If we aren't an installed library, pkg_resources.get_distribution() won't be + able to find setup.cfg's version, so we'll try to look at it from the filesystem + We can probably presume in this circumstance that we are not a properly installed + version and thus we're going to mark it with a dev label and a time entry + + This is the common case when you are developing graspologic itself. + """ + try: + cfg_parser = configparser.RawConfigParser() + cfg_parser.read(setup_config) + base_version = cfg_parser.get("metadata", "version") + now = datetime.datetime.today().strftime("%Y%m%d%H%M%S") + return f"{base_version}.dev{now}" + except BaseException: + return None + + +def __version() -> str: + """ + Distribution takes precedence, but otherwise we try to read it from the filesystem + + If all else fails, we have no way of knowing what version we are + """ + possible_version = __from_distribution() or __from_filesystem() + return possible_version if possible_version is not None else "unknown" diff --git a/graspologic/version/__init__.py b/graspologic/version/__init__.py deleted file mode 100644 index 9a0454564..000000000 --- a/graspologic/version/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. diff --git a/graspologic/version/version.py b/graspologic/version/version.py deleted file mode 100644 index 5a5e6af52..000000000 --- a/graspologic/version/version.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -import datetime -from typing import List -import pkg_resources - -__all__: List[str] = ["version", "name"] - -name = "graspologic" - -# manually updated -__semver = "0.1.0" -# full version (may be same as __semver on release) -__version_file = "version.txt" - - -def _from_resource() -> str: - version_file = pkg_resources.resource_stream(__name__, __version_file) - version_file_contents = version_file.read() - return version_file_contents.decode("utf-8").strip() - - -def local_build_number() -> str: - return datetime.datetime.today().strftime("%Y%m%d%H%M%S") - - -def get_version() -> str: - version_file_contents = _from_resource() - if len(version_file_contents) == 0: - return f"{__semver}.dev{local_build_number()}" - else: - return version_file_contents - - -version = get_version() diff --git a/graspologic/version/version.txt b/graspologic/version/version.txt deleted file mode 100644 index 8b1378917..000000000 --- a/graspologic/version/version.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..7fd26b970 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 000000000..822131256 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,60 @@ +[metadata] +name = graspologic +version = 0.1.0 + +description = A set of python modules for graph statistics +long_description = file: README.md +author = Eric Bridgeford, Jaewon Chung, Benjamin Pedigo, Bijan Varjavand +author_email = j1c@jhu.edu +maintainer = Dwayne Pryce +maintainer_email = dwpryce@microsoft.com +url = https://github.com/microsoft/graspologic +license = MIT +classifiers = + Development Status :: 3 - Alpha + Intended Audience :: Science/Research + Topic :: Scientific/Engineering :: Mathematics + License :: OSI Approved :: MIT License + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + +[options] +packages = find: +include_package_data = True +install_requires = + anytree>=2.8.0 + gensim>=3.8.0,<=3.9.0 # methods signatures changed in the 4.0.0beta release + graspologic-native + hyppo>=0.1.3 + joblib>=0.17.0 # Older versions of joblib cause issue #806. Transitive dependency of hyppo. + matplotlib>=3.0.0,<=3.3.0 + networkx>=2.1 + numpy>=1.8.1 + POT>=0.7.0 + seaborn>= 0.11.0 + scikit-learn>=0.19.1 + scipy>=1.4.0 + umap-learn>=0.4.6 + +[options.packages.find] +exclude = + tests + +[options.extras_require] +dev = + black + ipykernel>=5.1.0 + ipython>=7.4.0 + mypy + nbsphinx + numpydoc + pandoc + pytest + pytest-cov + sphinx + sphinxcontrib-rawfiles + sphinx-rtd-theme + testfixtures + diff --git a/setup.py b/setup.py index 2e161c3f5..9222603af 100644 --- a/setup.py +++ b/setup.py @@ -1,82 +1,6 @@ # Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. -import os -import sys -from setuptools import setup, find_packages +import setuptools - -MINIMUM_PYTHON_VERSION = 3, 6 # Minimum of Python 3.6 - -if sys.version_info < MINIMUM_PYTHON_VERSION: - sys.exit("Python {}.{}+ is required.".format(*MINIMUM_PYTHON_VERSION)) - -sys.path.insert(0, os.path.join("graspologic", "version")) -from version import version - -sys.path.pop(0) - -version_path = os.path.join("graspologic", "version", "version.txt") -with open(version_path, "w") as version_file: - version_file.write(f"{version}") - -with open("README.md", "r") as f: - LONG_DESCRIPTION = f.read() - -setup( - name="graspologic", - version=version, - description="A set of python modules for graph statistics", - long_description=LONG_DESCRIPTION, - long_description_content_type="text/markdown", - author="Eric Bridgeford, Jaewon Chung, Benjamin Pedigo, Bijan Varjavand", - author_email="j1c@jhu.edu", - maintainer="Dwayne Pryce", - maintainer_email="dwpryce@microsoft.com", - url="https://github.com/microsoft/graspologic", - license="MIT", - classifiers=[ - "Development Status :: 3 - Alpha", - "Intended Audience :: Science/Research", - "Topic :: Scientific/Engineering :: Mathematics", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - ], - packages=find_packages(exclude=["tests", "tests.*", "tests/*"]), - include_package_data=True, - package_data={"version": [os.path.join("graspologic", "version", "version.txt")]}, - install_requires=[ - "anytree>=2.8.0", - "gensim>=3.8.0,<=3.9.0", #methods signatures changed in the 4.0.0beta release - "graspologic-native", - "hyppo>=0.1.3", - "joblib>=0.17.0", # Older versions of joblib cause issue #806. Transitive dependency of hyppo. - "matplotlib>=3.0.0,<=3.3.0", - "networkx>=2.1", - "numpy>=1.8.1", - "POT>=0.7.0", - "seaborn>= 0.11.0", - "scikit-learn>=0.19.1", - "scipy>=1.4.0", - 'umap-learn>=0.4.6', - ], - extras_require={ - "dev": [ - "black", - "ipykernel>=5.1.0", - "ipython>=7.4.0", - "mypy", - "nbsphinx", - "numpydoc", - "pandoc", - "pytest", - "pytest-cov", - "sphinx", - "sphinxcontrib-rawfiles", - "sphinx-rtd-theme", - "testfixtures", - ] - }, -) +setuptools.setup() From 1a46e028b0d2125bb050026c594800efbed8902b Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Fri, 8 Jan 2021 09:41:47 -0800 Subject: [PATCH 081/140] Got the refs incorrect Previously I had `refs/head/main` and it should have been `refs/heads/main` - note the plural in heads. --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index e9cd11917..3578640fb 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -91,11 +91,11 @@ jobs: python -m pip install --upgrade pip setuptools wheel twine pip install -r requirements.txt - name: Build Release with setuptools - if: github.ref=='refs/head/main' + if: github.ref=='refs/heads/main' run: | python setup.py sdist - name: Build Prerelease with setuptools - if: github.ref=='refs/head/dev' + if: github.ref=='refs/heads/dev' run: | python setup.py egg_info -b "dev$GITHUB_RUN_ID" sdist - name: Publish with twine From 7f27560c8c8f2f45b60b0cf92d86760b77464f15 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Fri, 8 Jan 2021 11:08:19 -0800 Subject: [PATCH 082/140] Content type seems to be wrong on pypi.org The long description is currently showing Markdown in raw form instead of as rendered html. This... might fix it? --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 822131256..5b26fd0e9 100644 --- a/setup.cfg +++ b/setup.cfg @@ -4,6 +4,7 @@ version = 0.1.0 description = A set of python modules for graph statistics long_description = file: README.md +long_description_content_type = text/markdown author = Eric Bridgeford, Jaewon Chung, Benjamin Pedigo, Bijan Varjavand author_email = j1c@jhu.edu maintainer = Dwayne Pryce From 30a0d06212e584d3d67ea2ee218a72b1cf1c110c Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Mon, 11 Jan 2021 14:23:40 -0500 Subject: [PATCH 083/140] add pronouns and links to ROLES.md (#623) * add pronouns for me and links for all * Update ROLES.md * Update ROLES.md * Update ROLES.md * Update ROLES.md * Update ROLES.md Co-authored-by: Dwayne Pryce Co-authored-by: Nick Co-authored-by: Carolyn Buractaon <60894394+carolyncb@users.noreply.github.com> Co-authored-by: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Co-authored-by: Jaewon Chung --- ROLES.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ROLES.md b/ROLES.md index 3c36d3c73..8c309658b 100644 --- a/ROLES.md +++ b/ROLES.md @@ -7,35 +7,35 @@ your issue! ## Core Contributors -### Ali Saad-Eldin (@asaadeldin11) +### Ali Saad-Eldin ([@asaadeldin11](https://github.com/asaadeldin11)) (he/him) Ali is a Masters Student at Johns Hopkins University. He contributes and reviews code mostly for the `match` and `embed` modules. -### Anton Alyakin (@alyakin314) +### Anton Alyakin ([@alyakin314](https://github.com/alyakin314)) Anton is an Assistant Research Engineer at Johns Hopkins University. His primary contributions to `graspologic` are within `align` and `inference` modules. Ask him anything about those. -### Benjamin Pedigo (@bdpedigo) +### Benjamin Pedigo ([@bdpedigo](https://github.com/bdpedigo)) (he/him) Ben is a PhD student at Johns Hopkins University in the NeuroData lab. Ask Ben about network model fitting and sampling, clustering, and spectral embedding (`models`, `simulations`, `cluster`, and `embed`, respectfully). Ben is also happy to hear how we can improve our tutorials. -### Carolyn Buractaon (@carolyncb) +### Carolyn Buractaon ([@carolyncb](https://github.com/carolyncb)) (she/her) Carolyn is a Technical Program Manager at Microsoft. Ask Carolyn about where the project is going and how it’s organized. -### Dwayne Pryce (@dwaynepryce) +### Dwayne Pryce ([@dwaynepryce](https://github.com/dwaynepryce)) (he/him) Dwayne Pryce is a Software Engineer at Microsoft Research. His primary contributions to `graspologic` are on the steering committee, quality of life utility functions, and build and release processes. -### Jaewon Chung (@j1c) +### Jaewon Chung ([@j1c](https://github.com/j1c)) (he/him) Jaewon is a PhD student at Johns Hopkins University. He is a maintainer and developer for `graspologic`, and is responsible for reviewing code contributions, merging pull requests, and making decisions on the `graspologic` API. -### Nick Caurvina (@nyecarr) +### Nick Caurvina ([@nyecarr](https://github.com/nyecarr)) (he/him) Nick is a Software Engineer at Microsoft Research. Ask Nick about the network -embeddings and their application to business problems. \ No newline at end of file +embeddings and their application to business problems. From 990c6abe50843089fa220dcda215dec511ecf354 Mon Sep 17 00:00:00 2001 From: Ali Saad-Eldin <54996865+asaadeldin11@users.noreply.github.com> Date: Wed, 13 Jan 2021 15:27:16 -0500 Subject: [PATCH 084/140] Sparse ASE/LSE (#523) * Update utils.py * Update utils.py * Update utils.py * Update test_spectral_embed.py * Update utils.py * Update svd.py * Update utils.py * Update ase.py * Update utils.py * Update lse.py * fixing merge conflicts * fixing build errors * Update utils.py * only accept csr matrices * get rid of redundant code * scipy.csr_matrix -> scipy.sparse.csr_matrix * Update svd.py * adding warnings and sparse functionality accross embedding * update docs to remove redundancy * Update graspologic/embed/lse.py * remove import, resort * minor changes * formatting * Update utils.py Co-authored-by: Benjamin Pedigo --- graspologic/embed/ase.py | 7 +-- graspologic/embed/lse.py | 6 +-- graspologic/embed/mase.py | 12 ++--- graspologic/embed/omni.py | 5 +++ graspologic/embed/svd.py | 12 ++++- graspologic/utils/utils.py | 87 +++++++++++++++++++++--------------- tests/test_spectral_embed.py | 58 +++++++++++++++++++++++- 7 files changed, 136 insertions(+), 51 deletions(-) diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index 3ed507055..74a638ec2 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -47,6 +47,7 @@ class AdjacencySpectralEmbed(BaseSpectralEmbed): :func:`sklearn.utils.extmath.randomized_svd` - 'full' Computes full svd using :func:`scipy.linalg.svd` + Does not support ``graph`` input of type scipy.sparse.csr_matrix - 'truncated' Computes truncated svd using :func:`scipy.sparse.linalg.svds` @@ -142,7 +143,7 @@ def fit(self, graph, y=None): Parameters ---------- - graph : array_like or networkx.Graph + graph : array-like, scipy.sparse.csr_matrix, or networkx.Graph Input graph to embed. y: Ignored @@ -166,7 +167,7 @@ def fit(self, graph, y=None): if self.diag_aug: A = augment_diagonal(A) - self.n_features_in_ = len(A) + self.n_features_in_ = A.shape[0] self._reduce_dim(A) # for out-of-sample @@ -186,7 +187,7 @@ def transform(self, X): Parameters ---------- - X : array_like or tuple, original shape or (n_oos_vertices, n_vertices). + X : array-like or tuple, original shape or (n_oos_vertices, n_vertices). The original fitted matrix ("graph" in fit) or new out-of-sample data. If ``X`` is the original fitted matrix, returns a matrix close to diff --git a/graspologic/embed/lse.py b/graspologic/embed/lse.py index 39618f169..e26961cf4 100644 --- a/graspologic/embed/lse.py +++ b/graspologic/embed/lse.py @@ -141,8 +141,8 @@ def fit(self, graph, y=None): Parameters ---------- - graph : array_like or networkx.Graph - Input graph to embed. see :func:`~graspologic.utils.import_graph` + graph : array-like, scipy.sparse.csr_matrix, or networkx.Graph + Input graph to embed. see graspologic.utils.import_graph Returns ------- @@ -160,7 +160,7 @@ def fit(self, graph, y=None): ) warnings.warn(msg, UserWarning) - self.n_features_in_ = len(A) + self.n_features_in_ = A.shape[0] L_norm = to_laplace(A, form=self.form, regularizer=self.regularizer) self._reduce_dim(L_norm) return self diff --git a/graspologic/embed/mase.py b/graspologic/embed/mase.py index 17e7f27e4..df8ad3a85 100644 --- a/graspologic/embed/mase.py +++ b/graspologic/embed/mase.py @@ -198,9 +198,9 @@ def fit(self, graphs, y=None): Parameters ---------- - graphs : list of nx.Graph or ndarray, or ndarray + graphs : list of nx.Graph, ndarray or scipy.sparse.csr_matrix If list of nx.Graph, each Graph must contain same number of nodes. - If list of ndarray, each array must have shape (n_vertices, n_vertices). + If list of ndarray or csr_matrix, each array must have shape (n_vertices, n_vertices). If ndarray, then array must have shape (n_graphs, n_vertices, n_vertices). Returns @@ -222,11 +222,11 @@ def fit(self, graphs, y=None): self.latent_left_ = Uhat if not undirected: self.latent_right_ = Vhat - self.scores_ = Uhat.T @ graphs @ Vhat + self.scores_ = np.asarray([Uhat.T @ graph @ Uhat for graph in graphs]) self.singular_values_ = (sing_vals_left, sing_vals_right) else: self.latent_right_ = None - self.scores_ = Uhat.T @ graphs @ Uhat + self.scores_ = np.asarray([Uhat.T @ graph @ Uhat for graph in graphs]) self.singular_values_ = sing_vals_left return self @@ -238,9 +238,9 @@ def fit_transform(self, graphs, y=None): Parameters ---------- - graphs : list of nx.Graph or ndarray, or ndarray + graphs : list of nx.Graph, ndarray or scipy.sparse.csr_matrix If list of nx.Graph, each Graph must contain same number of nodes. - If list of ndarray, each array must have shape (n_vertices, n_vertices). + If list of ndarray or csr_matrix, each array must have shape (n_vertices, n_vertices). If ndarray, then array must have shape (n_graphs, n_vertices, n_vertices). Returns diff --git a/graspologic/embed/omni.py b/graspologic/embed/omni.py index 9832467df..ea7deca94 100644 --- a/graspologic/embed/omni.py +++ b/graspologic/embed/omni.py @@ -7,6 +7,7 @@ from ..utils import import_graph, is_fully_connected from .base import BaseEmbedMulti +from scipy.sparse import isspmatrix_csr def _get_omni_matrix(graphs): @@ -164,6 +165,10 @@ def fit(self, graphs, y=None): self : object Returns an instance of self. """ + if any([isspmatrix_csr(g) for g in graphs]): + msg = "OmnibusEmbed does not support scipy.sparse.csr_matrix inputs" + raise TypeError(msg) + graphs = self._check_input_graphs(graphs) # Check if Abar is connected diff --git a/graspologic/embed/svd.py b/graspologic/embed/svd.py index 58ce1d6e8..aada2615f 100644 --- a/graspologic/embed/svd.py +++ b/graspologic/embed/svd.py @@ -5,6 +5,7 @@ import scipy import sklearn from scipy.stats import norm +from scipy.sparse import isspmatrix_csr def _compute_likelihood(arr): @@ -92,8 +93,10 @@ def select_dimension( pp.918-930. """ # Handle input data - if not isinstance(X, np.ndarray): - msg = "X must be a numpy array, not {}.".format(type(X)) + if not isinstance(X, np.ndarray) and not isspmatrix_csr(X): + msg = "X must be a numpy array or scipy.sparse.csr_matrix, not {}.".format( + type(X) + ) raise ValueError(msg) if X.ndim > 2: msg = "X must be a 1d or 2d-array, not {}d array.".format(X.ndim) @@ -199,6 +202,7 @@ def selectSVD(X, n_components=None, n_elbows=2, algorithm="randomized", n_iter=5 :func:`sklearn.utils.extmath.randomized_svd` - 'full' Computes full svd using :func:`scipy.linalg.svd` + Does not support ``graph`` input of type scipy.sparse.csr_matrix - 'truncated' Computes truncated svd using :func:`scipy.sparse.linalg.svds` n_iter : int, optional (default = 5) @@ -232,6 +236,10 @@ def selectSVD(X, n_components=None, n_elbows=2, algorithm="randomized", n_iter=5 msg = "algorithm must be one of {full, truncated, randomized}." raise ValueError(msg) + if algorithm == "full" and isspmatrix_csr(X): + msg = "'full' agorithm does not support scipy.sparse.csr_matrix inputs." + raise TypeError(msg) + if n_components is None: elbows, _ = select_dimension(X, n_elbows=n_elbows, threshold=None) n_components = elbows[-1] diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index 9c3002b34..d1f292b50 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -7,12 +7,12 @@ from pathlib import Path from typing import Any, List, Optional, Tuple, Union - import networkx as nx import numpy as np -import scipy import pandas as pd +import scipy.sparse from scipy.optimize import linear_sum_assignment +from scipy.sparse import csr_matrix, diags, isspmatrix_csr from sklearn.metrics import confusion_matrix from sklearn.utils import check_array, check_consistent_length, column_or_1d from sklearn.utils.multiclass import type_of_target, unique_labels @@ -26,7 +26,7 @@ def import_graph(graph, copy=True): ---------- graph: object Either array-like, shape (n_vertices, n_vertices) numpy array, - or an object of type networkx.Graph. + a scipy.sparse.csr_matrix, or an object of type networkx.Graph. copy: bool, (default=True) Whether to return a copied version of array. If False and input is np.array, @@ -43,7 +43,7 @@ def import_graph(graph, copy=True): """ if isinstance(graph, (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)): out = nx.to_numpy_array(graph, nodelist=sorted(graph.nodes), dtype=np.float) - elif isinstance(graph, (np.ndarray, np.memmap)): + elif isinstance(graph, (np.ndarray, np.memmap, csr_matrix)): shape = graph.shape if len(shape) > 3: msg = "Input tensor must have at most 3 dimensions, not {}.".format( @@ -62,6 +62,7 @@ def import_graph(graph, copy=True): out = check_array( graph, dtype=[np.float64, np.float32], + accept_sparse=True, ensure_2d=True, allow_nd=True, # For omni tensor input ensure_min_features=min_features, @@ -69,7 +70,10 @@ def import_graph(graph, copy=True): copy=copy, ) else: - msg = "Input must be networkx.Graph or np.array, not {}.".format(type(graph)) + msg = "Input must be networkx.Graph, np.array, or scipy.sparse.csr_matrix,\ + not {}.".format( + type(graph) + ) raise TypeError(msg) return out @@ -169,7 +173,7 @@ def is_loopless(X): def is_unweighted( graph: Union[ np.ndarray, - scipy.sparse.csr.csr_matrix, + scipy.sparse.csr_matrix, nx.Graph, nx.DiGraph, nx.MultiGraph, @@ -182,10 +186,10 @@ def is_unweighted( Parameters ---------- - graph : Union[np.ndarray, scipy.sparse.csr.csr_matrix, nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDigraph] + graph : Union[np.ndarray, scipy.sparse.csr_matrix, nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDigraph] The graph to test for weightedness. If a networkx graph, we can just ask it directly by querying the weight attribute specified on every edge. It's possible an individual edge can be weighted but the full graph is not. - If an adjacency matrix defined by a numpy.ndarray or scipy.sparse.csr.csr_matrix, we check every value; if + If an adjacency matrix defined by a numpy.ndarray or scipy.sparse.csr_matrix, we check every value; if they are only 0 and 1, we claim the graph is unweighted. weight_attribute : Any Default is ``weight``. Only used for networkx, and used on the edge data dictionary as a key to look up the @@ -199,11 +203,11 @@ def is_unweighted( Raises ------ TypeError - If the provided graph is not a numpy.ndarray, scipy.sparse.csr.csr_matrix, or nx.Graph + If the provided graph is not a numpy.ndarray, scipy.sparse.csr_matrix, or nx.Graph """ if isinstance(graph, np.ndarray): return ((graph == 0) | (graph == 1)).all() - elif isinstance(graph, scipy.sparse.csr.csr_matrix): + elif isinstance(graph, csr_matrix): # brute force. if anyone has a better way, please PR rows, columns = graph.nonzero() for i in range(0, len(rows)): @@ -214,7 +218,7 @@ def is_unweighted( return nx.is_weighted(graph, weight=weight_attribute) else: raise TypeError( - "This function only works on numpy.ndarray or scipy.sparse.csr.csr_matrix instances" + "This function only works on numpy.ndarray or scipy.sparse.csr_matrix instances" ) @@ -261,17 +265,21 @@ def symmetrize(graph, method="avg"): [1, 1, 1]]) """ # graph = import_graph(graph) + sparse = isspmatrix_csr(graph) + pac = scipy.sparse if sparse else np + if method == "triu": - graph = np.triu(graph) + graph = pac.triu(graph) elif method == "tril": - graph = np.tril(graph) + graph = pac.tril(graph) elif method == "avg": - graph = (np.triu(graph) + np.tril(graph)) / 2 + graph = (pac.triu(graph) + pac.tril(graph)) / 2 else: msg = "You have not passed a valid parameter for the method." raise ValueError(msg) - # A = A + A' - diag(A) - graph = graph + graph.T - np.diag(np.diag(graph)) + + dia = diags(graph.diagonal()) if sparse else np.diag(np.diag(graph)) + graph = graph + graph.T - dia return graph @@ -291,7 +299,10 @@ def remove_loops(graph): the graph with self-loops (edges between the same node) removed. """ graph = import_graph(graph) - graph = graph - np.diag(np.diag(graph)) + + dia = diags(graph.diagonal()) if isspmatrix_csr(graph) else np.diag(np.diag(graph)) + + graph = graph - dia return graph @@ -310,7 +321,7 @@ def to_laplace(graph, form="DAD", regularizer=None): ---------- graph: object Either array-like, (n_vertices, n_vertices) numpy array, - or an object of type networkx.Graph. + scipy.sparse.csr_matrix, or an object of type networkx.Graph. form: {'I-DAD' (default), 'DAD', 'R-DAD'}, string, optional @@ -354,14 +365,15 @@ def to_laplace(graph, form="DAD", regularizer=None): [0.70710678, 0. , 0. ]]) """ + valid_inputs = ["I-DAD", "DAD", "R-DAD"] if form not in valid_inputs: raise TypeError("Unsuported Laplacian normalization") A = import_graph(graph) - in_degree = np.sum(A, axis=0) - out_degree = np.sum(A, axis=1) + in_degree = np.reshape(np.asarray(A.sum(axis=0)), (-1,)) + out_degree = np.reshape(np.asarray(A.sum(axis=1)), (-1,)) # regularize laplacian with parameter # set to average degree @@ -382,14 +394,16 @@ def to_laplace(graph, form="DAD", regularizer=None): in_root = 1 / np.sqrt(in_degree) # this is 10x faster than ** -0.5 out_root = 1 / np.sqrt(out_degree) + diag = diags if isspmatrix_csr(graph) else np.diag + in_root[np.isinf(in_root)] = 0 out_root[np.isinf(out_root)] = 0 - in_root = np.diag(in_root) # just change to sparse diag for sparse support - out_root = np.diag(out_root) + in_root = diag(in_root) # just change to sparse diag for sparse support + out_root = diag(out_root) if form == "I-DAD": - L = np.diag(in_degree) - A + L = diag(in_degree) - A L = in_root @ L @ in_root elif form == "DAD" or form == "R-DAD": L = out_root @ A @ in_root @@ -434,12 +448,12 @@ def is_fully_connected(graph): >>> is_fully_connected(a) False """ - if type(graph) is np.ndarray: + if isinstance(graph, (np.ndarray, csr_matrix)): if is_symmetric(graph): g_object = nx.Graph() else: g_object = nx.DiGraph() - graph = nx.from_numpy_array(graph, create_using=g_object) + graph = nx.to_networkx_graph(graph, create_using=g_object) if type(graph) in [nx.Graph, nx.MultiGraph]: return nx.is_connected(graph) elif type(graph) in [nx.DiGraph, nx.MultiDiGraph]: @@ -627,7 +641,8 @@ def augment_diagonal(graph, weight=1): Parameters ---------- - graph: nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray + graph: nx.Graph, nx.DiGraph, nx.MultiDiGraph, nx.MultiGraph, np.ndarray, + scipy.scr_matrix. Input graph in any of the above specified formats. If np.ndarray, interpreted as an :math:`n \times n` adjacency matrix weight: float/int @@ -649,17 +664,19 @@ def augment_diagonal(graph, weight=1): [1. , 0.5, 0. ], [1. , 0. , 0.5]]) """ + graph = import_graph(graph) graph = remove_loops(graph) divisor = graph.shape[0] - 1 - in_degrees = np.sum(np.abs(graph), axis=0) - out_degrees = np.sum(np.abs(graph), axis=1) - degrees = (in_degrees + out_degrees) / 2 + in_degrees = np.squeeze(np.asarray(abs(graph).sum(axis=0))) + out_degrees = np.squeeze(np.asarray(abs(graph).sum(axis=1))) + degrees = (in_degrees + out_degrees) / 2 diag = weight * degrees / divisor - graph += np.diag(diag) + + graph += diags(diag) if isspmatrix_csr(graph) else np.diag(diag) return graph @@ -935,7 +952,7 @@ def to_weighted_edge_list( nx.MultiGraph, nx.MultiDiGraph, np.ndarray, - scipy.sparse.csr.csr_matrix, + scipy.sparse.csr_matrix, ], is_directed: Optional[bool] = None, is_weighted: Optional[bool] = None, @@ -948,9 +965,9 @@ def to_weighted_edge_list( Parameters ---------- - graph : Union[List[Tuple[Any, Any, Union[float, int]]], nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph, np.ndarray, scipy.sparse.csr.csr_matrix] + graph : Union[List[Tuple[Any, Any, Union[float, int]]], nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph, np.ndarray, scipy.sparse.csr_matrix] A representation of a graph either as a list of tuples, a networkx graph, or an - adjacency matrix from numpy or scipy.sparse.csr.csr_matrix + adjacency matrix from numpy or scipy.sparse.csr_matrix is_directed : Optional[bool] Default is ``None``. Ignored if an edge tuple list or networkx graph is provided, but can be used to short circuit an exhaustive matrix symmetry check @@ -1018,7 +1035,7 @@ def to_weighted_edge_list( " could be cast to float in one of the edges" ) - if isinstance(graph, (np.ndarray, scipy.sparse.csr.csr_matrix)): + if isinstance(graph, (np.ndarray, csr_matrix)): shape = graph.shape if len(shape) != 2 or shape[0] != shape[1]: raise ValueError( @@ -1061,7 +1078,7 @@ def to_weighted_edge_list( raise TypeError( f"The type of graph provided {type(graph)} is not a list of 3-tuples, networkx " - f"graph, numpy.ndarray, or scipy.sparse.csr.csr_matrix" + f"graph, numpy.ndarray, or scipy.sparse.csr_matrix" ) diff --git a/tests/test_spectral_embed.py b/tests/test_spectral_embed.py index ed7be854c..75e3d97da 100644 --- a/tests/test_spectral_embed.py +++ b/tests/test_spectral_embed.py @@ -15,6 +15,7 @@ from sklearn.cluster import KMeans from sklearn.metrics import adjusted_rand_score, pairwise_distances from sklearn.base import clone +from scipy.sparse import csr_matrix np.random.seed(9002) @@ -63,18 +64,20 @@ def _test_output_dim_directed(self, method): self.assertEqual(embed.latent_right_.shape, (n, 4)) -def _test_output_dim(self, method, *args, **kwargs): +def _test_output_dim(self, method, sparse=False, *args, **kwargs): n_components = 4 embed = method(n_components=n_components) n = 10 M = 20 A = er_nm(n, M) + 5 + if sparse: + A = csr_matrix(A) embed._reduce_dim(A) self.assertEqual(embed.latent_left_.shape, (n, 4)) self.assertTrue(embed.latent_right_ is None) -def _test_sbm_er_binary(self, method, P, directed=False, *args, **kwargs): +def _test_sbm_er_binary(self, method, P, directed=False, sparse=False, *args, **kwargs): np.random.seed(8888) num_sims = 50 @@ -88,6 +91,9 @@ def _test_sbm_er_binary(self, method, P, directed=False, *args, **kwargs): for sim in range(0, num_sims): sbm_sample = sbm(verts_per_community, P, directed=directed) er = er_np(verts, 0.5, directed=directed) + if sparse: + sbm_sample = csr_matrix(sbm_sample) + er = csr_matrix(er) embed_sbm = method(n_components=2, concat=directed) embed_er = method(n_components=2, concat=directed) @@ -260,6 +266,27 @@ def test_exceptions(self): ase.transform(a) +class TestAdjacencySpectralEmbedSparse(unittest.TestCase): + def test_output_dim(self): + _test_output_dim(self, AdjacencySpectralEmbed, sparse=True) + + def test_sbm_er_binary_undirected(self): + P = np.array([[0.8, 0.2], [0.2, 0.8]]) + _test_sbm_er_binary( + self, AdjacencySpectralEmbed, P, directed=False, sparse=True + ) + + def test_sbm_er_binary_directed(self): + P = np.array([[0.8, 0.2], [0.2, 0.8]]) + _test_sbm_er_binary(self, AdjacencySpectralEmbed, P, directed=True, sparse=True) + + def test_unconnected_warning(self): + A = csr_matrix(er_nm(100, 10)) + with self.assertWarns(UserWarning): + ase = AdjacencySpectralEmbed() + ase.fit(A) + + class TestLaplacianSpectralEmbed(unittest.TestCase): def test_output_dim(self): _test_output_dim(self, LaplacianSpectralEmbed) @@ -288,5 +315,32 @@ def test_unconnected_warning(self): lse.fit(A) +class TestLaplacianSpectralEmbedSparse(unittest.TestCase): + def test_output_dim(self): + _test_output_dim(self, LaplacianSpectralEmbed, sparse=True) + + def test_sbm_er_binary_undirected(self): + P = np.array([[0.8, 0.2], [0.2, 0.3]]) + _test_sbm_er_binary( + self, LaplacianSpectralEmbed, P, directed=False, sparse=True + ) + + def test_sbm_er_binary_directed(self): + P = np.array([[0.8, 0.2], [0.2, 0.3]]) + _test_sbm_er_binary(self, LaplacianSpectralEmbed, P, directed=True, sparse=True) + + def test_different_forms(self): + f = csr_matrix(np.array([[1, 2], [2, 1]])) + lse = LaplacianSpectralEmbed(form="I-DAD") + + def test_unconnected_warning(self): + n = [50, 50] + p = [[1, 0], [0, 1]] + A = csr_matrix(sbm(n, p)) + with self.assertWarns(UserWarning): + lse = LaplacianSpectralEmbed() + lse.fit(A) + + if __name__ == "__main__": unittest.main() From 3cf2eac5479f3a864daea895e83fd19dea1f5329 Mon Sep 17 00:00:00 2001 From: vmdhhh <58106524+vmdhhh@users.noreply.github.com> Date: Thu, 14 Jan 2021 23:38:25 +0100 Subject: [PATCH 085/140] 615 seed automatic layout generation (#628) * #615 Adding Random seed for Automatic layout generation * #615 Removing random seed from _simulate_walks * 615 Black formatting * 615 Sorting Imports * #615 Random State addition for deterministic reproducible results between interpreter launches * #615 Black Formatting * #615 Adding test for n2v * #615 Black formatting for tests * #615 n2vgraph init parameter change * #615 Re-formatting and rephrasing docstrings * #615 Black Code Reformatting Co-authored-by: Dwayne Pryce Co-authored-by: Nick --- graspologic/embed/n2v.py | 51 +++++++++++++++++++++++++++++-------- graspologic/layouts/auto.py | 50 +++++++++++++++++++++++------------- tests/embed/__init__.py | 0 tests/embed/test_n2v.py | 25 ++++++++++++++++++ 4 files changed, 99 insertions(+), 27 deletions(-) create mode 100644 tests/embed/__init__.py create mode 100644 tests/embed/test_n2v.py diff --git a/graspologic/embed/n2v.py b/graspologic/embed/n2v.py index dccdbe4bf..a7bfbfcaf 100644 --- a/graspologic/embed/n2v.py +++ b/graspologic/embed/n2v.py @@ -3,7 +3,6 @@ import logging import math -import random import time from typing import Any, List, Optional, Tuple, Union @@ -22,6 +21,7 @@ def node2vec_embed( workers: int = 8, iterations: int = 1, interpolate_walk_lengths_by_node_degree: bool = True, + random_seed: Optional[int] = None, ) -> Tuple[np.ndarray, np.ndarray]: """ Generates a node2vec embedding from a given graph. Will follow the word2vec algorithm to create the embedding. @@ -57,6 +57,11 @@ def node2vec_embed( number of walks as a high degree node (which it will if this setting is not on), then the lower degree nodes will take a smaller breadth of random walks when compared to the high degree nodes. This will result in your lower degree walks dominating your higher degree nodes. + random_seed : int + Seed to be used for reproducible results. Default is None and will produce a random output. Note that for a fully + deterministically-reproducible run, you must also limit to a single worker thread (`workers=1`), to eliminate + ordering jitter from OS thread scheduling. In addition the environment variable ``PYTHONHASHSEED`` must be set + to control hash randomization. Returns ------- @@ -92,7 +97,11 @@ def node2vec_embed( interpolate_walk_lengths_by_node_degree, ) - node2vec_graph = _Node2VecGraph(graph, return_hyperparameter, inout_hyperparameter) + random_state = np.random.RandomState(seed=random_seed) + + node2vec_graph = _Node2VecGraph( + graph, return_hyperparameter, inout_hyperparameter, random_state + ) logging.info( f"Starting preprocessing of transition probabilities on graph with {str(len(graph.nodes()))} nodes and " @@ -110,7 +119,9 @@ def node2vec_embed( ) logging.info(f"Learning embeddings at time {str(time.time())}") - model = _learn_embeddings(walks, dimensions, window_size, workers, iterations) + model = _learn_embeddings( + walks, dimensions, window_size, workers, iterations, random_seed + ) end = time.time() logging.info( @@ -168,7 +179,12 @@ def _preconditions( def _learn_embeddings( - walks: List[Any], dimensions: int, window_size: int, workers: int, iterations: int + walks: List[Any], + dimensions: int, + window_size: int, + workers: int, + iterations: int, + random_seed: Optional[int], ): """ Learn embeddings by optimizing the skip-gram objective using SGD. @@ -186,6 +202,7 @@ def _learn_embeddings( sg=1, # Training algorithm: 1 for skip-gram; otherwise CBOW workers=workers, iter=iterations, + seed=random_seed, ) return model @@ -204,15 +221,23 @@ class _Node2VecGraph: Return hyperparameter inout_hyperparameter : float Inout hyperparameter + random_state : np.random.RandomState + Random State for reproducible results. Default is None and will produce random + results """ def __init__( - self, graph: nx.Graph, return_hyperparameter: float, inout_hyperparameter: float + self, + graph: nx.Graph, + return_hyperparameter: float, + inout_hyperparameter: float, + random_state: Optional[np.random.RandomState] = None, ): self.graph: nx.Graph = graph self.is_directed = self.graph.is_directed() self.p = return_hyperparameter self.q = inout_hyperparameter + self.random_state = random_state def node2vec_walk( self, @@ -251,7 +276,9 @@ def node2vec_walk( walk.append( current_neighbors[ _alias_draw( - alias_nodes[current][0], alias_nodes[current][1] + alias_nodes[current][0], + alias_nodes[current][1], + self.random_state, ) ] ) @@ -261,6 +288,7 @@ def node2vec_walk( _alias_draw( alias_edges[(prev, current)][0], alias_edges[(prev, current)][1], + self.random_state, ) ] walk.append(next) @@ -325,7 +353,8 @@ def _simulate_walks( logging.info( "Walk iteration: " + str(walk_iteration + 1) + "/" + str(num_walks) ) - random.shuffle(nodes) + + self.random_state.shuffle(nodes) for node in nodes: walks.append( self.node2vec_walk( @@ -469,14 +498,16 @@ def _alias_setup(probabilities: List[float]): return sampled_probabilities, alias -def _alias_draw(probabilities: List[float], alias: List[float]): +def _alias_draw( + probabilities: List[float], alias: List[float], random_state: np.random.RandomState +): """ Draw sample from a non-uniform discrete distribution using alias sampling. """ number_of_outcomes = len(probabilities) - random_index = int(np.floor(np.random.rand() * number_of_outcomes)) + random_index = int(np.floor(random_state.rand() * number_of_outcomes)) - if np.random.rand() < alias[random_index]: + if random_state.rand() < alias[random_index]: return random_index else: return probabilities[random_index] diff --git a/graspologic/layouts/auto.py b/graspologic/layouts/auto.py index 6e448419b..06b734956 100644 --- a/graspologic/layouts/auto.py +++ b/graspologic/layouts/auto.py @@ -1,24 +1,21 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. -from typing import Any, Dict, List, Tuple - import logging import math import time +from typing import Any, Dict, List, Optional, Tuple import networkx as nx import numpy as np - -from sklearn.manifold import TSNE import umap - -from .classes import NodePosition -from .nooverlap import remove_overlaps +from sklearn.manifold import TSNE from ..embed import node2vec_embed from ..partition import leiden from ..preprocessing import cut_edges_by_weight, histogram_edge_weight +from .classes import NodePosition +from .nooverlap import remove_overlaps logger = logging.getLogger(__name__) @@ -30,6 +27,7 @@ def layout_tsne( perplexity: int, n_iter: int, max_edges: int = 10000000, + random_seed: Optional[int] = None, ) -> Tuple[nx.Graph, List[NodePosition]]: """ Automatic graph layout generation by creating a generalized node2vec embedding, @@ -71,6 +69,11 @@ def layout_tsne( ``max_edges`` exist. Warning: this pruning is approximate and more edges than are necessary may be pruned. Running in 32 bit enviornment you will most likely need to reduce this number or you will out of memory. + random_seed : int + Seed to be used for reproducible results. Default is None and will produce + a new random state. Specifying a random state will provide consistent results + between runs. In addition the environment variable ``PYTHONHASHSEED`` must be + set to control hash randomization. Returns ------- @@ -88,9 +91,11 @@ def layout_tsne( .. [1] van der Maaten, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008. """ - lcc_graph, tensors, labels = _node2vec_for_layout(graph, max_edges) - points = TSNE(perplexity=perplexity, n_iter=n_iter).fit_transform(tensors) - positions = _node_positions_from(lcc_graph, labels, points) + lcc_graph, tensors, labels = _node2vec_for_layout(graph, max_edges, random_seed) + points = TSNE( + perplexity=perplexity, n_iter=n_iter, random_state=random_seed + ).fit_transform(tensors) + positions = _node_positions_from(lcc_graph, labels, points, random_seed=random_seed) return lcc_graph, positions @@ -99,6 +104,7 @@ def layout_umap( min_dist: float = 0.75, n_neighbors: int = 25, max_edges: int = 10000000, + random_seed: Optional[int] = None, ) -> Tuple[nx.Graph, List[NodePosition]]: """ Automatic graph layout generation by creating a generalized node2vec embedding, @@ -142,6 +148,9 @@ def layout_umap( ``max_edges`` exist. Warning: this pruning is approximate and more edges than are necessary may be pruned. Running in 32 bit enviornment you will most likely need to reduce this number or you will out of memory. + random_seed : int + Seed to be used for reproducible results. Default is None and will produce + random results. Returns ------- @@ -163,11 +172,11 @@ def layout_umap( 2007.08902v1, 17 Jul 2020. """ - lcc_graph, tensors, labels = _node2vec_for_layout(graph, max_edges) - points = umap.UMAP(min_dist=min_dist, n_neighbors=n_neighbors).fit_transform( - tensors - ) - positions = _node_positions_from(lcc_graph, labels, points) + lcc_graph, tensors, labels = _node2vec_for_layout(graph, max_edges, random_seed) + points = umap.UMAP( + min_dist=min_dist, n_neighbors=n_neighbors, random_state=random_seed + ).fit_transform(tensors) + positions = _node_positions_from(lcc_graph, labels, points, random_seed=random_seed) return lcc_graph, positions @@ -201,13 +210,19 @@ def _approximate_prune(graph: nx.Graph, max_edges_to_keep: int = 1000000): def _node2vec_for_layout( graph: nx.Graph, max_edges: int = 10000000, + random_seed: Optional[int] = None, ) -> Tuple[nx.Graph, np.ndarray, np.ndarray]: graph = _approximate_prune(graph, max_edges) graph = _largest_connected_component(graph) start = time.time() tensors, labels = node2vec_embed( - graph=graph, dimensions=128, num_walks=10, window_size=2, iterations=3 + graph=graph, + dimensions=128, + num_walks=10, + window_size=2, + iterations=3, + random_seed=random_seed, ) embedding_time = time.time() - start logger.info(f"embedding completed in {embedding_time} seconds") @@ -218,12 +233,13 @@ def _node_positions_from( graph: nx.Graph, labels: np.ndarray, down_projection_2d: np.ndarray, + random_seed: Optional[int] = None, ) -> List[NodePosition]: degree = graph.degree() sizes = _compute_sizes(degree) covered_area = _covered_size(sizes) scaled_points = _scale_points(down_projection_2d, covered_area) - partitions = leiden(graph) + partitions = leiden(graph, random_seed=random_seed) positions = [ NodePosition( node_id=key, diff --git a/tests/embed/__init__.py b/tests/embed/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/embed/test_n2v.py b/tests/embed/test_n2v.py new file mode 100644 index 000000000..9a0dd6805 --- /dev/null +++ b/tests/embed/test_n2v.py @@ -0,0 +1,25 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import unittest +import networkx as nx +import numpy as np +from graspologic.embed import n2v + + +class TestN2V(unittest.TestCase): + def test_node2vec_embed(self): + g = nx.florentine_families_graph() + + for s, t in g.edges(): + g.add_edge(s, t, weight=1) + + embedding = n2v.node2vec_embed(g, random_seed=1) + + embedding2 = n2v.node2vec_embed(g, random_seed=1) + + np.testing.assert_array_equal(embedding[0], embedding2[0]) + + +if __name__ == "__main__": + unittest.main() From fedc17b491cf3c4880954de8c7a290f117768390 Mon Sep 17 00:00:00 2001 From: Vivek Gopalakrishnan Date: Fri, 15 Jan 2021 08:59:57 -0500 Subject: [PATCH 086/140] Include participants in mouse data given to users (#634) * Include participants in mouse data given to users * Update docstring --- graspologic/datasets/base.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/graspologic/datasets/base.py b/graspologic/datasets/base.py index 37b71c0bd..2764db7f2 100644 --- a/graspologic/datasets/base.py +++ b/graspologic/datasets/base.py @@ -133,6 +133,10 @@ def load_mice(): DataFrame of block assignments for each ROI features : pd.DataFrame DataFrame of anatomical features for each ROI in each connectome + participants : pd.DataFrame + DataFrame of subject IDs and genotypes for each connectome + meta : Dictionary + Dictionary with meta information about the dataset (n_subjects and n_vertices) References ---------- @@ -176,5 +180,6 @@ def load_mice(): atlas=atlas, blocks=blocks, features=features, + participants=participants, meta=meta, ) From 71b10b0500ddc1e562bf4961cbcdf17a0692a58f Mon Sep 17 00:00:00 2001 From: tliu68 <54865879+tliu68@users.noreply.github.com> Date: Mon, 18 Jan 2021 02:39:03 +0800 Subject: [PATCH 087/140] DivisiveCluster: "model_" attribute for all nodes & fcluster option (#574) * Update test_divisive_cluster.py * Update divisive_cluster.py * update fcluster wording & GM model * fcluster wording * update `.model_` attribute * reformat * formating retry Co-authored-by: Benjamin Pedigo --- graspologic/cluster/divisive_cluster.py | 132 +++++++++++++++++++++++- tests/cluster/test_divisive_cluster.py | 68 +++++++----- 2 files changed, 173 insertions(+), 27 deletions(-) diff --git a/graspologic/cluster/divisive_cluster.py b/graspologic/cluster/divisive_cluster.py index ab28c46f7..b94a763d7 100644 --- a/graspologic/cluster/divisive_cluster.py +++ b/graspologic/cluster/divisive_cluster.py @@ -3,10 +3,11 @@ import numpy as np from sklearn.base import BaseEstimator +from sklearn.mixture import GaussianMixture from sklearn.utils import check_array from sklearn.utils.validation import check_is_fitted -from anytree import NodeMixin +from anytree import NodeMixin, LevelOrderIter from .autogmm import AutoGMMCluster from .kclust import KMeansCluster @@ -29,6 +30,18 @@ def _check_common_inputs(min_components, max_components, cluster_kws): raise TypeError("cluster_kws must be a dict") +def _check_fcluster(fcluster, level): + if level is not None: + if not isinstance(level, int): + raise TypeError("level must be an int") + elif level < 1: + raise ValueError("level must be positive") + elif fcluster is False: + msg = "level-specific flat clustering is availble\ + only if 'fcluster' is enabled" + raise ValueError(msg) + + class DivisiveCluster(NodeMixin, BaseEstimator): """ Recursively clusters data based on a chosen clustering algorithm. @@ -137,7 +150,7 @@ def fit(self, X): self.fit_predict(X) return self - def fit_predict(self, X): + def fit_predict(self, X, fcluster=False, level=None): """ Fits clustering models to the data as well as resulting clusters and using fitted models to predict a hierarchy of labels @@ -145,12 +158,22 @@ def fit_predict(self, X): Parameters ---------- X : array-like, shape (n_samples, n_features) + fcluster: bool, default=False + if True, returned labels will be re-numbered so that each column + of labels represents a flat clustering at current level, + and each label corresponds to a cluster indexed the same as + the corresponding node in the overall clustering dendrogram + level: int, optional (default=None) + the level of a single flat clustering to generate + only available if ``fcluster`` is True Returns ------- labels : array_label, shape (n_samples, n_levels) + if no level specified; otherwise, shape (n_samples,) """ X = check_array(X, dtype=[np.float64, np.float32], ensure_min_samples=1) + _check_fcluster(fcluster, level) if self.max_components > X.shape[0]: msg = "max_components must be >= n_samples, but max_components = " @@ -163,6 +186,13 @@ def fit_predict(self, X): if (labels.shape[1] > 1) and (np.max(labels[:, -1]) == 0): labels = labels[:, :-1] + if level is not None: + if level > labels.shape[1]: + msg = "input exceeds max level = {}".format(labels.shape[1]) + raise ValueError(msg) + if fcluster: + labels = self._relabel(labels, level) + return labels def _cluster_and_decide(self, X): @@ -238,26 +268,86 @@ def _fit(self, X): (labels, np.zeros((len(X), 1), dtype=int)) ) labels[inds, 1 : child_labels.shape[1] + 1] = child_labels + else: + # make a "GaussianMixture" model for clusters + # that were not fitted + if self.cluster_method == "gmm": + cluster_idx = len(dc.parent.children) - 1 + parent_model = dc.parent.model_ + model = GaussianMixture() + model.weights_ = np.array([1]) + model.means_ = parent_model.means_[cluster_idx].reshape(1, -1) + model.covariance_type = parent_model.covariance_type + if model.covariance_type == "tied": + model.covariances_ = parent_model.covariances_ + model.precisions_ = parent_model.precisions_ + model.precisions_cholesky_ = ( + parent_model.precisions_cholesky_ + ) + else: + cov_types = ["spherical", "diag", "full"] + n_features = model.means_.shape[-1] + cov_shapes = [ + (1,), + (1, n_features), + (1, n_features, n_features), + ] + cov_shape_idx = cov_types.index(model.covariance_type) + model.covariances_ = parent_model.covariances_[ + cluster_idx + ].reshape(cov_shapes[cov_shape_idx]) + model.precisions_ = parent_model.precisions_[ + cluster_idx + ].reshape(cov_shapes[cov_shape_idx]) + model.precisions_cholesky_ = ( + parent_model.precisions_cholesky_[cluster_idx].reshape( + cov_shapes[cov_shape_idx] + ) + ) + + dc.model_ = model return labels - def predict(self, X): + def predict(self, X, fcluster=False, level=None): """ Predicts a hierarchy of labels based on fitted models Parameters ---------- X : array-like, shape (n_samples, n_features) + fcluster: bool, default=False + if True, returned labels will be re-numbered so that each column + of labels represents a flat clustering at current level, + and each label corresponds to a cluster indexed the same as + the corresponding node in the overall clustering dendrogram + level: int, optional (default=None) + the level of a single flat clustering to generate + only available if ``fcluster`` is True Returns ------- labels : array-like, shape (n_samples, n_levels) + if no level specified; otherwise, shape (n_samples,) """ check_is_fitted(self, ["model_"], all_or_any=all) X = check_array(X, dtype=[np.float64, np.float32], ensure_min_samples=1) + _check_fcluster(fcluster, level) labels = self._predict_labels(X) + if (level is not None) and (level > labels.shape[1]): + msg = "input exceeds max level = {}".format(labels.shape[1]) + raise ValueError(msg) + + if fcluster: + # convert labels to stacked flat clusterings + # based on the flat clusterings on fitted data + inds = [(labels == row).all(1).any() for row in self._labels] + labels = self._new_labels[inds] + if level is not None: + labels = labels[:, level - 1] + return labels def _predict_labels(self, X): @@ -281,3 +371,39 @@ def _predict_labels(self, X): pred_labels = np.zeros((len(X), 1), dtype=int) return pred_labels + + def _relabel(self, labels, level=None): + # re-number "labels" so that each cluster at each level recieves + # a unique label = index of corresponding node in overall dendrogram + + # assign each cluster a new label based on its index in the dendrogram + all_clusters = [node for node in LevelOrderIter(self)][1:] + for indx in range(len(all_clusters)): + all_clusters[indx]._label = indx + + new_labels = labels.copy() + for lvl in range(1, self.height): + uni_paths, path_inds = np.unique( + labels[:, : lvl + 1], axis=0, return_index=True + ) + for p in range(len(uni_paths)): + label_inds = (labels[:, : lvl + 1] == uni_paths[p]).all(1) + current_path = labels[path_inds[p], : lvl + 1] + cluster = self.root + current_lvl = 0 + # find the cluster corresponding to "current_path" + while current_lvl <= lvl: + if not cluster.is_leaf: + cluster = cluster.children[current_path[current_lvl]] + current_lvl += 1 + else: + break + new_labels[label_inds, lvl] = cluster._label + + # stored to do relabeling for non-fitted data + self._labels = labels + self._new_labels = new_labels + + if level is not None: + new_labels = new_labels[:, level - 1] + return new_labels diff --git a/tests/cluster/test_divisive_cluster.py b/tests/cluster/test_divisive_cluster.py index 6bfca955b..acc7846eb 100644 --- a/tests/cluster/test_divisive_cluster.py +++ b/tests/cluster/test_divisive_cluster.py @@ -47,6 +47,46 @@ def test_inputs(): dc = DivisiveCluster(max_components=101) dc.fit(X) + # level not an int + with pytest.raises(TypeError): + rc = DivisiveCluster(max_components=2) + rc.fit_predict(X, fcluster=True, level="1") + + with pytest.raises(TypeError): + rc = DivisiveCluster(max_components=2) + rc.fit(X) + rc.predict(X, fcluster=True, level="1") + + # level not positive + with pytest.raises(ValueError): + rc = DivisiveCluster(max_components=2) + rc.fit_predict(X, fcluster=True, level=0) + + with pytest.raises(ValueError): + rc = DivisiveCluster(max_components=2) + rc.fit(X) + rc.predict(X, fcluster=True, level=0) + + # level exceeds n_level + with pytest.raises(ValueError): + dc = DivisiveCluster(max_components=2) + dc.fit_predict(X, fcluster=True, level=100) + + with pytest.raises(ValueError): + dc = DivisiveCluster(max_components=2) + dc.fit(X) + dc.predict(X, fcluster=True, level=100) + + # level is given but fcluster disabled + with pytest.raises(ValueError): + dc = DivisiveCluster(max_components=2) + dc.fit_predict(X, level=1) + + with pytest.raises(ValueError): + dc = DivisiveCluster(max_components=2) + dc.fit(X) + dc.predict(X, level=1) + def test_predict_without_fit(): # Generate random data @@ -163,18 +203,12 @@ def _test_hierarchical_four_class(**kws): """ np.random.seed(1) dc = DivisiveCluster(max_components=2, **kws) - pred = dc.fit_predict(X) - - # re-number "pred" so that each column represents - # a flat clustering at current level - for lvl in range(1, pred.shape[1]): - _, inds = np.unique(pred[:, : lvl + 1], axis=0, return_inverse=True) - pred[:, lvl] = inds + pred = dc.fit_predict(X, fcluster=True) # Assert that the 2-cluster model is the best at level 1 assert_equal(np.max(pred[:, 0]) + 1, 2) # Assert that the 4-cluster model is the best at level 1 - assert_equal(np.max(pred[:, 1]) + 1, 4) + assert_equal(len(np.unique(pred[:, 1])), 4) # Assert that we get perfect clustering at level 1 ari_lvl1 = adjusted_rand_score(y_lvl1, pred[:, 0]) @@ -221,25 +255,11 @@ def test_hierarchical_six_class_delta_criter(): # Perform clustering without setting delta_criter dc = DivisiveCluster(max_components=2) - pred = dc.fit_predict(X) - - # re-number "pred" so that each column represents - # a flat clustering at current level - for lvl in range(1, pred.shape[1]): - _, inds = np.unique(pred[:, : lvl + 1], axis=0, return_inverse=True) - pred[:, lvl] = inds + pred = dc.fit_predict(X, fcluster=True) # Perform clustering while setting delta_criter dc = DivisiveCluster(max_components=2, delta_criter=10) - pred_delta_criter = dc.fit_predict(X) - - # re-number "pred_delta_criter" so that each column represents - # a flat clustering at current level - for lvl in range(1, pred_delta_criter.shape[1]): - _, inds = np.unique( - pred_delta_criter[:, : lvl + 1], axis=0, return_inverse=True - ) - pred_delta_criter[:, lvl] = inds + pred_delta_criter = dc.fit_predict(X, fcluster=True) # Assert that pred has more levels than pred_delta_criter assert_equal(pred.shape[1] - 1, pred_delta_criter.shape[1]) From 28d8c50483955b8042b7b772895ba50eac997689 Mon Sep 17 00:00:00 2001 From: tliu68 <54865879+tliu68@users.noreply.github.com> Date: Mon, 18 Jan 2021 06:05:44 +0800 Subject: [PATCH 088/140] Update AutoGMM initialization (#589) * Update autogmm.py * Update autogmm.py * resolve conflicts * fix typo Co-authored-by: Benjamin Pedigo --- graspologic/cluster/autogmm.py | 83 +++++++++++++++++++++++++--------- 1 file changed, 61 insertions(+), 22 deletions(-) diff --git a/graspologic/cluster/autogmm.py b/graspologic/cluster/autogmm.py index 5e1b353eb..c6c939e1e 100644 --- a/graspologic/cluster/autogmm.py +++ b/graspologic/cluster/autogmm.py @@ -376,7 +376,7 @@ def __init__( self.max_agglom_size = max_agglom_size self.n_jobs = n_jobs - def _fit_cluster(self, X, y, params): + def _fit_cluster(self, X, X_subset, y, params, agg_clustering): label_init = self.label_init if label_init is not None: onehot = _labels_to_onehot(label_init) @@ -388,15 +388,6 @@ def _fit_cluster(self, X, y, params): gm_params["means_init"] = means_init gm_params["precisions_init"] = precisions_init elif params[0]["affinity"] != "none": - agg = AgglomerativeClustering(**params[0]) - n = X.shape[0] - - if self.max_agglom_size is None or n <= self.max_agglom_size: - X_subset = X - else: # if dataset is huge, agglomerate a subset - subset_idxs = np.random.choice(np.arange(0, n), self.max_agglom_size) - X_subset = X[subset_idxs, :] - agg_clustering = agg.fit_predict(X_subset) onehot = _labels_to_onehot(agg_clustering) weights_init, means_init, precisions_init = _onehot_to_initial_params( X_subset, onehot, params[1]["covariance_type"] @@ -416,8 +407,8 @@ def _fit_cluster(self, X, y, params): while gm_params["reg_covar"] <= 1 and criter == np.inf: model = GaussianMixture(**gm_params) try: - # ignoring warning here because if convergence is not reached, the regularization - # is automatically increased + # ignoring warning here because if convergence is not reached, + # the regularization is automatically increased with warnings.catch_warnings(): warnings.simplefilter("ignore", ConvergenceWarning) model.fit(X) @@ -526,17 +517,41 @@ def fit(self, X, y=None): n_components=range(lower_ncomponents, upper_ncomponents + 1), random_state=[self.random_state], ) - param_grid = list(ParameterGrid(param_grid)) - param_grid = _process_paramgrid(param_grid) + param_grid_ag, param_grid = _process_paramgrid(param_grid) + + n = X.shape[0] + if self.max_agglom_size is None or n <= self.max_agglom_size: + X_subset = X + else: # if dataset is huge, agglomerate a subset + subset_idxs = np.random.choice(np.arange(0, n), self.max_agglom_size) + X_subset = X[subset_idxs, :] + + ag_labels = [] + if self.label_init is None: + for p_ag in param_grid_ag: + if p_ag["affinity"] != "none": + agg = AgglomerativeClustering( + n_clusters=self.min_components, **p_ag + ) + agg.fit(X_subset) + hierarchical_labels = _hierarchical_labels( + agg.children_, lower_ncomponents, upper_ncomponents + ) + ag_labels.append(hierarchical_labels) def _fit_for_data(p): - return self._fit_cluster(X, y, p) + n_clusters = p[1]["n_components"] + if (p[0]["affinity"] != "none") and (self.label_init is None): + index = param_grid_ag.index(p[0]) + agg_clustering = ag_labels[index][:, n_clusters - self.min_components] + else: + agg_clustering = [] + return self._fit_cluster(X, X_subset, y, p, agg_clustering) results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( delayed(_fit_for_data)(p) for p in param_grid ) - results = pd.DataFrame(results) self.results_ = results @@ -647,7 +662,12 @@ def _process_paramgrid(paramgrid): paramgrid_processed : list pairs of dicts For each pair, the first dict are the options for AgglomerativeClustering. The second dict include the options for GaussianMixture. + ag_paramgrid_processed : list of dicts + options for AgglomerativeClustering """ + gm_keys = ["covariance_type", "n_components", "random_state"] + ag_keys = ["affinity", "linkage"] + ag_params_processed = [] paramgrid_processed = [] for params in paramgrid: @@ -663,14 +683,33 @@ def _process_paramgrid(paramgrid): ): continue else: - - gm_keys = ["covariance_type", "n_components", "random_state"] gm_params = {key: params[key] for key in gm_keys} - - ag_keys = ["affinity", "linkage"] ag_params = {key: params[key] for key in ag_keys} - ag_params["n_clusters"] = params["n_components"] + if ag_params not in ag_params_processed: + ag_params_processed.append(ag_params) paramgrid_processed.append([ag_params, gm_params]) + return ag_params_processed, paramgrid_processed + + +def _hierarchical_labels(children, min_components, max_components): + n_samples = len(children) + 1 + hierarchical_labels = np.arange(n_samples).reshape((-1, 1)) + merge_start = n_samples - max_components - 1 + merge_end = n_samples - min_components - 1 + + for n in range(merge_end + 1): + inds = np.where(np.isin(hierarchical_labels[:, n], children[n, :]))[0] + hierarchical_labels[inds, -1] = n_samples + n + if n < merge_end: + hierarchical_labels = np.hstack( + (hierarchical_labels, hierarchical_labels[:, -1].reshape((-1, 1))) + ) + + hierarchical_labels = hierarchical_labels[:, merge_start:] + for i in range(hierarchical_labels.shape[1]): + _, hierarchical_labels[:, i] = np.unique( + hierarchical_labels[:, i], return_inverse=True + ) - return paramgrid_processed + return hierarchical_labels[:, ::-1] From 43ca7d653380ff48572e5061683071ce1806c213 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 25 Jan 2021 15:26:35 -0800 Subject: [PATCH 089/140] Doc split (#632) * Split our documentation into two parts; one for reference documentation generation that RTD will continue building and publishing for us, and one for tutorials that we will be managing ourselves via the `gh-pages` branch. We still need to make changes in the github actions around capturing the output of build-tutorial-documentation and making it available for the publish job to use if we want to. After I figure out the github-workflow incantations for that we'll publish just `dev` and `main`, then we'll determine whether to link against dev or main via intersphinx (in most cases, we'll link against dev). I'm sure this may break some things in the future, but doc generation and publication via RTD is broken _today_ and I'm not going to let perfect be the enemy of good. * Fixing the developer Makefile to point to the right location for reference docs and added a target for tutorials * The new build should publish to our gh-pages, in theory * Testing this out and writing to gh-pages * Setting a config file for rtd * Fixing RTD config * Coverage was trying to run itself over our documentation generation ... 'modules', which is not something we want? * We were trying to run the publish-to-twine task and we shouldn't have * Triggering a new build? * In theory this should now link all of the appropriate tutorial documentation * Committing the Makefile and requirements.txt for netlify to continue working --- .github/workflows/build.yml | 47 ++++- .gitignore | 3 +- .readthedocs.yml | 9 + Makefile | 9 +- docs/.touch | 0 docs/CNAME | 1 - docs/README.md | 45 ----- docs/_templates/footer.html | 9 - docs/build_docs.sh | 9 - docs/index.rst | 84 --------- docs/{ => reference}/Makefile | 1 + .../_templates/numpydoc_docstring.rst | 0 docs/{ => reference}/cli.rst | 0 docs/{ => reference}/conf.py | 5 - docs/{ => reference}/contributing.rst | 0 docs/reference/index.rst | 102 ++++++++--- docs/{ => reference}/install.rst | 0 docs/{ => reference}/license.rst | 0 docs/reference/{ => reference}/align.rst | 0 docs/reference/{ => reference}/cluster.rst | 0 docs/reference/{ => reference}/datasets.rst | 0 docs/reference/{ => reference}/embed.rst | 2 + docs/reference/reference/index.rst | 24 +++ docs/reference/{ => reference}/inference.rst | 0 docs/reference/{ => reference}/layouts.rst | 0 docs/reference/{ => reference}/match.rst | 0 docs/reference/{ => reference}/models.rst | 0 docs/reference/{ => reference}/nominate.rst | 0 docs/reference/{ => reference}/partition.rst | 0 docs/reference/{ => reference}/plotting.rst | 0 .../{ => reference}/preprocessing.rst | 0 .../reference/{ => reference}/simulations.rst | 0 docs/reference/{ => reference}/subgraph.rst | 0 docs/reference/{ => reference}/utils.rst | 0 docs/{ => reference}/release.rst | 0 .../{ => reference}/release/release_0.0.1.rst | 0 .../{ => reference}/release/release_0.0.2.rst | 0 .../{ => reference}/release/release_0.0.3.rst | 0 docs/{ => reference}/release/release_0.1.rst | 0 docs/{ => reference}/release/release_0.2.rst | 0 docs/{ => reference}/release/release_0.3.rst | 0 .../release/release_template.rst | 0 docs/{ => reference}/requirements.txt | 3 +- docs/tutorials/Makefile | 25 +++ .../_templates/numpydoc_docstring.rst | 16 ++ docs/tutorials/conf.py | 171 ++++++++++++++++++ docs/{tutorial.rst => tutorials/index.rst} | 81 +++++---- docs/tutorials/requirements.txt | 8 + graspologic/embed/ase.py | 3 +- graspologic/embed/lse.py | 2 - graspologic/embed/mase.py | 3 +- graspologic/embed/omni.py | 3 +- .../inference/latent_distribution_test.py | 3 +- graspologic/inference/latent_position_test.py | 3 +- graspologic/models/er.py | 6 +- graspologic/models/rdpg.py | 3 +- graspologic/models/sbm.py | 6 +- graspologic/nominate/spectralVN.py | 9 +- graspologic/plot/plot.py | 13 +- graspologic/simulations/rdpg_corr.py | 4 +- graspologic/simulations/simulations.py | 15 +- 61 files changed, 483 insertions(+), 244 deletions(-) create mode 100644 .readthedocs.yml create mode 100644 docs/.touch delete mode 100644 docs/CNAME delete mode 100644 docs/README.md delete mode 100644 docs/_templates/footer.html delete mode 100644 docs/build_docs.sh delete mode 100644 docs/index.rst rename docs/{ => reference}/Makefile (99%) rename docs/{ => reference}/_templates/numpydoc_docstring.rst (100%) rename docs/{ => reference}/cli.rst (100%) rename docs/{ => reference}/conf.py (98%) rename docs/{ => reference}/contributing.rst (100%) rename docs/{ => reference}/install.rst (100%) rename docs/{ => reference}/license.rst (100%) rename docs/reference/{ => reference}/align.rst (100%) rename docs/reference/{ => reference}/cluster.rst (100%) rename docs/reference/{ => reference}/datasets.rst (100%) rename docs/reference/{ => reference}/embed.rst (83%) create mode 100644 docs/reference/reference/index.rst rename docs/reference/{ => reference}/inference.rst (100%) rename docs/reference/{ => reference}/layouts.rst (100%) rename docs/reference/{ => reference}/match.rst (100%) rename docs/reference/{ => reference}/models.rst (100%) rename docs/reference/{ => reference}/nominate.rst (100%) rename docs/reference/{ => reference}/partition.rst (100%) rename docs/reference/{ => reference}/plotting.rst (100%) rename docs/reference/{ => reference}/preprocessing.rst (100%) rename docs/reference/{ => reference}/simulations.rst (100%) rename docs/reference/{ => reference}/subgraph.rst (100%) rename docs/reference/{ => reference}/utils.rst (100%) rename docs/{ => reference}/release.rst (100%) rename docs/{ => reference}/release/release_0.0.1.rst (100%) rename docs/{ => reference}/release/release_0.0.2.rst (100%) rename docs/{ => reference}/release/release_0.0.3.rst (100%) rename docs/{ => reference}/release/release_0.1.rst (100%) rename docs/{ => reference}/release/release_0.2.rst (100%) rename docs/{ => reference}/release/release_0.3.rst (100%) rename docs/{ => reference}/release/release_template.rst (100%) rename docs/{ => reference}/requirements.txt (86%) create mode 100644 docs/tutorials/Makefile create mode 100644 docs/tutorials/_templates/numpydoc_docstring.rst create mode 100644 docs/tutorials/conf.py rename docs/{tutorial.rst => tutorials/index.rst} (74%) create mode 100644 docs/tutorials/requirements.txt diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 3578640fb..ca762c3e7 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,7 +1,7 @@ name: graspologic CI on: [push, pull_request] jobs: - build-documentation: + build-reference-documentation: runs-on: ubuntu-latest continue-on-error: true steps: @@ -10,12 +10,34 @@ jobs: uses: actions/setup-python@v1 with: python-version: 3.8 - - name: "Run Documentation Generation" + - name: "Run Reference Documentation Generation" run: | sudo apt install pandoc pip install -U pip setuptools pip install -r requirements.txt - sphinx-build -W -a docs/ docs/_build/html + sphinx-build -W -a docs/reference/ docs/reference/_build/html + build-tutorial-documentation: + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: "Run Tutorial Documentation Generation" + run: | + sudo apt install pandoc + pip install -U pip setuptools + pip install -r requirements.txt + sphinx-build -W -a docs/tutorials/ docs/tutorials/_build/html + touch docs/tutorials/_build/html/.nojekyll + - name: Archive production artifacts + uses: actions/upload-artifact@v2 + with: + name: tutorials + path: | + docs/tutorials/_build/html code-format-check: runs-on: ubuntu-latest continue-on-error: true @@ -57,7 +79,7 @@ jobs: run: | pip install -U pip setuptools pip install -r requirements.txt - python -m pytest --co --cov=graspologic + python -m pytest --co --cov=graspologic graspologic tests unit-and-doc-test: runs-on: ${{matrix.os}} continue-on-error: true @@ -78,8 +100,8 @@ jobs: pytest tests # note: should be pytest graspologic tests, but we're skipping doctests for now and re-enabling them in another PR publish: runs-on: ubuntu-latest - needs: [build-documentation, code-format-check, unit-and-doc-test] - if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' + needs: [build-reference-documentation, build-tutorial-documentation, code-format-check, unit-and-doc-test] + if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' || github.ref=='refs/heads/doc-split' steps: - uses: actions/checkout@v2 - name: Set up Python 3.8 @@ -99,8 +121,21 @@ jobs: run: | python setup.py egg_info -b "dev$GITHUB_RUN_ID" sdist - name: Publish with twine + if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | twine upload dist/* + - name: Download a single artifact + uses: actions/download-artifact@v2 + with: + name: tutorials + path: docs/tutorials + - name: Publish tutorials + if: github.ref=='refs/heads/dev' || github.ref=='refs/heads/doc-split' + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/tutorials + destination_dir: tutorials diff --git a/.gitignore b/.gitignore index 4d51b2b24..a0be4a955 100644 --- a/.gitignore +++ b/.gitignore @@ -65,7 +65,8 @@ instance/ .scrapy # Sphinx documentation -docs/_build/ +docs/reference/_build/ +docs/tutorials/_build/ # PyBuilder target/ diff --git a/.readthedocs.yml b/.readthedocs.yml new file mode 100644 index 000000000..f1ad56f2b --- /dev/null +++ b/.readthedocs.yml @@ -0,0 +1,9 @@ +version: 2 +sphinx: + configuration: docs/reference/conf.py + +python: + version: 3.8 + install: + - requirements: requirements.txt + diff --git a/Makefile b/Makefile index 29cf82fc7..d81d6bb06 100644 --- a/Makefile +++ b/Makefile @@ -1,8 +1,11 @@ docs: - sphinx-build -W -a docs/ docs/_build/html + sphinx-build -W -a docs/reference/ docs/reference/_build/html + +tutorials: + sphinx-build -W -a docs/tutorials/ docs/tutorials/_build/html coverage: - python -m pytest --co --cov=graspologic + python -m pytest --co --cov=graspologic graspologic tests lint: black --check --diff ./graspologic ./tests @@ -15,4 +18,4 @@ type-check: validate: lint coverage docs test -.PHONY: docs coverage lint test type-check validate \ No newline at end of file +.PHONY: docs tutorials coverage lint test type-check validate diff --git a/docs/.touch b/docs/.touch new file mode 100644 index 000000000..e69de29bb diff --git a/docs/CNAME b/docs/CNAME deleted file mode 100644 index ffc506c9a..000000000 --- a/docs/CNAME +++ /dev/null @@ -1 +0,0 @@ -graspy.neurodata.io \ No newline at end of file diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index 5ceccf453..000000000 --- a/docs/README.md +++ /dev/null @@ -1,45 +0,0 @@ -# Building docs - -We currently use Sphinx - -If you only want to get the documentation, note that pre-build versions can be found at - - https://graspy.neurodata.io - -## Instructions - -### Python Dependencies - -You will need to install all the dependencies as defined in `requirements.txt` file. The following packages are needed: - - sphinx>=1.8.1 - sphinx_rtd_theme>=0.4.2 - sphinxcontrib-rawfiles - numpydoc - nbsphinx - -The above can be installed by entering: - - pip3 install -r requirements.txt - -in the `doc/` directory. - -### Pandoc dependency - -In addition, you need to install `pandoc` for `nbsphinx`. If you are on linux, you can enter: - - sudo apt-get install pandoc - -If you are on macOS and have `homebrew` installed, you can enter: - - brew install pandoc - -Otherwise, you can visit [pandoc installing page](https://pandoc.org/installing.html) for more information. - -## Generating the documentation - -To build the HTML documentation, enter: - - make html - -in the `doc/` directory. If all goes well, this will generate a `_build/html/` subdirectory containing the built documentation. \ No newline at end of file diff --git a/docs/_templates/footer.html b/docs/_templates/footer.html deleted file mode 100644 index f875f363a..000000000 --- a/docs/_templates/footer.html +++ /dev/null @@ -1,9 +0,0 @@ -{% extends "!footer.html" %} -{% block extrafooter %} -

- - - -

-{{ super() }} -{% endblock %} \ No newline at end of file diff --git a/docs/build_docs.sh b/docs/build_docs.sh deleted file mode 100644 index 4ebf13548..000000000 --- a/docs/build_docs.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/usr/bin/env bash -set -ex - -pip install -r docs/requirements.txt -cd docs -make html -cd .. - -set +ex \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index dbab253fe..000000000 --- a/docs/index.rst +++ /dev/null @@ -1,84 +0,0 @@ -.. -*- coding: utf-8 -*- - -.. _contents: - -Overview of graspologic_ -======================== - -.. _graspologic: https://graspy.neurodata.io - -graspologic is a Python package for analysis of graphs, or networks. - -Motivation ----------- - -A graph, or network, provides a mathematically intuitive representation of data with -some sort of relationship between items. For example, a social network can be -represented as a graph by considering all participants in the social network as nodes, -with connections representing whether each pair of individuals in the network are friends -with one another. Naively, one might apply traditional statistical techniques to a graph, -which neglects the spatial arrangement of nodes within the network and is not utilizing -all of the information present in the graph. In this package, we provide utilities and -algorithms designed for the processing and analysis of graphs with specialized graph -statistical algorithms. - -Python ------- - -Python is a powerful programming language that allows concise expressions of network -algorithms. Python has a vibrant and growing ecosystem of packages that -graspologic uses to provide more features such as numerical linear algebra and -plotting. In order to make the most out of graspologic you will want to know how -to write basic programs in Python. Among the many guides to Python, we -recommend the `Python documentation `_. - -Free software -------------- - -graspologic is free software; you can redistribute it and/or modify it under the -terms of the :doc:`MIT ` license. We welcome contributions. -Join us on `GitHub `_. - -History -------- - -``graspologic`` first released in September 2020, but it got its start as a pair of Python libraries -written by Johns Hopkins University's NeuroData lab and Microsoft Research's Project Essex. -Both teams worked on many of the same algorithms, shared research, findings, and generally duplicated a lot of effort. - -``GraSPy`` - the NeuroData library - and ``topologic`` - the Microsoft Research library began merging in September of 2020, but both got their starts far earlier, with GraSPy starting in September 2018 and topologic starting just a short time later, on October 2nd, 2018. - -GraSPy was originally designed and written by Jaewon Chung, Benjamin Pedigo, and Eric Bridgeford. - -Topologic was originally designed and written by Patrick Bourke, Jonathan McLean, Nick Caurvina, and Dwayne Pryce. - -Documentation -============= - -graspologic is a graph statistics package in python. - -.. toctree:: - :maxdepth: 1 - - install - tutorial - reference/index - cli - contributing - release - license - -.. toctree:: - :maxdepth: 1 - :caption: Useful Links - - graspologic @ GitHub - graspologic @ PyPI - Issue Tracker - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`search` diff --git a/docs/Makefile b/docs/reference/Makefile similarity index 99% rename from docs/Makefile rename to docs/reference/Makefile index c53dae070..6f3ad867d 100644 --- a/docs/Makefile +++ b/docs/reference/Makefile @@ -22,3 +22,4 @@ html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) "$(SOURCEDIR)" "$(BUILDDIR)" @echo @echo "Build finished. The HTML pages are in build/html." + diff --git a/docs/_templates/numpydoc_docstring.rst b/docs/reference/_templates/numpydoc_docstring.rst similarity index 100% rename from docs/_templates/numpydoc_docstring.rst rename to docs/reference/_templates/numpydoc_docstring.rst diff --git a/docs/cli.rst b/docs/reference/cli.rst similarity index 100% rename from docs/cli.rst rename to docs/reference/cli.rst diff --git a/docs/conf.py b/docs/reference/conf.py similarity index 98% rename from docs/conf.py rename to docs/reference/conf.py index 86a5700f4..c38f024b0 100644 --- a/docs/conf.py +++ b/docs/reference/conf.py @@ -46,14 +46,9 @@ "numpydoc", "sphinx.ext.ifconfig", "sphinx.ext.githubpages", - "sphinxcontrib.rawfiles", - "nbsphinx", "sphinx.ext.intersphinx", ] -# -- sphinxcontrib.rawfiles -rawfiles = ["CNAME"] - # -- numpydoc # Below is needed to prevent errors numpydoc_show_class_members = False diff --git a/docs/contributing.rst b/docs/reference/contributing.rst similarity index 100% rename from docs/contributing.rst rename to docs/reference/contributing.rst diff --git a/docs/reference/index.rst b/docs/reference/index.rst index cdb8fc633..ed245e2aa 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -1,24 +1,84 @@ -.. _reference: +.. -*- coding: utf-8 -*- -Reference -********* +.. _contents: + +Overview of graspologic_ +======================== + +.. _graspologic: https://graspologic.readthedocs.org/en/latest + +graspologic is a Python package for analysis of graphs, or networks. + +Motivation +---------- + +A graph, or network, provides a mathematically intuitive representation of data with +some sort of relationship between items. For example, a social network can be +represented as a graph by considering all participants in the social network as nodes, +with connections representing whether each pair of individuals in the network are friends +with one another. Naively, one might apply traditional statistical techniques to a graph, +which neglects the spatial arrangement of nodes within the network and is not utilizing +all of the information present in the graph. In this package, we provide utilities and +algorithms designed for the processing and analysis of graphs with specialized graph +statistical algorithms. + +Python +------ + +Python is a powerful programming language that allows concise expressions of network +algorithms. Python has a vibrant and growing ecosystem of packages that +graspologic uses to provide more features such as numerical linear algebra and +plotting. In order to make the most out of graspologic you will want to know how +to write basic programs in Python. Among the many guides to Python, we +recommend the `Python documentation `_. + +Free software +------------- + +graspologic is free software; you can redistribute it and/or modify it under the +terms of the :doc:`MIT ` license. We welcome contributions. +Join us on `GitHub `_. + +History +------- + +``graspologic`` first released in September 2020, but it got its start as a pair of Python libraries +written by Johns Hopkins University's NeuroData lab and Microsoft Research's Project Essex. +Both teams worked on many of the same algorithms, shared research, findings, and generally duplicated a lot of effort. + +``GraSPy`` - the NeuroData library - and ``topologic`` - the Microsoft Research library began merging in September of 2020, but both got their starts far earlier, with GraSPy starting in September 2018 and topologic starting just a short time later, on October 2nd, 2018. + +GraSPy was originally designed and written by Jaewon Chung, Benjamin Pedigo, and Eric Bridgeford. + +Topologic was originally designed and written by Patrick Bourke, Jonathan McLean, Nick Caurvina, and Dwayne Pryce. + +Documentation +============= + +graspologic is a graph statistics package in python. .. toctree:: - :maxdepth: 2 - - align - cluster - datasets - embed - cluster - inference - layouts - match - models - nominate - partition - plotting - preprocessing - simulations - subgraph - utils + :maxdepth: 1 + + install + reference/index + Tutorials + cli + contributing + release + license + +.. toctree:: + :maxdepth: 1 + :caption: Useful Links + + graspologic @ GitHub + graspologic @ PyPI + Issue Tracker + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/install.rst b/docs/reference/install.rst similarity index 100% rename from docs/install.rst rename to docs/reference/install.rst diff --git a/docs/license.rst b/docs/reference/license.rst similarity index 100% rename from docs/license.rst rename to docs/reference/license.rst diff --git a/docs/reference/align.rst b/docs/reference/reference/align.rst similarity index 100% rename from docs/reference/align.rst rename to docs/reference/reference/align.rst diff --git a/docs/reference/cluster.rst b/docs/reference/reference/cluster.rst similarity index 100% rename from docs/reference/cluster.rst rename to docs/reference/reference/cluster.rst diff --git a/docs/reference/datasets.rst b/docs/reference/reference/datasets.rst similarity index 100% rename from docs/reference/datasets.rst rename to docs/reference/reference/datasets.rst diff --git a/docs/reference/embed.rst b/docs/reference/reference/embed.rst similarity index 83% rename from docs/reference/embed.rst rename to docs/reference/reference/embed.rst index 22e9a821f..37984c004 100644 --- a/docs/reference/embed.rst +++ b/docs/reference/reference/embed.rst @@ -1,3 +1,5 @@ +.. _ase_tutorial: https://microsoft.github.io/graspologic/tutorials/embedding/AdjacencySpectralEmbed.html + Embedding ========= diff --git a/docs/reference/reference/index.rst b/docs/reference/reference/index.rst new file mode 100644 index 000000000..cdb8fc633 --- /dev/null +++ b/docs/reference/reference/index.rst @@ -0,0 +1,24 @@ +.. _reference: + +Reference +********* + +.. toctree:: + :maxdepth: 2 + + align + cluster + datasets + embed + cluster + inference + layouts + match + models + nominate + partition + plotting + preprocessing + simulations + subgraph + utils diff --git a/docs/reference/inference.rst b/docs/reference/reference/inference.rst similarity index 100% rename from docs/reference/inference.rst rename to docs/reference/reference/inference.rst diff --git a/docs/reference/layouts.rst b/docs/reference/reference/layouts.rst similarity index 100% rename from docs/reference/layouts.rst rename to docs/reference/reference/layouts.rst diff --git a/docs/reference/match.rst b/docs/reference/reference/match.rst similarity index 100% rename from docs/reference/match.rst rename to docs/reference/reference/match.rst diff --git a/docs/reference/models.rst b/docs/reference/reference/models.rst similarity index 100% rename from docs/reference/models.rst rename to docs/reference/reference/models.rst diff --git a/docs/reference/nominate.rst b/docs/reference/reference/nominate.rst similarity index 100% rename from docs/reference/nominate.rst rename to docs/reference/reference/nominate.rst diff --git a/docs/reference/partition.rst b/docs/reference/reference/partition.rst similarity index 100% rename from docs/reference/partition.rst rename to docs/reference/reference/partition.rst diff --git a/docs/reference/plotting.rst b/docs/reference/reference/plotting.rst similarity index 100% rename from docs/reference/plotting.rst rename to docs/reference/reference/plotting.rst diff --git a/docs/reference/preprocessing.rst b/docs/reference/reference/preprocessing.rst similarity index 100% rename from docs/reference/preprocessing.rst rename to docs/reference/reference/preprocessing.rst diff --git a/docs/reference/simulations.rst b/docs/reference/reference/simulations.rst similarity index 100% rename from docs/reference/simulations.rst rename to docs/reference/reference/simulations.rst diff --git a/docs/reference/subgraph.rst 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docs/requirements.txt rename to docs/reference/requirements.txt index cc99e0408..ce3c0df17 100644 --- a/docs/requirements.txt +++ b/docs/reference/requirements.txt @@ -4,4 +4,5 @@ sphinxcontrib-rawfiles nbsphinx>=0.4.2 ipython>=7.4.0 ipykernel>=5.1.0 -numpydoc==0.9.2 \ No newline at end of file +numpydoc==0.9.2 + diff --git a/docs/tutorials/Makefile b/docs/tutorials/Makefile new file mode 100644 index 000000000..6f3ad867d --- /dev/null +++ b/docs/tutorials/Makefile @@ -0,0 +1,25 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = . +BUILDDIR = _build/html + +.PHONY: help clean html + + +# Put it first so that "make" without argument is like "make help". +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + +clean: + -rm -rf _build/* + +html: + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) "$(SOURCEDIR)" "$(BUILDDIR)" + @echo + @echo "Build finished. The HTML pages are in build/html." + diff --git a/docs/tutorials/_templates/numpydoc_docstring.rst b/docs/tutorials/_templates/numpydoc_docstring.rst new file mode 100644 index 000000000..db4cdfabc --- /dev/null +++ b/docs/tutorials/_templates/numpydoc_docstring.rst @@ -0,0 +1,16 @@ +{{index}} +{{summary}} +{{extended_summary}} +{{parameters}} +{{returns}} +{{yields}} +{{other_parameters}} +{{attributes}} +{{raises}} +{{warns}} +{{warnings}} +{{see_also}} +{{notes}} +{{references}} +{{examples}} +{{methods}} \ No newline at end of file diff --git a/docs/tutorials/conf.py b/docs/tutorials/conf.py new file mode 100644 index 000000000..09fcc96d4 --- /dev/null +++ b/docs/tutorials/conf.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +sys.path.insert(0, os.path.abspath("..")) + +# -- Project information ----------------------------------------------------- + +project = "graspologic tutorials" +copyright = "2020" +authors = "Microsoft, NeuroData" + +realpath = os.path.realpath(__file__) +dir_realpath = os.path.dirname(os.path.dirname(realpath)) +sys.path.append(dir_realpath) + +import graspologic + +release = graspologic.__version__ +try: + split = release.split(".") + version = f"{split[0]}.{split[1]}" +except BaseException: + version = release + +# -- Extension configuration ------------------------------------------------- +extensions = [ + "sphinx.ext.mathjax", + "sphinx.ext.ifconfig", + "nbsphinx", + "sphinx.ext.intersphinx", +] + + +# -- numpydoc +# Below is needed to prevent errors +numpydoc_show_class_members = False +numpydoc_attributes_as_param_list = True +numpydoc_use_blockquotes = True + +# -- sphinx.ext.intersphinx +intersphinx_mapping = { + "anytree": ("https://anytree.readthedocs.io/en/latest/", None), + "hyppo": ("https://hyppo.neurodata.io", None), + "matplotlib": ("https://matplotlib.org", None), + "networkx": ("https://networkx.org/documentation/stable", None), + "numpy": ("https://numpy.org/doc/stable", None), + "python": ("https://docs.python.org/3", None), + "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), + "seaborn": ("https://seaborn.pydata.org", None), + "sklearn": ("https://scikit-learn.org/dev", None), +} + +# -- sphinx options ---------------------------------------------------------- +source_suffix = ".rst" +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] +master_doc = "index" +source_encoding = "utf-8" + +# -- Options for HTML output ------------------------------------------------- +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] +html_static_path = [] +modindex_common_prefix = ["graspologic."] + +pygments_style = "sphinx" +smartquotes = False + +# Use RTD Theme +import sphinx_rtd_theme + +html_theme = "sphinx_rtd_theme" +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] +html_theme_options = { + #'includehidden': False, + "navigation_depth": 2, + "collapse_navigation": False, + "navigation_depth": 3, +} + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = "graspologicdoc" + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, "graspologic.tex", "graspologic Documentation", authors, "manual") +] + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, "graspologic", "graspologic Documentation", [authors], 1)] + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "graspologic", + "graspologic Documentation", + authors, + "graspologic", + "One line description of project.", + "Miscellaneous", + ) +] + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ["search.html"] diff --git a/docs/tutorial.rst b/docs/tutorials/index.rst similarity index 74% rename from docs/tutorial.rst rename to docs/tutorials/index.rst index 09af92a46..eaf4c5fe9 100644 --- a/docs/tutorial.rst +++ b/docs/tutorials/index.rst @@ -1,6 +1,10 @@ -******** -Tutorial -******** +********* +Tutorials +********* +.. toctree:: + :hidden: + + Reference Documentation .. _models_tutorials: @@ -11,8 +15,9 @@ stochastic block model (SBM), degree-corrected SBM, and random dot product graph .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/models/models + models/models .. _simulations_tutorials: @@ -23,13 +28,14 @@ stochastic block model, and random dot product graph (RDPG). .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/simulations/erdos_renyi - tutorials/simulations/sbm - tutorials/simulations/mmsbm - tutorials/simulations/rdpg - tutorials/simulations/corr - tutorials/simulations/rdpg_corr + simulations/erdos_renyi + simulations/sbm + simulations/mmsbm + simulations/rdpg + simulations/corr + simulations/rdpg_corr .. _cluster_tutorials: @@ -40,9 +46,10 @@ clustering algorithms, as well as the advantages of these to comparable implemen .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/clustering/autogmm - tutorials/clustering/kclust + clustering/autogmm + clustering/kclust .. _embed_tutorials: @@ -52,11 +59,12 @@ Inference on random graphs depends on low-dimensional Euclidean representation o .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/embedding/AdjacencySpectralEmbed - tutorials/embedding/OutOfSampleEmbed - tutorials/embedding/Omnibus - tutorials/embedding/MASE + embedding/AdjacencySpectralEmbed + embedding/OutOfSampleEmbed + embedding/Omnibus + embedding/MASE .. _inference_tutorials: @@ -68,9 +76,10 @@ are tutorials for robust statistical hypothesis testing on multiple graphs. .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/inference/latent_position_test - tutorials/inference/latent_distribution_test + inference/latent_position_test + inference/latent_distribution_test .. _plot_tutorials: @@ -80,11 +89,12 @@ The following tutorials present ways to visualize the graphs, such as its adjace .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/plotting/heatmaps - tutorials/plotting/gridplot - tutorials/plotting/pairplot - tutorials/plotting/pairplot_with_gmm + plotting/heatmaps + plotting/gridplot + plotting/pairplot + plotting/pairplot_with_gmm .. _matching_tutorials: @@ -95,10 +105,11 @@ including an introduction to the module, and how to utilize the seeding feature. .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/matching/faq - tutorials/matching/sgm - tutorials/matching/padded_gm + matching/faq + matching/sgm + matching/padded_gm .. _subgraph_tutorials: @@ -108,8 +119,9 @@ The following tutorial demonstrates how to estimate the signal-subgraph of sampl .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/subgraph/subgraph + subgraph/subgraph .. _vertex_nomination_tutorials: @@ -119,9 +131,10 @@ The following tutorials demonstrate how to use unattributed single graph spectra .. toctree:: :maxdepth: 1 + :titlesonly: - tutorials/vertex_nomination/SpectralVertexNomination - tutorials/nominate/vertex_nomination_via_SGM + vertex_nomination/SpectralVertexNomination + nominate/vertex_nomination_via_SGM .. _aligning_tutorials: @@ -131,16 +144,18 @@ The following tutorials shows how to align two seperate datasets with each other .. toctree:: :maxdepth: 1 - - tutorials/aligning/aligning + :titlesonly: + + aligning/aligning .. _connectomics_tutorials: Connectomics ============ -The following tutorials demonstrate how to apply methods in this package to the analysis of connectomics datasets. +The following tutorials demonstrate how to apply methods in this package to the analysis of connectomics datasets. .. toctree:: :maxdepth: 1 - - tutorials/connectomics/mcc + :titlesonly: + + connectomics/mcc diff --git a/docs/tutorials/requirements.txt b/docs/tutorials/requirements.txt new file mode 100644 index 000000000..ce3c0df17 --- /dev/null +++ b/docs/tutorials/requirements.txt @@ -0,0 +1,8 @@ +sphinx==1.8.5 +sphinx_rtd_theme>=0.4.2 +sphinxcontrib-rawfiles +nbsphinx>=0.4.2 +ipython>=7.4.0 +ipykernel>=5.1.0 +numpydoc==0.9.2 + diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index 74a638ec2..dd5bc445f 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -25,7 +25,8 @@ class AdjacencySpectralEmbed(BaseSpectralEmbed): the dimensionality to the specified k, or if k is unspecified, can find a number of dimensions automatically (see :class:`~graspologic.embed.selectSVD`). - Read more in the :ref:`tutorials ` + Read more in the `Adjacency Spectral Embedding Tutorial + `_ Parameters ---------- diff --git a/graspologic/embed/lse.py b/graspologic/embed/lse.py index e26961cf4..62c8a3042 100644 --- a/graspologic/embed/lse.py +++ b/graspologic/embed/lse.py @@ -16,8 +16,6 @@ class LaplacianSpectralEmbed(BaseSpectralEmbed): the dimensionality to the specified k, or if k is unspecified, can find a number of dimensions automatically. - Read more in the :ref:`tutorials ` - Parameters ---------- form : {'DAD' (default), 'I-DAD', 'R-DAD'}, optional diff --git a/graspologic/embed/mase.py b/graspologic/embed/mase.py index df8ad3a85..af8ec9839 100644 --- a/graspologic/embed/mase.py +++ b/graspologic/embed/mase.py @@ -27,7 +27,8 @@ class MultipleASE(BaseEmbedMulti): :math:`R^{(i)}` can be assymetric and non-square, but all graphs still share a common latent position matrices :math:`U` and :math:`V`. - Read more in the :ref:`tutorials ` + Read more in the `Multiple Adjacency Spectral Embedding (MASE) Tutorial + `_ Parameters ---------- diff --git a/graspologic/embed/omni.py b/graspologic/embed/omni.py index ea7deca94..49fbd21aa 100644 --- a/graspologic/embed/omni.py +++ b/graspologic/embed/omni.py @@ -52,7 +52,8 @@ class OmnibusEmbed(BaseEmbedMulti): :math:`M_{ij} = \frac{1}{2}(A_i + A_j)`. The omnibus matrix is then embedded using adjacency spectral embedding. - Read more in the :ref:`tutorials ` + Read more in the `Omnibus Embedding for Multiple Graphs Tutorial + `_ Parameters ---------- diff --git a/graspologic/inference/latent_distribution_test.py b/graspologic/inference/latent_distribution_test.py index 422d9b794..0285afe65 100644 --- a/graspologic/inference/latent_distribution_test.py +++ b/graspologic/inference/latent_distribution_test.py @@ -50,7 +50,8 @@ def latent_distribution_test( between the vertices of the two graphs, or even when the number of vertices is different. Currently, testing is only supported for undirected graphs. - Read more in the :ref:`tutorials ` + Read more in the `Latent Distribution Two-Graph Testing Tutorial + `_ Parameters ---------- diff --git a/graspologic/inference/latent_position_test.py b/graspologic/inference/latent_position_test.py index ca41084a4..8e8026d4c 100644 --- a/graspologic/inference/latent_position_test.py +++ b/graspologic/inference/latent_position_test.py @@ -25,7 +25,8 @@ def latent_position_test( have their vertices sorted in the same order. Currently, the function only supports undirected graphs. - Read more in the :ref:`tutorials ` + Read more in the `Latent Position Two-Graph Testing Tutorial + `_ Parameters ---------- diff --git a/graspologic/models/er.py b/graspologic/models/er.py index b726fd00b..ad0ed7331 100644 --- a/graspologic/models/er.py +++ b/graspologic/models/er.py @@ -16,7 +16,8 @@ class EREstimator(SBMEstimator): :math:`P_{ij} = p` for all i, j - Read more in the :ref:`tutorials ` + Read more in the `Erdos-Renyi (ER) Model Tutorial + `_ Parameters ---------- @@ -76,7 +77,8 @@ class DCEREstimator(DCSBMEstimator): :math:`P_{ij} = \theta_i \theta_j p` - Read more in the :ref:`tutorials ` + Read more in the `Erdos-Renyi (ER) Model Tutorial + `_ Parameters ---------- diff --git a/graspologic/models/rdpg.py b/graspologic/models/rdpg.py index 90c8d29ec..d6c10679f 100644 --- a/graspologic/models/rdpg.py +++ b/graspologic/models/rdpg.py @@ -26,7 +26,8 @@ class RDPGEstimator(BaseGraphEstimator): is undirected, then :math:`x_i = y_i`. Latent positions can be estimated via :class:`~graspologic.embed.AdjacencySpectralEmbed`. - Read more in the :ref:`tutorials ` + Read more in the `Random Dot Product Graph (RDPG) Model Tutorial + `_ Parameters ---------- diff --git a/graspologic/models/sbm.py b/graspologic/models/sbm.py index 3579e987b..4141565be 100644 --- a/graspologic/models/sbm.py +++ b/graspologic/models/sbm.py @@ -56,7 +56,8 @@ class SBMEstimator(BaseGraphEstimator): where :math:`B \in \mathbb{[0, 1]}^{K x K}` and :math:`\tau` is an `n\_nodes` length vector specifying which block each node belongs to. - Read more in the :ref:`tutorials ` + Read more in the `Stochastic Block Model (SBM) Tutorial + `_ Parameters ---------- @@ -243,7 +244,8 @@ class DCSBMEstimator(BaseGraphEstimator): where :math:`\theta` and :math:`\eta` need not be the same. - Read more in the :ref:`tutorials ` + Read more in the `Stochastic Block Model (SBM) Tutorial + `_ Parameters ---------- diff --git a/graspologic/nominate/spectralVN.py b/graspologic/nominate/spectralVN.py index 8279974d2..3327aa68f 100644 --- a/graspologic/nominate/spectralVN.py +++ b/graspologic/nominate/spectralVN.py @@ -17,10 +17,11 @@ class SpectralVertexNomination(BaseEstimator): (the "seed"), Single Graph Vertex Nomination is the problem of ranking all :math:`V` in order of relation to members of :math:`S`. Spectral Vertex Nomination solves this problem by embedding :math:`G` into a low dimensional euclidean space - (:ref:`tutorials `), and then generating a nomination list by some - distance based algorithm. In the simple unattributed case, for each seed vertex - :math:`u`, the other vertices are ranked in order of euclidean distance from - :math:`u`. + (see: `Adjacency Spectral Embed Tutorial + `_ + ), and then generating a nomination list by some distance based algorithm. In the + simple unattributed case, for each seed vertex :math:`u`, the other vertices are + ranked in order of euclidean distance from :math:`u`. Parameters ---------- diff --git a/graspologic/plot/plot.py b/graspologic/plot/plot.py index d118f0e20..0051c3791 100644 --- a/graspologic/plot/plot.py +++ b/graspologic/plot/plot.py @@ -163,7 +163,8 @@ def heatmap( The nodes will be sorted within each group if labels are also provided. - Read more in the :ref:`tutorials ` + Read more in the `Heatmap: Visualizing a Graph Tutorial + `_ Parameters ---------- @@ -365,7 +366,8 @@ def gridplot( The size of the dots correspond to the edge weights of the graphs, and colors represent input graphs. - Read more in the :ref:`tutorials ` + Read more in the `Gridplot: Visualize Multiple Graphs Tutorial + `_ Parameters ---------- @@ -542,7 +544,8 @@ def pairplot( The diagonal axes show the univariate distribution of the data for that dimension displayed as either a histogram or kernel density estimates (KDEs). - Read more in the :ref:`tutorials ` + Read more in the `Pairplot: Visualizing High Dimensional Data Tutorial + `_ Parameters ---------- @@ -758,7 +761,9 @@ def pairplot_with_gmm( The diagonal axes show the univariate distribution of the data for that dimension displayed as either a histogram or kernel density estimates (KDEs). - Read more in the :ref:`tutorials ` + Read more in the `Pairplot with GMM: Visualizing High Dimensional Data and + Clustering Tutorial + `_ Parameters ---------- diff --git a/graspologic/simulations/rdpg_corr.py b/graspologic/simulations/rdpg_corr.py index a1a33efc7..225242317 100644 --- a/graspologic/simulations/rdpg_corr.py +++ b/graspologic/simulations/rdpg_corr.py @@ -18,7 +18,9 @@ def rdpg_corr(X, Y, r, rescale=False, directed=False, loops=False): matrix to get probabilities between 0 and 1, or remove loops. A binary random graph is then sampled from the P matrix described by X (and possibly Y). - Read more in the :ref:`tutorials ` + + Read more in the `Correlated Random Dot Product Graph (RDPG) Graph Pair Tutorial + `_ Parameters ---------- diff --git a/graspologic/simulations/simulations.py b/graspologic/simulations/simulations.py index 74b11abc8..3add22cd6 100644 --- a/graspologic/simulations/simulations.py +++ b/graspologic/simulations/simulations.py @@ -75,7 +75,8 @@ def er_np(n, p, directed=False, loops=False, wt=1, wtargs=None, dc=None, dc_kws= Erdos Renyi (n, p) graph is a simple graph with n vertices and a probability p of edges being connected. - Read more in the :ref:`tutorials ` + Read more in the `Erdos-Renyi (ER) Model Tutorial + `_ Parameters ---------- @@ -174,7 +175,8 @@ def er_nm(n, m, directed=False, loops=False, wt=1, wtargs=None): Erdos Renyi (n, m) graph is a simple graph with n vertices and exactly m number of total edges. - Read more in the :ref:`tutorials ` + Read more in the `Erdos-Renyi (ER) Model Tutorial + `_ Parameters ---------- @@ -318,7 +320,8 @@ def sbm( SBM produces a graph with specified communities, in which each community can have different sizes and edge probabilities. - Read more in the :ref:`tutorials ` + Read more in the `Stochastic Block Model (SBM) Tutorial + `_ Parameters ---------- @@ -633,7 +636,8 @@ def rdpg(X, Y=None, rescale=False, directed=False, loops=False, wt=1, wtargs=Non A binary random graph is then sampled from the P matrix described by X (and possibly Y). - Read more in the :ref:`tutorials ` + Read more in the `Random Dot Product Graph (RDPG) Model Tutorial + `_ Parameters ---------- @@ -819,7 +823,8 @@ def mmsbm( another node. Each node's membership is determined according to those probabilities. Finally, interactions are sampled according to the assigned memberships. - Read more in the :ref:`tutorials ` + Read more in the `Mixed Membership Stochastic Blockmodel (MMSBM) Tutorial + `_ Parameters ---------- From fa3031963e283995051197bed4ed0fba00261807 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 25 Jan 2021 15:29:15 -0800 Subject: [PATCH 090/140] Remove temporary publication from doc-split branch The doc-split branch was added as a valid github.ref in our workflow to allow publication to gh-pages. This was fine as we were testing things out but now that we have merged back to dev we do not want this branch to be capable of publishing documentation site from it. --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ca762c3e7..754ea6d63 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -101,7 +101,7 @@ jobs: publish: runs-on: ubuntu-latest needs: [build-reference-documentation, build-tutorial-documentation, code-format-check, unit-and-doc-test] - if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' || github.ref=='refs/heads/doc-split' + if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' steps: - uses: actions/checkout@v2 - name: Set up Python 3.8 @@ -133,7 +133,7 @@ jobs: name: tutorials path: docs/tutorials - name: Publish tutorials - if: github.ref=='refs/heads/dev' || github.ref=='refs/heads/doc-split' + if: github.ref=='refs/heads/dev' uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} From 99bc9e8ede0f593518e1a3e4509ce90bc9fb9d07 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 26 Jan 2021 12:43:40 -0800 Subject: [PATCH 091/140] Preparing a Makefile so that netlify can use it (#638) --- .gitignore | 1 + docs/Makefile | 11 +++++++++++ 2 files changed, 12 insertions(+) create mode 100644 docs/Makefile diff --git a/.gitignore b/.gitignore index a0be4a955..49e59e2fe 100644 --- a/.gitignore +++ b/.gitignore @@ -65,6 +65,7 @@ instance/ .scrapy # Sphinx documentation +docs/_build docs/reference/_build/ docs/tutorials/_build/ diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 000000000..3b3b0dca3 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,11 @@ +.PHONY: docs + + +# Put it first so that "make" without argument is like "make help". +docs: + cd reference && pip install -r requirements.txt && make html + cd tutorials && pip install -r requirements.txt && make html + mkdir -p _build/html + cp -r reference/_build/html _build/html/reference + cp -r tutorials/_build/html _build/html/tutorials + From d9bbbfd167fb8111a0b79c02c8a7c45f39e00c23 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 26 Jan 2021 13:14:41 -0800 Subject: [PATCH 092/140] prior to 0.22.0, scikit-learn did not expose gaussian mixture model the same way (#639) --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 5b26fd0e9..6b34816aa 100644 --- a/setup.cfg +++ b/setup.cfg @@ -35,7 +35,7 @@ install_requires = numpy>=1.8.1 POT>=0.7.0 seaborn>= 0.11.0 - scikit-learn>=0.19.1 + scikit-learn>=0.22.0 scipy>=1.4.0 umap-learn>=0.4.6 From 6bc9bccc3cb2a9bf9876595a22edf5119c638efb Mon Sep 17 00:00:00 2001 From: Pratyush Raj Date: Wed, 27 Jan 2021 23:24:39 +0530 Subject: [PATCH 093/140] Updated the README.md by removing the Dependencies (#643) * Update README.md * Update README.md --- README.md | 32 -------------------------------- 1 file changed, 32 deletions(-) diff --git a/README.md b/README.md index 433a2f3dd..811ac84b1 100644 --- a/README.md +++ b/README.md @@ -52,38 +52,6 @@ If you try to use `graspologic` for a different platform than the ones listed an please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users if we have concrete examples of things not working! -### Python Dependencies -`graspologic` has the following direct dependencies: -``` -hyppo -matplotlib -networkx -numpy -POT -seaborn -scikit-learn -scipy -umap-learn -``` - -Developers of `graspologic` will also have the following dependencies: -``` -black -ipykernel -ipython -myp -nbsphinx -numpydoc -pandoc -pytest -pytest-cov -sphinx -sphinxcontrib-rawfiles -sphinx-rtd-theme -``` - -Please note that `pandoc` will also [need to be installed for your system](https://pandoc.org/installing.html). - # Installation Guide ## Install from pip ``` From c97d7561c45de66cbc09ec057ca109b77e489135 Mon Sep 17 00:00:00 2001 From: Bryan Tower Date: Wed, 27 Jan 2021 15:19:25 -0800 Subject: [PATCH 094/140] fixxing incorrect document string --- graspologic/partition/modularity.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/graspologic/partition/modularity.py b/graspologic/partition/modularity.py index 1a36f16ae..074f43d3f 100644 --- a/graspologic/partition/modularity.py +++ b/graspologic/partition/modularity.py @@ -69,8 +69,8 @@ def modularity( Returns ------- - Dict[int, float] - A dictionary of the community id to the modularity component of that community + float + The sum of the modularity of each of the communities. Raises ------ From 0829a91a301b65114d8331082520d780210b65e1 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Mon, 1 Feb 2021 10:32:55 -0800 Subject: [PATCH 095/140] Fix for `Fix modularity docstring` (#649) * fixxing incorrect document string * Running black Co-authored-by: Bryan Tower --- graspologic/partition/modularity.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graspologic/partition/modularity.py b/graspologic/partition/modularity.py index 074f43d3f..5640b0278 100644 --- a/graspologic/partition/modularity.py +++ b/graspologic/partition/modularity.py @@ -70,7 +70,7 @@ def modularity( Returns ------- float - The sum of the modularity of each of the communities. + The sum of the modularity of each of the communities. Raises ------ From f670281caa1476760c383597e169499d475fd92c Mon Sep 17 00:00:00 2001 From: tliu68 <54865879+tliu68@users.noreply.github.com> Date: Tue, 2 Feb 2021 05:38:23 +0800 Subject: [PATCH 096/140] reformat gmm.covariances_ in pairplot_with_gmm (#642) * reformat gmm.covariances_ for diff cov_types * code formatting * fix code format * Update test_plot.py * fix pairplot_with_gmm title Co-authored-by: Benjamin Pedigo --- graspologic/plot/plot.py | 20 +++++++++++++++++++- tests/test_plot.py | 40 ++++++++++++++++++++++++++++++++++++---- 2 files changed, 55 insertions(+), 5 deletions(-) diff --git a/graspologic/plot/plot.py b/graspologic/plot/plot.py index 0051c3791..b8a0bb576 100644 --- a/graspologic/plot/plot.py +++ b/graspologic/plot/plot.py @@ -827,6 +827,24 @@ def pairplot_with_gmm( Y_, means, covariances = gmm.predict(X), gmm.means_, gmm.covariances_ data = pd.DataFrame(data=X) n_components = gmm.n_components + + # reformat covariances in preparation for ellipse plotting + if gmm.covariance_type == "tied": + covariances = np.repeat( + gmm.covariances_[np.newaxis, :, :], n_components, axis=0 + ) + elif gmm.covariance_type == "diag": + covariances = np.array( + [np.diag(gmm.covariances_[i]) for i in range(n_components)] + ) + elif gmm.covariance_type == "spherical": + covariances = np.array( + [ + np.diag(np.repeat(gmm.covariances_[i], X.shape[1])) + for i in range(n_components) + ] + ) + # setting up the data DataFrame if labels is None: lab_names = [i for i in range(n_components)] @@ -898,7 +916,7 @@ def pairplot_with_gmm( ) # formatting if title: - pairplot.suptitle(title) + plt.suptitle(title) for i in range(dimensions): for j in range(dimensions): if X.shape[1] == 2: diff --git a/tests/test_plot.py b/tests/test_plot.py index eca0013c4..389db3b22 100644 --- a/tests/test_plot.py +++ b/tests/test_plot.py @@ -200,9 +200,9 @@ def test_pairplot_outputs(): ) -def test_pairplot_with_gmm_inputs(): +def _test_pairplot_with_gmm_inputs(**kws): X = np.random.rand(15, 3) - gmm = GaussianMixture(n_components=3, covariance_type="full").fit(X) + gmm = GaussianMixture(n_components=3, **kws).fit(X) labels = ["A"] * 5 + ["B"] * 5 + ["C"] * 5 # test data with pytest.raises(ValueError): @@ -215,9 +215,25 @@ def test_pairplot_with_gmm_inputs(): pairplot_with_gmm(X, gmm=None) -def test_pairplot_with_gmm_outputs(): +def test_pairplot_with_gmm_inputs_type_full(): + _test_pairplot_with_gmm_inputs(covariance_type="full") + + +def test_pairplot_with_gmm_inputs_type_diag(): + _test_pairplot_with_gmm_inputs(covariance_type="diag") + + +def test_pairplot_with_gmm_inputs_type_tied(): + _test_pairplot_with_gmm_inputs(covariance_type="tied") + + +def test_pairplot_with_gmm_inputs_type_spherical(): + _test_pairplot_with_gmm_inputs(covariance_type="spherical") + + +def _test_pairplot_with_gmm_outputs(**kws): X = np.random.rand(15, 3) - gmm = GaussianMixture(n_components=3, covariance_type="full").fit(X) + gmm = GaussianMixture(n_components=3, **kws).fit(X) labels = ["A"] * 5 + ["B"] * 5 + ["C"] * 5 cluster_palette = {0: "red", 1: "blue", 2: "green"} label_palette = {"A": "red", "B": "blue", "C": "green"} @@ -231,6 +247,22 @@ def test_pairplot_with_gmm_outputs(): ) +def test_pairplot_with_gmm_outputs_type_full(): + _test_pairplot_with_gmm_outputs(covariance_type="full") + + +def test_pairplot_with_gmm_outputs_type_diag(): + _test_pairplot_with_gmm_outputs(covariance_type="diag") + + +def test_pairplot_with_gmm_outputs_type_tied(): + _test_pairplot_with_gmm_outputs(covariance_type="tied") + + +def test_pairplot_with_gmm_outputs_type_spherical(): + _test_pairplot_with_gmm_outputs(covariance_type="spherical") + + def test_sort_inds(): B = np.array( [ From 9d57318e4e0a3f9ee76bf4385088e9d3deaaae53 Mon Sep 17 00:00:00 2001 From: Pratyush Raj Date: Tue, 2 Feb 2021 21:14:06 +0530 Subject: [PATCH 097/140] Cleaner function names (#647) * Update README.md * Update README.md * cleaner function names * Cleaned Function names Co-authored-by: Benjamin Pedigo --- docs/reference/reference/utils.rst | 8 +-- docs/reference/release/release_0.0.2.rst | 2 +- docs/reference/release/release_0.2.rst | 2 +- graspologic/embed/ase.py | 2 +- graspologic/embed/lse.py | 8 +-- graspologic/embed/omni.py | 2 +- graspologic/models/sbm.py | 6 +-- graspologic/simulations/simulations.py | 4 +- graspologic/utils/__init__.py | 20 +++---- graspologic/utils/utils.py | 18 +++---- tests/test_models.py | 4 +- tests/test_sims.py | 7 ++- tests/test_utils.py | 66 ++++++++++++------------ 13 files changed, 77 insertions(+), 72 deletions(-) diff --git a/docs/reference/reference/utils.rst b/docs/reference/reference/utils.rst index 2fcde74d7..f25ca2877 100644 --- a/docs/reference/reference/utils.rst +++ b/docs/reference/reference/utils.rst @@ -8,7 +8,7 @@ Transformations .. autofunction:: pass_to_ranks -.. autofunction:: to_laplace +.. autofunction:: to_laplacian .. autofunction:: augment_diagonal @@ -21,11 +21,11 @@ Connected Components .. autofunction:: is_fully_connected -.. autofunction:: get_lcc +.. autofunction:: largest_connected_component -.. autofunction:: get_multigraph_union_lcc +.. autofunction:: multigraph_lcc_union -.. autofunction:: get_multigraph_intersect_lcc +.. autofunction:: multigraph_lcc_intersection IO -- diff --git a/docs/reference/release/release_0.0.2.rst b/docs/reference/release/release_0.0.2.rst index a0e04f38e..cb7cdc1ff 100644 --- a/docs/reference/release/release_0.0.2.rst +++ b/docs/reference/release/release_0.0.2.rst @@ -34,7 +34,7 @@ API Changes ----------- - ``check_lcc`` argument in ``AdjacencySpectralEmbed``, ``LaplacianSpectralEmbed``, and ``OmnibusEmbed`` classes, which checks if input graph(s) are fully connected when ``check_lcc`` is True. - ``gridplot`` and ``heatmap`` now have a ``inner_hier_labels`` and ``outer_hier_labels``, which are used for hierarchical labeling of nodes. -- ``to_laplace`` function now has ``regularizer`` arg for when ``form`` is 'R-DAD'. +- ``to_laplacian`` function now has ``regularizer`` arg for when ``form`` is 'R-DAD'. - ``sbm`` function now has ``dc`` and ``dc_kws`` arguments for sampling SBM with degree-correction. Deprecations diff --git a/docs/reference/release/release_0.2.rst b/docs/reference/release/release_0.2.rst index 82d9d8343..063f01050 100644 --- a/docs/reference/release/release_0.2.rst +++ b/docs/reference/release/release_0.2.rst @@ -22,7 +22,7 @@ This release is the result of over 8 months of work with over 25 pull requests b Improvements ------------ - Diagonal augmentation is default behavior in AdjacencySpectralEmbed. -- Added functionality in ``to_laplace`` to allow for directed graphs. +- Added functionality in ``to_laplacian`` to allow for directed graphs. - Updated docstrings. - Updated documentation website. - Various bug fixes. diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index dd5bc445f..0e9162290 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -161,7 +161,7 @@ def fit(self, graph, y=None): msg = ( "Input graph is not fully connected. Results may not" + "be optimal. You can compute the largest connected component by" - + "using ``graspologic.utils.get_lcc``." + + "using ``graspologic.utils.largest_connected_component``." ) warnings.warn(msg, UserWarning) diff --git a/graspologic/embed/lse.py b/graspologic/embed/lse.py index 62c8a3042..2a74c4181 100644 --- a/graspologic/embed/lse.py +++ b/graspologic/embed/lse.py @@ -4,7 +4,7 @@ import warnings from .base import BaseSpectralEmbed -from ..utils import import_graph, to_laplace, is_fully_connected +from ..utils import import_graph, to_laplacian, is_fully_connected class LaplacianSpectralEmbed(BaseSpectralEmbed): @@ -81,7 +81,7 @@ class LaplacianSpectralEmbed(BaseSpectralEmbed): -------- graspologic.embed.selectSVD graspologic.embed.select_dimension - graspologic.utils.to_laplace + graspologic.utils.to_laplacian Notes ----- @@ -154,11 +154,11 @@ def fit(self, graph, y=None): msg = ( "Input graph is not fully connected. Results may not" + "be optimal. You can compute the largest connected component by" - + "using ``graspologic.utils.get_lcc``." + + "using ``graspologic.utils.largest_connected_component``." ) warnings.warn(msg, UserWarning) self.n_features_in_ = A.shape[0] - L_norm = to_laplace(A, form=self.form, regularizer=self.regularizer) + L_norm = to_laplacian(A, form=self.form, regularizer=self.regularizer) self._reduce_dim(L_norm) return self diff --git a/graspologic/embed/omni.py b/graspologic/embed/omni.py index 49fbd21aa..6b67217d8 100644 --- a/graspologic/embed/omni.py +++ b/graspologic/embed/omni.py @@ -178,7 +178,7 @@ def fit(self, graphs, y=None): msg = ( "Input graphs are not fully connected. Results may not" + "be optimal. You can compute the largest connected component by" - + "using ``graspologic.utils.get_multigraph_union_lcc``." + + "using ``graspologic.utils.multigraph_lcc_union``." ) warnings.warn(msg, UserWarning) diff --git a/graspologic/models/sbm.py b/graspologic/models/sbm.py index 4141565be..4ebc74418 100644 --- a/graspologic/models/sbm.py +++ b/graspologic/models/sbm.py @@ -8,7 +8,7 @@ from ..embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed from ..utils import ( augment_diagonal, - cartprod, + cartesian_product, import_graph, is_unweighted, remove_loops, @@ -467,7 +467,7 @@ def _calculate_block_p(graph, block_inds, block_vert_inds, return_counts=False): """ n_blocks = len(block_inds) - block_pairs = cartprod(block_inds, block_inds) + block_pairs = cartesian_product(block_inds, block_inds) block_p = np.zeros((n_blocks, n_blocks)) for p in block_pairs: @@ -492,7 +492,7 @@ def _block_to_full(block_mat, inverse, shape): block mat : k x k inverse : array like length n, """ - block_map = cartprod(inverse, inverse).T + block_map = cartesian_product(inverse, inverse).T mat_by_edge = block_mat[block_map[0], block_map[1]] full_mat = mat_by_edge.reshape(shape) return full_mat diff --git a/graspologic/simulations/simulations.py b/graspologic/simulations/simulations.py index 3add22cd6..529aa15b8 100644 --- a/graspologic/simulations/simulations.py +++ b/graspologic/simulations/simulations.py @@ -3,7 +3,7 @@ import numpy as np -from ..utils import symmetrize, cartprod +from ..utils import symmetrize, cartesian_product from sklearn.utils import check_array, check_scalar import warnings @@ -586,7 +586,7 @@ def sbm( block_p = p[i, j] # identify submatrix for community i, j # cartesian product to identify edges for community i,j pair - cprod = cartprod(cmties[i], cmties[j]) + cprod = cartesian_product(cmties[i], cmties[j]) # get idx in 1d coordinates by ravelling triu = np.ravel_multi_index((cprod[:, 0], cprod[:, 1]), A.shape) pchoice = np.random.uniform(size=len(triu)) diff --git a/graspologic/utils/__init__.py b/graspologic/utils/__init__.py index 9ddeaf610..5b0efdc78 100644 --- a/graspologic/utils/__init__.py +++ b/graspologic/utils/__init__.py @@ -10,14 +10,14 @@ is_almost_symmetric, symmetrize, remove_loops, - to_laplace, + to_laplacian, is_fully_connected, - get_lcc, - get_multigraph_union_lcc, - get_multigraph_intersect_lcc, + largest_connected_component, + multigraph_lcc_union, + multigraph_lcc_intersection, augment_diagonal, binarize, - cartprod, + cartesian_product, fit_plug_in_variance_estimator, remove_vertices, remap_labels, @@ -34,14 +34,14 @@ "is_almost_symmetric", "symmetrize", "remove_loops", - "to_laplace", + "to_laplacian", "is_fully_connected", - "get_lcc", - "get_multigraph_union_lcc", - "get_multigraph_intersect_lcc", + "largest_connected_component", + "multigraph_lcc_union", + "multigraph_lcc_intersection", "augment_diagonal", "binarize", - "cartprod", + "cartesian_product", "pass_to_ranks", "fit_plug_in_variance_estimator", "remove_vertices", diff --git a/graspologic/utils/utils.py b/graspologic/utils/utils.py index d1f292b50..fa44c4bcf 100644 --- a/graspologic/utils/utils.py +++ b/graspologic/utils/utils.py @@ -307,7 +307,7 @@ def remove_loops(graph): return graph -def to_laplace(graph, form="DAD", regularizer=None): +def to_laplacian(graph, form="DAD", regularizer=None): r""" A function to convert graph adjacency matrix to graph Laplacian. @@ -359,7 +359,7 @@ def to_laplace(graph, form="DAD", regularizer=None): ... [0, 1, 1], ... [1, 0, 0], ... [1, 0, 0]]) - >>> to_laplace(a, "DAD") + >>> to_laplacian(a, "DAD") array([[0. , 0.70710678, 0.70710678], [0.70710678, 0. , 0. ], [0.70710678, 0. , 0. ]]) @@ -460,7 +460,7 @@ def is_fully_connected(graph): return nx.is_weakly_connected(graph) -def get_lcc(graph, return_inds=False): +def largest_connected_component(graph, return_inds=False): r""" Finds the largest connected component for the input graph. @@ -510,7 +510,7 @@ def get_lcc(graph, return_inds=False): return lcc -def get_multigraph_union_lcc(graphs, return_inds=False): +def multigraph_lcc_union(graphs, return_inds=False): r""" Finds the union of all multiple graphs, then compute the largest connected component. @@ -550,7 +550,7 @@ def get_multigraph_union_lcc(graphs, return_inds=False): msg = "Expected list or np.ndarray, but got {} instead.".format(type(graphs)) raise ValueError(msg) - _, idx = get_lcc(bar, return_inds=True) + _, idx = largest_connected_component(bar, return_inds=True) idx = np.array(idx) if isinstance(graphs, np.ndarray): @@ -563,7 +563,7 @@ def get_multigraph_union_lcc(graphs, return_inds=False): return graphs -def get_multigraph_intersect_lcc(graphs, return_inds=False): +def multigraph_lcc_intersection(graphs, return_inds=False): r""" Finds the intersection of multiple graphs's largest connected components. @@ -594,7 +594,7 @@ def get_multigraph_intersect_lcc(graphs, return_inds=False): lcc_by_graph = [] inds_by_graph = [] for graph in graphs: - lcc, inds = get_lcc(graph, return_inds=True) + lcc, inds = largest_connected_component(graph, return_inds=True) lcc_by_graph.append(lcc) inds_by_graph.append(inds) inds_intersection = reduce(np.intersect1d, inds_by_graph) @@ -615,7 +615,7 @@ def get_multigraph_intersect_lcc(graphs, return_inds=False): recurse = True break if recurse: - new_graphs, new_inds_intersection = get_multigraph_intersect_lcc( + new_graphs, new_inds_intersection = multigraph_lcc_intersection( new_graphs, return_inds=True ) # new inds intersection are the indices of new_graph that were kept on recurse @@ -709,7 +709,7 @@ def binarize(graph): return graph -def cartprod(*arrays): +def cartesian_product(*arrays): """ Compute the cartesian product of multiple arrays """ diff --git a/tests/test_models.py b/tests/test_models.py index 3a2383310..6a857bb9e 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -12,7 +12,7 @@ DCEREstimator, ) from graspologic.simulations import er_np, sbm, sample_edges -from graspologic.utils import cartprod, is_symmetric +from graspologic.utils import cartesian_product, is_symmetric from sklearn.metrics import adjusted_rand_score from sklearn.exceptions import NotFittedError @@ -538,7 +538,7 @@ def _block_to_full(block_mat, inverse, shape): block mat : k x k inverse : array like length n, """ - block_map = cartprod(inverse, inverse).T + block_map = cartesian_product(inverse, inverse).T mat_by_edge = block_mat[block_map[0], block_map[1]] full_mat = mat_by_edge.reshape(shape) return full_mat diff --git a/tests/test_sims.py b/tests/test_sims.py index 54ce4e105..9debe75cd 100644 --- a/tests/test_sims.py +++ b/tests/test_sims.py @@ -6,7 +6,12 @@ import numpy as np import networkx as nx from graspologic.simulations import * -from graspologic.utils.utils import is_symmetric, is_loopless, symmetrize, cartprod +from graspologic.utils.utils import ( + is_symmetric, + is_loopless, + symmetrize, + cartesian_product, +) def remove_diagonal(A): diff --git a/tests/test_utils.py b/tests/test_utils.py index fe1dc6ccd..6aac713c7 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -49,73 +49,73 @@ def setUpClass(cls): cls.A = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]]) cls.B = np.array([[0, 1, 1, 1], [0, 0, 0, 1], [0, 1, 0, 1], [0, 1, 1, 0]]) - def test_to_laplace_IDAD(self): + def test_to_laplacian_IDAD(self): expected_L_normed = [ [1, -1 / (sqrt(2)), 0], [-1 / (sqrt(2)), 1, -1 / (sqrt(2))], [0, -1 / (sqrt(2)), 1], ] - L_normed = gus.to_laplace(self.A, form="I-DAD") + L_normed = gus.to_laplacian(self.A, form="I-DAD") self.assertTrue(np.allclose(L_normed, expected_L_normed, rtol=1e-04)) - def test_to_laplace_DAD(self): + def test_to_laplacian_DAD(self): expected_L_normed = [ [0, 1 / sqrt(2), 0], [1 / sqrt(2), 0, 1 / sqrt(2)], [0, 1 / sqrt(2), 0], ] - L_normed = gus.to_laplace(self.A, form="DAD") + L_normed = gus.to_laplacian(self.A, form="DAD") self.assertTrue(np.allclose(L_normed, expected_L_normed, rtol=1e-04)) - def test_to_laplace_RDAD(self): + def test_to_laplacian_RDAD(self): expected_L_normed = [ [0, 3 / sqrt(70), 0], [3 / sqrt(70), 0, 3 / sqrt(70)], [0, 3 / sqrt(70), 0], ] - L_normed = gus.to_laplace(self.A, form="R-DAD") + L_normed = gus.to_laplacian(self.A, form="R-DAD") self.assertTrue(np.allclose(L_normed, expected_L_normed, rtol=1e-04)) - def test_to_laplace_regularizer_kwarg(self): + def test_to_laplacian_regularizer_kwarg(self): expected_L_normed = [ [0, 1 / sqrt(6), 0], [1 / sqrt(6), 0, 1 / sqrt(6)], [0, 1 / sqrt(6), 0], ] - L_normed = gus.to_laplace(self.A, form="R-DAD", regularizer=1.0) + L_normed = gus.to_laplacian(self.A, form="R-DAD", regularizer=1.0) self.assertTrue(np.allclose(L_normed, expected_L_normed, rtol=1e-04)) - def test_to_laplace_symmetric(self): - L_normed = gus.to_laplace(self.A, form="DAD") + def test_to_laplacian_symmetric(self): + L_normed = gus.to_laplacian(self.A, form="DAD") self.assertTrue(gus.is_symmetric(L_normed)) - def test_to_laplace_unsuported(self): + def test_to_laplacian_unsuported(self): with self.assertRaises(TypeError): - gus.to_laplace(self.A, form="MOM") + gus.to_laplacian(self.A, form="MOM") - def test_to_laplace_unsuported_regularizer(self): + def test_to_laplacian_unsuported_regularizer(self): with self.assertRaises(TypeError): - gus.to_laplace(self.A, form="R-DAD", regularizer="2") + gus.to_laplacian(self.A, form="R-DAD", regularizer="2") with self.assertRaises(TypeError): - gus.to_laplace(self.A, form="R-DAD", regularizer=[1, 2, 3]) + gus.to_laplacian(self.A, form="R-DAD", regularizer=[1, 2, 3]) with self.assertRaises(ValueError): - gus.to_laplace(self.A, form="R-DAD", regularizer=-1.0) + gus.to_laplacian(self.A, form="R-DAD", regularizer=-1.0) - def test_to_laplace_directed(self): + def test_to_laplacian_directed(self): expected_L_normed = [ [0, 1 / 5, sqrt(5) / 10, 0.2], [0, 0, 0, sqrt(15) / 15], [0, sqrt(5) / 10, 0, sqrt(5) / 10], [0, sqrt(5) / 10, 0.25, 0], ] - L_normed = gus.to_laplace(self.B, form="R-DAD") + L_normed = gus.to_laplacian(self.B, form="R-DAD") self.assertTrue(np.allclose(L_normed, expected_L_normed, rtol=1e-04)) @@ -176,11 +176,11 @@ def test_lcc_networkx(self): g.add_edge(3, 6) g.add_edge(6, 3) g.add_edge(4, 2) - lcc, nodelist = gus.get_lcc(g, return_inds=True) + lcc, nodelist = gus.largest_connected_component(g, return_inds=True) lcc_matrix = nx.to_numpy_array(lcc) np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix) np.testing.assert_array_equal(nodelist, expected_nodelist) - lcc = gus.get_lcc(g) + lcc = gus.largest_connected_component(g) lcc_matrix = nx.to_numpy_array(lcc) np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix) @@ -203,11 +203,11 @@ def test_lcc_networkx_undirected(self): g.add_edge(3, 6) g.add_edge(6, 3) g.add_edge(4, 2) - lcc, nodelist = gus.get_lcc(g, return_inds=True) + lcc, nodelist = gus.largest_connected_component(g, return_inds=True) lcc_matrix = nx.to_numpy_array(lcc) np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix) np.testing.assert_array_equal(nodelist, expected_nodelist) - lcc = gus.get_lcc(g) + lcc = gus.largest_connected_component(g) lcc_matrix = nx.to_numpy_array(lcc) np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix) @@ -232,10 +232,10 @@ def test_lcc_numpy(self): g.add_edge(6, 3) g.add_edge(4, 2) g = nx.to_numpy_array(g) - lcc_matrix, nodelist = gus.get_lcc(g, return_inds=True) + lcc_matrix, nodelist = gus.largest_connected_component(g, return_inds=True) np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix) np.testing.assert_array_equal(nodelist, expected_nodelist) - lcc_matrix = gus.get_lcc(g) + lcc_matrix = gus.largest_connected_component(g) np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix) def test_multigraph_lcc_numpystack(self): @@ -261,7 +261,7 @@ def test_multigraph_lcc_numpystack(self): f.add_edge(3, 1) f = nx.to_numpy_array(f) g = nx.to_numpy_array(g) - lccs, nodelist = gus.get_multigraph_intersect_lcc( + lccs, nodelist = gus.multigraph_lcc_intersection( np.stack([f, g]), return_inds=True ) for i, graph in enumerate(lccs): @@ -291,7 +291,7 @@ def test_multigraph_lcc_recurse_numpylist(self): expected_mats = [expected_g_lcc, expected_f_lcc] expected_nodelist = np.array([1, 3, 4]) - lccs, nodelist = gus.get_multigraph_intersect_lcc([f, g], return_inds=True) + lccs, nodelist = gus.multigraph_lcc_intersection([f, g], return_inds=True) for i, graph in enumerate(lccs): np.testing.assert_array_equal(graph, expected_mats[i]) np.testing.assert_array_equal(nodelist, expected_nodelist) @@ -319,11 +319,11 @@ def test_multigraph_lcc_numpylist(self): f.add_edge(3, 1) f = nx.to_numpy_array(f) g = nx.to_numpy_array(g) - lccs, nodelist = gus.get_multigraph_intersect_lcc([f, g], return_inds=True) + lccs, nodelist = gus.multigraph_lcc_intersection([f, g], return_inds=True) for i, graph in enumerate(lccs): np.testing.assert_array_equal(graph, expected_mats[i]) np.testing.assert_array_equal(nodelist, expected_nodelist) - lccs = gus.get_multigraph_intersect_lcc([f, g], return_inds=False) + lccs = gus.multigraph_lcc_intersection([f, g], return_inds=False) for i, graph in enumerate(lccs): np.testing.assert_array_equal(graph, expected_mats[i]) @@ -348,11 +348,11 @@ def test_multigraph_lcc_networkx(self): f.add_edge(5, 4) f.remove_edge(4, 2) f.add_edge(3, 1) - lccs, nodelist = gus.get_multigraph_intersect_lcc([f, g], return_inds=True) + lccs, nodelist = gus.multigraph_lcc_intersection([f, g], return_inds=True) for i, graph in enumerate(lccs): np.testing.assert_array_equal(nx.to_numpy_array(graph), expected_mats[i]) np.testing.assert_array_equal(nodelist, expected_nodelist) - lccs = gus.get_multigraph_intersect_lcc([f, g], return_inds=False) + lccs = gus.multigraph_lcc_intersection([f, g], return_inds=False) for i, graph in enumerate(lccs): np.testing.assert_array_equal(nx.to_numpy_array(graph), expected_mats[i]) @@ -360,8 +360,8 @@ def test_multigraph_union(self): A = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 0]]) B = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0]]) - out_list = gus.get_multigraph_union_lcc([A, B]) - out_tensor = gus.get_multigraph_union_lcc(np.stack([A, B])) + out_list = gus.multigraph_lcc_union([A, B]) + out_tensor = gus.multigraph_lcc_union(np.stack([A, B])) np.testing.assert_equal(out_list, [A, B]) np.testing.assert_array_equal(out_tensor, np.stack([A, B])) @@ -409,7 +409,7 @@ def test_augment_diagonal_directed(self): def test_lcc_bad_matrix(self): A = np.array([0, 1]) with self.assertRaises(ValueError): - gus.get_lcc(A) + gus.largest_connected_component(A) def test_binarize(): From 53215bcc869cd087751d06a673354ebb2f17386d Mon Sep 17 00:00:00 2001 From: Alex Loftus Date: Sun, 7 Feb 2021 11:38:01 -0500 Subject: [PATCH 098/140] include nominate (#654) fix #653 --- graspologic/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/graspologic/__init__.py b/graspologic/__init__.py index ad156e719..9d6596604 100644 --- a/graspologic/__init__.py +++ b/graspologic/__init__.py @@ -8,6 +8,7 @@ import graspologic.inference import graspologic.layouts import graspologic.models +import graspologic.nominate import graspologic.partition import graspologic.preprocessing import graspologic.plot From bd1b84662008667c199db91a779b3c387a89e64a Mon Sep 17 00:00:00 2001 From: Pratyush Raj Date: Mon, 8 Feb 2021 22:58:59 +0530 Subject: [PATCH 099/140] Added Reference for Seedless Procrustes (#658) * Added Reference for Seedless Procrustes * Reformatted --- graspologic/align/seedless_procrustes.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/graspologic/align/seedless_procrustes.py b/graspologic/align/seedless_procrustes.py index f7a8d6651..7cff0b9cc 100644 --- a/graspologic/align/seedless_procrustes.py +++ b/graspologic/align/seedless_procrustes.py @@ -104,8 +104,10 @@ class SeedlessProcrustes(BaseAlign): References ---------- - .. [1] Agterberg, J. - # TODO Cite the Seedless Procrustes preprint whenever available. + + .. [1] Agterberg, J., Tang, M., Priebe., C. E. (2020). + "Nonparametric Two-Sample Hypothesis Testing for Random Graphs with Negative and Repeated Eigenvalues" + arXiv:2012.09828 .. [2] Agterberg, J., Tang, M., Priebe., C. E. (2020). "On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models" From a3a4b660f815950a4c5efe4544cd1a7fbfb97c84 Mon Sep 17 00:00:00 2001 From: elias Date: Mon, 8 Feb 2021 19:48:43 -0300 Subject: [PATCH 100/140] Fix hyppo version to avoid import error --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 6b34816aa..25a3c8dd6 100644 --- a/setup.cfg +++ b/setup.cfg @@ -28,7 +28,7 @@ install_requires = anytree>=2.8.0 gensim>=3.8.0,<=3.9.0 # methods signatures changed in the 4.0.0beta release graspologic-native - hyppo>=0.1.3 + hyppo==0.1.3 # Newer versions of hyppo cause issue #659. joblib>=0.17.0 # Older versions of joblib cause issue #806. Transitive dependency of hyppo. matplotlib>=3.0.0,<=3.3.0 networkx>=2.1 From 1eb6c74418408ba015adc7cf78b432bcd33b7323 Mon Sep 17 00:00:00 2001 From: Pratyush Raj Date: Wed, 10 Feb 2021 20:35:03 +0530 Subject: [PATCH 101/140] supressed the warnings by hyppo library and raised during runtime (#663) * supressed warnings in mcc tutorial * Supressed runtime and hyppo library warnings Co-authored-by: Benjamin Pedigo --- docs/tutorials/connectomics/mcc.ipynb | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/docs/tutorials/connectomics/mcc.ipynb b/docs/tutorials/connectomics/mcc.ipynb index 3db28cb14..41caa4fc3 100644 --- a/docs/tutorials/connectomics/mcc.ipynb +++ b/docs/tutorials/connectomics/mcc.ipynb @@ -21,7 +21,7 @@ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", - "\n", + "import warnings\n", "pd.options.mode.chained_assignment = None\n", "%matplotlib inline" ] @@ -273,21 +273,24 @@ "metadata": {}, "outputs": [], "source": [ - "vertex_pvals = []\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(\"ignore\", module=\"hyppo\")\n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n", + " vertex_pvals = []\n", "\n", - "for vertex_i in range(n_vertices):\n", + " for vertex_i in range(n_vertices):\n", " \n", " # Get the embedding of the i-th vertex for \n", - " samples = [\n", - " omni_embedding[mice.labels==genotype, vertex_i, :] \n", - " for genotype in np.unique(mice.labels)\n", - " ]\n", + " samples = [\n", + " omni_embedding[mice.labels==genotype, vertex_i, :] \n", + " for genotype in np.unique(mice.labels)\n", + " ]\n", " \n", " # Calculate the p-value for the i-th vertex\n", - " statistic, pvalue, _ = KSample(\"MGC\").test(\n", - " *samples, reps=250, workers=-1\n", - " )\n", - " vertex_pvals.append([vertex_i, statistic, pvalue])" + " statistic, pvalue, _ = KSample(\"MGC\").test(\n", + " *samples, reps=250, workers=-1\n", + " )\n", + " vertex_pvals.append([vertex_i, statistic, pvalue])" ] }, { From b0a164672ea65fabeeaba92991827517f0d70cf4 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Tue, 9 Feb 2021 11:20:13 -0800 Subject: [PATCH 102/140] Initial commit of all contributors I could find in the contribution insights on the project as well as individuals I know should be included --- .all-contributorsrc | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 .all-contributorsrc diff --git a/.all-contributorsrc b/.all-contributorsrc new file mode 100644 index 000000000..2e8798323 --- /dev/null +++ b/.all-contributorsrc @@ -0,0 +1,5 @@ +{ + "files": ["CONTRIBUTORS.md"], + "contributorsSortAlphabetically": true, + "contributors": ["asaadeldin11", "alyakin314", "AnshuTrivedi", "bdpedigo", "bryantower", "bvarjavand", "carolyncb", "CaseyWeiner", "darkyed", "dfrancisco1998", "dtborders", "dwaynepryce", "ebridge2", "emasquil", "falkben", "gkang7", "hhelm10", "idc9", "j1c", "jdey4", "jheiko1", "jonmclean", "Jingyan230", "kareef928", "kikiwink", "loftusa", "nyecarr", "pauladkisson", "PerifanosPrometheus", "PSSF23", "rajpratyush", "rflperry", "SHAAAAN", "shaderein", "shiyussy", "spencer-loggia", "tathey1", "tliu68", "v715", "vikramc1", "vmdhhh", "zeou1"] +} From d0b15ddb25144515e583b51b6235eed9fffc0981 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 07:58:36 -0800 Subject: [PATCH 103/140] Removing nyecarr so that we can test everything out by adding nyecarr later --- .all-contributorsrc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 2e8798323..ee4466da5 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,5 +1,5 @@ { "files": ["CONTRIBUTORS.md"], "contributorsSortAlphabetically": true, - "contributors": ["asaadeldin11", "alyakin314", "AnshuTrivedi", "bdpedigo", "bryantower", "bvarjavand", "carolyncb", "CaseyWeiner", "darkyed", "dfrancisco1998", "dtborders", "dwaynepryce", "ebridge2", "emasquil", "falkben", "gkang7", "hhelm10", "idc9", "j1c", "jdey4", "jheiko1", "jonmclean", "Jingyan230", "kareef928", "kikiwink", "loftusa", "nyecarr", "pauladkisson", "PerifanosPrometheus", "PSSF23", "rajpratyush", "rflperry", "SHAAAAN", "shaderein", "shiyussy", "spencer-loggia", "tathey1", "tliu68", "v715", "vikramc1", "vmdhhh", "zeou1"] + "contributors": ["asaadeldin11", "alyakin314", "AnshuTrivedi", "bdpedigo", "bryantower", "bvarjavand", "carolyncb", "CaseyWeiner", "darkyed", "dfrancisco1998", "dtborders", "dwaynepryce", "ebridge2", "emasquil", "falkben", "gkang7", "hhelm10", "idc9", "j1c", "jdey4", "jheiko1", "jonmclean", "Jingyan230", "kareef928", "kikiwink", "loftusa", "pauladkisson", "PerifanosPrometheus", "PSSF23", "rajpratyush", "rflperry", "SHAAAAN", "shaderein", "shiyussy", "spencer-loggia", "tathey1", "tliu68", "v715", "vikramc1", "vmdhhh", "zeou1"] } From 1bb2cc7bfe968bb819d64ca7a490f3e95ce98a8d Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 08:37:39 -0800 Subject: [PATCH 104/140] Adding a contributors md file so all contributors will use it --- CONTRIBUTORS.md | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 CONTRIBUTORS.md diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md new file mode 100644 index 000000000..9cd69e285 --- /dev/null +++ b/CONTRIBUTORS.md @@ -0,0 +1,2 @@ +# Contributors + From 250f9166e2844eca0ad3c4296bfd09171299dbe1 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 08:46:25 -0800 Subject: [PATCH 105/140] Maybe the list has to be empty --- .all-contributorsrc | 1 - 1 file changed, 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index ee4466da5..38a399222 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,5 +1,4 @@ { "files": ["CONTRIBUTORS.md"], "contributorsSortAlphabetically": true, - "contributors": ["asaadeldin11", "alyakin314", "AnshuTrivedi", "bdpedigo", "bryantower", "bvarjavand", "carolyncb", "CaseyWeiner", "darkyed", "dfrancisco1998", "dtborders", "dwaynepryce", "ebridge2", "emasquil", "falkben", "gkang7", "hhelm10", "idc9", "j1c", "jdey4", "jheiko1", "jonmclean", "Jingyan230", "kareef928", "kikiwink", "loftusa", "pauladkisson", "PerifanosPrometheus", "PSSF23", "rajpratyush", "rflperry", "SHAAAAN", "shaderein", "shiyussy", "spencer-loggia", "tathey1", "tliu68", "v715", "vikramc1", "vmdhhh", "zeou1"] } From 6af1289b7731594c82947fd6b0cc8ff1c843ea28 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 08:47:30 -0800 Subject: [PATCH 106/140] No dangling comma, ok. --- .all-contributorsrc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 38a399222..afde66a4a 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,4 +1,4 @@ { "files": ["CONTRIBUTORS.md"], - "contributorsSortAlphabetically": true, + "contributorsSortAlphabetically": true } From fb65aa381d3383fcdeb51d415823482a50359383 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 09:40:53 -0800 Subject: [PATCH 107/140] Updating our CI build to NOT trigger on pushes/PRs that include changes to the all contributors file(s) --- .all-contributorsrc | 1 - .github/workflows/build.yml | 10 +++++++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index afde66a4a..cc38f628e 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,4 +1,3 @@ { - "files": ["CONTRIBUTORS.md"], "contributorsSortAlphabetically": true } diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 754ea6d63..fd1d7736d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,5 +1,13 @@ name: graspologic CI -on: [push, pull_request] +on: + push: + paths-ignore: + - '.all-contributorsrc' + - 'CONTRIBUTORS.md' + pull_request: + paths-ignore: + - '.all-contributorsrc' + - 'CONTRIBUTORS.md' jobs: build-reference-documentation: runs-on: ubuntu-latest From b0e5357befed7da69f143284fa9cae2d84328ca8 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 10:12:21 -0800 Subject: [PATCH 108/140] Maybe it has to be fully empty the first time? --- .all-contributorsrc | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index cc38f628e..0967ef424 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,3 +1 @@ -{ - "contributorsSortAlphabetically": true -} +{} From 823a1ec9768e6137a9e28c8f4486373c1d5154ef Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 10:19:43 -0800 Subject: [PATCH 109/140] Adding an html comment into the CONTRIBUTORS.md to hopefully get this stupid bot to actually create the damn table. WHY ISN'T THIS DOCUMENTED ANYWHERE --- .all-contributorsrc | 5 ++++- CONTRIBUTORS.md | 3 +++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 0967ef424..afde66a4a 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1 +1,4 @@ -{} +{ + "files": ["CONTRIBUTORS.md"], + "contributorsSortAlphabetically": true +} diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 9cd69e285..3a580a9be 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,2 +1,5 @@ # Contributors + + + From fb9e6b456b9c3eb850b885441e1a88add95b89ef Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 10 Feb 2021 18:23:20 +0000 Subject: [PATCH 110/140] docs: update CONTRIBUTORS.md [skip ci] --- CONTRIBUTORS.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 3a580a9be..61846f323 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,5 +1,16 @@ # Contributors + + + + + + +

Nick
💻
+ + + + From 6fd786366347c65b7a623f5da0d4829d83d8d621 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 10 Feb 2021 18:23:21 +0000 Subject: [PATCH 111/140] docs: update .all-contributorsrc [skip ci] --- .all-contributorsrc | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index afde66a4a..5f2cb8609 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,4 +1,23 @@ { - "files": ["CONTRIBUTORS.md"], - "contributorsSortAlphabetically": true + "files": [ + "CONTRIBUTORS.md" + ], + "contributorsSortAlphabetically": true, + "projectName": "graspologic", + "projectOwner": "microsoft", + "repoType": "github", + "repoHost": "https://github.com", + "skipCi": true, + "contributors": [ + { + "login": "Nyecarr", + "name": "Nick", + "avatar_url": "https://avatars.githubusercontent.com/u/4693255?v=4", + "profile": "https://github.com/Nyecarr", + "contributions": [ + "code" + ] + } + ], + "contributorsPerLine": 7 } From 251020c9415803246198623480e3b5e23e1751c6 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 10 Feb 2021 18:39:02 +0000 Subject: [PATCH 112/140] docs: update CONTRIBUTORS.md [skip ci] --- CONTRIBUTORS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 61846f323..577d41f26 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -5,6 +5,7 @@ +

Ali Saad-Eldin
💻
Nick
💻
From ecc4874ebd69adb8c587f6fc96454f4ff419c08a Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 10 Feb 2021 18:39:03 +0000 Subject: [PATCH 113/140] docs: update .all-contributorsrc [skip ci] --- .all-contributorsrc | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/.all-contributorsrc b/.all-contributorsrc index 5f2cb8609..a401e659c 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -17,6 +17,15 @@ "contributions": [ "code" ] + }, + { + "login": "asaadeldin11", + "name": "Ali Saad-Eldin", + "avatar_url": "https://avatars.githubusercontent.com/u/54996865?v=4", + "profile": "https://github.com/asaadeldin11", + "contributions": [ + "code" + ] } ], "contributorsPerLine": 7 From 980de231f4d63cea652d382c43697f411183cfb8 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Wed, 10 Feb 2021 11:06:52 -0800 Subject: [PATCH 114/140] Initialization of all contributors using the cli instead of the damn bot that didn't work --- .all-contributorsrc | 363 +++++++++++++++++++++++++++++++++++++++++++- CONTRIBUTORS.md | 50 ++++++ 2 files changed, 412 insertions(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index a401e659c..b0904591b 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -26,7 +26,368 @@ "contributions": [ "code" ] + }, + { + "login": "alyakin314", + "name": "alyakin314", + "avatar_url": "https://avatars.githubusercontent.com/u/25692376?v=4", + "profile": "https://github.com/alyakin314", + "contributions": [ + "code" + ] + }, + { + "login": "AnshuTrivedi", + "name": "Anshu Trivedi", + "avatar_url": "https://avatars.githubusercontent.com/u/47869948?v=4", + "profile": "https://www.linkedin.com/in/anshu-trivedi-501a7b146/", + "contributions": [ + "code" + ] + }, + { + "login": "bdpedigo", + "name": "Benjamin Pedigo", + "avatar_url": "https://avatars.githubusercontent.com/u/25714207?v=4", + "profile": "https://bdpedigo.github.io/", + "contributions": [ + "code" + ] + }, + { + "login": "bryantower", + "name": "Bryan Tower", + "avatar_url": "https://avatars.githubusercontent.com/u/583566?v=4", + "profile": "https://github.com/bryantower", + "contributions": [ + "code" + ] + }, + { + "login": "bvarjavand", + "name": "Bijan Varjavand", + "avatar_url": "https://avatars.githubusercontent.com/u/8294669?v=4", + "profile": "https://github.com/bvarjavand", + "contributions": [ + "code" + ] + }, + { + "login": "carolyncb", + "name": "Carolyn Buractaon", + "avatar_url": "https://avatars.githubusercontent.com/u/60894394?v=4", + "profile": "https://github.com/carolyncb", + "contributions": [ + "projectManagement" + ] + }, + { + "login": "CaseyWeiner", + "name": "Casey Weiner", + "avatar_url": "https://avatars.githubusercontent.com/u/54880846?v=4", + "profile": "https://github.com/CaseyWeiner", + "contributions": [ + "code" + ] + }, + { + "login": "darkyed", + "name": "Saumitra Sharma", + "avatar_url": "https://avatars.githubusercontent.com/u/43502778?v=4", + "profile": "https://github.com/darkyed", + "contributions": [ + "code" + ] + }, + { + "login": "dfrancisco1998", + "name": "dfrancisco1998", + "avatar_url": "https://avatars.githubusercontent.com/u/40680427?v=4", + "profile": "https://github.com/dfrancisco1998", + "contributions": [ + "code" + ] + }, + { + "login": "dtborders", + "name": "Daniel Borders", + "avatar_url": "https://avatars.githubusercontent.com/u/32661399?v=4", + "profile": "https://github.com/dtborders", + "contributions": [ + "code" + ] + }, + { + "login": "dwaynepryce", + "name": "Dwayne Pryce", + "avatar_url": "https://avatars.githubusercontent.com/u/899411?v=4", + "profile": "https://github.com/dwaynepryce", + "contributions": [ + "code" + ] + }, + { + "login": "ebridge2", + "name": "Eric Bridgeford", + "avatar_url": "https://avatars.githubusercontent.com/u/8883547?v=4", + "profile": "http://ericwb.me/", + "contributions": [ + "code" + ] + }, + { + "login": "emasquil", + "name": "emasquil", + "avatar_url": "https://avatars.githubusercontent.com/u/29291336?v=4", + "profile": "https://github.com/emasquil", + "contributions": [ + "code" + ] + }, + { + "login": "falkben", + "name": "Ben Falk", + "avatar_url": "https://avatars.githubusercontent.com/u/653031?v=4", + "profile": "https://github.com/falkben", + "contributions": [ + "code" + ] + }, + { + "login": "gkang7", + "name": "gkang7", + "avatar_url": "https://avatars.githubusercontent.com/u/55039574?v=4", + "profile": "https://github.com/gkang7", + "contributions": [ + "code" + ] + }, + { + "login": "hhelm10", + "name": "hhelm10", + "avatar_url": "https://avatars.githubusercontent.com/u/31412718?v=4", + "profile": "https://github.com/hhelm10", + "contributions": [ + "code" + ] + }, + { + "login": "idc9", + "name": "Iain Carmichael", + "avatar_url": "https://avatars.githubusercontent.com/u/906398?v=4", + "profile": "https://idc9.github.io/", + "contributions": [ + "code" + ] + }, + { + "login": "j1c", + "name": "Jaewon Chung", + "avatar_url": "https://avatars.githubusercontent.com/u/5142539?v=4", + "profile": "https://twitter.com/j1chung", + "contributions": [ + "code" + ] + }, + { + "login": "jdey4", + "name": "Jayanta Dey", + "avatar_url": "https://avatars.githubusercontent.com/u/52499217?v=4", + "profile": "https://github.com/jdey4", + "contributions": [ + "code" + ] + }, + { + "login": "jheiko1", + "name": "jheiko1", + "avatar_url": "https://avatars.githubusercontent.com/u/39324337?v=4", + "profile": "https://github.com/jheiko1", + "contributions": [ + "code" + ] + }, + { + "login": "jonmclean", + "name": "Jon McLean", + "avatar_url": "https://avatars.githubusercontent.com/u/4429525?v=4", + "profile": "https://github.com/jonmclean", + "contributions": [ + "code" + ] + }, + { + "login": "Jingyan230", + "name": "jingyan230", + "avatar_url": "https://avatars.githubusercontent.com/u/69659070?v=4", + "profile": "https://github.com/Jingyan230", + "contributions": [ + "code" + ] + }, + { + "login": "kareef928", + "name": "kareef928", + "avatar_url": "https://avatars.githubusercontent.com/u/51966539?v=4", + "profile": "https://github.com/kareef928", + "contributions": [ + "code" + ] + }, + { + "login": "Kikiwink", + "name": "kikiwink", + "avatar_url": "https://avatars.githubusercontent.com/u/50060770?v=4", + "profile": "https://github.com/Kikiwink", + "contributions": [ + "code" + ] + }, + { + "login": "loftusa", + "name": "Alex Loftus", + "avatar_url": "https://avatars.githubusercontent.com/u/12386450?v=4", + "profile": "https://github.com/loftusa", + "contributions": [ + "code" + ] + }, + { + "login": "pauladkisson", + "name": "Paul Adkisson", + "avatar_url": "https://avatars.githubusercontent.com/u/34703136?v=4", + "profile": "https://github.com/pauladkisson", + "contributions": [ + "code" + ] + }, + { + "login": "PerifanosPrometheus", + "name": "perifanosprometheus", + "avatar_url": "https://avatars.githubusercontent.com/u/69210497?v=4", + "profile": "https://github.com/PerifanosPrometheus", + "contributions": [ + "code" + ] + }, + { + "login": "rajpratyush", + "name": "Pratyush Raj", + "avatar_url": "https://avatars.githubusercontent.com/u/53184883?v=4", + "profile": "https://www.linkedin.com/in/pratyush-raj-737809193/", + "contributions": [ + "code" + ] + }, + { + "login": "PSSF23", + "name": "Haoyin Xu", + "avatar_url": "https://avatars.githubusercontent.com/u/20309845?v=4", + "profile": "https://github.com/PSSF23", + "contributions": [ + "code" + ] + }, + { + "login": "rflperry", + "name": "Ronan Perry", + "avatar_url": "https://avatars.githubusercontent.com/u/13107341?v=4", + "profile": "https://rflperry.github.io/", + "contributions": [ + "code" + ] + }, + { + "login": "SHAAAAN", + "name": "ShanQiu", + "avatar_url": "https://avatars.githubusercontent.com/u/45969738?v=4", + "profile": "https://github.com/SHAAAAN", + "contributions": [ + "code" + ] + }, + { + "login": "shaderein", + "name": "Jinhan", + "avatar_url": "https://avatars.githubusercontent.com/u/47577845?v=4", + "profile": "https://github.com/shaderein", + "contributions": [ + "code" + ] + }, + { + "login": "shiyussy", + "name": "Shiyu Sun", + "avatar_url": "https://avatars.githubusercontent.com/u/50231743?v=4", + "profile": "https://github.com/shiyussy", + "contributions": [ + "code" + ] + }, + { + "login": "spencer-loggia", + "name": "spencer-loggia", + "avatar_url": "https://avatars.githubusercontent.com/u/36051767?v=4", + "profile": "https://github.com/spencer-loggia", + "contributions": [ + "code" + ] + }, + { + "login": "tathey1", + "name": "Thomas Athey", + "avatar_url": "https://avatars.githubusercontent.com/u/18537350?v=4", + "profile": "https://github.com/tathey1", + "contributions": [ + "code" + ] + }, + { + "login": "tliu68", + "name": "tliu68", + "avatar_url": "https://avatars.githubusercontent.com/u/54865879?v=4", + "profile": "https://github.com/tliu68", + "contributions": [ + "code" + ] + }, + { + "login": "v715", + "name": "Vivek Gopalakrishnan", + "avatar_url": "https://avatars.githubusercontent.com/u/29757116?v=4", + "profile": "https://vivekg.dev/", + "contributions": [ + "code" + ] + }, + { + "login": "vmdhhh", + "name": "vmdhhh", + "avatar_url": "https://avatars.githubusercontent.com/u/58106524?v=4", + "profile": "https://github.com/vmdhhh", + "contributions": [ + "code" + ] + }, + { + "login": "vikramc1", + "name": "Vikram Chandrashekhar", + "avatar_url": "https://avatars.githubusercontent.com/u/7087830?v=4", + "profile": "https://github.com/vikramc1", + "contributions": [ + "code" + ] + }, + { + "login": "zeou1", + "name": "Ze Ou", + "avatar_url": "https://avatars.githubusercontent.com/u/38440136?v=4", + "profile": "https://github.com/zeou1", + "contributions": [ + "code" + ] } ], - "contributorsPerLine": 7 + "contributorsPerLine": 7, + "commitConvention": "none" } diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 577d41f26..c3ba6eb9b 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -5,8 +5,58 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Alex Loftus
💻
Ali Saad-Eldin
💻
Anshu Trivedi
💻
Ben Falk
💻
Benjamin Pedigo
💻
Bijan Varjavand
💻
Bryan Tower
💻

Carolyn Buractaon
📆
Casey Weiner
💻
Daniel Borders
💻
Dwayne Pryce
💻
Eric Bridgeford
💻
Haoyin Xu
💻
Iain Carmichael
💻

Jaewon Chung
💻
Jayanta Dey
💻
Jinhan
💻
Jon McLean
💻
Nick
💻
Paul Adkisson
💻
Pratyush Raj
💻

Ronan Perry
💻
Saumitra Sharma
💻
ShanQiu
💻
Shiyu Sun
💻
Thomas Athey
💻
Vikram Chandrashekhar
💻
Vivek Gopalakrishnan
💻

Ze Ou
💻
alyakin314
💻
dfrancisco1998
💻
emasquil
💻
gkang7
💻
hhelm10
💻
jheiko1
💻

jingyan230
💻
kareef928
💻
kikiwink
💻
perifanosprometheus
💻
spencer-loggia
💻
tliu68
💻
vmdhhh
💻
From 2d40449cc84dd2401ba807f3b703804cfbd3bf18 Mon Sep 17 00:00:00 2001 From: Alexandre Dulaunoy Date: Thu, 25 Feb 2021 03:25:20 +0100 Subject: [PATCH 115/140] Update broken link to the tutorial section (#651) Update broken link to tutorial section where the documentation is now accessible. Co-authored-by: Dwayne Pryce Co-authored-by: Benjamin Pedigo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 811ac84b1..613e2113e 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ A graph, or network, provides a mathematically intuitive representation of data # Documentation The official documentation with usage is at https://graspologic.readthedocs.io/en/latest/ -Please visit the [tutorial section](https://graspologic.readthedocs.io/en/latest/tutorial.html) in the official website for more in depth usage. +Please visit the [tutorial section](https://microsoft.github.io/graspologic/tutorials/index.html) in the official website for more in depth usage. # System Requirements ## Hardware requirements From 5467617eda4a2192cb5d7afa03fb82f16ca90ace Mon Sep 17 00:00:00 2001 From: Ze Ou <38440136+zeou1@users.noreply.github.com> Date: Thu, 25 Feb 2021 14:12:07 -0500 Subject: [PATCH 116/140] Add matrix plotting (#546) * added n_features_in_ to all four estimator classes * to trigger netlify * revert back for netlify * Fix grammar * wrong branch to commit but need to before switching * Create seaborn.ipynb * added ticks, separators, and labels to the plot * updated the notebook * finished colormap axis, fixed heatmap ticks * update the notebook with three plots * Created tutorial and test for plot_matrix Created separate tutorial and test files for plot_matrix. Potentially can merge with current plot test files * Delete unnecessary jupyter notebooks * reformat code and update name to graspologic changed package name from graspy to graspologic and used black to reformat test_plot_matrix.py, plot_matrix.py, plot/__init__.py * Fixing subplots typo fix subplots typo in plot_matrix.py * Update test_plot_matrix.py * Added matrix.ipynb to toctree * Delete unnecessary currentmodule in plottint.rst * Comment out failing code in test_rdpg_corr.py * Deleted junk code and deleted gridmap as an external method Deleted commented out code in gridmap and sort_meta * Got rid of more junk codes * Updates responding to PR comments (details in description) Changes made: 1. Added Microsoft header 2. Removed comments at the beginning 3. Fixed typos 4. Changed the data type of group_class and color_class to string or list of string in documentation 5. Renamed gridmap to scattermap 6. Created a private function _set_spines to set the spines for the tick axes 7. Created adjplot. adjplot is a wrapper for matrixplot. It allows the user to use one set of parameters for both axes. 8. Redesigned matrixplot to allow users input two separate sets of parameters for the axes. * Added adjplot to plotting.rst and test_plot_matrix.py and reformatted long lines of code * Reformatted long lines of code and updated test_plot_matrix.py with new matrixplot test * Update the tutorial title * Many updates from comments last week 1. Enabled multi palettes for each color_class 2. Made sure that palettes can be dict and are included in documentation 3. check palettes have the length of 1 or the length of color_class 4. check highlight class are part of the metadata 5. fixed a minor mistake in sort_meta, so each group_class is added to total_sort_by only once 6. Changed the visual for class_order tutorial so it looks more intuitive 7. Added a tutorial for multi class_order sorting, but there isn't any difference in the graph 8. Added a tutorial for multi-palettes 9. Added a tutorial for highlights * Update test_rdpg_corr.py * Increased the font size * Update matrixplot.ipynb * Adding r in front of documentation * Updated names or sorting_kws * Further changing names in test * Update test_plot_matrix.py * Fixed outer tick visual difference * Included in documentation of the possibility of array_like sorting_kws and included a tutorial for it * Update test_plot_matrix.py * Update plot_matrix.py * update and clear notebook * more notebook updates * Deleted back separators and made sure all lines are under 88 chars * Update version.txt * Update matrixplot.ipynb * Update .gitignore * Deleted redundant code in tutorial * Update .gitignore * Update version.txt * Added the changes back to the tutorial * Update matrixplot.ipynb Changed a few typos in the tutorial, not sure if this would change the environment * update docs to match seaborn better (matrixplot) * try to add pandas intersphinx * update docs for adjplot * try to get the last tick back * bug fix * try to fix bug again * Delete version.txt * try doc fix * hard code references to match new tutorial loc * rebuild me plz * black Co-authored-by: zeou1 Co-authored-by: Benjamin Pedigo --- docs/reference/conf.py | 1 + docs/reference/reference/plotting.rst | 8 + docs/tutorials/index.rst | 1 + docs/tutorials/plotting/matrixplot.ipynb | 576 ++++++++++++ graspologic/plot/__init__.py | 3 + graspologic/plot/plot_matrix.py | 1010 ++++++++++++++++++++++ tests/test_plot_matrix.py | 185 ++++ 7 files changed, 1784 insertions(+) create mode 100644 docs/tutorials/plotting/matrixplot.ipynb create mode 100644 graspologic/plot/plot_matrix.py create mode 100644 tests/test_plot_matrix.py diff --git a/docs/reference/conf.py b/docs/reference/conf.py index c38f024b0..fe8f102eb 100644 --- a/docs/reference/conf.py +++ b/docs/reference/conf.py @@ -70,6 +70,7 @@ "matplotlib": ("https://matplotlib.org", None), "networkx": ("https://networkx.org/documentation/stable", None), "numpy": ("https://numpy.org/doc/stable", None), + "pandas": ("https://pandas.pydata.org/pandas-docs/stable", None), "python": ("https://docs.python.org/3", None), "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), "seaborn": ("https://seaborn.pydata.org", None), diff --git a/docs/reference/reference/plotting.rst b/docs/reference/reference/plotting.rst index 2f7b43957..9c019b30d 100644 --- a/docs/reference/reference/plotting.rst +++ b/docs/reference/reference/plotting.rst @@ -28,3 +28,11 @@ Edgeplot Screeplot --------- .. autofunction:: screeplot + +Adjplot +------- +.. autofunction:: adjplot + +Matrixplot +---------- +.. autofunction:: matrixplot diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index eaf4c5fe9..7fdaf6507 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -94,6 +94,7 @@ The following tutorials present ways to visualize the graphs, such as its adjace plotting/heatmaps plotting/gridplot plotting/pairplot + plotting/matrixplot plotting/pairplot_with_gmm .. _matching_tutorials: diff --git a/docs/tutorials/plotting/matrixplot.ipynb b/docs/tutorials/plotting/matrixplot.ipynb new file mode 100644 index 000000000..e0c20dd87 --- /dev/null +++ b/docs/tutorials/plotting/matrixplot.ipynb @@ -0,0 +1,576 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.7.9 64-bit ('ndd': virtualenv)", + "metadata": { + "interpreter": { + "hash": "855c3730de18900ddca5ab1525c8a72e412e6f1abfd1a86b3ec4c3008017ba5b" + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Matrixplot and Adjplot: Visualize and sort matrices with metadata\n", + "\n", + "This guide introduces `Matrixplot` and `Adjplot`. They allow the user to sort a matrix according to some metadata and plot it as either a heatmap or a scattermap. These functions also allows the user to add color or tick axes to indicate the separation between different groups or attributes.\n", + "\n", + "Note: `Matrixplot` and `Adjplot` have almost identical inputs/functionality. `Adjplot` is just a convenient wrapper around `Matrixplot` which assumes the matrix to be plotted is square and has the same row and column metadata." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "from graspologic.simulations import sbm\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from graspologic.plot import adjplot, matrixplot\n", + "import seaborn as sns\n" + ], + "cell_type": "code", + "metadata": {}, + "outputs": [] + }, + { + "source": [ + "## Simulate a binary graph using stochastic block model\n", + "The 4-block model is defined as below:\n", + "\n", + "\\begin{align*}\n", + "n &= [50, 50, 50, 50]\\\\\n", + "P &= \n", + "\\begin{bmatrix}0.8 & 0.1 & 0.05 & 0.01\\\\\n", + "0.1 & 0.4 & 0.15 & 0.02\\\\\n", + "0.05 & 0.15 & 0.3 & 0.01\\\\\n", + "0.01 & 0.02 & 0.01 & 0.4\n", + "\\end{bmatrix}\n", + "\\end{align*}\n", + "\n", + "Thus, the first 50 vertices belong to block 1, the second 50 vertices belong to block 2, the third 50 vertices belong to block 3, and the last 50 vertices belong to block 4.\n", + "\n", + "Each block is associated with some metadata:\n", + "\n", + "| Block Number | Hemisphere | region |\n", + "|:------------:|:----------:|:----:|\n", + "| 1 | 0 | 0 |\n", + "| 2 | 0 | 1 |\n", + "| 3 | 1 | 0 |\n", + "| 4 | 1 | 1 |\n", + "\n", + "\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "N = 50\n", + "n_communities = [N, N, N, N]\n", + "p = [[0.8, 0.1, 0.05, 0.01],\n", + " [0.1, 0.4, 0.15, 0.02],\n", + " [0.05, 0.15, 0.3, 0.01],\n", + " [0.01, 0.02, 0.01, 0.4]]\n", + "\n", + "np.random.seed(2)\n", + "A = sbm(n_communities, p)\n", + "meta = pd.DataFrame(\n", + " data={\n", + " 'hemisphere': np.concatenate((np.full((1, 2*N), 0), np.full((1, 2*N), 1)), axis=1).flatten(),\n", + " 'region': np.concatenate((np.full((1, N), 0), np.full((1, N), 1), np.full((1, N), 0), np.full((1, N), 1)), axis=1).flatten(),\n", + " 'cell_size': np.arange(4*N)},\n", + ")" + ] + }, + { + "source": [ + "With no randomization, the original data looks like this:" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + ")" + ] + }, + { + "source": [ + "Randomize the data, so we can see the visual importance of matrix sorting:" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "rnd_idx = np.arange(4*N)\n", + "np.random.shuffle(rnd_idx)\n", + "A = A[np.ix_(rnd_idx, rnd_idx)]\n", + "meta = meta.reindex(rnd_idx)" + ] + }, + { + "source": [ + "The data immediately after randomization:" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + ")" + ] + }, + { + "source": [ + "## The use of `group`\n", + "\n", + "The parameter group can be a list or strings or np.array by which to group the matrix" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "### Group the matrix by one metadata (hemisphere)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\"],\n", + " sizes=(5, 5),\n", + ")" + ] + }, + { + "source": [ + "### Group the matrix by another metadata (region), but with a color axis to label the hemisphre" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"region\"],\n", + " color=[\"hemisphere\"],\n", + " sizes=(5, 5),\n", + ")" + ] + }, + { + "source": [ + "### Group by two metadata at the same time\n", + "\n", + "Notice that the order of the list matters." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + ")\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"region\", \"hemisphere\"],\n", + ")" + ] + }, + { + "source": [ + "## The use of `class_order`" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "### Sort the grouped classes by their sizes\n", + "\n", + "If the grouped classes are of different sizes, we can sort them based on the size in ascending order." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "from numpy.random import normal\n", + "N = 10\n", + "n_communities = [N, 3*N, 2*N, N]\n", + "p = [[0.8, 0.1, 0.05, 0.01],\n", + " [0.1, 0.4, 0.15, 0.02],\n", + " [0.05, 0.15, 0.3, 0.01],\n", + " [0.01, 0.02, 0.01, 0.4]]\n", + "wt = [[normal]*4]*4\n", + "wtargs = [[dict(loc=5, scale=1)]*4]*4\n", + "\n", + "np.random.seed(2)\n", + "A = sbm(n_communities, p, wt=wt, wtargs=wtargs)\n", + "meta = pd.DataFrame(\n", + " data={\n", + " 'hemisphere': np.concatenate((np.full((1, 4*N), 0), np.full((1, 3*N), 1)), axis=1).flatten(),\n", + " 'region': np.concatenate((np.full((1, N), 0), np.full((1, 3*N), 1), np.full((1, 2*N), 0), np.full((1, N), 1)), axis=1).flatten(),\n", + " 'cell_size': np.arange(7*N),\n", + " 'axon_length': np.concatenate((np.random.normal(5, 1, (1, N)), np.random.normal(2, 1, (1, 3*N)), np.random.normal(5, 1, (1, 2*N)), np.random.normal(2, 1, (1, N))), axis=1).flatten()},\n", + ")\n", + "rnd_idx = np.arange(7*N)\n", + "np.random.shuffle(rnd_idx)\n", + "A = A[np.ix_(rnd_idx, rnd_idx)]\n", + "meta = meta.reindex(rnd_idx)\n" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " group_order=\"size\", # note that this is a special keyword which was not in `meta`\n", + " color=[\"cell_size\", \"axon_length\"],\n", + " palette=[\"Purples\", \"Blues\"],\n", + " sizes=(1, 30),\n", + ")" + ] + }, + { + "source": [ + "If the metadata has other fields, we can also sort by the mean of certain fields in ascending order" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " group_order=[\"cell_size\"],\n", + " color=[\"cell_size\", \"axon_length\"],\n", + " palette=[\"Purples\", \"Blues\"],\n", + ")" + ] + }, + { + "source": [ + "We can also sort by multiple fields at the same time, including the size of the `group_class`." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " group_order=[\"cell_size\", \"axon_length\"],\n", + " color=[\"cell_size\", \"axon_length\"],\n", + " palette=[\"Purples\", \"Blues\"],\n", + ")" + ] + }, + { + "source": [ + "## The use of `item_order`\n", + "\n", + "The parameter `item_order` is used to sort items within each specific class" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "Without sorting by `item_order`, the matrix remain randomized in each grouped class:" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " color=[\"cell_size\"],\n", + " palette=\"Purples\",\n", + ")" + ] + }, + { + "source": [ + "But with sorting with `item_order`, the items are ordered within each grouped class:" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " item_order=[\"cell_size\"],\n", + " color=[\"cell_size\"],\n", + " palette=\"Purples\",\n", + ")" + ] + }, + { + "source": [ + "## The use of `highlight`\n", + "`highlight` can be used to highlight separators of a particular class with a different style" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "highlight_kws = dict(color=\"red\", linestyle=\"-\", linewidth=5)\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " highlight=[\"hemisphere\"],\n", + " highlight_kws=highlight_kws,\n", + ")" + ] + }, + { + "source": [ + "## The use of multiple palettes\n", + "Each color can be plotted with the same `palette`, or different palettes can be specified for each color" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " meta=meta,\n", + " plot_type=\"scattermap\",\n", + " group=[\"hemisphere\", \"region\"],\n", + " ticks=False,\n", + " item_order=[\"cell_size\"],\n", + " color=[\"hemisphere\", \"region\", \"cell_size\", \"axon_length\"],\n", + " palette=[\"tab10\", \"tab20\", \"Purples\", \"Blues\"],\n", + ")" + ] + }, + { + "source": [ + "## Label the row and column axes with different metadata\n", + "If you would like to group the row and columns by different metadata, you can use the `matrixplot` function to specify the parameters for both of the axes. Most arguments are the same, with the addition of `row_` or `col_` to specify the corresponding axis of `data`.\n", + "\n", + "Note: for adjacency matrices, one should not sort the rows and columns separately, as it breaks the representation of the graph. Here we do so just for demonstration, assuming `A` is not an adjacency matrix." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "matrixplot(\n", + " data=A,\n", + " ax=ax,\n", + " col_meta=meta,\n", + " row_meta=meta,\n", + " plot_type=\"scattermap\",\n", + " col_group=[\"hemisphere\"],\n", + " row_group=[\"region\"],\n", + " col_item_order=[\"cell_size\"],\n", + " row_item_order=[\"cell_size\"],\n", + " row_color=[\"region\"],\n", + " row_ticks=False,\n", + ")" + ] + }, + { + "source": [ + "## Plot using `heatmap` instead of `scattermap`" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "matrixplot(\n", + " data=A,\n", + " ax=ax,\n", + " col_meta=meta,\n", + " row_meta=meta,\n", + " plot_type=\"heatmap\",\n", + " col_group=[\"hemisphere\"],\n", + " row_group=[\"region\"],\n", + " col_item_order=[\"cell_size\"],\n", + " row_item_order=[\"cell_size\"],\n", + " row_color=[\"region\"],\n", + " row_ticks=False,\n", + ")" + ] + }, + { + "source": [ + "## Supply array-likes instead of `meta`\n", + "If `meta` is not provided, each of the sorting/grouping/coloring keywords can be supplied with array-like data structures." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "group_0 = np.concatenate((np.full((4*N, 1), 0), np.full((3*N, 1), 1)), axis=0)\n", + "group_1 = np.concatenate((np.full((N, 1), 0), np.full((3*N, 1), 1), np.full((2*N, 1), 0), np.full((N, 1), 1)), axis=0)\n", + "group = np.concatenate((group_0, group_1), axis=1)\n", + "\n", + "group = group[rnd_idx, :]\n" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "adjplot(\n", + " data=A,\n", + " ax=ax,\n", + " plot_type=\"scattermap\",\n", + " group=group,\n", + " sizes=(1, 30),\n", + ")" + ] + } + ] +} \ No newline at end of file diff --git a/graspologic/plot/__init__.py b/graspologic/plot/__init__.py index 7ff08c096..9754e3778 100644 --- a/graspologic/plot/__init__.py +++ b/graspologic/plot/__init__.py @@ -13,6 +13,7 @@ edgeplot, screeplot, ) +from .plot_matrix import adjplot, matrixplot __all__ = [ "heatmap", @@ -22,4 +23,6 @@ "degreeplot", "edgeplot", "screeplot", + "adjplot", + "matrixplot", ] diff --git a/graspologic/plot/plot_matrix.py b/graspologic/plot/plot_matrix.py new file mode 100644 index 000000000..8b78e76e0 --- /dev/null +++ b/graspologic/plot/plot_matrix.py @@ -0,0 +1,1010 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np +import seaborn as sns +from mpl_toolkits.axes_grid1 import make_axes_locatable +import matplotlib as mpl +from matplotlib.colors import ListedColormap + + +def _check_data(data): + if not isinstance(data, np.ndarray): + raise TypeError("Data must be a np.ndarray.") + if data.ndim != 2: + raise ValueError("Data must have dimension 2.") + + +def _check_item_in_meta(meta, item, name): + if item is None: + return [] + if isinstance(item, str): + item = [item] + else: + try: + iter(item) + except TypeError: + msg = ( + f"{name} must be an iterable or string corresponding to columns in meta" + ) + raise TypeError(msg) + for col_name in item: + if col_name not in meta.columns: + if (name == "group_order") and (col_name == "size"): + pass + else: + raise ValueError(f"{col_name} is not a column in the meta dataframe.") + + return item + + +def _check_length(item, name, length): + if length != len(item): + raise ValueError( + f"Length of {name} must be the same as corresponding data axis" + ) + + +def _check_plotting_kws(palette, color): + if isinstance(palette, str) or isinstance(palette, dict): + palette = [palette] + elif len(palette) != len(color) and len(palette) != 1: + raise ValueError("There is not enough palette value for each color class") + + return palette + + +def _check_sorting_kws(length, meta, group, group_order, item_order, color, highlight): + if isinstance(meta, pd.DataFrame): + # if meta is here, than everything else must be column item in meta + _check_length(meta, "meta", length) + group = _check_item_in_meta(meta, group, "group") + group_order = _check_item_in_meta(meta, group_order, "group_order") + item_order = _check_item_in_meta(meta, item_order, "item_order") + color = _check_item_in_meta(meta, color, "color") + highlight = _check_item_in_meta(meta, highlight, "highlight") + else: + # otherwise, arguments can be a hodgepodge of stuff + group_meta, group = _item_to_df(group, "group", length) + group_order_meta, group_order = _item_to_df(group_order, "group_order", length) + item_order_meta, item_order = _item_to_df(item_order, "item_order", length) + color_meta, color = _item_to_df(color, "color", length) + highlight_meta, highlight = _item_to_df(highlight, "highlight", length) + metas = [] + for m in [ + group_meta, + group_order_meta, + item_order_meta, + color_meta, + highlight_meta, + ]: + if m is not None: + metas.append(m) + if len(metas) > 0: + meta = pd.concat(metas, axis=1) + else: + meta = pd.DataFrame() + + return meta, group, group_order, item_order, color, highlight + + +def _get_separator_info(meta, group): + if meta is None or group is None: + return None + + sep = meta.groupby(by=group, sort=False).first() + sep_inds = sep["sort_idx"].values + sep_inds = list(sep["sort_idx"].values) + last = meta.groupby(group, sort=False).last() + sep_inds.append(last["sort_idx"].values[-1] + 1) + return np.array(sep_inds) + + +def _item_to_df(item, name, length): + if item is None: + return None, [] + elif (name == "group_order") and (item == "size"): + return None, ["size"] + + if isinstance(item, pd.Series): + _check_length(item, name, length) + item_meta = item.to_frame(name=f"{name}_0") + elif isinstance(item, list): + if len(item) == length: # assuming elements of list are metadata + item = [item] + for elem in item: + _check_length(elem, name, length) + item_meta = pd.DataFrame({f"{name}_{i}": elem for i, elem in enumerate(item)}) + elif isinstance(item, np.ndarray): + if item.ndim > 2: + raise ValueError(f"Numpy array passed as {name} must be 1 or 2d.") + _check_length(item, name, length) + if item.ndim < 2: + item = np.atleast_2d(item).T + item_meta = pd.DataFrame( + data=item, columns=[f"{name}_{i}" for i in range(item.shape[1])] + ) + else: + raise ValueError(f"{name} must be a pd.Series, np.array, or list.") + + item = list(item_meta.columns.values) + + return item_meta, item + + +def _set_spines(ax, left, right, top, bottom): + ax.spines["right"].set_visible(right) + ax.spines["top"].set_visible(top) + ax.spines["left"].set_visible(left) + ax.spines["bottom"].set_visible(bottom) + + return ax + + +def _remove_shared_ax(ax): + """ + Remove ax from its sharex and sharey + """ + # Remove ax from the Grouper object + shax = ax.get_shared_x_axes() + shay = ax.get_shared_y_axes() + shax.remove(ax) + shay.remove(ax) + + # Set a new ticker with the respective new locator and formatter + for axis in [ax.xaxis, ax.yaxis]: + ticker = mpl.axis.Ticker() + axis.major = ticker + axis.minor = ticker + # No ticks and no labels + loc = mpl.ticker.NullLocator() + fmt = mpl.ticker.NullFormatter() + axis.set_major_locator(loc) + axis.set_major_formatter(fmt) + axis.set_minor_locator(loc) + axis.set_minor_formatter(fmt) + + +def draw_colors(ax, ax_type="x", meta=None, divider=None, color=None, palette="tab10"): + r""" + Draw colormap onto the axis to separate the data + + Parameters + ---------- + ax : matplotlib axes object + Axes in which to draw the colormap + ax_type : char, optional + Setting either the x or y axis, by default "x" + meta : pd.DataFrame, pd.Series, list of pd.Series or np.array, optional + Metadata of the matrix such as class, cell type, etc., by default None + divider : AxesLocator, optional + Divider used to add new axes to the plot + color : str, list of str, or array_like, optional + Attribute in meta by which to draw colorbars, by default None + palette : str or dict, optional + Colormap of the colorbar, by default "tab10" + + Returns + ------- + ax : matplotlib axes object + Axes in which to draw the color map + """ + classes = meta[color] + uni_classes = np.unique(classes) + # Create the color dictionary + if isinstance(palette, dict): + color_dict = palette + elif isinstance(palette, str): + color_dict = dict( + zip(uni_classes, sns.color_palette(palette, len(uni_classes))) + ) + + # Make the colormap + class_map = dict(zip(uni_classes, range(len(uni_classes)))) + color_sorted = np.vectorize(color_dict.get)(uni_classes) + color_sorted = np.array(color_sorted) + if len(color_sorted) != len(uni_classes): + color_sorted = color_sorted.T + lc = ListedColormap(color_sorted) + class_indicator = np.vectorize(class_map.get)(classes) + + if ax_type == "x": + class_indicator = class_indicator.reshape(1, len(classes)) + elif ax_type == "y": + class_indicator = class_indicator.reshape(len(classes), 1) + sns.heatmap( + class_indicator, + cmap=lc, + cbar=False, + yticklabels=False, + xticklabels=False, + ax=ax, + square=False, + ) + if ax_type == "x": + ax.set_xlabel(color, fontsize=20) + ax.xaxis.set_label_position("top") + elif ax_type == "y": + ax.set_ylabel(color, fontsize=20) + return ax + + +def draw_separators( + ax, + ax_type="x", + meta=None, + group=None, + plot_type="heatmap", + gridline_kws=None, +): + r""" + Draw separators between groups on the plot + + Parameters + ---------- + ax : matplotlib axes object + Axes in which to draw the ticks + ax_type : char, optional + Setting either the x or y axis, by default "x" + meta : pd.DataFrame, pd.Series, list of pd.Series or np.array, optional + Metadata of the matrix such as class, cell type, etc., by default None + group : str, list of str, or array_like, optional + Attribute in meta to group the graph in the plot, by default None + plot_type : str, optional + One of "heatmap" or "scattermap", by default "heatmap" + gridline_kws : dict, optional + Plotting arguments for the separators, by default None + + Returns + ------- + ax : matplotlib axes object + Axes in which to draw the ticks + """ + if len(group) > 0: + if gridline_kws is None: + gridline_kws = dict(color="grey", linestyle="--", alpha=0.7, linewidth=1) + + sep_inds = _get_separator_info(meta, group) + if plot_type == "scattermap": + sep_inds = sep_inds - 0.5 + + if ax_type == "x": + lims = ax.get_xlim() + drawer = ax.axvline + elif ax_type == "y": + lims = ax.get_ylim() + drawer = ax.axhline + + # Draw the separators + for t in list(sep_inds): + if t not in lims: + drawer(t, **gridline_kws) + + return ax + + +def draw_ticks( + ax, + ax_type="x", + meta=None, + group=None, + group_border=True, + plot_type="heatmap", +): + r""" + Draw ticks onto the axis of the plot to separate the data + + Parameters + ---------- + ax : matplotlib axes object + Axes in which to draw the ticks + ax_type : char, optional + Setting either the x or y axis, by default "x" + meta : pd.DataFrame, pd.Series, list of pd.Series or np.array, optional + Metadata of the matrix such as class, cell type, etc., by default None + group : str, list of str, or array_like, optional + Attribute in meta to group the graph in the plot, by default None + group_border : bool, optional + Whether to draw separator on the tick axes, by default True + plot_type : str, optional + One of "heatmap" or "scattermap", by default "heatmap" + + Returns + ------- + ax : matplotlib axes object + Axes in which to draw the ticks + """ + # Identify the locations of the ticks + if meta is None or group is None: + tick_inds = None + tick_labels = None + else: + # Identify the center of each class + center = meta.groupby(by=group, sort=False).mean() + tick_inds = np.array(center["sort_idx"].values) + if plot_type == "heatmap": + tick_inds = tick_inds + 0.5 + tick_labels = list(center.index.get_level_values(group[0])) + + if ax_type == "x": + ax.set_xticks(tick_inds) + ax.set_xticklabels(tick_labels, ha="center", va="bottom", fontsize=20) + ax.xaxis.tick_top() + ax.set_xlabel(group[0], fontsize=20) + ax.xaxis.set_label_position("top") + elif ax_type == "y": + ax.set_yticks(tick_inds) + ax.set_yticklabels(tick_labels, ha="right", va="center", fontsize=20) + ax.set_ylabel(group[0], fontsize=20) + + if group_border: + sep_inds = _get_separator_info(meta, group) + if plot_type == "scattermap": + sep_inds = sep_inds - 0.5 + + if ax_type == "x": + drawer = ax.axvline + elif ax_type == "y": + drawer = ax.axhline + + for t in list(sep_inds): + drawer(t, color="black", linestyle="-", alpha=1, linewidth=1) + + return ax + + +def scattermap(data, ax=None, legend=False, sizes=(5, 10), **kws): + r""" + Draw a matrix using points instead of a heatmap. Helpful for larger, sparse + matrices. + + Parameters + ---------- + data : np.narray, ndim=2 + Matrix to plot + ax: matplotlib axes object, optional + Axes in which to draw the plot, by default None + legend : bool, optional + [description], by default False + sizes : tuple, optional + min and max of dot sizes, by default (5, 10) + spines : bool, optional + whether to keep the spines of the plot, by default False + border : bool, optional + [description], by default True + **kws : dict, optional + Additional plotting arguments + + Returns + ------- + ax: matplotlib axes object, optional + Axes in which to draw the plot, by default None + """ + + if ax is None: + _, ax = plt.subplots(1, 1, figsize=(20, 20)) + n_verts = data.shape[0] + inds = np.nonzero(data) + edges = data[inds] + scatter_df = pd.DataFrame() + scatter_df["weight"] = edges + scatter_df["x"] = inds[1] + scatter_df["y"] = inds[0] + sns.scatterplot( + data=scatter_df, + x="x", + y="y", + size="weight", + legend=legend, + sizes=sizes, + ax=ax, + linewidth=0, + **kws, + ) + ax.set_xlim((-1, n_verts + 1)) + ax.set_ylim((n_verts + 1, -1)) + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_ylabel("") + ax.set_xlabel("") + return ax + + +def sort_meta(length, meta, group, group_order=["size"], item_order=None): + r""" + Sort the data and metadata according to the sorting method + + Parameters + ---------- + length : int + Number of nodes + meta : pd.DataFrame, pd.Series, list of pd.Series or np.array, optional + Metadata of the matrix such as class, cell type, etc., by default None + group : str, list of str, or array_like, optional + Attribute in meta to group the graph in the plot, by default None + group_order : str, list of str, or array_like, optional + Attribute of the sorting class to sort classes within the graph, + by default "size" + item_order : str, list of str, or array_like, optional + Attribute in meta by which to sort elements within a class, by default None + + Returns + ------- + inds : + The index of the data after sorting + meta : + The metadata after sorting + """ + + # if metadata does not exist, return the original data + if meta is None or len(meta) == 0: + return np.arange(length), meta + + meta = meta.copy() + # total_sort_by keeps track of what attribute was used to sort + total_sort_by = [] + # create new columns in the dataframe that correspond to the sorting order + # one problem with this current sorting algorithm is that we cannot sort + # classes by size and other class attributes at the same time. + + for gc in group: + for co in group_order: + if co == "size": + class_size = meta.groupby(gc).size() + # map each node from the sorting class + # to their sorting class size + meta[f"{gc}_size_order"] = meta[gc].map(class_size) + total_sort_by.append(f"{gc}_size_order") + else: + class_value = meta.groupby(gc)[co].mean() + # map each node from the sorting class + # to certain sorting class attribute + meta[f"{gc}_{co}_order"] = meta[gc].map(class_value) + total_sort_by.append(f"{gc}_{co}_order") + total_sort_by.append(gc) + total_sort_by += item_order + + # arbitrarily sort the data from 1 to length + meta["sort_idx"] = range(len(meta)) + # if we actually need to sort, sort by group_order, group, item_order + if len(total_sort_by) > 0: + meta.sort_values(total_sort_by, inplace=True, kind="mergesort") + + inds = np.copy(meta["sort_idx"].values) + meta["sort_idx"] = range(len(meta)) + return inds, meta + + +def matrixplot( + data, + ax=None, + col_meta=None, + row_meta=None, + plot_type="heatmap", + col_group=None, + row_group=None, + col_group_order="size", + row_group_order="size", + col_item_order=None, + row_item_order=None, + col_color=None, + row_color=None, + col_highlight=None, + row_highlight=None, + col_palette="tab10", + row_palette="tab10", + col_ticks=True, + row_ticks=True, + col_tick_pad=None, + row_tick_pad=None, + col_color_pad=None, + row_color_pad=None, + border=True, + center=0, + cmap="RdBu_r", + sizes=(5, 10), + square=True, + gridline_kws=None, + spinestyle_kws=None, + highlight_kws=None, + **kws, +): + r""" + Sorts and plots a matrix in various ways, and with optional information added to the + margin of the matrix plot. + + Read more in the `Tutorial https://microsoft.github.io/graspologic/tutorials/plotting/matrixplot.html` + + Parameters + ---------- + data : np.ndarray with ndim=2 + Matrix to plot + ax : matplotlib axes object (default=None) + Axes in which to draw the plot. If no axis is passed, one will be created. + plot_type : str in {"heatmap", "scattermap"} (default="heatmap") + "heatmap" will draw the matrix using :func:`seaborn.heatmap`, "scattermap" will + draw each nonzero element of the matrix using :func:`seaborn.scatterplot`. + "scattermap" is recommended for larger sparse matrices. + {row,col}_meta : pd.DataFrame or None, (default=None) + Metadata of the matrix. + + - ``{row,col}_meta`` is pd.DataFrame + All sorting keywords should only be str or list of str. + They should contain references to columns in ``meta``. + - ``{row,col}_meta`` is None + All sorting keywords should only array-like with the same length as the + corresponding axis of ``data``. + {row,col}_group : str, list of str, or array-like, (default=None) + Attribute(s) by which to group rows/columns of ``data``. If multiple groups are + specified, rows/columns will be sorted hierarchically (first by the first group), + then within that group by a possible second group, etc.). Behaves similarly to + :meth:`pandas.DataFrame.sort_values`. + {row,col}_group_order : str, list of str, or array-like, (default="size") + Attribute(s) by which to sort the groups if provided by ``{row,col}_group``. + Groups are sorted by the mean of this attribute in ascending order. "size" is a + special keyword which will sort groups by the number of elements per group. + {row,col}_item_order : str, list of str, or array-like (default=None) + Attribute(s) by which to sort the individual rows/columns within each group. + {row,col}_color : str, list of str, or array-like (default=None) + Attribute(s) to use for drawing colors on the borders of the matrix plot. + {row,col}_highlight : str, list of str, or array-like (default=None) + Attribute(s) in meta by which to draw highlighted separators between specific + groups, can be useful for emphasizing a particular region of the plot. Styling + of the highlighted separators can be specified via `spinestyle_kws`. + {row,col}_palette : str, dict, list of str or dict (default="tab10") + Colormap(s) of the color axes if specified by ``{row,col}_color``. + {row,col}_ticks : bool, optional (default=True) + Whether the plot has labels for the groups specified by ``{row,col}_group``. + {row,col}_tick_pad : int or float (default=None) + Custom padding to use for the distance between tick axes + {row,col}_color_pad : int or float (default=None) + Custom padding to use for the distance between color axes + border : bool (default=True) + Whether the plot should have a border. + center : int (default=0) + The value at which to center the colormap when plotting divergent data (only + used when ``plot_type="heatmap"``). + cmap : str (default="RdBu_r") + Colormap of the heatmap (only used when ``plot_type="heatmap"``). + sizes : tuple (default=(5, 10)) + Min and max sizes of dots (only used when ``plot_type="scattermap"``). + square : bool (default=False) + Whether the plot should be square. + gridline_kws : dict (default=None) + Plotting arguments for the separators. + spinestyle_kws : dict (default=None) + Plotting arguments for the spine border. + highlight_kws : dict (default=None) + Plotting arguments for the highlighted separators. + **kwargs + Additional plotting arguments passed down to the plotting function which will + draw the matrix ``data``, see ``plot_type`` for more information. + + Returns + ------- + ax : matplotlib axes object + Axes in which to draw the plot, by default None + divider : AxesLocator + Divider used to add new axes to the plot + """ + # check for the type and dimension of the data + _check_data(data) + + # check for the plot type + plot_type_opts = ["scattermap", "heatmap"] + if plot_type not in plot_type_opts: + raise ValueError(f"`plot_type` must be one of {plot_type_opts}") + + # check for the types of the sorting arguments + ( + col_meta, + col_group, + col_group_order, + col_item_order, + col_color, + col_highlight, + ) = _check_sorting_kws( + data.shape[0], + col_meta, + col_group, + col_group_order, + col_item_order, + col_color, + col_highlight, + ) + + ( + row_meta, + row_group, + row_group_order, + row_item_order, + row_color, + row_highlight, + ) = _check_sorting_kws( + data.shape[0], + row_meta, + row_group, + row_group_order, + row_item_order, + row_color, + row_highlight, + ) + + # check for the types of the plotting arguments + col_palette = _check_plotting_kws(col_palette, col_color) + row_palette = _check_plotting_kws(row_palette, row_color) + + # sort the data and the metadata according to the sorting method + col_inds, col_meta = sort_meta( + data.shape[0], + col_meta, + col_group, + group_order=col_group_order, + item_order=col_item_order, + ) + row_inds, row_meta = sort_meta( + data.shape[0], + row_meta, + row_group, + group_order=row_group_order, + item_order=row_item_order, + ) + data = data[np.ix_(row_inds, col_inds)] + + # draw the main heatmap/scattermap + if ax is None: + _, ax = plt.subplots(1, 1, figsize=(10, 10)) + + if plot_type == "heatmap": + sns.heatmap( + data, + cmap=cmap, + ax=ax, + center=center, + cbar=False, + xticklabels=False, + yticklabels=False, + **kws, + ) + elif plot_type == "scattermap": + scattermap(data, ax=ax, sizes=sizes, **kws) + + # extra features of the graph + if square: + ax.axis("square") + if plot_type == "scattermap": + ax_pad = 0.5 + else: + ax_pad = 0 + ax.set_ylim(data.shape[0] - ax_pad, 0 - ax_pad) + ax.set_xlim(0 - ax_pad, data.shape[1] - ax_pad) + + # make_axes_locatable allows us to create new axes like colormap + divider = make_axes_locatable(ax) + + # draw separators + # draw top separators + draw_separators( + ax, + ax_type="x", + meta=col_meta, + group=col_group, + plot_type=plot_type, + gridline_kws=gridline_kws, + ) + # draw left separators + draw_separators( + ax, + ax_type="y", + meta=row_meta, + group=row_group, + plot_type=plot_type, + gridline_kws=gridline_kws, + ) + + # draw highlighted separators + if highlight_kws is None: + highlight_kws = dict(color="black", linestyle="-", linewidth=1) + # draw top highlights + if col_highlight is not None: + draw_separators( + ax, + ax_type="x", + meta=col_meta, + group=col_highlight, + plot_type=plot_type, + gridline_kws=highlight_kws, + ) + # draw left highlights + if row_highlight is not None: + draw_separators( + ax, + ax_type="y", + meta=row_meta, + group=row_highlight, + plot_type=plot_type, + gridline_kws=highlight_kws, + ) + + # these two variables are used to identify the first axes + col_first_ax = True + row_first_ax = True + # draw colors + # draw top colors + if len(col_color) > 0: + if col_color_pad is None: + col_color_pad = len(col_color) * [0.5] + if col_first_ax: + col_color_pad[0] = 0 + col_first_ax = False + + rev_color = list(col_color[::-1]) + if len(col_palette) > 1: + rev_color_palette = list(col_palette[::-1]) + else: + rev_color_palette = list(col_palette * len(col_color)) + + for i, sc in enumerate(rev_color): + color_ax = divider.append_axes( + "top", size="3%", pad=col_color_pad[i], sharex=ax + ) + _remove_shared_ax(color_ax) + draw_colors( + color_ax, + meta=col_meta, + divider=divider, + ax_type="x", + color=rev_color[i], + palette=rev_color_palette[i], + ) + # draw left colors + if len(row_color) > 0: + if row_color_pad is None: + row_color_pad = len(row_color) * [0.5] + if row_first_ax: + row_color_pad[0] = 0 + row_first_ax = False + + rev_color = list(row_color[::-1]) + if len(row_palette) > 1: + rev_color_palette = list(row_palette[::-1]) + else: + rev_color_palette = list(row_palette * len(row_color)) + + for i, sc in enumerate(rev_color): + color_ax = divider.append_axes( + "left", size="3%", pad=row_color_pad[i], sharey=ax + ) + _remove_shared_ax(color_ax) + draw_colors( + color_ax, + meta=row_meta, + divider=divider, + ax_type="y", + color=rev_color[i], + palette=rev_color_palette[i], + ) + + # draw ticks + # draw top ticks + if len(col_group) > 0 and col_ticks: + if col_tick_pad is None: + col_tick_pad = len(col_group) * [0.8] + if col_first_ax: + col_tick_pad[0] = 0 + col_first_ax = False + else: + col_tick_pad[0] = 0.5 + + # Reverse the order of the group class, so the ticks are drawn + # in the opposite order + rev_group = list(col_group[::-1]) + for i, sc in enumerate(rev_group): + + # Add a new axis when needed + tick_ax = divider.append_axes( + "top", size="1%", pad=col_tick_pad[i], sharex=ax + ) + _remove_shared_ax(tick_ax) + tick_ax = _set_spines( + tick_ax, left=False, right=False, top=True, bottom=False + ) + + # Draw the ticks for the x axis + draw_ticks( + tick_ax, + ax_type="x", + meta=col_meta, + group=rev_group[i:], + plot_type=plot_type, + ) + ax.xaxis.set_label_position("top") + # draw left ticks + if len(row_group) > 0 and row_ticks: + if row_tick_pad is None: + row_tick_pad = len(row_group) * [0.8] + if row_first_ax: + row_tick_pad[0] = 0 + row_first_ax = False + else: + row_tick_pad[0] = 0.5 + + # Reverse the order of the group class, so the ticks are drawn + # in the opposite order + rev_group = list(row_group[::-1]) + for i, sc in enumerate(rev_group): + # Add a new axis when needed + tick_ax = divider.append_axes( + "left", size="1%", pad=row_tick_pad[i], sharey=ax + ) + _remove_shared_ax(tick_ax) + tick_ax = _set_spines( + tick_ax, left=True, right=False, top=False, bottom=False + ) + + # Draw the ticks for the y axis + draw_ticks( + tick_ax, + ax_type="y", + meta=row_meta, + group=rev_group[i:], + plot_type=plot_type, + ) + + # spines + if spinestyle_kws is None: + spinestyle_kws = dict(linestyle="-", linewidth=1, alpha=0.7) + if border: + for spine in ax.spines.values(): + spine.set_visible(True) + spine.set_linewidth(spinestyle_kws["linewidth"]) + spine.set_linestyle(spinestyle_kws["linestyle"]) + spine.set_alpha(spinestyle_kws["alpha"]) + + return ax, divider + + +def adjplot( + data, + ax=None, + meta=None, + plot_type="heatmap", + group=None, + group_order="size", + item_order=None, + color=None, + highlight=None, + palette="tab10", + ticks=True, + tick_pad=None, + color_pad=None, + border=True, + center=0, + cmap="RdBu_r", + sizes=(5, 10), + square=True, + gridline_kws=None, + spinestyle_kws=None, + highlight_kws=None, + **kws, +): + r""" + Sorts and plots a square matrix in various ways, and with optional information added + to the margin of the matrix plot. + + This function is a wrapper around ``matrixplot`` which assumes that the plotted + matrix is square, and that the rows and columns represent the same items and have + the same metadata (e.g. the adjacency matrix of a graph). + + Read more in the `Tutorial https://microsoft.github.io/graspologic/tutorials/plotting/matrixplot.html` + + Parameters + ---------- + data : np.ndarray with ndim=2 + Matrix to plot, must be square. + ax : matplotlib axes object (default=None) + Axes in which to draw the plot. If no axis is passed, one will be created. + plot_type : str in {"heatmap", "scattermap"} (default="heatmap") + "heatmap" will draw the matrix using :func:`seaborn.heatmap`, "scattermap" will + draw each nonzero element of the matrix using :func:`seaborn.scatterplot`. + "scattermap" is recommended for larger sparse matrices. + meta : pd.DataFrame or None, (default=None) + Metadata of the matrix. + + - ``meta`` is pd.DataFrame + All sorting keywords should only be str or list of str. + They should contain references to columns in ``meta``. + - ``meta`` is None + All sorting keywords should only array-like with the same length as the + corresponding axis of ``data``. + group : str, list of str, or array-like, (default=None) + Attribute(s) by which to group rows/columns of ``data``. If multiple groups are + specified, rows/columns will be sorted hierarchically (first by the first group), + then within that group by a possible second group, etc.). Behaves similarly to + :meth:`pandas.DataFrame.sort_values`. + group_order : str, list of str, or array-like, (default="size") + Attribute(s) by which to sort the groups if provided by ``group``. + Groups are sorted by the mean of this attribute in ascending order. "size" is a + special keyword which will sort groups by the number of elements per group. + item_order : str, list of str, or array-like (default=None) + Attribute(s) by which to sort the individual rows/columns within each group. + color : str, list of str, or array-like (default=None) + Attribute(s) to use for drawing colors on the borders of the matrix plot. + highlight : str, list of str, or array-like (default=None) + Attribute(s) in meta by which to draw highlighted separators between specific + groups, can be useful for emphasizing a particular region of the plot. Styling + of the highlighted separators can be specified via `spinestyle_kws`. + palette : str, dict, list of str or dict (default="tab10") + Colormap(s) of the color axes if specified by ``color``. + ticks : bool, optional (default=True) + Whether the plot has labels for the groups specified by ``group``. + tick_pad : int or float (default=None) + Custom padding to use for the distance between tick axes + color_pad : int or float (default=None) + Custom padding to use for the distance between color axes + border : bool (default=True) + Whether the plot should have a border. + center : int (default=0) + The value at which to center the colormap when plotting divergent data (only + used when ``plot_type="heatmap"``). + cmap : str (default="RdBu_r") + Colormap of the heatmap (only used when ``plot_type="heatmap"``). + sizes : tuple (default=(5, 10)) + Min and max sizes of dots (only used when ``plot_type="scattermap"``). + square : bool (default=False) + Whether the plot should be square. + gridline_kws : dict (default=None) + Plotting arguments for the separators. + spinestyle_kws : dict (default=None) + Plotting arguments for the spine border. + highlight_kws : dict (default=None) + Plotting arguments for the highlighted separators. + **kwargs + Additional plotting arguments passed down to the plotting function which will + draw the matrix ``data``, see ``plot_type`` for more information. + + Returns + ------- + ax : matplotlib axes object + Axes in which to draw the plot, by default None + divider : AxesLocator + Divider used to add new axes to the plot + """ + outs = matrixplot( + data, + ax=ax, + col_meta=meta, + row_meta=meta, + plot_type=plot_type, + col_group=group, + row_group=group, + col_group_order=group_order, + row_group_order=group_order, + col_item_order=item_order, + row_item_order=item_order, + col_color=color, + row_color=color, + col_highlight=highlight, + row_highlight=highlight, + col_palette=palette, + row_palette=palette, + col_ticks=ticks, + row_ticks=ticks, + col_tick_pad=tick_pad, + row_tick_pad=tick_pad, + col_color_pad=color_pad, + row_color_pad=color_pad, + border=border, + center=center, + cmap=cmap, + sizes=sizes, + square=square, + gridline_kws=gridline_kws, + spinestyle_kws=spinestyle_kws, + highlight_kws=highlight_kws, + **kws, + ) + + return outs diff --git a/tests/test_plot_matrix.py b/tests/test_plot_matrix.py new file mode 100644 index 000000000..2c835802b --- /dev/null +++ b/tests/test_plot_matrix.py @@ -0,0 +1,185 @@ +# Copyright (c) Microsoft Corporation and contributors. +# Licensed under the MIT License. + +import numpy as np +import pandas as pd +import pytest + +from graspologic.plot.plot_matrix import adjplot, matrixplot +from graspologic.simulations.simulations import er_np + + +def test_adjplot_inputs(): + X = er_np(100, 0.5) + meta = pd.DataFrame( + { + "hemisphere": np.random.randint(2, size=100), + "region": np.random.randint(2, size=100), + "cell_size": np.random.randint(10, size=100), + } + ) + + # test matrix + with pytest.raises(TypeError): + adjplot(data="input", meta=meta) + with pytest.raises(ValueError): + adjplot(data=np.zeros((2, 2, 2)), meta=meta) + + # test meta + with pytest.raises(ValueError): + bad_meta = pd.DataFrame( + { + "hemisphere": np.random.randint(2, size=1), + "region": np.random.randint(2, size=1), + "cell_size": np.random.randint(10, size=1), + } + ) + adjplot(X, meta=bad_meta) + + # test plot type + with pytest.raises(ValueError): + adjplot(X, plot_type="bad plottype") + + # test sorting_kws + with pytest.raises(TypeError): + adjplot(X, meta=meta, group=123) + with pytest.raises(TypeError): + adjplot(X, meta=meta, group_order=123) + with pytest.raises(TypeError): + adjplot(X, meta=meta, item_order=123) + with pytest.raises(TypeError): + adjplot(X, meta=meta, color=123) + with pytest.raises(ValueError): + adjplot(X, meta=meta, group="bad value") + with pytest.raises(ValueError): + adjplot(X, meta=meta, group_order="bad value") + with pytest.raises(ValueError): + adjplot(X, meta=meta, item_order="bad value") + with pytest.raises(ValueError): + adjplot(X, meta=meta, color="bad value") + + +def test_adjplot_output(): + """ + simple function to see if plot is made without errors + """ + X = er_np(10, 0.5) + meta = pd.DataFrame( + { + "hemisphere": np.random.randint(2, size=10), + "region": np.random.randint(2, size=10), + "cell_size": np.random.randint(10, size=10), + } + ) + ax = adjplot(X, meta=meta) + ax = adjplot(X, meta=meta, group="hemisphere") + ax = adjplot(X, meta=meta, group="hemisphere", group_order="size") + ax = adjplot(X, meta=meta, group="hemisphere", item_order="cell_size") + + +def test_matrix_inputs(): + X = er_np(100, 0.5) + meta = pd.DataFrame( + { + "hemisphere": np.random.randint(2, size=100), + "region": np.random.randint(2, size=100), + "cell_size": np.random.randint(10, size=100), + } + ) + + # test matrix + with pytest.raises(TypeError): + matrixplot(data="input", col_meta=meta, row_meta=meta) + with pytest.raises(ValueError): + matrixplot(data=np.zeros((2, 2, 2)), col_meta=meta, row_meta=meta) + + # test meta + with pytest.raises(ValueError): + bad_meta = pd.DataFrame( + { + "hemisphere": np.random.randint(2, size=1), + "region": np.random.randint(2, size=1), + "cell_size": np.random.randint(10, size=1), + } + ) + matrixplot(X, col_meta=bad_meta, row_meta=bad_meta) + + # test plot type + with pytest.raises(ValueError): + matrixplot(X, plot_type="bad plottype", col_meta=meta, row_meta=meta) + + # test sorting_kws + with pytest.raises(TypeError): + matrixplot(X, col_meta=meta, row_meta=meta, col_group=123, row_group=123) + with pytest.raises(TypeError): + matrixplot( + X, col_meta=meta, col_group_order=123, row_meta=meta, row_group_order=123 + ) + with pytest.raises(TypeError): + matrixplot( + X, col_meta=meta, col_item_order=123, row_meta=meta, row_item_order=123 + ) + with pytest.raises(TypeError): + matrixplot(X, col_meta=meta, col_color=123, row_meta=meta, row_color=123) + with pytest.raises(ValueError): + matrixplot( + X, + col_meta=meta, + col_group="bad value", + row_meta=meta, + row_group="bad value", + ) + with pytest.raises(ValueError): + matrixplot( + X, + col_meta=meta, + col_group_order="bad value", + row_meta=meta, + row_group_order="bad value", + ) + with pytest.raises(ValueError): + matrixplot( + X, + col_meta=meta, + col_item_order="bad value", + row_meta=meta, + row_item_order="bad value", + ) + with pytest.raises(ValueError): + matrixplot( + X, + col_meta=meta, + col_color="bad value", + row_meta=meta, + row_color="bad value", + ) + + +def test_matrix_output(): + """ + simple function to see if plot is made without errors + """ + X = er_np(10, 0.5) + meta = pd.DataFrame( + { + "hemisphere": np.random.randint(2, size=10), + "region": np.random.randint(2, size=10), + "cell_size": np.random.randint(10, size=10), + } + ) + ax = matrixplot(X, col_meta=meta, row_meta=meta) + ax = matrixplot(X, col_meta=meta, row_meta=meta, row_group="hemisphere") + ax = matrixplot( + X, + col_meta=meta, + row_meta=meta, + row_group="hemisphere", + col_group_order="size", + ) + ax = matrixplot( + X, + col_meta=meta, + row_meta=meta, + col_group="hemisphere", + row_item_order="cell_size", + ) From 3b5dbe79dc7cc754f75ba26aeb23d5dd077a9e97 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Thu, 25 Feb 2021 14:49:01 -0800 Subject: [PATCH 117/140] Quick and dirty release notes prior to release 0.1.0 so we can move along --- docs/reference/release.rst | 65 ++++------------------ docs/reference/release/graspy_releases.rst | 62 +++++++++++++++++++++ 2 files changed, 73 insertions(+), 54 deletions(-) create mode 100644 docs/reference/release/graspy_releases.rst diff --git a/docs/reference/release.rst b/docs/reference/release.rst index 75cb7a8e9..eda6ae977 100644 --- a/docs/reference/release.rst +++ b/docs/reference/release.rst @@ -5,64 +5,21 @@ Release Log graspologic 0.1.0 ----------------- -Release date: ? +This release represents the first release of the `GraSPy` and `topologic`_ libraries. -GraSPy 0.3 ----------- -Release date: 04 Aug 2020 -Supports Python 3.6, and 3.7 +In addition to all of the features that ``GraSPy`` had by version +:ref:`release/release_0.3`, this release includes a number of feature enhancements and +bug fixes - frankly, too many to go into. -.. toctree:: - :maxdepth: 1 - - release/release_0.3.rst - -GraSPy 0.2 ----------- -Release date: 02 Mar 2020 -Supports Python 3.5, 3.6, and 3.7 - -.. toctree:: - :maxdepth: 1 - - release/release_0.2.rst - -GraSPy 0.1 ----------- -Release date: 05 Aug 2019 -Supports Python 3.5, 3.6, and 3.7 +Please treat this as a brand new first release, informed heavily by the prior `GraSPy`, +and extra special thanks to **all** of our +`contributors `_! +Previous GraSPy Releases +------------------------ .. toctree:: :maxdepth: 1 - release/release_0.1.rst - -GraSPy 0.0.3 ------------- -Release date: 11 June 2019 -Supports Python 3.5, 3.6, and 3.7. - -.. toctree:: - :maxdepth: 1 - - release/release_0.0.3.rst - -GraSPy 0.0.2 ------------- -Release date: 26 March 2019 -Supports Python 3.5, 3.6, and 3.7. - -.. toctree:: - :maxdepth: 1 - - release/release_0.0.2.rst - -GraSPy 0.0.1 ------------- -Release date: 14 December 2018 -Supports Python 3.5, 3.6, and 3.7. - -.. toctree:: - :maxdepth: 1 + release/graspy_releases.rst - release/release_0.0.1.rst +.. _topologic: https://github.com/microsoft/topologic diff --git a/docs/reference/release/graspy_releases.rst b/docs/reference/release/graspy_releases.rst new file mode 100644 index 000000000..54bfaf675 --- /dev/null +++ b/docs/reference/release/graspy_releases.rst @@ -0,0 +1,62 @@ +GraSPy Release Log +================== + +GraSPy 0.3 +---------- +Release date: 04 Aug 2020 +Supports Python 3.6, and 3.7 + +.. toctree:: + :maxdepth: 1 + + release_0.3.rst + +GraSPy 0.2 +---------- +Release date: 02 Mar 2020 +Supports Python 3.5, 3.6, and 3.7 + +.. toctree:: + :maxdepth: 1 + + release_0.2.rst + +GraSPy 0.1 +---------- +Release date: 05 Aug 2019 +Supports Python 3.5, 3.6, and 3.7 + +.. toctree:: + :maxdepth: 1 + + release_0.1.rst + +GraSPy 0.0.3 +------------ +Release date: 11 June 2019 +Supports Python 3.5, 3.6, and 3.7. + +.. toctree:: + :maxdepth: 1 + + release_0.0.3.rst + +GraSPy 0.0.2 +------------ +Release date: 26 March 2019 +Supports Python 3.5, 3.6, and 3.7. + +.. toctree:: + :maxdepth: 1 + + release_0.0.2.rst + +GraSPy 0.0.1 +------------ +Release date: 14 December 2018 +Supports Python 3.5, 3.6, and 3.7. + +.. toctree:: + :maxdepth: 1 + + release_0.0.1.rst From a64b87abf2920f73946568b653fcecf5df6bfe48 Mon Sep 17 00:00:00 2001 From: Dwayne Pryce Date: Thu, 25 Feb 2021 15:16:44 -0800 Subject: [PATCH 118/140] Got the docs to work correctly --- docs/reference/release.rst | 13 +++++++------ docs/reference/release/release_0.3.rst | 2 ++ 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/docs/reference/release.rst b/docs/reference/release.rst index eda6ae977..c2544abf6 100644 --- a/docs/reference/release.rst +++ b/docs/reference/release.rst @@ -5,14 +5,15 @@ Release Log graspologic 0.1.0 ----------------- -This release represents the first release of the `GraSPy` and `topologic`_ libraries. +This release represents the first release of the ``GraSPy`` and `topologic`_ libraries +merger! -In addition to all of the features that ``GraSPy`` had by version -:ref:`release/release_0.3`, this release includes a number of feature enhancements and -bug fixes - frankly, too many to go into. +In addition to all of the features that ``GraSPy`` had in :ref:`last-graspy-label`, +this release includes a number of feature enhancements and bug fixes - frankly, too +many to go into. -Please treat this as a brand new first release, informed heavily by the prior `GraSPy`, -and extra special thanks to **all** of our +Please treat this as a brand new first release, informed heavily by the prior +``GraSPy``, and extra special thanks to **all** of our `contributors `_! Previous GraSPy Releases diff --git a/docs/reference/release/release_0.3.rst b/docs/reference/release/release_0.3.rst index db571a289..741429f23 100644 --- a/docs/reference/release/release_0.3.rst +++ b/docs/reference/release/release_0.3.rst @@ -1,3 +1,5 @@ +.. _last-graspy-label: + Release Notes: GraSPy 0.3 ========================= From 2a867b6bd4022dc731b277de2f0222fc3199662c Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Fri, 5 Nov 2021 08:02:46 -0700 Subject: [PATCH 119/140] Fixing a sort issue that... somehow only exists on main? I'm not sure what happened. --- tests/test_n2v.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/test_n2v.py b/tests/test_n2v.py index db217708c..334bcb9b7 100644 --- a/tests/test_n2v.py +++ b/tests/test_n2v.py @@ -2,8 +2,10 @@ # Licensed under the MIT License. import unittest + import networkx as nx import numpy as np + import graspologic.embed.n2v as n2v from graspologic.embed.n2v import _Node2VecGraph From 4d9c86fa677fc8c9c280f2807bf6dc22acf00c67 Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Wed, 22 Sep 2021 10:53:09 -0700 Subject: [PATCH 120/140] Preparing for 0.3.1 --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 7a2030e1f..1ce0be867 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = graspologic -version = 0.3.0 +version = 0.3.1 description = A set of python modules for graph statistics long_description = file: README.md From d2e326c88d9896fda5b8c5f577ffaed78ca2b5f7 Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Fri, 24 Sep 2021 12:55:30 -0400 Subject: [PATCH 121/140] Update matplotlib version to only disallow 3.3.* (#854) --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 1ce0be867..30d2f8a96 100644 --- a/setup.cfg +++ b/setup.cfg @@ -31,7 +31,7 @@ install_requires = graspologic-native>=1.0.0 hyppo>=0.2.0 joblib>=0.17.0 # Older versions of joblib cause issue #806. Transitive dependency of hyppo. - matplotlib>=3.0.0,<=3.3.0 + matplotlib>=3.0.0,!=3.3.* networkx>=2.1 numpy>=1.8.1 POT>=0.7.0 From 0d81046bc984b48587e0e690eedbb0f7b226f3e8 Mon Sep 17 00:00:00 2001 From: Alex Loftus Date: Wed, 13 Oct 2021 13:22:48 -0400 Subject: [PATCH 122/140] Add references to BaseEmbed class (#857) * Update setup.cfg * add references to base embed --- graspologic/embed/base.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/graspologic/embed/base.py b/graspologic/embed/base.py index 4107143bf..fc31ae0ce 100644 --- a/graspologic/embed/base.py +++ b/graspologic/embed/base.py @@ -269,6 +269,16 @@ def transform(self, X): directed case, if A is the original in-sample adjacency matrix, the tuple (A.T, A) will need to be passed to ``transform`` if you do not wish to use ``fit_transform``. + + References + ---------- + .. [1] Sussman, D.L., Tang, M., Fishkind, D.E., Priebe, C.E. "A + Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," + Journal of the American Statistical Association, Vol. 107(499), 2012 + + .. [2] Levin, K., Roosta-Khorasani, F., Mahoney, M. W., & Priebe, C. E. (2018). + Out-of-sample extension of graph adjacency spectral embedding. PMLR: Proceedings + of Machine Learning Research, 80, 2975-2984 """ # checks From 4039a9c6363f26b25f2888d16a04c9f1aa4c032c Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Fri, 15 Oct 2021 13:56:57 -0400 Subject: [PATCH 123/140] Updated CONTRIBUTING.md and PR template to include PR title specifications (#861) * Update CONTRIBUTING.md * Update PULL_REQUEST_TEMPLATE.md --- .github/PULL_REQUEST_TEMPLATE.md | 2 +- CONTRIBUTING.md | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 996950cd0..08ef882db 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -2,7 +2,7 @@ Thanks for contributing a pull request! Please ensure you have taken a look at the contribution guidelines: https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md --> - +- [ ] Does this PR have a descriptive title that could go in our release notes? - [ ] Does this PR add any new dependencies? - [ ] Does this PR modify any existing APIs? - [ ] Is the change to the API backwards compatible? diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 3c5c7b8dc..05d6b78fd 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -127,7 +127,10 @@ branch. Steps: We recommended that your contribution complies with the following rules before you submit a pull request: - Follow the [coding-guidelines](#guidelines). -- Give your pull request a helpful title that summarizes what your contribution does. +- Give your pull request (PR) a helpful title that summarizes what your contribution does. We are using PR titles to automatically generate release notes; examples of helpful PR title formats include: + - `Added Feature[Set] {Title|Short Descriptor} in ModuleOrPackageName` + - `Fixed bug in [ClassName.method_name|ModuleOrPackageName.function_name] where ShortDescription` + - `Updated [ClassName[.method_name]|ModuleOrPackageName.function_name] to ShortDescription` - Link your pull request to the issue (see: [closing keywords](https://docs.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue) for an easy way of linking your issue) From 8932ee3fb94ca3db5776f9841aae88de16f7b934 Mon Sep 17 00:00:00 2001 From: Alex Loftus Date: Fri, 22 Oct 2021 15:12:29 -0400 Subject: [PATCH 124/140] Remove dtype requirement in vertex nomination (#865) * Remove dtype requirement in vertex nomination Why are we requiring that the adjacency matrix that SVN eats is type `int`? our `sbm` simulation from `models` returns a float64. Seems weird to make an sbm model, then have to cast it to a different type to do anything. if it really matters and we care, let's just do `X = X.astype(int)` inside the code? * fix up VN tutorial * allow lists for y too * make tests pass * update mypy * use Union operator instead * remove typing import * ran isort --- .../SpectralVertexNomination.ipynb | 126 +++++++++--------- graspologic/nominate/spectralVN.py | 10 +- tests/test_spectral_nomination.py | 6 - 3 files changed, 70 insertions(+), 72 deletions(-) diff --git a/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb b/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb index 36877f475..12d383935 100644 --- a/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb +++ b/docs/tutorials/vertex_nomination/SpectralVertexNomination.ipynb @@ -2,18 +2,24 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "# Spectral Vertex Nomination\n", "\n", "This demo shows how to use the Spectral Vertex Nomination (SVN) class. We will use SVN to nominate vertices in a Stochastic Block Model (SBM)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# imports\n", @@ -24,16 +30,16 @@ "from matplotlib import pyplot as plt\n", "\n", "%matplotlib inline" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "## What is Spectral Vertex Nomination?\n", "\n", @@ -42,22 +48,24 @@ "\n", "SVN has the advantage of being extremely efficient on very large graphs, and requires very little prior knowledge of a graph's properties or structure. However, it does somewhat conflate the notion of community membership with vertex similarity.\n", "This might not be appropriate for all vertex nomination use cases. Synthetic SBM data, with inherently defined communities, is therefore a good setting for demonstrating SVN." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
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BhxTw9F+ca3z+GSNrlr+4Dr4kJCQkAMBu4/OwpnmRMCCRAp7+i/nG5z8ysn/w1EtISEgoE+b8srJpXiQMSKSAp//iOONzByO7BkB3/vnJSilVF48SEhIGOv7N+PyTpnmRMCCRAp7+i7HG5y2UoNb6EIDi5+iDkd3AnJCQkFAalFKnAnh7/vUAgBua6E7CAEQKePovRhqfDwjk9xufR5XsS0JCwgCGUmoqgG/jyJrzEa31mia6lDAAkQKehISEhIS6QSk1AtkDUKfnh34C4LPN8yhhoCIFPP0Xe4zPQwXy5i8mdnulEhISEoRQSg0F8H8AnpsfugfA63R6AFxCE5ACnv6LHcbniZRg/pDC0fnXLgB76+RTQkLCAEH+apvv48h7/B4A8BKtdZpfEpqCFPD0X/zd+DyHkZ2BI+9PeiztvhISEmqBUqoV2UMGz8sP/RnAi9O7+hKaiRTw9F88bHx+FiP7bE+9hISEhCDkjPE3Abw8P/RXAOcEPI05IaEuSAFP/8XPjc8vYmTNpytzT2VOSEhIcEIp1QLg68jejA4Ay5C9tDi9ny+h6UgBT//FrwBszj+frZQ60SWUvzz09fnXA8h+TZGQkJAQBKXUIAD/BeB1+aFHASzI39eXkNB0pICnnyJ/mOCn8q8KwFeVUuNMmfwXFP+NIw8avCXtxBISEkKRP5393wG8JT/0GIAXaq03NM+rhISjodL9qf0X+a8kbgdwen5oNbJJ6TFkNypfAGBeXrYMwKla652N9jOhb0EpdQyysWHiaQBeln/+DYC7rfLvaa3/XG/fEvomlFKfBvDB/GsXgPcie2UNh19orffVzbGEBAMp4OnnyFmd7+LIT0Nd+BOAV2qtVzXGq4S+DKXUmchSoiF4u9b6K6U7k1AJKKXuAnBGRNVjtNYd5XqTkOBGSmn1c+S/jDgb2X06PwGwDkAngI0A7kT2Mr+TU7CTkJCQkNCfkRiehISEhISEhH6PxPAkJCQkJCQk9HukgCchISEhISGh3yMFPAkJCQkJCQn9HingGWBQSj1dKfVdpRT3uomEhB4opd6ilPq6UmpUs31JqAaUUoOUUjcppT7cbF8SEoB00/KAg1JqC4AJANZqrWc025+Evo/83Uhd+dePaa2vaqY/CdWAUurlOPLk9ula63XN9CchITE8Awj501An5F+nN9OXhEphvvH5aU3zIqFqeKbx+YSmeZGQkCMFPAMLx5hf8ldLJCRwOM34nFJaCVI82/jsfJdfQkIjkQKegQX7SagxT0ZNGHgwx8nzlVLDmuZJQiWQp0Gfbxw6p1m+JCQUSAHPwMKbrO+vd0olJOTIX01yvnFouPU9IcGFlwIYY3w/Wyk1qVnOJCQAKeAZMFBKvRpH3qe1JP//ZqXU85rkUkIfR37P13UAhgHYA+DXedGnlVJjm+VXQt+GUmo0gM/kX+8FsB3AEADX52MqIaEpSAFPP4dSapRS6oMA/ic/dCeA1wD4G4AWAL9QSv0/pdSQZvmY0PeQvzH9Gzjy1vQrAVyE7D1sTwLwW6XUC9MCllBAZTgDwG8APAVAN4B3Ixs7APAWAN9SSh3bJBcTBjjSz9L7CZRSgwCMBTAN2WRzAoBTAJwFoLg5eTmAM7XWm5RSxyF7I3Z7XrYLwC8B/B7AXwF0AFgLYJdOg6TfIr/XYiKAWQDmIbu59CwAzwJQBDNfRfY29MNKqTfk3wfnZSsB/ALAXwA8AmA1gHVa6wONakNC45HfxzUZwFxkc81JAM4FMDsX6UY2Zr6WB8X/gSPBM5DNM78G8CCAvyObazZorbsb04KEgYgU8JSMPPAYYv0NdRzzHQ+VHYls4pmEI4uQjf0AvgTgw1rrPYavU5BRz29Gxva4sAfAVgA7rb89ud4DzN/B/M/87PreOZADqzzwaOS4GYts3BSPKXBhDYCrAPyneW6UUicDuB5H35RqYxuyVIY9bvbCP1aK8eQbI65jhwbquMkDiVbUZ3y4ZIchC44nI5t3fLgPwPu01vdZvr4dwMcBzPTU6wawGUfGyo78/y70nmtcc4943KTAamBiQAc8SqnhyJ5HMx3AeGQ32RV/YwGMQHbxD8v/25+L7+ak0NrINniwFxmb8zCy3ffPtNbbfcJKqanIbkQ9E9lObR78AVA9YU9SvslLuhjuBrDJ+Nuotd5Xi4P5xD0O2ZiZhmycjMWRcTMa2Y29vjFjfjbHTTP628YmAMsA/AnAUgC/1lp3uQTzfjgRwMsBnAzg6chYokZDQzYWpMcOIFtozXGzydcPUiilWpAFCjPy/2Nx9HwzGrIxY4+bZqcUNYAVyOabewD8RGv9kE9YKdUK4HRkNzU/E9l8M64BftroRrnjZj+yjeFGHBk327TWhxvWogQWAybgUUqNQPbTyAXILrITkQU5zUItF5d5bC+syRkZNRx9oeWT8xQcCQbH4ehAcAx6L+q+ibqvBIEF1iJb1B9CFgzeTaVflFLtyILB05CNm+OQtasZOIzyxs1O9B4z22pxLn+uUzuOBIPmeCn+XBsG1xjqK0EgkC2OjyEbN38A8DMAf/YxS3kw+FRk4+bZyB7WOAfNa88h1LZxMI9twZExsxlZ+rLWTcRoZGOmCAbHoHcwyI0Xc+PZ7CCwwD5kgeAyAL8FsFRrvbq5Lg1s9PuAJw90PgzgQtA0rEmh+tI2LlrVt0ukJpGugUTD52m+NvgpcymtHlpvDLIJtLiHyYXNAD4L4HPmLl4p9SRk6b5XwT+BHkLvMWOmbSgaPnjcaK0PEe3od8gD73qPEfvYUGQB/mTQAcpDAK7QWi8xDyqlXgzgamSMlw8Fi+RK24SkbqTjZsCwDHmwaaaHGzVuhiFL91EpYiB71cYirfXfy2hvQhj6dcCjlJqM7FdJxVM+DyGLtB9ANmGtQLbjX6+17myKkwl1RT4BjkC2gLUju6H7RACnAngOjgQzdwN4idZ6r1LqLGQTU/FU4Z0A7kCW6nkEwCpk42bzQFpMBhKMHwFMRnYj7jxkr9h4IY5+YvkntNYfzet8GMAnjLIVyH4Y8CCAR5HdE7UG6YcA/RZ5ym4CMob8WGQ3dD8dWWZhbC62F8ArtNa3N8HFAY3+HvDchewpsYcAfArAzbXS9gn9B3m66gMA3pMf+hqADyH7ldpYAOsBXAbgO7Xew5HQP5AH0M8GcC2ye94A4I3Ifq7/3fz7b5GNmwdSYJMA9Pwo4VXI2OQZyLIHJ2qtVzXVsQGG/h7wFI17tdb6+011JqHPQil1KYDP5V+/B+DVyO5VeFaakBJcyBewHwN4MTK2rxMZ83M7gPMGWvoxQQal1Axk94FNAfAVrfXbm+zSgMJACHh+prU+r9m+JPRd5OmLh5ClurqQ3WR9sdb65qY6ltCnkT+c8XEcfY/XU7TWjzbJpYQKQCn1TgCLARzQWjfrBxADEgPhScv/w4skDGTk9+F8Lf/aiuyntt9qnkcJVYDWegWypwoX+EMKdhIE+CayX/5RP6ZIqAMGQsDzy2Y7kFAJ/Nr4/LDWemPTPEmoEu4yPv+iWU4kVAda653IfgCR0GD094Bni9Z6Q7OdSKgE/mJ8Xt8sJxIqB3PherhpXiRUDSngaQL6e8CzrNkOJFQD+cMHi0cT7G2mLwmVgvkE861N8yKhakjP4WkC+nvA8+dmO5BQKWxG9rC2J5rtSEJlsA3ZmOlE9lqBhAQJliPNMw1HZX+lpZSaBOAbyF4XYeIgsod83b1u7doLd+zcCQD4woveiq7Vm/CBJ36F/QeyNwncesbrcf3K32HRiS8EVPZDi4/c90O0tbXhI896SY/CT/35Z9i/fz8+eeorswPd3bh2+V3YvHkz9uzdi8XnX4DDe/fj0MbteN+jv8Ch7uy9dOPHjcPHZp6GtllTsHDJbQCA9mnT0NXVhc1btmDx+dnLgzufWIeWiWNw4d3/CwAYOmQIpk2bhrXr1uGmc9+cmdyxB93bd6HtmPYevw5t2AqtNVqnTcTCJbdh8fkXoLNjPVrGjMCF93wHANA6eDCuP+4cDJ42AYOGDQEAHN69D9evvA8rV67E4fz8T5wwoafdha83n/QyXLn6Hmzdlj26aPFL3o7Ojg24YuXduO6s12PhktswfNgwXH3MmWidNQVqcPZg2u5tu3DNqvuwes2aHl+nTZ2KD808Be/5/Q96jh0zZw62bt2KXbt3Z+fo7Deja90WLHrsDhzs7MTi8y9A9449uH717/D+p57dU+/jD/wYSil87Dnn9/g6a+ZMLJr1PLSMGw0AuOhn/43rjzun53wvPv8CHN69D4c270DbMdOOOt9DhgzB2nXrAAB7du/G8ccfjwMHD+KTp7wi61ej3Xv2ZuTPkXZPhhqcvbP1Q7/9HkaNGoVVq1f3nNur/vATdHd348qTXw4AWLjkNtx0wnlomTAaF/7mW1h8/gXQBztx3T9+g3Xr1+PGc94EAPjA3d/BVTNOQ9vc9p5ze2jjNujuw2htn3hUu3fv3o3tO3Zkug4dwnV/+zU2btyIffv3Z+3esw/Xd9yHRSechYU//TIAYMKECRhqtHvx+Rega80mqCFtGDxpbNbRWqNzxXoMnjQWg0YNx8Ilt2HY0KGYOnUq1qxZg5tf/FYsXHIbxo0d29PuQteVv1+CK2aegsFTJxwZmyvW4aOr78FnXvDPmfq83Zc/6XSooW0AgPf/+tuYMGECVnR09Ogq2n3Rn37UMwZmzpiBvXv3Ytv27Xm7u9G1aiMGTx2Pd9/5DQDAyBEj8KnZL0DbnKnAoEFYuOQ2fP55r4HefxAXPfjjHl03P/1lUEPa8J77v5fZfOk7erX71gVvwqH1W9E6czJUa+/zvWf3bnztDZega/0WKKXwnj/8sEf/nNmzcdnM5+HC32b3wX/h3Lfgur/fjfXr1+PAwYMAgNGjRh3VbgCYNHEiWltbsW79kQxr0e5PnfaqrA8PdeO6v92FTZs2Ye++fT3tnjRpElatWoXuw9lzMSeMH49hw4Zhzdq1Pbrap01DZ2cntmzNiCmlVObr7Ofh3b/O5qKe8712Lbq6skdR3XLaa3Hd6t9h5apVPeO8a/1WQAEXGe2ePWsWLp/1PLSMHYWFS25DW1sbpre3O9vd0dGBYiUy290zR67aiCtX34Nt2zNCbXBLC2bOnNnT7sXnX4DL7vwmJk+ejEXzzsTCpV/pafdHZ5yKtllTAGTX383PeDlU62AMnjwOC5fc1tPuLVu34tozXgsAeO/t38DUqVOxdu1adObtHjtmDMaMGYOVq448qWLK5MkAgI2bNh3V7p07d6JYe9paWzF9+nRs2LChZ+0ZNWoUJk6YgKHDhvWV12AMCFQy4FFKjQJwP7KnnwLZLyX+F9kzMB4v3oS7Zs0aPXHCBFwyfJ5bEYMb9y0HgJ76xXcTlwyfd9Rx25atwzxu6rX1+PTZOm3blD2XXtMPn6ytz2fDV9fln6++6ZOrnbYflA4Kvnptc9tx7cN3eP2N6Xvbn1CffWOjVtlQ2NeBZNz49EiuJZdNro7rPMRctwXsMWZftwVWdHTgmDlzvDal56We56/e+qnrowydxfcC3FwjlaOuaVf92LbZ9bZs3YoZM2akgKeBqGrAcyOAi5E9M+VNWutvu+SWL1+ubzrhPHJhBNwBBBc8UAEDZ6cAN5lTPnE6Yi54lz0K3ERRyPj6ivLZZ4vyn+ofnw67TrFwuXT4FjuqD31+cX6EtEUSvHI++IKZWtvF2bN9pfzggn/7mNSH0HNj1weAi5ct7TVuQvWVUSc2QCjDx9j69Q40OB2h81MtY96ea+bNm5cCngZicLMdCIVSajyA/5d//agv2AHQQ4dyO0puR+6q61sYqAlHGkzYZdQi4PPDblfIxepb7AoUumOCPsqW3YecH7ZtaQBFTZytra29ZDnYjJSvXSFwBRausSNlNKSLh22L81HaP1Td0P6hAjsT1Dii7Eo3Fqbu1tZWclyZZVzbqWCQCwyoNoYE0bHBCuWvD3Z/+HyWBOwuOS7okx5z+RSyMa5lzCeUgyretPxSZA9s2gfgFkrw8P6DDXEoof9gxvTpzXZhQCA2QO6rSONmYCNmPA8bmp472GhULqWllPoqgDcD+D+t9T9Rsgf279fcToqj7Iu6XKpAykb4ynw+unx2wd5RcTt4n86Y/pBSwWYZx4z52srZoiDZVQ+eNgGH1h/5dbHPHrebt+tLKHtOj7QtElC71LL6NNSP0HoFJGM2RM6W9dk2ZTZs2ICpU6d65WN291zfxM4hEru2Tvval/QxZ8PFjJTVNyEog3VxzY2uc2737Rd1x4BLaSmlpiG7JeVkZC/iHQngLK31XcL68wDcAOD5yH4d+WMA79Nab2HrVjDgeQjAUwF8SGt9NSX7+GOP6euenP26J5YOdcm44Lt4fRNFSIBApbTMOlSKoZZALFSfT84Fqi0cJHKhaTHzXgwuKOPOk8vPkImamiwpGp3S4Spz6aslQIsNiGsZK1K/pQGQS96Gb9yEoIzggdNpl7n0lxFA1EuHdJz6dHB9KtnQ1HLd+rB27Voc+6QnDcSA50wAvwLwGIBNAE6FMODJX776ZwA7AHweWbB0GYAOACdrrbuo+pW6h0cp1YIjv8x6kJMvfk6YkJCQkJDQlzCA16c/Apiotd6qlHoFgB8w8iY+BGAYgKdrrdcCgFLqAWSvkHozgP+iKleK4VFKTQdQPNzlBK01GUJfdOypuvOJdb2OS3eYHB1ZlFHpLp+clHGQ6qfqhexUyki/2D6GshG2/5Q+X9ukO0VbdsuWLZg4cSKr39Ybkway/bDLTDSS6q+nDo6RCWXCavFVwsj56tj1zHHj0i8dOxQLLPGrjHEYkkorKw3k0l2USVhKTo7ywTffSdlRG1L76VdagBHwSBmejQDu0Fq/0Tr+KIAVWusXk/UrFvA8F9nzdwBgjNZ6FyW/ft06feX0UwHUntKSIiTFIAmobB0hCwHlh0sftRj52mDbCl08JKmxkACQC06p9nC2ylgUpPpCgkNKjvLLV0+a3uHGdhnpBhdC0g3UufXJShc46houYwGW9g0V3HPXo0SO2oiYcJ2T0A1TPYIaTr+kTq1rhOscbd++HdPa21PAIwx4DMLjfVrrz1llXwNwrtZ6CqWjar/SKn4KsYcLdgDgypmnHfXdHLTU5E3J+fRRspSOeiM2gJMsIBx8/RjbHyELYqxcx8qVTluSwMwukwYpIailbQVMv6RBm9RWiL5a+6OMcSSdB+wy+3Mxbii5WJRxDUvHQwhzIW2n1H+pjrLntJA6ZZzPAmPGjClNVzOglNrB/ZVsclr+f72jbD2AyfltL15U6h4eABPy/5slwm1zpqHziXW9IvTisx31m3IuedfEwE26scFTAelkVJSbbQmFXVfKwLh8oPpbojt2V0XtuF1ytq0vvPQd3smVY2ckwQ61Q7bPn6SOlCksdEsWDsqeS69LH8c4SNomtWsft/1w+egKvqhNDxXsANm4ufbhO2pemKmxR50vW87XTmkgz10D3DzDzSVUG6gyqT6J/hCfag10inFo6lm5ahXmzWvsZvidak6ZKZ2dJeqSYFj+/6Cj7IAhs8enoGoBTxESB3W0L+iQTrZSGte2IZ2wKLgWQR+9LvHDp9Oli/Lf9kMywVLyLv9dkxsXGPnayflVfF80f4FTF2XLNZlJF0xqPPr6iNMXGqS6givufLhsS0BdBy7fuTa7gk1KX6zfZj1XsHbxsqVkvRAWjOp7iX6unu+a44JUiR8+1BIwuPqOmtMa4VMoYuf/vgyt9dgGm9yf/x/iKBtqyThRtYBnbP5/h0S4eFEjF8VzkxHF5nADWboTpWz7FmufPy5dvoU1NFAyj/sWQGrSNvuS2ona9iWBEQUuaC3KPrHxAXI8hO5YQxZxV31bl8sn83gIC+CCZGL2BaKUDd+55DYgvkWXYy5cx6g2m//tNlJ6i8+78xfg+vRLgoniey0bprJkOFDXY1EubbMUkuvNZ4u6pl3jkZs/JbA3lraetrntuPnxe0W6EgAcSWVNc5RNA7CpeI+mD1W7hyeI4SneZJuQIEVbW1uzXagrYtJH9UB/2+3293GTUD66NmzlhRJ6kP8MfTOyhxXaeC6Av3A6qsbwBAU8V8zKfqFVNp1I0alc+kyyA6/FL4qNcvlRC7NCMUyu7zajw9HwFDvmkqNYHOocmX4tmr+Ate+yFbKDl0KSkqRsh9gvwy+fPm63L9k9U+OIkqPOoWQcFZ997E/xed369d63pdeSaqHmE1ed0JRhjI/U/GEzGq4y234tbKO0nmvecfla5vxM9UGzNh4tFfpNmFLqWADQWj9uHP4egDcrpaYbz+FZAOA4ANdxOqsW8IzM/3tvSjLROnUCrn34jqOOSVMKJrjFnlpUqFQSNeFSkMr52ildCHxtoWhaW3/IQucKRFy++forJECx/Sh0LFxyW686khSOrbPMAJtLD0nGn3SMSSZirq7UlmvBoa4Xys9arx2qj7nrxQRlPyZVFRoMhOiSzh+UXd+5s4+5zqVEP9c/kgDTTi1JzmVIsBUqW/ixoqNDVKc/Qin14fxjcTLerJR6PoAdWuviHZnF4j3HqPppAP8M4FdKqeJJy5cjexDxVzm7VQt4RDcmJSQk9G2UGRAmJCRUDp+wvr8j/78SxEvBtdarlVJnAPgcgM8ge5fWEgDv1Vp3ckar9uDBOwGcBeCTWuuPcPLLly/XN51wHgD5zotiN0J3xRyLQTETFKsQs5sN8dGsL+k3n78ueZ9cSL9IWICQHbnpS1dXF1pbW3v5LfHDlI8Zby4bUp0uP6mdrq//7f7w+e0qkwQxIWkVX3tCUx1Uqsr8LGXvnP61tuDGnQ977YcyNbWAOrcu2QKx/UvZlZ5rl0/UWOTq1RMxrJNdBvTctNzQJNNFLceUtuDf3L2iQgmyDFULeO4FcAqAD2utP8XJ79u7V7935IkA3IsolwoJWcQ5vdwC6bPn0hHaFu7Co+QpStqU8U10XFtcdl3HuaAwpJ2+hQ8ArtpwPz469WRWX+ikKhkfUv0haZ8QvT5bMYFMjA5Ov4mQzYhkoxK7uSnk3jvt2fjc+j8QLfAjNCCJ1VVvhI4V6VivR0BYIGQD6dIRuwm5ZPg8YJDCFxscNKSAp1oBz58APAPAZVrrz3Ly757wVP3ZNQ94F12A3lVKFx1TlmNSpOwH5aPUDyroCAkObB0UqODNtiENZFxlPlu1+A5k77ep9a3XIQGJy98QHT5fbEj7jTrmslNrMETJSQIS1+JZRn9zftpy9rgpm5kI1SdleKSIDbykdSTnTLKpkiA2wImdC01bpu1mvFpioAc8VftZenEPzwFSKkfL2FF1dCWhCqCYrEaijF1qGTr6Sn8kJJSBKo/nHTsb/aDi7FdaZf1VEQPipuWYhYKr49uJSHeiUvuUfMwu0uWv/Zmj8yXUu0+/j2mi0mKmPNd/VL/50lhmvZZBg3rJ+/T7fKfOs0uW8zE0ZSMZO1Qd8zxJfefOn33cNZa4+r46Pr+kqRPp2KN0mOPG9Iuq4wPXNy4fbdsxoPyVsMy+MeWrI2VMQv2hfDKPhcyfkmvWLo/xO6G+qFpKaz2AqQD+RWv9P5z84489pqdPnx6dVvFR/67JkaJeJQhJZ/gWbgkdTAUa3AIhaaO9AFFpJl8fcD6GtNeFkEDArhe6iFPg2iJZdF19Ix1LZaQ6bJ99+iT+h9iRBiSSAN7ns0uOk5X4a+qMGW+Uf9RYqAqkGzCqXuix4ngZY9Fnyy5bu3Ytjn3SkxrKlVw6uLyU1g2HUkqr3ghKaU2fPp0XSkgwsHmz6L20CQlHIY2bhFA0Y31qUaq0vyqiagzPfmRBz/la659w8h0rVujPzD2LZHEAOr1jf3aV+XS4yu3jJqS7PhckDEhRHuKjdKcoSRfZcj5/JefKpdvlj0TO9PniZUthP8pAkhLhbIWmVTg7oTvWmN2rBBTrKZW3y1zlMTt9nx7O3xhGJ/ZmdxfqycLZ+qnjknMlZXol/vhs+exK6nFjUVJWiw4TttyGDRsw55hjGho5XNY6t7QF//quJyoX9VQm4FFKKQCH869na63voOQB4KJjT9XXPnwHmcLyTSohqZ5Cxlfmk5MEBuZxV7Diquuz6bLr0+fSH7MQUBOHJN3iKpPoD02LmQGP79c2nL9l0+G2rM+21JYpHzuhc/0Y6rvPtnSshwbXIe3k2mVi0fwF6HxiHRvIhejkxlRIoBBaLyRYkfhYVsAWuimUblpc+mN9l9Zb0dGBefPmpYCngajSTcvm0+AONs2LhH6NEcOHN9uFAYF6sE3NxOG9+5vtQkICi6r+uqosVInhGQVgV/71OVpr9ilfF046SV+/6nckCyCJ5Kk0TAFu10uli0J1xKSqQqh9jvGh6GQpi+PT69Ljq+PavXF0fagOjoGw67rKJCxOCDXuap9PBzXGQq+JkN081U7bD6pNvrZIx5xPLzW+QhhGn48xctx8VBbrUou/BWoNWEP6V3refXbKCK7L0mNiy9atmDFjRkNDkPe3lcfwXNNZPYanSgHPRGSvhgeAp2mt/8rVObB/v5YEGmZZAW5h9k3coUGOeVxCh3N+SOr5/KQWf1cdSaqA0ycJNKQpEWlg4PMPAN736C8wc+ZMUbtc9V02YxYqauxQn0N8lQRoLv+kQTVlVzo+Y9I2oRsJThd1bRVlq1ev7hk3IT5SGx/OD6mdWoOf0HPrqiPddLnsxqaWQhGzGZGUmzJ2W4YOG5YCngaiSimtIcZnUUqrY+XKns+uAWlPNtxuOGait225Ao3QyZ9jPux6LvjaYn8PDcIoWSmLIQ0Uuf7zBZ/URFzci+Hz0dc30kDa9IUKKEOCmpCFgBs7XBAp8d2G67ybdnw6fdcLtyDbvocGOtJxauLz570N5v2C3GIvPY8xi71pL2ZshOqQtMU1hqV6qSBEEjRxOlw+x/YhBdtex8qVeMpTnlKKbimq+uuqslCln6UHBzxVYa8SEiSo9w5XijL8CAmaExL6I9L61HhUieEZanyOvmmZSxuYcuZxjqmohTa3bfh2HVRKyOeX3Z4QJsJXT0rfcvqlO3jKJpVusOUkO/4r7vk+xowZ49TJMToSRu3Gfcu959dni9Nt1pWcGw7U2JYwT9zO2aXTZcv211WH8ykmTRHDaFxxz/d76XfZdNWnxkBMcEnNASHjqBZGO8R2jM4Qlih2vgspM2VqZWET6ocq3cPzDAB/yr+O01rv4Oq8a9Q8/blNfyIneYpSl9YxIU2dhEw+tr7QIIQLwmLTFC45XxDjakMIDS1pcy0plp6y0fNx466HvXVFOhxlkvQjNyZCzqPrmKR/pCmAkHEXEixLIN20uPywj9n+2Xqoxd/EoUOHMHjw0ftHad+XMW5DELNwx9ZpxkIvnTNtmbLtc3r37NmDiZMmNTTH9JGhx5a24H/iwOOVy4/175RW16E6uZLQX9E2a0qzXejzSOmo3li9Zk2zXUioGFpbW3mhhFJRJYbnDAB35V9btNaHCXEAwPLly3Xx1FwT3O7WlpGwFlRdl4zLJ1e5dJcVuruSsCRmuXR3H7qTj2FramGNXHXM7ys6OmCOmVpTSRTb4zouZSGk6R2Jj5StGNbPxb6EMl4Se4UeiQylrwwdZT5pOcQvUxagr0lfeYxOaZ2YuaLMtFIoyxTbVy7bHJrx4MGBzvBU6R6eguE5JAl2THATOLdAUHUoW6ZOFzVuy/hoeluO8tHlM1WPukC5iSiUsuf88NWP8d0u840BLp0hsc0tMlRbTJu+8XfJ8HnO77YeewzEBL2hlL+pw7fIuXyV6pZsJOzvlA1Kjkp9cdfm4vMvwLUP+x/+HpPeCVnwKd2xaaaYYIvyq4xxKmlLiN/2XBCycfH52Jcx0H+lVaWAp7hpWZTOsmFP6kDvCdWWD1kIzc+xF43PX99EQQVelA7KP98xbhEI2aW5bFGLPaePC2RcAaVP/zXb/+L0SaLft/hLFym7H6m+d+nhgizppCwZv9Q1QOnzjRNXmW98U4EoVd+3yNoBpQvcuVBDWtnNg+Sa4MpiricK3GbP5xcVhPjs++Zg7rrwXfOSgInajEnOCeVDSLnpd4h8Qrmo4j08ojelA8CM9Lb0SkPCknAInVi6urpqtlmGHy40oz/q5Ud/g2qt0t6xb6CMcdRXrokYpPWp8ajSVVoEPGKGZ//+/eTul0tFSHcQvvo+u5TNQieVKghJ//h8lLBEIewXBY6hsI+7GBmJ7xxDJWFaVnR0YOTIkUfZkvR3DFNo++eT8em17UrqSJg+n1/2ebHlpUyTT7/P50I+9lqSfOYYKm7+oO7hCWVnQuFjBl2QLO6SsRgaJHBMTijzYbPdnGxMmekbJ8cxgK5rZ//+/Rg1ejRrv0xUieGoB6p00/IFAP4TwAqt9VxJnYuOPVX7npoLxC9G9kCWTDBcSkZKc1OTW+yE6gvsqBSLpN8oW7Z+V59KUjnSvpMGBovmL2DvxXC1g9IrCYak48SlQ+qj9Nz6wKU2fH6G0v0SxCxUIcEhpccuA4CLly3FMXPmeK+RejBr1LUp2axR+mv1V5IW4/ygNmploGx9UptA1rZm3LR81bAnlbbgf3T/Y5W7IahKAV8ww5OQkJCQkJCQAPTzlBa6u8ldAhfhS3bdrjrUjt70w1eHotuptAS1Q6PaXEbaTdpmn++mDjuVZMpKd2UuHa6UoO0nxe6YcnZqTVLHZVvK7En0hVD31JjzXSNUaiOUHaDGO1cnNO0h1ReTXrT7qRbmKWQcUWVUf0rOUy1zpqQPXdcmJWseDx0DIag3G2aPxZZBjecbBvqvtKqU0voggE8DuF9r/TxJnXeqORrgJ3MuVRBTbsvY4CYRajGibEjSHjGpHl99lw7fxEH5Ji2jJk5XG1xlrvYX37du3YoJEyY49UvOuc9HF6gxELuwcGlIqr9d/lO2Q+u56lL96wtYpekiLj0oXVi58wQAH1n9255xUyakKc7QDUGoLWn9egUiBUI3CGX6JBmnlI8uNPpt6Z8a/uTSFvwr9v2jctFTv05pDZ4yrk6uJPRX7Nq9u9kuJFQQadwkhGLTpk3NdmHAoUoMz9UAPgDgl1rrcyV1li9frqmnn4YwNzGpB+kOntNB+edjq0LkQtgKs04Mk0AdD0n3hMhI2wT0/rWNpC0+XZwvoYwOlwZxpWhidrfcbpljAuqx06cYGRNc30vHs8+urdM3buoFboyFjCHORoGyGJ8YBikmzcSxniG2XfrKwCXD56FtbjtufvzehrIknxlRHsPzgb3VY3j69z08BiTUuA1z4eAmWNfFIU1TcReUbcPUY9t1LXZcSouaPM3PvoXf1TdcSs7u+5AUju+8hPSpCVN26JAhvWxJUlrUgknZkwaRtdDmLl8kgQvVBokc1XZp0MSlsaggT7qRCF0QXbJDhwxxSB5tT5KW4uR8fkrazPW97zyHXJu+MSaxT/nM+cVdO6FwbXRqDd5M3Lgv+5VWQmNRJYbnVgDvAvA9rfVrJHVWrVypJ0+eDMB/UdoIvbClCxG3+5ZMzpytUFbD1xZuEvHZ59rhsu0qky7AIYEc1RYqqIlpC3cuXYGjZDG0j1G+U37Uc+daL4SMRde15pKT2pKW2bYb2c/1YLJqteX6PtBh9semTZswa/bsxPA0EFW6h6d4tYT4SctFsJOQIMXadf7nNjUbUqal3joSeqMvj5uEvolmrE8tSpX2V0VUieH5OoB/AfBfWusLJHUumnOy7ly58ahjHCsghW9HH5I2oFgnahfm2/n72KsQRkrKWkgZE19b7Hqcj1yKzKeDO7e2b663pVP9bbfB10Yu5RCaVpGOHcl5l9oOofZDUkuSdJRUH+d3rSyiD8W4qReTEcrAxPYVpRMov98kdiVzYWzayq7LXetUXZcsxa6tXr0aTz7uuIZGDteNPK60Bf/yPX+vXNRTpYDnOwBeA2Cx1vpdkjrFk5ZjF3xThhrIdv1Qitd18Ybqd4FKg0j0hQRsvrb46vrsufzy+R+6KErGgOvmU8mkygVoXMDG1bERO8FTgYdPb2xZiF+cjlqDNcoWN45MeZ8f9hO6ywo0QvVJUm7cGADqn5Iru3+aBW4jSNVrxk3LnxtVXsDz3t3VC3iqlNIakv8/2FQvEvo1xjT43TYJ/QPdO/Y024WEhAQGVWJ4fg7gXADXaK0/IKnz7rEn6u6dewHIUkTUrlq6+/TVkbARMXSqlBWQ7Oqk/rsYFy61FOqThIUKYVJsv319f8PeZVBGfjpmJ+qzY/sUwtb5IGEdXb5xFL3EX6nuEBmKzaMQmj6jyqTtNO1qrXuNG5+PZrl0zJbNFvns+eRjxxTHjNVqO0SPbTskLRYyJgpwTH/LmBG4dccjieFpIKoU8NwF4AwAV2qtPy6p887WYzUOdXsnbekC40pp2fq4RZ2S41Jm3CJl67FlY9IrFGpNf5jl1ERB9ZUEVODlW9gXzV+A4oWzLhlJUEotnjGpCW5ipsaepA61UJmyIeOUul58dm19krq1BDg2OJsU2ua2w3xRcew1VO9UT9lpq9hAplaE9FMZfUrNmdRcS+k4dOgQRo4a1dCg4abRx5e24F+869EU8NQLSqnfATgZwIe01ldL6lx07Km6yKtLJrjYwWuWUwuCz5Zvd83pdoELZorjMf1B2aJ0UyxG6K6aW8Qltqh2XLxsaa+bln1++Oz4yqkAqqzFwhdsuc5/2axBo1gWatPC7eAlATYXiLl0cG9L9+mlEBss+3SEBOYx+iR1C9TarhCbMbolm6Na/WjG29IHesCT7uFJSGgAGrn7pdBX/EioNqTMV0JCX0KVGJ5lAOYBuFBrfaukjvkrrQLcjseEpB614+dYnRjmg0oVSBgMaZpGoj90FxS6M3fZkjIrsf22b98+DB8+vJecpC3S/g9lEkLZxVqYCoqZoBiTkHSQ9Lry6Q9lTmJ25pI6pr/ScROCZqWLOJTJmthlHEMX62vo+QzVbx8rQOlrBsNzy5jyGJ4Ld1aP4Ql+tYTK7sx7LYA3A3g6gEkAtgFYBuCbAL6itT5Uoo8FRub/90krdK3dXCp9zy0Wpoz9+cZ9vV9TYH4OTT9JfS10+Mqouj7bdn+62sX1j63b1OkKJKjUjLRtEkr9ipmn4IatD3p1uOpTftjn1q7L6bV1UfVd5XZ/u+qGLjLS4MYnZ/eNNLCjgp8yA3hpusU8rurwILaQfpac51oQE3y55glbF1UW2xbXXCJBrcGVS5/pk32sfdq0mm0khCEopaWUGgfgdgD/C+ClAKYDaAMwFcALAfwHgPuVUrPKdDIPsiblX8WvmFWtVXpVWEJfQOu0ic12YUCgLzEWZWDDxo3NdiGhYujq6mq2CwMO4pSWUqoNWbBzen5oNYAvAXgMwAwA70CWcgIytucUrfWuUpxUahSAQtdztda/l9Qz35YeugN0gaLyJWkNLh0Qk7Yxj0lZHWoHS6UhfP765Hz1XH5Ru1TKd98xn05f2wrYD5CjQJ0/iS1Ol2RshqbuQsepLesbL1RfU+csNj0n6Wuunn2MAnfNFTe7h7AfLv/ssljGQZrCKVDLvFgGmmGbu3aosVfrdQo0J6W1eOxTSktpLdzxt8qltEICnosB3Jh//ROAs7XW243yoQB+COBF+aHrtdaXl+KkUsciC6wAYI7WeqWknutJy4AsxcNNRBJQwQS3SIVOeiG2zGOui9dVN8RHX7Di0hcT8IQEr762+I65fqVV63k39XMTLBdMuHT42iLtD8pPanyEjGEJpGNYiphg3mVPMhbtn6WbOqRBKVcWA+q8uHyV+hETlNUjeOOub7teyPiXBqWxSAFP4yFKaSmlBgO4Iv+qAbzFDHYAQGt9AMBbAOzND71HKTWhJD/NZOdmebVq3JCd0Hcwa+bMZruQUEF0dqxvtgsJFUPlooV+ABHDo5Q6F8DP86+3a63PIWT/E0Dxcs8LtNb/VbOTSl0A4D8BrNNaT5fWe6eao0N3uVSappZUAQXXjiSEZjf9cH3nKFgqPUDtmiRMENVvXHtctkLTLZRfdn0A2LFjBz7eforTlu2rLz0T4pOrHZzvlP8x5zp2x23XpXbWoeBYENuO5JqWMhCmLMcSFWXmuLFRD0ajVjatqMOxvz5btc5zkvEXwoxRNmNZcs7/WvwqMHTYsIbGPV8aVx7D82/bq8fwSAOe6wG8L/96mdb6s4TsawB8J//6Ha31a2t2UqnPAngvmGDLxto1a/QnZj4fAJ2DlwRF0tSMDWrxse35ggtbH5UGonw2/eF84HwOSSX5fOIWKsovUz508ado8OIBci67PoQsNlI/6pUe8OkoQ5+ttwAXALog2WS49Jk6y+gbl18uFC+drSW4ouRi546QBbzeKZwyUa/z7tLn0hlzndo+b926FdNnzEgBTwMh/RnTfOPzHxnZP3jq1YKT8/8Ph1TatXt3z2fX5G5PDhy7QS26XGBjD3hqMaXYE9sPV11bv2tClOxgQ4Km0P6gwE1mlO+xk11Rz3XTMrc4Ff1EBRCSANAnTwXUpiw1xqSBvk831y5fv0iOucq5nTm1kHBtcOmQyLv0SsebLcsFcj45l8+cXruuT7bW6yr0+vPNq5KNT8zYCgV37do+2eAC//xt6WW5myCANOA5zvjcwciuAdANoAXAk5VSStfwdMP8PqCCK749Vk9CggS6qx6PkOo7KGNR6Cu7/L6E1sHpERgJfR8tleNkyoU0pbUNwLj86yit9Z4y5RldVwH4CLKfpU/TWosfPPie6c/SXeu2srvDAtxuKnSX7tLh0m/r8X229VNMCOUPV0fKErn0U+1y6ZaWhbTHpYNiscxjkvMkQUjazSVnw+c/5ZM0teSSC+0P6c48NhXBpcIkjAnF9FBsJldPAgkTEMOOuPwKQSyDG2OHuhYk7GgIE2S3hbr+OLbbxSKGMIwu327ctxzr16/HMXPnNjQEuW18eSmtC7ZVL6UlDXg6AbTmX1u5JykrpdYCaM+/tmut10c5p9Q/IbsfqBXA1VrrD4XUN29all6wksmc+k6lEexjdpnEH2nwQwUClG2O/jf9iFnsKNuSetRCxS1OPrtm2Qee+BWmTp0a1C6XfkkQ7Cu35VxjWLoIUEG1pF0uP3xydlmordjAMqYtLhuhi7pv3IQiNpioNQiR6gsJsEPnBJ8PPlsx5yg2SK0ninZ8UXekgKeB6HM8rFJqEoAzALwVwPn54WUAPhmqq3XGJF4oIcHA/gMHmu1Cn4ePhWq0jr6ENG4SQtGM9amlDq9AqRKkAc8eHElRDc2/UxhmfN7tlcqhlHoasocWHuMo/i2A14Sksnr0trX2fObSNDY4dkSyG3Udd+1oqfSO67hkJ0ZR9HYdSSqJSjHZtm3/Jb5LYfrL+STRYddffP4FPQ+Qo/pKumssfHS1m9LvOi5JiYSyaD6fzDIfC+O7luz/vnHNgWIzJXVc1w7FKHEpFtsP8/OKjo5e9UxdFFMhmY9c5RKWJOQ6i5kLfQwrxaxzzG2t7KPLP9ecYZdRqAczZK5PCY2BNODZgSMBz0QQAU/+kMLR+dcuHHkQIYUb4Q52/hvAO7TWh4V+8oaYgEE6+dj6qIXKp9+nw1wgbT99aQDKD9ekzU1YLjn7mCR95FtUKL3SQJGCdDKzyy+bfUrUpGp/t2UlwYp0wfGNUypg5erZtmKug9Byl25pH0jOUajvlA7fwl0cv2LVb0j/JXMLFwBLx2VMH4SAaktIgC299svwVXKdUZD2fYi+IkhOaBykAc/fcSQgmQP6l1ozkP1CCwAeE/5C63oAZzmOvxXADKXU67XWW2SuHsEHf/PdoAueWvy5xYIKGuw6Ljku8LB1+fynvlO+uOpxzIXLH1cwRNXndnlU0EhBGpS5zoPPV7ue9FhIsBY6kYZOuNRuvCj3jVNqsQhhlri6tp82XL5T541rM+WPKwB02SjKJIs4Z5tqU+jCzemwjxXHqYBYGgxT84x0rvP5a/tF2TXrhgRolB8hfVDUsftx7JgxYttlYaD/Skv6tnTz+TfPYmSf7annhdb6p1prlfvTDuBtOPIT9AUAfqmUGuap7sWYJgyohPIgYXLKxsqVote0JdSIZpzbemKgjZsyzl/ZbE7VkNanxkP6K61zAPwi//pLrfW5hGxpr5ZQSr0WwP8gY4w+obX+aEj94m3pUiZFuuNxlZsI0enSz9mKYRnMYxzzI2EBXO322XBNjr4ds6+MYtd8dSW7R7veio6OnpeHcn6Yx0IYDsmO1XXOpP1B7T5DbVN1fGUuP0xQY4xj2zh/KUaQYi1iUz+FHtfb0n1jQnI9+2yVGSRwfS1lw6RsR4xftfjG6ZdetxzLHMI6m3LNeHno1ybOK+1XWm/esrxyfJE04BkMYB2AScjeyPlUrfUjDrnJAJ4AMALAAQAztNZba3JQqU8D+CCy5/BM1Vrvl9Zdvny5dk1CUlreBhWQcMGKaxGRLgh2HUmQUMsFyi1GkgnI15exFDK32Pu+u2xTgYD5tnSXXarvXfrscskC7KvH2XQhNKiWBhkSH0IDf0q3dLMghbS/pYti8WoJib3Qa4nTKQ1IQspcdkxbMbZrPWec/hiZegRslG2zD5oR8PzPpBNKC3jeuHlZ5QIeUUorf+7Op/KvCsBXlVLjTBml1FBkNxmPyA/dUmuwk+MGAIeR3Qjtus8nIaE0TJwwodkuJFQQadwkJPR9hDyHZzGAVwM4HcAzATyolPp3AI8hu1H5AgDFViDquTkuaK03K6XuA3Aasvt5fiqtO2XyZFYmJKqndn0+JoTaqVI7XcmuzOUDx7BI2B6f7RimgmIpQlggU78kTSVNZdmybW1trC++45LzZx6XpnNc50zKEEjHtpSB8TEytl1bN9U3Eh9NHdS5pVg+ynfuevMxpwWu2f4XZ92iTkibY85ZiIxrDuJYp9hzFlrHBWpcmTIUYylhpEKYsFB9rnEnWZ8SyoU44NFad+ZPPv4ugBcCmAl3UPMnAK/UWu8sx0UAwAPIAp6TQipdMfNUAHGBhQm7jmuBpy4WSq8PkgWbSne5fHYdly6YLn+oydJcnIpj3ILjCwZrCUpd/lMT0br168kFk0oJUYGor53cJG4e4xZdChJq3+WP3a4Ye6EBkkvGlpMGMmY9Ln0RGpSZdtetX9/rHh4K0rSVXScmhWPbCgnii+Nl+GvPK75UmWS8U0G1HWD6+k2y+bN9s/VL+8V1TtSwIVi871GyXtlIv9IKgNZ6O4CzAbwewE+Q3dfTCWAjgDsB/BuAk7XWq0r286H8f9Db11unJZo5IaEvIoSBS0joj0jrU+Mhumm52VBKnQHgrvzrEK11p6Re8SstwM9GULsQaoctZW5CGB7fjkdax5aj/PUxHxQrIl2kqN1bSH2ONaPYm9gFtW1uO659+I6jbFGsAHXc57uEOi+D1aLscuPKpd8lF8qWhuqO7QP7c2gZZcdVXvy6T+Kv7zxLGQefLOd7CKQpIp/NkHPH2Q3tU4l/MZCuIZR/Zr1m3LT8rcnl3bT8uk3Vu2m5KgHPkwD8I/96jNa6Q1LvomNP1dc+fAeb2qDofFM2VM62wS04VLDlsscFVhL6ntMf6xO1sLh85lIpknQGZcsH2+9LxzwVN+z8ay87LluUv3YdSSBT9gInHevSRcZ1jmsNcnz6QjYUIRsQlw/UxofSZ8pcu+NBLBp7Uk2LbkyAZ+suK9CIse3SFxpESvssNDAKnU8pWxQkgWCBtrntuPnxexsaNHx3yomlLfiv2fhICnjqgfyhg8W7tJ6vtb5HUm/f3r160KBBUdF/6CJgQhJohNr2yUt239LFuZCVMCbSAIgq5/zl/JAGay4dvgBg165dGD16dE8dCWNCTe7SMeDz1Wc3dEdfy4Ig1ecLErjgypSTbCp8um3ZmHHp8lFSb9euXfjo1JOjgsHQvnfpl1zrIfpCfIz1v4CEHYnVLwn0Y/TH+mHi8OHDGD5ixIALeJRSQwBcBeDNyF5Z9SCAK7TWd5AVs7pnA/gwgKciuyXnbwBu0Fp/W2I76B6eZiF/9k4R8IyX1tu5s8z7phMGArZu29ZsF7ygWIdGoq/40ZfQl8dNQt/EAF6fvgLgUgBfB3AxssfOLFVKnUJVUkqdj+wByIMBfAzARwB0A/iWUuoCqm6BkJ+lNxs7AQwHIH4e946dO3Hl9KN/qWVG2iG7OB/zQVHwISyNz6+Y1I7PdnGc64PQnSOXsqmV2qdYJ9d5otph+2Cfv4uXLXXWsY9xaRSffl+7bDluxy1lEkJ3/pJx6rMn8cNVRzpuqTFg+h3K5pp6OEjOSShqvV5MOem1w/lAlXPzFadPyqjYY4abYym45KTjNZY189UtkKe0RHrKQrN/paWUei6yHz1dqrW+MT/2VWSvoboGwAuI6u8GsB7AAq31wbzufyB72PFbANzG2q9CSgsAlFLLATwFwIVa61sldS469lTd+cQ6b7lrMaI++9Iq1IQdS+fHphi4trj6QCoX0y4puABCkvrh0mSS87TohLNw7bJfOcu44FPio+0bVearHxKsSFJEoWPM/OxbhCSLAjVmfe3w+Su9BkP1+a55G/a44VBr2i0EZaej6mm77LbH+hGiN7ZeM+7h+cHU8lJar9wQntJSSl0L4BIA47XWe4zjH0T2cOPpWuv1nrr3ABijtZ5vHX8YwBqt9Ys5+5VIaeUo+D8xw6O7DtXJlYT+is6ODc12oc+jjJRWf0uLpXGTEIoBuj49A8DfzGAnxwPI3uLwdKLurwGcqJT6hFLq2PzvEwCOA/BZifGqpbQAYKy0wnWP/hoAzUBQu3HqM8c0uI5x1LuPIeD02sdsn7j2UzS3q684PykfQ3ZTph0fCyBhBCS+FuVXrLyb9YfTUcsuMma3KU1b1WP3LE2lSHRQflH6pO0MYQdtP1xj3vw8ePJRb9phEcP6UZDMA1I/JGlN6jtn02WbYzRd8rWmzWKZHdO2lM10oWv1puA6taJFlUcoKaV2cDJa67HWoWkA1jpEC1annVD3KQDHArgC2Y3LALAHwMu11r/kfAGqldL6LrJXW3xBa/1uSZ0L25+lr3/8t6LghIJ0kXLVo9Jftn4unRRygXLt8qVpXDK2HGXLngB8OqXpPZ/fZh0q7eA75uvTRfMX9DyHRxrQUXAFuqHgUnWULWkwZOs1ERpMhAZe0oCY881ly6eD6xsffLpdz+EpM23FBRrSejHgrn1JYOSrC9QW3HPXd5l2ywiwzPINGzZgzjHHNDSl9X/T5pe24P/ThkfYu67tgEcp9TiAR7TWL7eOzwXwOID3aK1vcenKX2L+MWSMzg8AtCB72PEzkN3X83vOnyoFPF8H8C8A/ktrLboj23xbumSCsRdC6QVUyLh02xNszI5bGgxQqCV4kdSXyMVMvtwiHhtQ+fRfvGwpzIdVSoJNX9slPoUETq52UnJcv0kCXV9bJL7G7vxdPtn+hTJmZt3iuOT6phZYE8UDKyUBZsg8U2tgJJGX6i7DnstuGUFZPSENdgAZO1WgGQ8eLDPgefn6h2Pu4XkYwFqt9Yus4ycAeATAv2qtnTcfK6UWA3gugOdorQ/nx1rzepu11qdx9qt0D8+B/P/QpnqRIEJMUFYPHaEYPmxYXfxoRltc6MsLS5VxeN8BXijhKKSx2HgMUqq0v0isR5bWslEcc/7KSCnVBuBfASwpgh0A0Fp3AVgK4Lk5A0SiSvfwBAc8t57xegB0ntleiMzv9mdqxyrZLVMpLWqnR00Mdh0pTS9JiVDtCqGyXW2WskS2rOv8SHy02+WzNWXKlF6+cu3kWKBa0w9cX1G7SNs3auy79Nt6pYyD7ZeP2ZIyTdw14esPX/9IrxWqn2z/YpgKrk2h1x/HRFE2KQbQdy6kzIfLbugmgBt7FKtaCwPNsVDUWKT6Z9TIkd6yfoy/ALhYKTXSunH55Pz/g556E5DFKy2Osta8jI3CqpTSug7AZQCWaq1fIqnzTjVHA/KLq+z0gqsed6HaOl0TjySlY+rlLmqpPspnyUTrgqSdIbqolI7pu68P3vfoL/DZ48/tVceUcZWZ5dwiwJ0jqm4hG5rqoPSZOkIW7ZAAxVXPtZBIggtOZyx8QZMkEF29ejVmzpxJ6q6V0ahVB3dOQtIxPrmQOjGbgJhrTGLD1hk6tmPH39Bhwxqa0lrS/tTSFvzz1/01JqV1MoDf4ejn8AxB9hyejVrr5+fHZgEYrrX+W/69BcAWZAzRSTmzA6XUSADLAezQWj+Vs9+vGZ62Y6ahc8X6OrmT0B9xqLu72S4MCNS6+Pc1pHGTEIqOlSvxlKc8paE2VZOfPKi1vl8p9R0A1yqlpiG7UfmtAGYDeJsh+lUAZyBnbbTW3Uqp6wF8EsB9+T29LQAuADADGRnCol8HPFCKnVjNXQlHr1O7amkqifLDV8/+TO3GpXSqSy6E/nXR+Vw77Paa9VyyXNqDSzdIUjb2Dn5FR4fIfw6+sRK7U+RSOj4doUxQSEqE0udrp7QdIefP9juGObB9kTCbIUGbhFWVzFWhkKafQuYt6hzGMIUU7P6RsK5SptGuZ+oL6StO3oeqZFfqgLcA+ET+fxyAhwC8hHtHptb6U0qpFcheR/ExAEPyuq/SWv9AYrhKKa33AbgewJ+11s+U1DHfll5AGrjYZSE6zDqS4MQFaSpJUo8K5Mw63OQQkj7j0iWcvLR/OX9c5S6avPj+vvbn4LPrfn9UmU9/TOpHmh5w+R6TYqBkuLESoq8W1CNN4dJv2qDGQMimooA9bpoJLg3E9XeZ51uaNpVsmGKuP2rjym0qJHpDrkVbfzN+pfWTmU8rbcF/6eqHKve29CoxPPvz/2KG59Dm7XVyJaG/onv3Pl4oIcFCGjcJoZg4YULDbQ5q9su0mowqMTzvQPZysA6t9TGSOjt37NBDhgzxsgcUG2Eep3YCsTfI2aB2m7acJGURsksNTdtwbBG1W6Z0cufJBsXicLI+NujiZUtx0wnnHSUj8cN3XkLZGKpPXb5ziGFPYsZOGewLpYe6zqTXo9SWKcuxAIVc29x2mO/tK4tdk5T1FYQyJGWzKSG6QvTVMsdT4/LgwYMYM3ZsQyOQpXNOKm3BP6/jwcpFT1UKeP4F2evk12utqcdP92D58uW6eIicC9wiKE1p2TqkC7E0DeQ7RgU/Pp+4wELSThc9G5OK4RYgbmHl+lmyENp6zSctczrKTMW4xkJoaonSQdUtK11kgktNcEG7K4h0+VtLeoRKN0g2FSYWzV8A+0XFjQxQQtNWnK4yxnSMH7GbydgxYZZJ/QzdIPjQjJRWCniqE/C8BsB3AGzTWou4wCLgCVlYzWOSnaKEvSjkpExTyC7P1zbJQu3y0WePCi6kgaPrs8QXKTvjawNVz/bBfNIyV18ywbp8cung6pTBpEgnZd95D93lSttm13PJcfWljAx1XDIP+PSt6OgAtbny2Q7Z0JSNslmjmHMcIlsry+KTiw2wCt2x9ZoR8Pzs2GeUtuC/+PE/Vy7gqdKTlg/m/4c01YuEfo1pU6c22wUvylicuMA8IQ59edwkJCRkqBLDcy6AnwPo1lqLbrZ+Z9uxGl3d7M48ZvcYSodzVKqJkJSFWYdL27h0u3ZbHINFMTOutsTsvl3yPkj0S1muT23+A0aMGOHUb4LzW7IDDE05SdgPn4+xqR+XvjJSAJyPFAMYy0S6/OLawDG9RdnevXtxxaRns+0K8SOWgaHmo1A9dr3YtlA+xrI4nH6pvpjxbMvH9HFXVxdGjR6dGJ4GokoBzwsA/Dr/OlhrzT7pa9PGjXr06NHsBOubJH1pD05H6CLABTJmHcpHW79Lzue/rx6lU2JPMvHZOkNTRGUGlWZqgvPDp48LtCRlrnb4FvWQ9IFZJzRAM/WFBAnSdtn6pemGWq9NWy5m/HIvD7XbJj0u6cfQlIp0cxYT8MQi5vqO7Y/Qc+QaHxJwfuzatQuTp0xpaNDw8+OeWdqC/6K//6lyAU8VU1qAMK21ddu2OrmSkDBwUY8FLyFhoCGtT41HlRiepwP4c/51vNaafcgOd9MytbOj5Hy67GPm8RD2xrebiE1t2P6G7JYp9svXphC2R7LDpOpKGRjbf1+dYqfOIZSF8slQY9MHKaMj3bXb9akdtpQplCKGDQtNKVBMlqtOCOtZYEVHR8/jDDjZUEbGpS+GxSjDbhngxlg99IfWq4XJkoyrS4bPQ9vcdtz8+L2J4WkgqhTwzAOwLP86TWu9gavz90cf1Z97yovYFA6XCnEFCTGLgCS94NJD+WbLx6QcJHo53ZIFOiRIoPqbaoeEGnf5FNpvIcFvzNhx+R1aLg0gJDbsdrl8Dm2XrTM2WKk1OJSco1oD0XogNGALGdsxwZo0SKCuOeraCQnyygygbNtUwG2DCuxWrVqF444/vqFBwy/nPau0Bf+c5X9MAU+9oJSai+xFYwAwR2u9kqtzYP9+7bq4pBF4AYq1oBC6u+IupJDJl/JFYstVr9aggGM0QtvlWqhcfri+u+QBoGXCaHRv3XWUbz55aTDhs2W3hSszy0Mn8tAFjQswuTaEsA/SNlHXrelDzOJjf6eCIbN+cXzr1q2Y4HlybhmBgMvfshEzL5al36czJHCW+iPdPNUKic5Gvy19oAc8VbqH54DxWfR6ic2bN9fJlQQOIcFYX0LLmJHNdsGLMvq0r+hoNBtSb+zavbshdvpKv/UVP8pAs+aqtD41HlVieCYA2JJ/PUlr/RBXZ/ny5fqmE84Tp0F8VDx3QZTFIEnSDb7dbCy1ateNSe+47HJMGdcWrp21+OFqc4EynrRcy7nl/LURu+P2+SRhmOqxE7bt+GR8clR6xNfOMtpR2HA9sDKm36RsSi2siHSuCtVfC+tZKzh/pSxpKPsTyuaass148ODtJz6ntAX/7Ed+XzmGp0oBz0gAxTbquVpr9tXEFx17qi4e904N7gIcHR4ayNiy0rSNL/CydbjaEjOpUr5zfoboCJkcXfI+fdJzJglm3//4nZg2bZpTn/Tc2rIhi4AkgIld0CSBNCdHgevrUH2UHelCKpXlAucCvvF8+ZNOx3WP/SasIQH+hgRQscFVzHmi5iPp+Q2VNX2qdSzF2grtG1cbU8DTeFQppXXQ+DxEUuHw3v11ciWhv8IX7CSUi3ru9puBrnVbeKGEBAMjhg9vtgsDDlVieBSAbgAKwNlaa/a3wwf279eAP61iIzZ1Qu2MJDuSkPRFCE0em+7gdLn0USkoKk0iYRkotoQ6Jk0lmbj8yafjun/8ppecr92mborJCmW5XD5y/eDzj2qvq34Ms8AxDj7/yhqjtbCv0uuPwrp169DeLnqnMQkJo0rVcTF0ppyUnQhBPVkW206tbGEIm1RrPcl5afRNy3c+7bmlLfgvfOiByjE8lQl4AEAptQ/AMADna61/wsn/4+9/1589/lwAYUFEIS9ZgCm4FnvOlk+GaoPLJ2phoXRJ/JXokCyKsZN7bPAosWe/BDLk/IWmESifYtNHPj2S4FCiL9Q3ie+S64q6JrhgWxLk+Y656rl8se/hKQtlpnBiF3tKj3QjGHJ9mPVD5pKy+0R6fYesIQVu3Lccq1evxpOPOy4FPA1ElVJaQOALRA91s2+fSEhIaAL6W0orISEUaX1qPKrG8GwAMAXAG7XW3+TkLzr2VG2+36aAhMqWUOFmmXTXy9ly1YtJe3D6Y3bTZjnFTMSkgXxyLj8ku/3YBfV9M07GZ9fcf5Rubocp2V1KmJyQXWpMikyq13d+Q5k1jlUK9cv2Q+KvhCGQtMdl28S2bdswfvx4QUv8iD1/UlaEqiudL7hrlJpnJNcL1QcUK+djUzg5KaTzlG2LQjNuWv7VM04ubcE/68/3V47hqVrAsxLALABv11p/hZPfuGGD/ti05wFwX2xcesGWo+jJkAvCZzsmzWTLSNIDtpx0gokJHF2+mXZqmXAlqROObu+1YCrgxr3uhZBrP5c6sSE5j5JjZU7gsYE5Z0tyLbnOeegiGbJYUykL6QJf4Ia9y6CUErWlDNSS6pH0R4ztRoCaZ2vte2mfxuhwybWMGYFbdzySAp4GomoBz98BPBnAO7XW/87Jv7P1WH3jroePOiZlTAA6eJDuBiWBiD3hlhV0SHepobs36W6e8p/yz1UeGwyG7tQXzV8A36MMuMmWC3i4NnF97LMVM9HXsmCG6HPJFXCNextcwCNpIzeOXHIuHyiEPL8pBpyOMhiNmMCZA7XJCNXhmj+kQWqtG4RadbjafOjQIYwcNSoFPA1E1e7hKZ62PEQi3DZrSh1dSUiQo1k74r6K1B8JAx2r16xpuE3VMqi0vyqiagzP7wE8G8D7tdbXcvLFgwelqRNqxxDLcJiQ7uDtOpRMjF0Ju+Ky7/MjJKVF7UR9OqWpCGpXzbFwhSy3U/fpCIFkx8qlgXztotjKUGre5aNLr8tf2yeK6fPZlzBNkuvIxypy59Wlz9cO7knLElDn2SUr8deUDR0DlA6XPapvJexniB8hdaQ6fQx0vYLzZtzDc9ezTyltwT/zD/dVjuGpWsDzWwCnAfio1voTnLz5agnJRWqWuSbwmPSRS7dtW0pDc4FWSBAlWTx8tnz2OLs+X2x9ocFbqCxV770Tn47PbfkL6SvnoylbFr3uCyhcYyo0BSUNFF0BSMyiIA16Q/X5dEgCtJiNkKlv3759GC58kFxIKiZGX6wcd65dspKglNLtqicJrqT2JPNDs/o/BTyNx+BmOxCIg/l/UUqrPT01NyEQVdoANAuS4J5DvXbNzUL2XNSEBDmasT4NahnY47RqDM9PAZwH4Hqt9eWc/LtGzdOf2/QnAPJJmksJ2HLU7seW89nhymPTHtLUia8PQuTMY6FpBq4sZEccYttVvqKjAzedcJ63rs9P6e6Z21FSjJ0LlD4uzeSza8tLmJAQxsRlz3Vcymq56ofKStpF1bEfWGnbkbCtjWLNuDnC913SHsq/WEZPwibF+ET5WG9cMnweBo0chi/sXt7QCOTuk08tbcF/wf33Vi56qlrA830ArwTwea31RZx8kdIC4u6RkSwCxfEQGVuO0+ebKKggxG4HpSNm8opBSLohVE9RTxp0+MouXrbUG/CUkUqy/aACnFB9VB3fouGyHRLomcdC0x6hgZDkmqB0cbZ9bZGkXIpxE7NgSjY0nI8+nSak51UiW0Y9n66YuckXrMUGWVIfbfshG5pmpLQGesBT1ZTW0JBK1CCMCTRc+s06IWyIaZuywaGw6/JByhaYkLQ5lI1wtZ3rg9BdrzSYsPXbbfOVSXVQMGUlTILPZlE/dHFyyUtYDNuuqYvTaR6jzqWkLa7+o2TN9oQESHYZ1Vb7eMiY8LWHqi9pt+mLDWrcU5DWC70mJOfFLrfrUNdtTNtCNgQhY7KZUIMqF6OUiqr9tizoHp5GIpb1MFHGhVLWDqYvoBl9OmvmzJpt9mX0lTHWlxcFFzh/qzRu+sr1XQaqNo4SmouqpbT+HcC/AfiW1vr1nPw71ZyexnE7B5ecNIVEpbR8aYoY2pxiKlxslIQVoSY/CW3LyVIyPl9idnkuv6TtNuu1jBuJz679Peknp5OzWwZrxKWofDq4cSU5txJ/qRRDSB/4fJCkgeqV0nJhx44dGDt2LNsmyg/JOa8H4+irE8OqhvgROj+FsHIxYzhEdyybZ6PRb0v/zSmnlbbgn37fPZWji6oW8NwM4D0Afqi1fiUn/+5JJ+nuLTvFFyxAp56oYILbNVF6Od98E3iIPi5QouxxciF+2PKS/uMWJkkw5NNp++F6W7p0kTR115I68ZVR9jhQC4DLdyqAMOV8On32qb4JbZNZr9aFlfOR01GMG6mtekMawIZukFx2qKDb55OvzOVn7Lhw+ReD0GBQ2o9bt27F9BkzGho03PP800tb8E/77W8qF/BULaUV9KTlltEj6uhKQsLAxEBMaSUklI1du3c324UBh3590/LCJbd5fyoKhO38fBQmx1RIqE+bqZCmsaS5eJdPxTHXbt7HblFytm7pghaqw3WcYomku9ni+2V//6VoR+mSMWV9fcWxVfVKWdi2bR0+fb42Fd851tFVV2LL1kNdByF+UOeB6g9uLLYOHuyVo0Bdm7UwhVJWUjIGQs6Zyz+JrESGY5mpOlJ2ScpWSeYFjp1PaDyqFvDszf+Pkgh/4ew347rHfgPAPeDsSVSaluJATQ7comwfi6VTKf3UQhKSmuHSHq4y3wQr1eHqQ19wKAnebHvXH3eOUz5En6sNXDsl50gamFPjmBvjVHDv8lWyGEjHVOw48sHWJWm7ry5ne8aMGWK/fOPZDpw5fWUHELFypv8xC7q0na75I3QjEbJhigkA7frU2B86RJSoKBVqgD94sGoprU35/0kS4a51W+roSkJ/xOBpE5rtwoBAf9vpbtiwodkuJFQM09KbABqOqt20fD6AHyO7l2e4Zpx//PHH9fT2djLKDk1f2LKx6QVqNyuliaX0awgDQVHqlJ+SMq6/Y+l7nxxH57t8sl8C6apj16MYG1dKizuHrjLbZ2kdU45i1KTMhM++SzeVmqFYHp9cCCPGtUEq57tG7frFE7opFpHr79i5xPY3JI0Vk2qRso223tjrW+KjdG6K8dtV5utv7tya9dauW4djjz22oZTLvWe8oLQF/9Rf3105uqhqAc9zAdyffx2ttSbv+rro2FP1tQ/fwU6UrsHqCkgkAZGPZqWCDor69Plsg0plcGkOH3x+SBfmWvRL22yWURORrcMXkLjeeu3yVzpx1lrPPC4NyLnxTNmxy2tZgCX2fIG/9Nrz2ebqUP6F2PONG65OrQxXiI6YzUK9IZl3pfWl9oDa2kYF8BJbts/NeNLyfS88o7QF/5Q7f50CnnpCKTUHwIr861O01o9S8kXAYyJkEZAsJHZdLkigggbJDp4qj9kBcW3x6Yhlayg/bN2SAEvqE6XPxOBJY3H9yvt61XHZ8rVL2m++z5TvNqjAPIRJkEzgLt2SRYBb0GLGka8tscGEdGdu+mLW2bxlCyZNnBjFAsQGx5zvXJlpuwxbseCCXlvWVxYjR/lRj8DP9CsFPI1H1e7hWYMjv9Q6nhPu3rGnvt4k9Dsc2ryj2S7UFTFMXz109DdMmjix2S4kVAxjRo9utgsDDpVieABAKfUggKcB+IDW+hpK9p2D5ugb98rvBaAm8lBGx6WPY3G43biEAeJYkVCmyddOzg+XjG2f2tlJGZla9Lu+X7r8Z7hh3ouP0i0515yclIlz6XOB2tFTMmUwMlI5ab9RvvvGfAybSdmgrk3J+Vp04gtx7SN3snI+HyT2YhmIUOYwlAEsA7XqL4PZK4PVCdGntcaw4cMbypLcf85ZpS34J//yV5VjeKoY8HwdwL8A+JHW+hWU7EXHnqo7n1jHXrxlpF9smUKu1gnGZd8FbtddZhrBZTs2ZeHT55PlUgOSlIut26y3aP4CdD6xTuyrS0dM+6hF167nqhvqr11PErRzQW+Mfp9cSCBA2ZemenzjSnJegGzcmPcLSjYldjkV4JeRwolNfZUdCPjKgXLGdS1+UHK1+OPS14yU1kAPeKqW0gKAX+b/FyilhjfVk4SEhISEhIRKoIoMzyQAq5G9XuIyrfVnfbLLly/XN51w3lHHJLs1gN/x2Dpd5dQO3iznUhehaSsKHOsUmpri7IYwMqa+EGpYsiOWnrNPbHwAo0a5n2tJsQC2TmlqzdcW6dhx1YlhblxtlPjrQi0sH+W7hAkK8Ys6R6Fprd27d3vHTaMR006O0aDORS1+1qqjDFscw18rK+1DMxieB178wtIW/Of+7M7KMTyVC3gAQCl1K4B3IbuB+bVa6/9zyW3bulV/aOKz2MCFmuhMSBYtKm3l0sWVUXpCJh5JGk4aeNkTp8+OT08hx02wXErLhdgFs/h+7Y4H0dbW5rQX0jb7GIeQCTm2byT2bR9iUgAxGwKfD1yQIwmuJDpsf6kg0lXPHje2P2WkQSSbgFBb1LmQBkZSO2WmzGoJquupP+S87Nu3D+MnTEgBTwNRtVdLFPgggHMAPBnAj5RSvwTwDQB3A+goHkioVO/z4ZoMfRMitdMtvnOLoFnHJ1+Pnb50obb1+fxyTXpcWyg52y+7D3y2pcyNfdz8TrEz5tvSXZOUlFlx2fL57NJNlYUGty6b1FiX6qW+235S15V5bqXXk22DChhdeqk+8p13qn3muOHsSzc09nHJGAhd6H1BOqVfsskKDXBCdfrOMwcqeON89tmRXEeuOVkNa8PifX9nfU4oD5UMeLTWu5RSLwDwLQAvQBb8FC9B6lRKHQTw6LJly5rlYtNQ686mLIQuXAnVQV8ZY1KUMRbTeD4aVRsD9UZMf7ROa/yjDAYNqhwpUyoqGfAAgNZ6g1LqtQD+HcCLkd3TAwBt+d+T9+zejba57VjR0YHW1lbMmD4dg6dNwLvv+DoAYMuWLbhx33IsOvGFgFJYNH8BPrHxAbS1tWHR/AVYuOQ2AMCM6dNx1Yb7saKjAwDQMmgQbty3HJs3b8aevdn7TEcMH47Jkydj9erVWDR/AQDginu+j4/NPA1ts6b06Lpm+1/Q1dWFFR0dWHz+BQCAax++A1u3bsWu3dmDo4cOGYIb92WPHi9sjhk9GjfsXYZF8xfg4mVLAWSU6BUzT+m5cC5ethTd3d3YvXs3Ll62FIvmL4DuOoQb9y3Hhg0bsP/AASw+/wJcNvsU3LhvOVauXIkVHR24eNlSTJwwAW1tbVi3fn2PX5eOeSquXH0Ptm7bluk74Sx0dmzAFSvv7vFj+LBhmDJlClavXo1D3d1Y0dGBW079Z1y/669YvWZNj9y0qVPRnZcDQNvcdlz78B1438yT0TJmJBbNX4D3P34npk2bhsuffDre9cuvYfH5F2TPUlLo6VMAOLRhK7TWuOiPP+rpi1kzZ2LRrOf16L/s77/E9cedgw888SvsP3AAAHDrGW/Aoc07cOnyn/Xo+8TGBzBkyJCeehs2bMD09nYcOHiwZ+wMUgqzZ8/uaTcAHN53ADfuW441eRsB4OPr7sOoUaOO8lUNbcN7Jz0D7/n9D44avy0TRqNlzEgsXHIbhgwZgvZp07Bu/XocPHiw53yPGzcOHStXom1uOxbNX4BDG7dBdx/GRX/6Uc856ly5AS2jhqNl/Gi0zW3HmjVrAACDp47HoOFDsWj+Ahzesy/r8zlTsfCnXwYAvHfqs3Dtjgd7xtji8y9A15pNUEPaMHjS2OIiA5A9VG/Pnj24eNlSDBs6FJcMn4fWmZPRNrcdC5fchnFjx6K7uxttc9t72nfl75fgvROfjsFTJ/Sc784V67Bt2zZ85gX/nKk/2AkAuPxJp0MNzdJBH1n9W0yYMKHHp7a57Ti0cRsOHDjQc64XzV9wVLsBoHXWFHSt2ojL5p7WI3frmW/EJcPn4b1/+zm6Dx/OzuXoEdD7D6J15uSea/LSsU/FVavv7am3+KXv6Gn3dWe+DguX3IbB0ybg0PqtuPz4M/Cun/83AOCW0/4Z3Tv3om3ONLzyPz+FxedfgCt/vwRaa2zcVLz2D5gzezZaxo/Ghb/9Vtbe416ArjWb0do+Ee+6/WsAgNGjRuETM5/f4wOQPdvn4MGDWLd+fc9YnDljBvbu3YtPnfaqzK+WFsycOROXHXMqBo0YhoVLbsPIESMwadIkrFq1Ct2HDwMAJowfj4/OPBUXP7ikR//Nz3g5rlp1D7Zs3YqLly2FUgpzZs/Gli1bsHtP9gyzYUOHZuN87Vp0dXUBAD629l4cPnwYK1et6tE1dcoUfGjmKWjNz/fFy5bikhEn4GNr7sGOnTtx8bKlWLtuHaa3t+P9j9/Zc72NHjUKN+5bjo6ODmhkTNmkiRPR2tqKjzzrJT36O1dtxJXrf9dTr2j3pk2bsHdfNr5vPeuNOLRxO977t5/jlpe8HQDw4Xt/gGHDhuGKZ7y4x6+DBw+iq6urp0+Ldn949W+P6M/fqbd27Vp0dnXh4mVLMXbMGIwZM6ZHBgBufvYroIYPOerY7FmzsHPnTuzYuTMb+62tuO7JZ+MDT/zqiP6JY5DQeFTyHh4AUEqdCeB7AMYbh9cA2IDsXVsPLVu27F02zczRmJKctut4zL0GPnC0a4Eycszc/RZSUPdNSOrFyIXc90L5aqKzs7PXPTwmJKnJkFSPaYeiyjmd1HGJ/5J7NELSt5J+c9H8XHqQsinxN+a6sv1x6XXdw+NLMXO6qf5w+SjtU6qdVMpaen1Rdn11XHolY9GlM/Sa5/TXOjdSa80lw+ehbW47bn783oZSLn94yYLSFvxn//SOytFFlWR4lFJPAbAEwAgA2wFcB+BbWusnTLm9e/a8qwnuJVQYBw4e9N58mlAe+ltKJI2bhFB0dqxvuE3VUsUn0ZSHSgY8AD6HLNhZA+D5WuuVLqFFs56H7u17SmFCqBstzf/ULlR6wxy1U+d28RzL4LoZM9ZfSo9kNyuxT+mj2ADXd8kOsEi1mToopsD2k/PNlHXdSBvKIFHgGEafPurcxp4vykYok2X77qrnk3X5ILmGOdZl69atGG39LF3KmPj6N6Svubb5dFKMtkTOJcv5S+kMZeok/eU7ZxxzFXq9uXTYPpu6WsaMCNKbUDsql9JSSj0ZQHFr+yu11j/0yS5fvlybv7gBei/+UqrZPh5Ch0vBUb6hFG+M3ZCLW0J1S/smZLGXLATcOfOVrejoQPHsJm4RMH2T+u+zK0kj+NoiCY5dY73W8y7VJ/HVJxe76Jg6fefDZyMm8CrGTa2LrnSccpCOQamd2HlF2h/Scx967XNzNXdeffV8bZHIFmhGSuuPLzuntAX/WT/+ZUppNQAvz/+vB+B8/k5CQiwGOR5lkJDAIY2bhCpgUMvAHqdVDHhOz///TGt9mBL8woveiuse/XUv+ly6S5GwCmWV+XY8rh0Jx0i5vlO2bEjbLaG5zV0Upc91XHrMV+6zZx83P8+ePZusT+mrlfanfOR2wZR/Lh9NWWpH79LpY0uptkh8tGUkfSZNxUggveZCmSwzPcLJ+vRL2FTzODeXuHyw00Su9E4IpP3BjWEpqyOZL1xtljBDseeW0lf8ojKhcahiSmsFgDkALtRa30rJHti/X3P0PzWpSCcz6WJE0bi+eiEBmq1DsriF0NdSyl8SyLnKbHDpDIkf3OJh675i5d341OwXiOxxvrhsUD5JdZv6pX3KBQZShOjhzl8hw6WcKH3c+JEu/lL/fOd18OSxOLRpB2mLGnd2ua3DpVO68ZHIxc45nC3pJoAazz7dIQgN0iW+SmRNuOoNHTasoZTLn19xbmkL/jN++IvK0UWVYniUUkORBTsA8DAnv2HDBnLCsuGL6gs2w94B+eArlyzAnK6QC4/a8VDBi2/SDtl5cf67dnkxExFlz2yX/dk8l+b3RfMXBC8yrglOsqByQZ5kZ2t/pgInUz5k8aVs1RpMSL8X16BdVks/SzY6toxvgV80fwFu7LhPvMDb9U0ZyTxD+c+1zTXHmf5xdSQ+Fd+pIJWzzQV1kk2MbUuiU3rcJRdia/C0Cbhl3R9ZvWVCDfCUVtV+ozbd+LzKK5WjeOBcQkJChlhmp2zUEtQmJPQ1xFxXg4YNqYMnCRQqldLKXyfx6/zrUK31QUq+eFs6tWug0lEhOx4uTWEzCa66JqQpi1DqmWOMJP1h64npUxuu/pZQ3qHttXXYcpfNPQ2HNmw7yqdQul2awpHWiYWE7XH55GNruPNngxo7nB4ps1KUSXbtIUyYhF0zdXxwxV2YMmWKiOHhfJfIhVz7Lt22/7G6a5mDOMaOm6tCbFFy1Njx6aDmNGq8mbqa8bb0v7z6RaUt+E//3s8rRxdVLeB5HYD/BbBVa82+iOTCyU/X16+8r9dxycUQEliEUKEhQQPno8uWtG0uxLRR6pdtWxJMSIKVGMrbJVuAW/x9n01wwZsLIUEBpYOyRQXjBWL89kE6zkKDspAynxx1jqSbEsqHWhgs3wZIMndwGySJb65rh1rUQ4I7u67EH1dbfN9d+qTnRRL8FDokG1lKBwAMnjQWt2z6S0ODhgdfe15pC/5J315auYCnaimtcfn/rRLhQ5t31M+ThH6J9MuJhBikcZMQirQ+NR6VumkZwNj8/06JcNsx0wDwbEHMTsOEZIdZ2Aplcag0ilnX/l9Lm6mdnU9OooNqp2vn5tqZmXLUTlG687Lrmi/+dPnh+iyRqRUh7Fforpcbz1Q/x1D7UvjGvX2cYz6KcrvfqGvF1y6fT12HDnltSdlLV/upvpNcY65rQmLXVde069ITygJz48g1fij/OSaWK+PaEsr8+cZbgZUrnS8ISKgjqpbSuhrABwDcrrU+h5O/6NhTdecT66JTG75yKgDhbPhsSdIlVJqBo3GpSSSEHnfpiKXvpTQxt+BIFkZb3rc4L5q/AJ1PrHOWSf2n4BuLMUG0T07irykXYlviv/S8SRBaT9qn0gDNJ2vj4mVLYb+o2EZIasUn4+t7+5qVXgeUf5LggvPX5SNVRrWllrm7Fp98301INlZ2/WY8afmvb3hJaQv+U7/508qltKoW8HwBwEIA39Nav4aTN18tYYKbNCg5u8ylx3W8lnqSQIObKKhgQrLTte1LLv4yd1cSSHf6drlZ9+Pr7sPYsWNJ3UV9atKT9L3LZ2mA66ojkad8jZEN3fXGLhpUACzZLNg2uHNH6XP5uWPHDue4MXW6bHF6fbpiNl2SzYxZVzrPcL76/OHaQ10voX1FXY8FYuZ/ap7h9DfjpuWBHvBU7R6eMfl/UUpr4oQJIqUhO86+jr7Slqr6Mcp6AWSMDhdiWbD+ir4yPqTg/HWNm2b4USUd/Qkx/SFdnxLKQ9UYnh8C+CcAn9daX8TJ79yxQ79/3NN7HQ9dfCTUKkcTx9i063E74hhmhNrB1rq78tnzoR5BQegOk0ppcSkQjuFxfacgYdtcx327WakOqV8SpkGiI9RWaCrC1OWzV2v6YkVHR6+UlsSWKSdhklzgWGVTjmMdpPakDJOU7ZDaso/HzEmhc1AIExmCgwcPYszYsQ1lSR7+l5eWtuDP/8ZPonxXSg0BcBWANyP7IdKDAK7QWt8hrP9GAJcAOBHAQQB/BXC51voBtm7FAp6fAXgRgGu11u/n5F338ABy+lq64EhRdqompMwEtyj6/JUsniG0LpWmoCANPCXn1tZlBjxcCooCFUT65Kh+c9VxgVsguHa47EoXAclC4vIjJh3l6zeXT5z/1OLMXVNFuR3wcD6GLqzUAi/ZCJntkmxiuLmKuva5+lLdZQQWkjmXGjtl+WH7csnw5tzD00cCnm8CeDWAGwE8BuBtAJ4N4Ayt9X1M3U8CeD+ArwG4F8AIACcB+KHWmn2ZeNV+pTU0/58eoZyQ0CSkdEZCQjUxqKW5d7EopZ4L4PUALtVa35gf+yqyV0VdA8D9IsNM7lQAHwLwaq31D6LsV4zh+R2AkwF8SGt9NSfvetKyjZhdNKWH2tVQ+qndEbVDs+vHpNZCGBP7s90eaUpBuuumjlOQMgS2vBrahhu2PXjUMc4XSk6auvHpLI5JmBCOZaFsSfyU6nf5K/VDaiukXyWMl6nLNda58W2OG58+bm7g5GxZqi1cHUk9l45Q26H6pPVCWBvJeaZ0cnOazy9ujDXjpuVlb3lZaQv+CV/9cbDvSqlrkaWjxmut9xjHPwjgUwCma63Xe+p+C8AcrfXJSqlBAIabOkT2Kxbw/AUZffVerfUNnPyunTv1orEnAeDvYSjKQmlcl0xogEX5Rl00nBxlV+K/dDKj0hRSOcmCQPktgWRSeu+kZ+Dw3qMJRN/iKg0SXD745KhzSwWcLntlMDHc4k+1QRK0UgExFQBRfS9JJYX2Ddevn9r8B4wYMUIcXIQu3LEBXuw1HAJJAMEFCb7PlC3Xd5d8WQEZNdak+kxdqnUwFnc+NtACnl8CmKK1fpp1fAGA2wG8RGu91FN3C7I3LewC8B4AIwGsRHb/zzck9quW0hqW/98vET6QXh6aEIjBU8b3umk5IYHDps2bccyIEc12I6FCUE14eWiZb0tXSu3gZLTWY61D0wCsdYgWrE67x9Y4ABOQpcO6kd3Hsw3AuwF8XSm1T5LmqlrAE3QPzydPfWWvCDw0pWPKSFgLblcTugN37Zp8Ow4p9SrdlVBsjE9Oqs/+7rLFMTtSVoiqZ9dfuOQ23HTCeUeV+XRRDEetLBnFDFH9H5I6k+zGpeeauq5CjnOpI5fvpn1uFx7KoNm2uHHlaovtA1WfG/ehbIWLMXHNdxQrxKVmfHZ93wt9vuvFthN67bv8C2WJOWY2ZGz6/Bg8cUwvuQGAYch+WWXjgFHuwsj8/wQAz9Na3w8ASqkfILvx+aMA2ICnaimtjQAmA3iD1vp/OXnJgwelVDO3iFH1XMfNMtMXCi5/KZ1mPZ8cNSH66lJ2pRS9LSvti5CgVDJZ2jIrOjp6Ah7bF2oSpBZJ238u5cPZioUkMJKMQ58sF1hQdiVykgWS89enPza4L8rb5rbj2ofviEpVSVJCvvquely7Qjd41Hzn0xOSauIC3VrnJWmbJXo4H131CruuuabR9/Asf/vLS1vw5335/2JSWg8DWKu1fpF1/AQAjwD4V631bY56EwFsBrBCaz3XKrsBwMUARnP39FQt4NkJYDSAV2qtf8jJX3TM83Rnx4aamYoQVsTUK1347O+xQYO0jssXU06ycNUKSd+HLLyuOtJdO7fQhPohCVxt3SHBQ4h+U0eIf5Igj0PMOaWuTV8QxLWNW1hjg0mzfVprKHVk/nfpl2ysbH9CWAwpJMGIWc5dG2XNm7Z9l01JWcgmRVLm6o+YNttlq1atwnHHH9/QgOdv//qK0hb8p/znDxt2D09+k/I+AH/WWp9ilX0AwNUAZmitXemyHlTtSctBKa3Ojg11dCWhP6Jlwuhmu+BFGQFnGUxRQm9s37692S4kVAyzZs1qtgvNwF8APEUpNdI6fnL+/0E4oLU+nNed7iiegey+nm2c8cowPHmE151/faHW+ldcndWrVulPzX5BUDRO7SJd4NJMFKVJ+eLbFYaks0J3lC4fQ1mRkD6V6q+FhrbtcbJFasLltzT9IGXTOFmfbc4nbncq8YNiQiRtlfaHr05oXUqPb5yax2KYWBMXL1sK8xEYFFPq0y9JDdUyL3BzkAR2iojy23ceJOM01jfKpxAdXH9K2B5XHVP/5s2bMXPWrIHG8JwM4Hc4+jk8Q5A9h2ej1vr5+bFZyH52/jej7vsAXA/gXK31L/Njo5Hdw/M3rbX3GT4FqnTTcpvx+aCkwnVnvZ4sl9KzHL1OXdg+2PWpyVG6aNl++SZZn21X+yVlnI+SPnVNgBK6n5twQifVhUuOTh+HLNzcwhoqF5Oq4OpwE3XRpxJ/pEECZdc31l32fPooH32ypo/SNBjlx4qOjqN0STYvlM++Mqp9LltlM3rSoMk1R0p9ig3QJJvJkPkixmbIpm3P3r1BtsqAGtTcpI7W+n6l1HcAXKuUmgbgcQBvBTAb2ROXC3wVwBkAzKBqMYB/BfC9/L6d7QAuADAWwAcl9qsU8AwxPqffmyfUBUOGDOGFEhIspHGTkCDGWwB8Iv8/DsBDyO7duYeqpLXep5Q6C8B1yJ7DMwzAHwGczdUtUKWU1mQAG/Ov87XWj3B1VnZ06KuPOfOoY1Iq35bxReuushh6lkqPhFCmMQih1GN0S9MjvnoSG7auUJq/AJfe8Onn0liStAqnRwLOJ47toHxz6aw1RWTrkLBI1PVH6eP6lhqnLtuhiGF6YtM0vuuLui5CrvUyUlA+nyS+S+fdWlN4ZcP0cePGjZg9Z05DU1qP/turSlvwj//S9xvqexmoUsAzE8Cq/OuTtdaPcXUO7N8valytiy9FmVILgdSetA5VLtFNQdp+n1+cTm6RiQ0IJYGoqWPd+vVonzbNqTt2wZTo4AIeSSDGnQeuDyR2ag0AqPNcli3JWKHGGBcAurDo8TvRPm2aeLxL/PX5KQEVTHPjhRsrLr9sOVcg6vNH4pNdxtn3IWRuDdkUSTYKLv+GDhvW0KDhHwtfU9qC/+TF300BT72glHoSgH/kX2dprVdzdd4z+2R93d/u8paXsaBRkC7Gpu4QuZDJQbJzdvkRMynZ+qQMEvVZyj5xu2hbvy3reuu1Xdd13AcqgKDq2DZiglSfLkqndDIPtW/bMW1J2ZSYfiyDTZL0zaL5C3pudnfpCpkvJIs5F6z49Jv6YtkPSSAgCeBjffbpCmGGJG2R+OfyV7LpBIDWWVPw+ZX3p4CngajSz9KHGp8PSiqowS11ciWhESiDio5ZpJuJvkS/1wqqLVVrZ9X8pVBGW/pKf1StLeZ8lNanxqNKDM+zAPwh/zpGa72Lq1M8aVnKEIRQvJLdhM+eXS8kHeWTo3zkdPt2ZVLq16ePkg/ZzVL2KVsxu+xt27Zh/PjxrI8ufZI0gk+e8j/WB0rO5y+lw6UzVEcI4+XTL2WFpGNMMoY4xqJlwmh8dvX93rbEsilSHTHzUYjdAtw8ImGcKf22jRj/XYwO1T+UbfOYlKnmfCvQNrcdNz9+b0NZkscufG1pC/6Tbvl25RieKgU8pwIo7sQeqrVmWZ53j5uvP7vu90cdoyZbboGXXPgchS65MCgal5oMfHQthZiALeSi5iZAbkGjbHOTYQztr7XGpSNOcPpPLbQuWddxnx8hVH0tY9gHSeqHC+gkqQ6JLz7/Qq5DVz27rg2q7zibkjErGcOSMW37EbuxofTVOgeE+GXXiQmapL6UMe+69IX0S4GdO3diytSpKeBpIKqU0hpifO6UVOjeva9OriT0V3SsXNlsF/o8+ko6oy+hba7zJc8JCV6MGDGi2S4MOFSJ4XkxgKUAOrXWQzh5IEtpFU8/laQbfLtBCQsi2QH6ylyg0hmhtKuL4pWUcf5JdFJ1bL9jWTMpuN0xcPSTlqW6bMSwHa42SsaARBfnY1kIse+qR9WVps+olFYo+2nXoeraT1p2gUurlDlvmAhlmaXltoyUma0FFNsZw6RK+yO2/ymdzUhpPX7x60tb8I+96X8rx/BU6cGDxU3LbCqrwOLzL+gZcK6Bx03Q1EQhWdBMm5IFxqznu1BCUjg+P2x9IYEdVy+kr6TtctmQwBeUuc5XUbZo/gIyQPH55pOx+4OaoH1ttHXY5bY9iZ9cEM31m62PC8qo8+cbf5I6Lls+Od945jYLPp9cbQ49v+a1Luknn46QgNjnKzV2fHNTPYMflw5pn/pAzXHc+JD4yLXN9LF4OndC41DFlFZ6ynJC3XBoI/v+uYSEXpg8aVKzXUhISGBQpZTWWwD8N4A1WuuZkjoLhx2nb9j2IMt8cAyElNZ2yUt3USF1pNSrj2XxtcNmO1zlrroxO30u3SClp0N2VK422WWXjj8J+kAn2V4b3HmVshF2mY95iGFduHMrAee3lMmTXBPctUmND1t3DCtCjTGXv2poG27Y5nzRsxghDEgIc1OrH1wfSK9Hqn2SebeMucpnn4KUDbXlub5QQ9uweP/fG5oWeuLSN5a24M+94X9SSquOKBgecUpLtRwhsKhBKJnofNQltXDbkAQ7HCQ+uexKFjqOQpbS9WVOeq56koU6dPEo5C760496HjwoDQgkgYudWpHoolI/vkDOV59ajKQBSqGD0sPZotI0kra79HHBlvS6c9mXBkaL5i/oVc/VRl8bON/tBV56Hdi6qIDY1UYfJKkkas605UMCWNd3alzatl0Bnc9/yjefv1J5c31KaAyqxPBcCODzAJZprU+U1Cmew2MiZFdA1fOVcbsB8zi1e5XsoKS7TxsxgRE1mYXsniQBD7XIuOxJdodSLJq/AJ1PrHPqp2xJd5cxoJga6e6TqiOtZ9blAgFbp0+3xAcXqGvHlgmF5JzbZRcvWwp7rqEg8Z+r5yuPLQvxgdPDBT+2rKSedOzEQjLubdRyvlZ0dGDevHmJ4WkgqsjwpHt4EgYkyprU+4sfCQkJYVAtA/vpzlUKeIJ/pWWC2k1IdisUwxBKzRbfqRSAS08tqSLJDt5Fm/t02J9rXcAo/ZScy09b3vaR6sfOlRt6HaOYEYm/kjquHauZSjL/UwyEb6fOMSGSNFAIXc+xNZJde8yuOjbtw7GqvrICs2bOZNkI6vqx2+Aro+YMF3zXsw3peY1lkEL1mD5J2TApexxzTPo95LzkP0tnZRPKQ5UCnoLhEQc85r0YofS4K8iRBAI+W3YwwYFanFx+uCZ3ycUrnRxi2uzSK6nnm/BC/eRsu+q0jBqO7h17vHKcLz4/XG2hghwpuMmX8olbxF1yZaRfqMDGFyRIUxsucLLUGKT8Nc/X7t27vTqpwMelz5SnAlizPjW2pQGDT5+vXVQZN8/4NiBUPe5cS3yjxrfv+qM2WHY9yeaL8zWhfqjSXVPBAc/WrVvr5EpCf0XL+NHNdiGhgti+Y0ezXUioGJqxPqmWQaX9VRH9muG55ozX4tqH72DTGRLGg6NPJRG+zw/X7kPCCEh2US5/pWyEDd9OVJpisBHDNFGMC7XTtfX55Nrmtot3lRQb4fPRrk+dX5+dUBbIp5/a9VIskZTx8dH8sbtg37XD+UBd+6Hw2Vs0f4F4nqHYKu4adslKGE4fYhkfHytu6uJ0c3OgxEcJa+azySFk3EjnOLMtu3bvxnSxNwlloIoBT9BNy9xkTtGpIfBd0BxdSvnJybp8901EVJsl+m1I6XGqDdQk4Sv3tcunx7Tns21+XrNmDRkYSIIa7rvPD1Mvl6oyP0tSGK4+lQbfNjgZV3tj9FFjRLIIuspccq6+kQSzZp2LfvbfvexJNkXSYIXrC2nQJAkAXP5ygYXvuw9ccG/b9+nwbZSkczw1F8QGR1RflRF0J8SjSj9L/zKAtwH4mtb6LZI6K554Qk+bNk000CQLTC07RWpxC9HJXcycLordCfFHwhZQF3zMrjEUkonHF8TYoGS4RY5a8CR+hwbtRTkXhNl1fLqpAMqn3wcqSKf8ipHjNgQmJOOZss2d4xCWwiUrXSy568ol7yujbPj8lUISzLt0hwY/Ln9D61Oy0nXCJbd+/XocM3duQ3/aveqDby9twZ919Zcr97P0KiXiCobnoLTCtGnTRHIxF2xfRRk7iIGooxgDg6eOr1lHaFmMHIUy+k0S/NVLf9lohK2NGzfW3UZfQtWYipBNYAxirm/p+pRQHqrE8HwPwKsA3Kq1vlBS54nHH9ft7e1RVKJ0pxG6M3Dp8JW7ZEw5rsy3U5TYKGQ5ypiDlIp3lZn+SXa9HEtn13fZa5vb3vPgQW6XLtndh5xTjiXxtUXCcLh8kS4CEibO5auv7aF1bD9c7ZWyYVK9Pp9857ZtbjuuffgOkqWU7vxjmF+XPu577NwVwq5J2RUXQlhKnx57fLhsc/0RMmdSde0669atw9xjj00MTwNRpYBnCYCXAvic1vp9kjoXHXuqvvbhO3odl9LOoakFSqckjSEJLnyBWOzixfkuXWTtOmVQ+762lBFs+WTMJy1zC7CJmACTCkA4uxJZaaBCnS+7vm+8Sxmh2MWc0x8TKEl0UzpMUE9a5sahfV6o65sKqHz2KP22jEtfSHtizm9IIC7VQ82ZUl2S68cVcEs2ZJcM73kOT0ODhtUfvqC0BX/mJ2+rXMBTxZRWetJyQt1weM++ZrtQV8QuJiakQWC9/ehLGDliRLNdSEhIYFAlhuduAKcD+JjW+ipJnXVr1+rx48eLd1iAPL0j3VVz8lQdHzhqnLIp2b2ZsjE7/5B+otJAIb7a/lI2Xb5LaHWOqZCkenz+SNieULbNRgijFMpCcSyflNnj2ipplykfmy6RIISJlDKzFFvA2eQgTe1wPnL+h/oZwqxLmaUYZp2au7gxGzr/Adkzv27d+lBieBqIKgU8DwB4DoAPaK2vkdR556A5+sa95e1oY2hcipK25aiJzqejlknJ1kG10eVrLYEiRd9TOn19Y7clNohsmzMVnR0bvD5y7bJtS+l6V5AQUuayZSM0jROiIyatJNEvDfKkASsXvPr85QLAlStXYvbs2WTbYhAT/MfOQXYdSZqG80Nqy0RoUGPXkfobOhZCwfWV1hrDhg9vaNCw5mP/X2kL/owr/6NyAU8VU1oHpRXajmmvkysJ/RaDqnRJJPQVHK7IxjGh76Bj5cpmuzDgUMUHD4oDnoVLbmNlOAai+GyWmbhxn/+dW9zO0azHsTBmHY4J8vlllhXfQ2luX1soGSlrIdlJ2TLU7iyEoi6wcMltR918SqVmfO0O3QW7dHM6Q1I1PvlYtsbXHxKEpJlcftnH7DKXr67rzNXe0P4NZV04hs5nTwLqXIbYc+mj5qkQ+Nhd6Xgz7btY5hh/ffOJdFxT/UnNHbX0Y0I8glJaSqkWAPMAPBvAs/L/JwEYlotcqbX+eMk+FrY3AJgC4E1a629I6ixfvlybLxAtEJtGkaSIbEgWPmrClQRCtp+cbMjk7tMV4ocPodSzbTuEJvfJ23bfO/VZOLxr31F1qAnXtCUNdCnd1Hnnxof0vErqSQIIzh7lLxfI+PrWZ4cKwrgxRl3f1MJq4qqND2D0qFGkjGTxk16zLv99tlzHKdu+PjLrSBZvrr9d37m2UHNmjD4pfJvL0MDFrNuMX2mtvfL/lUZFTv/Yv1cupRUa8BTPwvGhLgGPUmoQgC5kKbhztNa3S+pt27pVDx8+vNdxyUXDIfaiKcAtBtIFx9bHXYxc8ObyR3qcsiv1rSiLCYY4276AxOyPa3c8iEVjT+p13OUTZVfCgkiDDi5wkQbOkmBKEry5/LcRy5hI7IVcs9Kdesx5Mj9fOuapuGHnX522pf3EBcohwaUk0KBA6XDZ4/woYxMk3dD59LoQEgxJ5y+JzI37lmPfvn0YP2FCCngaiNAbFlqs79sA/KMkXyiMxxFfN0krKVW585HQZKxdt67XsZiAuK+iP7WlL6F15uRmu5DQREgDaxNpfWo8QhmeDwEYBeCPAP6otV6hlHobgC/nIvVieE4A8Ej+dZrWeoOknpnSKhBDzfrquo7bkO5qfLKhVLZEjqsn7RPJrp3T4fJJ6p9klxXiAwCs6OjATSecd1Q9auftsilJgXA+SRkpl84QJkfir5R5irkmfOc9hNUyIWUBQllDH7NRfF/R0eF98KCtN4adCmFbQuYLaWrKluGuCcpHygbXFh8DWXbaKlRXDDvYjJTWuk8uLI3haf/w4spFbEE3LWutP10vRxjMyP8fBrBVWmnx+Rcc9bh3oPegjZkcKR0+SCdwrqxsKt+18MXQxtI+8aVf7HZSE7ck/cJNXD4/Fp9/gWiBly6Ytn5bhw+u8158jwkO7e9cqsDU7+pnyTj0BTISn2wdvjYVul3n1vXdBS4VQQVixfeLly1l9UvSJDHBvn0d+eSo/nW1y9ZpHpcEMJyctC2uMm4TVyaTWZwXaoxJNl/SjVBC/VCV3+AWo+QxrXVXUz1J6NfoWiPOmA5YlLmY9BdMb0+PwEhI6Ouoys/ST8j/Lwup1NmxHkDv3XEBFyvh2oG7dk9SajiGnbFBMR2+3antg9kWijXyMTW+na39ndul2n1I9Y+PNqd2WnZ9btdn21BD2kS7MG635uu3WMaOgoTBdPka2s5adqc+1oXa3UsYNR+z4bLLsSc+Rsp3rk1du3fvxqKxJ3l9lIwV6XGXvwV8/Wr6wTFp3PXrY1oodqYMSBlAGyGMZGgqTDKefCjWp0ZCtVSF46gPqhLwnJH//3NIpZYxR95v45oApFS9ZOH0TTBmOadfshBytK4LoakCaepCEohxiwWV6vD54erXEP+odg2eNBbXbvxjj5wPrnNFpat88lw/SfTZkC7qlH9c+yg7oZsLqg+oPvUdsyHpb2lw4bNNpbQ4vSGBZIyfVLlvgQ8JEszPkvQdFRRJ6pmfuXPIza3SOddn39ZB6bFlduzYwcollIuaXy1R75uWlVInAng4/3qy1voBad3ibemSi5dibaS7H66sAMeCSH0MWYAkQUzIAuDbNcb0D7Urtf2hdpeU39L+XjR/Aa59+A6nDz79scyHdOdPtZMKVGx/qUmfYgTKhs+2q50uP7gg1xesSftC4qv9fdH8Beh8Yp2zXow9ypb0Gi70hW4IKJ1lMzd2QEJ9Nn2h/OA2hbXMhdK1xOWjfXxFRwfmzZvX0Bt/11/97tJuWp72wVv7903LjYbKfrdXvCj0MQB/qIcd6aQesgP3oewJox6Q+lhGW8pYUEtdlPvwKwLqEXz40FfGaSPHWE3Xd4njpox5pgqo2pxZRt83uy2DUkqrb0IpNQrAdTjyoMOrtNaHQ3TorkMAaqeBAX4S4naPLkrfVz/ET5uu9+3UTXmfvTJ39FKWQJIO8u36XDp8zBO1q45BaBpCkiKibEgYDor94dJB1E46JG1E2XOV+dIElI2QXXutwY702uxcsf6ovrLrcWXFcY5tk/RRDHtswlU/hLl2fXeND985s4+bOijWRML+2G2i+opilShbnL4CrYP77PLbb9Fnejxnc+YAeD6AM5EFOmPz4v8G8PVQnV2r3b+4Cb0wOFnJAJdcWD5ZySIeQrNytL+kf6STGeWPb0Hgghr7mPS7T78pt3nLll4+uQIq2w6VmpGmFCgd1IJp64+d+Dlbvrb7ynztNP3k6nFBm6uMWoR9gZ80sPPpvmLVb7x9JV0cKR9c1xS1EPtsSMaKJGjiriXJBjFmHpaA6ysqgCrg6g/fnEFdty55s+yLuiOobQm1oU8EPNZ9QCb2A/gUgKt1xM1Gg6dNqNGzhIGGPXv2YNLEic12o9+jLCaxr2DPnj3NdiGhYmjG+qQGpZRWX8C/eI7/BMCXQ1NZBd59x9edTIFvx0PtRDm2hksZ2MdcOxxq9+nzw/TdRw272ubTY3/2+RbCePl2trb/vno+Vkuy6/XtIn31Ll62VJzCMVkQSs5lx+U/xUZw7IHPpjSwoBgpH8NoynKsHJW+kDKKLp+4Nkl2367voXMEVcaNRZfPxWcpu+a71qUMnQsUi0PZ4+C6ll3+ur5L/JceM21wMtR86WOnXe0wy1Z0dHjtJdQHfSXgeTuAqwE8DdnDEOfnx18D4IVKqVdorX8Tq7xsypRKRRTffXalQYjPbi2Lv8S3EErWN+FSkC6KLhmXv1ybfHp8dYYNHXqUvC+F4fKRC3DtYy45CbgJVkLZS1NFdp2QAMUnRwVy0nEk2QTY/trlPp2mnO9as+tcduzzcf3jvz3quESH61yWPV/Zvto+uMqoulQ9OxiWBDWu8kJX6Nik/OZ89vnB+ejT75urE5qHPhHwaK3XAHhz8V0pNQ3AGwBcgezFoT9WSj1Da70iRO/IkSNL9TOh/2Pq1KnNdiGhgji0XvzGm4QEAM1Znwb6gwf79HN4lFLHALgPwBQA39RavzGk/oH9+9nGSRiHEHAMiWTX6yrnbPnsuXRz5dxO1JYLYZdCWByOHaF2xJxPvvNw+fFn9Nzs7jpnvr70MUGc/67jdjnXVyG2JH1A+UMxMiFto9rjqsPtwimffP1o66f8587FmjVrcP1x50QxGj5Q9aQ6ORZHyo76xncsc0Gx4hLd0r4JYSV9Y9vFFBagmG/7uEt+6LBhDX2WzeYbLi3t+QmTLr0hPYenTORvY1+E7Fdar1NKXaK1Fr/saNGJL0TnivUA+LRIAckiwA3yGEqW8lEqT9mjJn3ppEr1IUXfc3UoGeq4azKTUs02imNtc9t7HXf1AZXSssuoYIg7X5J6XEBoHvdN6JRtUzfV35Lx4fpOgQsgCl3UGOD6ShJwcYFD16FDovHqq++ySV1D0rlCch3a9qn5jbp2OHu+scIFLy7dIe2n5mfXd5cOqU1fu1x92nbMNNz8xH2idiSUgz4d8OT4FoBbAYwE8BIAX5FWXPiT/8JNJ5xH7t4A2T0PrnpmXZcMtQO27fpAMRou29wEJtmRUBMf56/Pb1e90IDQrkNNRKHBUIGFS27DTSec18t3qi8LW9S5dh2nGBhqwfXBVcenNyS49vVBSNAU2jeuAE8S2Pl899Xjjrv8dY0p+2Z36XijfOaufeo8uMpshGxofH746lFzE1XfVWaO3VqCDk6ek5WOMd9xOwBqxk3LAz2lFRTw5CmmC6zDTzM+v1ApZev8ntY66B1YJrTWB5VSvwLwMgCnISDgSUgIxbixY3sdCw3KEgYeXOMmISGhbyGU4ZmN7EZiH07P/0w8hsCXfjrwZ2QBz0khlSZO6P2cAyrVAfB5/kKGoioljAZHE/v8pVgLKv1if47dfZr+StgwajfP7Txd7XTpplIXXH/Y37u7u8m2+Jgequ99Prra7LMby8jUcp6Lej4d1Dkq7Lt8suGTk7AzHBNGXWccoySBPW4o5oM7n9wxCeMaM05CEMIYmTZc82Ko/7XMWy4Wn2KaqHmdGxsuH11zzqBRw/GFXcuC2pFQG6qQ0gKOvDz0+JBKQ4YMIelWjp6VTvSUrKlLMhm6ZDl/zDrcJCKhbLk+cC1uMbS9L00RsxhIfJCcT/MeHtfEJqHvqb5x9Z0LXMBH9YckZWTKS88d1S57QfCliELab9qk6vkCF/u/z3efTZdOX5tXrV7dy1+ubbYOV3t8cpLrWCInnUtCzpnLrqvNrsCDC+6lQbA5trlghRsfVHloYGx+7+zsFNUtEwP9wYNBrdda36W1VoF/XynBz9X5/9FKKfFv+dauW8cLJSQkJCQkNBhpfWo8qsLwrDE+TwfwqKTS4vMvwLUP39HreAiFypXZuxUTrp2GWebSRSGECvbteimqltvpUzv1UJag6DMX1Wz77/M3NC0mYWeotlApIt8umOsn6RhzfafquHyXIkTWVU/azzaovqIYJJ8dlz4X6+NiG32g5HzXeChbW4YfXF3bX25OtPteYsM3J1HXAMX4Uikz7hhVLulvqq9qZbsTGoeqBDwbAGgACkA7hAHPwiW3OY9z1Lx9zFXfDhJqzaf75F1pH4kcp9u3eHJBhE+XtIxqi60jdOKiJkRfu+wJ68rfLwmm8ilQ6S5ugffp4gJYl35XWyRt4yZzaZDqqmMHpXYZlRazdVPpEZ9fvoXLFbi4/DLlrt76p15tDtkwuUClZmLOu/S6cm3ipBsou32u+pxtn46QwJHb6HA+cfO4dJ2Q6G8kBrW0NMVuX0HNDx5sFJRSOwCMAfAqrfUPJHUWtj1J37Dzrz3fXbt+ClI5iQ6XHsnFyul2BS6uNkp33zFBl3R37LNL+SixXdTxsT++Oma94vN7Jz4dh/cd7FXm8sulwwZVx3fMPm7LU+NFurBI+tRVRxpo+cp8+nx+Seu57Jo6XO31BQUh46j4vm/fPnxo4rPYtkja5wLXp9Q14dPhakcoYufIUB999V06bEjmQK48tJ1cn14yfB5U62As7nysoQ/v2774A6Ut+OMWfqZyDx6s0h1MO/P/Y6QV1LC2ns+17tQbjdgJiNuZhKLe/Ra7Q6qXjsFT6/MG45hJndLRTJQxrsre4UrYFCB+1831/cZN/PNQY4OdGMQEDvW2VVa9GB1lzCVl2zLXp4TGoEoMz1+RvVT0Uq31jZI6y5cv18fMmRNlj9v1SlkH6pjEtpTyDtHng727pfQV8iE+SRd9KYMksRV6DhbNX4DOJ9YdJedjE0ImUckO39fmWDnKXsjia49FKRNn1qcYK+o4x0hy7ZGwbxzjw9kGsgcPHjNnjoi5CGHUKNvU2KZYP+5aqoW1kfQXNc9ImJEY33z+SOYZl+0YP2z9Kzo6MG/evIayJDu+9KHSFvyx//bpyjE8VbmHB4hgeHw3LfsmB/OYPZCpNIJ9PDbQcaEeu6CQBdMlV+hwtS0k1SGFz1+JLe5Ycbw41ja3XRxs+nTYtkLodqo93EJPLe7SAN5e/F0yRbnvXNh1XWOsOGbbovz3tVMaPHPXputcSM+zGSi7/LL7yrQtDe5dOkMgZS+4wMfnI3WMO8eucqldly6JnB14hQQ/ofZC9CXUD/06pYXDh+vjSUK/ReeK9FNRDs1Mg/RVpHGTEIpBqnIESeVRpZTWtwH8M4Avaq0XSur43pbO7W5d8tzu2Le7l+4aQlJJPlDMjcQut4sM1eerL4WE6o89Zz5927Ztw1UzTgvyU5r24HRIWYzQcSAt42xTrI+UKXSlM2JSP9L0i1RHSJtd+OiaezB+/HixD7HXguQa5Or6ykLY6FrnBc6vshBqQ9pXvjKzXNLmRr8tfedtHy5twR9zwScrF7FVKeD5KoA3A/iK1vrtkjoXTnm6PrRpR9AFxaUNOHluwi4j4CkroKD0Uj65Jl+JD9wE4PpMlXELcQwFTqUmqHb6AkwqRWTrjgk0zHIq8ArtU1e7OJ22f5I2U+DGhKmDuq7sOsVxafBAbZAKFPfw+OrHbkZMm2WndLjxbKKRjFxIgBZ63jn9Mb7G6Llk+DwMnjwWt2z8Swp4GogqpbT25/+HSSsMGjm8Tq4kJCTUgjLSYgkJVUZanxqPKt20fCD/PzSkEpcakKajTJhl3K7addzWQZXZbZEc8/lcyFOLjXTXJPWB2wGZZb7PFHtC2ZfS9GaZPtgZnMIx/eRsUZCmhThbEsbNp59jESU6ijLJ9VMWi0j5HcumuOxdMtz9tO11jtcEhI49ri2S8Rw6p0nLKB9D2UGuzdR1FcIO+uQkbJ6LXQxl1yhm78Z9y7Gio4PVVzYG+ru0qpTSugbAIgA/11q/WFJnZUeHnjJlSnDaiZOx5SjK25ey4PSHlnGpByligxpbTpJm8emVLF6hCE1bSHyUBAW1wNeXIZM95SMVTFATv9R3SfAfEpD52s/5xfUH1WZOP9UuyZgNCeCp4FC6iEvKYq+10DknZq6l5KS++AIZasMRM4dy/g2eOh63rP9TQ9NCu7780dIW/NFvv6pyKa0qBTxXAvgogF9rrc+U1HmnmqNd0bl0ApfsDG2drvqmDuluyKU7ZDfPXbCSBcXXP9yORzpZSAJF33dKrw+SXenlTzod1z32m17+hSz2tl5JPWrypXzhFmdJkMDtZmMDFA7cAkwtQK52+PRLgreQ/nDh8iedjq51W8gg0mxjqJwLMZsa6XUbE4SEgtsUUee+lvZSQZ6pnwqIJfYkgfkXdceAC3iUUkMAXIXsntxxAB4EcIXWuvczZGg9PwVwHoCbtNaXSOpUKaW1P/8vTmm1zppcJ1cS+ivU0N5PP61lUk9wo7/1qWvcJFQD9WBnJWjG+qRa+kRK6ysAXg3gRgCPAXgbgKVKqTO01vdJFCilXgrgBaGGq8TwXALgBgAPaa1PktS56NhTdecT68QUcQw1LNVB7TRsnTH1qF0RtwuOYZ1ifZSyZrGsjpQh8J0z15OWXXKxKRdff0t2sRLmhqvPnQsXEyllf2rxzdYXO2ZjdFD+ce0tylZ0dBz1K61Y5kZ6zZk2YuYVzq50fPhshLRD6kfxnbLL6ZTWd9UJ1cHJNuNJy7u/+vHSFvxRb/l4sO9KqecCuB/GGxOUUkMBPAxgndaaDWKUUm25/NcBXIl+yvAE37S8cMltRz3uHTh60NkDkFuYap2IfKDkpIu+9JhPp08uNu3h87sMKtyll/PflLtkeO+nHxffP7L6t5gwYULPMaluV7lpyzfuJIsztwOlJmZqoXL1n+vcumxRx4v6VDtjFiFbl91Glxw1BqhUhW98mDDlbzn9dejeuss7V8QEAJLg0lUWs4mjIL32Y4Mr21aoXIitkA2TWUcS4Nll3Nhsm9uOmx+/V9CKfoXXAOgC8J/FAa31AaXUbQA+pZSaprVez+i4GNmvta9HFvCI0a8DnnFjx9bHk4R+iyLYSagvygh4+xK6t+5qtgsJFUP3tsaPmT6Q0noGgL9prfdYxx8AoAA8HYA34FFKTQXwEQDv1lrvU4FPq65SwHMw/z9EWmHUqFEAwtJFEpj17B1PyO7Kp5NK/YRQ/ebnGNo7huGg7MT2t88njtXy2aN8WNHRgZtOOI+1x+2MKRrc50coayT1hbMVmirjGBP7u0uXZPxJd9bSXbyE4XLV8R0zddkvD/XJuXRSDDR1Dft84q4Rl05zTBXHJcw4xfZIrz9f/3BzicuXEB0cwyjtewpUH3Z3d0fp7CtQSu3gZLTWY61D0wCsdYgWQU47o/JqAI8iS2cFo18HPKtWr/Y+/RSQX3g2Ne5aPCg95nEJRctN3pIJxmeLm3x9/kuDH27xCU1fUHVCF1mfblPH4vMvOMqWHVz4zgeXjpIsfiEUvm+ytv11+UJBEpi55F0LtWQc+eSoa4wLoKS+2z5Jx4ypu6iz+PwLYN8v6JsvpAFayGbBtdGh/OaOhdjzlUsCI1sHdW5r2UxRKScuyOHmGV9AxAWbq1avxrx5YWOuH2AYjqzlJg4Y5U7k9/+8BcAZOvLm4yrdtPxSAEsA7Ndaix5RWdy0bCJ2AqQCCR+rIrnIXcGUZOdp6/O1UTrRhLbFpZ/adVH9wfkhRcykb8u1zW3vWbg4fyWTvm1TysZQgawtxwWtlA5Txq7LLUTSQD/kevHVd7XHV2bacV23sUGnzwfgyLhxtdenQyorYSKoxZmzUwtqOQ8+fWXPEbX2AXXeQ+ZrG824aXnvNz9Z2oI/4g0fjrlp+WEAa7XWL7KOnwDgEQD/qrW+zVFPAbgXwGqt9WuN4xr99KblIiocqpRSsREeB2pBosokOzZOX4wOyg+pv1SZdPIuw1ZIWZl1TBzauK0Un0IWNp8cFVBIz4s0KKH8CGU+QvWXPQZsSMeptMzl06GN27xlEv0+lNH+egU7Ev3Svo/VH3NdxaLe43SAYD2ytJaN4ljvR5ZneCWA5wL4kFJqjlU2Oj+2UWu9nzJepYCnoLwUMr+7uArmg8AkoHaEkp10CKjdP7fbdX2mZDkKXbqLklL01I5Isgt01fO1y3XcrM/t1u3yAwcOOOUo/12MoO2Xrx4FCcvj+m6PW5/vLvvFMQmj49JZHPOd9xv39b4/RNJv5mcpc+bqN+6zq8xum6vNl44/ifTD/O8Dd53GBK8U8yL1Q6qfmhekOqX+UufMJUeNU0qnzweOUfP5b+qcNnWqs239HH8BcLFSaqR14/LJ+f8HPfVmIXv3552Osrfnf+cB+BllvEoprecgu5MbAEY57vLuha1btugRI0ZEUZDcJChZ4EN3ezEXURmUNRWESCCllDn9IYFYUc6VSXSYuHjZ0qNuWrb9C2WypIuzXcadB9/CTemrNWANZZWkATzXPuqY7VdI8E0dlwSlpp3iZvfQgIILBKTBii84dtkNDYAo/23blF8hZTE+FXCNe2o8+XS4jkv0S/tm7969mDBxYkNTWvu+dXVpC/7w130wJqV1MoDf4ejn8AxB9lydjVrr5+fHZgEYrrX+W/79WABPdaj8AbJbXW4DcK/WehNlv0oMz0Hj8xAAbMCzafNmHDNiRP08SkioGMpYXBISEmrHps2bMWHixGa70VBore9XSn0HwLVKqWkAHgfwVgCzkT1xucBXAZyBLKMDrfXjuexRyH+W/rjW+ocS+1VieI4H8Lf863SttS/X14Ply5fr4qei0h1+6C7dR33b9agdo5QKltalZLgdV8iupxZ6nLJF7VJD9JuQ+m4+aVkKjvkx7VNMB1XukzfrSMZJLQxHSEolJEXk8oPbmceyqBJQ17dPrvhZuhRS5oO6JiTzQYjN0HPE2eaYlVj/Q+RcNu3PkjnTx4xJ5ymXj/mDBwcUwwP0PFn5EwDehOxdWg8B+JDW+nZD5i5kv8YibYTetFylgGcOgBX512O11k9wdcyAxwS30Lo+lwFJaoqy7ZrwfBcvd4GGLFb2MZe/Ph99kKSaQvs9JFXgs3PJqBOB7sM9vknOmSRNIJWNWey44IJrS0zg4vLXlosN0H3++nynxrrLN58On0+SsktGnYgbdz/ibJv02nH5QumSjKnYBZlCTPDp0iG5ViUbUqoet3kKSUHVsja42tGMX2nt+8615QU8/7yocm9Lb/pjFwNg3k06RFJhzuzZdXIlob+iZZToiQcJCUchjZuEUKT1qfGoEsMzDsC2/OsztNZ/4eqsW7tWXzXjtOjdmy1D7dhC0yqcrFlHQs2HUMfSHWAMY0LpkMi6/I7ZwUrZCFvWldKS7Kp9ci5Q50XK3FA+1eIv5wPlbyxCr09qh035SLFO0rSNz/fi5aGUj7WySz5IUziuepJrR/Ld52soU02hjPkoRH8Bbt6OZdS2bduG9unTE8PTQFQp4BkOYG/+9Xla6/u5Ohcde6q+9uE7SJmYgU1Njr4JVnIhcSkAm76XphskbZPIUbZ8Oql0gpTa5/o+dJKiYL8tXZJKig18XHViUjO2HsnCSoEL1qQBkV3PJUONZ1f9EJ99NjmdIdd3AftXWiELoSQ9wgXwtaaVpH5w+gpwm0SqLDboDw3yQubTkPlf2pam3MPzvevLC3hefVnlAp4q/0pLDOrClu5cuJ2iWc830UoXHZ8MNWn4fHbB9kOy0IRe1NzOnOobW4bqs9B2+FDIt86a4vRDwgJwiz8VoHFjIzaIsm24/LDLKB2mzCXD/a+xkIxhrj7VP762uNrL+V7GAv++R39B9rfkmpEE1662cG2gzq3rOxcIUP5QY5OaPyXzVshmzwe7z3zjSHIt25+lG9aE5qEy9/BorbsBFG9bEwU8+mBn/RxK6JfoWrWx17EyJqm+MtH1FT+kQVtVMHPmzGa7MCBRxjgq45qI8SOtT41HZVJaAKCU2gNgBICXa61/zMm/U83paRxFd9aaAoiBlHYNrcPtWiVULbfjdDEEEj9CaH7Tnq89En2htP9lc0/D9U/c08u+DZe+mElPsqu2dUtZAG7HacpRY4DzndNBgfOfYwo5n+wyinlz6eHOaSG3ceNGTJkypZf/Pp2Sa8flR+h1IL3GpHNdyDi1/Q+ddyVzlctWCCQsbcg4Dp3vhg4b1tC00P4f3lDagj/sFZemlFadcRBZwCNieBY9fifap0076hhHDVOgBnUoRU3BN6H4Jju7rn1cmiqh0gIm1UxdyPb/2AlX6r+vLa42Sc7LxcuWHlU3JIiRBCK+4yG2fNS7rT9EB5VikAYJMUGZpL7rmDQgCw06OPj6u21uO3z3C9p1fHY5Oem5poJ+TpaDb9xJ/JCU23Of7zyVsRmVBmESxPTHuvXrMXfuXLGNhNpRmZRWjuI+HlHAc/DgQV4oISEhCGUsNgkJAx1pfWo8qsjwAAE3LbsoWIpVoHYwUuZGuqPy7WS41JVLF+W/XYfb3YbugqUMkukLl74IqSfxXarv1jPfiEObtovaQvlg+iFhXah0oo9Z9PnkY/liUm62XkkKTTLGJGyQrdMnY/rlOu8+xtKnz9UWyjYAXDbn1N6NJEDNRyEpFJeP1Ln2yVE6KH2x7JVvvqPOHzV3m22TMOE+OSkbLb0O+hwGtTTbg6aiavfw/A3A8QDepbVezMm/e+LTdPfWXVGLPycnmQyKcp9O13G7rGzE5sV9KTuJv5LJgQI3CUkmTl8dytfCNudjGWPALpcuOD47ti1Oj++cuvqUGgsuW9L+CTkvnH2zzKePC3pMPWUsuhxC5gEuwIm5bl3n3pZzbSApHTGg+iFUf8iG16efarNko2KXF2iZMBq3bnmosffw/N/N5d3D8/KLKncPT9VSWsXTlodKhLlgxwS1i/NBuruJDWgoPyh/pW2J2fHHTmQhi4ukXmybKVwyfB7a5kyNqusC1VfSfqxlBx2qI6RPY/wv49za9WPOdez1TWHVqlWiehLmJURHvcdRiE6JjpBzG2qXAzUWY/TXOgd1b90lspNQHqrG8NwP4LkAPqi1/gwnbz54UMqu+OBjRVyQUp+2vlDK1OUft5Phditcmc8Paf/4dqYxiwTVZpdfEv0XL1uKm044z6kvhrUo9EtZKMmOXLKbd/ngO+by3TfGKP8pv6jxIWWvJMySqx7nn6uc2ii5+nLR/AVH3bRMnVvquO/akTIrrjLKJ5+cjRid1HmWzo2UjyFzchly1NwaGnjfuK8579Lav+TW8hie899dOYanagHP3QBOB/BxrfWVnHzx8lApQgIZXz0uCKCCGmmwIvU/ZhKUTAghk6+rDaF17HrSACekLQWKJ+ZKYC/okvFhtkWq39Yh3RVLzoPvuG/R9dmUBuiUPKdDEmhRfof2Jwd73ITMNRQkmwBTzpQNufZqCa5iWRaX/9KNFXduqaA+dF502TARs07YuprxpOUU8FQr4PklgLMBXK21/hAn73u1hG9HJd2RcLvV2B2Pr44tI72wY3RwQRlnS9q2kH7iyiTshZRhGDR6BD634Q9sO1xjg1pIuD6TjA0pmxEqJw3QqCCdg6RvqDZwtlz6qYXbFfBQ8wA3R1y14X6MHj3a2a6Q4M0Hzie7H2wf7PbZsiE+SeZFTic1xrh2S2yFttnVRumGhvKX8jsxPI1Hv/6V1qGN23ihhAQDev9BXqjCKGNnntAbQ4eKbitMSOjB5EmTGm5TDarabbvlomoMz3cBvBrArVrrCzn5Hdu36w+Mf4Z45yylkENoc6kt6Q6L2l1J6nFpD5evLnAUMifn8i+Eljd118Ie2Mfa5rYf9bZ06Y7Q5bcEIYwG1c92uc9fH1vD2ZbISRgTqk2u8lrAtdG0yfUtZQM48tJZ6trh2FeXz6FMkK03ZPza9WOCY9e5t/0I0RUyZ8QyRiF9UiCUwXXZOnDgAMaOG9dQluTATxeXtuAPfcnCxPDUGcX2W7SdWr9hQ69FMYTStEFdUC499uRF0Z0h6QGfTy69hUxIuoNL41CBhjSQoxY+qX67P33ngDpm61i45LaeezEuGd77GTq+ccEFD74+4AIAl+1YSt1XLzbo4M6nK7jibEkDQF+6wbYRu8BLg/4CxbgJDTRcfUONWV/AKgk2XSij31x+uvziNi42uE1cTBBlwpWqouZ9yp7v+qYCo/UbNmDsuHE1tSEhDFUNeEQprYSEhISEhIQc6cGDlUppLQbwTgDf0Vq/lpP33bRMQZpyMiFJ24Tu5CXMClWH8p3aZUva4vJLihCWQpp+CQVFa1869qm4Ycdf2XrUeLD9CkkdhTImti2Xz5x+yg/Of1NGOv5sHZLdvU9HzHXks0WBY3yu2f4XvH/c02tmHUwbvuvcLuNSO2XNC5y/JnyMk6QexSRL/LFtUCyLhM2Mmd/suq45rRk3LR/42ZfKS2m9+N9SSqvOCGJ4OlduYKlU+5gJaSBAXTQuirn4z/kl9dPUwaVwJJMaR88Wn2MmUmoyps6DlKL3QUKhA8BVq+8Nnlg56t3WY44Bn4wU0kWq8FEScFKLqavcV8dVRi1eVH9QKRxqDFHXhH0tUuedu/Y6OzvZsS29rihIrm9TT0y7uOBH4od9zKVTet5dn6WbLS6Y941T6TiyZX0y9twKALNmziR9rwsSw1MphuczAN4P4Oda6xdz8hvWr9cfbz9FPBEV3wtQOwMbXDBELYqhTEko80Et9rG7Osonib8xiyjns2t3aH6WLJK+Bw9Su03KJxu+c0ZNzNJgTVLukqHOi6uurzxmged0UG2w9XF1bP12HV+ZZKG7eNnSXvfwUHNC6OIdG+hzfhfgfKyHX7Fzjc+uVF9of7uuS0nwTdm6ZPg8tIwdiVu3P9xYhucXt5XH8Jx7QeUYnqr9Rq1geEQ3LW/fsaN+niRUAmUsEmUtNLWir7SFCyoapSMhoa8gZjy3jB/NCyWUiqqltHbm/0W3ti8+/4Kez1LqtgC1A+PSO76oXmLLpcPeTXApAluHbUPCEnA6OMaEo6Vd/Sb1j2JCJAs6xXAsfuk7eukzZan+cPlk++VrF5UG8vlcC0x9Ev8oPQUo1kiSEqGYhxDdFKSy1PXn8m/RiS/EtY/cSTJZZlrDPCYZR1x/hbDDvnMd4pPvmvD547o2YxhtCtycXXwPnS9ctkNYRVPOPJ8rOjpE9cpEeg5PtVJabwLwNQAbtNbTOPn3zH6u7lq1iR3UUnqSq2vWDwWV7pJQ6rZtSXqkOC4Jasy6RR3fYuQ75tLh8kOaVnGBmuioOtIF2OWDy4Y0pSUJGMqQs2W5hdy3iEnGgK0zxj+fXz5blH5qo2J/dpVJbPlshoCbc2LSR1xQVIu/Lp+k1x83t0h0cPoL+IJiewxzfrjAzc/UGGudNRmfX/lAQ9NCB2//cmkL/pCz355SWnXGpvz/JKUU63vXqk2cSELCUdi8ZUuzXejzqJVdAvpfSiuNm4RQpPWp8agaw/N0AH/Ov07UWm+l5C+c9kx9/RP39Does2PwwZX2cEGye5WyIjG7fZfP3HEpuyTdvVE+mXKunZ2PRQnZfUmod/Ot1xRzIz13th/1ZnHstkkDC99uOqZdnB++9kgZp9B+87E6Ph85lsH1ubjZPYallbCqdjttnyT2QpgszlbMPEPNYz5w50/a32WwRBIfQ7Bx40bMnjOnsQzPnV8tj+F54Vsqx/BU7R6e9cbnWQDIgOfdd34jmiIPXexDBzyVHgjVU/jkm8AoatWUrYXWdvnks8fVcYGjnSV6JJPjwiW39arDBQ2ShYQbYy45219J0GTr90E6Brjgigo8uOtHsnhx/hfj3mfH7h9Jn0g2Ly65kGCc0u3qj5AF1ydLBYrUfCTtP5c913fqmgi5fql2FqDGLDV3h5w/XxBM+QgA+/bvF+lPKA9VTGntyD/3L048oU9gWHoJZEMQG1z3VaRxk5DQ91EphkdrrZVSywCcCuAETv7WM98AgN8F+yJ8aaReyLvkfLtx6a6M0imt7/I1lAp2+VProhVbn2q32TdUmoJiiUzdFDsm2flK+tMs8zFZVB2Xz7Z9joWyP0vaY8v4xnoIO8GdC1cdCj4Whkp9uepz7Npn5p7FMg4cM1z4EMIsFPUkKRu7nk+f63jMuaTKpGM6hvGywTFDPv2xbZa07cZ9yzFyxAiB9yUjPXiwOvfwAIBS6mYA7wFwj9b6+ZTsO9UcLZkACvgmXteEGDLB+GzYdbi0jWRiltrx6ff571pIqYWV0u9rs6SMS8/Zsi5/qTqXH38Grnv010fVl54XH0L6w1dfKselu7iFm7JHjQ9XuS+g4uqFQhLISfRT1zeXslizZg1mzJhxlD7pHEH5WAYTxgVtfQlS37hr06fThGtD47MdE2RL/P+i7mjsPTx3faO8e3jO/Jd0D08D8DNkAc8pSqmZWuvVPsG2OVNFk55rggyZpEJlXLAXQsq+5IKi7HDHQneANkJ2c3a9Ql4ayFATEbeQu/Cun/93r8WNmlxte662+CZczjdfIOALoEIZoZjxwwVUvuO+4NgH6aIVWhYaXLnmD9d5vnjZUu+4p1gA6txSvoQswFR7pLprmXNcOqSBBqfD5b8tX+vGQjIfceuB3eYb9y3HypUrvToT6oMqBjx3ANgAYCqATyql3qZ9NNUAf8hSQkJfRSybk5DQX3C4CdkV1TKwU1qVC3i01geVUp8EcAuAtwDYr5R6v9Z6py27cMltPe+3oZibAiE7+FjE7JR8u0FXu+xdD6XP5Ru1m/Udl6aLuJ2jxK8QW7aMJLV2y2n/7JX3sRuhi3dMCse2J0nbuPw160nZmZhrJoSpcOkrZCSpH8qWxHfXeaTSZC4d75v+HJZp9DHJtl3fmJWwptw1XCtzY58XyXzB2ebsU7Y49tU+b9J5xvXZp5cap77Plwyfh7a57bj58Xu97U4oH5W7hwcA8ocOfh3AG/JDewEsAXA3suf0rAHQtXHjxvWjR43yTo7URGQilj52wUXJ2n5I6hf1XHoldn0XtqmD6w/KJhVccH64/JIs8LXQzj36R54AHD5yTVATnQ2qH1xyIW2263N6Od98/egai1QfS4LImPQFFXRxgXPouORs2WUuHd3d3WiJ2D3Hzi3SfqTqcnOFqV8SDEp0u+pQZTFjR6rP5XNsABiDXbt3Y/LkyQ29D6bzN/9b2oLfdvrr0z08jYDW+rBS6s0AHgLwYQAjALwu/yuwdeiQIc1wL6HCaJszDZ1PrGu2G/0eZW0g+gpWrV6NY+bMabYbCRVCU9anAX6bRyUZngJKqZcBuA7A8Y7i3Q/cf/+okaNGAQBaW1sxY/p0bNiwAfsPHAAAjBo5EhMnTsSiE18IKIWFS27DxAkT0NbWhnXr1/couumk8zFo+FC8577vAgBaBg3CrFmzsHnzZuzZuxeLz78Al935TUyePBmrV6/G5897GwDginu+jxEjRmD1mjUAspeZdq3dDNU6GO954PtH9J9wHj6y+rfYtXs3gOxCmDZtGtauW4fOzk4sPv8CdO/Yg+tX/w7vf+rZPQ/HmzJ5MpRS2LBxY4/+qx/8BXbv3t3zpvjWwYMxY8aMo9o9cuRITJo4EStXruzJI7vaPb29HQcOHMDWbdsy/S95Ozo7NmDw5HF4913/g8XnX4DD+w7g0IZteN+jv8Ch7m4AwC2n/jO6d+9D2+ypALLU4rSpU9Hd3Y0rT355zzG73V84+83oWrcFix67Awc7OwEAY0aPxrhx4/D+p57dU89uNwDMmjlT3O7ifAPAR+77IYYMGYK167IgZ8OGDXjWM5+JAwcPYuvWrVa7x2LQyOEAgPfd8T+YMmUK1qxZg65DhwAA48aOxahRo7Bq9ZH76G9+5j/hk6vvxabNm3vO0TV/vR3bt2/Hzl27AABDhgxB+7RpWLd+PW48500AgO6de9C9dRfa5rb3nO/PP+eV0N2H0do+seeY2e7F51+Ai37235gxYwY2btyIzy54IxYuuQ0jR4zApEmTjjrfEyZMwMdmnIrWmZN7dE1vb8fBgwexZevW7MW7WqNzxXpcseo32LNnDwDg1gVvwvWP/5Zt99QpU6C1xsZNm3ra3bliHT66+p4j7W5rQ3t7Oy5/0ulQQ9uwcMltuOX016F76y5cvGxpz8t/D23cBt19GBf96Uc9+m966kvRMmo4Lrz3OwCAwS0tmDlzZk+7AeDwnv24vuNeLDrhLGDQICxcchsmjB+PoUOH4sPPPK+n3e3TpqGzsxNbivP90nfg2kfuxOYtW45q96H1W3HZ33/Z0+5bTvtnXLvqd1i1ejU2bNiAH/zrFehavwVKKQyeOqHH184V69AybjRaxo4EAOjOLnSt2YzW9ol41+1fy67vXXvRvWXn0e3etB1Xr74P69av7zn26b/8HHv37sW27dux+PwLoA91o2vVRnyo49fYu29fdk3u2Y9Dm7ajbfYUIGedDm3dicP7DqBt5pQev7rWboZqG4zBk/J3Mefne/CkMRg0KvvJ9OH9B3Fo/Va0zpwM1Zrtj7u378Z1q3+HlatWYfH5F2Dhktt6zvfHn/uyHv2feeiX2LlzJ3bszO42+MK5b8nbPQFqaLbYF+1umzsNQDYHT5o4Ea2trfjIs15ypA9XbUTLiKFomTAGAPCepV/BZ48/t6fdAHDrWW/EoY29233NqvtwxTNefHS7Wwdj8ORxWLjkNiilMGf2bFw263kYNDpr93tv/wY+M/cstM6YBNXWioVLbsPYMWMwZswYfOBp5xzRtSEbM635+V645DbcNP8laBk7Ei1jR2HhktvQ1tqK6dOn47Jjn4933/H1zNcXvB6DRo/AzY/f21iG555vl8fwnPbayjE8lQ14lFIfAXCVcehRALcDeAxZiuux98w95U7qNQFlIYTWrJUypdJMJjj9knsjKH1cKpDKd1PtCAVHj/v898F8tYStP4a+p2RruYchVIcUkhSOKwVg1/HJcePEp5vTEZPWkYxFLgVSoBg3oelPKn3oqkOVu+SkdULtcNd0bLtctiUpLWreCulTKo1M2aLOtc/Oio4OzJs3LwU8DUQlAx6l1D8B+GH+9S4Al2qt/2LLXXTsqbrziXVR+eTQSZSaqCV1pGWxF70pU1bQF7oQhwZlpq4yAkrJArx//34MGzaM1W+jlvtRTJ+44I2SK46XEQTFjGHpYmzXl1x/0jaHBEOSQE7af5dOOAl6fyd535JkUfSVcwEgt6i7EBLUUPokQapdP8T30P6igvRaj0l8lvjatIDnvu+VF/Cc8urKBTyVu4cnv2H5mvzrrwCcq7U+5JLtWpPeRpsQhr68Aehv9730Jyil0HdHTt9EPRjJZvkRg+nt7U2xO5BROYZHKfV8AL/Jv56ktX7IJ7t50yY9Kr+Hpww08g5+l13OdtnpjTJ01rqDl/gRw5T5sKKjo9fNp1wfSNIeEgaQa2MtTGTsWKyFlZPW8e2qpeNDcn7sMko/lY7x+bBo/oKjbnaP6aeinoTd5VAGO0H1gY8Nk6SjJPV8fpj+lJHik/Z3DIPpYvlMDBo1HF/YtSwxPA1E5RgeAOfn/x+igh0A2LJ1K6iAJ3Qx9E2IFELTPq569kXj0xkymVHUraSd0j7wybh8j9ETUiY931T/Suy7Fk9TJmbs1GLblAtN24QEci4fXN9tXT67IUGOfW2YZb6F22XPbov9nfKJu1a4/vadK85HaVATmoqRyHKLusS2NF0p1WfLUcdMHbXMab7xR+lY0dEhki0TaoC/S6uKv1F7bv7/DlIqISGhLugrqYiEhISEEFQqpaWUUgA2A5gA4K1a669S8o/+7W969uzZRx2Tpk64XZ29Uw6lwGNSBdxOUbJDqQeLI4Uk9UPVteVq8c93/rTWUPlP1qXnNfT8S9IB3BiVso0SRoY6HqJD6q+kbi0pOOm1VIBjyWwZ57ER82DexFNLqkRyPimEMjm2Xcl45o5RfoTMmdJUV0j6KfSajmWSzLqu8bRy5Uoc/5SnNDQt1HX/D0tb8FtPfkXlUlpVC3jGANiRf32u1vr3lPw71RwN0FRwGQuohHrnKHqJPik1btu2ZX0Xc2jbfZNZDOoVeHHBhd0PLeNHo3vbrqPkpH0lSRPacqZ8SCBj6pIEL9LAyOeXrU+C0PSZ7UdsQBXTb7XqeN+Mk3vGjc93SX9Igx+fvyGLs88Wd12HBBecDy6dPhlKXhrkUL6HpMlqtVWg0W9L7/r9/5UX8Dzn5ZULeKqW0ppufF7DCQ+eNLZ+niT0SxQPh0tICEEaNwmhSOtT41E1hudcAD8H0A1giNa6m5Jfvny5dv3iRsok1Eq/S3f8zUYjfeor7fcxPBcvW9rzKy2KPTHr2sdN1JKaCklxFTIcQ+AbkzFytbCDUnDjRcLGlDHmOB3Fr7R88wfVdyYoJrIsZqWW9JHruglhl1w6bb1SPRRzb8tIfQwd66H9YdZtxnN4BjrDU7WA580AvgpgndZ6Oid/0bGn6uLpp81eYCmU5V8MFS9Nifj0FKB0xKRYJHZdNkMncBuXH/cCXPf3u506uAk2NFgue1zWY5zXmoa1y6SpCGqB5xZtaXDoOu4q89UzbV1+3AvQtWaz2Cep/2WmWFxtkQY/MXOE9LzHBPqFbqkOyZzBBaGSoInrQ/NY/rb0hgYNh/74k9IW/MHPemkKeOoJpdR7ANwMYJnW+kROvmPFCj11avY+p5hFvIBkAmz2btc1UUrlCrh2oj77Uv19EaEsX63nWLpoSXTEMoXS8SHRw9l1LUaxLItkB0/JxbYxlFGiFmBqTglhBHzXLhXwUIGAz89YNkkaQLn0SYItaqNG1XF999mNDe65/neNjw0bNmDOMcekgKeBqNo9PGPy/zslwkWwk5AgRWv7xGa74EVfCST7ih99CX153CT0TaT1qfGoGsNzHYDLAPxMa30eJ//YP/6hZ8yY0eu4dKfeCNbGZ7dWe/VIGblkODmpvhhwbEyMPdeTlsvU70MtqUWfDulOlNvRh9iX1I9lzSgWJ4bBlTKipqyvny5ethQ3neCekjgGJyTdImGOqP7lzosPoekcTjZEvyQtRumt1WdXn4aeF9veJcPnoXXmZHx+1QONZXj+/LPyGJ5nvLhyDE/VAp7/APCvAL6ltX49J++6aTkWXE7bJRcymcWko2J8p/yg6sYuurZ9nx8hkKbWbDuSFMuKjo6ehSt0UfalWiRBWWi60rRDpbtCAwHpoiil/n16OJ0hY73WNJPkHHB2XW9LN0H5QY0PCr6xbfsorS+1KwEVeJnlti8hmxjuuvDZteW5dksCK65dNi4ZPq859/AM8ICnaimt0fl/UUorISEUo0t891p/RUpp9Ub3rr3NdiEhIYFB1d6lNSz/v08iPG7s2NIMx1LllJxPvqwFRbKjpNgRzheXftduOZZB4vzndnwUw+Hz6RMzn+8t486LZPfo6/vCV5+cJDVRtN/XPzYk54gaLzFpL05/jJyvPygG0JRz6ZecPxPdW3b2kqeYA2pcUufWLJNcO65ylw4Ovn7w9Zv9X+qfi00JZd5cfrvsUsekbKOrjGOVi+87duzgG1A2BlWN4ygXVUtp/QLAOQA+o7X+ICe/d88e/b5R89lBL10gJKi3jtgylywQN4mU0UafPwVqTWeE1insm/diUIFGbABcyzmidPp84uoUx6lrwpbj/HHZ8MmF6PPplm4wYsYUF1QXKFKhksBdOqYkKRpbh+u7Sz7Ghq/MpZeSlejyyVMBoCkvneMl14etk5Kzy324ZPg8YJDCF7tXNDal9eAvyktpnXRu5VJaVQt47gZwOoCPa62v5OTNe3hckbaUVXDJ+C682MWtVlB+SOpydWrZzdfC4rjkJQEqFdS4+skMeFz3fVETZQFpYOIqc/koAdVOSo80WOFYEAkrQtmoNeDmFjCfrCvQkgQ5vjYVDx40IQkwQ+YL6RiUBhrcuJHIudrgsuU7JvHZd91KAyaXzsKPkEAuNECz69g+N+UengEe8FQ1pbW/qV4kDCjETqxl6+hPSP2R0FcwkMaiamlptgtNRdUYnr8CmA/gYq31zZy8i+EB5Hl5CvZOQ0LBl8X21MK2SHzhqFsqLcHZpXSYkLJVoSyPbc/edX1y0+/x4cnP8fooTSVJ7IUwPL62cJM1xYbVRMfDv8t2pVjs+r5+9OnmznHoNVHruLHtvHfyM3F4z/6jyqg5IiQ1xPlh16VYBQkoHVS6iPKRY4woFkXCGErPmQs+/WWzPrZsMxie7ofvKG3Bb5m/oHIMT9UCnn8AeBKA/6e1/hInv33bNj1sWEYKUYu4hG6nFhwqdSJFTNDEUfv1TqdJ2xmaHqklEPXpl/gEAAcPHsSQIUNEstJFVjJ2QtMEIeO4KJMETaFtjA3efHUoGYmfZY957rwU3w8ePIj3j3v6UXKS9I7tuzSwlQYytaR+bD0uP+wy+7PUxwLcPOY7H9JgKDSdJtFB6aPmtP3792Pc+PEp4GkgqnbL9tD8/wGJcJWCuYS+gXXr1zfbhQGBegfjjUYaNwmhaMr6NKilvL8KomoMz2YAEwG8Vmv9HU6+SGmF7Pa5XZh93KVLYovS49tN9KVFgqO1uR03l2Jp1A7e1t82t/2om09j0h6mPqm/tbRPOn5DxnMZKQyqTMrySFNfkh28JNVmH5cypdzb0n3tCr2OfAyLNMXlsxXLzkhtmfakrA1V5vIxdlxKrosYVoer15SU1rK7ymN4TjizcgxP1QKePQBGAPgnrfX/cfK+X2kV4PK+oSkpWz91nKtT5mJK6S4rFRab3iozkJH6TrXZfNIyBemiHQJpilKSIgspk/rkKwfC78so6nDpuZjUVlljyaXfV+Z60jIV9LngajPVH1TqxDzuCzY5+FJVXJlZbiI0OHH5I92QSgIvlz3JWHQd8/WHL0C7cd9yrOjowLx581LA00BU7VdaQ/L/opRWQkIoZjrevZZQPvoSW1kGOldtbLYLCQk8KpqKKguVYXiUUi0ADuVfz9Ra/5qrs3/fPq1UXBBa1k6R0lkG2yHZAYawS30F9eh/CXbu3ImPTXtez3cpu8HtMEMZGVt/SFktcoVsvZk3KSNahh+crVr1A73HjY2QtEcMAyFNW5UBCUvEsXshujk2SSInYWl8+mydNiRtdLGZWmsMGz68sQzP335THsPzlNOjfFdKDQFwFYA3AxgH4EEAV2it72DqvQrA6wA8F8AUAKsA/BjAJ7XWotdNVSngGQ6geGHN87TW93N13j3hafqza1gxL2JTTrXqL1tHbHrORmiKhbMRk6aITcFJdRSpCU4upM0h9aTpDJ9uu5zzK0Se8tH2kysr2yeprrKvueJz8YTu0DSQWV6AS49w7ZOeF2lKKCQNFgLu+qeCFduvkDbbn33BUGxKy9VG1zlvGT8at259aCAGPN8E8GoANwJ4DMDbADwbwBla6/uIelsArAPwQ2TBzlMBvBPAPwA8W2vNZn6qFPCMA7At//oMrfVfuDoXHXuqNhevAvVmbhqpQxrwFHDtNP7/9r49yrKrrPO369nv6upXdVU/qtMhQGIMQZRxIpBAKwiDazHjjI6zFB+wFB0WSSS0iIJLBNQkQhRi4gMVnZnlQmccZyEZkUhGCcaoPGMSIN1dXf1+VLq7+l2P3vPHvad61+69v8c++95bp+r81urVdc/Z+/u+vc8+e//27zv33FYTOyouLYHIXc7HvrExuM99UZOl1Bc1qUohUYyoepRKxNlNJdVatZGKg1uYtASCapeGRBbn/YfdQ365jUSKGsKB21jE+p6KhSIXqZCoOBwJKeM35k/qIyWeTjy0fOUbj2db8Lte+F3q2I0xLwfwjwDuttY+0Dy2DMBTAA5ba19F1L3DWvuYd+zNAD4B4MettX/ExqwNuIPod/6+3LEoaixq9CzxN5HWSIOdme10CDVqVAH/EcA0gN8vDjSVmY8DeIUxZjhW0Sc7TfxF838R06ySwjMKYKz58Xpr7V6uzt49e+zIyMi8Y6kpER8xWVRTP0ccEvutVmo00KgFEuk9t+/UctzOLqZs5EjvpIxFbqcvVXokyg2l8HDXk0uLcQoGZStWVmPTLyPZ6XP3PaW6cP7dsjFfnNJE2aPKlUFsTFHtotpB3RPUWJfaj9mKlQ35PHz4MHZef/1SU3j+BsCQtfYW7/guAJ8F8AZr7SMKezcA+AaA3dba+9jyFSI8LwLwbPPjVmvtIa7OpYsXr2lc6mQZsiMp5/ulFgFpCoCKQxsXZzMlhePbSiWD1MQntc0ttP7xe667DTPHTl3jSxOvC3/CLUNIypLWdhLf1DSeZqFyIUmVae4rKq4Qjh8/jg/tuJ2ML0f6hbsfuTZLCARVzj1Wxrdvwz8WslcGkngldYvPErIs8fWwHWsv4XnuiXzP8Nzwb9kHha21a93PxpinAByy1r7OO34TgH8F8FZr7celMRhjfh+NZ4BeZK3dw5WvakpL9LX0I/XbT2so0bVyeadDqFFBnL9wodMh1KgYekc2dDqETmA5wo+kXHLOi2CM+S8A3gLgXgnZAar1Hh71MzyXLjeKuUzbZePUDoNLOWl3iKHy0l0atbv0j5VNY1E7FL8PJbvllBhCfiRt1/iP9f1Pf+rjcw8tc9DsiCU2Qp/dY6F6LqhdNBeD39eSnXks3lBcsfipFI00lZKqNmpsxNpQHH/ojW9hfUvsUjH6f1OpGO4epmKSzDehuKRKUPE5Nu+6x7hrxvVrcV5yP8bGPbUWFO2Q9KtfxizrY2NayPDVGyEuYv5aXmCZc56FMeaVaDz381cA3it1XqWU1isA/H3zY5+1dpqr475p2UVKOsoFN4FT9mMLFVWOipNK2XCTdKsg9a2Rw1P9ShfWAidOnMAHR69+UYBadEO2JGW0hIEbM5LxRsnyoWuiHache9TYjB2XED9qI8KNMQlJiMVE4Z4dt+H+sS8kpWdi/UOBqsMRAYqgSGykgLvW2jRTzK5rP3Ze2jehWGP1KcLm/1187sSblq/seTLfMzzXv7wjz/AYY14C4DE0nt15jbX2PFV+Xt0KEZ6iQyyAbisI/NDBg3b9+vVkmTJKCFDuWZmc4CZ+7nhO35Jz0njd84BsAfSPpYJaRKQEM2RTq3ikEAEuBs5GSj9SKkMBDVkpCw35yeHLhZQwSEkRVZdTRfw4JYu3JhbuOsdilPiiNj4a4hyyxyF2L3DkUnJd7lpxI7rXr8GDJ9v7Hp4FQHjuA3AngHXW2nPO8fcA+CCALdbaw0T96wF8HsAkgO+y1p5UxawNuIMoZLDLErIDABzZqVHDx/j4+DXHFhKpXQg2cmChxJELfaNDnQ6hcshxXy2UezMFsxOTnQ6hE/hzAL0A3locaL55+ccBPF6QHWPMdmPMi92KxpjNAD4D4AqA12nJDlAthec/APifAE5bawcldVJ+Lb0sNDvHlBQIVS9VZdCkREI2Wrlz5mxqlCxJf7s/Hkrt3nx7KTFSNjhVR2q7nSqGpr+lu3aunH+OS8VRKQZp7G6Z4nzox0MpSFI4kjnLL8fFLfEnuc6USihFShqLqkPF78aYov5I20yNI/9+7EhKa+8/51N4dn576puWPwngTQA+AmAPgB8F8B0AXm2tfbxZ5jE03rxsnHpfBvASAPcC+Jpndg/1luY5GxUiPD8E4H8AOGat3Sypk4vwFPWpCZKDdvHpVMqMS7kAPNHKlcaS+JdMxL4viqC5b1rWxkjFy02WZa6zJF3AkbKQLSo26T0VQsoY0NzDUpJK2afSGCG7u2/eham9h9VEhkuPUHX9+F0bEpJJpbckqSbfBnVfURsQv12atsRik6SZYn5DNqTETtNvHXnT8sIgPMsA/AqAH0bjt7S+CuA91trPOmUew7WEh4r9E9baH+N8V/FbWpc7GkWNRY3169Z1OoQFj8WWjsqBmQnRbxfWqLHk0Xyz8rua/2Jl7ggcK00OFzXh2bRxIwB65x875u4s/P+lKKPq5EjBpahEVFlpTLFy0p19KO7YDlDaNmnK4n3bbsMDZ56aV0+bfgmVDe18uR2vVNWK+Yr5jdmJtcUtL+1z6e4+5f4I1YuNlVg9Tl2L9Vss3isXrn01mFTtkaoRsbipNI17LFW1CMVLKWqxeP3jknkiZksyTmPn3XOxe07bHynnzp8Xf7koG6yp0mO7+bGoCU93d3dwkpZIpmVSDCEb3GTDxcjF22pwPqUxSRY6jlilksiQfd/enV/5FBmjlAjEbKQuMtRnKTGiyrp1KPu5IOkrrjwH/7pICZNPSn1/oXj7tg3N+/FQisho2uDXp+YF1wdHBEKfJeBSexR5SyFGGpIm6V+OoFHlQtc9VEZKWM2yPjx08RtsLDXyoUp0r3gxkegtywBw5OjRFoVSo0aNGjVqpGOJvmm5o1jUCg+gUybK7Hgk6RmNChJLS2jqcWVd29r6LkKKQIpcHUsjxNJAxXGqj3MpU375UIyUXW5nzPmifHP13Ri4sZRDPZSmgajzOdNgIbWGUu80Ka6iTt/Oa3+k2K8vvX+pNBD3tx8jdy+2Qr3zY/F9aRRKvy0StSc0zrm0YqyeG3tOBaljWOIprSp9S+sDAH4BwN9aa3dJ6sTetFyAmgy05KEdaabQAu+e8yGJiVuoc0C6UKUsaJStFHJ19+At+Mipr5LxcTFKFxyN/VQynmN8U/UkC1rIRoGy6UnNmIm1mbp3pGmguwdvgb3Mvvx9zrYk7tCi68fnx0QtzNzCLd1YcfVD53z7MWLAbSJifZIyF0qJid9vFBl3fXF934mvpc+OfTnfj4fuuLWtsedAlQjPfQDuAfCItfYNkjrnzp61PT0NEUs7WWoXrVZP9qG4KP/S+DXtDPnQtoPa5Wl39JI60h1mcezs2bNYvXp10H6ZvkpBLHZqDGiIh1+P+uzHQdmnSLR0AU7tt9RrRO3YJb5+dujbcOXsRTI2Lq7Y2A3FqFEStIuzX0dCPIpyHOnhYg+NQ4k6E/OlQao6E7o/qbJAM9buLjw8s7cmPG1ElfSt/ub/4pRWJ56Cr1FtnJyY6HQIUbRDRWwXFlNbAKBno+hdqDVqzKF79Yr2OzUm378KokoKz+8A+EkAf2qt/SFJHTellSK9F2hXysr3CeifNYjZ0qonbhmJr1S1p1W7e0nZAu6YuPPpR+a9aVkTl0a90sQoHQNSqT1miypTBtLxR6kMnD1NLIVtiT9p7MWLBylI0k5+OSnK2tMoE9JrqUnDcT45pChe2tRamT4O+enEiwdn938ln8Iz+pLKsZ4qEZ5PAHgzhG9UBPhneAqUIRBcOkDitwA3IUrkdUkuOWazzMKsbbOGTHBSvqSvpOmNu575v9gxOiqOPxajFpL4UshhbhJW1oZ2wSwTRyqBDi3GnI3d3/IaTO07EjwXskWNl5Q0UI5zVHqrLCmTpn24FFUKGfTrUeMsNS0mTQ265WrC034s6m9p9XR3tyiUGosVFNmpUSOGGNmpUSMGOzPbfqddVXqKJT+qpPD8BRo/OPZRa+07JHXeZnZYamcr3UXmVD6o1Bol//rHtL5yQqNGUGWlqgu3A86hMhQ4efIkPrD9lUH7MRm+uJbStkhVF0mKIRRLGYTGZIp6pxkDBVLGlK9scmmVmC9KJZIoMD0bB3D//ifE6RfKlltOooSEYktVQiR+Jec4Vcuv58fv1pFc95hNTq2KKU9lVKgYQvUetmPtVXgOfC2fwrPtWyun8FSJ8HwawOsB3G+tjf4Gh4v9Y2N2aGgoWwoqBirdoCEdrU6LhHylpAKoPtSkWnJfh1S4vt0fDy0TU87UV1mC59qRjFOOeJUlmJL0CEUitddFmjJLuWbFub6dI9FneKjFM1Y+5DclfRSKuUCMwHP+pLFoNmixa+Qe48aHJD0Xio/baHLQ1HF99mxeh48d+WJNeNqIKhGeRwG8BsAHrLXvldQJPcMj3W0CcpIinVAkJCDkO4U0aRbTHERDOsG6KKtS5CBNfp19Y2NzDy27sUjVOk38XCw5beSyXYAiyqHz2k1ALlLjI4XgS/wWhIcjlGUgsZODXPnnqPMcaYrVj22QJIpMLJ6YfYlCl6qIUZsFLu5OPMMzc/Bfsy34PVu/pXKEp0oJveKnJcTP8NSoocXyZcuuOZZjsVpMyEGQO6XmtQpXLi6taSn3JqmTqMfz0kGVFJ5/BvAyALuttfdJ6hwYH7cbm7+YHkPuVAqn6kjk9VZI+bGdVKo6oVWvJDFq61D1uJhS20+lPST+XGgVDonCkqKaaHfBUn9UHK7N2E44t3LKKY8xm1p1trClTaf452LnffvcONGmZiRqmUSd5WLnlCGu/zQpN6nqErIRGqOcMsXFCAA9mwbxsWNfqhWeNqJKhOdrAG4GcKe19rckdS5dvHhN48qkQaiJk5t8WpXmkCzA1E2uvbHbCQk5TLUbW9wOHjqErVu2RP3H4kshExpyIm23lAyUJeaSFF8ZMpQj3aW9t0NtkxKZ3m2bMH3geNQ+11exMn5MrVJFtAQtRGxS02muDa6cDwnRkI5h6h5OiV3SH+1+aHnm0DP5CM+W9v4sRg6IU1rGmAFjzA8YYx4yxvyjMWbCGDNtjDlljPmKMea3jTHf0cJY1Smt8fHxFoVSY7Fielr2e0hLGQslFbGQYHqr9IaPGgsBfTs2dzqEJQeRwmOM2Q3g/bj6LhwK/w3AT1lrL5SMzY/heQCDAP6TtfbPJXXKvniQSgNJbLRiJ+r7kiowOZWaVqYyUuOR2OB2p8D8b2lp/Ep2lKH6Keqd1Aa1g01R8kIqgzQ1I0EZ9Y5TMTW+U3b+3Le0/HikioaWXGpTPSk23LiodBplM6Ysa9vOpb+4e5OyoVX5JPG5nzvy0PISV3ikhOf3Abyl+XEvgM8C+DKAk2iQkF0Avh9A8aa/zwB4vbX2SpYgjenDVWXndmvt30nqveP62+y9Tz0675gkd+yXkyB36id1sWtVSiA1PSJd1P2YqRSDJCbfvrTcLx36AgYHZb+LRE2QmlSKX7f4TBFCKflOIemx8sV5rnzquOTKSRY1Kg6uP1OJKQC8c8vLMXvqLGkjFK8W1H0Wsi0hL5oYU+5lrjxHylOIhzQVKBk7oTikRN+/Lu65Tvxa+szhr+cjPCMvqhzhkeqwFsBfAbjPWvv/Aud/1xjzSgCfBrAKwGsB/CiAP8wSJbDB+TueKPcwc/JMJvc1lgoGBgY6HUKNCmL2zLlOh1CjYli/bl2nQ1hykCo8g9baU4Jybwfw0ebHv7PW3l4yvsLurQC+1Py43lr7vKTe5Jkztq+vL3o+ZYcbstEKJUi7M5fG4u94QjucXKkmDTSqU4FWpOdi7+GR2ND2KbWL5OzH4uBshD5rUmuUKkr5o9oiUflC8YT8c32qVUClfeqnQqmdf2rKSaK6aPxyqaQy40irznB9E0Ps/kpVvCRjncsMSOOemprCmoGBWuFpI7J+S8sYMwTgaPPjKWttFgprjPn3AP4XgIsAVklTZW5KS0sStGmJ3MSgDKmR2KYWO+nCWiBX2yn5N8VGCqhneLjFKFaWmhA1KZfYOb+chMhQC5WEdEggTXdRMWltSOMIHddcX98G9eLBoj61mLYKFFFOTX25dSmb0rgkZbl4ORIm2UBKfcXKhc5R5TryDM+Rb+YjPMM3VI7w5P5qwVnn7+UZ7d7U/P+Z1OeCpLtZCXwbKRO6xr57PKUeVSZUntvtaSZADVKuUasWa64+pyxIiYwWFBks/EgWEola4NvVkm/J2PLHle8rtmBo+pQbzyHiI71OvsKjVat8W1JlLBYvdb+GbEsXbv9Yyvwk8Z2LGMZscEQmNtZjdigbVLkHLjyDfWNj6nbVKIfcb1q+2fl7f0a739r8P6+MUqOGh81DQ50OIYrcKmIn0S6Fo11YyOOmRo0aDeROabnf5vqwtfadGWx2ATiGxoPLb7fWPiit+9P9L7B2aiZpt63Z/VI7Oany0eq0WCtTZDl8S1NroR1azvjv3nAr7IXL83yE4vXjzK3olEnvaMacxJ4mRm05zp+kLaljQKPGcH114cIFvGfDy6LjwIXmemnVDq4c5Zvyxd2DEhtUnJziJmlPShyxmKQ2uL7y4ZY1fT146PJzbU0LTR/dk23B7918feVSWtkIjzHmNgB/j4ZqdAnADdbagxnsvgGNb4gBwPXW2r3SuieOH7erV68uGwIJbZqKk6SLzy5yLuhlFgctOfHPxc5r4pQukBpfLnbfvAv+qwxC9jULsCamMmTF90VBGr+U1KSCS88V0G5OijqScZTjevXtHMG9Tz2qJiWhOYEj+lpI25ayqJclRpLYY3OmpA7VRi6dV/Ye42yePXsWGzdtqglPG5ElpWWM2Qzgk46992YiO2sAFL+b9VkN2QGAkxMTZUOoUaNGjRo1sqNen9qP0g8tG2NWAvhLAFuah/4KwG+UtNmFxssMP4zGA8szAH4+xRa3Y9akoEK7A2mKKFSe8tXK9Ja0rFverRfaOVMqgEbV4cpzsn/Z9M6dTz+SlPYM9Y97XKKYSNNCvr9YGzW7akkfxmL2Y9IoQZwiILGToo7G+pKqQ9nZffOuUjt/QJbCDR3nbEriiqkxMdsxVUQCrm7s3onVTfVJzVXa1KtkvSij0mWDyf3YbrVQKqVljFmGBsF5TfPQ4wBeZ609r7RjALwcjV9DfwWAV+EqgZoF8BPW2j/Wxvf1Z5+1o6OjAHQLvg8/HaW5EVKIhnTR09qP1fP/TrFVlqDltpGaFrxr5U144PzTorhSU3VuHQnBlpLq1DSQpt/LpH84W5wfCaHi7hHt/SIdl1euXEFX19XFhFvsuHSK75vqd7cOV49qmzSFI128JcSJilFDAKl4Qz5D5TTpMykppT7v378fL3rxi9ub0jq2L19Ka+i6yqW0kglP8+ce/jeA1zcPPQnge6y1kwm2HgbwU4FT/wDgLmvtkykxvs3ssJKdPXfjUDswzoamjO835KsstAQs9cYuypQlMj7Z1MSuKV/g1KlT0Z+WkJJbjc+YApOyi841MVM2Uwh3mTHAkZVkYsuQAi0xKsaNNkaKpPuQlPPvOepviS9J/GVilpCnkA1u/EqVMb9vqDhjNvxykjFQoN2/lr7UCU+SvmWM6QXwZ7hKdr4E4HtTyE4TvZHjmwCMJNpEz8a1qVVrlEQOopZDAtbGcfrMwv05ktzkt0Y+tGvc5LgnFkRqBQtnjuhUf3RkfTIm378KQq3wGGN6APwpGj8WCgBfA/Bqa23yE1hOSusFaLzL53UAXuoUebe19te1dsv+WnoKNHJyAU3KokxKrlXQ5rulNgqE0m7uuZg96XnX3p1PPzL3pmV/10elRLRpFiqmAqlqknQ8t2rc+59zqzCp95V7PnRdyyg+/k+SuGVTF1Sp6uf70qSSQscLcOk2yrZfhlM7KBVKqh5rVaTQdQ/V0fgKtSnUlrtWdObX0qePj+VTeDbtqBzrUREeY0w3gP8O4Aebh55Gg+yIf9BT4etGAA8CeHXz0PdYaz+rsSElPC60Cytnwz8uWcS4hVMykWhSDDlTUFqkLLpUndDEorF/6PBh3PeCXXOfKbLlgkvxSQishMRI2qLpn1h9KUHTXAPfr8RPbNHTEJIyxE7q610vfBWmD55Q2w+BW4BT6hbQbCSkBEKaavNjyJleS0lVaQgpd59KN0VuvU78WvpSJzzilFbzm1N/gKtk5+sAdrWC7ACAtfYZNJSeJ5qHPtRUgsRYvmxZ9rhqLG5sGUnOoNZQYKGkVXIhF9mpsXTQkfXJdOX7V0FIfy3dAPhdAG9tHnoOwO3W2sMtjK3w/UoAf9f8eFOTCIlw6eLFLGw2p/Qfs6/Z+Up2KynKjyZGTVlOmqbsFGWkO/1UFPZ/bs/fYnh4OIstQPbAu6ScX6eMUiixEbPHqYMxv61Ks6WqRBpIlLfekfWYPjwhVmcoZUKiKMbAqcqS1KCvinDqD5WO0sZIpdk4m1KlRqswlbFPKWoAsGz58vYqPCfG8yk8G7dXTuGREp4P4ep7cKYB/CwAyYsFP2OtvZAe3hzZGgewFcC7rLX3S+s+981v2q1bt847lmOR1ErqWt8SwqABF29qKkV6Y+cgXCGUIWEx+L+WXna8cAtOAW1KL7Zo5SBDVPwaGxQxT/UdiyEl9SUhaKG4Q9dt9827MLX3cLS85J6gymkWZx/SzRBXz68bgjY9FKoTizdlnkpNl2ltSMa6X6Z32yZ8dPzJmvC0EdIXD97m/N0L4KPCetcBGNME5MNaa40xnwPwIwC+XVN3emamjOsaNWrUqFGjJTC9pd/7q4ataCoqF6QKz2MAbk+wf521diyhnu//nQDuB/CstVa8xeEeWuZ2kKlpJqlMLLWtQdk0kDRlIZHGQzYoXxJorlmKvYmJCaxfvz54LkWd0SgqZdJM7t8pY0mi0LVSvQzZl5al1BhpPaqdXOoHAN65/Tsxe/KMWo2gQCk8krSS74/rF0l/UEoGp4pJ508/Bsp+rC8pRayMWubWL8CNP7ec67sT39KaOnkwm8LTt2Fr5RSerL+W3ioYY/4dgE8BuAxguRUGffTIEbt27VrV4iyB1J6WNGn8A2npIYp4+TGWJXkx3yEbmoVV6lNKUKSLLpdKouLSnKfKcZBK+KkEhTonSTNJIUlnFaBIqWRcSQgTd60l6SjKp1uHGm8xwiOBdKHPSdokflLsxghfzEasT7lzZXyHrpFrv3twFR58/qma8LQRVSE8t6LxckMA2GitPSmpd/7cOdvd3Q2A3uWl7Kr9+trJvdPEq0DuxYg6T+3QyvQFtevldqn+BHfX049gB6MKhuylqDwadcM/nrIj9suG4qDKSeLzy7hxhfym2NeoGJKYCsTIG0fsAGD3za/B1N4jQXtSBcAvS80zVLycHQ4SZSUWL0V6qfvUbyfXppAdyh51z8fscUQ6ZCMWh2tnDl0GD8/uay/hef5wPsKzbqRyhKcqCT33Aekt0VIexg8caEEoNRYzWkX/cyp7nUaOOBZKW/KhcnN/jQ6jb8dwp0NYcqiKwmMATKHxkPX3Wmv/WlIv9gyPVOHIoT60GhLZmdq5UPb8sinKELU7lqYtNGpSipriwn1jrjS9Ffqc0p7YzpFLEXEyOnU8dI67JtT1dP+WpkYkO+eU8cZdo5T7O2ajeEO35rq554rzZdNNUsWDapdERaTsx+KUQNIHqWUl0Chg3DmuXCdePLjUFZ72PyaegOY3tc4AWA9gQFvfv8mlNyOVLkpJYfnlcxKqkA3tghOLT+JLGleZun5cD1x4RmxPOjlu3LAhukBwREUq30uIaCqhdOtSi78Pyfgu4tXG7LfTv34xX1w7Q+co4iWpH4rR/RxqGwCcO3eOXQQ5GxKyEptHWrlxo8ZwLO7QvELZoEiqH0uoTVKyzPVxKD6KwFM2JGm0tmOJf0urSq0vfp1PTHg2Dw21KJQaixW9vbHfsa2REzlI/kJCPW5qaFGvT+1HJVJaAGCM+SIaPygqfvng8xMTdsWKFdHzOVIqGpu5oVGeqONaybtsjDlR5jqF6lAvkKPqadqZI5VStpwbBxVLSmqpzDVP6d+UVKM2Ru6a+ePGhVQlkig8sfpUGiWHDQox1TolPUfFm5LCCqXnUtUwqWobij2ErhX9+O3zX29vSuvU0XwprcHNlUtpVYnwfA7AHQA+YK19r6TOO66/zU7tPRwdkByk+dyyCKVE/HP+336MZRYYaoKJlQvZKerEbMZiTz3XCtKx++ZduPepR6N1JH1AxewidM3LkpAUgkL5oK5jO8ixlsD756ixqOlr7nzfzhEUc43fltD9Tc0tOdIfFJmg7vEycUjGfCgGzqbkmnH3hIQQS9NWoXKSvvL9duQ9PKeP5yM8azdVjvBU4hmeJiab/6/RVCoGWcqNrSUXkvPauu5niTojKRcrS5Xj7Pl9KFmoqIlPE69rIzQhUvFSyLEr5Oprj/vn/bbH+pSapCVlY+U5VYKC5H5LIU5StUdCKKn67ufdN+8SKS0cUZOoBdyifNeK+HNWfnzcwh46RvWvW156P1BEgyIkfj9x5Jw6V5ZkFjaodqWO5xr5UKVneC42/1+QP4FeZjdWIMfNUN9Q86G9LlPjx645tpj6NMc4zYGFEocUXLyhcaO1IcFCmSMWyvVbKP2xmOaIxYwqpbT+EMCPAfgTa+2bJXUuXrhgjTGllBApJLt8qk4u9h/aYUraLrGbolyl2KR2ujE7RbnUPi3q/fKRJzAwMHCN3Vj5Aho/lD3JLpWT+TmFgLKRMi5CvjTqqNRGrC1uXakq59tLHduFv+6BlZg9c36eb+q6UOoMpaZIlRuJekEprKHYQjak5amxqLEhGfdcvLE+TVHUQrYp1XreOQM8fGWsvSmtMyfzpbQGNlQupVUlwvMQgLcB+DNr7Q9I6hw+dMi+f+t3sRNWGaKhWZRzEwYNysZJyfkpi4wkRmriyGE/BPfX0qXXTDpx+tAu/twkzBF4bjHl4qCgGd9lr7M0Xs2xlI2Ki+IZHjd2KRGNtcVFiMhQkGwkqDpS0hSzUUYBipGl2DgqykhISqj9OWLmEPLRvW4NHpz4ak142ogqpbQuNf8Xp7TOTE7yhWrUqNF21CmAGksd3WtXdTqEJYcqKTy/CuDdAP7GWvtaSZ1nnnnG/uZNrxdLlZrUSWq5sjtn6W6U28FL7GkUAalNKSS+tSkrSTsPHDiAbdu2kXYA+cOsoXrSlBPXHh+cwhOL37fXzvvAPU9dW0p5jNnQxMvt+rkYi3GTkgrhxpK0HhW/Jp6irHaccmOSUrykMbmQKjwx+7E6obZw97N0rnLrdeJNy5cnn8+24PevWVc5hadKhOd9AH4ZwOetta+U1NmzZ4/dMjJyzXEt6aBApTNiZf2/qXKtgmTB9Mvn6i/OF1cvlhIpk6ZIgYYkhOppSQdHmooyUgKbErsGZciVlgC69bQbAqoel0ah6scWxZAP12bKWA/V9xGKQ0IEfP8pBIUjSjlTcCF/XH1JjKFzoRgpYume6926ER898E814WkjFnVKK0R2atSgcPz48U6HUKOC6Bka7HQINSqG6YMn2u/UdOX7V0FUSeF5B4DfBPCUtfZbJXX27d1rh4eHo+clqRmAl4c1EnrZlFbZsqk7Z6mvMsoKtdNNiSklltCPh0qUFS6OAtwuP7fKEPLht8VFSKLnYg+1oR0KW8h34T9WRjOWKFu+veJh91TFhGtHLmUl5jsUX8r4kNzDUuWGU22k867UjibllmrPrXfkyBFct3NnexWes6fzKTyr11ZO4akS4flJAL8DYI+19gWSOu+4/jZbvDU3ZaFqJ6TpLqmNMjGUWTy0pCN18fbrxmLVprvcb2lpkGPs5CKRKSk+TfpFStq0hJXyRY1J7t6hFq6U+ELtcolyCNK0noTQFHWkqZ7UmMqmhaTXoYyvUFs5ghmzqel7Pw73HJV2dOPoxJuWa8JTHcLzZgCfAHDQWht/qtTBM888Y92vGANpz3loVZxU5LTZTtKUwxfVxy64SS/kI1QvZuOe627DzLFTc3WkC3yKeuDG5ZNNzQLJxReqJ0UZG2XGc457LpV8p2yK3HETg3ZxDakMsYU75scvK7nHNJAqMFy8sXsi9FlST3NfSfpU0i6OuPn1OkJ4zp3JR3hWDVSO8FQpEXe5+X+/tMKa1atbFEqNxQpu0apRI4R63CxtpJD62cnzfKEaWVElhedNAP4CwKS1dkBS521mh5WmOUJlUneSIV+cv1RIVIZWKFQp6QaubCqotmiv9fj4OLZv367y1Qo1LfWaUddFkzYNpQe4602ltMqqM9Q5zZjSjhVpHLtvvANT+4+RfR/77ILrq5jqyflz49WqOpJrHIqXUk84VSfkn1MbqVRSrF0SVYqbWyXxxhS6h21737S81BWeKhGe1wP4NIApa61I5Qm9h4dDChmSpj00MeRcQEM3GlUutFC5dbTxpaRjqFgpmb9syoL6tXTXBmfHLR/r09wEmBqXWnKVI5YC0uunsRmzncOG9BrPIzw375r3pmUOseuSunlw62hILxVbzA81R1Cg2kwRKGk6SjK+tQQoFIe0bZzfjqS0zp/NR3hWrq4c4aliSqvPGFO5jq5Ro8ZVtELtrFGjRg0KVVJ4bgPwePPjMmvtZao8kKbwSBGTkGM7eG5Xn7rT1ewO3fikqQhpPFTdVikaoTgKaNWDApOTk1izZs08m5I0Cbfr5XaiGtVQEgt3TqJKhiBRAH07KSmG1N28D0m90HGqjaFzXQMr8eEj/xz167ZJYi/WByFwKa2c0KiqOWKQKFqhcxoFnlPbUq+LH49voyNvWl7iCk+VCM/LABQzyoC1lv2hrJ9ZfaP98PEvAkh7noBL/3DnQudTbbplyhAHSdqnLDFJsZEj5cItXBJMT0+jt7dX5DcHkZOQzRQC6iOFIGjLSlJB2uvMpU40fZ86xiQL/PT0NN41cAs7HxQxU+koCaiyqSmnkB0g3H4JGaAIpsRXrGzIr28jdDxkU0rGY+D6l2pX16rl+O2zz7SVNFy6cD7bgr9sxcqk2I0x/QDeD+BHAAwC+AqAX7DWxp8luFp3C4CPAHgtGhmqvwVwt7V2n8R3T0rAHYKr6PRLKtjpGbYMteBQkym1IKUuVlIlwbcd8hn7TCkfnH3NxOZOjGXaHppgYzGHYirqStv8Cy/93rlnMShfsVi5tvjn/bIxFSAWQygmLTHy46F2upo4JO2l4k5VdXz/hW3pZie0gFFjHGiMm1hd319I/XHLha695l6kfPsxcL5C9d3zsfb6xzlSQI2x2PWg4pYS29jczdkqzoeuC3XNXFuS9WmR4o8AfD+ABwA8B+DHADxijLndWvsPsUrGmFUAPgdgNYAPApgBcDeAx4wxt1pr2a9KLmrC07tlo8hwWUUjl40cSN3N5UaO/sjRloXSH3VbOofFdH8vlPtqoWChXNsUG9L1aTHBGPNyAP8ZDVXmgeaxPwbwFIBfB/AqovrPAHgBgJdZa7/UrPtIs+7dAN7H+q9QSms7gP3NjzdYa5/j6hQvHtTK9AUWyiQnBSfLA+nP6aTEkmo/R1qNsxPrjzuffuSaN+Zq1Q7OH/d3zD63q9coarG2uMdDviT2KHDKijS1IU2dUOqBa0uialFx9e0cmfctLS52ibLC1ZGOnZidmHqiVSooVYuyr6njnnd9hsYL1becfcoXFa9EifPRiW9pXbp4MV9Ka/lydezGmHsB3AVgnbX2nHP859FQbbZYa49E6j4JYMZae5t3/K8BjFprX8z5r5LCc8n5W6TwPPTGt+Depx5VLZ7uwKUmFOmC3mpiIYU/MZRJFXD1qJs8RjTcY9QklXItJeeKz5cvX45O5rE2cxOt299UHFzbpBOwBBobksWUakcsvaFdfGJjIIVEUYRSSnLc63r34C3z6vq+tIus68c9F0qlUAREQnBifqnNX9kNjaSuhKxI+pUi0rF4/Pnf9xW7F2PXiCJGSwwvBfCsS3aaeBKAAXArgGsIjzGmC8AtAH43YPNJAN9jjFlhrb1AOa8S4VGntKbGj7UolBqLFdPT0+jvFw2vGiXQ6Q1AbpjeHtjL050Oo0aFUPX1yRhzmitjrV3rHRoGcChQtCA5IxFT69BY90PqzxE0yNIwgD1UPIua8HSvXCYyHGL43E5eu2MIlZWkzzhfMdsxcLtjibQfKiNVKjS785jNVqhmodREsYPn/El2gi7Kxi1JJ4SgkdtDKoYmDmpMSMZKaEccuw+k6Sj/nF8uZl8Sb8+mQUydu5iUqtGoFpT6yNmm/ITaLFXiYteIU4hi6S7KPnUsFDuHWP/4Sk3sPuBUM+rcmTNnRDHmREoaisDphDrLMX8tL3DJOR+rh8S6c6jSMzxdAGabH19trX2Mq8O9hyc1naNBbDHUpB7KxhiSUilJNndMKQSlldeGsu3/WnqsLEV0gfjiycXlglrUYz4pkuDbpOJIiT9mg/Jfxr5rT7J5iPnVblpcFOeLcaMlCZRN7r6Vpkck5UIEkNrsxNoZG7Pc/RKLgyLpUlIWi9FvSyj+GKTzaSwGoDPP8HQaxpinAByy1r7OO34TgH8F8FZr7ccD9TYAOAHgPdbaX/XO/QyABwG8wFpLKjyVedOytfYKgEIzlkk3NWooUb/Eu0YK6nFTo4YIR9BIPfkojsV+n+V5NNSdWF2LcLprHiqj8ACAMWYSje/gv8la+5dc+W9+4xt227Zt0d0QJ3n7x3MqLZ2or7VJ7VZiZal+0+ykqXipOEJ+OXucHYk/v6yLFDWvTAqnbMpPqmpRcaQoWxJFi4sjl6Lm2+YUn5C6FoP0fpMqKVJ/UnWXUpM4X1QMEnVGek9L5y2pqhSql6ImhezG4j1w4ABueOELlxRTNsbcB+BOXPstrffg6re0gqTHGPNPAKYD39L6DBrf0noR679ihOcEgA0AftBa+0mu/KWLF22ORTaEnMQoR9qAmpg1k0OO+KmJP3Vh0i5G1DH/uHvuFw98HhvWryfbVtSXSOEacujXdW1Qi46EGKQuEFR5atGSkAnKN7fIStMSEqSORffve7Z/J2ZOnhERUb8d1ELLHffPhUgLFYeEVFPXkiMJoXKxNoSQSjq4Pg+d04xnym+sf93PBTI/U7PgYYz5NwCewPz38PSj8S6dY9baVzSPbQewwlr7rFP35wD8Kua/h+dFaKTCfs1a+4us/4oRnoMAtgB4s7X2T7jyb9/8Unv/vi+IJ9XimF+Og2RHnIPUxM4D6WQlNzTxauppy3H+YnCf4SlDjKWkhlMMOP9SYhGKSUoUpZCMRY6kUzaoxUsbh29POg/EYurbOYJ7n3pUVI8jExJiUwYUGYj5klyz1Fg1ZE5CQqhxRI0/t50ucpCmkP3jx49j++jokiI8AGCM+SSAN6HxExF7APwogO9A49ncx5tlHgNwu7XWOPVWA/gSgJUAfgONNy3/LJpfZ7fWTnC+q/QtLeDqE9r9ksJdK5e3MJQaNaqHThPiAjkW7ho1qozzF8hXxixmvBnArzT/HwTwVQBvKMhODNbas8aYO9AgSu9F4xnkzwG4S0J2gOopPE8DuBHA2621D3LlY29alu5ipTtsye4wh2yujT8nUpWbVHutgGSn2DO8Hvfv+Xy0Hmc3dj53W1PGsCbtI1E/XKTeS2VVUIlKlZKmkNy77vGjR4/i13a+Olo/Zp9LCUlSOtq0FafIaBRAqm9i9kOf/bIxO7GyKamvMvcO5S9kI4Sl+C2tTqNqhOeLaLyp8R5r7W9w5Q+Mj9sPjr4qeaGSIteCVoaIURNRjvhyEx7OhmSR8eu5/sukyHIQXWlaJWaTIxzSvvFji9mMlW8l2aZiCm1SJG3Wbiwo/+5xily45akFmEtpcSkQTVos1sYYNAQwFLfkGOVP2h+amFPqxOYPaRwasnXixAls2769JjxtRGW+lt6EKqW1cePS+3G2GuVw6FDoJaA1XLSC7FQdvVvruaaGDvX61H5UTeF5DMDtAN5vrf0lrvw3vv51u3379pbGlLrD5HbjZdUZafqC8qVRdSRyOHc8R0qorAKxb2xs3o+HllV/tAqSREaXXiPpOKKuO7XTlez6OWh8SXxT44qLT6sUuud237xr3o+HhqBV1aT3n1+Hg1YBSk0LadWaok7KGJAqopwyJklNcf1BKYAu+kaH8Ftj/1grPG1E1QjPXwN4LYBft9a+mytfPMPjg0o3SBcjzTm3TBkylLqY505v5bLJtbNAavpFGwtA/1o6J3NLSE6ovFsmlWDGykvJUCw2KjUTii1UV0O0KBvSxSgGrj/K2Ai9aTlk2/chJZDFeYoQh+pK7q+yoMhESorItUuRfimB4uKQkMVYf0uvd8hG/QxP+1E1wvN/AHwfgAestXdz5d2HlgtwE0OOxdO1nVuZkEzMEr9lF49UaElj0ZYyZJOalPxJ6JcOfQGDg4PRchJyrFFFYvUoYqEhPJRfbvEM+aIWoJhfjsCmjH2qXVT5HHG45Yuyp06dwuDgoFph8f1SJNo9Jlns/Rh9X5xvqX2p71gdP54Utce3laMfqRi1CN07+8bGcOONN9aEp42o2jM8xY+EiX5aYv26dSKjrVjYU5CLZC0W5GiL1sbAwMA1xxbK+Gjntc3ha6H0WzsQGjetQI4+XUrXZSFDuj7VyIeqKTx/AuCHAfyhtfYnuPJnJyftuwZuEe+IY9DsqjuF1BhT0mxUyoZSOyg5ORU57Lvl/B8PLc4D6WNHAq1taSqkbArHL+sitkNOvaZc2kO685fYzz32imd4tEqFC2lqxS/D9ZmkrEbJy6FwxGKR+qLaFbJHKTyc+iPpD+7ahRS16elprF6zplZ42ohF/eLBg4cOzQ00Kj3AIZTOyAlqoU6NUUoEQsdDC3zs79Ax3750gkxJW/lxueW4SZE7LolVak9LvFwfVF3JtYiVlZSTLtZUPQ258u1xC6Hk/ubGs1teE6M0XePGEqqbsqj75zSEJFSWqhOaS7SkRGM/dJw7x0Haj1QcblsoGzH/frwHDx3CjWvWiNtQozyqltJSEZ4aNWrUqFGjRg2geimtDwO4G8CnrLXfx5V3v6UV2r2VUW1Sd62ULS79wMmnlKwr9Un1SStTeq1U0DS4e8Ot+MjJL6vqpPaLJgXHXWtpPNR1l9iN1ZPG5tdJSUFJFVDtdaEUpNj8UcCs6Ie9cBkuqDQHlwKJQVqPS9OEPsdsuOCuN+fPLZPaB65tTikL+cyRnnOhUejcz/W3tNqPqhGeDwH4eQB/Y619LVf+zOnTtr8/LgZxi2wZUlCg3Yu4lthRkwZXR7pIcvYkcYVsSq6LdNErypkV/fMITywO7UKv6VcNpGOYi4+yWYY4pNoI1Uu9H7XpRI5QheK4e8OtpQmPND0UOx/zxdUJ1eOIQRl/0rSYpM0phEUTK8BfPyp+14b/2fT34qFL36wJTxtRtZRW8S2tFZLCU1NTLQylxmJE7+b1nQ6hRgVRj5saWpi+qj1CW31UTeH5rwA+BuBZay1L7UMpLeBadq5FjnqadEZZv61AbGeqSYnkaE9ZG3790Le0/PIFJDt/jRom9RMrG0shaH1wqpYmntC5VihSLjTXRWJD0k+7b96Fe596lFQqOFVHo2hw1ys1VRayl5KS06gvVOpLmi6L1dOq0JIYpb5D/uuUVmdRNYp5vPn/plQDuQiDdoH3b4TUybydpIfy5R+XpBRS49bE4daJlfNjytWnIRuhtkueJ+AWIOm4Sk0phmxoFhGNTfdvyfXUkObQ/eP79W36kLTzpz/1cXZBLuP7rhU3zqsTs83FTqXuJPak533fFHmJ9YV/b7r9RqXguPZIyCyFUNtT7uN9Y2MifzXyoWoKzx0APtf82GetnabKP/vss3bH6CgA/TMXLiSTKjdR51hQqd1CK0iQlAhQO8zUfpG2TbpTl7blypUr6OrqCsYoXVwl5C9Uh1JWtDFQcUjKSpUEypZ2U8DFqh3jvroWi0Vzf0cXs1U3AVfsvHOc0uVDQlY5YhO7brG4OEUp5DNmI9TmFPu+H8l8xxEaSTwh3xpQ8fqx3bXiRvRdN4zf2vsPtcLTRlTtGZ7jzt/sT80WZKdGDSnOnDnTErvaybNVNmq0Bt1rV3U6hBodRMqGc2rfkRZEUoNC1RSeNQCKFemV1trPU+UPHjhgN2zYoPIh2ZVpJHXKT1lVpqzioC2njS1kk5J+cypWnBoRO+f+Wjq3Y5OoLr7K0IqxIlVqKAUpVp+ykdLHof5ISQfEoBlTZfvNBfVr6VK1gIsvRZ3QKDqSuEKqGRVj6nhPiVHax6njOWYvVo6b43o2DuBjx79SKzxtRKUIDwAYYw4A2ArgJ621v0eV5R5a1iD34txKKb+oV0BK3ig7Ghtl0g+a2DQ2paAeWuYmR2kKg5LcpZCmRPx4U/yl1pGWj6VupP2rTbVp4qDKuWVcouyf41IbsWsUKkeljyh/EkjTUNIUmZQMa9JAkjj8OtJrQZ2X9ok0DgD1j4d2AFVLaQHA083/X9LRKGosSvT19nY6hBoVRD1uatRY+KiiwvM+AL8M4JvW2hdSZcf27bObN28m7eWS6N3PBXKmiFxf2h1VGftlbGls5FKkQvZC9TSytnTXSvmSjp0cY0a6w0yxGxsfXFu06pc0jSdJOXGqiFatkyDWphC4sRkqyykOsVj8crH+oHxxqcdYXUp1iSlesbZxflLvbwqUEpSiEvUMr8fHDv9LrfC0EVX7WjoAbeeXGAAABnxJREFUfBoNwnODMeY2a+0XYgU3b97MTly5ZX5O/ozZSpF/fb9Sm8X5WN2yqTbJ4hoqQ6W0UtJAVL/5fxc23733c9fYlyzSmvRLKAZJe2KgFn9/LJZZ8FOJKEf4JOOZQgrxTyGofl23zD3XvwIzRybUhNg97vcHRTRi84xm/vF9S21QY4oiK1zsIcTm5+I4Ryx9kh6KTUoiQ+30QV1nP46ZIxNRfzVagyoqPAbAPwF4GYAvArjDWns2VPa5556zW7dsAZCWn6d2DVT9VPuxchr70pi4OEI7qBwLMhcXVydWLmbfPSex6T7DQ+3ipDFSu0tu4s2pkFA7Z2rx5PzEoLnmHDmkiJxmfPjlU0hSrJz/DE8MnDrC3Y+xY7791AU5VjZWh4KUoFG2pKqKD2k7JRsTDtp7ruiL3m2b8NHxJ2uFp42o3DM8tsHQ7gZwBcC3AXjCGPNGY0y3X3Z6mnxNT40ItApVq5BKsBYiUnbdPqT9odmdp/iSxpHj+i2UMZDjnqj7Yz4WSltyxJFyX5neKiZYqo3KKTwFjDE/AeD3cJW0nQLwOIAvAfg6gKeffvrpL/rfuElJ9Uh3k2WRkgaR2tMoXKEdi2Z3JY1Ns4BTKZEcKNrXs2EAMyfnv4sntiOUKhApyohbltv5l93ppipZlHqlqef6TVFROZVP21dUHDF792z/Ttw//gQZI3dfhVQQiTrIqdES1VoDSbticYaOU7apGKgx7JeT2PDjlsz/nFpK3WP1t7Taj8pSTGvtHxhjvgLg1wB8N4BBAG9s/gOAf+kyZu713b29vdi6ZQt6htfPHVu9ahU2bNiAsf37URC/rtXLYadm0Ltl49VXf/d2o2vFsrnP3V1d2L59O06cOIFz588DAFauWIFNmzbhwIEDmJmdBQCsGxzEypUrceDgwbm4R4aHMT09jRMnT84du27HDkxMTKBv5wgA4MiRIxgeHsahw4cxNTWFvp0jmD19DrOnJue9jnxo0yYYY3D02LG5Y9u3bcPZs2dx6vRp9O0cgZ2eAdB4QK5reT8A4MTJk9i4YQP279+PK812b1i/Hn19fejbOTLnw/T2YHJyEhPPP9+I7coVTI0dRc+mwbkyK5Yvx9DQENvu4c2bMTs7i+MnTgDAXFsnJiYwebaRkVzW34/h4WEcPnwYl5s//DqwZg1gEGy3G6vbbgDo7enB1q1bcfToUVy81PjN2VWrVgXb3d/fPxcPAJgz52GW96Fnw1oAwP79+zE6OooTJ07MlTvW7PPe7Zuwb2wMfTtHcPr0aaxevRrjBw7Mlbt06RK6Vi5Dz9A6AI127BgdxalTp3BmchIA0N/fj5HhYRw+cmSu3uyZc7DWYmz//rl2dq1cBjt7Bb0jG672R3cXTp8+fU27jx07hgsXL6Jv5wiunLuAmeOn57W7a80KTE1N4dDhw3M+pw8eh+nvm7PdZQxGR0fRs3EtulavwL6xMSxftgybN2/GwYMH5+IaXLsW6DLzrtHmoSF0rehHz+ar91vR7qJef19fI+aRDTDLGn6716/B7MT8cb5p40Z0d3fjyNGjc2NnZmYG58+fx/NNe3ZmFtPjx+baDQCrVq4EAIyPj2P2yhX07RzB5OQkli1bhkOHr74zZ2R4eO4+K643APRsXDsXR9Hu3m2brs4Dg6swOzuL8QMH0LVmJfaNjWHz0BCstfPG5o7RUXSvW3N1LtraeGfqEfd6T56fa1uBmeOncPny5blxsW9sDNu2bsX58+fnytmZxj3XMzSIrpXLAQAnintsdGhubM5MnMH09PQ8+5cvX0bX6uXo2TjYOGAtpvYdQc/GAXStbvTdlYuXMXNkAr3bNs0pEqdOncLAwAD2j4/PxbV5aAhmRf+86za6fTu6161G99rVAIBDhw9jy8jINe2ePXkGfTuHATTG0MYNG9Db2zsv1qnxY+heuQzd6wcAAAcOHAAAHD9+HOcvXGiM8/MXMXPsFPpGh4DuhuA/OTkJ9HSjb/vQnK3pQydgenvm+mZs/37sGB3FyYkJnG3ORT3DjR+DPXTo0FwcbrsLmBWNObX48dh9Y2NAl0H32lXoXru64aP5DT53Dj45MYG1AwOo0V5UVuGpUaNGjRo1atSQonLP8NSoUaNGjRo1amhRE54aNWrUqFGjxqJHTXhq1KhRo0aNGoseNeGpUaNGjRo1aix61ISnRo0aNWrUqLHoUROeGjVq1KhRo8aix/8HsJyX2vj2T/sAAAAASUVORK5CYII=\n" 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", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -74,36 +82,38 @@ "labels = np.array([0] * n_verts + [1] * n_verts + [2] * n_verts)\n", "adj = np.array(sbm(3 * [n_verts], p), dtype=np.int)\n", "fig = heatmap(adj, title='3-block SBM (undirected)', inner_hier_labels=labels)" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "There are two cases of single graph vertex nomination. In the unattributed case, the seed $S$ is simply a list of any number of vertices in the graph $G$. For each seed vertex $s \\in S$, SVN will return a list of the other vertices in order of distance to $s$.\n", "Any distance metric supported by sklearn's `NearestNeighbors` can be used, euclidean is default and is generally sufficient.\n", "\n", "The `n_neighbors` argument can be used to specify how many vertices should be nominated for each seed, otherwise all vertices will be ranked for each seed. Setting `n_neighbors` will improve runtime on large graphs and might also improve prediction accuracy in some cases." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "SpectralVertexNomination(n_neighbors=5)" + "text/plain": [ + "SpectralVertexNomination(n_neighbors=5)" + ] }, "execution_count": 3, "metadata": {}, @@ -115,36 +125,38 @@ "svn = SpectralVertexNomination(n_neighbors=5)\n", "# fit to the adjacency matrix\n", "svn.fit(X=adj)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "The predict method takes a numpy array of seed vertex indices as an argument. It generates the nomination list, and returns a tuple of the nomination list itself and the associated distance matrix.\n", - "\n", - "Here, we select a seed of 5 random vertices, and the top 5 vertices nominated for each seed are plotted along the first two dimensions of the embedded space." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The predict method takes a numpy array of seed vertex indices as an argument. It generates the nomination list, and returns a tuple of the nomination list itself and the associated distance matrix.\n", + "\n", + "Here, we select a seed of 5 random vertices, and the top 5 vertices nominated for each seed are plotted along the first two dimensions of the embedded space." + ] }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -168,25 +180,19 @@ " plt.xticks([], [])\n", " plt.yticks([], [])\n", " plt.title('Top 5 Nominees For Each Seed (black) in First Two Embeded Dimensions')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "The attributed case is not currently handled by Graspologic, and may be added in the future." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The attributed case is not currently handled by Graspologic, and may be added in the future." + ] } ], "metadata": { @@ -210,4 +216,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/graspologic/nominate/spectralVN.py b/graspologic/nominate/spectralVN.py index 8eb129bb1..8b04de070 100644 --- a/graspologic/nominate/spectralVN.py +++ b/graspologic/nominate/spectralVN.py @@ -6,6 +6,7 @@ import numpy as np from sklearn.base import BaseEstimator from sklearn.neighbors import NearestNeighbors +from sklearn.utils import check_array from ..embed import AdjacencySpectralEmbed, BaseSpectralEmbed, LaplacianSpectralEmbed @@ -99,16 +100,12 @@ def _check_x(self, X: np.ndarray): raise IndexError("Dim 1 of an embedding should be smaller than dim 0.") if not np.issubdtype(X.dtype, np.float): raise TypeError("Embedding should have type float") - elif not np.issubdtype(X.dtype, np.int): - raise TypeError("Adjacency matrix should have type int") elif X.shape[0] != X.shape[1]: raise IndexError("Adjacency Matrix should be square.") def _check_y(self, y: np.ndarray): # check y - if not isinstance(y, np.ndarray): - raise TypeError("y must be of type np.ndarray") - elif not np.issubdtype(y.dtype, np.integer): + if not np.issubdtype(y.dtype, np.integer): raise TypeError("y must have dtype int") elif np.ndim(y) > 2 or (y.ndim == 2 and y.shape[1] > 1): raise IndexError("y must have shape (n) or (n, 1).") @@ -196,7 +193,7 @@ def fit( self.nearest_neighbors_.fit(self.embedding_) return self - def predict(self, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + def predict(self, y: Union[list, np.ndarray]) -> Tuple[np.ndarray, np.ndarray]: """ Nominates vertices for each seed vertex. Methodology is distance based ranking. @@ -218,6 +215,7 @@ def predict(self, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: The matrix of distances associated with each element of the nomination list. """ + y = check_array(y, ensure_2d=False) self._check_y(y) y = y.reshape(-1) y_vec = self.embedding_[y.astype(np.int)] diff --git a/tests/test_spectral_nomination.py b/tests/test_spectral_nomination.py index c8c3c7b2f..dcb096c09 100644 --- a/tests/test_spectral_nomination.py +++ b/tests/test_spectral_nomination.py @@ -31,18 +31,12 @@ def _nominate(self, X, seed, nominator=None, k=None): return nom_list def test_seed_inputs(self): - with self.assertRaises(IndexError): - self._nominate(adj, np.zeros((5, 5, 5), dtype=np.int)) with self.assertRaises(IndexError): self._nominate(adj, np.zeros((1, 50), dtype=np.int)) with self.assertRaises(TypeError): self._nominate(adj, np.random.random((10, 2))) - with self.assertRaises(TypeError): - self._nominate(adj, [0] * 10) def test_X_inputs(self): - with self.assertRaises(TypeError): - self._nominate(np.random.random((10, 10)), np.zeros(3, dtype=np.int)) with self.assertRaises(IndexError): self._nominate(np.zeros((5, 5, 5), dtype=np.int), np.zeros(3, dtype=np.int)) with self.assertRaises(TypeError): From 561519e95c22ec76c40e4ec422a1b5f353a1a24c Mon Sep 17 00:00:00 2001 From: Jon McLean <4429525+jonmclean@users.noreply.github.com> Date: Mon, 1 Nov 2021 13:09:30 -0700 Subject: [PATCH 125/140] Added developer initialization documentation (#868) Merging despite RTD being unable to build our docs for now. * Added developer initialization documentation * Added setup instructions for CMD and PowerShell * Updated for py launcher as well * Added comment suggesting that you can use different versions of python Co-authored-by: Jon McLean --- CONTRIBUTING.md | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 05d6b78fd..081f7b246 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -122,6 +122,48 @@ branch. Steps: git push -u origin my-feature ``` +## Local Developer Setup +1. Make sure you have a compatible version of Python 3 installed +2. From the project root, create a virtual environment and install all development dependencies. This example uses Python 3.8 but you may use any Python version supported by graspologic. + + **Bash** + ```bash + # Create virtual environment + python3.8 -m venv venv + + # Activate the virtual environment + source venv/bin/activate + + # Install development dependencies + pip install -U pip setuptools + pip install -r requirements.txt + ``` + **CMD (Windows)** + ```cmd + rem Create virtual environment. Depending on your installation you might need "py -3.8 -m venv venv" instead + python3.8 -m venv venv + + rem Activate the virtual environment + .\venv\Scripts\activate.bat + + rem Install development dependencies + pip install -U pip setuptools + pip install -r requirements.txt + ``` + **PowerShell** + ```powershell + # Create virtual environment + python3.8 -m venv venv + + # Activate the virtual environment + .\venv\Scripts\Activate.ps1 + + # Install development dependencies + pip install -U pip setuptools + pip install -r requirements.txt + ``` +3. Start playing with Graspologic code! + ## Pull Request Checklist We recommended that your contribution complies with the following rules before you submit a pull request: From 30452bd071ab32ecafa0688ffb54228cee653704 Mon Sep 17 00:00:00 2001 From: dlee0156 <47963020+dlee0156@users.noreply.github.com> Date: Wed, 3 Nov 2021 17:20:37 -0400 Subject: [PATCH 126/140] Added function `networkplot` for drawing 2D layouts to `plot` (#860) * Adding function that plots network * Adding tests for csr_matrix * Make title default to empty * Address comments on networkplot * Add tests for csr_matrix, adjust for beartype * Update plot.py * Adding variables to choose hue and size * Update plot.py * Adding simple tutorial for networkplot * Adding sbm ref and plot A * change default figsize * updating networkplot tutorial * changing default node_alpha and remove ax ticks * Update docstring to changed default * minor docs change * minor matplotlib call changes * Add node_kws, edge_kws * Update graspologic/plot/plot.py Co-authored-by: Benjamin Pedigo * fixing sphinx cross ref for edge_kws Co-authored-by: Benjamin Pedigo --- docs/tutorials/index.rst | 1 + docs/tutorials/plotting/networkplot.ipynb | 166 +++++++++++++++ graspologic/plot/__init__.py | 2 + graspologic/plot/plot.py | 249 +++++++++++++++++++++- tests/test_plot.py | 110 +++++++++- 5 files changed, 525 insertions(+), 3 deletions(-) create mode 100644 docs/tutorials/plotting/networkplot.ipynb diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 8f86b3605..df9daae99 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -97,6 +97,7 @@ The following tutorials present ways to visualize the graphs, such as its adjace plotting/pairplot plotting/matrixplot plotting/pairplot_with_gmm + plotting/networkplot .. _matching_tutorials: diff --git a/docs/tutorials/plotting/networkplot.ipynb b/docs/tutorials/plotting/networkplot.ipynb new file mode 100644 index 000000000..e632062fa --- /dev/null +++ b/docs/tutorials/plotting/networkplot.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Networkplot: Visualizing 2D Layouts\n", + "\n", + "This example provides how to visualize 2D layouts using networkplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import graspologic\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing 2D layout using networkplot\n", + "\n", + "### Simulate an adjacency matrix using a stochastic block model\n", + "\n", + "The 2-block model is defined as below:\n", + "\n", + "\\begin{align*}\n", + "P = \n", + "\\begin{bmatrix}0.25 & 0.05 \\\\\n", + "0.05 & 0.25\n", + "\\end{bmatrix}\n", + "\\end{align*}\n", + "\n", + "More information on stochastic block models can be found [here](https://microsoft.github.io/graspologic/tutorials/simulations/sbm.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.simulations import sbm\n", + "\n", + "n_communities = [50, 50]\n", + "p = [[0.25, 0.05], [0.05, 0.25]]\n", + "\n", + "np.random.seed(1)\n", + "A, node_ids = sbm(n_communities, p, return_labels=True)\n", + "print(A.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate a 2D embedding using ASE\n", + "\n", + "Adjacency Spectral Embed (ASE) reduces the dimensionality of the input adjacency matrix and estimates its latent positions, generating an embedding in a lower-dimensional space. In this example, ASE embeds the adjacency matrix generated by SBM to two-dimensional space and calculates its coordinates. \n", + "\n", + "More information on ASE can be found [here](https://microsoft.github.io/graspologic/tutorials/embedding/AdjacencySpectralEmbed.html?highlight=adjacency)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from graspologic.embed import AdjacencySpectralEmbed\n", + "\n", + "ase = AdjacencySpectralEmbed(n_components=2)\n", + "X = ase.fit_transform(A)\n", + "\n", + "print(X.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize 2D layout\n", + "\n", + "Note that node colors were determined by communities that were randomly assigned and the node sizes are determined by the number of edges connected to each node. The edge colors for the plot below are based on their source nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from graspologic.plot.plot import networkplot\n", + "\n", + "x_pos = X[:,0]\n", + "y_pos = X[:,1]\n", + "degrees = np.sum(A, axis=0)\n", + "\n", + "plot = networkplot(adjacency=A, x=x_pos, y=y_pos, node_hue=node_ids, palette='deep', node_size=degrees, \n", + " node_sizes=(20, 200), edge_hue='source', edge_alpha=0.5, edge_linewidth=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can also use a pandas dataframe and use x, y, node_hue, and node_size as keys. Note that for the plot below, the edge colors are determined by their target nodes by assigning edge_hue as 'target'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "index = range(X.shape[0])\n", + "node_df = pd.DataFrame(index=index)\n", + "node_df.loc[:, 'x'] = X[:,0]\n", + "node_df.loc[:, 'y'] = X[:,1]\n", + "node_df.loc[:, 'id'] = node_ids\n", + "node_df.loc[:, 'degree'] = np.sum(A, axis=0)\n", + "\n", + "plot = networkplot(adjacency=A, node_data=node_df, x='x', y='y', node_hue='id', palette='deep', \n", + " node_size='degree', node_sizes=(20, 200), edge_hue='target', edge_alpha=0.5, edge_linewidth=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/graspologic/plot/__init__.py b/graspologic/plot/__init__.py index 70cb922b9..eeba43f91 100644 --- a/graspologic/plot/__init__.py +++ b/graspologic/plot/__init__.py @@ -10,6 +10,7 @@ edgeplot, gridplot, heatmap, + networkplot, pairplot, pairplot_with_gmm, screeplot, @@ -26,4 +27,5 @@ "screeplot", "adjplot", "matrixplot", + "networkplot", ] diff --git a/graspologic/plot/plot.py b/graspologic/plot/plot.py index 33cd7ac89..af252f688 100644 --- a/graspologic/plot/plot.py +++ b/graspologic/plot/plot.py @@ -1,18 +1,29 @@ -# Copyright (c) Microsoft Corporation and contributors. +# Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. +from typing import Optional, Tuple, Union + import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns +from beartype import beartype +from matplotlib.axes import Axes +from matplotlib.collections import LineCollection from matplotlib.colors import Colormap from mpl_toolkits.axes_grid1 import make_axes_locatable from scipy import linalg +from scipy.sparse import csr_matrix from sklearn.preprocessing import Binarizer from sklearn.utils import check_array, check_consistent_length, check_X_y from ..embed import select_svd +from ..preconditions import ( + check_argument, + check_argument_types, + check_optional_argument_types, +) from ..utils import import_graph, pass_to_ranks @@ -1126,6 +1137,242 @@ def edgeplot( return ax +@beartype +def networkplot( + adjacency: Union[np.ndarray, csr_matrix], + x: Union[np.ndarray, str], + y: Union[np.ndarray, str], + node_data: Optional[pd.DataFrame] = None, + node_hue: Optional[Union[np.ndarray, str]] = None, + palette: Optional[Union[str, list, dict]] = None, + node_size: Optional[Union[np.ndarray, str]] = None, + node_sizes: Optional[Union[list, dict, tuple]] = None, + node_alpha: float = 0.8, + edge_hue: str = "source", + edge_linewidth: float = 0.2, + edge_alpha: float = 0.2, + title: str = "", + context: str = "talk", + font_scale: float = 1.0, + figsize: Tuple[int, int] = (10, 10), + ax: Optional[Axes] = None, + legend: str = False, + node_kws: dict = {}, + edge_kws: dict = {}, +) -> Axes: + r""" + Plots a 2D layout of a network. Allows for an adjacency matrix + with ``x, y`` as 1D arrays that represent the coordinates of each + node, or an adjacency matrix with ``node_data`` and ``x, y`` as + keys. Note that the nodes in the adjacency matrix are assumed to be ordered the same + way as in ``x``, ``y``, and ``node_data``. + + Parameters + ---------- + adjacency: np.ndarray, csr_matrix + Adjacency matrix of input network. + x,y: np.ndarray, str + Variables that specify the positions on the x and y axes. Either an + array of x, y coordinates or a string that accesses a vector in + ``node_data``. If ``x, y`` are arrays, they must be indexed the + same way as the adjacency matrix of the input network. + node_data: pd.DataFrame, optional, default: None + Input data. When ``node_data`` is None, ``x, y`` must be np.ndarrays. + When ``node_data`` is a dataframe, ``x, y`` must be strings. Must be + indexed the same way as the adjacency matrix of the input network. + node_hue: np.ndarray, str, optional, default: None + Variable that produces nodes with different colors. Can be either + categorical or numeric, and colors are mapped based on ``palette``. + However if ``palette`` is None, ``node_hue`` is treated as numeric + and 'Set1' is used as ``palette``. + palette: str, list, dict, optional, default: None + Method for choosing colors specified in ``node_hue``. Can be a string + argument supported by :func:`seaborn.color_palette`, a list of colors, + or a dictionary with ``node_hue`` variables as keys and colors as its + values. Note that ``palette`` will not affect the plot if ``node_hue`` + is not given. + node_size: np.ndarray, str, optional, default: None + Variable that produces nodes with different sizes. Can be either categorical + or numeric, and sizes are determined based on ``node_sizes``. If the + argument ``node_sizes`` is None, ``node_size`` will be treated as + numeric variables. + node_sizes: list, dict, tuple, optional, default: None + Method for choosing sizes specified in ``node_size``. Can be a list of + sizes, a dictionary with ``node_size`` variables as keys and sizes as + its values, or a tuple defining the minimum and maximum size values. + Note that ``node_sizes`` will not affect the output plot if ``node_hue`` + is not given. + node_alpha: float, default: 0.8 + Proportional opacity of the nodes. + edge_hue: str, one of {source (default), target} + Determines edge color based on its source or target node. + edge_linewidth: float, default: 0.2 + Linewidth of the edges. + edge_alpha: float, default: 0.2 + Proportional opacity of the edges. + title: str + Plot title. + context : None, or one of {talk (default), paper, notebook, poster} + Seaborn plotting context + font_scale : float, optional, default: 1.0 + Separate scaling factor to independently scale the size of the font + elements. + figsize : tuple of length 2, default: (10, 10) + Size of the figure (width, height) + ax: matplotlib.axes.Axes, optional, default: None + Axes in which to draw the plot. Otherwise, will generate own axes. + legend: False (default), or one of {brief, full, auto} + How to draw the legend. If “brief”, numeric hue and size variables + will be represented with a sample of evenly spaced values. If “full”, + every group will get an entry in the legend. If “auto”, choose + between brief or full representation based on number of levels. If + False, no legend data is added and no legend is drawn. + node_kws: dict, optional + Optional arguments for :func:`seaborn.scatterplot`. + edge_kws: dict, optional + Optional arguments for :class:`matplotlib.collections.LineCollection`. + + Returns + ------- + ax : matplotlib axis object + Output plot + + Notes + ----- + Node colors are determined by ``node_hue`` and ``palette``, and if + ``node_hue`` is None, all nodes will have the same default color + used by :func:`seaborn.scatterplot`. If ``node_hue`` is given but + ``palette`` is None, ``palette`` is set to 'Set1' and ``node_hue`` + will be treated as numeric variables. Edge colors are determined by + its nodes, and ``edge_hue`` dictates whether the edges are colored + based on its source or target nodes. + + Node sizes can also vary based on ``node_size`` and ``node_sizes``, + and if ``node_size`` is None, all nodes will be of the same default + size used by :func:`seaborn.scatterplot`. If ``node_size`` is given + but ``node_sizes`` is None, ``node_size`` will be treated as numeric + variables. + + Note that ``palette`` and ``node_sizes`` will not affect the output + plot if ``node_hue`` and ``node_size`` are None, and ``node_hue`` and + ``node_size`` must be the same types as ``x, y``. + + """ + + _check_common_inputs( + figsize=figsize, title=title, context=context, font_scale=font_scale + ) + + index = range(adjacency.shape[0]) + if isinstance(x, np.ndarray): + check_consistent_length(adjacency, x, y) + check_argument( + node_data is None, "If x and y are numpy arrays, meta_data must be None." + ) + plot_df = pd.DataFrame(index=index) + x_key = "x" + y_key = "y" + plot_df.loc[:, x_key] = x + plot_df.loc[:, y_key] = y + if node_hue is not None: + check_argument( + isinstance(node_hue, np.ndarray), + "If x and y are numpy arrays, node_hue must be a list or a numpy array.", + ) + check_consistent_length(x, node_hue) + hue_key = "hue" + plot_df.loc[:, hue_key] = node_hue + if palette is None: + palette = "Set1" + else: + hue_key = None + elif isinstance(x, str): + check_consistent_length(adjacency, node_data) + check_argument( + node_data is not None, + "If x and y are strings, meta_data must be pandas DataFrame.", + ) + plot_df = node_data.copy() + x_key = x + y_key = y + if node_hue is not None: + check_argument( + isinstance(node_hue, str), + "If x and y are strings, node_hue must also be a string.", + ) + hue_key = node_hue + if palette is None: + palette = "Set1" + else: + hue_key = None + else: + raise TypeError("x and y must be numpy arrays or strings.") + + pre_inds, post_inds = adjacency.nonzero() + pre = np.array(index)[pre_inds.astype(int)] + post = np.array(index)[post_inds.astype(int)] + rows = {"source": pre, "target": post} + + edgelist = pd.DataFrame(rows) + pre_edgelist = edgelist.copy() + post_edgelist = edgelist.copy() + + pre_edgelist["x"] = pre_edgelist["source"].map(plot_df[x_key]) + pre_edgelist["y"] = pre_edgelist["source"].map(plot_df[y_key]) + post_edgelist["x"] = post_edgelist["target"].map(plot_df[x_key]) + post_edgelist["y"] = post_edgelist["target"].map(plot_df[y_key]) + pre_coords = list(zip(pre_edgelist["x"], pre_edgelist["y"])) + post_coords = list(zip(post_edgelist["x"], post_edgelist["y"])) + coords = list(zip(pre_coords, post_coords)) + + if node_hue is not None: + if isinstance(palette, str): + palette = sns.color_palette( + palette, n_colors=len(plot_df[hue_key].unique()) + ) + plot_palette = dict(zip(plot_df[hue_key].unique(), palette)) + elif isinstance(palette, list): + plot_palette = dict(zip(plot_df[hue_key].unique(), palette)) + elif isinstance(palette, dict): + plot_palette = palette + edgelist[hue_key] = edgelist[edge_hue].map(plot_df[hue_key]) + edge_colors = edgelist[hue_key].map(plot_palette) + else: + plot_palette = None + edge_colors = None + + with sns.plotting_context(context=context, font_scale=font_scale): + if ax is None: + _, ax = plt.subplots(1, 1, figsize=figsize) + sns.scatterplot( + data=plot_df, + x=x_key, + y=y_key, + hue=hue_key, + palette=plot_palette, + size=node_size, + sizes=node_sizes, + ax=ax, + legend=legend, + alpha=node_alpha, + zorder=1, + **node_kws, + ) + ax.set_title(title) + lc = LineCollection( + segments=coords, + alpha=edge_alpha, + linewidths=edge_linewidth, + colors=edge_colors, + zorder=0, + **edge_kws, + ) + ax.add_collection(lc) + ax.set(xticks=[], yticks=[]) + + return ax + + def screeplot( X, title="Scree plot", diff --git a/tests/test_plot.py b/tests/test_plot.py index f714cb450..f2488fcc4 100644 --- a/tests/test_plot.py +++ b/tests/test_plot.py @@ -3,13 +3,18 @@ import unittest +import beartype.roar +import matplotlib.pyplot as plt import numpy as np +import pandas as pd +from scipy.sparse import csr_matrix from sklearn.mixture import GaussianMixture from graspologic.plot.plot import ( _sort_inds, gridplot, heatmap, + networkplot, pairplot, pairplot_with_gmm, ) @@ -50,12 +55,17 @@ def _test_pairplot_with_gmm_outputs(**kws): class TestPlot(unittest.TestCase): def test_common_inputs(self): X = er_np(100, 0.5) + x = np.random.rand(100, 1) + y = np.random.rand(100, 1) grid_labels = ["Test1"] # test figsize + figsize = "bad figsize" with self.assertRaises(TypeError): - figsize = "bad figsize" heatmap(X, figsize=figsize) + with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, figsize=figsize) # test height height = "1" @@ -72,6 +82,9 @@ def test_common_inputs(self): gridplot([X], grid_labels, title=title) with self.assertRaises(TypeError): pairplot(X, title=title) + with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, title=title) # test context context = 123 @@ -81,6 +94,9 @@ def test_common_inputs(self): gridplot([X], grid_labels, context=context) with self.assertRaises(TypeError): pairplot(X, context=context) + with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, context=context) context = "journal" with self.assertRaises(ValueError): @@ -89,6 +105,8 @@ def test_common_inputs(self): gridplot([X], grid_labels, context=context) with self.assertRaises(ValueError): pairplot(X, context=context) + with self.assertRaises(ValueError): + networkplot(adjacency=X, x=x, y=y, context=context) # test font scales font_scales = ["1", []] @@ -98,7 +116,10 @@ def test_common_inputs(self): with self.assertRaises(TypeError): gridplot([X], grid_labels, font_scale=font_scale) with self.assertRaises(TypeError): - pairplot(X, cont_scale=font_scale) + pairplot(X, font_scale=font_scale) + with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, font_scale=font_scale) # ticklabels with self.assertRaises(TypeError): @@ -258,6 +279,91 @@ def test_pairplot_with_gmm_outputs_type_tied(self): def test_pairplot_with_gmm_outputs_type_spherical(self): _test_pairplot_with_gmm_outputs(covariance_type="spherical") + def test_networkplot_inputs(self): + X = np.random.rand(15, 3) + x = np.random.rand(15, 1) + y = np.random.rand(15, 1) + with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(TypeError): + networkplot(adjacency="test", x=x, y=y) + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=["A"], y=["A"]) + + with self.assertRaises(TypeError): + networkplot( + adjacency=csr_matrix(X), x="source", y="target", node_data="data" + ) + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, node_data="data") + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, node_hue=(5, 5)) + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, palette=4) + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, node_size=(5, 5)) + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, node_sizes=4) + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, node_alpha="test") + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, edge_hue=4) + + with self.assertRaises(TypeError): + networkplot(adjacency=csr_matrix(X), x=x, y=y, edge_alpha="test") + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, edge_linewidth="test") + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, ax="test") + + with self.assertRaises(TypeError): + networkplot(adjacency=X, x=x, y=y, legend=4) + + def test_networkplot_outputs(self): + X = np.random.rand(15, 3) + xarray = np.random.rand(15, 1) + yarray = np.random.rand(15, 1) + xstring = "source" + ystring = "target" + node_df = pd.DataFrame(index=range(X.shape[0])) + node_df.loc[:, "source"] = xarray + node_df.loc[:, "target"] = yarray + hue = np.random.randint(2, size=15) + palette = {0: (0.8, 0.4, 0.2), 1: (0, 0.9, 0.4)} + size = np.random.rand(15) + sizes = (10, 200) + + fig = networkplot(adjacency=X, x=xarray, y=yarray) + fig = networkplot(adjacency=csr_matrix(X), x=xarray, y=yarray) + fig = networkplot(adjacency=X, x=xstring, y=ystring, node_data=node_df) + fig = networkplot( + adjacency=csr_matrix(X), x=xstring, y=ystring, node_data=node_df + ) + fig = plt.figure() + ax = fig.add_subplot(211) + fig = networkplot( + adjacency=X, + x=xarray, + y=yarray, + node_hue=hue, + palette=palette, + node_size=size, + node_sizes=sizes, + node_alpha=0.5, + edge_alpha=0.4, + edge_linewidth=0.6, + ax=ax, + ) + def test_sort_inds(self): B = np.array( [ From a6cd53e94932175759e7a032c1d79d4e94b4cd41 Mon Sep 17 00:00:00 2001 From: Jon McLean <4429525+jonmclean@users.noreply.github.com> Date: Thu, 4 Nov 2021 15:04:22 -0700 Subject: [PATCH 127/140] Build documentation and release through GitHub actions (#874) * Started migrating toward a single Sphinx build * Removed duplicate label and invalid license link * Forced versions of documentation tools that we know work * Added flag to enable tutorials * Added filter to remove tutorials from table of contents * Removed extra configuration since it is handled at the root level now * Removed duplicate documentation * Removed old tutorials and reference links * Changed Sphinx build to create docs and tutorials * Removed tutorial step that is not used * Publish to gh-pages * Updated to use dev in the version * Renamed build artifact * Moved doc publish to the publish step * Append 'dev' and runid when on dev branch * Rolled back to the correct version * Publish docs to version directory * Test run of docs with version * Revert "Test run of docs with version" This reverts commit 4fe7dbed477f3a85ae91163045cf6504910fe224. * Added release & tag when on main branch * Moved reference docs down so that it will be next to tutorials in the final index * Added pip freeze output to the release artifacts * Download documentation so we can publish it on the gh-pages branch * Archived the versions at documentation build time * Added archive of the versions_at_release.txt * Changed to >= instead of pinned versions * Updated .PHONY in makefile * Added newline for code formatting Co-authored-by: Jon McLean Co-authored-by: Dax Pryce --- .github/workflows/build.yml | 68 ++++--- Makefile | 11 +- docs/Makefile | 11 -- .../_templates/numpydoc_docstring.rst | 0 docs/{reference => }/conf.py | 24 ++- docs/index.rst | 75 ++++++++ docs/{reference => }/license.rst | 0 docs/reference/Makefile | 25 --- docs/reference/index.rst | 75 +------- docs/reference/requirements.txt | 8 - docs/sphinx-ext/toctree_filter.py | 44 +++++ docs/tutorials/Makefile | 25 --- .../_templates/numpydoc_docstring.rst | 16 -- docs/tutorials/conf.py | 171 ------------------ docs/tutorials/index.rst | 2 - docs/tutorials/requirements.txt | 8 - setup.cfg | 22 +-- 17 files changed, 196 insertions(+), 389 deletions(-) delete mode 100644 docs/Makefile rename docs/{reference => }/_templates/numpydoc_docstring.rst (100%) rename docs/{reference => }/conf.py (89%) create mode 100644 docs/index.rst rename docs/{reference => }/license.rst (100%) delete mode 100644 docs/reference/Makefile delete mode 100644 docs/reference/requirements.txt create mode 100644 docs/sphinx-ext/toctree_filter.py delete mode 100644 docs/tutorials/Makefile delete mode 100644 docs/tutorials/_templates/numpydoc_docstring.rst delete mode 100644 docs/tutorials/conf.py delete mode 100644 docs/tutorials/requirements.txt diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c31182f1d..3f9cca038 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -40,29 +40,20 @@ jobs: sudo apt install pandoc pip install -U pip setuptools pip install -r requirements.txt - sphinx-build -W -a docs/reference/ docs/reference/_build/html - build-tutorial-documentation: - runs-on: ubuntu-latest - continue-on-error: true - steps: - - uses: actions/checkout@v2 - - name: Set up Python 3.8 - uses: actions/setup-python@v1 + pip freeze > versions_at_documentation_build.txt + sphinx-build -W -t build_tutorials -a docs/ docs/_build/html + - name: Archive documentation version artifact + uses: actions/upload-artifact@v2 with: - python-version: 3.8 - - name: "Run Tutorial Documentation Generation" - run: | - sudo apt install pandoc - pip install -U pip setuptools - pip install -r requirements.txt - sphinx-build -W -a docs/tutorials/ docs/tutorials/_build/html - touch docs/tutorials/_build/html/.nojekyll - - name: Archive production artifacts + name: versions_at_documentation_build + path: | + versions_at_documentation_build.txt + - name: Archive documentation artifacts uses: actions/upload-artifact@v2 with: - name: tutorials + name: documentation-site path: | - docs/tutorials/_build/html + docs/_build/html code-format-check: runs-on: ubuntu-latest continue-on-error: true @@ -126,7 +117,7 @@ jobs: pytest tests # note: should be pytest graspologic tests, but we're skipping doctests for now and re-enabling them in another PR publish: runs-on: ubuntu-latest - needs: [static-code-analysis, build-reference-documentation, build-tutorial-documentation, code-format-check, unit-and-doc-test] + needs: [static-code-analysis, build-reference-documentation, code-format-check, unit-and-doc-test] if: github.ref=='refs/heads/main' || github.ref=='refs/heads/dev' steps: - uses: actions/checkout@v2 @@ -138,6 +129,17 @@ jobs: run: | python -m pip install --upgrade pip setuptools wheel twine pip install -r requirements.txt + pip freeze > versions_at_release.txt + - name: Archive version artifact + uses: actions/upload-artifact@v2 + with: + name: versions_at_release + path: | + versions_at_release.txt + - name: Create version environment variable + run: | + echo "GRASPOLOGIC_VERSION=`python setup.py --version`" >> $GITHUB_ENV + echo "GRASPOLOGIC_TAG=v`python setup.py --version`" >> $GITHUB_ENV - name: Build Release with setuptools if: github.ref=='refs/heads/main' run: | @@ -153,15 +155,29 @@ jobs: TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | twine upload dist/* - - name: Download a single artifact + - name: Download documentation artifact uses: actions/download-artifact@v2 with: - name: tutorials - path: docs/tutorials - - name: Publish tutorials + name: documentation-site + path: docs/documentation-site + - name: Publish reference docs (dev branch) + uses: peaceiris/actions-gh-pages@v3 if: github.ref=='refs/heads/dev' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/documentation-site + destination_dir: pre-release + - name: Publish reference docs (main branch) uses: peaceiris/actions-gh-pages@v3 + if: github.ref=='refs/heads/main' with: github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: docs/tutorials - destination_dir: tutorials + publish_dir: docs/documentation-site + destination_dir: ${{ env.GRASPOLOGIC_VERSION }} + - name: Create tag and GitHub release + uses: ncipollo/release-action@v1 + if: github.ref=='refs/heads/main' + with: + tag: ${{ env.GRASPOLOGIC_TAG }} + token: ${{ secrets.GITHUB_TOKEN }} + artifacts: versions_at_release.txt diff --git a/Makefile b/Makefile index 336e94961..3c86d6263 100644 --- a/Makefile +++ b/Makefile @@ -1,8 +1,11 @@ +clean: + rm -rf docs/_build + docs: - sphinx-build -W -a docs/reference/ docs/reference/_build/html + sphinx-build -W -a docs/ docs/_build/html -tutorials: - sphinx-build -W -a docs/tutorials/ docs/tutorials/_build/html +docsWithTutorials: + sphinx-build -W -t build_tutorials -a docs/ docs/_build/html coverage: python -m pytest --co --cov=graspologic graspologic tests @@ -24,4 +27,4 @@ type-check: validate: lint coverage docs test -.PHONY: docs tutorials coverage lint test type-check validate +.PHONY: docs docsWithTutorials coverage lint format test type-check validate diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 3b3b0dca3..000000000 --- a/docs/Makefile +++ /dev/null @@ -1,11 +0,0 @@ -.PHONY: docs - - -# Put it first so that "make" without argument is like "make help". -docs: - cd reference && pip install -r requirements.txt && make html - cd tutorials && pip install -r requirements.txt && make html - mkdir -p _build/html - cp -r reference/_build/html _build/html/reference - cp -r tutorials/_build/html _build/html/tutorials - diff --git a/docs/reference/_templates/numpydoc_docstring.rst b/docs/_templates/numpydoc_docstring.rst similarity index 100% rename from docs/reference/_templates/numpydoc_docstring.rst rename to docs/_templates/numpydoc_docstring.rst diff --git a/docs/reference/conf.py b/docs/conf.py similarity index 89% rename from docs/reference/conf.py rename to docs/conf.py index 112aecdbf..9272f1108 100644 --- a/docs/reference/conf.py +++ b/docs/conf.py @@ -15,6 +15,7 @@ import os import sys +sys.path.append(os.path.abspath('./sphinx-ext/')) sys.path.insert(0, os.path.abspath("..")) # -- Project information ----------------------------------------------------- @@ -24,17 +25,17 @@ authors = "Microsoft, NeuroData" realpath = os.path.realpath(__file__) -dir_realpath = os.path.dirname(os.path.dirname(realpath)) +dir_realpath = os.path.dirname(realpath) sys.path.append(dir_realpath) import graspologic -release = graspologic.__version__ -try: - split = release.split(".") - version = f"{split[0]}.{split[1]}" -except BaseException: - version = release +version = graspologic.__version__ +# Append "dev" and the github run to the version when on the dev branch +if os.environ.get("GITHUB_REF", "") == "refs/heads/dev": + version = f"{version}dev{os.environ['GITHUB_RUN_ID']}" + +release = version # -- Extension configuration ------------------------------------------------- extensions = [ @@ -43,10 +44,12 @@ "sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx.ext.mathjax", + "nbsphinx", "numpydoc", "sphinx.ext.ifconfig", "sphinx.ext.githubpages", "sphinx.ext.intersphinx", + "toctree_filter", ] # -- numpydoc @@ -80,9 +83,14 @@ # -- sphinx options ---------------------------------------------------------- source_suffix = ".rst" -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints", "tutorials"] +toc_filter_exclude = ['tutorials/index'] master_doc = "index" source_encoding = "utf-8" +if tags.has("build_tutorials"): + # Tutorials are excluded by default. Remove the exclusion since we want to build the tutorials + exclude_patterns.remove("tutorials") + toc_filter_exclude = [] # -- Options for HTML output ------------------------------------------------- # Add any paths that contain templates here, relative to this directory. diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 000000000..361e29517 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,75 @@ +.. -*- coding: utf-8 -*- + +.. _contents: + +Overview of graspologic_ +======================== + +.. _graspologic: https://graspologic.readthedocs.org/en/latest + +graspologic is a Python package for analysis of graphs, or networks. + +Motivation +---------- + +A graph, or network, provides a mathematically intuitive representation of data with +some sort of relationship between items. For example, a social network can be +represented as a graph by considering all participants in the social network as nodes, +with connections representing whether each pair of individuals in the network are friends +with one another. Naively, one might apply traditional statistical techniques to a graph, +which neglects the spatial arrangement of nodes within the network and is not utilizing +all of the information present in the graph. In this package, we provide utilities and +algorithms designed for the processing and analysis of graphs with specialized graph +statistical algorithms. + +Python +------ + +Python is a powerful programming language that allows concise expressions of network +algorithms. Python has a vibrant and growing ecosystem of packages that +graspologic uses to provide more features such as numerical linear algebra and +plotting. In order to make the most out of graspologic you will want to know how +to write basic programs in Python. Among the many guides to Python, we +recommend the `Python documentation `_. + +Free software +------------- + +graspologic is free software; you can redistribute it and/or modify it under the +terms of the :doc:`MIT ` license. We welcome contributions. +Join us on `GitHub `_. + +History +------- + +``graspologic`` first released in September 2020, but it got its start as a pair of Python libraries +written by Johns Hopkins University's NeuroData lab and Microsoft Research's Project Essex. +Both teams worked on many of the same algorithms, shared research, findings, and generally duplicated a lot of effort. + +``GraSPy`` - the NeuroData library - and ``topologic`` - the Microsoft Research library began merging in September of 2020, but both got their starts far earlier, with GraSPy starting in September 2018 and topologic starting just a short time later, on October 2nd, 2018. + +GraSPy was originally designed and written by Jaewon Chung, Benjamin Pedigo, and Eric Bridgeford. + +Topologic was originally designed and written by Patrick Bourke, Jonathan McLean, Nick Caurvina, and Dwayne Pryce. + +.. toctree-filt:: + :maxdepth: 1 + :caption: Documentation + + license + reference/index + tutorials/index + +.. toctree:: + :maxdepth: 1 + :caption: Useful Links + + graspologic @ GitHub + graspologic @ PyPI + Issue Tracker + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/reference/license.rst b/docs/license.rst similarity index 100% rename from docs/reference/license.rst rename to docs/license.rst diff --git a/docs/reference/Makefile b/docs/reference/Makefile deleted file mode 100644 index 6f3ad867d..000000000 --- a/docs/reference/Makefile +++ /dev/null @@ -1,25 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = . -BUILDDIR = _build/html - -.PHONY: help clean html - - -# Put it first so that "make" without argument is like "make help". -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - -clean: - -rm -rf _build/* - -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) "$(SOURCEDIR)" "$(BUILDDIR)" - @echo - @echo "Build finished. The HTML pages are in build/html." - diff --git a/docs/reference/index.rst b/docs/reference/index.rst index abce77980..5e8b34a07 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -1,85 +1,12 @@ .. -*- coding: utf-8 -*- -.. _contents: - -Overview of graspologic_ -======================== - -.. _graspologic: https://graspologic.readthedocs.org/en/latest - -graspologic is a Python package for analysis of graphs, or networks. - -Motivation ----------- - -A graph, or network, provides a mathematically intuitive representation of data with -some sort of relationship between items. For example, a social network can be -represented as a graph by considering all participants in the social network as nodes, -with connections representing whether each pair of individuals in the network are friends -with one another. Naively, one might apply traditional statistical techniques to a graph, -which neglects the spatial arrangement of nodes within the network and is not utilizing -all of the information present in the graph. In this package, we provide utilities and -algorithms designed for the processing and analysis of graphs with specialized graph -statistical algorithms. - -Python ------- - -Python is a powerful programming language that allows concise expressions of network -algorithms. Python has a vibrant and growing ecosystem of packages that -graspologic uses to provide more features such as numerical linear algebra and -plotting. In order to make the most out of graspologic you will want to know how -to write basic programs in Python. Among the many guides to Python, we -recommend the `Python documentation `_. - -Free software -------------- - -graspologic is free software; you can redistribute it and/or modify it under the -terms of the :doc:`MIT ` license. We welcome contributions. -Join us on `GitHub `_. - -History -------- - -``graspologic`` first released in September 2020, but it got its start as a pair of Python libraries -written by Johns Hopkins University's NeuroData lab and Microsoft Research's Project Essex. -Both teams worked on many of the same algorithms, shared research, findings, and generally duplicated a lot of effort. - -``GraSPy`` - the NeuroData library - and ``topologic`` - the Microsoft Research library began merging in September of 2020, but both got their starts far earlier, with GraSPy starting in September 2018 and topologic starting just a short time later, on October 2nd, 2018. - -GraSPy was originally designed and written by Jaewon Chung, Benjamin Pedigo, and Eric Bridgeford. - -Topologic was originally designed and written by Patrick Bourke, Jonathan McLean, Nick Caurvina, and Dwayne Pryce. - -Documentation -============= - -graspologic is a graph statistics package in python. - .. toctree:: :maxdepth: 1 install - reference/index - Tutorials cli contributing in-the-wild release - license - -.. toctree:: - :maxdepth: 1 - :caption: Useful Links - - graspologic @ GitHub - graspologic @ PyPI - Issue Tracker - - -Indices and tables -================== + reference/index -* :ref:`genindex` -* :ref:`search` diff --git a/docs/reference/requirements.txt b/docs/reference/requirements.txt deleted file mode 100644 index ce3c0df17..000000000 --- a/docs/reference/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -sphinx==1.8.5 -sphinx_rtd_theme>=0.4.2 -sphinxcontrib-rawfiles -nbsphinx>=0.4.2 -ipython>=7.4.0 -ipykernel>=5.1.0 -numpydoc==0.9.2 - diff --git a/docs/sphinx-ext/toctree_filter.py b/docs/sphinx-ext/toctree_filter.py new file mode 100644 index 000000000..e916fe4f3 --- /dev/null +++ b/docs/sphinx-ext/toctree_filter.py @@ -0,0 +1,44 @@ +# Copied and modified from https://stackoverflow.com/questions/15001888/conditional-toctree-in-sphinx + +import re +from sphinx.directives.other import TocTree + + +def setup(app): + app.add_config_value('toc_filter_exclude', [], 'html') + app.add_directive('toctree-filt', TocTreeFilt) + return {'version': '1.0.0'} + +class TocTreeFilt(TocTree): + """ + Directive to notify Sphinx about the hierarchical structure of the docs, + and to include a table-of-contents like tree in the current document. This + version filters the entries based on a list of prefixes. We simply filter + the content of the directive and call the super's version of run. The + list of exclusions is stored in the **toc_filter_exclusion** list. Any + table of content entry prefixed by one of these strings will be excluded. + If `toc_filter_exclusion=['secret','draft']` then all toc entries of the + form `:secret:ultra-api` or `:draft:new-features` will be excuded from + the final table of contents. Entries without a prefix are always included. + """ + hasPat = re.compile('\s*(.*)$') + + # Remove any entries in the content that we dont want and strip + # out any filter prefixes that we want but obviously don't want the + # prefix to mess up the file name. + def filter_entries(self, entries): + excl = self.state.document.settings.env.config.toc_filter_exclude + filtered = [] + for e in entries: + m = self.hasPat.match(e) + if m != None: + if not m.groups()[0] in excl: + filtered.append(m.groups()[0]) + else: + filtered.append(e) + return filtered + + def run(self): + # Remove all TOC entries that should not be on display + self.content = self.filter_entries(self.content) + return super().run() diff --git a/docs/tutorials/Makefile b/docs/tutorials/Makefile deleted file mode 100644 index 6f3ad867d..000000000 --- a/docs/tutorials/Makefile +++ /dev/null @@ -1,25 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = . -BUILDDIR = _build/html - -.PHONY: help clean html - - -# Put it first so that "make" without argument is like "make help". -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - -clean: - -rm -rf _build/* - -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) "$(SOURCEDIR)" "$(BUILDDIR)" - @echo - @echo "Build finished. The HTML pages are in build/html." - diff --git a/docs/tutorials/_templates/numpydoc_docstring.rst b/docs/tutorials/_templates/numpydoc_docstring.rst deleted file mode 100644 index db4cdfabc..000000000 --- a/docs/tutorials/_templates/numpydoc_docstring.rst +++ /dev/null @@ -1,16 +0,0 @@ -{{index}} -{{summary}} -{{extended_summary}} -{{parameters}} -{{returns}} -{{yields}} -{{other_parameters}} -{{attributes}} -{{raises}} -{{warns}} -{{warnings}} -{{see_also}} -{{notes}} -{{references}} -{{examples}} -{{methods}} \ No newline at end of file diff --git a/docs/tutorials/conf.py b/docs/tutorials/conf.py deleted file mode 100644 index 09fcc96d4..000000000 --- a/docs/tutorials/conf.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file does only contain a selection of the most common options. For a -# full list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys - -sys.path.insert(0, os.path.abspath("..")) - -# -- Project information ----------------------------------------------------- - -project = "graspologic tutorials" -copyright = "2020" -authors = "Microsoft, NeuroData" - -realpath = os.path.realpath(__file__) -dir_realpath = os.path.dirname(os.path.dirname(realpath)) -sys.path.append(dir_realpath) - -import graspologic - -release = graspologic.__version__ -try: - split = release.split(".") - version = f"{split[0]}.{split[1]}" -except BaseException: - version = release - -# -- Extension configuration ------------------------------------------------- -extensions = [ - "sphinx.ext.mathjax", - "sphinx.ext.ifconfig", - "nbsphinx", - "sphinx.ext.intersphinx", -] - - -# -- numpydoc -# Below is needed to prevent errors -numpydoc_show_class_members = False -numpydoc_attributes_as_param_list = True -numpydoc_use_blockquotes = True - -# -- sphinx.ext.intersphinx -intersphinx_mapping = { - "anytree": ("https://anytree.readthedocs.io/en/latest/", None), - "hyppo": ("https://hyppo.neurodata.io", None), - "matplotlib": ("https://matplotlib.org", None), - "networkx": ("https://networkx.org/documentation/stable", None), - "numpy": ("https://numpy.org/doc/stable", None), - "python": ("https://docs.python.org/3", None), - "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), - "seaborn": ("https://seaborn.pydata.org", None), - "sklearn": ("https://scikit-learn.org/dev", None), -} - -# -- sphinx options ---------------------------------------------------------- -source_suffix = ".rst" -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] -master_doc = "index" -source_encoding = "utf-8" - -# -- Options for HTML output ------------------------------------------------- -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] -html_static_path = [] -modindex_common_prefix = ["graspologic."] - -pygments_style = "sphinx" -smartquotes = False - -# Use RTD Theme -import sphinx_rtd_theme - -html_theme = "sphinx_rtd_theme" -html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] -html_theme_options = { - #'includehidden': False, - "navigation_depth": 2, - "collapse_navigation": False, - "navigation_depth": 3, -} - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = "graspologicdoc" - -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, "graspologic.tex", "graspologic Documentation", authors, "manual") -] - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [(master_doc, "graspologic", "graspologic Documentation", [authors], 1)] - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - master_doc, - "graspologic", - "graspologic Documentation", - authors, - "graspologic", - "One line description of project.", - "Miscellaneous", - ) -] - -# -- Options for Epub output ------------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project - -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# -# epub_identifier = '' - -# A unique identification for the text. -# -# epub_uid = '' - -# A list of files that should not be packed into the epub file. -epub_exclude_files = ["search.html"] diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index df9daae99..131f3a269 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -4,8 +4,6 @@ Tutorials .. toctree:: :hidden: - Reference Documentation - .. _models_tutorials: Models diff --git a/docs/tutorials/requirements.txt b/docs/tutorials/requirements.txt deleted file mode 100644 index ce3c0df17..000000000 --- a/docs/tutorials/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -sphinx==1.8.5 -sphinx_rtd_theme>=0.4.2 -sphinxcontrib-rawfiles -nbsphinx>=0.4.2 -ipython>=7.4.0 -ipykernel>=5.1.0 -numpydoc==0.9.2 - diff --git a/setup.cfg b/setup.cfg index 30d2f8a96..c5a348179 100644 --- a/setup.cfg +++ b/setup.cfg @@ -49,15 +49,15 @@ dev = black ipykernel>=5.1.0 ipython>=7.4.0 - isort - mypy - nbsphinx - numpydoc - pandoc - pytest - pytest-cov - sphinx - sphinxcontrib-rawfiles - sphinx-rtd-theme - testfixtures + isort>=5.9.3 + mypy>=0.910 + nbsphinx>=0.8.7 + numpydoc>=1.1.0 + pandoc>=1.1.0 + pytest>=6.2.5 + pytest-cov>=3.0.0 + sphinx>=4.2.0 + sphinxcontrib-rawfiles>=0.1.1 + sphinx-rtd-theme>=1.0.0 + testfixtures>=6.18.3 From 810c1be408aa405046b4571801045f520df1bc0d Mon Sep 17 00:00:00 2001 From: Jon McLean <4429525+jonmclean@users.noreply.github.com> Date: Thu, 4 Nov 2021 15:46:54 -0700 Subject: [PATCH 128/140] Also publish documentation to /latest on push to main as well as version number Co-authored-by: Jon McLean --- .github/workflows/build.yml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 3f9cca038..b7fc15824 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -174,6 +174,13 @@ jobs: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: docs/documentation-site destination_dir: ${{ env.GRASPOLOGIC_VERSION }} + - name: Publish latest reference docs (main branch) + uses: peaceiris/actions-gh-pages@v3 + if: github.ref=='refs/heads/main' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/documentation-site + destination_dir: latest - name: Create tag and GitHub release uses: ncipollo/release-action@v1 if: github.ref=='refs/heads/main' From a3da9a14ce78e4af9553d493903522c5c2476829 Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Fri, 5 Nov 2021 09:02:33 -0700 Subject: [PATCH 129/140] Disabling non deterministic tests in test_n2v See issue #880 --- tests/embed/test_n2v.py | 94 +++++++++++++++++++++-------------------- 1 file changed, 48 insertions(+), 46 deletions(-) diff --git a/tests/embed/test_n2v.py b/tests/embed/test_n2v.py index 545e1203d..a876cb8d5 100644 --- a/tests/embed/test_n2v.py +++ b/tests/embed/test_n2v.py @@ -12,68 +12,70 @@ class Node2VecEmbedTest(unittest.TestCase): - def test_n2v_returns_same_labels_with_different_nodeid_types(self): - probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) - number_of_nodes_per_community = [20, 20] + # these are not deterministic tests and can't be trusted to work consistently. + # See Issue #880 +# def test_n2v_returns_same_labels_with_different_nodeid_types(self): +# probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) +# number_of_nodes_per_community = [20, 20] - sbm_sample = gc.simulations.sbm( - number_of_nodes_per_community, probability_matrix - ) - sbm_graph = nx.from_numpy_array(sbm_sample) +# sbm_sample = gc.simulations.sbm( +# number_of_nodes_per_community, probability_matrix +# ) +# sbm_graph = nx.from_numpy_array(sbm_sample) - graph = nx.Graph() - graph_as_strings = nx.Graph() - for s, t in sbm_graph.edges(): - graph.add_edge(s, t, weight=1) - graph_as_strings.add_edge(str(s), str(t), weight=1) +# graph = nx.Graph() +# graph_as_strings = nx.Graph() +# for s, t in sbm_graph.edges(): +# graph.add_edge(s, t, weight=1) +# graph_as_strings.add_edge(str(s), str(t), weight=1) - original_embedding = gc.embed.node2vec_embed(graph, random_seed=1) - string_embedding = gc.embed.node2vec_embed(graph_as_strings, random_seed=1) +# original_embedding = gc.embed.node2vec_embed(graph, random_seed=1) +# string_embedding = gc.embed.node2vec_embed(graph_as_strings, random_seed=1) - k = KMeans(n_clusters=2) - original_labels = k.fit_predict(original_embedding[0]) - string_labels = k.fit_predict(string_embedding[0]) +# k = KMeans(n_clusters=2) +# original_labels = k.fit_predict(original_embedding[0]) +# string_labels = k.fit_predict(string_embedding[0]) - expected_labels = np.zeros(40, dtype=int) - expected_labels[20:] = 1 +# expected_labels = np.zeros(40, dtype=int) +# expected_labels[20:] = 1 - original_ari = adjusted_rand_score(original_labels, expected_labels) - string_ari = adjusted_rand_score(string_labels, expected_labels) +# original_ari = adjusted_rand_score(original_labels, expected_labels) +# string_ari = adjusted_rand_score(string_labels, expected_labels) - self.assertEqual(original_ari, string_ari) +# self.assertEqual(original_ari, string_ari) - def test_n2v_directed_undirected_returns_same_clustering(self): - probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) - number_of_nodes_per_community = [20, 20] +# def test_n2v_directed_undirected_returns_same_clustering(self): +# probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) +# number_of_nodes_per_community = [20, 20] - sbm_sample = gc.simulations.sbm( - number_of_nodes_per_community, probability_matrix - ) - sbm_graph = nx.from_numpy_array(sbm_sample) +# sbm_sample = gc.simulations.sbm( +# number_of_nodes_per_community, probability_matrix +# ) +# sbm_graph = nx.from_numpy_array(sbm_sample) - graph = nx.Graph() - graph_directed = nx.DiGraph() - for s, t in sbm_graph.edges(): - graph.add_edge(s, t, weight=1) +# graph = nx.Graph() +# graph_directed = nx.DiGraph() +# for s, t in sbm_graph.edges(): +# graph.add_edge(s, t, weight=1) - graph_directed.add_edge(s, t, weight=1) - graph_directed.add_edge(t, s, weight=1) +# graph_directed.add_edge(s, t, weight=1) +# graph_directed.add_edge(t, s, weight=1) - undirected_embedding = gc.embed.node2vec_embed(graph, random_seed=1) - directed_embedding = gc.embed.node2vec_embed(graph_directed, random_seed=1) +# undirected_embedding = gc.embed.node2vec_embed(graph, random_seed=1) +# directed_embedding = gc.embed.node2vec_embed(graph_directed, random_seed=1) - k = KMeans(n_clusters=2, random_state=1234) - undirected_labels = k.fit_predict(undirected_embedding[0]) - k = KMeans(n_clusters=2, random_state=1234) - directed_labels = k.fit_predict(directed_embedding[0]) +# k = KMeans(n_clusters=2, random_state=1234) +# undirected_labels = k.fit_predict(undirected_embedding[0]) +# k = KMeans(n_clusters=2, random_state=1234) +# directed_labels = k.fit_predict(directed_embedding[0]) - expected_labels = np.zeros(40, dtype=int) - expected_labels[20:] = 1 +# expected_labels = np.zeros(40, dtype=int) +# expected_labels[20:] = 1 - undirected_ari = adjusted_rand_score(undirected_labels, expected_labels) - directed_ari = adjusted_rand_score(directed_labels, expected_labels) +# undirected_ari = adjusted_rand_score(undirected_labels, expected_labels) +# directed_ari = adjusted_rand_score(directed_labels, expected_labels) - self.assertEqual(undirected_ari, directed_ari) +# self.assertEqual(undirected_ari, directed_ari) def test_node2vec_embed(self): g = nx.florentine_families_graph() From ef5a9131c360311f16f6e5859d75af4a7f54d6cd Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Fri, 5 Nov 2021 09:27:40 -0700 Subject: [PATCH 130/140] Fixing formatting from trying to do this using the github editor --- tests/embed/test_n2v.py | 92 ++++++++++++++++++++--------------------- 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/tests/embed/test_n2v.py b/tests/embed/test_n2v.py index a876cb8d5..753ae3779 100644 --- a/tests/embed/test_n2v.py +++ b/tests/embed/test_n2v.py @@ -14,68 +14,68 @@ class Node2VecEmbedTest(unittest.TestCase): # these are not deterministic tests and can't be trusted to work consistently. # See Issue #880 -# def test_n2v_returns_same_labels_with_different_nodeid_types(self): -# probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) -# number_of_nodes_per_community = [20, 20] + # def test_n2v_returns_same_labels_with_different_nodeid_types(self): + # probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) + # number_of_nodes_per_community = [20, 20] -# sbm_sample = gc.simulations.sbm( -# number_of_nodes_per_community, probability_matrix -# ) -# sbm_graph = nx.from_numpy_array(sbm_sample) + # sbm_sample = gc.simulations.sbm( + # number_of_nodes_per_community, probability_matrix + # ) + # sbm_graph = nx.from_numpy_array(sbm_sample) -# graph = nx.Graph() -# graph_as_strings = nx.Graph() -# for s, t in sbm_graph.edges(): -# graph.add_edge(s, t, weight=1) -# graph_as_strings.add_edge(str(s), str(t), weight=1) + # graph = nx.Graph() + # graph_as_strings = nx.Graph() + # for s, t in sbm_graph.edges(): + # graph.add_edge(s, t, weight=1) + # graph_as_strings.add_edge(str(s), str(t), weight=1) -# original_embedding = gc.embed.node2vec_embed(graph, random_seed=1) -# string_embedding = gc.embed.node2vec_embed(graph_as_strings, random_seed=1) + # original_embedding = gc.embed.node2vec_embed(graph, random_seed=1) + # string_embedding = gc.embed.node2vec_embed(graph_as_strings, random_seed=1) -# k = KMeans(n_clusters=2) -# original_labels = k.fit_predict(original_embedding[0]) -# string_labels = k.fit_predict(string_embedding[0]) + # k = KMeans(n_clusters=2) + # original_labels = k.fit_predict(original_embedding[0]) + # string_labels = k.fit_predict(string_embedding[0]) -# expected_labels = np.zeros(40, dtype=int) -# expected_labels[20:] = 1 + # expected_labels = np.zeros(40, dtype=int) + # expected_labels[20:] = 1 -# original_ari = adjusted_rand_score(original_labels, expected_labels) -# string_ari = adjusted_rand_score(string_labels, expected_labels) + # original_ari = adjusted_rand_score(original_labels, expected_labels) + # string_ari = adjusted_rand_score(string_labels, expected_labels) -# self.assertEqual(original_ari, string_ari) + # self.assertEqual(original_ari, string_ari) -# def test_n2v_directed_undirected_returns_same_clustering(self): -# probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) -# number_of_nodes_per_community = [20, 20] + # def test_n2v_directed_undirected_returns_same_clustering(self): + # probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) + # number_of_nodes_per_community = [20, 20] -# sbm_sample = gc.simulations.sbm( -# number_of_nodes_per_community, probability_matrix -# ) -# sbm_graph = nx.from_numpy_array(sbm_sample) + # sbm_sample = gc.simulations.sbm( + # number_of_nodes_per_community, probability_matrix + # ) + # sbm_graph = nx.from_numpy_array(sbm_sample) -# graph = nx.Graph() -# graph_directed = nx.DiGraph() -# for s, t in sbm_graph.edges(): -# graph.add_edge(s, t, weight=1) + # graph = nx.Graph() + # graph_directed = nx.DiGraph() + # for s, t in sbm_graph.edges(): + # graph.add_edge(s, t, weight=1) -# graph_directed.add_edge(s, t, weight=1) -# graph_directed.add_edge(t, s, weight=1) + # graph_directed.add_edge(s, t, weight=1) + # graph_directed.add_edge(t, s, weight=1) -# undirected_embedding = gc.embed.node2vec_embed(graph, random_seed=1) -# directed_embedding = gc.embed.node2vec_embed(graph_directed, random_seed=1) + # undirected_embedding = gc.embed.node2vec_embed(graph, random_seed=1) + # directed_embedding = gc.embed.node2vec_embed(graph_directed, random_seed=1) -# k = KMeans(n_clusters=2, random_state=1234) -# undirected_labels = k.fit_predict(undirected_embedding[0]) -# k = KMeans(n_clusters=2, random_state=1234) -# directed_labels = k.fit_predict(directed_embedding[0]) + # k = KMeans(n_clusters=2, random_state=1234) + # undirected_labels = k.fit_predict(undirected_embedding[0]) + # k = KMeans(n_clusters=2, random_state=1234) + # directed_labels = k.fit_predict(directed_embedding[0]) -# expected_labels = np.zeros(40, dtype=int) -# expected_labels[20:] = 1 + # expected_labels = np.zeros(40, dtype=int) + # expected_labels[20:] = 1 -# undirected_ari = adjusted_rand_score(undirected_labels, expected_labels) -# directed_ari = adjusted_rand_score(directed_labels, expected_labels) + # undirected_ari = adjusted_rand_score(undirected_labels, expected_labels) + # directed_ari = adjusted_rand_score(directed_labels, expected_labels) -# self.assertEqual(undirected_ari, directed_ari) + # self.assertEqual(undirected_ari, directed_ari) def test_node2vec_embed(self): g = nx.florentine_families_graph() From 8c613205d722325a686260df33de86b632838315 Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Wed, 9 Feb 2022 14:32:05 -0800 Subject: [PATCH 131/140] Hyppo 0.2.2 (and possibly earlier) use a private scipy function that has been moved in 1.8.0+. Until hyppo fixes this we need to restrict scipy to an earlier version. --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 7dd8f75ec..be445964d 100644 --- a/setup.cfg +++ b/setup.cfg @@ -39,7 +39,7 @@ install_requires = POT>=0.7.0 seaborn>= 0.11.0 scikit-learn>=0.22.0 - scipy>=1.4.0 + scipy>=1.4.0,<1.8.0 # restricted due to hyppo 0.2.2 bug umap-learn>=0.4.6 [options.packages.find] From d78380469bfa4877b99e1f666e668ee29ad69ccd Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Wed, 9 Feb 2022 15:35:41 -0800 Subject: [PATCH 132/140] Someone, somewhere, thinks that dropping Python 3.6 support is only worth a patch increment in semver, and I just don't know where to start with that --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index be445964d..e01a359b0 100644 --- a/setup.cfg +++ b/setup.cfg @@ -36,7 +36,7 @@ install_requires = matplotlib>=3.0.0,!=3.3.* networkx>=2.1 numpy>=1.8.1 - POT>=0.7.0 + POT>=0.7.0,<0.8.1 # 0.8.1 drops python 3.6 support seaborn>= 0.11.0 scikit-learn>=0.22.0 scipy>=1.4.0,<1.8.0 # restricted due to hyppo 0.2.2 bug From fc0ef9c1d05fec439f25a1f95ada963a2db8e0e4 Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Thu, 10 Feb 2022 11:03:33 -0800 Subject: [PATCH 133/140] This hyppo fix is getting out of control. We have now officially deprecated Python 3.6, no longer need to limit POT, require the latest beartype in all the land just to avoid deprecation warnings if we use the old Exceptions instead of the Violations, and I did a lot of packaging junk to ensure it won't even try to install on Python 3.6 now --- .github/workflows/build.yml | 2 +- README.md | 11 +++---- docs/reference/release.rst | 2 ++ graspologic/partition/leiden.py | 4 +-- .../embed/adjacency_spectral_embedding.py | 2 +- graspologic/pipeline/embed/embeddings.py | 2 +- .../embed/laplacian_spectral_embedding.py | 2 +- .../pipeline/embed/omnibus_embedding.py | 2 +- pyproject.toml | 2 +- setup.cfg | 6 ++-- tests/partition/test_leiden.py | 30 +++++++++---------- .../test_adjacency_spectral_embedding.py | 16 +++++----- tests/pipeline/embed/test_embeddings.py | 8 ++--- .../test_laplacian_spectral_embedding.py | 22 +++++++------- .../pipeline/embed/test_omnibus_embedding.py | 16 +++++----- tests/test_plot.py | 10 +++---- 16 files changed, 70 insertions(+), 67 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 454c6f6b1..0ad74de13 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -89,7 +89,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python_version: [3.6, 3.7, 3.8, 3.9] + python_version: [3.7, 3.8, 3.9, 3.10] fail-fast: false steps: - uses: actions/checkout@v2 diff --git a/README.md b/README.md index d1ea38347..3284d1541 100644 --- a/README.md +++ b/README.md @@ -35,10 +35,11 @@ Please visit the [tutorial section](https://microsoft.github.io/graspologic/late - macOS x64 - Windows 10 x64 -And across the following versions of Python: -- 3.6 (x64) -- 3.7 (x64) -- 3.8 (x64) +And across the following **x86_64** versions of Python: +- 3.7 +- 3.8 +- 3.9 +- 3.10 If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users @@ -74,4 +75,4 @@ We appreciate detailed bug reports and feature requests (though we appreciate pu # Citing `graspologic` If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) -> Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. +> Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. \ No newline at end of file diff --git a/docs/reference/release.rst b/docs/reference/release.rst index 275c78a8b..bd19f92db 100644 --- a/docs/reference/release.rst +++ b/docs/reference/release.rst @@ -5,6 +5,8 @@ Release Log graspologic 1.0.0 ----------------- +- Removed Python 3.6 support +- Officially added Python 3.9 and 3.10 support - Fixed a type in an error message `#904 ` - Added support for arbitrarily indexed node data for networkplot diff --git a/graspologic/partition/leiden.py b/graspologic/partition/leiden.py index 579dc3e98..7b2fac7e4 100644 --- a/graspologic/partition/leiden.py +++ b/graspologic/partition/leiden.py @@ -274,7 +274,7 @@ def leiden( ------ ValueError TypeError - BeartypeCallHintPepParamException + BeartypeCallHintParamViolation See Also -------- @@ -539,7 +539,7 @@ def hierarchical_leiden( ------ ValueError TypeError - BeartypeCallHintPepParamException + BeartypeCallHintParamViolation See Also -------- diff --git a/graspologic/pipeline/embed/adjacency_spectral_embedding.py b/graspologic/pipeline/embed/adjacency_spectral_embedding.py index 65de295dd..2422014f7 100644 --- a/graspologic/pipeline/embed/adjacency_spectral_embedding.py +++ b/graspologic/pipeline/embed/adjacency_spectral_embedding.py @@ -106,7 +106,7 @@ def adjacency_spectral_embedding( Raises ------ - beartype.roar.BeartypeCallHintPepParamException if parameters do not match type hints + beartype.roar.BeartypeCallHintParamViolation if parameters do not match type hints ValueError if values are not within appropriate ranges or allowed values See Also diff --git a/graspologic/pipeline/embed/embeddings.py b/graspologic/pipeline/embed/embeddings.py index df44592fa..43629e2c4 100644 --- a/graspologic/pipeline/embed/embeddings.py +++ b/graspologic/pipeline/embed/embeddings.py @@ -56,7 +56,7 @@ def __init__(self, labels: np.ndarray, embeddings: np.ndarray): Raises ------ - beartype.roar.BeartypeCallHintPepParamException if the types are invalid + beartype.roar.BeartypeCallHintParamViolation if the types are invalid ValueError if the row count of labels does not equal the row count of embeddings """ if labels.shape[0] != embeddings.shape[0]: diff --git a/graspologic/pipeline/embed/laplacian_spectral_embedding.py b/graspologic/pipeline/embed/laplacian_spectral_embedding.py index ca631fe4c..160c84d58 100644 --- a/graspologic/pipeline/embed/laplacian_spectral_embedding.py +++ b/graspologic/pipeline/embed/laplacian_spectral_embedding.py @@ -109,7 +109,7 @@ def laplacian_spectral_embedding( Raises ------ - beartype.roar.BeartypeCallHintPepParamException if parameters do not match type hints + beartype.roar.BeartypeCallHintParamViolation if parameters do not match type hints ValueError if values are not within appropriate ranges or allowed values See Also diff --git a/graspologic/pipeline/embed/omnibus_embedding.py b/graspologic/pipeline/embed/omnibus_embedding.py index 202bc55b5..c145ef8b8 100644 --- a/graspologic/pipeline/embed/omnibus_embedding.py +++ b/graspologic/pipeline/embed/omnibus_embedding.py @@ -107,7 +107,7 @@ def omnibus_embedding_pairwise( Raises ------ - beartype.roar.BeartypeCallHintPepParamException if parameters do not match type hints + beartype.roar.BeartypeCallHintParamViolation if parameters do not match type hints ValueError if values are not within appropriate ranges or allowed values See Also diff --git a/pyproject.toml b/pyproject.toml index 092a7288c..69b4656bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools"] +requires = ["setuptools >= 24.2.0"] build-backend = "setuptools.build_meta" [tool.isort] diff --git a/setup.cfg b/setup.cfg index e01a359b0..3446b10c8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,7 +17,6 @@ classifiers = Topic :: Scientific/Engineering :: Mathematics License :: OSI Approved :: MIT License Programming Language :: Python :: 3 - Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 @@ -26,9 +25,10 @@ classifiers = [options] packages = find: include_package_data = True +python_requires = >=3.7, <3.11 install_requires = anytree>=2.8.0 - beartype>=0.9.0 + beartype>=0.10.0 gensim>=4.0.0 graspologic-native>=1.1.1 hyppo>=0.2.2 @@ -36,7 +36,7 @@ install_requires = matplotlib>=3.0.0,!=3.3.* networkx>=2.1 numpy>=1.8.1 - POT>=0.7.0,<0.8.1 # 0.8.1 drops python 3.6 support + POT>=0.7.0 seaborn>= 0.11.0 scikit-learn>=0.22.0 scipy>=1.4.0,<1.8.0 # restricted due to hyppo 0.2.2 bug diff --git a/tests/partition/test_leiden.py b/tests/partition/test_leiden.py index 23d3b3416..a9ab9c880 100644 --- a/tests/partition/test_leiden.py +++ b/tests/partition/test_leiden.py @@ -8,7 +8,7 @@ import numpy as np import pytest import scipy -from beartype.roar import BeartypeCallHintPepParamException +from beartype.roar import BeartypeCallHintParamViolation from graspologic.partition import ( HierarchicalCluster, @@ -91,7 +91,7 @@ def test_correct_types(self): graph.add_edge("2", "3", weight=4.0) leiden(graph=graph, **good_args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["starting_communities"] = 123 leiden(graph=graph, **args) @@ -100,7 +100,7 @@ def test_correct_types(self): args["starting_communities"] = None leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["extra_forced_iterations"] = 1234.003 leiden(graph=graph, **args) @@ -110,7 +110,7 @@ def test_correct_types(self): args["extra_forced_iterations"] = -4003 leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["resolution"] = "leiden" leiden(graph=graph, **args) @@ -120,7 +120,7 @@ def test_correct_types(self): args["resolution"] = 0 leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["randomness"] = "leiden" leiden(graph=graph, **args) @@ -130,12 +130,12 @@ def test_correct_types(self): args["randomness"] = 0 leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["use_modularity"] = 1234 leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["trials"] = "hotdog" leiden(graph=graph, **args) @@ -151,7 +151,7 @@ def test_correct_types(self): args["random_seed"] = None leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["random_seed"] = "leiden" leiden(graph=graph, **args) @@ -161,23 +161,23 @@ def test_correct_types(self): args["random_seed"] = -1 leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["is_weighted"] = "leiden" leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["weight_default"] = "leiden" leiden(graph=graph, **args) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["check_directed"] = "leiden" leiden(graph=graph, **args) # one extra parameter hierarchical needs - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): args = good_args.copy() args["max_cluster_size"] = "leiden" hierarchical_leiden(graph=graph, **args) @@ -349,7 +349,7 @@ def test_empty_edge_list(self): def test_assert_list_does_not_contain_tuples(self): edges = ["invalid"] - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): _edge_list_to_edge_list( edges=edges, identifier=_IdentityMapper(), @@ -357,7 +357,7 @@ def test_assert_list_does_not_contain_tuples(self): def test_assert_list_contains_misshapen_tuple(self): edges = [(1, 2, 1.0, 1.0)] - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): _edge_list_to_edge_list( edges=edges, identifier=_IdentityMapper(), @@ -365,7 +365,7 @@ def test_assert_list_contains_misshapen_tuple(self): def test_assert_wrong_types_in_tuples(self): edges = [(True, 4, "sandwich")] - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): _edge_list_to_edge_list( edges=edges, identifier=_IdentityMapper(), diff --git a/tests/pipeline/embed/test_adjacency_spectral_embedding.py b/tests/pipeline/embed/test_adjacency_spectral_embedding.py index 675cfc888..0c411df5d 100644 --- a/tests/pipeline/embed/test_adjacency_spectral_embedding.py +++ b/tests/pipeline/embed/test_adjacency_spectral_embedding.py @@ -6,7 +6,7 @@ import networkx as nx import numpy as np import pytest -from beartype.roar import BeartypeCallHintPepParamException +from beartype.roar import BeartypeCallHintParamViolation import graspologic.utils from graspologic.embed import AdjacencySpectralEmbed @@ -44,7 +44,7 @@ def parameters(): def test_argument_validation(self): # graph types - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): adjacency_spectral_embedding( graph=np.array([[1, 2], [2, 1]]), **self.default_parameters ) @@ -56,7 +56,7 @@ def test_argument_validation(self): # dimensions dimensions = [None, 1.3, "1"] for dimension in dimensions: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestAdjacencySpectralEmbedding.parameters() params["dimensions"] = dimension params["graph"] = self.graph @@ -65,19 +65,19 @@ def test_argument_validation(self): # elbow_cuts elbow_cuts = ["3", 1.3] for elbow_cut in elbow_cuts: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestAdjacencySpectralEmbedding.parameters() params["elbow_cut"] = elbow_cut params["graph"] = self.graph adjacency_spectral_embedding(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestAdjacencySpectralEmbedding.parameters() params["svd_solver_algorithm"] = 1 params["graph"] = self.graph adjacency_spectral_embedding(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestAdjacencySpectralEmbedding.parameters() params["svd_solver_algorithm"] = "sandwich" params["graph"] = self.graph @@ -86,7 +86,7 @@ def test_argument_validation(self): # svd_solver_iterations svd_solver_iterations = [None, "5", 5.1] for ssi in svd_solver_iterations: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestAdjacencySpectralEmbedding.parameters() params["svd_solver_iterations"] = ssi params["graph"] = self.graph @@ -95,7 +95,7 @@ def test_argument_validation(self): # svd_seed svd_seeds = ["5", 5.1] for svd_seed in svd_seeds: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestAdjacencySpectralEmbedding.parameters() params["svd_seed"] = svd_seed params["graph"] = self.graph diff --git a/tests/pipeline/embed/test_embeddings.py b/tests/pipeline/embed/test_embeddings.py index 806d8053a..fc0dc793f 100644 --- a/tests/pipeline/embed/test_embeddings.py +++ b/tests/pipeline/embed/test_embeddings.py @@ -4,7 +4,7 @@ import unittest import numpy as np -from beartype.roar import BeartypeCallHintPepParamException +from beartype.roar import BeartypeCallHintParamViolation from graspologic.pipeline.embed import Embeddings @@ -46,11 +46,11 @@ def test_view(self): np.testing.assert_array_equal(expected[key], view[key]) def test_argument_types(self): - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): Embeddings(None, None) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): Embeddings(np.array(["hello"]), None) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): Embeddings(["hello"], [1.0]) with self.assertRaises(ValueError): Embeddings(np.array(["hello"]), np.array([[1.1, 1.2], [2.1, 2.2]])) diff --git a/tests/pipeline/embed/test_laplacian_spectral_embedding.py b/tests/pipeline/embed/test_laplacian_spectral_embedding.py index 6d2201313..4c6de0a78 100644 --- a/tests/pipeline/embed/test_laplacian_spectral_embedding.py +++ b/tests/pipeline/embed/test_laplacian_spectral_embedding.py @@ -7,7 +7,7 @@ import networkx as nx import numpy as np import pytest -from beartype.roar import BeartypeCallHintPepParamException +from beartype.roar import BeartypeCallHintParamViolation import graspologic.utils from graspologic.embed import LaplacianSpectralEmbed @@ -43,7 +43,7 @@ def parameters(graph: Optional[Union[nx.Graph, nx.DiGraph]] = None): def test_argument_validation(self): # graph types - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters() params["graph"] = np.array([[1, 2], [2, 1]]) laplacian_spectral_embedding(**params) @@ -55,7 +55,7 @@ def test_argument_validation(self): # dimensions dimensions = [None, 1.3, "1"] for dimension in dimensions: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["dimensions"] = dimension laplacian_spectral_embedding(**params) @@ -63,17 +63,17 @@ def test_argument_validation(self): # elbow_cuts elbow_cuts = ["3", 1.3] for elbow_cut in elbow_cuts: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["elbow_cut"] = elbow_cut laplacian_spectral_embedding(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["svd_solver_algorithm"] = 1 laplacian_spectral_embedding(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["svd_solver_algorithm"] = "sandwich" laplacian_spectral_embedding(**params) @@ -81,7 +81,7 @@ def test_argument_validation(self): # svd_solver_iterations svd_solver_iterations = [None, "5", 5.1] for ssi in svd_solver_iterations: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["svd_solver_iterations"] = ssi laplacian_spectral_embedding(**params) @@ -89,7 +89,7 @@ def test_argument_validation(self): # svd_seed svd_seeds = ["5", 5.1] for svd_seed in svd_seeds: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["svd_seed"] = svd_seed laplacian_spectral_embedding(**params) @@ -97,17 +97,17 @@ def test_argument_validation(self): # form forms = [0, None] for form in forms: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["form"] = form laplacian_spectral_embedding(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["form"] = "formless" laplacian_spectral_embedding(**params) # regularizer - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = TestLaplacianSpectralEmbedding.parameters(self.graph) params["regularizer"] = "1" laplacian_spectral_embedding(**params) diff --git a/tests/pipeline/embed/test_omnibus_embedding.py b/tests/pipeline/embed/test_omnibus_embedding.py index ed86632e7..06ee7bc95 100644 --- a/tests/pipeline/embed/test_omnibus_embedding.py +++ b/tests/pipeline/embed/test_omnibus_embedding.py @@ -5,7 +5,7 @@ import networkx as nx import numpy as np -from beartype.roar import BeartypeCallHintPepParamException +from beartype.roar import BeartypeCallHintParamViolation from graspologic.pipeline.embed import omnibus_embedding_pairwise @@ -24,7 +24,7 @@ def _default_parameters(self): } def test_argument_validation(self): - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): omnibus_embedding_pairwise(graphs=[1]) with self.assertRaises(ValueError): @@ -35,7 +35,7 @@ def test_argument_validation(self): # dimensions dimensions = [None, 1.3, "1"] for dimension in dimensions: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = self._default_parameters() params["dimensions"] = dimension params["graphs"] = self.graphs @@ -44,19 +44,19 @@ def test_argument_validation(self): # elbow_cuts elbow_cuts = ["3", 1.3] for elbow_cut in elbow_cuts: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = self._default_parameters() params["elbow_cut"] = elbow_cut params["graphs"] = self.graphs omnibus_embedding_pairwise(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = self._default_parameters() params["svd_solver_algorithm"] = 1 params["graphs"] = self.graphs omnibus_embedding_pairwise(**params) - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = self._default_parameters() params["svd_solver_algorithm"] = "sandwich" params["graphs"] = self.graphs @@ -65,7 +65,7 @@ def test_argument_validation(self): # svd_solver_iterations svd_solver_iterations = [None, "5", 5.1] for ssi in svd_solver_iterations: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = self._default_parameters() params["svd_solver_iterations"] = ssi params["graphs"] = self.graphs @@ -74,7 +74,7 @@ def test_argument_validation(self): # svd_seed svd_seeds = ["5", 5.1] for svd_seed in svd_seeds: - with self.assertRaises(BeartypeCallHintPepParamException): + with self.assertRaises(BeartypeCallHintParamViolation): params = self._default_parameters() params["svd_seed"] = svd_seed params["graphs"] = self.graphs diff --git a/tests/test_plot.py b/tests/test_plot.py index 3a1d8c522..b92defa16 100644 --- a/tests/test_plot.py +++ b/tests/test_plot.py @@ -63,7 +63,7 @@ def test_common_inputs(self): figsize = "bad figsize" with self.assertRaises(TypeError): heatmap(X, figsize=figsize) - with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(beartype.roar.BeartypeCallHintParamViolation): with self.assertRaises(TypeError): networkplot(adjacency=X, x=x, y=y, figsize=figsize) @@ -82,7 +82,7 @@ def test_common_inputs(self): gridplot([X], grid_labels, title=title) with self.assertRaises(TypeError): pairplot(X, title=title) - with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(beartype.roar.BeartypeCallHintParamViolation): with self.assertRaises(TypeError): networkplot(adjacency=X, x=x, y=y, title=title) @@ -94,7 +94,7 @@ def test_common_inputs(self): gridplot([X], grid_labels, context=context) with self.assertRaises(TypeError): pairplot(X, context=context) - with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(beartype.roar.BeartypeCallHintParamViolation): with self.assertRaises(TypeError): networkplot(adjacency=X, x=x, y=y, context=context) @@ -117,7 +117,7 @@ def test_common_inputs(self): gridplot([X], grid_labels, font_scale=font_scale) with self.assertRaises(TypeError): pairplot(X, font_scale=font_scale) - with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(beartype.roar.BeartypeCallHintParamViolation): with self.assertRaises(TypeError): networkplot(adjacency=X, x=x, y=y, font_scale=font_scale) @@ -283,7 +283,7 @@ def test_networkplot_inputs(self): X = er_np(15, 0.5) x = np.random.rand(15, 1) y = np.random.rand(15, 1) - with self.assertRaises(beartype.roar.BeartypeCallHintPepParamException): + with self.assertRaises(beartype.roar.BeartypeCallHintParamViolation): with self.assertRaises(TypeError): networkplot(adjacency="test", x=x, y=y) From eec91af4e13abf2f57ebfe57219a7210be2effec Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Thu, 10 Feb 2022 12:18:51 -0800 Subject: [PATCH 134/140] Fixing documentation now that we enabled inherited members by default again (it worked when we used older sphinx, but new sphinx broke since we were using a deprecated manner to signal we wanted to document class membership and inheritence automatically) --- .github/workflows/build.yml | 2 +- docs/conf.py | 11 +++++++++-- docs/reference/reference/cluster.rst | 1 + graspologic/embed/ase.py | 1 - graspologic/embed/base.py | 10 ++++++---- graspologic/inference/latent_distribution_test.py | 2 +- graspologic/inference/latent_position_test.py | 2 +- 7 files changed, 19 insertions(+), 10 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0ad74de13..d0610d7b9 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -89,7 +89,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python_version: [3.7, 3.8, 3.9, 3.10] + python_version: ["3.7", "3.8", "3.9", "3.10"] fail-fast: false steps: - uses: actions/checkout@v2 diff --git a/docs/conf.py b/docs/conf.py index f829586da..5620e0eb5 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -63,8 +63,15 @@ # -- sphinx.ext.autodoc autoclass_content = "both" -autodoc_default_flags = ["members", "inherited-members"] -autodoc_member_order = "bysource" # default is alphabetical +autodoc_default_options = { + 'members': True, + 'inherited-members': True, + 'member-order': 'bysource', + 'special-members': '__init__', + 'undoc-members': True, +} +autodoc_class_signature = "separated" +autodoc_typehints = "description" # -- sphinx.ext.intersphinx intersphinx_mapping = { diff --git a/docs/reference/reference/cluster.rst b/docs/reference/reference/cluster.rst index c166c3b8c..702237a03 100644 --- a/docs/reference/reference/cluster.rst +++ b/docs/reference/reference/cluster.rst @@ -17,3 +17,4 @@ Gaussian Mixture Models Clustering Hierarchical Clustering ---------------------------------- .. autoclass:: DivisiveCluster + :no-inherited-members: diff --git a/graspologic/embed/ase.py b/graspologic/embed/ase.py index 44867377f..3c0cdd7cd 100644 --- a/graspologic/embed/ase.py +++ b/graspologic/embed/ase.py @@ -70,7 +70,6 @@ class AdjacencySpectralEmbed(BaseSpectralEmbed): randomized svd solver for deterministic, albeit pseudo-randomized behavior. - Attributes ---------- n_features_in_: int diff --git a/graspologic/embed/base.py b/graspologic/embed/base.py index cdfd46b46..428a94573 100644 --- a/graspologic/embed/base.py +++ b/graspologic/embed/base.py @@ -247,8 +247,10 @@ def fit_transform( def transform(self, X): # type: ignore """ Obtain latent positions from an adjacency matrix or matrix of out-of-sample - vertices. For more details on transforming out-of-sample vertices, see the - :ref:`tutorials `. For mathematical background, see [2]. + vertices. For more details on transforming out-of-sample vertices, see `Out-of-Sample (OOS) Embedding + `_ + + For mathematical background, see [2]. Parameters ---------- @@ -293,8 +295,8 @@ def transform(self, X): # type: ignore References ---------- .. [1] Sussman, D.L., Tang, M., Fishkind, D.E., Priebe, C.E. "A - Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," - Journal of the American Statistical Association, Vol. 107(499), 2012 + Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," + Journal of the American Statistical Association, Vol. 107(499), 2012 .. [2] Levin, K., Roosta-Khorasani, F., Mahoney, M. W., & Priebe, C. E. (2018). Out-of-sample extension of graph adjacency spectral embedding. PMLR: Proceedings diff --git a/graspologic/inference/latent_distribution_test.py b/graspologic/inference/latent_distribution_test.py index 72aa6e0b4..fb1b34866 100644 --- a/graspologic/inference/latent_distribution_test.py +++ b/graspologic/inference/latent_distribution_test.py @@ -203,7 +203,7 @@ def latent_distribution_test( d must be same. n_components attribute is ignored in this case. Returns - ---------- + ------- stat : float The observed difference between the embedded latent positions of the two input graphs. diff --git a/graspologic/inference/latent_position_test.py b/graspologic/inference/latent_position_test.py index a57fb50b8..1ef67d8a1 100644 --- a/graspologic/inference/latent_position_test.py +++ b/graspologic/inference/latent_position_test.py @@ -88,7 +88,7 @@ def latent_position_test( Supply -1 to use all cores available. Returns - ---------- + ------- stat : float The observed difference between the embedded positions of the two input graphs after an alignment (the type of alignment depends on ``test_case``) From 76b056876983c4971ebd138064f706bc7b225733 Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Thu, 10 Feb 2022 12:26:30 -0800 Subject: [PATCH 135/140] So much for adding 3.10 support - gensim is not working with 3.10, similar to our problems using gensim 3.8.3 on 3.6 last week --- .github/workflows/build.yml | 2 +- README.md | 3 +-- docs/reference/release.rst | 2 +- setup.cfg | 3 +-- 4 files changed, 4 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index d0610d7b9..68371060c 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -89,7 +89,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python_version: ["3.7", "3.8", "3.9", "3.10"] + python_version: ["3.7", "3.8", "3.9"] fail-fast: false steps: - uses: actions/checkout@v2 diff --git a/README.md b/README.md index 3284d1541..9ff814a10 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,6 @@ And across the following **x86_64** versions of Python: - 3.7 - 3.8 - 3.9 -- 3.10 If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users @@ -75,4 +74,4 @@ We appreciate detailed bug reports and feature requests (though we appreciate pu # Citing `graspologic` If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) -> Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. \ No newline at end of file +> Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. diff --git a/docs/reference/release.rst b/docs/reference/release.rst index bd19f92db..2a95947b1 100644 --- a/docs/reference/release.rst +++ b/docs/reference/release.rst @@ -6,7 +6,7 @@ Release Log graspologic 1.0.0 ----------------- - Removed Python 3.6 support -- Officially added Python 3.9 and 3.10 support +- Officially added Python 3.9 support - Fixed a type in an error message `#904 ` - Added support for arbitrarily indexed node data for networkplot diff --git a/setup.cfg b/setup.cfg index 3446b10c8..0752b2ff6 100644 --- a/setup.cfg +++ b/setup.cfg @@ -20,12 +20,11 @@ classifiers = Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 [options] packages = find: include_package_data = True -python_requires = >=3.7, <3.11 +python_requires = >=3.7, <3.10 install_requires = anytree>=2.8.0 beartype>=0.10.0 From 0699238f3bb54c0a61318484e9a198944f8a602c Mon Sep 17 00:00:00 2001 From: bdpedigo Date: Thu, 10 Feb 2022 15:49:34 -0500 Subject: [PATCH 136/140] try removing scipy cap but bumping hyppo --- setup.cfg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.cfg b/setup.cfg index 0752b2ff6..a213a454d 100644 --- a/setup.cfg +++ b/setup.cfg @@ -30,7 +30,7 @@ install_requires = beartype>=0.10.0 gensim>=4.0.0 graspologic-native>=1.1.1 - hyppo>=0.2.2 + hyppo>=0.3.0 joblib>=0.17.0 # Older versions of joblib cause issue #806. Transitive dependency of hyppo. matplotlib>=3.0.0,!=3.3.* networkx>=2.1 @@ -38,7 +38,7 @@ install_requires = POT>=0.7.0 seaborn>= 0.11.0 scikit-learn>=0.22.0 - scipy>=1.4.0,<1.8.0 # restricted due to hyppo 0.2.2 bug + scipy>=1.4.0 umap-learn>=0.4.6 [options.packages.find] From f5a3200560db28ec29b7f53aba7b3ca80ccfeb7a Mon Sep 17 00:00:00 2001 From: bdpedigo Date: Thu, 10 Feb 2022 16:11:20 -0500 Subject: [PATCH 137/140] bump to 0.3.1 --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index a213a454d..aeb5ff201 100644 --- a/setup.cfg +++ b/setup.cfg @@ -30,7 +30,7 @@ install_requires = beartype>=0.10.0 gensim>=4.0.0 graspologic-native>=1.1.1 - hyppo>=0.3.0 + hyppo>=0.3.1 joblib>=0.17.0 # Older versions of joblib cause issue #806. Transitive dependency of hyppo. matplotlib>=3.0.0,!=3.3.* networkx>=2.1 From c6d82686507f0a23a55723b5f9a06e8c4cb9c6b0 Mon Sep 17 00:00:00 2001 From: bdpedigo Date: Thu, 10 Feb 2022 16:51:45 -0500 Subject: [PATCH 138/140] bump hyppo again --- setup.cfg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.cfg b/setup.cfg index aeb5ff201..c060b6922 100644 --- a/setup.cfg +++ b/setup.cfg @@ -27,10 +27,10 @@ include_package_data = True python_requires = >=3.7, <3.10 install_requires = anytree>=2.8.0 - beartype>=0.10.0 + beartype>=0.10.0 gensim>=4.0.0 graspologic-native>=1.1.1 - hyppo>=0.3.1 + hyppo>=0.3.2 # bug with lower versions and scipy>=1.8 joblib>=0.17.0 # Older versions of joblib cause issue #806. Transitive dependency of hyppo. matplotlib>=3.0.0,!=3.3.* networkx>=2.1 From a23a5cb7dca485c36883b3c693de0bc75a0dab24 Mon Sep 17 00:00:00 2001 From: "microsoft-github-policy-service[bot]" <77245923+microsoft-github-policy-service[bot]@users.noreply.github.com> Date: Wed, 18 May 2022 22:05:25 +0000 Subject: [PATCH 139/140] Microsoft mandatory file --- SECURITY.md | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 SECURITY.md diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 000000000..869fdfe2b --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,41 @@ + + +## Security + +Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). + +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below. + +## Reporting Security Issues + +**Please do not report security vulnerabilities through public GitHub issues.** + +Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report). + +If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey). + +You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). + +Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: + + * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) + * Full paths of source file(s) related to the manifestation of the issue + * The location of the affected source code (tag/branch/commit or direct URL) + * Any special configuration required to reproduce the issue + * Step-by-step instructions to reproduce the issue + * Proof-of-concept or exploit code (if possible) + * Impact of the issue, including how an attacker might exploit the issue + +This information will help us triage your report more quickly. + +If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs. + +## Preferred Languages + +We prefer all communications to be in English. + +## Policy + +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd). + + From e5ed9ada3bfbc688470dc92f4e641d4916a7d742 Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Fri, 16 Sep 2022 10:20:17 -0700 Subject: [PATCH 140/140] Quotes. --- docs/conf.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 5620e0eb5..2f2e62fcb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -64,11 +64,11 @@ # -- sphinx.ext.autodoc autoclass_content = "both" autodoc_default_options = { - 'members': True, - 'inherited-members': True, - 'member-order': 'bysource', - 'special-members': '__init__', - 'undoc-members': True, + "members": True, + "inherited-members": True, + "member-order": "bysource", + "special-members": "__init__", + "undoc-members": True, } autodoc_class_signature = "separated" autodoc_typehints = "description"