diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 0000000..d062b05 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,57 @@ +name: Build + +on: + workflow_call: + inputs: + ref: + description: The reference to build + type: string + required: true + image: + description: The name of the image to build + type: string + required: true + context: + description: The context used to build the image + type: string + required: true + stage: + description: The stage to build + type: string + required: false + dockerfile: + description: The path to the Dockerfile + type: string + required: false + outputs: + image-id: + description: The ID of image that has been built + value: ${{ jobs.build.outputs.image-id }} + +jobs: + build: + runs-on: ubuntu-latest + outputs: + image-id: ${{ steps.build.outputs.image-id }} + steps: + - name: Checkout Code + uses: actions/checkout@v4 + with: + ref: ${{ inputs.ref }} + + - id: build + name: Build and push + uses: cern-sis/gh-workflows/.github/actions/docker-build@v6.2.0 + with: + image: ${{ inputs.image }} + context: ${{ inputs.context }} + stage: ${{ inputs.stage }} + dockerfile: ${{ inputs.dockerfile }} + registry: registry.cern.ch + cache: false + tags: | + type=ref,event=branch + type=ref,event=pr + type=ref,event=tag + username: ${{ secrets.HARBOR_USERNAME }} + password: ${{ secrets.HARBOR_SECRET }} \ No newline at end of file diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..5e30061 --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,26 @@ +name: Pre-Commit + +on: + workflow_call: + inputs: + ref: + description: The reference to build + type: string + required: true + +jobs: + linter: + runs-on: ubuntu-latest + steps: + - name: Checkout Code + uses: actions/checkout@v4 + with: + ref: ${{ inputs.ref }} + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Run pre-commit + uses: pre-commit/action@v3.0.0 \ No newline at end of file diff --git a/.github/workflows/pull-request-master.yml b/.github/workflows/pull-request-master.yml new file mode 100644 index 0000000..6487d4e --- /dev/null +++ b/.github/workflows/pull-request-master.yml @@ -0,0 +1,12 @@ +name: Pull request master + +on: + pull_request_target: + branches: [master] + +jobs: + pre-commit: + uses: ./.github/workflows/pre-commit.yml + with: + ref: ${{ github.event.pull_request.head.sha }} + secrets: inherit \ No newline at end of file diff --git a/.github/workflows/push-master.yml b/.github/workflows/push-master.yml new file mode 100644 index 0000000..1098387 --- /dev/null +++ b/.github/workflows/push-master.yml @@ -0,0 +1,30 @@ +name: Push master + +on: workflow_dispatch +# push: +# branches: [master] + +defaults: + run: + shell: bash + +jobs: + build: + uses: ./.github/workflows/build.yml + with: + ref: ${{ inputs.ref }} + image: cern-sis/inspire/classifier + context: . + secrets: inherit + + deploy: + needs: build + runs-on: ubuntu-latest + steps: + - name: send event + uses: cern-sis/gh-workflows/.github/actions/kubernetes-project-new-images@v6.3.0 + with: + event-type: update + images: | + cern-sis/inspire/classifier@${{ needs.build.outputs.image-id }} + token: ${{ secrets.PAT_FIRE_EVENTS_ON_CERN_SIS_KUBERNETES }} diff --git a/.gitignore b/.gitignore index c383071..1410c7b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,25 +1,3 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -30,7 +8,6 @@ __pycache__/ # Distribution / packaging .Python -env/ build/ develop-eggs/ dist/ @@ -42,9 +19,12 @@ lib64/ parts/ sdist/ var/ +wheels/ +share/python-wheels/ *.egg-info/ .installed.cfg *.egg +MANIFEST # PyInstaller # Usually these files are written by a python script from a template @@ -59,14 +39,17 @@ pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ +.nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml -*,cover +*.cover +*.py,cover .hypothesis/ .pytest_cache/ +cover/ # Translations *.mo @@ -75,63 +58,111 @@ coverage.xml # Django stuff: *.log local_settings.py +db.sqlite3 +db.sqlite3-journal -# Flask instance folder +# Flask stuff: instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy # Sphinx documentation docs/_build/ -docs/_api/ # PyBuilder +.pybuilder/ target/ -# IPython Notebook +# Jupyter Notebook .ipynb_checkpoints -# pyenv -.python-version - -# Eclipse/PyDev -.project -.pydevproject - -# Git merge backup files -*.orig +# IPython +profile_default/ +ipython_config.py -# Linux backup files -*~ +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ -# Mac folder attributes -.DS_Store +# Spyder project settings +.spyderproject +.spyproject -# Locally installed node modules -node_modules +# Rope project settings +.ropeproject -# IntelliJ IDE -.idea +# mkdocs documentation +/site -# Redis -dump.rdb +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json -# KDevelop4 -.kdev4/ -*.kdev4 -*.kate-swp +# Pyre type checker +.pyre/ -# Vim swapfiles -.*.sw? +# pytype static type analyzer +.pytype/ -# Selenium report -assets -selenium-report.html +# Cython debug symbols +cython_debug/ -# Rope project files -.ropeproject/ +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ -# Twisted -twistd.pid +# Ignore all dataset files +*.pkl +*.df +*.csv +*.h5 -# Build artifacts -AUTHORS -CHANGELOG +!/tests/integration/fixtures/inspire_test_data.df \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..5221851 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,14 @@ +repos: + - repo: https://github.com/psf/black + rev: '24.4.2' + hooks: + - id: black + - repo: https://github.com/pycqa/isort + rev: '5.13.2' + hooks: + - id: isort + - repo: https://github.com/pycqa/flake8 + rev: '7.1.0' + hooks: + - id: flake8 + args: ['--config=setup.cfg'] \ No newline at end of file diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 3069dea..0000000 --- a/.travis.yml +++ /dev/null @@ -1,26 +0,0 @@ -language: python -dist: trusty -python: -- 3.6 -before_install: -- travis_retry pip install --upgrade pip setuptools -- travis_retry pip install coveralls -install: -- travis_retry pip install --progress-bar off -e .[tests] -script: -- "./run-tests.sh" -deploy: - provider: pypi - user: inspirehep - password: - secure: 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 - on: - branch: master -after_deploy: -- "./deploy_to_openshift.sh" -env: - global: - #DEPLOY_TOKEN - - secure: 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 - #DEPLOY_URL - - secure: 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 diff --git a/docker/Dockerfile b/Dockerfile similarity index 51% rename from docker/Dockerfile rename to Dockerfile index c64094c..d64e9ed 100644 --- a/docker/Dockerfile +++ b/Dockerfile @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2016-2018 CERN. +# Copyright (C) 2016-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,20 +20,30 @@ # granted to it by virtue of its status as an Intergovernmental Organization # or submit itself to any jurisdiction. -FROM pytorch/pytorch:0.4_cuda9_cudnn7 -COPY instance-data /opt/conda/var/inspire_classifier.app-instance/ -COPY boot.sh requirements.txt /app/ +FROM python:3.11-slim-buster + +RUN ip install poetry==1.8.3p + +ENV POETRY_NO_INTERACTION=1 \ + POETRY_VIRTUALENVS_IN_PROJECT=1 \ + POETRY_VIRTUALENVS_CREATE=1 \ + POETRY_CACHE_DIR=/tmp/poetry_cache + WORKDIR /app -RUN apt-get update && apt-get -y install ffmpeg libglib2.0-0 libsm6 libxrender1 libxext6 build-essential git -RUN python -m pip install --upgrade pip \ - && pip install --upgrade setuptools wheel \ - && pip install --upgrade --no-deps --force-reinstall https://download.pytorch.org/whl/cpu/torch-0.3.1-cp36-cp36m-linux_x86_64.whl \ - && pip freeze \ - && pip install -r requirements.txt -v \ - && pip freeze \ - && apt-get -y remove build-essential git \ - && apt-get -y autoremove && apt-get clean -ENV LC_ALL=C.UTF-8 -ENV LANG=C.UTF-8 -ENTRYPOINT ["./boot.sh"] + +COPY pyproject.toml poetry.lock ./ + +# Workarround to avoid install GPU dependencies +RUN poetry remove fastai +RUN poetry install --without dev --no-root && rm -rf $POETRY_CACHE_DIR + +COPY inspire_classifier inspire_classifier/ +# COPY classifier/ app/instance/ +RUN poetry install --without dev + +# Workarround to avoid install GPU dependencies +RUN poetry run pip install torch==2.3.1+cpu -f https://download.pytorch.org/whl/torch_stable.html fastai==2.7.15 + + +CMD ["poetry", "run", "gunicorn", "-b", ":5000", "--access-logfile", "-", "--error-logfile", "-", "inspire_classifier.app:app", "--timeout 90"] \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 9cecc1d..0000000 --- a/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - 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But first, please read -. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 14c92f7..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1 +0,0 @@ -recursive-include generate_training_data_scripts * \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..845f680 --- /dev/null +++ b/README.md @@ -0,0 +1,61 @@ +# Inspire Classifier + +## About +INSPIRE module aimed at automatically classifying the new papers that are added to INSPIRE, such as if they are core or not, or the arXiv category corresponding to each of them. + +The current implemntation uses the ULMfit approach. Universal Language Model Fine-tuning, is a method for training text classifiers by first pretraining a language model on a large corpus to learn general language features (in this case a pre-loaded model, which was trained using the WikiText-103 dataset is used). The pretrained model is then fine-tuned on the title and abstract of the inpsire dataset before training the classifier on top. + + + +## Installation: +* Install and activate `python 3.11` enviroment (for exmaple using pyenv) +* Install poetry: `pip install poetry==1.8.3` +* Run poetry install: `poetry install` + + +## Train and upload new classifier model +### 1. Gather training data +Set the enviroment variables for inspire-prod es database and run the [`create_dataset.py`](scripts/create_dataset.py) file, passing the range of years. This will create a `inspire_classifier_dataset.pkl`, containing the label (core, non-core, rejected) as well as the title and abstract of the fetched records. This data will be used in the next step to train the model. + +``` +export ES_USERNAME=XXXX +export ES_PASSWORD=XXXX + +poetry run python scripts/create_dataset.py --year-from $YEAR_FROM --year-to $YEAR_TO +``` + + +### 2. Run training and validate model +The [`train_classifier.py`](scripts/train_classifier.py) script will run the commands to train and validate a new model. Configurations changes like the amount of training epochs as well as the train-test split can be adjusted here. In short, the script first splits the pkl file from the first step into a training and a test dataset inside the `classifier/data` folder. The training set is then used to train the model, while the test set is used to evaluate the model after the training is finished. The model will be saved into `classifier/models/language_model/finetuned_language_model_encoder.h5` + +``` +poetry run python scripts/train_classifier.py +``` + + +### 3. Upload model +The final step is to upload the new model to the s3 bucket and change the config file in the deployment, so after a redeployment, the pods make use of the new classifier model. Either use `rclone copy` to transfer the file into the s3 bucket or use the `upload_to_s3.py` script (small adjustments like adding the credential are needed). + +``` +poetry run python scripts/upload_to_s3.py +``` + + + +## How to build and deploy new classifier image: +**Currently new images have to deployed on [dockerhub](https://hub.docker.com/r/inspirehep/classifier). This is subject to change as images should go to the harbor registry, but changes in deployment are needed first** + +1. Build docker image: `docker build -t inspirehep/classifier: .` +2. Login with inspirehep user on dockerhub: `docker login` +3. Push image to dockerhub: `docker push inspirehep/classifier:` +4. Change `newTag` in the `kustomization.yml` file in the [k8s repo](https://github.com/cern-sis/kubernetes/tree/master/classifier). + + + + +## How to run +For testing, the cli of the classifier can be used via `poetry run inspire-classifier 'example title' 'exmaple abstract'`, with the `-b` flag, the basepath to check for the training data, can be passed (which currently should be `-b classifier`). + +In the production, the api is used to predict the 'coreness' of records using the `/api/predict/coreness` endpoint and passing `title` and `abstract` as json fields in a POST request (see [this file](inspire_classifier/app.py) for details). + + diff --git a/README.rst b/README.rst deleted file mode 100644 index e03fdd1..0000000 --- a/README.rst +++ /dev/null @@ -1,58 +0,0 @@ -.. - This file is part of INSPIRE. - Copyright (C) 2014-2018 CERN. - - INSPIRE is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - INSPIRE is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with INSPIRE. If not, see . - - In applying this license, CERN does not waive the privileges and immunities - granted to it by virtue of its status as an Intergovernmental Organization - or submit itself to any jurisdiction. - - -==================== - inspire-classifier -==================== - -.. image:: https://travis-ci.org/inspirehep/inspire-classifier.svg?branch=master - :target: https://travis-ci.org/inspirehep/inspire-classifier - -.. image:: https://coveralls.io/repos/github/inspirehep/inspire-classifier/badge.svg?branch=master - :target: https://coveralls.io/github/inspirehep/inspire-classifier?branch=master - - -About -===== - -INSPIRE module aimed at automatically classifying the new papers that are added to INSPIRE, such as if they are core or not, or the arXiv category corresponding to each of them. - -Run the development server with: - -.. highlight:: bash - $ FLASK_DEBUG=true FLASK_APP=inspire_classifier/app.py flask run - -Example: - -.. highlight:: bash - $ curl -i http://127.0.0.1:5000/api/predict/coreness --data "{\"title\": \"Alice In Wonderland\", \"abstract\": \"The reader is conveyed to Wonderland, a world that has no apparent connection with reality...\"}" - HTTP/1.0 200 OK - Content-Type: application/json - Content-Length: 52 - Server: Werkzeug/0.14.1 Python/3.6.4 - Date: Wed, 22 Aug 2018 13:00:16 GMT - - { - "score1": 0.1, - "score2": 0.2, - "score3": 0.7 - } \ No newline at end of file diff --git a/deploy_to_openshift.sh b/deploy_to_openshift.sh deleted file mode 100755 index c1a9be0..0000000 --- a/deploy_to_openshift.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash -xe -curl -X POST "${DEPLOY_URL}" \ - -F token=${DEPLOY_TOKEN} \ - -F ref=master \ - -F "variables[CACHE_DATE]=$(date +%Y-%m-%d:%H:%M:%S)" \ No newline at end of file diff --git a/docker/boot.sh b/docker/boot.sh deleted file mode 100755 index d97b107..0000000 --- a/docker/boot.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -exec gunicorn -b :5000 --access-logfile - --error-logfile - inspire_classifier.app:app --timeout 90 diff --git a/docker/requirements.txt b/docker/requirements.txt deleted file mode 100644 index 8ecd604..0000000 --- a/docker/requirements.txt +++ /dev/null @@ -1,27 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2016-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this licence, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -# work around the restriction on installing PyPI packages with non-PyPI deps -git+https://github.com/inspirehep/inspire-classifier.git#egg=inspire-classifier -gunicorn - - diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 298ea9e..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,19 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index f078414..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,197 +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 ----------------------------------------------------- - -from datetime import datetime - -project = u'inspire-classifier' -copyright = u'{0}, CERN'.format(datetime.now().year) -author = u'CERN' - -# The short X.Y version -version = datetime.utcnow().strftime("v%Y%m%d") -# The full version, including alpha/beta/rc tags -release = datetime.utcnow().strftime("v%Y%m%d") - - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.coverage', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.viewcode', - 'sphinx.ext.todo', - 'sphinx.ext.napoleon', -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The master toctree document. -master_doc = 'index' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'alabaster' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# - -html_theme_options = { - 'description': 'inspire-classifier source code repository based on the ULMFiT text classifier.', - 'github_user': 'inspirehep', - 'github_repo': 'inspire-next', - 'github_button': False, - 'github_banner': True, - 'show_powered_by': False, - 'extra_nav_links': { - 'inspire-classifier@GitHub': 'http://github.com/inspirehep/inspire-classifier', - 'inspire-classifier@PyPI': 'http://pypi.python.org/pypi/inspire-classifier/', - } -} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# 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 = 'inspire-classifierdoc' - - -# -- 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, 'inspire-classifier.tex', u'inspire-classifier Documentation', - u'CERN', '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, 'inspire-classifier', u'inspire-classifier Documentation', - [author], 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, 'inspire-classifier', u'inspire-classifier Documentation', - author, 'inspire-classifier', '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'] - - -# -- Extension configuration ------------------------------------------------- \ No newline at end of file diff --git a/docs/generate_training_data.rst b/docs/generate_training_data.rst deleted file mode 100644 index ec9a886..0000000 --- a/docs/generate_training_data.rst +++ /dev/null @@ -1,122 +0,0 @@ -.. - This file is part of INSPIRE. - Copyright (C) 2014-2018 CERN. - - INSPIRE is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - INSPIRE is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with INSPIRE. If not, see . - - In applying this license, CERN does not waive the privileges and immunities - granted to it by virtue of its status as an Intergovernmental Organization - or submit itself to any jurisdiction. - -========================== -Generate the training data -========================== - -We need annotated data to train the classifier in a supervised way. The data should contain title and abstract as well as their corresponding labels i.e. Rejected, Non-Core, or Core. We do have that data available in INSPIRE due to the large number of daily harvests and the corresponding curator actions to classify them in one of the aforementioned categories. - -The training data needs to be generated and organized in an appropriate format. The process, however, is somewhat complex. Data for Core and Non-Core records needs to be generated separately from data for the Rejected records. - -Generate Core and Non-Core data -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Core and Non-Core records can be extracted from the INSPIRE dump data migrated on a local inspire-next instance. First, we would need to `setup inspire-next locally `_. Next, we need to migrate the data from a recent `INSPIRE dump `_. The HEP dump is the relevant one in this case. Download that, and from the local inspire-next directory, go to the inspirehep terminal: - -:: - - docker-compose run --rm web bash - -In the terminal, you can migrate from the downloaded dump using: - -:: - - APP_DEBUG=False inspirehep migrate file -w - - -Then, the content of code snippet specified in ``generate_core_and_noncore_data.py`` is to be copied and executed from within the inspirehep shell [1]_. - -This produces two files: *inspire_core_records.json* and *inspire_noncore_records.json*. We will later combine this data with the Rejected records data. - -Here, we consider only data from 2016 onwards since before that the curation rules for classification as Core, Non-Core, and Rejected were different. However, the user is free to modify the STARTING_DATE variable in the script to specify a starting date of their choice. - -Generate Rejected data -^^^^^^^^^^^^^^^^^^^^^^ - -The data for Rejected articles is harvested from the local inspire-next instance in a hackish way. The workflows themselves need to be modified in our local inspire-next setup. First, the file *inspire-next/inspirehep/modules/workflows/workflows/article.py* needs to be modified as specified in ``article.py``. We need to add another file *inspire-next/inspirehep/modules/workflows/tasks/makejson.py* with the contents of ``makejson.py``. - -Once the workflow has been modified, we are ready to start the harvest. First, we need to deploy the harvest spiders. This can be done from the *inspire-next* instance folder: - -:: - - docker-compose -f docker-compose.deps.yml run --rm scrapyd-deploy - -To trigger the harvest, we need to copy the file ``trigger_harvest.sh`` to the local inspire-next directory and then run it as: - -:: - - chmod +x trigger_harvest.sh - ./trigger_harvest.sh -s -d -e - -For example: - -:: - - ./trigger_harvest.sh -s 2018-10-02 -d 2 -e 2018-10-10 - -This would trigger a harvest from 2nd October 2018 to 10th October 2018, both days included, with 2 days of records harvested at a time. If no arguments are specified, the script would only trigger harvests for today and yesterday. - -The core command in *trigger_harvest.sh* is as follows, which schedules a harvest from arXiv from the specified *from_date* to the specified *until_date* over a set of arXiv categories defined in the *sets* argument. - -:: - - docker-compose run --rm web inspirehep crawler schedule arXiv article --kwarg 'from_date=2018-11-06' --kwarg 'until_date=2018-11-07' --kwarg 'sets=cs,econ,eess,math,physics,physics:astro-ph,physics:cond-mat,physics:gr-qc,physics:hep-ex,physics:hep-lat,physics:hep-ph,physics:hep-th,physics:math-ph,physics:nlin,physics:nucl-ex,physics:nucl-th,physics:physics,physics:quant-ph,q-bio,q-fin,stat' - -**Important**: Make sure that the dates (especially the which is also the until_date) correspond to the date of the INSPIRE dump used for record migration. This is important as later on, we also need to generate the list of Core and Non-Core records from the same INSPIRE dump and any records harvested from after that date will be considered Rejected and that will corrupt our dataset. - -Since the harvest will generate a large number of files, it's necessary to delete files associated with records from the workflows which have already finished. Otherwise, the harvest will fill our disk space quickly. We can use any job or task scheduler for this as we need to delete the content created in *$DOCKER_DATA/tmp/virutalenv/var/data/workflows/files"* periodically, where *$DOCKER_DATA* is the environment variable corresponding to our *inspire-next* docker data. We can use *crontab* as the task scheduler in linux. In the bash terminal: - -:: - - crontab -e - -This will open our favorite text editor (or we'll be required to set it). Add the following line at the end of the file and save and quit: - -:: - - */15 * * * * find $DOCKER_DATA/tmp/virtualenv/var/data/workflows/files/* -mmin +30 -delete - -This will schedule a task to run every 15 minutes which will find and delete all files created before the last 30 minutes. It's recommended to schedule the cronjob after starting the harvests since the first harvests and workflows can take a few minutes to start. We can schedule the command to run more frequently or vice versa depending on our hardware specifications. - -The harvest produces a file named *inspire_harvested_data.json*. We can monitor the harvest status in the local holdingpen. However, it doesn't contain information on whether the harvested records were Core, Non-Core, or Rejected. To find this, we need to extract the list of arXiv identifiers of Core and Non-Core records from our local inspire-next instance. From the *inspirehep shell* [1]_, copy the contents of ``get_core_and_noncore_arXiv_identifiers.py`` and execute. This will produce two files, *inspire_core_list.txt* and *inspire_noncore_list.txt*. These files will be used to filter out Core and Non-Core records from the harvested data. - -Combine the Core, Non-Core, and Rejected data -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The Core, Non-Core, and Rejected data can be combined by using the python script found at ``combine_core_noncore_rejected_data.py``. The different required files paths need to be specified in the file before running the script. Finally, this will produce the file *inspire_data.df* which is a Pandas DataFrame and which can be used for training and evaluation of the INSPIRE classifier. This file should be placed at the path specified in *inspire-classifier/inspire_classifier/config.py* in the variable *CLASSIFIER_DATAFRAME_PATH*. - -The resulting pandas dataframe will contain 2 columns: *labels* and *text* where *text* is *title* and *abstract* concatenated with a ** token in between. - - - -.. [1] The inspirehep shell can be accessed by accessing the *inspire-next* bash terminal: - - :: - - docker-compose run --rm web bash - - Once inside, the terminal, you can run the following to access the inspirehep shell/cli: - - :: - - inspirehep shell - diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index c3e5c6a..0000000 --- a/docs/index.rst +++ /dev/null @@ -1,29 +0,0 @@ -.. - This file is part of INSPIRE. - Copyright (C) 2014-2018 CERN. - - INSPIRE is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - INSPIRE is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with INSPIRE. If not, see . - - In applying this license, CERN does not waive the privileges and immunities - granted to it by virtue of its status as an Intergovernmental Organization - or submit itself to any jurisdiction. - -User's Guide ------------- - -.. toctree:: - :maxdepth: 2 - - installation - generate_training_data \ No newline at end of file diff --git a/docs/installation.rst b/docs/installation.rst deleted file mode 100644 index 96a6456..0000000 --- a/docs/installation.rst +++ /dev/null @@ -1,27 +0,0 @@ -.. - This file is part of INSPIRE. - Copyright (C) 2014-2018 CERN. - - INSPIRE is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - INSPIRE is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with INSPIRE. If not, see . - - In applying this license, CERN does not waive the privileges and immunities - granted to it by virtue of its status as an Intergovernmental Organization - or submit itself to any jurisdiction. - -Installation ------------- - -:: - - pip install inspire-classifier \ No newline at end of file diff --git a/generate_training_data_scripts/article.py b/generate_training_data_scripts/article.py deleted file mode 100644 index 31b5df5..0000000 --- a/generate_training_data_scripts/article.py +++ /dev/null @@ -1,112 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -"""Workflow for processing single arXiv records harvested.""" - -from __future__ import absolute_import, division, print_function - -from workflow.patterns.controlflow import ( - IF, - IF_NOT, - IF_ELSE, -) - -from inspirehep.modules.workflows.tasks.refextract import extract_journal_info -from inspirehep.modules.workflows.tasks.arxiv import ( - arxiv_author_list, - arxiv_package_download, - arxiv_derive_inspire_categories, - populate_arxiv_document, -) -from inspirehep.modules.workflows.tasks.actions import ( - count_reference_coreness, - download_documents, - is_arxiv_paper, - is_submission, - mark, - normalize_journal_titles, - populate_journal_coverage, - populate_submission_document, - refextract, - save_workflow, - validate_record, -) -from inspirehep.modules.workflows.tasks.upload import set_schema - -from inspirehep.modules.workflows.tasks.makejson import makejson - - -ENHANCE_RECORD = [ - IF( - is_arxiv_paper, - [ - populate_arxiv_document, - arxiv_package_download, - arxiv_derive_inspire_categories, - arxiv_author_list("authorlist2marcxml.xsl"), - ] - ), - IF( - is_submission, - populate_submission_document, - ), - download_documents, - normalize_journal_titles, - refextract, - count_reference_coreness, - extract_journal_info, - populate_journal_coverage, -] - -INIT_MARKS = [ - mark('auto-approved', None), - mark('already-in-holding-pen', None), - mark('previously_rejected', None), - mark('is-update', None), - mark('stopped-matched-holdingpen-wf', None), - mark('approved', None), - mark('unexpected-workflow-path', None), - save_workflow -] - -PRE_PROCESSING = [ - # Make sure schema is set for proper indexing in Holding Pen - set_schema, - INIT_MARKS, - validate_record('hep') -] - -MAKE_JSON = [ - makejson -] - - -class Article(object): - """Article ingestion workflow for Literature collection.""" - name = "HEP" - data_type = "hep" - - workflow = ( - PRE_PROCESSING + - ENHANCE_RECORD + - MAKE_JSON - ) diff --git a/generate_training_data_scripts/combine_core_noncore_rejected_data.py b/generate_training_data_scripts/combine_core_noncore_rejected_data.py deleted file mode 100644 index a4084e4..0000000 --- a/generate_training_data_scripts/combine_core_noncore_rejected_data.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -import json -import numpy as np -import pandas as pd - -inspire_core_list_path = 'inspire_core_list.txt' -inspire_noncore_list_path = 'inspire_noncore_list.txt' -inspire_harvested_data_path = 'inspire_harvested_data.jsonl' -inspire_core_data_path = 'inspire_core_records.jsonl' -inspire_noncore_data_path = 'inspire_noncore_records.jsonl' -save_path = 'inspire_data.df' - -with open(inspire_core_list_path, 'r') as fd: - inspire_core_arxiv_ids = set(arxiv_id.strip() for arxiv_id in fd.readlines()) -with open(inspire_noncore_list_path, 'r') as fd: - inspire_noncore_arxiv_ids = set(arxiv_id.strip() for arxiv_id in fd.readlines()) - -def rejected_data(harvested_data_path): - with open(harvested_data_path, 'r') as fd: - for line in fd: - try: - record = json.loads(line) - if not (record['arxiv_identifier'] in inspire_core_arxiv_ids) and \ - not (record['arxiv_identifier'] in inspire_noncore_arxiv_ids): - del record['arxiv_identifier'] - yield record - except: - continue - -def core_data(): - with open(inspire_core_data_path, 'r') as fd: - for line in fd: - yield json.loads(line) - -def noncore_data(): - with open(inspire_noncore_data_path, 'r') as fd: - for line in fd: - yield json.loads(line) - -rejected_df = pd.DataFrame(rejected_data(inspire_harvested_data_path)) -rejected_df['labels'] = 0 -noncore_df = pd.DataFrame(core_data()) -noncore_df['labels'] = 1 -core_df = pd.DataFrame(noncore_data()) -core_df['labels'] = 2 - -inspire_data = pd.concat([rejected_df, noncore_df, core_df], ignore_index=True) -inspire_data['text'] = inspire_data['title'] + ' ' + inspire_data['abstract'] -inspire_data = inspire_data[['labels', 'text']] -inspire_data.to_pickle(save_path) diff --git a/generate_training_data_scripts/generate_core_and_noncore_data.py b/generate_training_data_scripts/generate_core_and_noncore_data.py deleted file mode 100644 index e3c216f..0000000 --- a/generate_training_data_scripts/generate_core_and_noncore_data.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -""" -Get Core and Non-Core records starting from an earliest date from INSPIRE. -Please run the code in this snippet from within the inspirehep shell. -""" - -import datetime -from invenio_db import db -from invenio_records.models import RecordMetadata -import json -from sqlalchemy import ( - and_, - cast, - not_, - or_, - type_coerce -) -from sqlalchemy.dialects.postgresql import JSONB - - -STARTING_DATE = datetime.datetime(2016, 1, 1, 0, 0, 0) - -base_query = db.session.query(RecordMetadata).with_entities(RecordMetadata.json['titles'][0]['title'], RecordMetadata.json['abstracts'][0]['value']) -filter_by_date = RecordMetadata.created >= STARTING_DATE -has_title_and_abstract = and_(type_coerce(RecordMetadata.json, JSONB).has_key('titles'), type_coerce(RecordMetadata.json, JSONB).has_key('abstracts')) -filter_deleted_records = or_(not_(type_coerce(RecordMetadata.json, JSONB).has_key('deleted')), not_(RecordMetadata.json['deleted'] == cast(True, JSONB))) -only_literature_collection = type_coerce(RecordMetadata.json, JSONB)['_collections'].contains(['Literature']) - -only_core_records = type_coerce(RecordMetadata.json, JSONB)['core'] == cast(True, JSONB) -only_noncore_records = or_(type_coerce(RecordMetadata.json, JSONB)['core'] == cast(False, JSONB), not_(type_coerce(RecordMetadata.json, JSONB).has_key('core'))) - -core_query_results = base_query.filter(filter_by_date, only_core_records, has_title_and_abstract, filter_deleted_records, only_literature_collection) -noncore_query_results = base_query.filter(filter_by_date, only_noncore_records, has_title_and_abstract, filter_deleted_records, only_literature_collection) - -with open('inspire_core_records.jsonl', 'w') as fd: - for title, abstract in core_query_results: - fd.write(json.dumps({ - 'title': title, - 'abstract': abstract, - }) + '\n') -with open('inspire_noncore_records.jsonl', 'w') as fd: - for title, abstract in noncore_query_results: - fd.write(json.dumps({ - 'title': title, - 'abstract': abstract, - }) + '\n') diff --git a/generate_training_data_scripts/get_core_and_non_core_arxiv_identifiers.py b/generate_training_data_scripts/get_core_and_non_core_arxiv_identifiers.py deleted file mode 100644 index 13b4d37..0000000 --- a/generate_training_data_scripts/get_core_and_non_core_arxiv_identifiers.py +++ /dev/null @@ -1,47 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -""" -Get the arxiv ids of all the Core and Non-Core records in INSPIRE. -Please run the code in this snippet from within the inspirehep shell. -""" - -from invenio_search import current_search_client as es -from elasticsearch.helpers import scan -import numpy as np - -core = [] -non_core = [] - -for hit in scan(es, query={"query": {"exists": {"field": "arxiv_eprints"}}, "_source": ["core", "arxiv_eprints"]}, - index='records-hep', doc_type='hep'): - source = hit['_source'] - arxiv_eprint = source['arxiv_eprints'][0]['value'] - if source.get('core') == True: - core.append(arxiv_eprint) - else: - non_core.append(arxiv_eprint) - -with open('inspire_core_list.txt', 'w') as fd: - fd.writelines("{}\n".format(arxiv_id) for arxiv_id in core) -with open('inspire_noncore_list.txt', 'w') as fd: - fd.writelines("{}\n".format(arxiv_id) for arxiv_id in non_core) \ No newline at end of file diff --git a/generate_training_data_scripts/makejson.py b/generate_training_data_scripts/makejson.py deleted file mode 100644 index 4e50a66..0000000 --- a/generate_training_data_scripts/makejson.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -from __future__ import absolute_import, division, print_function -import json - - -def makejson(obj, eng): - title = obj.extra_data['source_data']['data']['titles'][0]['title'] - abstract = obj.extra_data['source_data']['data']['abstracts'][0]['value'] - arxiv_identifier = obj.extra_data['source_data']['data']['arxiv_eprints'][0]['value'] - object_data = {"title": title, "abstract": abstract, "arxiv_identifier": arxiv_identifier} - - with open('./inspire_harvested_data.jsonl', 'a') as fd: - json.dump(object_data, fd) - fd.write("\n") diff --git a/generate_training_data_scripts/trigger_harvest.sh b/generate_training_data_scripts/trigger_harvest.sh deleted file mode 100755 index 91a975e..0000000 --- a/generate_training_data_scripts/trigger_harvest.sh +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env bash -LANG=en_us_88591 - -# Get the arguments specified on the command line -while getopts s:d:e: option -do - case "${option}" in - s) START_DATE=${OPTARG};; # Date Format: YYYY-MM-DD (Default: Today's date) - d) DATE_DIFF=${OPTARG};; # Specifies how many days to harvest at a time (Default: 2) - e) STOP_DATE=${OPTARG};; # Date Format: YYYY-MM-DD (Default: Today's date) - \? ) echo "Unknown option: -$OPTARG" >&2; exit 1;; - : ) echo "Missing option argument for -$OPTARG" >&2; exit 1;; - * ) echo "Unimplemented option: -$OPTARG" >&2; exit 1;; - esac -done - -# Set default arguments to use if not specified on the command line -if [ ${OPTIND} == 1 ] -then - STOP_DATE=$(date) - STOP_DATE=$(date -d"$STOP_DATE" +%Y-%m-%d) - DATE_DIFF=1 - START_DATE=$(date -d"${STOP_DATE} - $DATE_DIFF day" +%Y-%m-%d) -fi - -# Convert dates to integer like format for easier comparisons -init_stop_date_int=$(date -d"${STOP_DATE}" +%Y%m%d) -temp_stop_date=$(date --date="${START_DATE} + ${DATE_DIFF} day" +%Y-%m-%d) -temp_stop_date=$(date --date="${temp_stop_date} - 1 day" +%Y-%m-%d) -temp_start_date=$START_DATE -temp_start_date_int=$(date -d"${temp_start_date}" +%Y%m%d) -temp_stop_date_int=$(date -d"${temp_stop_date}" +%Y%m%d) - -# Specify the arXiv categories to harvest -sets="cs,econ,eess,math,physics,physics:astro-ph,physics:cond-mat,physics:gr-qc,physics:hep-ex,physics:hep-lat,physics:hep-ph,physics:hep-th,physics:math-ph,physics:nlin,physics:nucl-ex,physics:nucl-th,physics:physics,physics:quant-ph,q-bio,q-fin,stat" - -while [ "$temp_stop_date_int" -le "$init_stop_date_int" -a "$temp_start_date_int" -le "$init_stop_date_int" -a "$temp_start_date_int" -le "$temp_stop_date_int" ] -do - temp_start_date=$(date -d"${temp_start_date}" +%Y-%m-%d) - temp_stop_date=$(date -d"${temp_stop_date}" +%Y-%m-%d) - echo "Harvesting from ${temp_start_date} to ${temp_stop_date}" - docker-compose run --rm web inspirehep crawler schedule arXiv article --kwarg sets=$sets --kwarg from_date=$temp_start_date --kwarg until_date=$temp_stop_date - temp_start_date=$(date -d"${temp_stop_date} + 1 day" +%Y-%m-%d) - temp_start_date_int=$(date -d"${temp_start_date}" +%Y%m%d) - temp_stop_date=$(date -d"${temp_stop_date} + ${DATE_DIFF} day" +%Y-%m-%d) - temp_stop_date_int=$(date -d"${temp_stop_date}" +%Y%m%d) - if [ "$temp_stop_date_int" -gt "$init_stop_date_int" -a "$temp_start_date_int" -le "$init_stop_date_int" ]; - then - echo "Harvesting from ${temp_start_date} to ${STOP_DATE}" - docker-compose run --rm web inspirehep crawler schedule arXiv article --kwarg sets=$sets --kwarg from_date=$temp_start_date --kwarg until_date=$STOP_DATE - fi - # The command below fetches the crawler job list. Second, it finds the header which contains the terms 'id job_id', and - # it fetches all the line after that. Lastly, it selects only the top line, which corresponds to the most recent harvest job. - job=$(docker-compose run --rm web inspirehep crawler job list | sed -e '1,/id job_id/d' | head -1) - # We repeatedly check whether the output of the above command contains the term "None" (which can be found in the logs - # and results columns of the crawler job list), since "None" corresponds to a harvest which is unfinished. Only when that - # job finishes it exits the loop below and starts the next harvest. - while [[ $job = *"None"* ]] - do - sleep 15 - job=$(docker-compose run --rm web inspirehep crawler job list | sed -e '1,/id job_id/d' | head -1) - done -done diff --git a/inspire_classifier/__init__.py b/inspire_classifier/__init__.py index 693d916..6cf077a 100644 --- a/inspire_classifier/__init__.py +++ b/inspire_classifier/__init__.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/inspire_classifier/api.py b/inspire_classifier/api.py index b0dbb43..e746054 100644 --- a/inspire_classifier/api.py +++ b/inspire_classifier/api.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -25,116 +25,77 @@ """Classifier API.""" +from pprint import pprint + +import numpy as np from flask import current_app -from inspire_classifier.domain.models import ( - Classifier, - LanguageModel -) -from inspire_classifier.domain.preprocessor import ( - generate_and_save_classifier_tokens, - generate_and_save_language_model_tokens, - map_and_save_tokens_to_ids_for_classifier, - map_and_save_tokens_to_ids_for_language_model, - split_and_save_data_for_language_model_and_classifier -) +from sklearn.metrics import classification_report, confusion_matrix, f1_score +from tqdm import tqdm + +from inspire_classifier.domain.models import Classifier, LanguageModel +from inspire_classifier.domain.preprocessor import split_and_save_data_for_training from inspire_classifier.utils import path_for -import numpy as np -from sklearn.metrics import f1_score, classification_report, confusion_matrix -from pprint import pprint -import requests def create_directories(): """Create the project data and model directories""" - path_for('classifier_data').mkdir(parents=True, exist_ok=True) - path_for('language_model_data').mkdir(parents=True, exist_ok=True) - path_for('classifier_model').mkdir(parents=True, exist_ok=True) - (path_for('language_model') / 'wikitext_103').mkdir(parents=True, exist_ok=True) + path_for("data").mkdir(parents=True, exist_ok=True) + path_for("language_model").mkdir(parents=True, exist_ok=True) + path_for("classifier_model").mkdir(parents=True, exist_ok=True) -def preprocess_and_save_data(): +def split_data(): """ - Prepares the data for training. + Splits the data into training and validation set. """ try: - split_and_save_data_for_language_model_and_classifier( - dataframe_path=path_for('dataframe'), language_model_data_dir=path_for('language_model_data'), - classifier_data_dir=path_for('classifier_data'), - val_fraction=current_app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'] + split_and_save_data_for_training( + dataframe_path=path_for("dataframe"), + dest_dir=path_for("train_valid_data"), + val_fraction=current_app.config["CLASSIFIER_VALIDATION_DATA_FRACTION"], ) except IOError as error: - raise IOError('Training dataframe not found. Make sure the file is present in the right directory. ' - 'Please use the path specified in config.py for CLASSIFIER_DATAFRAME_PATH relative to the ' - 'CLASSIFIER_BASE_PATH.') from error - - try: - generate_and_save_language_model_tokens(language_model_data_dir=path_for('language_model_data')) - except IOError as error: - raise IOError('Language Model data directory does not exist.') from error - - try: - map_and_save_tokens_to_ids_for_language_model( - language_model_data_dir=path_for('language_model_data'), data_itos_path=path_for('data_itos'), - max_vocab_size=current_app.config['CLASSIFIER_MAXIMUM_VOCABULARY_SIZE'], - minimum_frequency=current_app.config['CLASSIFIER_MINIMUM_WORD_FREQUENCY'] - ) - except IOError as error: - raise IOError('Language Model data directory or the data directory do not exist.') from error - - try: - generate_and_save_classifier_tokens(classifier_data_dir=path_for('classifier_data')) - except IOError as error: - raise IOError('Classifier data directory does not exist.') from error - - try: - map_and_save_tokens_to_ids_for_classifier(classifier_data_dir=path_for('classifier_data'), - data_itos_path=path_for('data_itos')) - except IOError as error: - raise IOError('Classifier data directory or the data ITOS does not exist.') from error + raise IOError( + "Training dataframe not found. Make sure the file is present in the right directory. " + "Please use the path specified in config.py for CLASSIFIER_DATAFRAME_PATH relative to the " + "CLASSIFIER_BASE_PATH." + ) from error def finetune_and_save_language_model(): """ - Finetunes the pretrained (on wikitext103) language model on our dataset. + Finetunes the pretrained language model on our dataset. """ try: language_model = LanguageModel( - training_data_ids_path=path_for('language_model_data') / 'training_token_ids.npy', - validation_data_ids_path=path_for('language_model_data') / 'validation_token_ids.npy', - language_model_model_dir=path_for('language_model_data'), - data_itos_path=path_for('data_itos'), cuda_device_id=current_app.config['CLASSIFIER_CUDA_DEVICE_ID'], - batch_size=current_app.config['CLASSIFIER_LANGUAGE_MODEL_BATCH_SIZE'] + train_valid_data_dir=path_for("train_valid_data"), + data_itos_path=path_for("data_itos"), + cuda_device_id=current_app.config["CLASSIFIER_CUDA_DEVICE_ID"], + batch_size=current_app.config["CLASSIFIER_LANGUAGE_MODEL_BATCH_SIZE"], + minimum_word_frequency=current_app.config[ + "CLASSIFIER_MINIMUM_WORD_FREQUENCY" + ], + maximum_vocabulary_size=current_app.config[ + "CLASSIFIER_MAXIMUM_VOCABULARY_SIZE" + ], ) except IOError as error: - raise IOError('Training files, language model data directory, or data ITOS do not exist.') from error - - if not path_for('pretrained_language_model').exists(): - wikitext103_language_model_response = requests.get( - current_app.config['CLASSIFIER_WIKITEXT103_LANGUAGE_MODEL_URL'], allow_redirects=True) - wikitext103_language_model_response.raise_for_status() - with open(path_for('pretrained_language_model'), 'wb') as fd: - fd.write(wikitext103_language_model_response.content) - if not path_for('wikitext103_itos').exists(): - wikitext103_itos_response = requests.get(current_app.config['CLASSIFIER_WIKITEXT103_ITOS_URL'], - allow_redirects=True) - wikitext103_itos_response.raise_for_status() - with open(path_for('wikitext103_itos'), 'wb') as fd: - fd.write(wikitext103_itos_response.content) + raise IOError( + "Training files, language model data directory, or data ITOS do not exist." + ) from error try: - language_model.load_pretrained_language_model_weights( - pretrained_language_model_path=path_for('pretrained_language_model'), - wikitext103_itos_path=path_for('wikitext103_itos') + language_model.train( + finetuned_language_model_encoder_save_path=path_for( + "finetuned_language_model_encoder" + ), + cycle_length=current_app.config["CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH"], ) except IOError as error: - raise IOError('Wikitext103 pretrained language model and Wikitext103 ITOS do not exist.') from error - - try: - language_model.train(finetuned_language_model_encoder_save_path=path_for('finetuned_language_model_encoder'), - cycle_length=current_app.config['CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH']) - except IOError as error: - raise IOError('Unable to save the finetuned language model. Please check that the language model data directory ' - 'exists.') from error + raise IOError( + "Unable to save the finetuned language model. Please check that the language model data directory " + "exists." + ) from error def train_and_save_classifier(): @@ -142,37 +103,42 @@ def train_and_save_classifier(): Trains the classifier on our dataset and save the weights. """ try: - classifier = Classifier(data_itos_path=path_for('data_itos'), - number_of_classes=3, cuda_device_id=current_app.config['CLASSIFIER_CUDA_DEVICE_ID']) + classifier = Classifier( + cuda_device_id=current_app.config["CLASSIFIER_CUDA_DEVICE_ID"] + ) except IOError as error: - raise IOError('Data ITOS not found.') from error + raise IOError("Data ITOS not found.") from error try: classifier.load_training_and_validation_data( - training_data_ids_path=path_for('classifier_data') / 'training_token_ids.npy', - training_data_labels_path=path_for('classifier_data') / 'training_labels.npy', - validation_data_ids_path=path_for('classifier_data') / 'validation_token_ids.npy', - validation_data_labels_path=path_for('classifier_data') / 'validation_labels.npy', - classifier_data_dir=path_for('classifier_data'), - batch_size=current_app.config['CLASSIFIER_CLASSIFIER_BATCH_SIZE'] + train_valid_data_dir=path_for("train_valid_data"), + data_itos_path=path_for("data_itos"), + batch_size=current_app.config["CLASSIFIER_CLASSIFIER_BATCH_SIZE"], ) except IOError as error: - raise IOError('Training and Validation data for Classifier not found.') from error + raise IOError( + "Training and Validation data for Classifier not found." + ) from error classifier.initialize_learner() try: + print(path_for("finetuned_language_model_encoder")) classifier.load_finetuned_language_model_weights( - finetuned_language_model_encoder_path=path_for('finetuned_language_model_encoder') + finetuned_language_model_encoder_path=path_for( + "finetuned_language_model_encoder" + ) ) except IOError as error: - raise IOError('Finetuned Language Model Encoder does not exist.') from error + raise IOError("Finetuned Language Model Encoder does not exist.") from error try: - classifier.train(trained_classifier_save_path=path_for('trained_classifier'), - cycle_length=current_app.config['CLASSIFIER_CLASSIFIER_CYCLE_LENGTH']) + classifier.train( + trained_classifier_save_path=path_for("trained_classifier"), + cycle_length=current_app.config["CLASSIFIER_CLASSIFIER_CYCLE_LENGTH"], + ) except IOError as error: - raise IOError('Unable to save the trained classifier.') from error + raise IOError("Unable to save the trained classifier.") from error def train(): @@ -180,7 +146,7 @@ def train(): Runs the complete training pipeline. """ create_directories() - preprocess_and_save_data() + split_data() finetune_and_save_language_model() train_and_save_classifier() @@ -189,45 +155,53 @@ def predict_coreness(title, abstract): """ Predicts class-wise probabilities given the title and abstract. """ - text = title + ' ' + abstract - categories = ['rejected', 'non_core', 'core'] + text = title + " " + abstract + categories = ["rejected", "non_core", "core"] try: - classifier = Classifier(data_itos_path=path_for('data_itos'), - number_of_classes=3, cuda_device_id=current_app.config['CLASSIFIER_CUDA_DEVICE_ID']) + classifier = Classifier( + cuda_device_id=current_app.config["CLASSIFIER_CUDA_DEVICE_ID"] + ) except IOError as error: - raise IOError('Data ITOS not found.') from error + raise IOError("Data ITOS not found.") from error try: - classifier.load_trained_classifier_weights(path_for('trained_classifier')) + classifier.load_trained_classifier_weights(path_for("trained_classifier")) except IOError as error: - raise IOError('Could not load the trained classifier weights.') from error + raise IOError("Could not load the trained classifier weights.") from error - class_probabilities = classifier.predict(text) + class_probabilities = classifier.predict( + text, temperature=current_app.config["CLASSIFIER_SOFTMAX_TEMPERATUR"] + ) assert len(class_probabilities) == 3 predicted_class = categories[np.argmax(class_probabilities)] - output_dict = {'prediction': predicted_class} - output_dict['scores'] = dict(zip(categories, class_probabilities)) + output_dict = {"prediction": predicted_class} + output_dict["scores"] = dict(zip(categories, class_probabilities)) return output_dict -def validate_classifier(validation_df): +def validate(validation_df): + classifier = Classifier( + cuda_device_id=current_app.config["CLASSIFIER_CUDA_DEVICE_ID"] + ) try: - classifier = Classifier(data_itos_path=path_for('data_itos'), - number_of_classes=3, cuda_device_id=current_app.config['CLASSIFIER_CUDA_DEVICE_ID']) + classifier.load_trained_classifier_weights(path_for("trained_classifier")) except IOError as error: - raise IOError('Data ITOS not found.') from error - - classifier.load_trained_classifier_weights(path_for('trained_classifier')) + raise IOError("There was a problem loading the classifier model") from error predictions = [] - true_labels = validation_df.labels.values - for _, row in validation_df.iterrows(): - predicted_value = classifier.predict(row.text) + true_labels = [] + validation_df = validation_df.sample(frac=1, random_state=42) + for _, row in tqdm( + validation_df.iterrows(), total=len(validation_df.labels.values) + ): + predicted_value = classifier.predict( + row.text, temperature=current_app.config["CLASSIFIER_SOFTMAX_TEMPERATUR"] + ) predicted_class = np.argmax(predicted_value) predictions.append(predicted_class) - print(f'y pred: {predicted_class}, y true: {row.labels}') + true_labels.append(row.labels) - print('f1 score ', f1_score(true_labels, predictions, average='micro')) + print("f1 score ", f1_score(true_labels, predictions, average="micro")) pprint(classification_report(true_labels, predictions)) pprint(confusion_matrix(true_labels, predictions)) diff --git a/inspire_classifier/app.py b/inspire_classifier/app.py index 689f02f..eefadb1 100644 --- a/inspire_classifier/app.py +++ b/inspire_classifier/app.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -22,20 +22,19 @@ import datetime import logging -import os -from flask import Flask, jsonify, Response -from flask_apispec import use_kwargs, marshal_with, FlaskApiSpec -from inspire_classifier.api import predict_coreness +from flask import Flask, Response, jsonify from marshmallow import fields -from prometheus_flask_exporter.multiprocess import \ - GunicornInternalPrometheusMetrics +from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics +from webargs.flaskparser import use_args + +from inspire_classifier.api import predict_coreness from . import serializers class JsonResponse(Response): - """"By creaitng this Response class, we force the response to always be in json, getting rid of the jsonify function.""" + """ "By creaitng this Response class, we force the response to always be in json, getting rid of the jsonify function.""" @classmethod def force_type(cls, rv, environ=None): @@ -46,13 +45,16 @@ def force_type(cls, rv, environ=None): def create_app(): Flask.response_class = JsonResponse - app = Flask(__name__, instance_relative_config=True) - app.config['CLASSIFIER_BASE_PATH'] = app.instance_path - app.config.from_object('inspire_classifier.config') - app.config.from_pyfile('classifier.cfg', silent=True) - app.config['CLASSIFIER_CUDA_DEVICE_ID'] = int(os.environ.get('CLASSIFIER_CUDA_DEVICE_ID', -1)) - - docs = FlaskApiSpec(app) + coreness_schema = serializers.ClassifierOutputSerializer() + # TODO instance path should be removed... but needs changes in deployment file + app = Flask( + __name__, + instance_relative_config=True, + instance_path="/opt/conda/var/inspire_classifier.app-instance", + ) + app.config["CLASSIFIER_BASE_PATH"] = app.instance_path + app.config.from_object("inspire_classifier.config") + app.config.from_pyfile("classifier.cfg", silent=True) @app.route("/api/health") def date(): @@ -60,34 +62,30 @@ def date(): now = datetime.datetime.now() return jsonify(now) - docs.register(date) - @app.route("/api/predict/coreness", methods=["POST"]) - @use_kwargs({'title': fields.Str(required=True), 'abstract': fields.Str(required=True)}) - @marshal_with(serializers.ClassifierOutputSerializer) - def core_classifier(**kwargs): + @use_args( + {"title": fields.Str(required=True), "abstract": fields.Str(required=True)}, + location="json", + ) + def core_classifier(args): """Endpoint for the CORE classifier.""" - - return predict_coreness(kwargs['title'], kwargs['abstract']) - - docs.register(core_classifier) + prediction = predict_coreness(args["title"], args["abstract"]) + response = coreness_schema.dump(prediction) + return response return app @app.errorhandler(404) def page_not_found(e): - return { - "errors": [ - str(e) - ] - }, 404 + return {"errors": [str(e)]}, 404 app = create_app() -if app.config.get('PROMETHEUS_ENABLE_EXPORTER_FLASK'): + +if app.config.get("PROMETHEUS_ENABLE_EXPORTER_FLASK"): logging.info("Starting prometheus metrics exporter") metrics = GunicornInternalPrometheusMetrics.for_app_factory() metrics.init_app(app) -if __name__ == '__main__': - app.run(host='0.0.0.0') +if __name__ == "__main__": + app.run(host="0.0.0.0") diff --git a/inspire_classifier/cli.py b/inspire_classifier/cli.py index 4f2b7ff..3eda875 100644 --- a/inspire_classifier/cli.py +++ b/inspire_classifier/cli.py @@ -20,18 +20,14 @@ # granted to it by virtue of its status as an Intergovernmental Organization # or submit itself to any jurisdiction. -from copyreg import pickle import click import click_spinner +import pandas as pd from flask import current_app from flask.cli import FlaskGroup, with_appcontext -from inspire_classifier.api import ( - train, - predict_coreness, - validate_classifier -) + +from inspire_classifier.api import predict_coreness, train, validate from inspire_classifier.app import create_app -import pandas as pd @click.group(cls=FlaskGroup, create_app=create_app) @@ -39,42 +35,54 @@ def inspire_classifier(): "INSPIRE Classifier commands" -@inspire_classifier.command('predict-coreness') +@inspire_classifier.command("predict-coreness") @with_appcontext -@click.argument('title', type=str, required=True, nargs=1) -@click.argument('abstract', type=str, required=True, nargs=1) -@click.option('-b', '--base-path', type=click.Path(exists=True), required=False, nargs=1) +@click.argument("title", type=str, required=True, nargs=1) +@click.argument("abstract", type=str, required=True, nargs=1) +@click.option( + "-b", "--base-path", type=click.Path(exists=True), required=False, nargs=1 +) def predict(title, abstract, base_path): with click_spinner.spinner(): with current_app.app_context(): if base_path: - current_app.config['CLASSIFIER_BASE_PATH'] = base_path + current_app.config["CLASSIFIER_BASE_PATH"] = base_path click.echo(predict_coreness(title, abstract)) -@inspire_classifier.command('train') +@inspire_classifier.command("train") @with_appcontext -@click.option('-l', '--language-model-epochs', type=int, required=False, nargs=1) -@click.option('-c', '--classifier-epochs', type=int, required=False, nargs=1) -@click.option('-b', '--base-path', type=click.Path(exists=True), required=False, nargs=1) +@click.option("-l", "--language-model-epochs", type=int, required=False, nargs=1) +@click.option("-c", "--classifier-epochs", type=int, required=False, nargs=1) +@click.option( + "-b", "--base-path", type=click.Path(exists=True), required=False, nargs=1 +) def train_classifier(language_model_epochs, classifier_epochs, base_path): with click_spinner.spinner(): with current_app.app_context(): if language_model_epochs: - current_app.config['CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH'] = language_model_epochs + current_app.config["CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH"] = ( + language_model_epochs + ) if classifier_epochs: - current_app.config['CLASSIFIER_CLASSIFIER_CYCLE_LENGTH'] = classifier_epochs + current_app.config["CLASSIFIER_CLASSIFIER_CYCLE_LENGTH"] = ( + classifier_epochs + ) if base_path: - current_app.config['CLASSIFIER_BASE_PATH'] = base_path + current_app.config["CLASSIFIER_BASE_PATH"] = base_path train() -@inspire_classifier.command('validate') +@inspire_classifier.command("validate") @with_appcontext -@click.option('-p', '--dataframe-path', type=click.Path(exists=True), required=True, nargs=1) -@click.option('-b', '--base-path', type=click.Path(exists=True), required=False, nargs=1) +@click.option( + "-p", "--dataframe-path", type=click.Path(exists=True), required=True, nargs=1 +) +@click.option( + "-b", "--base-path", type=click.Path(exists=True), required=False, nargs=1 +) def validate_classifier(dataframe_path, base_path): if base_path: - current_app.config['CLASSIFIER_BASE_PATH'] = base_path + current_app.config["CLASSIFIER_BASE_PATH"] = base_path df = pd.read_pickle(dataframe_path) - validate_classifier(df) + validate(df) diff --git a/inspire_classifier/config.py b/inspire_classifier/config.py index 0b6526a..2d6d532 100644 --- a/inspire_classifier/config.py +++ b/inspire_classifier/config.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -30,20 +30,19 @@ CLASSIFIER_CLASSIFIER_CYCLE_LENGTH = 14 CLASSIFIER_LANGUAGE_MODEL_BATCH_SIZE = 32 CLASSIFIER_CLASSIFIER_BATCH_SIZE = 10 -CLASSIFIER_CUDA_DEVICE_ID = -1 # set to 0 to use a GPU +CLASSIFIER_SOFTMAX_TEMPERATUR = 0.25 +CLASSIFIER_CUDA_DEVICE_ID = 0 # set to 0 to use a GPU -CLASSIFIER_DATA_PATH = 'data' -CLASSIFIER_LANGUAGE_MODEL_PATH = 'models/language_model' -CLASSIFIER_CLASSIFIER_MODEL_PATH = 'models/classifier_model' -CLASSIFIER_LANGUAGE_MODEL_DATA_PATH = 'data/language_model_data' -CLASSIFIER_CLASSIFIER_DATA_PATH = 'data/classifier_data' -CLASSIFIER_DATAFRAME_PATH = 'data/inspire_data.df' -CLASSIFIER_PRETRAINED_LANGUAGE_MODEL_PATH = 'models/language_model/wikitext_103/fwd_wt103.h5' -CLASSIFIER_FINETUNED_LANGUAGE_MODEL_ENCODER_PATH = 'models/language_model/finetuned_language_model_encoder.h5' -CLASSIFIER_TRAINED_CLASSIFIER_PATH = 'models/classifier_model/trained_classifier_model.h5' -CLASSIFIER_WIKITEXT103_ITOS_PATH = 'models/language_model/wikitext_103/itos_wt103.pkl' -CLASSIFIER_DATA_ITOS_PATH = 'data/inspire_data_itos.pkl' - -CLASSIFIER_WIKITEXT103_LANGUAGE_MODEL_URL = 'http://files.fast.ai/models/wt103/fwd_wt103.h5' -CLASSIFIER_WIKITEXT103_ITOS_URL = 'http://files.fast.ai/models/wt103/itos_wt103.pkl' +CLASSIFIER_DATA_PATH = "data" +CLASSIFIER_LANGUAGE_MODEL_PATH = "models/language_model" +CLASSIFIER_CLASSIFIER_MODEL_PATH = "models/classifier_model" +CLASSIFIER_DATAFRAME_PATH = "data/inspire_data.df" +CLASSIFIER_TRAIN_VALID_DATA_PATH = "data/train_valid_data.csv" +CLASSIFIER_FINETUNED_LANGUAGE_MODEL_ENCODER_PATH = ( + "models/language_model/finetuned_language_model_encoder.h5" +) +CLASSIFIER_TRAINED_CLASSIFIER_PATH = ( + "models/classifier_model/trained_classifier_model.h5" +) +CLASSIFIER_DATA_ITOS_PATH = "data/inspire_data_itos.pkl" PROMETHEUS_ENABLE_EXPORTER_FLASK = False diff --git a/inspire_classifier/domain/__init__.py b/inspire_classifier/domain/__init__.py index 30b456f..cd24c61 100644 --- a/inspire_classifier/domain/__init__.py +++ b/inspire_classifier/domain/__init__.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/inspire_classifier/domain/models.py b/inspire_classifier/domain/models.py index 984e8c5..27b7dda 100644 --- a/inspire_classifier/domain/models.py +++ b/inspire_classifier/domain/models.py @@ -1,243 +1,172 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. -# -# Modified from the fastai library (https://github.com/fastai/fastai). - -"""Classifier Domain Models.""" - -import collections +import os + +import numpy as np +import pandas as pd import torch -import torch.optim as optim -from fastai.text import ( +from fastai.text.all import ( + AWD_LSTM, + ColReader, + ColSplitter, + DataBlock, + TextBlock, + TextDataLoaders, accuracy, - DataLoader, - get_rnn_classifer, - LanguageModelLoader, - LanguageModelData, - load_model, - ModelData, - RNN_Learner, - T, - TextDataset, - TextModel, - to_gpu, - to_np, - save_model, - seq2seq_reg, - SortishSampler, - SortSampler, - Variable + default_device, + language_model_learner, + load_learner, + multiprocessing, + text_classifier_learner, ) from sklearn.metrics import f1_score -from functools import partial -from inspire_classifier.utils import FastLoadTokenizer -import numpy as np -import pickle +from inspire_classifier.utils import ( + export_classifier_path, + load_encoder_path, + save_encoder_path, + softmax, +) -class LanguageModel(object): - def __init__(self, training_data_ids_path, validation_data_ids_path, language_model_model_dir, - data_itos_path, cuda_device_id=0, batch_size=32, dropout_multiplier=0.7): - torch.cuda.set_device(cuda_device_id) - self.use_cuda = True if cuda_device_id >= 0 else False - self.inspire_data_itos = pickle.load(open(data_itos_path, 'rb')) - self.vocabulary_size = len(self.inspire_data_itos) +class LanguageModel(object): + def __init__( + self, + train_valid_data_dir, + data_itos_path, + minimum_word_frequency, + maximum_vocabulary_size, + cuda_device_id, + batch_size=32, + dropout_multiplier=0.7, + learning_rate=1e-3, + weight_decay=1e-7, + ): + super(LanguageModel, self).__init__() + if torch.cuda.is_available() and cuda_device_id: + torch.cuda.set_device(cuda_device_id) + else: + default_device(False) number_of_backpropagation_through_time_steps = 70 - number_of_hidden_units = 1150 - number_of_layers = 3 - self.embedding_size = 400 - optimization_function = partial(optim.Adam, betas=(0.8, 0.99)) - - training_token_ids = np.load(training_data_ids_path) - training_token_ids = np.concatenate(training_token_ids) - validation_token_ids = np.load(validation_data_ids_path) - validation_token_ids = np.concatenate(validation_token_ids) - - training_dataloader = LanguageModelLoader(nums=training_token_ids, bs=batch_size, - bptt=number_of_backpropagation_through_time_steps) - validation_dataloader = LanguageModelLoader(nums=validation_token_ids, bs=batch_size, - bptt=number_of_backpropagation_through_time_steps) - model = LanguageModelData(path=language_model_model_dir, pad_idx=1, n_tok=self.vocabulary_size, - trn_dl=training_dataloader, val_dl=validation_dataloader, bs=batch_size, - bptt=number_of_backpropagation_through_time_steps) - - dropouts = np.array([0.25, 0.1, 0.2, 0.02, 0.15]) * dropout_multiplier - - self.learner = model.get_model(opt_fn=optimization_function, emb_sz=self.embedding_size, - n_hid=number_of_hidden_units, n_layers=number_of_layers, dropouti=dropouts[0], - dropout=dropouts[1], wdrop=dropouts[2], dropoute=dropouts[3], - dropouth=dropouts[4]) - self.learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1) - self.learner.clip = 0.3 - self.learner.metrics = [accuracy] - - def load_pretrained_language_model_weights(self, pretrained_language_model_path, wikitext103_itos_path): - weights = torch.load(pretrained_language_model_path, map_location=lambda storage, loc: storage) - - encoder_weights = to_np(weights['0.encoder.weight']) - row_m = encoder_weights.mean(0) - - wikitext103_itos = pickle.load(open(wikitext103_itos_path, 'rb')) - wikitext103_stoi = collections.defaultdict(lambda: -1, {v: k for k, v in enumerate(wikitext103_itos)}) - - nw = np.zeros((self.vocabulary_size, self.embedding_size), dtype=np.float32) - for i, w in enumerate(self.inspire_data_itos): - r = wikitext103_stoi[w] - if r >= 0: - nw[i] = encoder_weights[r] - else: - nw[i] = row_m - - weights['0.encoder.weight'] = T(nw, cuda=self.use_cuda) - weights['0.encoder_with_dropout.embed.weight'] = T(np.copy(nw), cuda=self.use_cuda) - weights['1.decoder.weight'] = T(np.copy(nw), cuda=self.use_cuda) - - self.learner.model.load_state_dict(weights) - - def train(self, finetuned_language_model_encoder_save_path, learning_rate=1e-3, weight_decay=1e-7, cycle_length=15): - print('language model training starts') - print(f'Loaded torch version: {torch.__version__}') - self.learner.freeze_to(-1) - self.learner.fit(learning_rate / 2, n_cycle=1, wds=weight_decay, use_clr=(32, 2), cycle_len=1) + + train_valid_data = pd.read_csv(train_valid_data_dir) + + dblock_lm = DataBlock( + blocks=TextBlock.from_df( + "text", + is_lm=True, + seq_len=number_of_backpropagation_through_time_steps, + max_vocab=maximum_vocabulary_size, + min_freq=minimum_word_frequency, + ), + get_x=ColReader("text"), + splitter=ColSplitter("is_valid"), + ) + + dls_lm = dblock_lm.dataloaders( + train_valid_data, + bs=batch_size // 2, + num_workers=multiprocessing.cpu_count() // 2, + pin_memory=True, + ) + + # save vocab + pd.to_pickle(dls_lm.vocab, data_itos_path) + + self.learner = language_model_learner( + dls_lm, + AWD_LSTM, + metrics=accuracy, + pretrained=True, + drop_mult=dropout_multiplier, + lr=learning_rate, + wd=weight_decay, + ).to_fp16() + + def train(self, finetuned_language_model_encoder_save_path, cycle_length=15): + print("language model training starts") + print(f"Loaded torch version: {torch.__version__}") + self.learner.fit_one_cycle(1, 1e-2) self.learner.unfreeze() - self.learner.fit(learning_rate, n_cycle=1, wds=weight_decay, use_clr=(20, 10), cycle_len=cycle_length) - save_model(self.learner.model[0], finetuned_language_model_encoder_save_path) - - -class Classifier(object): - def __init__(self, data_itos_path, cuda_device_id=0, dropout_multiplier=0.5, number_of_classes=3): - torch.cuda.set_device(cuda_device_id) - - inspire_data_itos = pickle.load(open(data_itos_path, 'rb')) - self.vocabulary_size = len(inspire_data_itos) - self.inspire_data_stoi = collections.defaultdict( - lambda: 0, {str(v): int(k) for k, v in enumerate(inspire_data_itos)}) - - dropouts = np.array([0.4, 0.5, 0.05, 0.3, 0.4]) * dropout_multiplier - - number_of_back_propagation_through_time_steps = 70 - number_of_hidden_units = 1150 - number_of_layers = 3 - embedding_size = 400 - - self.model = get_rnn_classifer(bptt=number_of_back_propagation_through_time_steps, - max_seq=20 * number_of_back_propagation_through_time_steps, - n_class=number_of_classes, n_tok=self.vocabulary_size, emb_sz=embedding_size, - n_hid=number_of_hidden_units, n_layers=number_of_layers, pad_token=1, - layers=[embedding_size * 3, 50, number_of_classes], drops=[dropouts[4], 0.1], - dropouti=dropouts[0], wdrop=dropouts[1], dropoute=dropouts[2], - dropouth=dropouts[3]) - - self.tokenizer = FastLoadTokenizer() - - def load_training_and_validation_data(self, training_data_ids_path, training_data_labels_path, - validation_data_ids_path, validation_data_labels_path, classifier_data_dir, - batch_size=10): - training_token_ids = np.load(training_data_ids_path) - validation_token_ids = np.load(validation_data_ids_path) - training_labels = np.load(training_data_labels_path) - validation_labels = np.load(validation_data_labels_path) - - training_labels = training_labels.flatten() - validation_labels = validation_labels.flatten() - training_labels -= training_labels.min() - validation_labels -= validation_labels.min() - - training_dataset = TextDataset(training_token_ids, training_labels) - validation_dataset = TextDataset(validation_token_ids, validation_labels) - training_data_sampler = SortishSampler(data_source=training_token_ids, key=lambda x: len(training_token_ids[x]), - bs=batch_size // 2) - validation_data_sampler = SortSampler(data_source=validation_token_ids, - key=lambda x: len(validation_token_ids[x])) - training_dataloader = DataLoader(dataset=training_dataset, batch_size=batch_size // 2, transpose=True, - num_workers=1, pad_idx=1, sampler=training_data_sampler) - validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=batch_size, transpose=True, - num_workers=1, pad_idx=1, sampler=validation_data_sampler) - self.model_data = ModelData(path=classifier_data_dir, trn_dl=training_dataloader, val_dl=validation_dataloader) - - def initialize_learner(self): - optimization_function = partial(optim.Adam, betas=(0.8, 0.99)) - - self.learner = RNN_Learner(data=self.model_data, models=TextModel(to_gpu(self.model)), - opt_fn=optimization_function) - self.learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1) - self.learner.clip = 25. - self.learner.metrics = [accuracy] - - def load_finetuned_language_model_weights(self, finetuned_language_model_encoder_path): - load_model(self.learner.model[0], finetuned_language_model_encoder_path) - - def train(self, trained_classifier_save_path, learning_rates=np.array([1e-4, 1e-4, 1e-4, 1e-3, 1e-2]), - weight_decay=1e-6, cycle_length=14): - print('Core classifier model training starts') - print(f'Loaded torch version: {torch.__version__}') - self.learner.freeze_to(-1) - self.learner.fit(learning_rates, n_cycle=1, wds=weight_decay, cycle_len=1, use_clr=(8, 3)) + self.learner.fit_one_cycle(cycle_length, 1e-3) + save_encoder_path(self.learner, finetuned_language_model_encoder_save_path) + + +class Classifier: + def __init__(self, cuda_device_id): + if torch.cuda.is_available() and cuda_device_id: + torch.cuda.set_device(cuda_device_id) + self.cpu = False + else: + default_device(False) + self.cpu = True + + def load_training_and_validation_data( + self, train_valid_data_dir, data_itos_path, batch_size=10 + ): + self.dls_lm_vocab = pd.read_pickle(os.path.join(data_itos_path)) + + train_valid_data = pd.read_csv(train_valid_data_dir) + self.dataloader = TextDataLoaders.from_df( + train_valid_data, + label_col="labels", + text_col="text", + valid_col="is_valid", + is_lm=False, + bs=batch_size // 2, + num_workers=multiprocessing.cpu_count() // 2, + pin_memory=True, + text_vocab=self.dls_lm_vocab, + ) + + def initialize_learner( + self, + dropout_multiplier=0.5, + weight_decay=1e-6, + learning_rates=np.array([1e-4, 1e-4, 1e-4, 1e-3, 1e-2]), + ): + self.learner = text_classifier_learner( + self.dataloader, + AWD_LSTM, + drop_mult=dropout_multiplier, + lr=learning_rates, + wd=weight_decay, + pretrained=False, + metrics=accuracy, + ) + + def load_finetuned_language_model_weights( + self, finetuned_language_model_encoder_path + ): + self.learner = load_encoder_path( + self.learner, finetuned_language_model_encoder_path + ) + + def train(self, trained_classifier_save_path, cycle_length=14): + print("Core classifier model training starts") + print(f"Loaded torch version: {torch.__version__}") + + self.learner.fit_one_cycle(1, 2e-2) self.learner.freeze_to(-2) - self.learner.fit(learning_rates, n_cycle=1, wds=weight_decay, cycle_len=1, use_clr=(8, 3)) - + self.learner.fit_one_cycle(1, slice(1e-2 / (2.6**4), 1e-2)) + self.learner.freeze_to(-3) + self.learner.fit_one_cycle(1, slice(5e-3 / (2.6**4), 5e-3)) self.learner.unfreeze() - self.learner.fit(learning_rates, n_cycle=1, wds=weight_decay, cycle_len=cycle_length, use_clr=(32, 10)) - save_model(self.learner.model, trained_classifier_save_path) + self.learner.fit_one_cycle(cycle_length, slice(1e-3 / (2.6**4), 1e-3)) + + export_classifier_path(self.learner, trained_classifier_save_path) self.calculate_f1_for_validation_dataset() def load_trained_classifier_weights(self, trained_classifier_path): - self.model.load_state_dict(torch.load(trained_classifier_path, map_location=lambda storage, loc: storage)) - - def predict(self, text): - self.model.reset() - self.model.eval() - - input_string = 'xbos xfld 1 ' + text - texts = [input_string] - tokens = self.tokenizer.proc_all(texts) - encoded_tokens = [self.inspire_data_stoi[p] for p in tokens[0]] - token_array = np.reshape(np.array(encoded_tokens), (-1, 1)) - token_array = Variable(torch.from_numpy(token_array)) - prediction_scores = self.model(token_array) - prediction_scores_numpy = prediction_scores[0].data.cpu().numpy() + self.model = load_learner(trained_classifier_path, cpu=self.cpu) - return numpy_softmax(prediction_scores_numpy[0])[0] + def predict(self, text, temperature=0.25): + prediction_scores = self.model.predict(text) + return softmax(prediction_scores[-1].numpy(), temperature) def calculate_f1_for_validation_dataset(self): - labels_list = [] - for batch in self.model_data.val_dl: - labels_list.extend((batch[1]).tolist()) - - labels = np.array(labels_list) - self.model.eval() - predictions = self.learner.predict_dl(self.model_data.val_dl) - y_pred = np.argmax(np.array(predictions), axis=1) - f1_validation_score = f1_score(y_pred, labels, average='micro') - - print(f'Validation score (f1): {f1_validation_score}') - return f1_validation_score - + predictions = self.learner.get_preds(dl=self.dataloader.valid) + y_pred = np.argmax(np.array(predictions[0]), axis=1) + f1_validation_score = f1_score(predictions[1], y_pred, average="micro") -def numpy_softmax(x): - if x.ndim == 1: - x = x.reshape((1, -1)) - max_x = np.max(x, axis=1).reshape((-1, 1)) - exp_x = np.exp(x - max_x) - return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1)) + print(f"Validation score (f1): {f1_validation_score}") + return f1_validation_score diff --git a/inspire_classifier/domain/preprocessor.py b/inspire_classifier/domain/preprocessor.py index fe10971..d867949 100644 --- a/inspire_classifier/domain/preprocessor.py +++ b/inspire_classifier/domain/preprocessor.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -24,150 +24,28 @@ """Classifier Domain Preprocessors.""" - -import collections -from fastai.text import partition_by_cores -import html -from inspire_classifier.utils import FastLoadTokenizer -import numpy as np import pandas as pd -import pickle -import re -import sklearn - - -BOS = 'xbos' # beginning-of-sentence tag -FLD = 'xfld' # data field tag -re1 = re.compile(r' +') +from fastai.text.all import RandomSplitter, range_of -def split_and_save_data_for_language_model_and_classifier(dataframe_path, language_model_data_dir, classifier_data_dir, - val_fraction=0.1): +def split_and_save_data_for_training(dataframe_path, dest_dir, val_fraction=0.1): """ Args: dataframe_path: The path to the pandas dataframe containing the records. The dataframe should have one column containing the title and abstract text appended (title + abstract). The second column should contain the label as an integer (0: Rejected, 1: Non-Core, 2: Core). - language_model_data_dir: Directory to store language model data. - classifier_data_dir: Directory to save classifier data. + dest_dir: Directory to save the training/validation csv. val_fraction: the fraction of data to use as the validation set. """ inspire_data = pd.read_pickle(dataframe_path) # Shuffle the data inspire_data = inspire_data.sample(frac=1).reset_index(drop=True) - # Swap the columns so that the labels are Column 0 and the text is Column 1 (and remove any additional columns) - inspire_data = inspire_data[['labels', 'text']] - - training_dataframe, validation_dataframe = sklearn.model_selection.train_test_split( - inspire_data, test_size=val_fraction) - - training_dataframe = training_dataframe.reset_index(drop=True) - validation_dataframe = validation_dataframe.reset_index(drop=True) - - # Save the data for the classifier - training_dataframe.to_csv(classifier_data_dir / 'training_data.csv', header=False, index=False) - validation_dataframe.to_csv(classifier_data_dir / 'validation_data.csv', header=False, index=False) - - training_texts = np.array(training_dataframe['text']) - validation_texts = np.array(validation_dataframe['text']) - - column_names = ['labels', 'text'] - training_dataframe_for_language_model = pd.DataFrame({'text': training_texts, 'labels': [0] * len(training_texts)}, - columns=column_names) - validation_dataframe_for_language_model = pd.DataFrame({'text': validation_texts, - 'labels': [0] * len(validation_texts)}, - columns=column_names) - - training_dataframe_for_language_model.to_csv(language_model_data_dir / 'training_data.csv', header=False, - index=False) - validation_dataframe_for_language_model.to_csv(language_model_data_dir / 'validation_data.csv', header=False, - index=False) - - -def generate_and_save_language_model_tokens(language_model_data_dir): - training_dataframe = pd.read_csv(language_model_data_dir / 'training_data.csv', header=None) - validation_dataframe = pd.read_csv(language_model_data_dir / 'validation_data.csv', header=None) - - training_tokens, training_labels = get_texts(training_dataframe) - validation_tokens, validation_labels = get_texts(validation_dataframe) - - assert len(training_tokens) == len(training_dataframe) - - np.save(language_model_data_dir / 'training_tokens.npy', training_tokens) - np.save(language_model_data_dir / 'validation_tokens.npy', validation_tokens) - np.save(language_model_data_dir / 'training_labels.npy', training_labels) - np.save(language_model_data_dir / 'validation_labels.npy', validation_labels) - - -def map_and_save_tokens_to_ids_for_language_model(language_model_data_dir, data_itos_path, max_vocab_size=60000, - minimum_frequency=2): - """ - Args: - language_model_data_dir: Directory for language model data. - data_itos_path: The path to save the data ITOS which maps the words in the vocabulary to numerical indices. - max_vocab_size: The maximum size of the vocabulary (default: 60000). - minimum_frequency: The minimum frequency that a word has to occur to be included in the vocabulary. This - prevents including words which occur rarely (default: 2) - """ - training_tokens = np.load(language_model_data_dir / 'training_tokens.npy') - validation_tokens = np.load(language_model_data_dir / 'validation_tokens.npy') - - word_frequency = collections.Counter(p for o in training_tokens for p in o) - inspire_data_itos = [o for o, c in word_frequency.most_common(max_vocab_size) if c > minimum_frequency] - inspire_data_itos.insert(0, '_pad_') - inspire_data_itos.insert(0, '_unk_') - inspire_data_stoi = collections.defaultdict(lambda: 0, {v: k for k, v in enumerate(inspire_data_itos)}) - - training_token_ids = np.array([[inspire_data_stoi[o] for o in p] for p in training_tokens]) - validation_token_ids = np.array([[inspire_data_stoi[o] for o in p] for p in validation_tokens]) - - np.save(language_model_data_dir / 'training_token_ids.npy', training_token_ids) - np.save(language_model_data_dir / 'validation_token_ids.npy', validation_token_ids) - pickle.dump(inspire_data_itos, open(data_itos_path, 'wb')) - - -def generate_and_save_classifier_tokens(classifier_data_dir): - training_dataframe = pd.read_csv(classifier_data_dir / 'training_data.csv', header=None) - validation_dataframe = pd.read_csv(classifier_data_dir / 'validation_data.csv', header=None) - - training_tokens, training_labels = get_texts(training_dataframe) - validation_tokens, validation_labels = get_texts(validation_dataframe) - - assert len(training_tokens) == len(training_dataframe) - - np.save(classifier_data_dir / 'training_tokens.npy', training_tokens) - np.save(classifier_data_dir / 'validation_tokens.npy', validation_tokens) - np.save(classifier_data_dir / 'training_labels.npy', training_labels) - np.save(classifier_data_dir / 'validation_labels.npy', validation_labels) - - -def map_and_save_tokens_to_ids_for_classifier(classifier_data_dir, data_itos_path): - training_tokens = np.load(classifier_data_dir / 'training_tokens.npy') - validation_tokens = np.load(classifier_data_dir / 'validation_tokens.npy') - - inspire_data_itos = pickle.load(open(data_itos_path, 'rb')) - inspire_data_stoi = collections.defaultdict(lambda: 0, {v: k for k, v in enumerate(inspire_data_itos)}) - - training_token_ids = np.array([[inspire_data_stoi[o] for o in p] for p in training_tokens]) - validation_token_ids = np.array([[inspire_data_stoi[o] for o in p] for p in validation_tokens]) - - np.save(classifier_data_dir / 'training_token_ids.npy', training_token_ids) - np.save(classifier_data_dir / 'validation_token_ids.npy', validation_token_ids) - - -def get_texts(df): - labels = df[0].values.astype(np.int64) - texts = f'\n{BOS} {FLD} 1 ' + df[1].astype(str) - texts = list(texts.apply(fixup).values) - - tokens = FastLoadTokenizer().proc_all_mp(partition_by_cores(texts)) - return tokens, list(labels) + splits = RandomSplitter(valid_pct=val_fraction, seed=42)(range_of(inspire_data)) + # Add is_valid column based on the splits + inspire_data["is_valid"] = False + inspire_data.loc[splits[1], "is_valid"] = True -def fixup(x): - x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace( - 'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace( - '
', "\n").replace('\\"', '"').replace('', 'u_n').replace(' @.@ ', '.').replace( - ' @-@ ', '-').replace('\\', ' \\ ') - return re1.sub(' ', html.unescape(x)) + # Save the data for the classifier and language model + inspire_data.to_csv(dest_dir, header=True, index=False) diff --git a/inspire_classifier/serializers.py b/inspire_classifier/serializers.py index 1c080c6..d030e67 100644 --- a/inspire_classifier/serializers.py +++ b/inspire_classifier/serializers.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -25,11 +25,13 @@ class ScoreSchema(Schema): - rejected = fields.Float(attribute='rejected', required=True) - non_core = fields.Float(attribute='non_core', required=True) - core = fields.Float(attribute='core', required=True) + rejected = fields.Float(attribute="rejected", required=True) + non_core = fields.Float(attribute="non_core", required=True) + core = fields.Float(attribute="core", required=True) class ClassifierOutputSerializer(Schema): - prediction = fields.Str(validate=OneOf(['core', 'non_core', 'rejected']), required=True) + prediction = fields.Str( + validate=OneOf(["core", "non_core", "rejected"]), required=True + ) scores = fields.Nested(ScoreSchema) diff --git a/inspire_classifier/utils.py b/inspire_classifier/utils.py index bc0d699..2e0d860 100644 --- a/inspire_classifier/utils.py +++ b/inspire_classifier/utils.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -22,43 +22,59 @@ # # Modified from the fastai library (https://github.com/fastai/fastai). -from fastai.text import ( - num_cpus, - Tokenizer -) -from flask import current_app +import warnings from pathlib import Path -from concurrent.futures import ProcessPoolExecutor -import re -from spacy.lang.en import English -from spacy.symbols import ORTH + +import numpy as np +import torch +from fastai.text.all import clean_raw_keys, distrib_barrier, get_model, rank_distrib +from flask import current_app def path_for(name): - base_path = Path(current_app.config.get('CLASSIFIER_BASE_PATH') or current_app.instance_path) - config_key = f'CLASSIFIER_{name}_PATH'.upper() - + base_path = Path( + current_app.config.get("CLASSIFIER_BASE_PATH") or current_app.instance_path + ) + config_key = f"CLASSIFIER_{name}_PATH".upper() return base_path / current_app.config[config_key] -class FastLoadTokenizer(Tokenizer): - """ - Tokenizer which avoids redundant loading of spacy language model +def save_encoder_path(self, path): + """Save the encoder path to the config file.""" + encoder = get_model(self.model)[0] + torch.save(encoder.state_dict(), path) + + +def load_encoder_path(model, path, device=None): + encoder = get_model(model.model)[0] + if device is None: + device = model.dls.device + if hasattr(encoder, "module"): + encoder = encoder.module + distrib_barrier() + encoder.load_state_dict(clean_raw_keys(torch.load(path))) + model.freeze() + return model + - The FastAI Tokenizer class loads all the pipeline components of the spacy model which significantly increases - loading time, especially when doing inference on CPU. This class inherits from the FastAI Tokenizer and is - refactored to avoid redundant loading of the classifier. - """ - def __init__(self): - self.re_br = re.compile(r'<\s*br\s*/?>', re.IGNORECASE) - self.tok = English() - for w in ('', '', ''): - self.tok.tokenizer.add_special_case(w, [{ORTH: w}]) +def export_classifier_path(model, path): + """Save the classifier path to the config file.""" + if rank_distrib(): + return # don't export if child proc + model._end_cleanup() + old_dbunch = model.dls + model.dls = model.dls.new_empty() + state = model.opt.state_dict() if model.opt is not None else None + model.opt = None + with warnings.catch_warnings(): + # To avoid the warning that come from PyTorch about model not being checked + warnings.simplefilter("ignore") + torch.save(model, path) + model.create_opt() + if state is not None: + model.opt.load_state_dict(state) + model.dls = old_dbunch - def proc_all(self, ss): - return [self.proc_text(s) for s in ss] - def proc_all_mp(self, ss, 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category corresponding to each of them." +authors = ["CERN "] +license = "MIT License" +homepage = "https://inspirehep.net" +repository = "https://github.com/inspirehep/inspire-classifier" +classifiers=[ + "Environment :: Web Environment", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Topic :: Internet :: WWW/HTTP :: Dynamic Content", + "Development Status :: 5 - Production/Stable", +] + +[tool.poetry.dependencies] +python = "^3.11" +click = "^8.1.7" +pandas = "^2.2.2" +tqdm = "^4.66.4" +click-spinner = "^0.1.10" +flask = "^3.0.3" +scikit-learn = "^1.5.0" +prometheus-flask-exporter = "^0.23.0" +fastai = "2.7.15" +webargs = "^8.4.0" +numpy = "1.26.4" +gunicorn = "^22.0.0" + +[tool.poetry.group.test.dependencies] +pytest = "^8.2.2" +mock = "^5.1.0" + + +[tool.poetry.group.dev.dependencies] +black = "^24.4.2" +pre-commit = "*" +elasticsearch-dsl = "^7.4.0" +elasticsearch = "<7.14.0" +inspire-utils = "3.0.22" + + +isort = "^5.13.2" +boto3 = "^1.34.130" +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + + +[tool.poetry.scripts] +inspire-classifier = 'inspire_classifier.cli:inspire_classifier' + +[tool.pytest.ini_options] +testpaths = [ + "tests", +] + +[tool.isort] +profile = "black" +multi_line_output = 3 +atomic = true \ No newline at end of file diff --git a/run-tests.sh b/run-tests.sh deleted file mode 100755 index a11f7d8..0000000 --- a/run-tests.sh +++ /dev/null @@ -1,26 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -set -e - -flake8 inspire_classifier tests -py.test tests diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 0000000..ee2c0cd --- /dev/null +++ b/scripts/README.md @@ -0,0 +1,11 @@ +# Inspire classifier training data generator +A script to prepare dataset compliant with inspire-classifier requirements. + +### How to run +``` +export ES_USERNAME=XXXX +export ES_PASSWORD=XXXX + +poetry install +poetry run python create_dataset.py --year-from $YEAR_FROM —year-to $YEAR_TO +``` diff --git a/scripts/create_dataset.py b/scripts/create_dataset.py new file mode 100644 index 0000000..79db467 --- /dev/null +++ b/scripts/create_dataset.py @@ -0,0 +1,154 @@ +import os + +import click +import pandas as pd +from elasticsearch_dsl import Q, Search +from elasticsearch_dsl.connections import connections +from inspire_utils.record import get_value +from tqdm import tqdm + +DECISIONS_MAPPING = { + "core": { + "index": "records-hep", + "filter_query": Q("term", core=True), + "label": 2, + }, + "non_core": { + "index": "records-hep", + "filter_query": Q("term", core=False) | ~Q("exists", field="core"), + "label": 1, + }, + "rejected": { + "index": "holdingpen-hep", + "filter_query": Q("term", _extra_data__approved=False), + "label": 0, + }, +} + + +class LiteratureSearch(Search): + connection_holdingpen = connections.create_connection( + hosts=["https://es-inspire-prod1.cern.ch/es"], + timeout=30, + http_auth=(os.environ["ES_USERNAME"], os.environ["ES_PASSWORD"]), + verify_certs=False, + use_ssl=True, + ) + connection_inspirehep = connections.create_connection( + hosts=["https://os-inspire-prod.cern.ch/es"], + timeout=30, + http_auth=(os.environ["ES_USERNAME"], os.environ["ES_PASSWORD"]), + verify_certs=False, + use_ssl=True, + ) + + def __init__(self, index, **kwargs): + if index == "holdingpen-hep": + connection = LiteratureSearch.connection_holdingpen + else: + connection = LiteratureSearch.connection_inspirehep + super().__init__( + using=kwargs.get("using", connection), + index=index, + ) + + +class InspireClassifierSearch(object): + def __init__(self, index, query_filters, year_from, year_to): + self.search = LiteratureSearch(index=index) + self.year_from = year_from + self.year_to = year_to + + # Training, validation and test data + + if index == "holdingpen-hep": + self.source_fields = [ + "metadata.abstracts", + "metadata.titles", + "metadata.inspire_categories", + ] + self.title_field = "metadata.titles[0].title" + self.abstract_field = "metadata.abstracts[0].value" + self.inspire_categories_field = "metadata.inspire_categories.term" + self.query_filters = [ + query_filters + & Q( + "range", + metadata__acquisition_source__datetime={ + "gte": self.year_from, + "lt": self.year_to, + }, + ), + ] + else: + self.source_fields = ["abstracts", "titles", "inspire_categories"] + self.title_field = "titles[0].title" + self.abstract_field = "abstracts[0].value" + self.inspire_categories_field = "inspire_categories.term" + self.query_filters = [ + query_filters + & Q("range", _created={"gte": self.year_from, "lt": self.year_to}), + ] + + def _postprocess_record_data(self, record_data): + title = get_value(record_data, self.title_field) + abstract = get_value(record_data, self.abstract_field) + inspire_categories = get_value(record_data, self.inspire_categories_field, []) + return { + "title": title, + "abstract": abstract, + "inspire_categories": inspire_categories, + } + + def get_decision_query(self): + query = self.search.query( + "bool", + filter=self.query_filters, + ).params(size=9999, _source=self.source_fields) + return query + + +def get_data_for_decisions(year_from, year_to): + for decision in DECISIONS_MAPPING: + inspire_search = InspireClassifierSearch( + index=DECISIONS_MAPPING[decision]["index"], + query_filters=DECISIONS_MAPPING[decision]["filter_query"], + year_from=year_from, + year_to=year_to, + ) + query = inspire_search.get_decision_query() + for record_es_data in tqdm(query.scan()): + record_classifier_data = inspire_search._postprocess_record_data( + record_es_data.to_dict() + ) + record_classifier_data["labels"] = DECISIONS_MAPPING[decision]["label"] + yield record_classifier_data + + +def prepare_inspire_classifier_dataset(data, save_data_path): + inspire_data_df = pd.DataFrame(data) + inspire_data_df = inspire_data_df.drop( + inspire_data_df[inspire_data_df.abstract.isna()].index + ) + inspire_data_df["text"] = ( + inspire_data_df["title"] + " " + inspire_data_df["abstract"] + ) + inspire_classifier_data_df = inspire_data_df[["labels", "text"]] + inspire_classifier_data_df.to_pickle(save_data_path) + + +@click.command() +@click.option("--year-from", type=int, required=True) +@click.option("--year-to", type=int, required=True) +def get_inspire_classifier_dataset(year_from, year_to): + if year_to < year_from: + raise ValueError("year_to must be before year_from") + inspire_classifier_dataset_path = os.path.join( + os.getcwd(), "inspire_classifier_dataset.pkl" + ) + data = get_data_for_decisions(year_from, year_to) + prepare_inspire_classifier_dataset(data, inspire_classifier_dataset_path) + + +if __name__ == "__main__": + get_inspire_classifier_dataset() diff --git a/scripts/train_classifier.py b/scripts/train_classifier.py new file mode 100644 index 0000000..3754076 --- /dev/null +++ b/scripts/train_classifier.py @@ -0,0 +1,66 @@ +import os + +import pandas as pd + + +def train_classifier( + text_path, + train_test_split, + number_of_classifier_epochs, + number_of_lanuage_model_epochs, +): + + os.makedirs(os.path.join(os.getcwd(), "classifier", "data"), exist_ok=True) + + df = pd.read_pickle(text_path) + print(df["labels"].value_counts()) + train_size = round(min(df["labels"].value_counts()) * train_test_split) + test_size = round(min(df["labels"].value_counts()) * (1 - train_test_split)) + + print(train_size) + print(test_size) + grouped_df = df.groupby("labels", as_index=False).sample( + n=train_size, random_state=42 + ) + test_df = df.drop(grouped_df.index) + grouped_test_df = test_df.groupby("labels", as_index=False).sample( + n=test_size, random_state=42 + ) + test_df = grouped_test_df.reset_index(drop=True) + df = grouped_df.reset_index(drop=True) + + df.to_pickle(os.path.join("classifier/data", "inspire_data.df")) + test_df.to_pickle(os.path.join("classifier/data", "test_data.df")) + + print("-----------------") + print("Inspire Data:") + print(f"dataframe size: {df.shape}") + print("categories: ") + print(df["labels"].value_counts()) + print("-----------------") + print("Test Data:") + print(f"dataframe size: {test_df.shape}") + print("categories: ") + print(test_df["labels"].value_counts()) + print("-----------------") + + os.system( + f"inspire-classifier train -b classifier --classifier-epochs {number_of_classifier_epochs} --language-model-epochs {number_of_lanuage_model_epochs}" + ) + print("training finished successfully!") + os.system( + "inspire-classifier validate -b classifier -p classifier/data/test_data.df" + ) + + +# Adjust necessary data +NUMBER_OF_CLASSIFIER_EPOCHS = 15 +NUMBER_OF_LANGUAGE_MODEL_EPOCHS = 15 +TRAIN_TEST_SPLIT = 0.8 + +train_classifier( + os.path.join(os.getcwd(), "inspire_classifier_dataset.pkl"), + TRAIN_TEST_SPLIT, + NUMBER_OF_CLASSIFIER_EPOCHS, + NUMBER_OF_LANGUAGE_MODEL_EPOCHS, +) diff --git a/scripts/upload_to_s3.py b/scripts/upload_to_s3.py new file mode 100644 index 0000000..1a07de5 --- /dev/null +++ b/scripts/upload_to_s3.py @@ -0,0 +1,41 @@ +import hashlib +import os +import time + +import boto3 + + +def upload_model_to_s3( + model_path, + output_bucket, +): + if os.path.exists(model_path) is False: + raise IOError(f"model file not found: {model_path}") + + hash = hashlib.sha1() + hash.update(str(time.time()).encode("utf-8")) + hash.hexdigest() + model_name = f"trained_classifier_model_{hash.hexdigest()}.h5" + + session = boto3.Session( + aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"), + aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"), + ) + + s3 = session.resource("s3", endpoint_url="https://s3.cern.ch") + s3.meta.client.upload_file(model_path, output_bucket, model_name) + print(f"model {model_name} uploaded to s3") + + +OUTPUT_BUCKET_NAME = "inspire-qa-classifier/data/models/classifier_model/" + +upload_model_to_s3( + os.path.join( + os.getcwd(), + "classifier", + "models", + "classifier_model", + "trained_classifier_model.h5", + ), + OUTPUT_BUCKET_NAME, +) diff --git a/setup.cfg b/setup.cfg index 433525d..d3feb6e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,34 +1,18 @@ # -*- coding: utf-8 -*- # -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -# -# Tests +# Copyright (C) 2024 CERN. # +# inspirehep is free software; you can redistribute it and/or modify it under +# the terms of the MIT License; see LICENSE file for more details. -[coverage:run] -include = inspire_classifier/*.py +[aliases] +test = pytest -[tool:pytest] -addopts = --cov=inspire_classifier --cov-report=term-missing:skip-covered +[bdist_wheel] +universal = 1 [flake8] -ignore = *.py E501 FI10 FI11 FI12 FI13 FI14 FI15 FI16 FI17 +# E501 black takes care of the line size +ignore = D401,W503,E501,E265,E203 +max-line-length = 88 +max-complexity = 15 \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index f504f2b..0000000 --- a/setup.py +++ /dev/null @@ -1,108 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -"""INSPIRE module aimed at automatically classifying the new papers that are added to INSPIRE, such as if they are core or not, or the arXiv category corresponding to each of them.""" - -from __future__ import absolute_import, division, print_function - -from setuptools import find_packages, setup - - -url = 'https://github.com/inspirehep/inspire-classifier' - -readme = open('README.rst').read() - -setup_requires = [ - 'autosemver~=0.0,>=0.5.2', -] - -install_requires = [ - 'click~=7.0,>=7.0', - 'click-spinner~=0.1,>=0.1.8', - 'fastai==0.7.0', - 'Flask~=1.0,>=1.0.2', - 'flask-apispec~=0.0,>=0.7.0', - 'marshmallow~=3.14.0', - 'numpy==1.15.4', - 'spacy~=2.0,>=2.0.0', - 'torchtext==0.2.3', - 'prometheus-flask-exporter~=0.20,>=0.20.1' -] - -docs_require = [] - -tests_require = [ - 'flake8-future-import~=0.0,>=0.4.3', - 'mock~=2.0,>=2.0.0', - 'pytest-cov~=2.0,>=2.5.1', - 'pytest~=3.0,>=3.9.0', -] - -extras_require = { - 'docs': docs_require, - 'tests': tests_require, -} - -extras_require['all'] = [] -for name, reqs in extras_require.items(): - if name not in ['all']: - extras_require['all'].extend(reqs) - -packages = find_packages(exclude=['docs', 'tests']) - -setup( - name='inspire-classifier', - autosemver={ - 'bugtracker_url': url + '/issues', - }, - url=url, - license='GPLv3', - author='cern', - author_email='admin@inspirehep.net', - packages=packages, - include_package_data=True, - zip_safe=False, - platforms='any', - description=__doc__, - long_description=readme, - setup_requires=setup_requires, - install_requires=install_requires, - tests_require=tests_require, - extras_require=extras_require, - classifiers=[ - 'Development Status :: 1 - Planning', - 'Environment :: Web Environment', - 'Intended Audience :: Developers', - 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', - 'Operating System :: OS Independent', - 'Programming Language :: Python', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', - 'Topic :: Software Development :: Libraries :: Python Modules', - ], - entry_points={ - 'console_scripts': [ - 'inspire-classifier = inspire_classifier.cli:inspire_classifier' - ] - } -) diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index c78d459..e81bad1 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -20,51 +20,46 @@ # granted to it by virtue of its status as an Intergovernmental Organization # or submit itself to any jurisdiction. -from inspire_classifier.app import create_app -from inspire_classifier.api import ( - create_directories, - train -) -from inspire_classifier.domain.models import RNN_Learner -from inspire_classifier.utils import path_for -from mock import patch +import shutil from pathlib import Path + import pytest -import shutil +from fastai.text.all import Learner +from mock import patch + +from inspire_classifier.api import create_directories, train +from inspire_classifier.app import create_app +from inspire_classifier.utils import path_for -@pytest.fixture(autouse=True, scope='session') +@pytest.fixture(autouse=True, scope="session") def app(): app = create_app() with app.app_context(): - app.config['CLASSIFIER_MAXIMUM_VOCABULARY_SIZE'] = 500 - app.config['CLASSIFIER_MINIMUM_WORD_FREQUENCY'] = 1 - app.config['CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH'] = 1 - app.config['CLASSIFIER_CLASSIFIER_CYCLE_LENGTH'] = 1 - app.config['CLASSIFIER_LANGUAGE_MODEL_BATCH_SIZE'] = 10 - app.config['CLASSIFIER_CLASSIFIER_BATCH_SIZE'] = 10 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'] = 0.2 + app.config["CLASSIFIER_MAXIMUM_VOCABULARY_SIZE"] = 500 + app.config["CLASSIFIER_MINIMUM_WORD_FREQUENCY"] = 1 + app.config["CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH"] = 1 + app.config["CLASSIFIER_CLASSIFIER_CYCLE_LENGTH"] = 1 + app.config["CLASSIFIER_LANGUAGE_MODEL_BATCH_SIZE"] = 10 + app.config["CLASSIFIER_CLASSIFIER_BATCH_SIZE"] = 10 + app.config["CLASSIFIER_VALIDATION_DATA_FRACTION"] = 0.2 yield app -# TODO: all fixtures using ``app`` must be replaced by ones that use ``isolated_app``. @pytest.fixture() def app_client(app): - """Flask test client for the application. - See: http://flask.pocoo.org/docs/0.12/testing/#keeping-the-context-around. - """ - with app.test_client() as client: - yield client + return app.test_client() -class Mock_RNN_Learner(RNN_Learner): +class Mock_Learner(Learner): """ - Mocks the fit method of the RNN_Learner. + Mocks the fit method of the Learner. This is done to reduce the model training time during testing by making the fit run once (as opposed to 2 times and 3 times for the LanguageModel and Classifier respectively). It stores the result of the first run and then returns the same result for the other times fit is run. """ + def fit(self, *args, **kwargs): try: return self._fit_result @@ -73,15 +68,13 @@ def fit(self, *args, **kwargs): return self._fit_result -@pytest.fixture(scope='session') -@patch( - 'fastai.text.RNN_Learner', Mock_RNN_Learner -) -@patch( - 'inspire_classifier.domain.models.RNN_Learner', Mock_RNN_Learner -) +@pytest.fixture(scope="session") +@patch("fastai.text.learner.text_classifier_learner", Mock_Learner) def trained_pipeline(app, tmp_path_factory): - app.config['CLASSIFIER_BASE_PATH'] = tmp_path_factory.getbasetemp() + app.config["CLASSIFIER_BASE_PATH"] = tmp_path_factory.getbasetemp() create_directories() - shutil.copy(Path(__file__).parent / 'fixtures' / 'inspire_test_data.df', path_for('dataframe')) + shutil.copy( + Path(__file__).parent / "fixtures" / "inspire_test_data.df", + path_for("dataframe"), + ) train() diff --git a/tests/integration/test_app.py b/tests/integration/test_app.py index 29f8f72..3d3e6e8 100644 --- a/tests/integration/test_app.py +++ b/tests/integration/test_app.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -25,31 +25,58 @@ def test_health_check(app_client): - assert app_client.get('/api/health').status_code == 200 + assert app_client.get("/api/health").status_code == 200 def test_classifier_accepts_only_post(app_client, trained_pipeline): - assert app_client.post('/api/predict/coreness', data=dict(title='foo bar', abstract='foobar foobar')).status_code == 200 - assert app_client.get('/api/predict/coreness').status_code == 405 + assert ( + app_client.post( + "/api/predict/coreness", + json=dict(title="foo bar", abstract="foobar foobar"), + ).status_code + == 200 + ) + assert app_client.get("/api/predict/coreness").status_code == 405 def test_classifier(app_client, trained_pipeline): - response = app_client.post('/api/predict/coreness', data=dict(title='foo bar', abstract='foobar foobar')) + response = app_client.post( + "/api/predict/coreness", json=dict(title="foo bar", abstract="foobar foobar") + ) result = json.loads(response.data) assert response.status_code == 200 - assert set(result.keys()) == {'prediction', 'scores'} - assert result['prediction'] in {'rejected', 'non_core', 'core'} - assert set(result['scores'].keys()) == {'rejected', 'non_core', 'core'} - assert isclose(result['scores']['rejected'] + result['scores']['non_core'] + result['scores']['core'], 1.0, - abs_tol=1e-2) + assert set(result.keys()) == {"prediction", "scores"} + assert result["prediction"] in {"rejected", "non_core", "core"} + assert set(result["scores"].keys()) == {"rejected", "non_core", "core"} + assert isclose( + result["scores"]["rejected"] + + result["scores"]["non_core"] + + result["scores"]["core"], + 1.0, + abs_tol=1e-2, + ) def test_classifier_serializes_input(app_client, trained_pipeline): - assert app_client.post('/api/predict/coreness', data=dict(title='foo bar')).status_code == 422 - assert app_client.post('/api/predict/coreness', data=dict(abstract='foo bar')).status_code == 422 + assert ( + app_client.post("/api/predict/coreness", json=dict(title="foo bar")).status_code + == 422 + ) + assert ( + app_client.post( + "/api/predict/coreness", json=dict(abstract="foo bar") + ).status_code + == 422 + ) -def test_classifier_accepts_extra_fields(app_client, trained_pipeline): - assert app_client.post('/api/predict/coreness', data=dict(title='foo bar', abstract='foo bar', author='foo')).status_code == 200 +def test_classifier_doesnt_accept_extra_fields(app_client, trained_pipeline): + assert ( + app_client.post( + "/api/predict/coreness", + json=dict(title="foo bar", abstract="foo bar", author="foo"), + ).status_code + == 422 + ) diff --git a/tests/integration/test_classifier_api.py b/tests/integration/test_classifier_api.py index 3b930ce..571cf25 100644 --- a/tests/integration/test_classifier_api.py +++ b/tests/integration/test_classifier_api.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,116 +20,78 @@ # granted to it by virtue of its status as an Intergovernmental Organization # or submit itself to any jurisdiction. -from inspire_classifier.api import ( - predict_coreness, -) -from inspire_classifier.utils import path_for +import pickle from math import isclose -import numpy as np + import pandas as pd -import pickle +from inspire_classifier.api import predict_coreness +from inspire_classifier.utils import path_for -TEST_TITLE = 'Pre-images of extreme points of the numerical range, and applications' -TEST_ABSTRACT = 'We extend the pre-image representation of exposed points of the numerical range of a matrix to all \ +TEST_TITLE = "Pre-images of extreme points of the numerical range, and applications" +TEST_ABSTRACT = "We extend the pre-image representation of exposed points of the numerical range of a matrix to all \ extreme points. With that we characterize extreme points which are multiply generated, having at least two linearly \ independent pre-images, as the extreme points which are Hausdorff limits of flat boundary portions on numerical ranges \ of a sequence converging to the given matrix. These studies address the inverse numerical range map and the \ maximum-entropy inference map which are continuous functions on the numerical range except possibly at certain \ multiply generated extreme points. This work also allows us to describe closures of subsets of 3-by-3 matrices having \ -the same shape of the numerical range.' +the same shape of the numerical range." def test_create_directories(trained_pipeline): - assert path_for('classifier_data').exists() - assert path_for('language_model_data').exists() - assert path_for('classifier_model').exists() - assert (path_for('language_model') / 'wikitext_103').exists() + assert path_for("classifier_model").exists() def test_preprocess_and_save_data(app, trained_pipeline): - dataframe = pd.read_pickle(path_for('dataframe')) - - # Test core/preprocessor:split_and_save_data_for_language_model_and_classifier - classifier_training_csv = pd.read_csv(path_for('classifier_data') / 'training_data.csv') - assert isclose(len(classifier_training_csv), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - classifier_validation_csv = pd.read_csv(path_for('classifier_data') / 'validation_data.csv') - assert isclose(len(classifier_validation_csv), len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], - abs_tol=1) - - language_model_training_csv = pd.read_csv(path_for('language_model_data') / 'training_data.csv') - assert isclose(len(language_model_training_csv), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - language_model_validation_csv = pd.read_csv(path_for('language_model_data') / 'validation_data.csv') - assert isclose(len(language_model_validation_csv), - len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], abs_tol=1) - - # Test core/preprocessor:generate_and_save_language_model_tokens - language_model_training_tokens = np.load(path_for('language_model_data') / 'training_tokens.npy') - assert isclose(len(language_model_training_tokens), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - language_model_validation_tokens = np.load(path_for('language_model_data') / 'validation_tokens.npy') - assert isclose(len(language_model_validation_tokens), - len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], abs_tol=1) - language_model_training_labels = np.load(path_for('language_model_data') / 'training_labels.npy') - assert isclose(len(language_model_training_labels), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - language_model_validation_labels = np.load(path_for('language_model_data') / 'validation_labels.npy') - assert isclose(len(language_model_validation_labels), - len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], abs_tol=1) - - # Test core/preprocessor:map_and_save_tokens_to_ids_for_language_model - data_itos = pickle.load(open(path_for('data_itos'), 'rb')) - assert len(data_itos) == app.config['CLASSIFIER_MAXIMUM_VOCABULARY_SIZE'] + 2 - - language_model_training_ids = np.load(path_for('language_model_data') / 'training_token_ids.npy') - assert isclose(len(language_model_training_ids), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - language_model_validation_ids = np.load(path_for('language_model_data') / 'validation_token_ids.npy') - assert isclose(len(language_model_validation_ids), - len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], abs_tol=1) - - # Test core/preprocessor:generate_and_save_classifier_tokens - classifier_training_tokens = np.load(path_for('classifier_data') / 'training_tokens.npy') - assert isclose(len(classifier_training_tokens), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - classifier_validation_tokens = np.load(path_for('classifier_data') / 'validation_tokens.npy') - assert isclose(len(classifier_validation_tokens), - len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], abs_tol=1) - classifier_training_labels = np.load(path_for('classifier_data') / 'training_labels.npy') - assert isclose(len(classifier_training_labels), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - classifier_validation_labels = np.load(path_for('classifier_data') / 'validation_labels.npy') - assert isclose(len(classifier_validation_labels), - len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], abs_tol=1) - - # Test core/preprocessor:map_and_save_tokens_to_ids_for_classifier - classifier_training_ids = np.load(path_for('classifier_data') / 'training_token_ids.npy') - assert isclose(len(classifier_training_ids), - len(dataframe) * (1 - app.config['CLASSIFIER_VALIDATION_DATA_FRACTION']), abs_tol=1) - classifier_validation_ids = np.load(path_for('classifier_data') / 'validation_token_ids.npy') - assert isclose(len(classifier_validation_ids), len(dataframe) * app.config['CLASSIFIER_VALIDATION_DATA_FRACTION'], - abs_tol=1) - - -def test_finetune_and_save_language_model(trained_pipeline): - assert path_for('pretrained_language_model').exists() - assert path_for('wikitext103_itos').exists() - assert path_for('finetuned_language_model_encoder').exists() + dataframe = pd.read_pickle(path_for("dataframe")) + + training_valid__csv = pd.read_csv(path_for("train_valid_data")) + training_csv = training_valid__csv[~training_valid__csv["is_valid"]] + validation_csv = training_valid__csv[training_valid__csv["is_valid"]] + + assert isclose( + len(training_csv), + len(dataframe) * (1 - app.config["CLASSIFIER_VALIDATION_DATA_FRACTION"]), + abs_tol=1, + ) + assert isclose( + len(validation_csv), + len(dataframe) * app.config["CLASSIFIER_VALIDATION_DATA_FRACTION"], + abs_tol=1, + ) + + +def test_vocab(app, trained_pipeline): + data_itos = pickle.load(open(path_for("data_itos"), "rb")) + # For performance when using mixed precision, the vocabulary is always made of size a multiple of 8, potentially by adding xxfake tokens. + adjusted_max_vocab = ( + app.config["CLASSIFIER_MAXIMUM_VOCABULARY_SIZE"] + + 8 + - app.config["CLASSIFIER_MAXIMUM_VOCABULARY_SIZE"] % 8 + ) + assert len(data_itos) == adjusted_max_vocab + + +def test_save_language_model(trained_pipeline): + assert path_for("finetuned_language_model_encoder").exists() def test_train_and_save_classifier(trained_pipeline): - assert path_for('trained_classifier').exists() + assert path_for("trained_classifier").exists() def test_predict_coreness(trained_pipeline): - assert path_for('data_itos').exists() - assert path_for('trained_classifier').exists() + assert path_for("data_itos").exists() + assert path_for("trained_classifier").exists() output_dict = predict_coreness(title=TEST_TITLE, abstract=TEST_ABSTRACT) - assert set(output_dict.keys()) == {'prediction', 'scores'} - assert output_dict['prediction'] in {'rejected', 'non_core', 'core'} - assert set(output_dict['scores'].keys()) == {'rejected', 'non_core', 'core'} - assert isclose(output_dict['scores']['rejected'] + output_dict['scores']['non_core'] + output_dict['scores']['core'], - 1.0, abs_tol=1e-2) + assert set(output_dict.keys()) == {"prediction", "scores"} + assert output_dict["prediction"] in {"rejected", "non_core", "core"} + assert set(output_dict["scores"].keys()) == {"rejected", "non_core", "core"} + assert isclose( + output_dict["scores"]["rejected"] + + output_dict["scores"]["non_core"] + + output_dict["scores"]["core"], + 1.0, + abs_tol=1e-2, + ) diff --git a/tests/integration/test_utils.py b/tests/integration/test_utils.py deleted file mode 100644 index 4d86d03..0000000 --- a/tests/integration/test_utils.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- coding: utf-8 -*- -# -# This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. -# -# INSPIRE is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# INSPIRE is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with INSPIRE. If not, see . -# -# In applying this license, CERN does not waive the privileges and immunities -# granted to it by virtue of its status as an Intergovernmental Organization -# or submit itself to any jurisdiction. - -from fastai.text import partition_by_cores -from inspire_classifier.utils import FastLoadTokenizer - -TEST_TEXT = ['Pre-images of extreme points of the numerical range, and applications'] - - -def test_fast_load_tokenizer_proc_all(): - tokenizer = FastLoadTokenizer() - expected = [['pre', '-', 'images', 'of', 'extreme', 'points', 'of', 'the', 'numerical', 'range', ',', 'and', - 'applications']] - result = tokenizer.proc_all(TEST_TEXT) - assert result == expected - - -def test_fast_load_tokenizer_proc_all_mp(): - tokenizer = FastLoadTokenizer() - expected = [['pre', '-', 'images', 'of', 'extreme', 'points', 'of', 'the', 'numerical', 'range', ',', 'and', - 'applications']] - result = tokenizer.proc_all_mp(partition_by_cores(TEST_TEXT)) - assert result == expected diff --git a/tests/unit/test_api.py b/tests/unit/test_api.py index bc37844..1d62d02 100644 --- a/tests/unit/test_api.py +++ b/tests/unit/test_api.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,11 +20,10 @@ # granted to it by virtue of its status as an Intergovernmental Organization # or submit itself to any jurisdiction. -from inspire_classifier import serializers - +import pytest from marshmallow.exceptions import ValidationError -import pytest +from inspire_classifier import serializers def test_output_serializer(): @@ -32,11 +31,7 @@ def test_output_serializer(): scores = { "prediction": "core", - "scores": { - "rejected": 0.1, - "non_core": 0.2, - "core": 0.7 - } + "scores": {"rejected": 0.1, "non_core": 0.2, "core": 0.7}, } assert output_serializer.load(scores) @@ -45,13 +40,7 @@ def test_output_serializer(): def test_output_serializer_raises_exception(): output_serializer = serializers.ClassifierOutputSerializer() - scores = { - "prediction": "core", - "scores": { - "rejected": 0.1, - "non_core": 0.2 - } - } + scores = {"prediction": "core", "scores": {"rejected": 0.1, "non_core": 0.2}} with pytest.raises(ValidationError): output_serializer.load(scores) @@ -62,12 +51,7 @@ def test_output_serializer_does_not_accept_extra_fields(): scores = { "prediction": "core", - "scores": { - "rejected": 0.1, - "non_core": 0.2, - "core": 0.7, - "score4": 0.0 - } + "scores": {"rejected": 0.1, "non_core": 0.2, "core": 0.7, "score4": 0.0}, } with pytest.raises(ValidationError): @@ -79,11 +63,7 @@ def test_output_accepts_only_certain_values_for_prediction(): scores = { "prediction": "non-rejected", - "scores": { - "rejected": 0.1, - "non_core": 0.2, - "core": 0.7 - } + "scores": {"rejected": 0.1, "non_core": 0.2, "core": 0.7}, } with pytest.raises(ValidationError): diff --git a/tests/unit/test_cli.py b/tests/unit/test_cli.py index 7d448a6..3b5fe30 100644 --- a/tests/unit/test_cli.py +++ b/tests/unit/test_cli.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # # This file is part of INSPIRE. -# Copyright (C) 2014-2018 CERN. +# Copyright (C) 2014-2024 CERN. # # INSPIRE is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,39 +20,41 @@ # granted to it by virtue of its status as an Intergovernmental Organization # or submit itself to any jurisdiction. -from click.exceptions import MissingParameter -from inspire_classifier.cli import ( - predict, - train_classifier -) import pytest +from click.exceptions import MissingParameter + +from inspire_classifier.cli import predict, train_classifier def test_classifier_predict_cli_with_classifier_base_path(): - input_arguments = predict.make_context('predict-coreness', args=['foo', 'bar', '-b', '.']) - assert input_arguments.params['title'] == 'foo' - assert input_arguments.params['abstract'] == 'bar' - assert input_arguments.params['base_path'] == '.' + input_arguments = predict.make_context( + "predict-coreness", args=["foo", "bar", "-b", "."] + ) + assert input_arguments.params["title"] == "foo" + assert input_arguments.params["abstract"] == "bar" + assert input_arguments.params["base_path"] == "." def test_classifier_predict_cli_without_classifier_base_path(): - input_arguments = predict.make_context('predict-coreness', args=['foo', 'bar']) - assert input_arguments.params['title'] == 'foo' - assert input_arguments.params['abstract'] == 'bar' - assert input_arguments.params['base_path'] is None + input_arguments = predict.make_context("predict-coreness", args=["foo", "bar"]) + assert input_arguments.params["title"] == "foo" + assert input_arguments.params["abstract"] == "bar" + assert input_arguments.params["base_path"] is None def test_classifier_predict_cli_fails_without_title_and_abstract(): with pytest.raises(MissingParameter): - predict.make_context('predict-coreness', args=[]) + predict.make_context("predict-coreness", args=[]) def test_classifier_train_cli_correctly_parses_arguments(): - input_arguments = train_classifier.make_context('train', args=['-l', '15', '-c', '14', '-b', '.']) - assert input_arguments.params['language_model_epochs'] == 15 - assert input_arguments.params['classifier_epochs'] == 14 - assert input_arguments.params['base_path'] == '.' + input_arguments = train_classifier.make_context( + "train", args=["-l", "15", "-c", "14", "-b", "."] + ) + assert input_arguments.params["language_model_epochs"] == 15 + assert input_arguments.params["classifier_epochs"] == 14 + assert input_arguments.params["base_path"] == "." def test_classifier_train_cli_works_with_no_arguments(): - assert train_classifier.make_context('train', args=[]) + assert train_classifier.make_context("train", args=[])