diff --git a/.gitignore b/.gitignore index 82c153cd..72ab359a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,9 @@ *.vscode *.html +# files generated by running the test suite +tests/cpp/__* + tags build/ operon/ diff --git a/README.md b/README.md index 41c69c89..d9e1e75b 100644 --- a/README.md +++ b/README.md @@ -98,13 +98,16 @@ In addition, Brush provides functionality that allows you to feed in more compli ```python # load data import pandas as pd + df = pd.read_csv('docs/examples/datasets/d_enc.csv') X = df.drop(columns='label') y = df['label'] # import and make a regressor -from brush import BrushRegressor -est = BrushRegressor() +from pybrush import BrushRegressor + +# you can set verbosity=1 to see the progress bar +est = BrushRegressor(verbosity=1) # use like you would a sklearn regressor est.fit(X,y) @@ -118,15 +121,18 @@ print('score:', est.score(X,y)) ```python # load data import pandas as pd + df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') X = df.drop(columns='target') y = df['target'] # import and make a classifier -from brush import BrushClassifier -est = BrushClassifier() +from pybrush import BrushClassifier +est = BrushClassifier(verbosity=1) + # use like you would a sklearn classifier est.fit(X,y) + y_pred = est.predict(X) y_pred_proba = est.predict_proba(X) @@ -237,4 +243,30 @@ If you are developing the cpp code and want to build the cpp tests, run the foll ./install tests ``` +## Building the docs locally + +To build the documentation you will need some additional requirements. +Before proceeding, make sure you have the python wrapper installed, as the documentation have some sample notebooks that will run the code. + +First go to the `docs` folder: + +```bash +cd docs/ +``` + +Then, install additional python packages in the same environemnt as brush is intalled with: + +```bash +conda activate brush +pip install -r requirements.txt +``` + +Now just run: + +```bash +make html +``` + +The static website is located in `-build/html` + diff --git a/docs/conf.py b/docs/conf.py index e2124945..0163b624 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -37,7 +37,6 @@ def configureDoxyfile(input_dir, output_dir): with open('Doxyfile', 'w') as fp2: fp2.write(filedata) - ## Only trigger readthedocs build if running on readthedocs servers: # read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True' @@ -58,7 +57,7 @@ def configureDoxyfile(input_dir, output_dir): author = 'William La Cava and Joseph D. Romano' # The full version, including alpha/beta/rc tags -release = '0.1a' +release = '0.1a' # TODO: use versionstr here # -- General configuration --------------------------------------------------- @@ -112,7 +111,8 @@ def configureDoxyfile(input_dir, output_dir): breathe_default_project = "brush" breathe_default_members = ('members', 'undoc-members') breathe_projects_source = { - "brush": ("../src/", list(glob('../src/', recursive=True))) + "brush" : ("../src/", list(glob('../src/', recursive=True)) ), + "pybrush": ("../pybrush/", list(glob('../pybrush/', recursive=True)) ), } html_theme_options = { diff --git a/docs/cpp_api/archive.rst b/docs/cpp_api/archive.rst new file mode 100644 index 00000000..02810868 --- /dev/null +++ b/docs/cpp_api/archive.rst @@ -0,0 +1,5 @@ +Archive +======= + +.. doxygenstruct:: Brush::Pop::Archive + :members: diff --git a/docs/cpp_api/engine.rst b/docs/cpp_api/engine.rst new file mode 100644 index 00000000..9129dfa3 --- /dev/null +++ b/docs/cpp_api/engine.rst @@ -0,0 +1,9 @@ +Engine (and parameters) +======================= + +.. doxygenstruct:: Brush::Parameters + :members: + +.. doxygenclass:: Brush::Engine + :members: + diff --git a/docs/cpp_api/evaluation.rst b/docs/cpp_api/evaluation.rst new file mode 100644 index 00000000..8803dc2d --- /dev/null +++ b/docs/cpp_api/evaluation.rst @@ -0,0 +1,8 @@ +Evaluation +========== + +.. doxygenclass:: Brush::Eval::Evaluation + :members: + +.. doxygenclass:: Brush::Eval::Scorer + :members: diff --git a/docs/cpp_api/index.md b/docs/cpp_api/index.md index 226702d5..5d6ba358 100644 --- a/docs/cpp_api/index.md +++ b/docs/cpp_api/index.md @@ -13,5 +13,11 @@ search_space program node nodetypes +individual +evaluation +population variation +selection +archive +engine ``` \ No newline at end of file diff --git a/docs/cpp_api/individual.rst b/docs/cpp_api/individual.rst new file mode 100644 index 00000000..155097ec --- /dev/null +++ b/docs/cpp_api/individual.rst @@ -0,0 +1,8 @@ +Individual and Fitness +====================== + +.. doxygenclass:: Brush::Pop::Individual + :members: + +.. doxygenstruct:: Brush::Fitness + :members: \ No newline at end of file diff --git a/docs/cpp_api/population.rst b/docs/cpp_api/population.rst new file mode 100644 index 00000000..d8616e56 --- /dev/null +++ b/docs/cpp_api/population.rst @@ -0,0 +1,5 @@ +Population +========== + +.. doxygenclass:: Brush::Pop::Population + :members: \ No newline at end of file diff --git a/docs/cpp_api/selection.rst b/docs/cpp_api/selection.rst new file mode 100644 index 00000000..b9fa1429 --- /dev/null +++ b/docs/cpp_api/selection.rst @@ -0,0 +1,14 @@ +Selection +========= + +.. doxygenclass:: Brush::Sel::Selection + :members: + +.. doxygenclass:: Brush::Sel::SelectionOperator + :members: + +.. doxygenclass:: Brush::Sel::NSGA2 + :members: + +.. doxygenclass:: Brush::Sel::Lexicase + :members: diff --git a/docs/cpp_api/variation.rst b/docs/cpp_api/variation.rst index f92847f5..55959d79 100644 --- a/docs/cpp_api/variation.rst +++ b/docs/cpp_api/variation.rst @@ -1,4 +1,8 @@ Variation (Crossover/Mutation) ============================== -.. doxygenfile:: variation.h \ No newline at end of file +.. doxygenclass:: Brush::Var::MutationBase + :members: + +.. doxygenclass:: Brush::Var::Variation + :members: diff --git a/docs/guide/archive.ipynb b/docs/guide/archive.ipynb new file mode 100644 index 00000000..81fc4fb9 --- /dev/null +++ b/docs/guide/archive.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The archive\n", + "\n", + "When you fit a brush estimator, two new attributes are created: `best_estimator_` and `archive_`.\n", + "\n", + "If you set `use_arch` to `True` when instantiating the estimator, then it will store the pareto front as a list in `archive_`. This pareto front is always created with individuals from the final population that are not dominated in objectives **error** and **complexity**.\n", + "\n", + "In case you need more flexibility, the archive will contain the entire final population if `use_arch` is `False`, and you can iterate through this list to select individuals with different criteria. It is also good to remind that Brush supports different optimization objectives using the argument `objectives`.\n", + "\n", + "Each element from the archive is a serialized individual (JSON object)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pybrush import BrushClassifier\n", + "\n", + "# load data\n", + "df = pd.read_csv('../examples/datasets/d_analcatdata_aids.csv')\n", + "X = df.drop(columns='target')\n", + "y = df['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Completed 100% [====================]\n", + "score: 0.7\n" + ] + } + ], + "source": [ + "est = BrushClassifier(\n", + " functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs'],\n", + " use_arch=True,\n", + " max_gens=100,\n", + " verbosity=1\n", + ")\n", + "\n", + "est.fit(X,y)\n", + "y_pred = est.predict(X)\n", + "print('score:', est.score(X,y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see individuals from archive using the index:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + }, + { + "data": { + "text/plain": [ + "{'fitness': {'complexity': 80,\n", + " 'crowding_dist': 0.0,\n", + " 'dcounter': 0,\n", + " 'depth': 3,\n", + " 'dominated': [],\n", + " 'loss': 0.5091069936752319,\n", + " 'loss_v': 0.5091069936752319,\n", + " 'rank': 1,\n", + " 'size': 12,\n", + " 'values': [0.5091069936752319, 12.0],\n", + " 'weights': [-1.0, -1.0],\n", + " 'wvalues': [-0.5091069936752319, -12.0]},\n", + " 'id': 10060,\n", + " 'objectives': ['error', 'size'],\n", + " 'parent_id': [9628],\n", + " 'program': {'Tree': [{'W': 15890.5,\n", + " 'arg_types': ['ArrayF', 'ArrayF'],\n", + " 'center_op': True,\n", + " 'feature': 'AIDS',\n", + " 'fixed': False,\n", + " 'is_weighted': False,\n", + " 'name': 'SplitBest',\n", + " 'node_type': 'SplitBest',\n", + " 'prob_change': 1.0,\n", + " 'ret_type': 'ArrayF',\n", + " 'sig_dual_hash': 9996486434638833164,\n", + " 'sig_hash': 10001460114883919497},\n", + " {'W': 1.0,\n", + " 'arg_types': ['ArrayF'],\n", + " 'center_op': True,\n", + " 'feature': '',\n", + " 'fixed': False,\n", + " 'is_weighted': False,\n", + " 'name': 'Logabs',\n", + " 'node_type': 'Logabs',\n", + " 'prob_change': 1.0,\n", + " 'ret_type': 'ArrayF',\n", + " 'sig_dual_hash': 10617925524997611780,\n", + " 'sig_hash': 13326223354425868050},\n", + " {'W': 2.7182815074920654,\n", + " 'arg_types': [],\n", + " 'center_op': True,\n", + " 'feature': 'Cf',\n", + " 'fixed': False,\n", + " 'is_weighted': False,\n", + " 'name': 'Constant',\n", + " 'node_type': 'Constant',\n", + " 'prob_change': 1.0,\n", + " 'ret_type': 'ArrayF',\n", + " 'sig_dual_hash': 509529941281334733,\n", + " 'sig_hash': 17717457037689164349},\n", + " {'W': 1572255.5,\n", + " 'arg_types': ['ArrayF', 'ArrayF'],\n", + " 'center_op': True,\n", + " 'feature': 'Total',\n", + " 'fixed': False,\n", + " 'is_weighted': False,\n", + " 'name': 'SplitBest',\n", + " 'node_type': 'SplitBest',\n", + " 'prob_change': 1.0,\n", + " 'ret_type': 'ArrayF',\n", + " 'sig_dual_hash': 9996486434638833164,\n", + " 'sig_hash': 10001460114883919497},\n", + " {'W': 0.2222222238779068,\n", + " 'arg_types': [],\n", + " 'center_op': True,\n", + " 'feature': 'MeanLabel',\n", + " 'fixed': False,\n", + " 'is_weighted': True,\n", + " 'name': 'MeanLabel',\n", + " 'node_type': 'MeanLabel',\n", + " 'prob_change': 1.0,\n", + " 'ret_type': 'ArrayF',\n", + " 'sig_dual_hash': 509529941281334733,\n", + " 'sig_hash': 17717457037689164349},\n", + " {'W': 0.5217871069908142,\n", + " 'arg_types': [],\n", + " 'center_op': True,\n", + " 'feature': 'Cf',\n", + " 'fixed': False,\n", + " 'is_weighted': False,\n", + " 'name': 'Constant',\n", + " 'node_type': 'Constant',\n", + " 'prob_change': 1.0,\n", + " 'ret_type': 'ArrayF',\n", + " 'sig_dual_hash': 509529941281334733,\n", + " 'sig_hash': 17717457037689164349}],\n", + " 'is_fitted_': True}}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(len(est.archive_[0]))\n", + "\n", + "est.archive_[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And you can call `predict` (or `predict_proba`, if your `est` is an instance of `BrushClassifier`) with the entire archive:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'id': 10060,\n", + " 'y_pred': array([False, True, True, True, True, False, True, True, True,\n", + " False, True, True, True, True, False, True, True, True,\n", + " True, True, True, True, True, True, True, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, True, False, True, True, True, True, True,\n", + " True, True, True, True, True])},\n", + " {'id': 9789,\n", + " 'y_pred': array([False, True, True, True, True, False, True, True, True,\n", + " False, True, True, True, True, False, True, True, True,\n", + " True, True, True, True, True, True, True, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, True, False, True, True, True, True, True,\n", + " True, True, True, True, True])},\n", + " {'id': 10049,\n", + " 'y_pred': array([False, True, True, True, True, False, True, True, True,\n", + " False, False, True, True, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False])},\n", + " {'id': 4384,\n", + " 'y_pred': array([False, True, True, True, True, False, True, True, True,\n", + " False, False, True, True, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False])},\n", + " {'id': 9692,\n", + " 'y_pred': array([ True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True])},\n", + " {'id': 9552,\n", + " 'y_pred': array([False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False])}]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est.predict_archive(X)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'id': 10060,\n", + " 'y_pred': array([0.22222222, 0.9999999 , 0.9999999 , 0.9999999 , 0.9999999 ,\n", + " 0.22222222, 0.9999999 , 0.9999999 , 0.9999999 , 0.22222222,\n", + " 0.5217871 , 0.9999999 , 0.9999999 , 0.5217871 , 0.22222222,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ,\n", + " 0.22222222, 0.22222222, 0.22222222, 0.22222222, 0.22222222,\n", + " 0.22222222, 0.22222222, 0.22222222, 0.22222222, 0.22222222,\n", + " 0.22222222, 0.22222222, 0.22222222, 0.5217871 , 0.22222222,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ],\n", + " dtype=float32)},\n", + " {'id': 9789,\n", + " 'y_pred': array([0.22222222, 0.99994993, 0.99994993, 0.99994993, 0.99994993,\n", + " 0.22222222, 0.99994993, 0.99994993, 0.99994993, 0.22222222,\n", + " 0.5217871 , 0.99994993, 0.99994993, 0.5217871 , 0.22222222,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ,\n", + " 0.22222222, 0.22222222, 0.22222222, 0.22222222, 0.22222222,\n", + " 0.22222222, 0.22222222, 0.22222222, 0.22222222, 0.22222222,\n", + " 0.22222222, 0.22222222, 0.22222222, 0.5217871 , 0.22222222,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ,\n", + " 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 , 0.5217871 ],\n", + " dtype=float32)},\n", + " {'id': 10049,\n", + " 'y_pred': array([0.39024392, 0.9999999 , 0.9999999 , 0.9999999 , 0.9999999 ,\n", + " 0.39024392, 0.9999999 , 0.9999999 , 0.9999999 , 0.39024392,\n", + " 0.39024392, 0.9999999 , 0.9999999 , 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392],\n", + " dtype=float32)},\n", + " {'id': 4384,\n", + " 'y_pred': array([0.39024392, 0.9999522 , 0.9999522 , 0.9999522 , 0.9999522 ,\n", + " 0.39024392, 0.9999522 , 0.9999522 , 0.9999522 , 0.39024392,\n", + " 0.39024392, 0.9999522 , 0.9999522 , 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392,\n", + " 0.39024392, 0.39024392, 0.39024392, 0.39024392, 0.39024392],\n", + " dtype=float32)},\n", + " {'id': 9692,\n", + " 'y_pred': array([0.5317098 , 0.93985564, 0.9835824 , 0.8686745 , 0.68970597,\n", + " 0.53089285, 0.8455727 , 0.9291562 , 0.7663612 , 0.6237519 ,\n", + " 0.5169323 , 0.7368382 , 0.794476 , 0.63628834, 0.5578266 ,\n", + " 0.50047225, 0.50908357, 0.51443684, 0.506959 , 0.50320625,\n", + " 0.5003231 , 0.50484663, 0.5051821 , 0.50173986, 0.5005965 ,\n", + " 0.5060892 , 0.5592239 , 0.56642807, 0.5267187 , 0.5222307 ,\n", + " 0.5185086 , 0.64804167, 0.68591666, 0.5714386 , 0.5314499 ,\n", + " 0.50612646, 0.5576549 , 0.5636914 , 0.5241404 , 0.5113072 ,\n", + " 0.50007457, 0.5010315 , 0.5013173 , 0.50085753, 0.50068355,\n", + " 0.5000373 , 0.50096935, 0.50095695, 0.5003852 , 0.500174 ],\n", + " dtype=float32)},\n", + " {'id': 9552,\n", + " 'y_pred': array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n", + " dtype=float32)}]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est.predict_proba_archive(X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "brush", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/guide/index.md b/docs/guide/index.md index ccd1c6a8..eb71a290 100644 --- a/docs/guide/index.md +++ b/docs/guide/index.md @@ -13,5 +13,7 @@ data search_space working_with_programs json +saving_loading_populations +archive deap ``` \ No newline at end of file diff --git a/docs/guide/saving_loading_populations.ipynb b/docs/guide/saving_loading_populations.ipynb new file mode 100644 index 00000000..af6fd4bd --- /dev/null +++ b/docs/guide/saving_loading_populations.ipynb @@ -0,0 +1,281 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Saving and loading populations\n", + "\n", + "Another feature Brush implements is the ability to save and load entire populations.\n", + "We use JSON notation to store the population into a file that is human readable. The same way, we can feed an estimator a previous population file to serve as starting point for the evolution.\n", + "\n", + "In this notebook, we will walk through how to use the `save_population` and `load_population` parameters. \n", + "\n", + "We start by getting a sample dataset and splitting it into `X` and `y`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pybrush import BrushRegressor\n", + "\n", + "# load data\n", + "df = pd.read_csv('../examples/datasets/d_enc.csv')\n", + "X = df.drop(columns='label')\n", + "y = df['label']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To save the population after finishing the evolution, you nee to set `save_population` parameter to a value different than an empty string. Then, the final population is going to be stored in that specific file.\n", + "\n", + "In this example, we create a temporary file." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation 1/10 [////// ]\n", + "Train Loss (Med): 11.75939 (74.37032)\n", + "Val Loss (Med): 11.75939 (74.37032)\n", + "Median Size (Max): 3 (19)\n", + "Median complexity (Max): 9 (432)\n", + "Time (s): 0.12205\n", + "\n", + "Generation 2/10 [/////////// ]\n", + "Train Loss (Med): 11.58283 (17.94969)\n", + "Val Loss (Med): 11.58283 (17.94969)\n", + "Median Size (Max): 3 (19)\n", + "Median complexity (Max): 9 (368)\n", + "Time (s): 0.27800\n", + "\n", + "Generation 3/10 [//////////////// ]\n", + "Train Loss (Med): 11.15674 (17.94969)\n", + "Val Loss (Med): 11.15674 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 10 (915)\n", + "Time (s): 0.41845\n", + "\n", + "Generation 4/10 [///////////////////// ]\n", + "Train Loss (Med): 10.62121 (17.94969)\n", + "Val Loss (Med): 10.62121 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (381)\n", + "Time (s): 0.56585\n", + "\n", + "Generation 5/10 [////////////////////////// ]\n", + "Train Loss (Med): 10.51181 (17.94969)\n", + "Val Loss (Med): 10.51181 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (412)\n", + "Time (s): 0.73561\n", + "\n", + "Generation 6/10 [/////////////////////////////// ]\n", + "Train Loss (Med): 10.51181 (17.94969)\n", + "Val Loss (Med): 10.51181 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (412)\n", + "Time (s): 0.89526\n", + "\n", + "Generation 7/10 [//////////////////////////////////// ]\n", + "Train Loss (Med): 10.51181 (17.94969)\n", + "Val Loss (Med): 10.51181 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (412)\n", + "Time (s): 1.03213\n", + "\n", + "Generation 8/10 [///////////////////////////////////////// ]\n", + "Train Loss (Med): 10.43982 (17.94969)\n", + "Val Loss (Med): 10.43982 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (412)\n", + "Time (s): 1.19282\n", + "\n", + "Generation 9/10 [////////////////////////////////////////////// ]\n", + "Train Loss (Med): 10.33524 (17.94969)\n", + "Val Loss (Med): 10.33524 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (368)\n", + "Time (s): 1.33781\n", + "\n", + "Generation 10/10 [//////////////////////////////////////////////////]\n", + "Train Loss (Med): 10.33524 (17.94969)\n", + "Val Loss (Med): 10.33524 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (368)\n", + "Time (s): 1.50192\n", + "\n", + "Saved population to file /tmp/tmpw7jkwa5m/population.json\n", + "score: 0.8856532915521027\n" + ] + } + ], + "source": [ + "import pickle\n", + "import os, tempfile\n", + "\n", + "pop_file = os.path.join(tempfile.mkdtemp(), 'population.json')\n", + "\n", + "# set verbosity==2 to see the full report\n", + "est = BrushRegressor(\n", + " functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs'],\n", + " max_gens=10,\n", + " save_population=pop_file,\n", + " verbosity=2\n", + ")\n", + "\n", + "est.fit(X,y)\n", + "y_pred = est.predict(X)\n", + "print('score:', est.score(X,y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading a previous population is done providing `load_population` a string value corresponding to a JSON file generated by Brush. In our case, we will use the same file from the previous code block.\n", + "\n", + "After loading the population, we run the evolution for 10 more generations, and we can see that the first generation started from the previous population. This means that the population was successfully saved and loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded population from /tmp/tmpw7jkwa5m/population.json of size = 200\n", + "Generation 1/10 [////// ]\n", + "Train Loss (Med): 10.33524 (17.94969)\n", + "Val Loss (Med): 10.33524 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (368)\n", + "Time (s): 0.16596\n", + "\n", + "Generation 2/10 [/////////// ]\n", + "Train Loss (Med): 10.33524 (17.94969)\n", + "Val Loss (Med): 10.33524 (17.94969)\n", + "Median Size (Max): 3 (18)\n", + "Median complexity (Max): 9 (240)\n", + "Time (s): 0.31669\n", + "\n", + "Generation 3/10 [//////////////// ]\n", + "Train Loss (Med): 10.26326 (17.94969)\n", + "Val Loss (Med): 10.26326 (17.94969)\n", + "Median Size (Max): 3 (20)\n", + "Median complexity (Max): 9 (368)\n", + "Time (s): 0.45045\n", + "\n", + "Generation 4/10 [///////////////////// ]\n", + "Train Loss (Med): 10.26326 (17.94969)\n", + "Val Loss (Med): 10.26326 (17.94969)\n", + "Median Size (Max): 3 (19)\n", + "Median complexity (Max): 9 (368)\n", + "Time (s): 0.63331\n", + "\n", + "Generation 5/10 [////////////////////////// ]\n", + "Train Loss (Med): 10.26326 (16.41696)\n", + "Val Loss (Med): 10.26326 (16.41696)\n", + "Median Size (Max): 5 (17)\n", + "Median complexity (Max): 33 (330)\n", + "Time (s): 0.78002\n", + "\n", + "Generation 6/10 [/////////////////////////////// ]\n", + "Train Loss (Med): 9.70269 (17.94969)\n", + "Val Loss (Med): 9.70269 (17.94969)\n", + "Median Size (Max): 3 (19)\n", + "Median complexity (Max): 9 (330)\n", + "Time (s): 0.91656\n", + "\n", + "Generation 7/10 [//////////////////////////////////// ]\n", + "Train Loss (Med): 9.67577 (17.94969)\n", + "Val Loss (Med): 9.67577 (17.94969)\n", + "Median Size (Max): 3 (19)\n", + "Median complexity (Max): 9 (330)\n", + "Time (s): 1.10225\n", + "\n", + "Generation 8/10 [///////////////////////////////////////// ]\n", + "Train Loss (Med): 9.67577 (16.41696)\n", + "Val Loss (Med): 9.67577 (16.41696)\n", + "Median Size (Max): 5 (19)\n", + "Median complexity (Max): 33 (330)\n", + "Time (s): 1.30773\n", + "\n", + "Generation 9/10 [////////////////////////////////////////////// ]\n", + "Train Loss (Med): 9.67577 (16.41696)\n", + "Val Loss (Med): 9.67577 (16.41696)\n", + "Median Size (Max): 5 (19)\n", + "Median complexity (Max): 33 (330)\n", + "Time (s): 1.44840\n", + "\n", + "Generation 10/10 [//////////////////////////////////////////////////]\n", + "Train Loss (Med): 9.67577 (15.67545)\n", + "Val Loss (Med): 9.67577 (15.67545)\n", + "Median Size (Max): 6 (19)\n", + "Median complexity (Max): 36 (723)\n", + "Time (s): 1.65144\n", + "\n", + "score: 0.892949582824199\n" + ] + } + ], + "source": [ + "est = BrushRegressor(\n", + " functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs'],\n", + " load_population=pop_file,\n", + " max_gens=10,\n", + " verbosity=2\n", + ")\n", + "\n", + "est.fit(X,y)\n", + "y_pred = est.predict(X)\n", + "print('score:', est.score(X,y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can open the serialized file and change individuals' programs manually.\n", + "\n", + "This also allow us to have checkpoints in the execution." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "brush", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/guide/search_space.ipynb b/docs/guide/search_space.ipynb index 9d072354..69faab2c 100644 --- a/docs/guide/search_space.ipynb +++ b/docs/guide/search_space.ipynb @@ -31,13 +31,11 @@ "cell_type": "code", "execution_count": 1, "id": "b667948a", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "from brush import Dataset, SearchSpace\n", + "from pybrush import Dataset, SearchSpace\n", "\n", "df = pd.read_csv('../examples/datasets/d_enc.csv')\n", "X = df.drop(columns='label')\n", @@ -105,10 +103,10 @@ "text": [ "Search Space\n", "===\n", - "terminal_map: {ArrayI: [x_5, x_7], ArrayF: [x_0, x_1, x_2, x_3, x_4, x_6]}\n", - "terminal_weights: {ArrayI: [1, 1], ArrayF: [1, 1, 1, 1, 1, 1]}\n", - "node_map[ArrayI][[\"ArrayI\", \"ArrayI\"]][SplitBest] = SplitBest[>0.000], weight = 0.2\n", - "node_map[ArrayF][[\"ArrayF\", \"ArrayF\"]][SplitBest] = SplitBest[>0.000], weight = 0.2\n", + "terminal_map: {\"ArrayB\": [\"1.00\"], \"ArrayI\": [\"x_5\", \"x_7\", \"1.00\"], \"ArrayF\": [\"x_0\", \"x_1\", \"x_2\", \"x_3\", \"x_4\", \"x_6\", \"1.00\", \"1.00*MeanLabel\"]}\n", + "terminal_weights: {\"ArrayB\": [-nan], \"ArrayI\": [0.011619061, 0.03579926, 0.023709161], \"ArrayF\": [0.6343385, 0.67299956, 0.42711574, 0.8625447, 0.8957853, 0.20750472, 0.6167148, 0.6167148]}\n", + "node_map[ArrayI][[\"ArrayI\", \"ArrayI\"]][SplitBest] = SplitBest, weight = 0.2\n", + "node_map[ArrayF][[\"ArrayF\", \"ArrayF\"]][SplitBest] = SplitBest, weight = 0.2\n", "node_map[ArrayF][[\"ArrayF\", \"ArrayF\"]][Div] = Div, weight = 0.1\n", "node_map[ArrayF][[\"ArrayF\", \"ArrayF\"]][Mul] = Mul, weight = 1\n", "node_map[ArrayF][[\"ArrayF\", \"ArrayF\"]][Sub] = Sub, weight = 0.5\n", @@ -158,7 +156,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/guide/working_with_programs.ipynb b/docs/guide/working_with_programs.ipynb index f48be082..0769c286 100644 --- a/docs/guide/working_with_programs.ipynb +++ b/docs/guide/working_with_programs.ipynb @@ -67,7 +67,6 @@ "execution_count": 1, "id": "102e3fcb", "metadata": { - "scrolled": true, "tags": [ "remove-output" ] @@ -75,14 +74,12 @@ "outputs": [], "source": [ "import pandas as pd\n", - "from brush import BrushRegressor\n", - "from pmlb import fetch_data\n", + "from pybrush import BrushRegressor\n", "\n", "# load data\n", "df = pd.read_csv('../examples/datasets/d_enc.csv')\n", "X = df.drop(columns='label')\n", - "y = df['label']\n", - "\n" + "y = df['label']" ] }, { @@ -91,32 +88,20 @@ "id": "ac39c9ca", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bill/mambaforge/envs/brush/lib/python3.11/site-packages/deap/tools/emo.py:139: RuntimeWarning: invalid value encountered in scalar divide\n", - " distances[cur[1]] += (next[0][i] - prev[0][i]) / norm\n", - "/home/bill/mambaforge/envs/brush/lib/python3.11/site-packages/deap/tools/emo.py:139: RuntimeWarning: invalid value encountered in scalar subtract\n", - " distances[cur[1]] += (next[0][i] - prev[0][i]) / norm\n", - "/home/bill/projects/brush/src/brush/estimator.py:251: RuntimeWarning: overflow encountered in square\n", - " np.sum((data.y- ind.prg.predict(data))**2),\n", - "/home/bill/mambaforge/envs/brush/lib/python3.11/site-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n", - " return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "best model: Add(10.57*x6,If(x0>0.75,Add(8.50*x6,If(x0>0.81,26.02,Add(-9.31*x4,127.74*x0))),Add(Add(13.60*x4,0.11*x2),-0.09*x1)))\n" + "Completed 100% [====================]\n", + "score: 0.8972961690538603\n" ] } ], "source": [ "# import and make a regressor\n", "est = BrushRegressor(\n", - " functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs']\n", + " functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs'],\n", + " verbosity=1 # set verbosity==1 to see a progress bar\n", ")\n", "\n", "# use like you would a sklearn regressor\n", @@ -125,6 +110,157 @@ "print('score:', est.score(X,y))" ] }, + { + "cell_type": "markdown", + "id": "5bbd24cd", + "metadata": {}, + "source": [ + "You can see the fitness of the final individual by accessing the `fitness` attribute. Each fitness value corresponds to the objective of same index defined earlier for the `BrushRegressor` class. By default, it will try to minimize `\"error\"` and `\"size\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "166415c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitness(9.282899 19.000000 )\n", + "['error', 'size']\n" + ] + } + ], + "source": [ + "print(est.best_estimator_.fitness)\n", + "print(est.objectives)" + ] + }, + { + "cell_type": "markdown", + "id": "38b6364e", + "metadata": {}, + "source": [ + "A `fitness` in Brush is actually more than a tuple. It is a class that has all boolean comparison operators overloaded to allow an ease of use when prototyping with Brush.\n", + "\n", + "It also infers the weight of each objective to automatically handle minimization or maximization objetives.\n", + "\n", + "To see the weights, you can try:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "13d0ac5f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[-1.0, -1.0]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est.best_estimator_.fitness.weights" + ] + }, + { + "cell_type": "markdown", + "id": "fe594691", + "metadata": {}, + "source": [ + "## Serialization \n", + "\n", + "Brush let's you serialize the entire individual, or just the program or fitness it wraps. It uses JSON to serialize the objects, and this is implemented with the get and set states of an object:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b01ab1fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fitness {'complexity': 304, 'crowding_dist': 3.4028234663852886e+38, 'dcounter': 0, 'depth': 3, 'dominated': [0, 2, 29, 62, 80, 127, 146], 'loss': 9.282898902893066, 'loss_v': 9.282898902893066, 'rank': 1, 'size': 19, 'values': [9.282898902893066, 19.0], 'weights': [-1.0, -1.0], 'wvalues': [-9.282898902893066, -19.0]}\n", + "id 1910\n", + "objectives ['error', 'size']\n", + "parent_id [1858]\n", + "program {'Tree': [{'W': 0.75, 'arg_types': ['ArrayF', 'ArrayF'], 'center_op': True, 'feature': 'x0', 'fixed': False, 'is_weighted': False, 'name': 'SplitBest', 'node_type': 'SplitBest', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 9996486434638833164, 'sig_hash': 10001460114883919497}, {'W': 0.8050000071525574, 'arg_types': ['ArrayF', 'ArrayF'], 'center_op': True, 'feature': 'x0', 'fixed': False, 'is_weighted': False, 'name': 'SplitBest', 'node_type': 'SplitBest', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 9996486434638833164, 'sig_hash': 10001460114883919497}, {'W': 30.494491577148438, 'arg_types': [], 'center_op': True, 'feature': 'MeanLabel', 'fixed': False, 'is_weighted': True, 'name': 'MeanLabel', 'node_type': 'MeanLabel', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 509529941281334733, 'sig_hash': 17717457037689164349}, {'W': 49.47871017456055, 'arg_types': [], 'center_op': True, 'feature': 'x0', 'fixed': False, 'is_weighted': True, 'name': 'Terminal', 'node_type': 'Terminal', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 509529941281334733, 'sig_hash': 17717457037689164349}, {'W': 1.0, 'arg_types': ['ArrayF', 'ArrayF'], 'center_op': True, 'feature': '', 'fixed': False, 'is_weighted': False, 'name': 'Add', 'node_type': 'Add', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 9996486434638833164, 'sig_hash': 10001460114883919497}, {'W': 0.018234524875879288, 'arg_types': [], 'center_op': True, 'feature': 'x1', 'fixed': False, 'is_weighted': True, 'name': 'Terminal', 'node_type': 'Terminal', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 509529941281334733, 'sig_hash': 17717457037689164349}, {'W': 10.46687126159668, 'arg_types': [], 'center_op': True, 'feature': 'x6', 'fixed': False, 'is_weighted': True, 'name': 'Terminal', 'node_type': 'Terminal', 'prob_change': 1.0, 'ret_type': 'ArrayF', 'sig_dual_hash': 509529941281334733, 'sig_hash': 17717457037689164349}], 'is_fitted_': True}\n" + ] + } + ], + "source": [ + "estimator_dict = est.best_estimator_.__getstate__()\n", + "\n", + "for k, v in estimator_dict.items():\n", + " print(k, v)" + ] + }, + { + "cell_type": "markdown", + "id": "6bcb071b", + "metadata": {}, + "source": [ + "With serialization, you can use pickle to save and load just programs or even the entire individual." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b4537631", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import os, tempfile\n", + "\n", + "individual_file = os.path.join(tempfile.mkdtemp(), 'individual.json')\n", + "with open(individual_file, \"wb\") as f:\n", + " pickle.dump(est.best_estimator_, f)\n", + "\n", + "program_file = os.path.join(tempfile.mkdtemp(), 'program.json')\n", + "with open(program_file, \"wb\") as f:\n", + " pickle.dump(est.best_estimator_.program, f)" + ] + }, + { + "cell_type": "markdown", + "id": "fff5693d", + "metadata": {}, + "source": [ + "Then we can load it later with:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ee7a20c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If(x0>0.75,If(x0>0.81,30.49*MeanLabel,49.48*x0),Add(0.02*x1,10.47*x6))\n" + ] + } + ], + "source": [ + "with open(individual_file, \"rb\") as f:\n", + " loaded_estimator = pickle.load(f)\n", + " print(loaded_estimator.get_model())" + ] + }, { "cell_type": "markdown", "id": "a355d8f3", @@ -138,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "316964d5", "metadata": {}, "outputs": [ @@ -146,7 +282,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Add(10.57*x6,If(x0>0.75,Add(8.50*x6,If(x0>0.81,26.02,Add(-9.31*x4,127.74*x0))),Add(Add(13.60*x4,0.11*x2),-0.09*x1)))\n" + "If(x0>0.75,If(x0>0.81,30.49*MeanLabel,49.48*x0),Add(0.02*x1,10.47*x6))\n" ] } ], @@ -166,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "dad68d01", "metadata": {}, "outputs": [ @@ -174,21 +310,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Add\n", - "|-10.57*x6\n", + "SplitBest\n", "|-SplitBest\n", - "| |-Add\n", - "| |-8.50*x6\n", - "| |-SplitBest\n", - "| | |-26.02\n", - "| | |-Add\n", - "| | | |--9.31*x4\n", - "| | | |-127.74*x0\n", - "| |-Add\n", - "| | |-Add\n", - "| | |-13.60*x4\n", - "| | |-0.11*x2\n", - "| | |--0.09*x1\n" + " |-30.49*MeanLabel\n", + " |-49.48*x0\n", + "|-Add\n", + "| |-0.02*x1\n", + "| |-10.47*x6\n" ] } ], @@ -209,11 +337,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "3ef1a735", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -221,195 +347,107 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "\n", - "5625d27dfb10\n", - "\n", - "Add\n", + "7f370003ebc0\n", + "\n", + "x0>0.75?\n", "\n", - "\n", + "\n", "\n", - "x6\n", - "\n", - "x6\n", + "7f37000b5410\n", + "\n", + "x0>0.81?\n", "\n", - "\n", + "\n", "\n", - "5625d27dfb10->x6\n", - "\n", - "\n", - "10.57\n", + "7f370003ebc0->7f37000b5410\n", + "\n", + "\n", + "Y\n", "\n", - "\n", + "\n", "\n", - "5625d1de0610\n", - "\n", - "x0>0.75?\n", + "7f370003f120\n", + "\n", + "Add\n", "\n", - "\n", + "\n", "\n", - "5625d27dfb10->5625d1de0610\n", - "\n", - "\n", + "7f370003ebc0->7f370003f120\n", + "\n", + "\n", + "N\n", "\n", - "\n", + "\n", "\n", - "5625d3c02820\n", - "\n", - "Add\n", + "7f370003ef80\n", + "\n", + "30.49*MeanLabel\n", "\n", - "\n", + "\n", "\n", - "5625d1de0610->5625d3c02820\n", - 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"\"5625d27dfb10\" [label=\"Add\"];\n", - "\"5625d27dfb10\" -> \"x6\" [label=\"10.57\"];\n", - "\"5625d27dfb10\" -> \"5625d1de0610\" [label=\"\"];\n", - "\"x6\" [label=\"x6\"];\n", - "\"5625d1de0610\" [label=\"x0>0.75?\"];\n", - "\"5625d1de0610\" -> \"5625d3c02820\" [headlabel=\"\",taillabel=\"Y\"];\n", - "\"5625d1de0610\" -> \"5625d1ddc200\" [headlabel=\"\",taillabel=\"N\"];\n", - "\"5625d3c02820\" [label=\"Add\"];\n", - "\"5625d3c02820\" -> \"x6\" [label=\"8.50\"];\n", - "\"5625d3c02820\" -> \"5625d27cb4a0\" [label=\"\"];\n", - "\"x6\" [label=\"x6\"];\n", - "\"5625d27cb4a0\" [label=\"x0>0.81?\"];\n", - "\"5625d27cb4a0\" -> \"5625d1c92dc0\" [headlabel=\"\",taillabel=\"Y\"];\n", - "\"5625d27cb4a0\" -> \"5625d2515750\" [headlabel=\"\",taillabel=\"N\"];\n", - "\"5625d1c92dc0\" [label=\"26.02\"];\n", - "\"5625d2515750\" [label=\"Add\"];\n", - "\"5625d2515750\" -> \"x4\" [label=\"-9.31\"];\n", - "\"5625d2515750\" -> \"x0\" [label=\"127.74\"];\n", - "\"x4\" [label=\"x4\"];\n", + "\"7f370003ebc0\" [label=\"x0>0.75?\"];\n", + "\"7f370003ebc0\" -> \"7f37000b5410\" [headlabel=\"\",taillabel=\"Y\"];\n", + "\"7f370003ebc0\" -> \"7f370003f120\" [headlabel=\"\",taillabel=\"N\"];\n", + "\"7f37000b5410\" [label=\"x0>0.81?\"];\n", + "\"7f37000b5410\" -> \"7f370003ef80\" [headlabel=\"\",taillabel=\"Y\"];\n", + "\"7f37000b5410\" -> \"x0\" [headlabel=\"49.48\",taillabel=\"N\"];\n", + "\"7f370003ef80\" [label=\"30.49*MeanLabel\"];\n", "\"x0\" [label=\"x0\"];\n", - "\"5625d1ddc200\" [label=\"Add\"];\n", - "\"5625d1ddc200\" -> \"5625d1d1bc10\" [label=\"\"];\n", - "\"5625d1ddc200\" -> \"x1\" [label=\"-0.09\"];\n", - "\"5625d1d1bc10\" [label=\"Add\"];\n", - "\"5625d1d1bc10\" -> \"x4\" [label=\"13.60\"];\n", - "\"5625d1d1bc10\" -> \"x2\" [label=\"0.11\"];\n", - "\"x4\" [label=\"x4\"];\n", - "\"x2\" [label=\"x2\"];\n", + "\"7f370003f120\" [label=\"Add\"];\n", + "\"7f370003f120\" -> \"x1\" [label=\"0.02\"];\n", + "\"7f370003f120\" -> \"x6\" [label=\"10.47\"];\n", "\"x1\" [label=\"x1\"];\n", + "\"x6\" [label=\"x6\"];\n", "}\n", "\n" ] @@ -493,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "f35b1e05", "metadata": {}, "outputs": [ @@ -503,195 +525,107 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "\n", - "5625d27dfb10\n", - "\n", - "Add\n", + "7f370003ebc0\n", + "\n", + "x0>0.75?\n", "\n", - "\n", + "\n", "\n", - "x6\n", - "\n", - "x6\n", + "7f37000b5410\n", + "\n", + "x0>0.81?\n", "\n", - 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"\n", - "5625d1d1bc10->x4\n", - "\n", - "\n", - "13.60\n", - "\n", - "\n", - "\n", - "x2\n", - "\n", - "x2\n", + "x6\n", + "\n", + "x6\n", "\n", - "\n", - "\n", - "5625d1d1bc10->x2\n", - "\n", - "\n", - "0.11\n", + "\n", + "\n", + "7f370003f120->x6\n", + "\n", + "\n", + "10.47\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -718,7 +652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/python_api/classifier.rst b/docs/python_api/classifier.rst index c0317657..789af014 100644 --- a/docs/python_api/classifier.rst +++ b/docs/python_api/classifier.rst @@ -1,7 +1,6 @@ BrushClassifier =============== - -.. autoclass:: brush.estimator.BrushClassifier +.. autoclass:: pybrush.BrushClassifier :members: :undoc-members: \ No newline at end of file diff --git a/docs/python_api/estimator.rst b/docs/python_api/estimator.rst index 7ed540ed..73b4d865 100644 --- a/docs/python_api/estimator.rst +++ b/docs/python_api/estimator.rst @@ -1,6 +1,6 @@ BrushEstimator ============== -.. autoclass:: brush.estimator.BrushEstimator +.. autoclass:: pybrush.BrushEstimator.BrushEstimator :members: :undoc-members: \ No newline at end of file diff --git a/docs/python_api/index.md b/docs/python_api/index.md index 74c1d0f2..7463ff63 100644 --- a/docs/python_api/index.md +++ b/docs/python_api/index.md @@ -2,6 +2,8 @@ ```{toctree} estimator +interface regressor classifier +python_api ``` \ No newline at end of file diff --git a/docs/python_api/interface.rst b/docs/python_api/interface.rst new file mode 100644 index 00000000..e35c8cb6 --- /dev/null +++ b/docs/python_api/interface.rst @@ -0,0 +1,6 @@ +EstimatorInterface +================== + +.. autoclass:: pybrush.EstimatorInterface.EstimatorInterface + :members: + :undoc-members: \ No newline at end of file diff --git a/docs/python_api/python_api.rst b/docs/python_api/python_api.rst index 49c1e879..701cd786 100644 --- a/docs/python_api/python_api.rst +++ b/docs/python_api/python_api.rst @@ -3,7 +3,7 @@ Python API .. With doxygennamespace: -.. .. doxygennamespace:: brush +.. .. doxygennamespace:: pybrush .. :members: diff --git a/docs/python_api/regressor.rst b/docs/python_api/regressor.rst index 9289f85d..6191bcef 100644 --- a/docs/python_api/regressor.rst +++ b/docs/python_api/regressor.rst @@ -1,6 +1,6 @@ BrushRegressor ============== -.. autoclass:: brush.estimator.BrushRegressor +.. autoclass:: pybrush.BrushEstimator.BrushRegressor :members: :undoc-members: \ No newline at end of file diff --git a/environment.yml b/environment.yml index 0325ff03..5d33457f 100644 --- a/environment.yml +++ b/environment.yml @@ -11,16 +11,19 @@ dependencies: - ninja - ceres-solver=2.1.0 - pybind11>=2.6.2 + - taskflow - pytest #=6.2.4 - pydot - scikit-learn - pandas - # these are not required for install + # not required for install the c++ library (but used in the wrapper) - jupyter - ipython - pip - nlohmann_json - pybind11_json + # Building documentation + - doxygen - sphinx - pip: - graphviz diff --git a/pybrush/BrushEstimator.py b/pybrush/BrushEstimator.py new file mode 100644 index 00000000..8daf0b38 --- /dev/null +++ b/pybrush/BrushEstimator.py @@ -0,0 +1,298 @@ +""" +sklearn-compatible wrapper for GP analyses. + +See engine.cpp for Python (via pybind11) modules that give more fine-grained +control of the underlying GP objects. +""" + +import numpy as np +import pandas as pd + +from sklearn.base import BaseEstimator, ClassifierMixin, \ + RegressorMixin, TransformerMixin + +from sklearn.utils.validation import check_is_fitted + +from pybrush import Parameters, Dataset, SearchSpace, brush_rng +from pybrush.EstimatorInterface import EstimatorInterface +from pybrush import RegressorEngine, ClassifierEngine, MultiClassifierEngine + +class BrushEstimator(EstimatorInterface, BaseEstimator): + """ + This is the base class for Brush estimators using the c++ engine. + + Parameters are defined and documented in + :py:class:`EstimatorInterface ` + + Attributes + ---------- + best_estimator_ : pybrush.Program + The final model picked from training. Used in subsequent calls to :func:`predict`. + archive_ : list[deap_api.DeapIndividual] + The final population from training. + data_ : pybrush.Dataset + The complete data in Brush format. + train_ : pybrush.Dataset + Partition of `data_` containing `(1-validation_size)`% of the data, in Brush format. + validation_ : pybrush.Dataset + Partition of `data_` containing `(validation_size)`% of the data, in Brush format. + search_space_ : a Brush `SearchSpace` object. + Holds the operators and terminals and sampling utilities to update programs. + """ + + def __init__(self, **kwargs): + EstimatorInterface.__init__(self, **kwargs) + + def fit(self, X, y): + """ + Fit an estimator to X,y. + + Parameters + ---------- + X : np.ndarray + 2-d array of input data. + y : np.ndarray + 1-d array of (boolean) target values. + """ + + self.feature_names_ = [] + if isinstance(X, pd.DataFrame): + self.feature_names_ = X.columns.to_list() + + self.data_ = self._make_data(X, y, + feature_names=self.feature_names_, + validation_size=self.validation_size) + + # set n classes if relevant + self.n_classes_ = 0 + if self.mode=="classification": + self.n_classes_ = len(np.unique(y)) + + # These have a default behavior to return something meaningfull if + # no values are set + self.train_ = self.data_.get_training_data() + self.train_.set_batch_size(self.batch_size) # TODO: update batch indexes at the beggining of every generation + self.validation_ = self.data_.get_validation_data() + + self.parameters_ = self._wrap_parameters(n_classes=self.n_classes_) + + self.search_space_ = SearchSpace(self.data_, self.parameters_.functions, self.weights_init) + + self.engine_ = None + if self.mode == 'classification': + self.engine_ = ( ClassifierEngine + if self.n_classes_ == 2 else + MultiClassifierEngine)(self.parameters_) + else: + self.engine_ = RegressorEngine(self.parameters_) + + self.engine_.fit(self.data_) + + self.archive_ = self.engine_.get_archive() + self.best_estimator_ = self.engine_.best_ind + + return self + + def _make_data(self, X, y=None, feature_names=[], validation_size=0.0): + """ + Prepare the data for training or prediction. + + Parameters: + - X: array-like or pandas DataFrame, shape (n_samples, n_features) + The input features. + - y: array-like or pandas Series, shape (n_samples,), optional (default=None) + The target variable. + - feature_names: list, optional (default=[]) + The names of the features. + - validation_size: float, optional (default=0.0) + The proportion of the data to be used for validation. + + Returns: + - dataset: Dataset + The prepared dataset object containing the input features, target variable, + feature names, and validation size. + """ + + # This function should not partition data (since it may be used in `predict`). + # partitioning is done by `fit`. Feature names should be inferred + # before calling _make_data (so predict can be made with np arrays or + # pd dataframes). + + if isinstance(y, pd.Series): + y = y.values + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + if y is None: + return Dataset(X=X, + feature_names=feature_names, c=self.mode == "classification", + validation_size=validation_size) + + return Dataset(X=X, y=y, + feature_names=feature_names, c=self.mode == "classification", + validation_size=validation_size) + + + def predict(self, X): + """Predict using the best estimator in the archive. """ + + check_is_fitted(self) + + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + data = Dataset(X=X, ref_dataset=self.data_, c=self.mode == "classification", + feature_names=self.feature_names_) + + # data = self._make_data(X, feature_names=self.feature_names_) + + return self.best_estimator_.program.predict(data) + + def get_params(self, deep=True): + out = dict() + for (key, value) in self.__dict__.items(): + if not key.endswith('_'): + if deep and hasattr(value, "get_params") and not isinstance(value, type): + deep_items = value.get_params().items() + out.update((key + "__" + k, val) for k, val in deep_items) + out[key] = value + return out + + def predict_archive(self, X): + """Returns a list of dictionary predictions for all models.""" + + check_is_fitted(self) + + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + data = Dataset(X=X, ref_dataset=self.data_, c=self.mode == "classification", + feature_names=self.feature_names_) + + archive = self.engine_.get_archive() + + preds = [] + for ind in archive: + tmp = { + 'id' : ind['id'], + 'y_pred' : self.engine_.predict_archive(ind['id'], data) + } + preds.append(tmp) + + return preds + + +class BrushClassifier(BrushEstimator, ClassifierMixin): + """Brush with c++ engine for classification. + + Parameters are defined and documented in + :py:class:`EstimatorInterface ` + + This class inherits from :py:class:`BrushEstimator `. + A full documentation of the methods and attributes can be found there. + + Examples + -------- + >>> import pandas as pd + >>> df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') + >>> X = df.drop(columns='target') + >>> y = df['target'] + >>> from pybrush import BrushClassifier + >>> est = BrushClassifier() + >>> est.fit(X,y) + >>> # print('score:', est.score(X,y)) + """ + def __init__( self, **kwargs): + super().__init__(mode='classification',**kwargs) + + def predict_proba(self, X): + """Predict class probabilities for X. + + Parameters + ---------- + X : {array-like} of shape (n_samples, n_features) + The input samples. + + Returns + ------- + p : ndarray of shape (n_samples, n_classes) + The class probabilities of the input samples. + + """ + + check_is_fitted(self) + + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + data = Dataset(X=X, ref_dataset=self.data_, c=True, + feature_names=self.feature_names_) + + # data = self._make_data(X, feature_names=self.feature_names_) + + prob = self.best_estimator_.program.predict_proba(data) + + if self.n_classes_ == 2: + prob = np.hstack( (np.ones(X.shape[0]).reshape(-1,1), prob.reshape(-1,1)) ) + prob[:, 0] -= prob[:, 1] + + return prob + + + def predict_proba_archive(self, X): + """Returns a list of dictionary predictions for all models.""" + + check_is_fitted(self) + + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + data = Dataset(X=X, ref_dataset=self.data_, c=True, + feature_names=self.feature_names_) + + archive = self.engine_.get_archive() + + preds = [] + for ind in archive: + tmp = { + 'id' : ind['id'], + 'y_pred' : self.engine_.predict_proba_archive(ind['id'], data) + } + preds.append(tmp) + + return preds + + +class BrushRegressor(BrushEstimator, RegressorMixin): + """Brush with c++ engine for regression. + + Parameters are defined and documented in + :py:class:`EstimatorInterface ` + + This class inherits from :py:class:`BrushEstimator `. + A full documentation of the methods and attributes can be found there. + + Examples + -------- + >>> import pandas as pd + >>> df = pd.read_csv('docs/examples/datasets/d_enc.csv') + >>> X = df.drop(columns='label') + >>> y = df['label'] + >>> from pybrush import BrushRegressor + >>> est = BrushRegressor() + >>> est.fit(X,y) + >>> # print('score:', est.score(X,y)) + """ + + def __init__(self, **kwargs): + super().__init__(mode='regressor',**kwargs) \ No newline at end of file diff --git a/pybrush/DeapEstimator.py b/pybrush/DeapEstimator.py new file mode 100644 index 00000000..eca83ccc --- /dev/null +++ b/pybrush/DeapEstimator.py @@ -0,0 +1,405 @@ +""" +sklearn-compatible wrapper for GP analyses. + +See brushgp.cpp for Python (via pybind11) modules that give more fine-grained +control of the underlying GP objects. +""" + +import functools + +import numpy as np +import pandas as pd + +from deap import algorithms, base, creator, tools + +from sklearn.metrics import average_precision_score +from sklearn.preprocessing import MinMaxScaler + +from sklearn.utils.validation import check_is_fitted +from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, \ + TransformerMixin + +from pybrush.EstimatorInterface import EstimatorInterface +from pybrush.deap_api import nsga2 +from pybrush import individual +from pybrush import RegressorEvaluator, ClassifierEvaluator, MultiClassifierEvaluator +from pybrush import RegressorSelector, ClassifierSelector, MultiClassifierSelector +from pybrush import RegressorVariator, ClassifierVariator, MultiClassifierVariator +from pybrush import brush_rng, Parameters, Dataset, SearchSpace + +class DeapEstimator(EstimatorInterface, BaseEstimator): + """ + This is the base class for Brush estimators in python. + + Parameters are defined and documented in pybrush.EstimatorInterface.EstimatorInterface + + Attributes + ---------- + best_estimator_ : pybrush.Program + The final model picked from training. Used in subsequent calls to :func:`predict`. + archive_ : list[deap_api.DeapIndividual] + The final population from training. + data_ : pybrush.Dataset + The complete data in Brush format. + train_ : pybrush.Dataset + Partition of `data_` containing `(1-validation_size)`% of the data, in Brush format. + validation_ : pybrush.Dataset + Partition of `data_` containing `(validation_size)`% of the data, in Brush format. + search_space_ : a Brush `SearchSpace` object. + Holds the operators and terminals and sampling utilities to update programs. + toolbox_ : deap.Toolbox + The toolbox used by DEAP for EA algorithm. + """ + + def __init__(self, **kwargs): + EstimatorInterface.__init__(self, **kwargs) + + def _setup_toolbox(self): + """Setup the deap toolbox""" + toolbox: base.Toolbox = base.Toolbox() + + # create Individual class, inheriting from self.Individual with a fitness attribute + if self.mode == 'classification': + self.Individual = ( individual.ClassifierIndividual + if self.n_classes_ == 2 else + individual.MultiClassifierIndividual) + self.eval_ = ( ClassifierEvaluator() + if self.n_classes_ == 2 else + MultiClassifierEvaluator() ) + self.sel_ = ( ClassifierSelector("nsga2", False) + if self.n_classes_ == 2 else + MultiClassifierSelector("nsga2", False) ) + self.surv_ = ( ClassifierSelector("nsga2", True) + if self.n_classes_ == 2 else + MultiClassifierSelector("nsga2", True) ) + else: + self.Individual = individual.RegressorIndividual + self.sel_ = RegressorSelector("lexicase", False) + self.surv_ = RegressorSelector("nsga2", True) + self.eval_ = RegressorEvaluator() + + toolbox.register("select", lambda pop: self.sel_.select(pop, self.parameters_)) + toolbox.register("survive", lambda pop: self.surv_.survive(pop, self.parameters_)) + + # it could be both sel or surv. + toolbox.register("migrate", lambda pop: self.surv_.migrate(pop, self.parameters_)) + + def update_current_gen(gen): self.parameters_.current_gen = gen + toolbox.register("update_current_gen", update_current_gen) + + def assign_fit(ind, validation=False): + ind.program.fit(self.data_.get_training_data()) + self.eval_.assign_fit(ind, self.data_, self.parameters_, validation) + return ind + + toolbox.register("assign_fit", assign_fit) + + toolbox.register("Clone", lambda ind: self.Individual(ind.program.copy())) + + toolbox.register("mate", self.variator_.cross) + toolbox.register("mutate", self.variator_.mutate) + toolbox.register("vary_pop", lambda pop: self.variator_.vary_pop(pop, self.parameters_)) + + # When solving multi-objective problems, selection and survival must + # support this feature. This means that these selection operators must + # accept a tuple of fitnesses as argument) + # if self.algorithm=="nsga2" or self.algorithm=="nsga2island": + # toolbox.register("select", tools.selTournamentDCD) + # toolbox.register("survive", tools.selNSGA2) + # elif self.algorithm=="ga" or self.algorithm=="gaisland": + # toolbox.register("select", tools.selTournament, tournsize=3) + # def offspring(pop, MU): return pop[-MU:] + # toolbox.register("survive", offspring) + + + # toolbox.population will return a list of elements by calling toolbox.individual + toolbox.register("createRandom", self._make_individual) + toolbox.register("population", tools.initRepeat, list, toolbox.createRandom) + + toolbox.register("get_objectives", lambda: self.objectives) + + return toolbox + + def fit(self, X, y): + """ + Fit an estimator to X,y. + + Parameters + ---------- + X : np.ndarray + 2-d array of input data. + y : np.ndarray + 1-d array of (boolean) target values. + """ + + self.feature_names_ = [] + if isinstance(X, pd.DataFrame): + self.feature_names_ = X.columns.to_list() + + self.data_ = self._make_data(X, y, + feature_names=self.feature_names_, + validation_size=self.validation_size) + + # set n classes if relevant + self.n_classes_ = 0 + if self.mode=="classification": + self.n_classes_ = len(np.unique(y)) + + # These have a default behavior to return something meaningfull if + # no values are set + self.train_ = self.data_.get_training_data() + self.train_.set_batch_size(self.batch_size) + + self.validation_ = self.data_.get_validation_data() + + self.parameters_ = self._wrap_parameters(n_classes=self.n_classes_) + self.search_space_ = SearchSpace(self.data_, self.parameters_.functions, self.weights_init) + + if self.mode == "classification": + self.variator_ = (ClassifierVariator + if self.n_classes_ == 2 else + MultiClassifierVariator + )(self.parameters_, self.search_space_) + elif self.mode == "regressor": + self.variator_ = RegressorVariator(self.parameters_, self.search_space_) + + # from pybrush import RegressorEngine + # brush_estimator = RegressorEngine(self.parameters_) + # brush_estimator.run(self.data_) + # print(brush_estimator.is_fitted) + # print(brush_estimator.best_ind) + else: + raise("Unsupported mode") + + self.toolbox_ = self._setup_toolbox() + + # nsga2 and ga differ in the toolbox + self.archive_, self.logbook_ = nsga2( + self.toolbox_, self.max_gens, self.pop_size, self.cx_prob, + (0.0 0: + print(f'best model {self.best_estimator_.program.get_model()}' + + f' with size {self.best_estimator_.program.size()}, ' + + f' depth {self.best_estimator_.program.depth()}, ' + + f' and fitness {self.archive_[final_ind_idx].fitness}') + + return self + + def _make_data(self, X, y=None, feature_names=[], validation_size=0.0): + # This function should not partition data (since it may be used in `predict`). + # partitioning is done by `fit`. Feature names should be inferred + # before calling _make_data (so predict can be made with np arrays or + # pd dataframes). + + if isinstance(y, pd.Series): + y = y.values + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + if y is None: + return Dataset(X=X, + feature_names=feature_names, validation_size=validation_size) + + return Dataset(X=X, y=y, + feature_names=feature_names, validation_size=validation_size) + + + def _make_individual(self): + # C++'s PTC2-based `make_individual` will create a tree of at least + # the given size. By uniformly sampling the size, we can instantiate a + # population with more diversity + + if self.initialization not in ["uniform", "max_size"]: + raise ValueError(f"Invalid argument value for `initialization`. " + f"expected 'max_size' or 'uniform'. got {self.initialization}") + + ind = self.Individual() + ind.init(self.search_space_, self.parameters_) + ind.objectives = self.objectives + + return ind + + def predict(self, X): + """Predict using the best estimator in the archive. """ + + check_is_fitted(self) + + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + data = Dataset(X=X, ref_dataset=self.data_, + feature_names=self.feature_names_) + + # data = self._make_data(X, feature_names=self.feature_names_) + + return self.best_estimator_.program.predict(data) + + # def _setup_population(self): + # """initialize programs""" + # if self.mode == 'classification': + # generate = self.search_space_.make_classifier + # else: + # generate = self.search_space_.make_regressor + + # programs = [ + # DeapIndividual(generate(self.max_depth, self.max_size)) + # for i in range(self.pop_size) + # ] + # # return [self._create_deap_individual_(p) for p in programs] + # return programs + + def get_params(self, deep=True): + out = dict() + for (key, value) in self.__dict__.items(): + if not key.endswith('_'): + if deep and hasattr(value, "get_params") and not isinstance(value, type): + deep_items = value.get_params().items() + out.update((key + "__" + k, val) for k, val in deep_items) + out[key] = value + return out + + +class DeapClassifier(DeapEstimator,ClassifierMixin): + """Deap-based Brush for classification. + + For options, see :py:class:`DeapEstimator `. + + Examples + -------- + >>> import pandas as pd + >>> df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') + >>> X = df.drop(columns='target') + >>> y = df['target'] + >>> from pybrush import DeapClassifier + >>> est = DeapClassifier() + >>> est.fit(X,y) + >>> # print('score:', est.score(X,y)) + """ + def __init__( self, **kwargs): + super().__init__(mode='classification',**kwargs) + + def predict_proba(self, X): + """Predict class probabilities for X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Internally, it will be converted to + ``dtype=np.float32``. + + Returns + ------- + p : ndarray of shape (n_samples, n_classes) + The class probabilities of the input samples. The order of the + classes corresponds to that in the attribute :term:`classes_`. + + """ + + check_is_fitted(self) + + if isinstance(X, pd.DataFrame): + X = X.values + + assert isinstance(X, np.ndarray) + + data = Dataset(X=X, ref_dataset=self.data_, + feature_names=self.feature_names_) + + # data = self._make_data(X, feature_names=self.feature_names_) + + prob = self.best_estimator_.program.predict_proba(data) + + if self.n_classes_ <= 2: + prob = np.hstack( (np.ones(X.shape[0]).reshape(-1,1), prob.reshape(-1,1)) ) + prob[:, 0] -= prob[:, 1] + + return prob + + +class DeapRegressor(DeapEstimator, RegressorMixin): + """Deap-based Brush for regression. + + For options, see :py:class:`DeapEstimator `. + + Examples + -------- + >>> import pandas as pd + >>> df = pd.read_csv('docs/examples/datasets/d_enc.csv') + >>> X = df.drop(columns='label') + >>> y = df['label'] + >>> from pybrush import DeapRegressor + >>> est = DeapRegressor() + >>> est.fit(X,y) + >>> # print('score:', est.score(X,y)) + """ + def __init__(self, **kwargs): + super().__init__(mode='regressor',**kwargs) + +# Under development +# class DeapRepresenter(DeapEstimator, TransformerMixin): +# """Deap-based Brush for representation learning. + +# For options, see :py:class:`DeapEstimator `. + +# Examples +# -------- +# >>> import pandas as pd +# >>> df = pd.read_csv('docs/examples/datasets/d_enc.csv') +# >>> X = df.drop(columns='label') +# >>> y = df['label'] +# >>> from pybrush import DeapRegressor +# >>> est = DeapRegressor() +# >>> est.fit(X,y) +# >>> # print('score:', est.score(X,y)) +# """ +# def __init__(self, **kwargs): +# super().__init__(mode='regressor',**kwargs) + +# def _fitness_function(self, ind, data: Dataset): +# ind.program.fit(data) +# return ( +# # todo: need to return a matrix from X for this +# np.sum((data.get_X()- ind.program.predict(data))**2), +# ind.program.size() +# ) + +# def _make_individual(self): +# return creator.Individual( +# self.search_space_.make_representer(self.max_depth, self.max_size) +# ) + +# def transform(self, X): +# """Transform X using the best estimator in the archive. """ +# return self.predict(X) \ No newline at end of file diff --git a/pybrush/EstimatorInterface.py b/pybrush/EstimatorInterface.py new file mode 100644 index 00000000..0b94a439 --- /dev/null +++ b/pybrush/EstimatorInterface.py @@ -0,0 +1,226 @@ +""" +Estimator interface for GP implementations. + +This interface defines all the hyperparameters for Brush estimators and +provides documentation for the hyperparameters. +""" + +import numpy as np +from pybrush import Parameters + +class EstimatorInterface(): + """ + Interface class for all estimators in pybrush. + + Parameters + ---------- + mode : str, default 'classification' + The mode of the estimator. Used by subclasses + pop_size : int, default 100 + Population size. + max_gens : int, default 100 + Maximum iterations of the algorithm. + max_time: int, optional (default: -1) + Maximum time terminational criterion in seconds. If -1, not used. + max_stall: int, optional (default: 0) + How many generations to continue after the validation loss has + stalled. If 0, not used. + verbosity : int, default 0 + Controls level of printouts. + max_depth : int, default 0 + Maximum depth of GP trees in the GP program. Use 0 for no limit. + max_size : int, default 0 + Maximum number of nodes in a tree. Use 0 for no limit. + num_islands : int, default 5 + Number of independent islands to use in evolutionary framework. + This also corresponds to the number of parallel threads in the c++ + engine. + mig_prob : float, default 0.05 + Probability of occuring a migration between two random islands at the + end of a generation, must be between 0 and 1. + cx_prob : float, default 1/7 + Probability of applying the crossover variation when generating the offspring, + must be between 0 and 1. + Given that there are `n` mutations, and either crossover or mutation is + used to generate each individual in the offspring (but not both at the + same time), we want to have by default an uniform probability between + crossover and every possible mutation. By setting `cx_prob=1/(n+1)`, and + `1/n` for each mutation, we can achieve an uniform distribution. + mutation_probs : dict, default {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, "toggle_weight_on":1/6, "toggle_weight_off":1/6} + A dictionary with keys naming the types of mutation and floating point + values specifying the fraction of total mutations to do with that method. + The probability of having a mutation is `(1-cx_prob)` and, in case the mutation + is applied, then each mutation option is sampled based on the probabilities + defined in `mutation_probs`. The set of probabilities should add up to 1.0. + functions: dict[str,float] or list[str], default {} + A dictionary with keys naming the function set and values giving the probability + of sampling them, or a list of functions which will be weighted uniformly. + If empty, all available functions are included in the search space. + initialization : {"uniform", "max_size"}, default "uniform" + Distribution of sizes on the initial population. If `max_size`, then every + expression is created with `max_size` nodes. If `uniform`, size will be + uniformly distributed between 1 and `max_size`. + objectives : list[str], default ["error", "size"] + list with one or more objectives to use. Options are `"error", "size", "complexity"`. + If `"error"` is used, then it will be the mean squared error for regression, + and accuracy for classification. + algorithm : {"nsga2island", "nsga2", "gaisland", "ga"}, default "nsga2" + Which Evolutionary Algorithm framework to use to evolve the population. + This is used only in DeapEstimators. + weights_init : bool, default True + Whether the search space should initialize the sampling weights of terminal nodes + based on the correlation with the output y. If `False`, then all terminal nodes + will have the same probability of 1.0. + validation_size : float, default 0.0 + Percentage of samples to use as a hold-out partition. These samples are used + to calculate statistics during evolution, but not used to train the models. + The `best_estimator_` will be selected using this partition. If zero, then + the same data used for training is used for validation. + val_from_arch: boolean, optional (default: True) + Validates the final model using the archive rather than the whole + population. + use_arch: boolean, optional (default: False) + Determines if we should save pareto front of the entire evolution + (when set to True) or just the final population (False). + batch_size : float, default 1.0 + Percentage of training data to sample every generation. If `1.0`, then + all data is used. Very small values can improve execution time, but + also lead to underfit. + save_population: str, optional (default "") + string containing the path to save the final population. Ignored if + not provided. + load_population: str, optional (default "") + string containing the path to load the initial population. Ignored + if not provided. + shuffle_split: boolean, optional (default False) + whether if the engine should shuffle the data before splitting it + into train and validation partitions. Ignored if `validation_size` + is set to zero. + logfile: str, optional (default: "") + If specified, spits statistics into a logfile. "" means don't log. + random_state: int or None, default None + If int, then the value is used to seed the c++ random generator; if None, + then a seed will be generated using a non-deterministic generator. It is + important to notice that, even if the random state is fixed, it is + unlikely that running brush using multiple threads will have the same + results. This happens because the Operating System's scheduler is + responsible to choose which thread will run at any given time, thus + reproductibility is not guaranteed. + """ + + def __init__(self, + mode='classification', + pop_size=100, + max_gens=100, + max_time=-1, + max_stall=0, + verbosity=0, + max_depth=3, + max_size=20, + num_islands=1, + n_jobs=1, + mig_prob=0.05, + cx_prob= 1/7, + mutation_probs = {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, + "toggle_weight_on":1/6, "toggle_weight_off":1/6}, + functions: list[str]|dict[str,float] = {}, + initialization="uniform", + algorithm="nsga2", + objectives=["error", "size"], + random_state=None, + logfile="", + save_population="", + load_population="", + shuffle_split=False, + weights_init=True, + val_from_arch=True, + use_arch=False, + validation_size: float = 0.0, + batch_size: float = 1.0 + ): + self.pop_size=pop_size + self.max_gens=max_gens + self.max_stall=max_stall + self.max_time=max_time + self.verbosity=verbosity + self.algorithm=algorithm + self.mode=mode + self.max_depth=max_depth + self.max_size=max_size + self.num_islands=num_islands + self.mig_prob=mig_prob + self.n_jobs=n_jobs + self.cx_prob=cx_prob + self.logfile=logfile + self.save_population=save_population + self.load_population=load_population + self.mutation_probs=mutation_probs + self.val_from_arch=val_from_arch # TODO: val from arch implementation (in cpp side) + self.use_arch=use_arch + self.functions=functions + self.objectives=objectives + self.shuffle_split=shuffle_split + self.initialization=initialization + self.random_state=random_state + self.batch_size=batch_size + self.weights_init=weights_init + self.validation_size=validation_size + + def _wrap_parameters(self, **extra_kwargs): + """ + Creates a `Parameters` class to send to c++ backend the settings for + the algorithm to use. + """ + + if isinstance(self.functions, list): + self.functions_ = {k:1.0 for k in self.functions} + else: + self.functions_ = self.functions + + params = Parameters() + + params.classification = self.mode == "classification" + params.n_classes = self.n_classes_ + params.verbosity = self.verbosity + params.n_jobs = self.n_jobs + params.pop_size = self.pop_size + params.max_gens = self.max_gens + params.logfile = self.logfile + params.save_population = self.save_population + params.load_population = self.load_population + params.max_stall = self.max_stall + params.max_time = self.max_time + params.num_islands = self.num_islands + params.max_depth = self.max_depth + params.max_size = self.max_size + params.objectives = self.objectives + params.shuffle_split = self.shuffle_split + params.cx_prob = self.cx_prob + params.use_arch = self.use_arch + params.val_from_arch = self.val_from_arch + params.mig_prob = self.mig_prob + params.functions = self.functions_ + params.mutation_probs = self.mutation_probs + params.validation_size = self.validation_size + params.batch_size = self.batch_size + params.feature_names = self.feature_names_ + + params.scorer_ = "mse" + if self.mode == "classification": + params.scorer_ = "log" if self.n_classes_ == 2 else "multi_log" + + if self.random_state is not None: + seed = 0 + if isinstance(self.random_state, np.random.Generator): + seed = self.random_state.integers(1_000_000) + elif isinstance(self.random_state, int): + seed = self.random_state + else: + raise ValueError("random_state must be either a numpy random generator or an integer") + + params.random_state = seed + + for k, v in extra_kwargs.items(): + setattr(params, k, v) + + return params \ No newline at end of file diff --git a/pybrush/__init__.py b/pybrush/__init__.py new file mode 100644 index 00000000..21172afd --- /dev/null +++ b/pybrush/__init__.py @@ -0,0 +1,21 @@ +# Interfaces for Brush data structures. Use to prototype with Brush +from _brush import Dataset +from _brush import SearchSpace +from _brush import Parameters + +# geting random floats with brush (avoid random state issues in parallel exec) +from _brush import rnd_flt as brush_rng + +from _brush import individual # Individual classes (specific for each task) + +# c++ learning engines +from _brush.engine import * + +# Evaluation, selection, and variation. used in python estimators +from _brush import RegressorEvaluator, ClassifierEvaluator, MultiClassifierEvaluator +from _brush import RegressorSelector, ClassifierSelector, MultiClassifierSelector +from _brush import RegressorVariator, ClassifierVariator, MultiClassifierVariator + +# full estimator implementations -------------------- +from pybrush.DeapEstimator import DeapClassifier, DeapRegressor +from pybrush.BrushEstimator import BrushClassifier, BrushRegressor diff --git a/pybrush/_versionstr.py b/pybrush/_versionstr.py new file mode 100644 index 00000000..6cf57698 --- /dev/null +++ b/pybrush/_versionstr.py @@ -0,0 +1 @@ +__version__="0.1" \ No newline at end of file diff --git a/pybrush/deap_api/__init__.py b/pybrush/deap_api/__init__.py new file mode 100644 index 00000000..e13697ee --- /dev/null +++ b/pybrush/deap_api/__init__.py @@ -0,0 +1 @@ +from pybrush.deap_api.nsga2 import nsga2 \ No newline at end of file diff --git a/pybrush/deap_api/nsga2.py b/pybrush/deap_api/nsga2.py new file mode 100644 index 00000000..822feda2 --- /dev/null +++ b/pybrush/deap_api/nsga2.py @@ -0,0 +1,78 @@ +from deap import tools +from deap.benchmarks.tools import hypervolume +import numpy as np +import functools + +def nsga2(toolbox, NGEN, MU, CXPB, use_batch, verbosity, rnd_flt): + # NGEN = 250 + # MU = 100 + # CXPB = 0.9 + # rnd_flt: random number generator to sample crossover prob + + def calculate_statistics(ind): + on_train = ind.fitness.values + # TODO: make this work again + on_val = ind.fitness.values #toolbox.evaluateValidation(ind) + + return (*on_train, *on_val) + + stats = tools.Statistics(calculate_statistics) + + stats.register("avg", np.nanmean, axis=0) + stats.register("med", np.nanmedian, axis=0) + stats.register("std", np.nanstd, axis=0) + stats.register("min", np.nanmin, axis=0) + stats.register("max", np.nanmax, axis=0) + + logbook = tools.Logbook() + logbook.header = ['gen', 'evals'] + \ + [f"{stat} {partition} O{objective}" + for stat in ['avg', 'med', 'std', 'min', 'max'] + for partition in ['train', 'val'] + for objective in toolbox.get_objectives()] + + pop = toolbox.population(n=MU) + pop = list(toolbox.map(toolbox.assign_fit, pop)) + + record = stats.compile(pop) + logbook.record(gen=0, evals=len(pop), **record) + + if verbosity > 0: + print(logbook.stream) + + # Begin the generational process + for gen in range(1, NGEN+1): + + # this is used in cpp to decide if we are going to do some calculations or not + toolbox.update_current_gen(gen) + + # Vary the population + + parents = toolbox.select(pop) # , len(pop) # select method from brush's cpp side will use the values in self.parameters_ to decide how many individuals it should select + + offspring = toolbox.vary_pop(parents) + offspring = list(toolbox.map(toolbox.assign_fit, offspring)) + + # Select the next generation population (no sorting before this step, as + # survive==offspring will cut it in half) + pop = toolbox.survive(pop + offspring) + + pop = toolbox.migrate(pop) + + pop.sort(key=lambda x: x.fitness, reverse=True) + + record = stats.compile(pop) + logbook.record(gen=gen, evals=len(offspring)+(len(pop) if use_batch else 0), **record) + + if verbosity > 0: + print(logbook.stream) + print(pop[0].fitness.values, pop[0].fitness.weights, pop[0].fitness.wvalues, + pop[0].program.get_model(),) + + # if verbosity > 0: + # print("Final population hypervolume is %f" % hypervolume(pop, [1000.0, 50.0])) + + archive = tools.ParetoFront() + archive.update(pop) + + return archive, logbook \ No newline at end of file diff --git a/setup.py b/setup.py index 0dd66c13..5e8277fe 100644 --- a/setup.py +++ b/setup.py @@ -45,7 +45,7 @@ def build_extension(self, ext): "-DEXAMPLE_VERSION_INFO={}".format(self.distribution.get_version()), "-DCMAKE_BUILD_TYPE={}".format(cfg), # not used on MSVC, but no harm "-DGTEST=OFF", - "-DDOCS=OFF", + "-DDOCS=ON", "-DGTEST_INCLUDE_DIRS={}/include/".format(conda_prefix), "-DGTEST_LIBRARIES={}/lib/libgtest.so".format(conda_prefix), "-DEIGEN3_INCLUDE_DIR={}/include/eigen3/".format(conda_prefix), @@ -99,15 +99,15 @@ def build_extension(self, ext): ) # # # Clean old build/ directory if it exists -# try: -# remove_tree("./build") -# print("Removed old build directory.") -# except FileNotFoundError: -# print("No existing build directory found - skipping.") +try: + remove_tree("./build") + print("Removed old build directory.") +except FileNotFoundError: + print("No existing build directory found - skipping.") setup( name="pybrush", - version="0.0.1", + version="0.0.1", # TODO: use versionstr here author="William La Cava, Joseph D. Romano", author_email="joseph.romano@pennmedicine.upenn.edu", # can change to Bill license="GNU General Public License v3.0", @@ -117,9 +117,9 @@ def build_extension(self, ext): project_urls={ "Bug Tracker": "https://github.com/lacava/brush/issues", }, - package_dir={"": "src"}, - packages=find_packages(where="src"), - # cmake_install_dir="src/brush", + package_dir={"": "."}, + packages=find_packages(where="."), + #cmake_install_dir="src/", python_requires=">=3.6", install_requires=[ 'numpy', diff --git a/src/bindings/bind_dataset.cpp b/src/bindings/bind_dataset.cpp index 872750d5..41cbb94a 100644 --- a/src/bindings/bind_dataset.cpp +++ b/src/bindings/bind_dataset.cpp @@ -9,77 +9,52 @@ namespace nl = nlohmann; void bind_dataset(py::module & m) { py::class_(m, "Dataset") - - // construct from X - // .def(py::init &>()) - // construct from X (and optional validation and batch sizes) with constructor 3. - .def(py::init([](const Ref& X, - const float validation_size=0.0, - const float batch_size=1.0){ - return br::Data::Dataset( - X, {}, validation_size, batch_size); - }), - py::arg("X"), - py::arg("validation_size") = 0.0, - py::arg("batch_size") = 1.0 - ) - // construct from X, feature names - // .def(py::init< - // const Ref&, - // const vector& - // >() - // ) // construct from X, feature names (and optional validation and batch sizes) with constructor 3. .def(py::init([](const Ref& X, - const vector& feature_names, + const vector& feature_names=vector(), + const bool c=false, const float validation_size=0.0, const float batch_size=1.0){ return br::Data::Dataset( - X, feature_names, validation_size, batch_size); + X, feature_names, c, validation_size, batch_size); }), py::arg("X"), - py::arg("feature_names"), + py::arg("feature_names") = vector(), + py::arg("c") = false, py::arg("validation_size") = 0.0, py::arg("batch_size") = 1.0 ) - - // construct from X, y arrays - // .def(py::init &, Ref &>()) - // construct from X, y arrays (and optional validation and batch sizes) with constructor 2. + // construct from X, y, feature names (and optional validation and batch sizes) with constructor 2. .def(py::init([](const Ref& X, const Ref& y, + const vector& feature_names=vector(), + const bool c=false, const float validation_size=0.0, const float batch_size=1.0){ return br::Data::Dataset( - X, y, {}, {}, false, validation_size, batch_size); + X, y, feature_names, {}, c, validation_size, batch_size); }), py::arg("X"), py::arg("y"), + py::arg("feature_names") = vector(), + py::arg("c") = false, py::arg("validation_size") = 0.0, py::arg("batch_size") = 1.0 ) - - // construct from X, y, feature names - // .def(py::init< - // const Ref&, - // const Ref&, - // const vector& - // >() - // ) - // construct from X, y, feature names (and optional validation and batch sizes) with constructor 2. + // construct from X, feature names, but copying the feature types from a + // reference dataset with constructor 4. Useful for predicting (specially + // because the user can provide a single element matrix, or an array with + // no feature names). .def(py::init([](const Ref& X, - const Ref& y, + const br::Data::Dataset& ref_dataset, const vector& feature_names, - const float validation_size=0.0, - const float batch_size=1.0){ - return br::Data::Dataset( - X, y, feature_names, {}, false, validation_size, batch_size); + const bool c=false){ + return br::Data::Dataset(X, ref_dataset, feature_names, c); }), py::arg("X"), - py::arg("y"), + py::arg("ref_dataset"), py::arg("feature_names"), - py::arg("validation_size") = 0.0, - py::arg("batch_size") = 1.0 + py::arg("c") = false ) .def_readwrite("y", &br::Data::Dataset::y) diff --git a/src/bindings/bind_engines.cpp b/src/bindings/bind_engines.cpp new file mode 100644 index 00000000..619b7bbf --- /dev/null +++ b/src/bindings/bind_engines.cpp @@ -0,0 +1,16 @@ +#include "module.h" +#include "bind_engines.h" + +namespace py = pybind11; +namespace br = Brush; +namespace nl = nlohmann; + +void bind_engines(py::module& m) +{ + bind_engine(m, "RegressorEngine"); + bind_engine(m, "ClassifierEngine"); + + // TODO: make these work + bind_engine(m, "MultiClassifierEngine"); + bind_engine(m, "RepresenterEngine"); +} \ No newline at end of file diff --git a/src/bindings/bind_engines.h b/src/bindings/bind_engines.h new file mode 100644 index 00000000..034aceb1 --- /dev/null +++ b/src/bindings/bind_engines.h @@ -0,0 +1,104 @@ +#include "module.h" +#include "../engine.h" +#include "../engine.cpp" + +// TODO: figure out why do I need to include the whole thing (otherwise it gives me symbol errors) +#include "../selection/selection.h" +#include "../selection/selection.cpp" +#include "../selection/selection_operator.h" +#include "../selection/selection_operator.cpp" +#include "../selection/nsga2.h" +#include "../selection/nsga2.cpp" +#include "../selection/lexicase.h" +#include "../selection/lexicase.cpp" + +#include "../eval/evaluation.h" +#include "../eval/evaluation.cpp" + +#include "../pop/population.cpp" +#include "../pop/population.h" + +#include "../pop/archive.cpp" +#include "../pop/archive.h" + +using Reg = Brush::RegressorEngine; +using Cls = Brush::ClassifierEngine; +using Rep = Brush::RepresenterEngine; +using MCls = Brush::MulticlassClassifierEngine; + +namespace nl = nlohmann; +namespace br = Brush; + +using stream_redirect = py::call_guard; + +template +void bind_engine(py::module& m, string name) +{ + using RetType = std::conditional_t< + std::is_same_v, ArrayXf, + std::conditional_t, ArrayXb, + std::conditional_t, ArrayXi, ArrayXXf>>>; + + py::class_ engine(m, name.data() ); + engine.def(py::init<>()) + .def(py::init([](br::Parameters& p){ T e(p); + return e; }) + ) + .def_property("params", &T::get_params, &T::set_params) + .def_property_readonly("is_fitted", &T::get_is_fitted) + .def_property_readonly("best_ind", &T::get_best_ind) + // .def("run", &T::run, py::call_guard(), "run from brush dataset") + .def("fit", + static_cast(&T::fit), + py::call_guard(), + "fit from Dataset object") + .def("fit", + static_cast &X, const Ref &y)>(&T::fit), + py::call_guard(), + "fit from X,y data") + .def("predict", + static_cast(&T::predict), + "predict from Dataset object") + .def("predict", + static_cast &X)>(&T::predict), + "predict from X data") + .def("predict_archive", + static_cast(&T::predict_archive), + "predict from individual in archive") + .def("predict_archive", + static_cast &X)>(&T::predict_archive), + "predict from individual in archive") + .def("get_archive", &T::get_archive, py::arg("front") = false) + .def(py::pickle( + [](const T &p) { // __getstate__ + /* Return a tuple that fully encodes the state of the object */ + // return py::make_tuple(p.value(), p.extra()); + nl::json j = p; + return j; + }, + [](nl::json j) { // __setstate__ + T p = j; + return p; + }) + ) + ; + + // specialization for subclasses + if constexpr (std::is_same_v) + { + engine.def("predict_proba", + static_cast(&T::predict_proba), + "predict from Dataset object") + .def("predict_proba", + static_cast &X)>(&T::predict_proba), + "predict from X data") + .def("predict_proba_archive", + static_cast(&T::predict_proba_archive), + "predict from individual in archive") + .def("predict_proba_archive", + static_cast &X)>(&T::predict_proba_archive), + "predict from individual in archive") + + ; + } +} \ No newline at end of file diff --git a/src/bindings/bind_evaluator.cpp b/src/bindings/bind_evaluator.cpp new file mode 100644 index 00000000..ae8a6450 --- /dev/null +++ b/src/bindings/bind_evaluator.cpp @@ -0,0 +1,16 @@ +#include "module.h" +#include "bind_evaluator.h" + +namespace py = pybind11; +namespace br = Brush; +namespace nl = nlohmann; + +using stream_redirect = py::call_guard; + +void bind_evaluators(py::module &m) +{ + bind_evaluator(m, "RegressorEvaluator"); + bind_evaluator(m, "ClassifierEvaluator"); + bind_evaluator(m, "MultiClassifierEvaluator"); + bind_evaluator(m, "RepresenterEvaluator"); +} \ No newline at end of file diff --git a/src/bindings/bind_evaluator.h b/src/bindings/bind_evaluator.h new file mode 100644 index 00000000..90ea3ab5 --- /dev/null +++ b/src/bindings/bind_evaluator.h @@ -0,0 +1,21 @@ +#include "module.h" +#include "../eval/evaluation.h" +#include "../eval/evaluation.cpp" + +namespace py = pybind11; +namespace br = Brush; +namespace nl = nlohmann; + +using stream_redirect = py::call_guard; + +template +void bind_evaluator(py::module& m, string name) +{ + using Class = br::Eval::Evaluation; + // TODO: will this part of c++ be exposed? + py::class_ eval(m, name.data() ); + eval.def(py::init<>()) + .def("assign_fit", &Class::assign_fit) + .def_property("scorer", &Class::get_scorer, &Class::set_scorer) + ; +} \ No newline at end of file diff --git a/src/bindings/bind_fitness.cpp b/src/bindings/bind_fitness.cpp new file mode 100644 index 00000000..c483acfc --- /dev/null +++ b/src/bindings/bind_fitness.cpp @@ -0,0 +1,50 @@ +#include "module.h" + +#include "../ind/fitness.h" + +namespace nl = nlohmann; +namespace br = Brush; + +using stream_redirect = py::call_guard; + +void bind_fitness(py::module& m) +{ + py::class_(m, "Fitness", py::dynamic_attr()) + .def(py::init<>()) + .def(py::init&>(), "Constructor with weights") + .def_property("values", &br::Fitness::get_values, &br::Fitness::set_values) + .def_property_readonly("weights", &br::Fitness::get_weights) + .def_property_readonly("wvalues", &br::Fitness::get_wvalues) + .def("dominates", &br::Fitness::dominates) + .def("clearValues", &br::Fitness::clearValues, "Clear the weighted values vector") + .def_property("rank", &br::Fitness::get_rank, &br::Fitness::set_rank) + .def_property("loss", &br::Fitness::get_loss, &br::Fitness::set_loss) + .def_property("loss_v", &br::Fitness::get_loss_v, &br::Fitness::set_loss_v) + .def_property("crowding_dist", &br::Fitness::get_crowding_dist, &br::Fitness::set_crowding_dist) + + .def("valid", &br::Fitness::valid, "Check if the fitness is valid") + .def("__hash__", &br::Fitness::hash, py::is_operator()) + .def("__eq__", &br::Fitness::operator==, py::is_operator()) + .def("__ne__", &br::Fitness::operator!=, py::is_operator()) + .def("__lt__", &br::Fitness::operator<, py::is_operator()) + .def("__gt__", &br::Fitness::operator>, py::is_operator()) + .def("__le__", &br::Fitness::operator<=, py::is_operator()) + .def("__ge__", &br::Fitness::operator>=, py::is_operator()) + .def("__str__", &br::Fitness::toString, "String representation of the Fitness object") + .def("__repr__", &br::Fitness::repr, "Representation for debugging the Fitness object") + .def(py::pickle( + [](const br::Fitness &f) { // __getstate__ + /* Return a tuple that fully encodes the state of the object */ + // return py::make_tuple(p.value(), p.extra()); + nl::json j = f; + return j; + }, + [](nl::json j) { // __setstate__ + br::Fitness f = j; + return f; + } + ) + ) + ; + +} \ No newline at end of file diff --git a/src/bindings/bind_individuals.cpp b/src/bindings/bind_individuals.cpp new file mode 100644 index 00000000..8b5a9851 --- /dev/null +++ b/src/bindings/bind_individuals.cpp @@ -0,0 +1,15 @@ +#include "module.h" +#include "bind_individuals.h" + +namespace py = pybind11; +namespace br = Brush; +namespace nl = nlohmann; + + +void bind_individuals(py::module& m) +{ + bind_individual(m, "RegressorIndividual"); + bind_individual(m, "ClassifierIndividual"); + bind_individual(m, "MultiClassifierIndividual"); + // bind_individual(m, "RepresenterIndividual"); +} \ No newline at end of file diff --git a/src/bindings/bind_individuals.h b/src/bindings/bind_individuals.h new file mode 100644 index 00000000..5777c5e2 --- /dev/null +++ b/src/bindings/bind_individuals.h @@ -0,0 +1,81 @@ +#include "module.h" + +#include "../ind/individual.h" + +namespace nl = nlohmann; +namespace br = Brush; + +using Reg = Brush::RegressorIndividual; +using Cls = Brush::ClassifierIndividual; +using MCls = Brush::MulticlassClassifierIndividual; +using Rep = Brush::RepresenterIndividual; + +using stream_redirect = py::call_guard; + +template +void bind_individual(py::module& m, string name) +{ + using Class = br::Pop::Individual; + + using RetType = std::conditional_t< + std::is_same_v, ArrayXf, + std::conditional_t, ArrayXb, + std::conditional_t, ArrayXi, ArrayXXf>>>; + + py::class_ ind(m, name.data() ); + ind.def(py::init<>()) + .def(py::init([](br::Program& prg){ Class i(prg); + return i; }) + ) + .def(py::init([](const json& j){ br::Program prg = j; + Class i(prg); + return i; }) + ) + .def("init", &Class::init) + .def_property("objectives", &Class::get_objectives, &Class::set_objectives) + .def_property_readonly("program", &Class::get_program) + .def_property_readonly("fitness", &Class::get_fitness) + .def("get_model", &Class::get_model, + py::arg("fmt") = "compact", + py::arg("pretty") = false) + .def("get_dot_model", &Class::get_dot_model, + py::arg("extras") = "") + .def("fit", + static_cast(&Class::fit), + "fit from Dataset object") + .def("fit", + static_cast &X, const Ref &y)>(&Class::fit), + "fit from X,y data") + .def("predict", + static_cast(&Class::predict), + "predict from Dataset object") + .def("predict", + static_cast &X)>(&Class::predict), + "predict from X data") + .def(py::pickle( + [](const Class &p) { // __getstate__ + /* Return a tuple that fully encodes the state of the object */ + // return py::make_tuple(p.value(), p.extra()); + nl::json j = p; + return j; + }, + [](nl::json j) { // __setstate__ + Class p = j; + return p; + } + ) + ) + ; + + if constexpr (std::is_same_v) + { + ind.def("predict_proba", + static_cast(&Class::predict_proba), + "predict from Dataset object") + .def("predict_proba", + static_cast &X)>(&Class::predict_proba), + "predict from X data") + ; + } + +} \ No newline at end of file diff --git a/src/bindings/bind_params.cpp b/src/bindings/bind_params.cpp index 75521ab3..a4db4ae6 100644 --- a/src/bindings/bind_params.cpp +++ b/src/bindings/bind_params.cpp @@ -6,16 +6,54 @@ namespace br = Brush; void bind_params(py::module& m) { - // py::object params = Brush::PARAMS; - // m.attr("PARAMS") = params; - - // py::class_(m, "Params", py::dynamic_attr()) - // .def(py::init<>()) - - m.def("set_params", &br::set_params); - m.def("get_params", &br::get_params); m.def("set_random_state", [](unsigned int seed) { br::Util::r = *br::Util::Rnd::initRand(); br::Util::r.set_seed(seed); }); m.def("rnd_flt", [](){ return br::Util::r.rnd_flt(); }); + + py::class_(m, "Parameters") + .def(py::init([](){ Brush::Parameters p; return p; })) + .def_property("verbosity", &Brush::Parameters::get_verbosity, &Brush::Parameters::set_verbosity) + .def_property("pop_size", &Brush::Parameters::get_pop_size, &Brush::Parameters::set_pop_size) + .def_property("max_gens", &Brush::Parameters::get_max_gens, &Brush::Parameters::set_max_gens) + .def_property("max_stall", &Brush::Parameters::get_max_stall, &Brush::Parameters::set_max_stall) + .def_property("max_time", &Brush::Parameters::get_max_time, &Brush::Parameters::set_max_time) + .def_property("current_gen", &Brush::Parameters::get_current_gen, &Brush::Parameters::set_current_gen) + .def_property("scorer_", &Brush::Parameters::get_scorer_, &Brush::Parameters::set_scorer_) + .def_property("random_state", &Brush::Parameters::get_random_state, &Brush::Parameters::set_random_state) + .def_property("load_population", &Brush::Parameters::get_load_population, &Brush::Parameters::set_load_population) + .def_property("save_population", &Brush::Parameters::get_save_population, &Brush::Parameters::set_save_population) + .def_property("logfile", &Brush::Parameters::get_logfile, &Brush::Parameters::set_logfile) + .def_property("num_islands", &Brush::Parameters::get_num_islands, &Brush::Parameters::set_num_islands) + .def_property("use_arch", &Brush::Parameters::get_use_arch, &Brush::Parameters::set_use_arch) + .def_property("val_from_arch", &Brush::Parameters::get_val_from_arch, &Brush::Parameters::set_val_from_arch) + .def_property("n_classes", &Brush::Parameters::get_n_classes, &Brush::Parameters::set_n_classes) + .def_property("n_jobs", &Brush::Parameters::get_n_jobs, &Brush::Parameters::set_n_classes) + .def_property("classification", &Brush::Parameters::get_classification, &Brush::Parameters::set_classification) + .def_property("shuffle_split", &Brush::Parameters::get_shuffle_split, &Brush::Parameters::set_shuffle_split) + .def_property("validation_size", &Brush::Parameters::get_validation_size, &Brush::Parameters::set_validation_size) + .def_property("feature_names", &Brush::Parameters::get_feature_names, &Brush::Parameters::set_feature_names) + .def_property("batch_size", &Brush::Parameters::get_batch_size, &Brush::Parameters::set_batch_size) + .def_property("max_depth", &Brush::Parameters::get_max_depth, &Brush::Parameters::set_max_depth) + .def_property("max_size", &Brush::Parameters::get_max_size, &Brush::Parameters::set_max_size) + .def_property("objectives", &Brush::Parameters::get_objectives, &Brush::Parameters::set_objectives) + .def_property("sel", &Brush::Parameters::get_sel, &Brush::Parameters::set_sel) + .def_property("surv", &Brush::Parameters::get_surv, &Brush::Parameters::set_surv) + .def_property("cx_prob", &Brush::Parameters::get_cx_prob, &Brush::Parameters::set_cx_prob) + .def_property("mig_prob", &Brush::Parameters::get_mig_prob, &Brush::Parameters::set_mig_prob) + .def_property("functions", &Brush::Parameters::get_functions, &Brush::Parameters::set_functions) + .def_property("mutation_probs", &Brush::Parameters::get_mutation_probs, &Brush::Parameters::set_mutation_probs) + .def(py::pickle( + [](const Brush::Parameters &p) { // __getstate__ + /* Return a tuple that fully encodes the state of the object */ + // return py::make_tuple(p.value(), p.extra()); + nl::json j = p; + return j; + }, + [](nl::json j) { // __setstate__ + Brush::Parameters p = j; + return p; + }) + ) + ; } \ No newline at end of file diff --git a/src/bindings/bind_programs.cpp b/src/bindings/bind_programs.cpp index 136bae5f..905b3dfa 100644 --- a/src/bindings/bind_programs.cpp +++ b/src/bindings/bind_programs.cpp @@ -11,15 +11,8 @@ namespace nl = nlohmann; void bind_programs(py::module& m) { - py::class_(m, "Fitness", py::dynamic_attr()) - .def(py::init<>()) - .def_readwrite("values", &br::Fitness::values) - .def_readwrite("valid", &br::Fitness::valid) - ; - bind_program(m, "Regressor"); bind_program(m, "Classifier"); bind_program(m, "MultiClassifier"); bind_program(m, "Representer"); - } \ No newline at end of file diff --git a/src/bindings/bind_programs.h b/src/bindings/bind_programs.h index 96a36b71..49ca8ff7 100644 --- a/src/bindings/bind_programs.h +++ b/src/bindings/bind_programs.h @@ -24,7 +24,6 @@ void bind_program(py::module& m, string name) .def(py::init( [](const json& j){ T p = j; return p; }) ) - .def_readwrite("fitness", &T::fitness) .def("fit", static_cast(&T::fit), "fit from Dataset object") @@ -46,12 +45,15 @@ void bind_program(py::module& m, string name) .def("get_dot_model", &T::get_dot_model, py::arg("extras")="") .def("get_weights", &T::get_weights) .def("size", &T::size, py::arg("include_weight")=true) + .def("complexity", &T::complexity) .def("depth", &T::depth) - .def("cross", &T::cross, py::return_value_policy::automatic, - "Performs one attempt to stochastically swap subtrees between two programs and generate a child") - .def("mutate", &T::mutate, py::return_value_policy::automatic, - "Performs one attempt to stochastically mutate the program and generate a child") + // .def("cross", &T::cross, py::return_value_policy::automatic, + // "Performs one attempt to stochastically swap subtrees between two programs and generate a child") + // .def("mutate", &T::mutate, py::return_value_policy::automatic, + // "Performs one attempt to stochastically mutate the program and generate a child") .def("set_search_space", &T::set_search_space) + //.def("copy", &T::copy<>, py::return_value_policy::copy) + .def("copy", [](const T& self){ T clone(self); return clone; }) .def(py::pickle( [](const T &p) { // __getstate__ /* Return a tuple that fully encodes the state of the object */ @@ -74,7 +76,8 @@ void bind_program(py::module& m, string name) "predict from Dataset object") .def("predict_proba", static_cast &X)>(&T::predict_proba), - "fit from X,y data"); + "predict from X data") + ; } } \ No newline at end of file diff --git a/src/bindings/bind_search_space.cpp b/src/bindings/bind_search_space.cpp index 29dc468c..5bb2c795 100644 --- a/src/bindings/bind_search_space.cpp +++ b/src/bindings/bind_search_space.cpp @@ -1,5 +1,5 @@ #include "module.h" -#include "../search_space.h" +#include "../vary/search_space.h" #include "../program/program.h" namespace py = pybind11; namespace br = Brush; @@ -13,16 +13,35 @@ void bind_search_space(py::module &m) // constructing it with a Dataset object, rather than initializing it as an // empty struct and then calling init() with the Dataset object. py::class_(m, "SearchSpace") - .def(py::init([](br::Data::Dataset data) - { + .def(py::init([](br::Data::Dataset data, bool weights_init=true){ SearchSpace SS; - SS.init(data); - return SS; })) - .def(py::init&>()) - .def("make_regressor", &br::SearchSpace::make_regressor) - .def("make_classifier", &br::SearchSpace::make_classifier) - .def("make_multiclass_classifier", &br::SearchSpace::make_multiclass_classifier) - .def("make_representer", &br::SearchSpace::make_representer) + SS.init(data, {}, weights_init); + return SS; + }), + py::arg("data"), + py::arg("weights_init") = true ) + .def(py::init&, + bool>(), + py::arg("data"), + py::arg("user_ops"), + py::arg("weights_init") = true ) + .def("make_regressor", &br::SearchSpace::make_regressor, + py::arg("max_d") = 0, + py::arg("max_size") = 0, + py::arg("params") = Brush::Parameters() ) + .def("make_classifier", &br::SearchSpace::make_classifier, + py::arg("max_d") = 0, + py::arg("max_size") = 0, + py::arg("params") = Brush::Parameters() ) + .def("make_multiclass_classifier", + &br::SearchSpace::make_multiclass_classifier, + py::arg("max_d") = 0, + py::arg("max_size") = 0, + py::arg("params") = Brush::Parameters() ) + .def("make_representer", &br::SearchSpace::make_representer, + py::arg("max_d") = 0, + py::arg("max_size") = 0, + py::arg("params") = Brush::Parameters() ) .def("print", &br::SearchSpace::print, stream_redirect() diff --git a/src/bindings/bind_selection.cpp b/src/bindings/bind_selection.cpp new file mode 100644 index 00000000..427ead9e --- /dev/null +++ b/src/bindings/bind_selection.cpp @@ -0,0 +1,15 @@ +#include "module.h" +#include "bind_selection.h" + +namespace py = pybind11; +namespace br = Brush; +namespace nl = nlohmann; + +void bind_selections(py::module& m) +{ + bind_selection(m, "RegressorSelector"); + bind_selection(m, "ClassifierSelector"); + + bind_selection(m, "MultiClassifierSelector"); + // bind_selection(m, "RepresenterSelector"); +} \ No newline at end of file diff --git a/src/bindings/bind_selection.h b/src/bindings/bind_selection.h new file mode 100644 index 00000000..b8c9d45b --- /dev/null +++ b/src/bindings/bind_selection.h @@ -0,0 +1,103 @@ +#include "module.h" + +// TODO: figure out why im having symbol errors (if i dont include the cpp here as well) +#include "../selection/selection.h" +#include "../selection/selection.cpp" +#include "../selection/selection_operator.h" +#include "../selection/selection_operator.cpp" +#include "../selection/nsga2.h" +#include "../selection/nsga2.cpp" +#include "../selection/lexicase.h" +#include "../selection/lexicase.cpp" + +#include "../pop/population.cpp" +#include "../pop/population.h" + +namespace py = pybind11; +namespace nl = nlohmann; +namespace br = Brush; + +template +void bind_selection(py::module& m, string name) +{ + using Class = br::Sel::Selection; + + // TODO: make selection a non-templated class + py::class_ sel(m, name.data() ); + + sel.def(py::init<>()) + .def(py::init( + [](string type, bool survival){ Class s(type, survival); return s; }) + ) + .def("select", [](Class &self, + std::vector>& individuals, + const Parameters& params) { + + // auto sel = Class("nsga2", false); + auto pop = br::Pop::Population(); + + pop.init(individuals, params); + + vector> pool; + pool.resize(0); + + for (int island = 0; island < params.num_islands; ++island) + { + vector selected = self.select(pop, island, params); + + // std::cout << "selecting in island " << island << std::endl; + + for (size_t idx : selected) { + pool.push_back(pop[idx]); + } + } + + return pool; + }) + .def("survive", [](Class &self, + std::vector>& individuals, + const Parameters& params) { + + // auto sel = Class("nsga2", false); + auto pop = br::Pop::Population(); + + pop.init(individuals, params); + + vector> pool; + pool.resize(0); + + for (int island = 0; island < params.num_islands; ++island) + { + vector selected = self.survive(pop, island, params); + + for (size_t idx : selected) { + pool.push_back(pop[idx]); + } + } + + return pool; + }) + .def("migrate", [](Class &self, + std::vector>& individuals, + const Parameters& params) { + + auto pop = br::Pop::Population(); + + pop.init(individuals, params); + pop.migrate(); // this will modify island indexes inplace + + vector> pool; + pool.resize(0); + + for (int island = 0; island < params.num_islands; ++island) + { + vector selected = pop.get_island_indexes(island); + + for (size_t idx : selected) { + pool.push_back(pop[idx]); + } + } + return pool; + }) + ; +} \ No newline at end of file diff --git a/src/bindings/bind_variation.cpp b/src/bindings/bind_variation.cpp new file mode 100644 index 00000000..739d115e --- /dev/null +++ b/src/bindings/bind_variation.cpp @@ -0,0 +1,14 @@ +#include "module.h" +#include "bind_variation.h" + +namespace py = pybind11; +namespace br = Brush; +namespace nl = nlohmann; + +void bind_variations(py::module& m) +{ + bind_variation(m,"RegressorVariator"); + bind_variation(m, "ClassifierVariator"); + bind_variation(m, "MultiClassifierVariator"); + bind_variation(m, "RepresenterVariator"); +} \ No newline at end of file diff --git a/src/bindings/bind_variation.h b/src/bindings/bind_variation.h new file mode 100644 index 00000000..c08032f2 --- /dev/null +++ b/src/bindings/bind_variation.h @@ -0,0 +1,69 @@ +#include "module.h" + +#include "../vary/variation.h" +#include "../vary/variation.cpp" +#include "../pop/population.h" +#include "../pop/population.cpp" + +namespace py = pybind11; +namespace nl = nlohmann; +namespace br = Brush; + +template +void bind_variation(py::module& m, string name) +{ + using Class = br::Var::Variation; + + // TODO: make variation a non-templated class + py::class_ vary(m, name.data() ); + + vary.def(py::init<>([](br::Parameters& p, br::SearchSpace& ss){ + Class variation(p, ss); + return variation; })) + .def("mutate", &Class::mutate, py::return_value_policy::automatic) + .def("cross", &Class::cross, py::return_value_policy::automatic) + .def("vary_pop", [](Class &self, + std::vector>& individuals, + const Parameters& params) { + if (individuals.size() != params.pop_size) { + string msg = "Individual vector has different number of " + "individuals than pop_size. When calling " + "variation, they should be the same. popsize is "+ + to_string(params.pop_size)+", number of " + "individuals is "+to_string(individuals.size()); + + throw std::runtime_error(msg); + } + + auto pop = br::Pop::Population(); + + pop.init(individuals, params); + + vector> pool; + pool.resize(0); + + for (int island = 0; island < params.num_islands; ++island) + { + // I am assuming the individual vector passed as argument + // will contain the selected parents already + vector parents = pop.get_island_indexes(island); + + // including offspring indexes (the vary method will store the + // offspring in the second half of the index vector) + pop.add_offspring_indexes(island); + + self.vary(pop, island, parents); + + // making copies of the second half of the island individuals + vector indices = pop.get_island_indexes(island); + int start = indices.size()/2; + for (unsigned i = start; i #include #include #include + // json support #include "pybind11_json/pybind11_json.hpp" #include "nlohmann/json.hpp" \ No newline at end of file diff --git a/src/brush/__init__.py b/src/brush/__init__.py deleted file mode 100644 index 8e705ae4..00000000 --- a/src/brush/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .estimator import BrushClassifier, BrushRegressor -from _brush import Dataset, SearchSpace \ No newline at end of file diff --git a/src/brush/deap_api/__init__.py b/src/brush/deap_api/__init__.py deleted file mode 100644 index b2b2dfa8..00000000 --- a/src/brush/deap_api/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .nsga2 import nsga2 -from .utils import DeapIndividual \ No newline at end of file diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py deleted file mode 100644 index e45d011b..00000000 --- a/src/brush/deap_api/nsga2.py +++ /dev/null @@ -1,105 +0,0 @@ -from deap import tools -from deap.benchmarks.tools import diversity, convergence, hypervolume -import numpy as np -import functools - - -def nsga2(toolbox, NGEN, MU, CXPB, use_batch, verbosity, rnd_flt): - # NGEN = 250 - # MU = 100 - # CXPB = 0.9 - # rnd_flt: random number generator to sample crossover prob - - def calculate_statistics(ind): - on_train = ind.fitness.values - on_val = toolbox.evaluateValidation(ind) - - return (*on_train, *on_val) - - stats = tools.Statistics(calculate_statistics) - - stats.register("avg", np.mean, axis=0) - stats.register("med", np.median, axis=0) - stats.register("std", np.std, axis=0) - stats.register("min", np.min, axis=0) - stats.register("max", np.max, axis=0) - - logbook = tools.Logbook() - logbook.header = "gen", "evals", "avg (O1 train, O2 train, O1 val, O2 val)", \ - "med (O1 train, O2 train, O1 val, O2 val)", \ - "std (O1 train, O2 train, O1 val, O2 val)", \ - "min (O1 train, O2 train, O1 val, O2 val)", \ - "max (O1 train, O2 train, O1 val, O2 val)" - - pop = toolbox.population(n=MU) - - batch = toolbox.getBatch() # everytime this function is called, a new random batch is generated - - # OBS: evaluate calls fit in the individual. It is different from using it to predict. The - # function evaluateValidation don't call the fit - fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), pop) - - for ind, fit in zip(pop, fitnesses): - ind.fitness.values = fit - - # This is just to assign the crowding distance to the individuals - # no actual selection is done - pop = toolbox.survive(pop, len(pop)) - - record = stats.compile(pop) - logbook.record(gen=0, evals=len(pop), **record) - - if verbosity > 0: - print(logbook.stream) - - # Begin the generational process - for gen in range(1, NGEN): - # The batch will be random only if it is not the size of the entire train set. - # In this case, we dont need to reevaluate the whole pop - if (use_batch): - batch = toolbox.getBatch() - fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), pop) - - for ind, fit in zip(pop, fitnesses): - ind.fitness.values = fit - - # Vary the population - # offspring = tools.selTournamentDCD(pop, len(pop)) - parents = toolbox.select(pop, len(pop)) - # offspring = [toolbox.clone(ind) for ind in offspring] - offspring = [] - - for ind1, ind2 in zip(parents[::2], parents[1::2]): - off1, off2 = None, None - if rnd_flt() < CXPB: - off1, off2 = toolbox.mate(ind1, ind2) - else: - off1 = toolbox.mutate(ind1) - off2 = toolbox.mutate(ind2) - - # avoid inserting empty solutions - if off1 is not None: offspring.extend([off1]) - if off2 is not None: offspring.extend([off2]) - - # archive.update(offspring) - # Evaluate the individuals with an invalid fitness - invalid_ind = [ind for ind in offspring if not ind.fitness.valid] - fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), invalid_ind) - for ind, fit in zip(invalid_ind, fitnesses): - ind.fitness.values = fit - - # Select the next generation population - pop = toolbox.survive(pop + offspring, MU) - record = stats.compile(pop) - logbook.record(gen=gen, evals=len(offspring)+(len(pop) if use_batch else 0), **record) - - if verbosity > 0: - print(logbook.stream) - - if verbosity > 0: - print("Final population hypervolume is %f" % hypervolume(pop, [1000.0, 50.0])) - - archive = tools.ParetoFront() - archive.update(pop) - - return archive, logbook \ No newline at end of file diff --git a/src/brush/deap_api/utils.py b/src/brush/deap_api/utils.py deleted file mode 100644 index 9a9bdcb3..00000000 --- a/src/brush/deap_api/utils.py +++ /dev/null @@ -1,4 +0,0 @@ -class DeapIndividual(): - """Class that wraps brush program for creator.Individual class from DEAP.""" - def __init__(self, prg): - self.prg = prg \ No newline at end of file diff --git a/src/brush/estimator.py b/src/brush/estimator.py deleted file mode 100644 index fd4913af..00000000 --- a/src/brush/estimator.py +++ /dev/null @@ -1,473 +0,0 @@ -""" -sklearn-compatible wrapper for GP analyses. - -See brushgp.cpp for Python (via pybind11) modules that give more fine-grained -control of the underlying GP objects. -""" -from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin -# from sklearn.metrics import mean_squared_error -import numpy as np -import pandas as pd -# import deap as dp -from deap import algorithms, base, creator, tools -# from tqdm import tqdm -from types import NoneType -import _brush -from .deap_api import nsga2, DeapIndividual -# from _brush import Dataset, SearchSpace - - -class BrushEstimator(BaseEstimator): - """ - This is the base class for Brush estimators. - This class shouldn't be called directly; instead, call a child class like - :py:class:`BrushRegressor ` or :py:class:`BrushClassifier `. - All of the shared parameters are documented here. - - Parameters - ---------- - mode : str, default 'classification' - The mode of the estimator. Used by subclasses - pop_size : int, default 100 - Population size. - max_gen : int, default 100 - Maximum iterations of the algorithm. - verbosity : int, default 0 - Controls level of printouts. - max_depth : int, default 0 - Maximum depth of GP trees in the GP program. Use 0 for no limit. - max_size : int, default 0 - Maximum number of nodes in a tree. Use 0 for no limit. - cx_prob : float, default 1/7 - Probability of applying the crossover variation when generating the offspring, - must be between 0 and 1. - Given that there are `n` mutations, and either crossover or mutation is - used to generate each individual in the offspring (but not both at the - same time), we want to have by default an uniform probability between - crossover and every possible mutation. By setting `cx_prob=1/(n+1)`, and - `1/n` for each mutation, we can achieve an uniform distribution. - mutation_options : dict, default {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, "toggle_weight_on":1/6, "toggle_weight_off":1/6} - A dictionary with keys naming the types of mutation and floating point - values specifying the fraction of total mutations to do with that method. - The probability of having a mutation is `(1-cx_prob)` and, in case the mutation - is applied, then each mutation option is sampled based on the probabilities - defined in `mutation_options`. The set of probabilities should add up to 1.0. - functions: dict[str,float] or list[str], default {} - A dictionary with keys naming the function set and values giving the probability - of sampling them, or a list of functions which will be weighted uniformly. - If empty, all available functions are included in the search space. - initialization : {"grow", "full"}, default "grow" - Strategy to create the initial population. If `full`, then every expression is created - with `max_size` nodes. If `grow`, size will be uniformly distributed. - validation_size : float, default 0.0 - Percentage of samples to use as a hold-out partition. These samples are used - to calculate statistics during evolution, but not used to train the models. - The `best_estimator_` will be selected using this partition. If zero, then - the same data used for training is used for validation. - batch_size : float, default 1.0 - Percentage of training data to sample every generation. If `1.0`, then - all data is used. Very small values can improve execution time, but - also lead to underfit. - random_state: int or None, default None - If int, then the value is used to seed the c++ random generator; if None, - then a seed will be generated using a non-deterministic generator. It is - important to notice that, even if the random state is fixed, it is - unlikely that running brush using multiple threads will have the same - results. This happens because the Operating System's scheduler is - responsible to choose which thread will run at any given time, thus - reproductibility is not guaranteed. - - Attributes - ---------- - best_estimator_ : _brush.Program - The final model picked from training. Used in subsequent calls to :func:`predict`. - archive_ : list[deap_api.DeapIndividual] - The final population from training. - data_ : _brush.Dataset - The complete data in Brush format. - train_ : _brush.Dataset - Partition of `data_` containing `(1-validation_size)`% of the data, in Brush format. - validation_ : _brush.Dataset - Partition of `data_` containing `(validation_size)`% of the data, in Brush format. - search_space_ : a Brush `SearchSpace` object. - Holds the operators and terminals and sampling utilities to update programs. - toolbox_ : deap.Toolbox - The toolbox used by DEAP for EA algorithm. - - """ - - def __init__( - self, - mode='classification', - pop_size=100, - max_gen=100, - verbosity=0, - max_depth=3, - max_size=20, - cx_prob= 1/7, - mutation_options = {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, - "toggle_weight_on":1/6, "toggle_weight_off":1/6}, - functions: list[str]|dict[str,float] = {}, - initialization="grow", - random_state=None, - validation_size: float = 0.0, - batch_size: float = 1.0 - ): - self.pop_size=pop_size - self.max_gen=max_gen - self.verbosity=verbosity - self.mode=mode - self.max_depth=max_depth - self.max_size=max_size - self.cx_prob=cx_prob - self.mutation_options=mutation_options - self.functions=functions - self.initialization=initialization - self.random_state=random_state - self.batch_size=batch_size - self.validation_size=validation_size - - - def _setup_toolbox(self, data_train, data_validation): - """Setup the deap toolbox""" - toolbox: base.Toolbox = base.Toolbox() - - # creator.create is used to "create new functions", and takes at least - # 2 arguments: the name of the newly created class and a base class - - # Minimizing/maximizing problem: negative/positive weight, respectively. - # Our classification is using the error as a metric - # Comparing fitnesses: https://deap.readthedocs.io/en/master/api/base.html#deap.base.Fitness - creator.create("FitnessMulti", base.Fitness, weights=self.weights) - - # create Individual class, inheriting from self.Individual with a fitness attribute - creator.create("Individual", DeapIndividual, fitness=creator.FitnessMulti) - - toolbox.register("mate", self._crossover) - toolbox.register("mutate", self._mutate) - - # When solving multi-objective problems, selection and survival must - # support this feature. This means that these selection operators must - # accept a tuple of fitnesses as argument) - toolbox.register("select", tools.selTournamentDCD) - toolbox.register("survive", tools.selNSGA2) - - # toolbox.population will return a list of elements by calling toolbox.individual - toolbox.register("createRandom", self._make_individual) - toolbox.register("population", tools.initRepeat, list, toolbox.createRandom) - - toolbox.register("getBatch", data_train.get_batch) - toolbox.register("evaluate", self._fitness_function, data=data_train) - toolbox.register("evaluateValidation", self._fitness_validation, data=data_validation) - - return toolbox - - - def _crossover(self, ind1, ind2): - offspring = [] - - for i,j in [(ind1,ind2),(ind2,ind1)]: - child = i.prg.cross(j.prg) - if child: - offspring.append(creator.Individual(child)) - else: # so we'll always have two elements to unpack in `offspring` - offspring.append(None) - - return offspring[0], offspring[1] - - - def _mutate(self, ind1): - # offspring = (creator.Individual(ind1.prg.mutate(self.search_space_)),) - offspring = ind1.prg.mutate() - - if offspring: - return creator.Individual(offspring) - - return None - - - def fit(self, X, y): - """ - Fit an estimator to X,y. - - Parameters - ---------- - X : np.ndarray - 2-d array of input data. - y : np.ndarray - 1-d array of (boolean) target values. - """ - _brush.set_params(self.get_params()) - - if self.random_state is not None: - _brush.set_random_state(self.random_state) - - self.data_ = self._make_data(X,y, validation_size=self.validation_size) - - # set n classes if relevant - if self.mode=="classification": - self.n_classes_ = len(np.unique(y)) - - # These have a default behavior to return something meaningfull if - # no values are set - self.train_ = self.data_.get_training_data() - self.train_.set_batch_size(self.batch_size) - self.validation_ = self.data_.get_validation_data() - - if isinstance(self.functions, list): - self.functions_ = {k:1.0 for k in self.functions} - else: - self.functions_ = self.functions - - self.search_space_ = _brush.SearchSpace(self.train_, self.functions_) - self.toolbox_ = self._setup_toolbox(data_train=self.train_, data_validation=self.validation_) - - archive, logbook = nsga2( - self.toolbox_, self.max_gen, self.pop_size, self.cx_prob, - (0.0 0: - print(f'best model {self.best_estimator_.get_model()}'+ - f' with size {self.best_estimator_.size()}, ' + - f' depth {self.best_estimator_.depth()}, ' + - f' and fitness {self.archive_[0].fitness}' ) - - return self - - def _make_data(self, X, y=None, validation_size=0.0): - # This function should not partition data (as it is used in predict). - # partitioning is done in fit(). - - if isinstance(y, pd.Series): - y = y.values - if isinstance(X, pd.DataFrame): - # self.data_ = _brush.Dataset(X.to_dict(orient='list'), y) - feature_names = X.columns.to_list() - X = X.values - if isinstance(y, NoneType): - return _brush.Dataset(X, - feature_names=feature_names, validation_size=validation_size) - else: - return _brush.Dataset(X, y, - feature_names=feature_names, validation_size=validation_size) - - assert isinstance(X, np.ndarray) - - # if there is no label, don't include it in library call to Dataset - if isinstance(y, NoneType): - return _brush.Dataset(X, validation_size=validation_size) - - return _brush.Dataset(X, y, validation_size=validation_size) - - - def predict(self, X): - """Predict using the best estimator in the archive. """ - data = self._make_data(X) - return self.best_estimator_.predict(data) - - # def _setup_population(self): - # """initialize programs""" - # if self.mode == 'classification': - # generate = self.search_space_.make_classifier - # else: - # generate = self.search_space_.make_regressor - - # programs = [ - # DeapIndividual(generate(self.max_depth, self.max_size)) - # for i in range(self.pop_size) - # ] - # # return [self._create_deap_individual_(p) for p in programs] - # return programs - - def get_params(self): - return {k:v for k,v in self.__dict__.items() if not k.endswith('_')} - - -class BrushClassifier(BrushEstimator,ClassifierMixin): - """Brush for classification. - - For options, see :py:class:`BrushEstimator `. - - Examples - -------- - >>> import pandas as pd - >>> df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') - >>> X = df.drop(columns='target') - >>> y = df['target'] - >>> from brush import BrushClassifier - >>> est = BrushClassifier() - >>> est.fit(X,y) - >>> print('score:', est.score(X,y)) - """ - def __init__( self, **kwargs): - super().__init__(mode='classification',**kwargs) - - # Weight of each objective (+ for maximization, - for minimization) - self.weights = (+1.0,-1.0) - - def _fitness_validation(self, ind, data: _brush.Dataset): - # Fitness without fitting the expression, used with validation data - return ( # (accuracy, size) - (data.y==ind.prg.predict(data)).sum() / data.y.shape[0], - ind.prg.size() - ) - - def _fitness_function(self, ind, data: _brush.Dataset): - ind.prg.fit(data) - return ( # (accuracy, size) - (data.y==ind.prg.predict(data)).sum() / data.y.shape[0], - ind.prg.size() - ) - - def _make_individual(self): - # C++'s PTC2-based `make_individual` will create a tree of at least - # the given size. By uniformly sampling the size, we can instantiate a - # population with more diversity - - if self.initialization not in ["grow", "full"]: - raise ValueError(f"Invalid argument value for `initialization`. " - f"expected 'full' or 'grow'. got {self.initialization}") - - return creator.Individual( - self.search_space_.make_classifier( - self.max_depth,(0 if self.initialization=='grow' else self.max_size)) - if self.n_classes_ == 2 else - self.search_space_.make_multiclass_classifier( - self.max_depth, (0 if self.initialization=='grow' else self.max_size)) - ) - - def predict_proba(self, X): - """Predict class probabilities for X. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - The input samples. Internally, it will be converted to - ``dtype=np.float32``. - - Returns - ------- - p : ndarray of shape (n_samples, n_classes) - The class probabilities of the input samples. The order of the - classes corresponds to that in the attribute :term:`classes_`. - - """ - data = self._make_data(X) - return self.best_estimator_.predict_proba(data) - -class BrushRegressor(BrushEstimator, RegressorMixin): - """Brush for regression. - - For options, see :py:class:`BrushEstimator `. - - Examples - -------- - >>> import pandas as pd - >>> df = pd.read_csv('docs/examples/datasets/d_enc.csv') - >>> X = df.drop(columns='label') - >>> y = df['label'] - >>> from brush import BrushRegressor - >>> est = BrushRegressor() - >>> est.fit(X,y) - >>> print('score:', est.score(X,y)) - """ - def __init__(self, **kwargs): - super().__init__(mode='regressor',**kwargs) - - # Weight of each objective (+ for maximization, - for minimization) - self.weights = (-1.0,-1.0) - - def _fitness_validation(self, ind, data: _brush.Dataset): - # Fitness without fitting the expression, used with validation data - - MSE = np.mean( (data.y-ind.prg.predict(data))**2 ) - if not np.isfinite(MSE): # numeric erros, np.nan, +-np.inf - MSE = np.inf - - return ( MSE, ind.prg.size() ) - - def _fitness_function(self, ind, data: _brush.Dataset): - ind.prg.fit(data) - - MSE = np.mean( (data.y-ind.prg.predict(data))**2 ) - if not np.isfinite(MSE): # numeric erros, np.nan, +-np.inf - MSE = np.inf - - return ( MSE, ind.prg.size() ) - - def _make_individual(self): - if self.initialization not in ["grow", "full"]: - raise ValueError(f"Invalid argument value for `initialization`. " - f"expected 'full' or 'grow'. got {self.initialization}") - - return creator.Individual( # No arguments (or zero): brush will use PARAMS passed in set_params. max_size is sampled between 1 and params['max_size'] if zero is provided - self.search_space_.make_regressor( - self.max_depth, (0 if self.initialization=='grow' else self.max_size)) - ) - -# Under development -# class BrushRepresenter(BrushEstimator, TransformerMixin): -# """Brush for representation learning. - -# For options, see :py:class:`BrushEstimator `. - -# Examples -# -------- -# >>> import pandas as pd -# >>> df = pd.read_csv('docs/examples/datasets/d_enc.csv') -# >>> X = df.drop(columns='label') -# >>> y = df['label'] -# >>> from brush import BrushRegressor -# >>> est = BrushRegressor() -# >>> est.fit(X,y) -# >>> print('score:', est.score(X,y)) -# """ -# def __init__(self, **kwargs): -# super().__init__(mode='regressor',**kwargs) - -# def _fitness_function(self, ind, data: _brush.Dataset): -# ind.prg.fit(data) -# return ( -# # todo: need to return a matrix from X for this -# np.sum((data.get_X()- ind.prg.predict(data))**2), -# ind.prg.size() -# ) - -# def _make_individual(self): -# return creator.Individual( -# self.search_space_.make_representer(self.max_depth, self.max_size) -# ) - -# def transform(self, X): -# """Transform X using the best estimator in the archive. """ -# return self.predict(X) \ No newline at end of file diff --git a/src/data/data.cpp b/src/data/data.cpp index b80668df..5ca46f1d 100644 --- a/src/data/data.cpp +++ b/src/data/data.cpp @@ -100,7 +100,17 @@ State check_type(const ArrayXf& x) } } return tmp; +} +template +State cast_type(const ArrayXf& x, const StateRef& x_ref) +{ + if (std::holds_alternative(x_ref)) + return ArrayXi(x.cast()); + else if (std::holds_alternative(x_ref)) + return ArrayXb(x.cast()); + + return x; } /// return a slice of the data using indices idx @@ -130,6 +140,9 @@ Dataset Dataset::operator()(const vector& idx) const return Dataset(new_features, new_y, this->classification); } + +// TODO: i need to improve how get batch works. Maybe a function to update batch indexes, and always using the same dataset? +// TODO: also, i need to make sure the get batch will sample only from training data and not test Dataset Dataset::get_batch() const { // will always return a new dataset, even when use_batch is false (this case, returns itself) @@ -214,6 +227,7 @@ void Dataset::init() } } +// TODO: use integer instead of percentage (or even better, have both) float Dataset::get_batch_size() { return batch_size; } void Dataset::set_batch_size(float new_size) { batch_size = new_size; @@ -222,9 +236,9 @@ void Dataset::set_batch_size(float new_size) { /// turns input data into a feature map map Dataset::make_features(const ArrayXXf& X, - const map& Z, - const vector& vn - ) + const map& Z, + const vector& vn + ) { // fmt::print("Dataset::make_features()\n"); map tmp_features; @@ -265,6 +279,57 @@ map Dataset::make_features(const ArrayXXf& X, return tmp_features; }; +/// turns input into a feature map, with feature types copied from a reference +map Dataset::copy_and_make_features(const ArrayXXf& X, + const Dataset& ref_dataset, + const vector& vn + ) +{ + vector var_names; + if (vn.empty()) + { + for (int i = 0; i < X.cols(); ++i) + { + string v = "x_"+to_string(i); + var_names.push_back(v); + } + } + else + { + if (vn.size() != X.cols()) + HANDLE_ERROR_THROW( + fmt::format("Variable names and data size mismatch: " + "{} variable names and {} features in X", + vn.size(), + X.cols() + ) + ); + var_names = vn; + } + + if (ref_dataset.features.size() != var_names.size()) + HANDLE_ERROR_THROW( + fmt::format("Reference dataset with incompatible number of variables: " + "Reference has {} variable names, but X has {}", + ref_dataset.features.size(), + var_names.size() + ) + ); + + map tmp_features; + for (int i = 0; i < X.cols(); ++i) + { + State tmp = cast_type( + X.col(i).array(), + ref_dataset.features.at(var_names.at(i)) + ); + + tmp_features[var_names.at(i)] = tmp; + } + + return tmp_features; +}; + ostream& operator<<(ostream& os, DataType dt) { os << DataTypeName[dt]; diff --git a/src/data/data.h b/src/data/data.h index 629c02b5..a5d8ee26 100644 --- a/src/data/data.h +++ b/src/data/data.h @@ -36,6 +36,10 @@ namespace Data /// determines data types of columns of matrix X. State check_type(const ArrayXf& x); DataType StateType(const State& arg); + +template +State cast_type(const ArrayXf& x, const StateRef& x_ref); + /////////////////////////////////////////////////////////////////////////////// /*! @@ -77,7 +81,7 @@ class Dataset /// @brief percentage of original data used for train. if 0.0, then all data is used for train and validation float validation_size; - bool use_validation; + bool use_validation; // TODO: shuffle before validation (this should be a parameter) /// @brief percentage of training data size to use in each batch. if 1.0, then all data is used float batch_size; @@ -94,6 +98,14 @@ class Dataset const vector& vn = {} ); + // TODO: let the user specify the datatypes + + /// turns input into a feature map, with feature types copied from a reference + map copy_and_make_features(const ArrayXXf& X, + const Dataset& ref_dataset, + const vector& vn = {} + ); + /// 1. initialize data from a map. Dataset(std::map& d, const Ref& y_ = ArrayXf(), @@ -133,14 +145,34 @@ class Dataset /// 3. initialize data from X and feature names Dataset(const ArrayXXf& X, const vector& vn, + bool c = false, float validation_size = 0.0, - float batch_size = 1.0) - : classification(false) + float batch_size = 1.0 + ) + : classification(c) , features(make_features(X,map{},vn)) , validation_size(validation_size) , use_validation(validation_size > 0.0 && validation_size < 1.0) , batch_size(batch_size) , use_batch(batch_size > 0.0 && batch_size < 1.0) + { + init(); + Xref = optional>{X}; + } + + //// 4. initialize data from X, but feature types are copied from a + //// reference dataset. Useful for bypass Brush's type sniffer and + //// doing predictions with small number of samples + Dataset(const ArrayXXf& X, const Dataset& ref_dataset, + const vector& vn, + bool c = false + ) + : classification(c) + , features(copy_and_make_features(X,ref_dataset,vn)) + , validation_size(0.0) + , use_validation(false) + , batch_size(1.0) + , use_batch(false) { init(); Xref = optional>{X}; @@ -173,7 +205,7 @@ class Dataset // if split is not set, then training = validation. Dataset get_training_data() const; Dataset get_validation_data() const; - + // TODO: shuffle split inline int get_n_samples() const { return std::visit( [&](auto&& arg) -> int { return int(arg.size());}, @@ -217,6 +249,7 @@ template <> struct fmt::formatter: formatter { return formatter::format(Brush::DataTypeName.at(x), ctx); } }; + // TODO: fmt overload for Data // template <> struct fmt::formatter: formatter { // template diff --git a/src/data/io.cpp b/src/data/io.cpp index 8293f478..d81559ae 100755 --- a/src/data/io.cpp +++ b/src/data/io.cpp @@ -81,9 +81,10 @@ Dataset read_csv ( // check if endpoint is binary bool binary_endpoint = (y.array() == 0 || y.array() == 1).all(); - auto result = Dataset(features,y,binary_endpoint); - return result; - + // using constructor 1. (initializing data from a map) + auto result = Dataset(features, y, binary_endpoint); + + return result; } } // Brush diff --git a/src/engine.cpp b/src/engine.cpp new file mode 100644 index 00000000..3550b2f9 --- /dev/null +++ b/src/engine.cpp @@ -0,0 +1,528 @@ +#include "engine.h" + + +#include +#include + + +namespace Brush{ + + +using namespace Pop; +using namespace Sel; +using namespace Eval; +using namespace Var; + +/// @brief initialize Feat object for fitting. +template +void Engine::init() +{ + r.set_seed(params.get_random_state()); + + set_is_fitted(false); + + this->pop = Population(); + + this->evaluator = Evaluation(); + + // TODO: make these classes have a default constructor, and stop recreating instances + this->variator.init(params, ss); + + this->selector = Selection(params.sel, false); + this->survivor = Selection(params.surv, true); + + this->best_score = MAX_FLT; + this->best_complexity = MAX_FLT; + + this->archive.set_objectives(params.objectives); + + timer.Reset(); + + // reset statistics + this->stats = Log_Stats(); +} + +template +void Engine::print_progress(float percentage) +{ + int val = (int) (percentage * 100); + int lpad = (int) (percentage * PBWIDTH); + int rpad = PBWIDTH - lpad; + + printf ("\rCompleted %3d%% [%.*s%*s]", val, lpad, PBSTR.c_str(), rpad, ""); + + fflush (stdout); + + if(val == 100) + cout << "\n"; +} + + +template +void Engine::calculate_stats() +{ + int pop_size = 0; + for (int island=0; island::weightsMap[params.scorer_]; + + int index = 0; + for (int island=0; islandpop.individuals.at(indices[i]); + + // Fitness class will store every information that can be used as + // fitness. you just need to access them. Multiplying by weight + // so we can find best score. From Fitness::dominates: + // the proper way of comparing weighted values is considering + // everything as a maximization problem + scores(index) = p->fitness.get_loss(); + scores_v(index) = p->fitness.get_loss_v(); + sizes(index) = p->get_size(); + complexities(index) = p->get_complexity(); + ++index; + } + } + + assert (pop_size == this->params.pop_size); + + // Multiply by weight to make it a maximization problem. + // Then, multiply again to get rid of signal + float best_score = (scores*error_weight).maxCoeff()*error_weight; + float best_score_v = (scores_v*error_weight).maxCoeff()*error_weight; + float med_score = median(scores); + float med_score_v = median(scores_v); + unsigned med_size = median(sizes); + unsigned med_complexity = median(complexities); + unsigned max_size = sizes.maxCoeff(); + unsigned max_complexity = complexities.maxCoeff(); + + // update stats + stats.update(params.current_gen, + timer.Elapsed().count(), + best_score, + best_score_v, + med_score, + med_score_v, + med_size, + med_complexity, + max_size, + max_complexity); +} + + +template +void Engine::log_stats(std::ofstream& log) +{ + // print stats in tabular format + string sep = ","; + if (params.current_gen == 0) // print header + { + log << "generation" << sep + << "time" << sep + << "best_score" << sep + << "best_score_val" << sep + << "med_score" << sep + << "med_score_val" << sep + << "med_size" << sep + << "med_complexity" << sep + << "max_size" << sep + << "max_complexity" << "\n"; + } + log << params.current_gen << sep + << timer.Elapsed().count() << sep + << stats.best_score.back() << sep + << stats.best_score_v.back() << sep + << stats.med_score.back() << sep + << stats.med_score_v.back() << sep + << stats.med_size.back() << sep + << stats.med_complexity.back() << sep + << stats.max_size.back() << sep + << stats.max_complexity.back() << "\n"; +} + +template +void Engine::print_stats(std::ofstream& log, float fraction) +{ + // progress bar + string bar, space = ""; + for (unsigned int i = 0; i<50; ++i) + { + if (i <= 50*fraction) bar += "/"; + else space += " "; + } + + std::cout.precision(5); + std::cout << std::scientific; + + if(params.max_time == -1) + std::cout << "Generation " << params.current_gen+1 << "/" + << params.max_gens << " [" + bar + space + "]\n"; + else + std::cout << std::fixed << "Time elapsed "<< timer + << "/" << params.max_time + << " seconds (Generation "<< params.current_gen+1 + << ") [" + bar + space + "]\n"; + + std::cout << std::fixed + << "Train Loss (Med): " << stats.best_score.back() << " (" << stats.med_score.back() << ")\n" + << "Val Loss (Med): " << stats.best_score_v.back() << " (" << stats.med_score_v.back() << ")\n" + << "Median Size (Max): " << stats.med_size.back() << " (" << stats.max_size.back() << ")\n" + << "Median complexity (Max): " << stats.med_complexity.back() << " (" << stats.max_complexity.back() << ")\n" + << "Time (s): " << timer + <<"\n\n"; +} + +template +vector Engine::get_archive(bool front) +{ + vector archive_vector; // Use a vector to store serialized individuals + + // TODO: use this front argument (or remove it). I think I can remove + for (const auto& ind : archive.individuals) { + json j; // Serialize each individual + to_json(j, ind); + archive_vector.push_back(j); + } + + return archive_vector; +} + +// TODO: private function called find_individual that searches for it based on id. Then, +// use this function in predict_archive and predict_proba_archive. +template +auto Engine::predict_archive(int id, const Dataset& data) +{ + if (id == best_ind.id) + return best_ind.predict(data); + + for (int i = 0; i < this->archive.individuals.size(); ++i) + { + Individual& ind = this->archive.individuals.at(i); + + if (id == ind.id) + return ind.predict(data); + } + for (int island=0; islandid) + return ind->predict(data); + } + } + + std::runtime_error("Could not find id = " + + to_string(id) + "in archive or population."); + + return best_ind.predict(data); +} + +template +auto Engine::predict_archive(int id, const Ref& X) +{ + Dataset d(X); + return predict_archive(id, d); +} + +template +template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) +auto Engine::predict_proba_archive(int id, const Dataset& data) +{ + if (id == best_ind.id) + return best_ind.predict_proba(data); + + for (int i = 0; i < this->archive.individuals.size(); ++i) + { + Individual& ind = this->archive.individuals.at(i); + + if (id == ind.id) + return ind.predict_proba(data); + } + for (int island=0; islandid) + return ind->predict_proba(data); + } + } + + std::runtime_error("Could not find id = " + + to_string(id) + "in archive or population."); + + return best_ind.predict_proba(data); +} + +template +template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) +auto Engine::predict_proba_archive(int id, const Ref& X) +{ + Dataset d(X); + return predict_proba_archive(id, d); +} + +template +bool Engine::update_best(const Dataset& data, bool val) +{ + float error_weight = Individual::weightsMap[params.scorer_]; + + float f; + bool updated = false; + float bs = this->best_score; + + vector hof = this->pop.hall_of_fame(1); + + for (int i=0; i < hof.size(); ++i) + { + const auto& ind = *pop.individuals.at(hof[i]); + + // TODO: dataset arg here with null default value. if the user provides a dataset, we use it to update + // if there is no validation, then loss_v==loss and this should work just fine + f = ind.fitness.loss_v; + + if (f*error_weight > bs*error_weight + || (f == bs && ind.fitness.complexity < this->best_complexity) ) + { + bs = f; + this->best_ind = ind; + this->best_complexity = ind.fitness.complexity; + + updated = true; + } + } + + this->best_score = bs; + + return updated; +} + + +template +void Engine::run(Dataset &data) +{ + //TODO: i need to make sure i initialize everything (pybind needs to have constructors + // without arguments to work, and i need to handle correcting these values before running) + this->ss = SearchSpace(data, params.functions); + + this->init(); + + if (params.load_population != "") + this->pop.load(params.load_population); + else + this->pop.init(this->ss, this->params); + + // log file stream + std::ofstream log; + if (!params.logfile.empty()) + log.open(params.logfile, std::ofstream::app); + + evaluator.set_scorer(params.scorer_); + + Dataset &batch = data; + + int threads; + if (params.n_jobs == -1) + threads = std::thread::hardware_concurrency(); + else if (params.n_jobs == 0) + threads = params.num_islands; + else + threads = params.n_jobs; + + tf::Executor executor(threads); + + assert( (executor.num_workers() > 0) && "Invalid number of workers"); + + tf::Taskflow taskflow; + + // stop criteria + unsigned generation = 0; + unsigned stall_count = 0; + float fraction = 0; + + bool use_arch; + + auto stop = [&]() { + return ( (generation == params.max_gens) + && ((params.max_stall == 0 || stall_count < params.max_stall) + && (params.max_time == -1 || params.max_time > timer.Elapsed().count()) ) + ); + }; + + // TODO: check that I dont use pop.size() (or I use correctly, because it will return the size with the slots for the offspring) + // vectors to store each island separatedly + vector> island_parents; + vector> survivors; + island_parents.clear(); + island_parents.resize(pop.num_islands); + + survivors.clear(); + survivors.resize(pop.num_islands); + + for (int i=0; i< params.num_islands; i++){ + size_t idx_start = std::floor(i*params.pop_size/params.num_islands); + size_t idx_end = std::floor((i+1)*params.pop_size/params.num_islands); + + auto delta = idx_end - idx_start; + + survivors.at(i).clear(); + island_parents.at(i).clear(); + + survivors.at(i).resize(delta); + island_parents.at(i).resize(delta); + } + + // heavily inspired in https://github.com/heal-research/operon/blob/main/source/algorithms/nsga2.cpp + auto [init, cond, body, back, done] = taskflow.emplace( + [&]() { /* done nothing to do */ }, // init (entry point for taskflow) + + stop, // loop condition + + [&](tf::Subflow& subflow) { // loop body (evolutionary main loop) + auto prepare_gen = subflow.emplace([&]() { + params.set_current_gen(generation); + batch = data.get_batch(); // will return the original dataset if it is set to dont use batch + }).name("prepare generation");// set generation in params, get batch + + auto run_generation = subflow.for_each_index(0, this->params.num_islands, 1, [&](int island) { + evaluator.update_fitness(this->pop, island, data, params, true); // fit the weights with all training data + + // TODO: have some way to set which fitness to use (for example in params, or it can infer based on split size idk) + // TODO: if using batch, fitness should be called before selection to set the batch + if (data.use_batch) // assign the batch error as fitness (but fit was done with training data) + evaluator.update_fitness(this->pop, island, batch, params, false); + + vector parents = selector.select(this->pop, island, params); + + for (int i=0; i< parents.size(); i++){ + island_parents.at(island).at(i) = parents.at(i); + } + + this->pop.add_offspring_indexes(island); + variator.vary(this->pop, island, island_parents.at(island)); + evaluator.update_fitness(this->pop, island, data, params, true); + + if (data.use_batch) // assign the batch error as fitness (but fit was done with training data) + evaluator.update_fitness(this->pop, island, batch, params, false); + + // select survivors from combined pool of parents and offspring + vector island_survivors = survivor.survive(this->pop, island, params); + + for (int i=0; i< island_survivors.size(); i++){ + survivors.at(island).at(i) = island_survivors.at(i); + } + }).name("runs one generation at each island in parallel"); + + auto update_pop = subflow.emplace([&]() { + this->pop.update(survivors); + this->pop.migrate(); + }).name("update, migrate and disentangle indexes between islands"); + + auto finish_gen = subflow.emplace([&]() { + bool updated_best = this->update_best(data); + + if ( (params.verbosity>1 || !params.logfile.empty() ) + || params.use_arch ) { + calculate_stats(); + } + + if (params.use_arch) + archive.update(pop, params); + + fraction = params.max_time == -1 ? ((generation+1)*1.0)/params.max_gens : + timer.Elapsed().count()/params.max_time; + + if(params.verbosity>1) + print_stats(log, fraction); + else if(params.verbosity == 1) + print_progress(fraction); + + if (!params.logfile.empty()) + log_stats(log); + + if (generation == 0 || updated_best ) + stall_count = 0; + else + ++stall_count; + + ++generation; + + }).name("update best, log, archive, stall"); + + // set-up subflow graph + prepare_gen.precede(run_generation); + run_generation.precede(update_pop); + update_pop.precede(finish_gen); + }, + + [&]() { return 0; }, // jump back to the next iteration + + [&]() { + if (params.save_population != "") + this->pop.save(params.save_population); + + this->set_is_fitted(true); + + // TODO: open, write, close? (to avoid breaking the file and allow some debugging if things dont work well) + if (log.is_open()) + log.close(); + + // if we're not using an archive, let's store the final population in the + // archive + if (!params.use_arch) + { + archive.individuals.resize(0); + for (int island =0; island< pop.num_islands; ++island) { + vector indices = pop.get_island_indexes(island); + + for (unsigned i = 0; i + +namespace Brush +{ + +using namespace Pop; +using namespace Sel; +using namespace Eval; +using namespace Var; +using namespace nlohmann; + +template +/** + * @brief The `Engine` class represents the core engine of the brush library. + * + * It encapsulates the functionality for training and predicting with programs + * in a genetic programming framework. The `Engine` class manages the population + * of programs, selection algorithms, evaluation code, variation operators, and + * survival algorithms. It also provides methods for training the model, making + * predictions, and accessing runtime statistics. + * + * The `Engine` class is parameterized by the program type `T`, which determines + * the type of programs that can be evolved and evaluated by the engine. + */ +class Engine{ +public: + Engine(const Parameters& p=Parameters()) + : params(p) + , ss(SearchSpace()) // we need to initialize ss and variator. TODO: make them have a default way so we dont have to initialize here + , variator(Variation(params, ss)) + {}; + + ~Engine(){}; + + // outputs a progress bar, filled according to @param percentage. + void print_progress(float percentage); + void calculate_stats(); + void print_stats(std::ofstream& log, float fraction); + void log_stats(std::ofstream& log); + + // all hyperparameters are controlled by the parameter class. please refer to that to change something + inline Parameters& get_params(){return params;} + inline void set_params(Parameters& p){params=p;} + + inline bool get_is_fitted(){return is_fitted;} + + /// updates best score by searching in the population for the individual that best fits the given data + bool update_best(const Dataset& data, bool val=false); + + // TODO: hyperparameter to set how the best is picked (MCDM, best on val, pareto front, etc). one of the options should be getting the pareto front + + // TODO: best fitness (the class) instead of these. use fitness comparison + float best_score; + int best_complexity; + Individual& get_best_ind(){return best_ind;}; + + Engine &fit(Dataset& data) { + run(data); + return *this; + }; + Engine &fit(const Ref& X, const Ref& y) + { + // Using constructor 2 to create the dataset + Dataset d(X,y,params.feature_names,{},params.classification, + params.validation_size, params.batch_size); + return fit(d); + }; + + auto predict(const Dataset& data) { return this->best_ind.predict(data); }; + auto predict(const Ref& X) + { + Dataset d(X); + return predict(d); + }; + + template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) + auto predict_proba(const Dataset &d) { return this->best_ind.predict_proba(d); }; + template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) + auto predict_proba(const Ref& X) + { + Dataset d(X); + return predict_proba(d); + }; + + ///return archive size + int get_archive_size(){ return this->archive.individuals.size(); }; + + ///return population as string + vector get_archive(bool front); + + /// predict on unseen data from the archive + auto predict_archive(int id, const Dataset& data); + auto predict_archive(int id, const Ref& X); + + template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) + auto predict_proba_archive(int id, const Dataset& data); + template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) + auto predict_proba_archive(int id, const Ref& X); + + // TODO: predict/predict_proba/archive with longitudinal data + + /// train the model + void run(Dataset &d); + + Parameters params; ///< hyperparameters of brush, which the user can interact + Individual best_ind; + + Archive archive; ///< pareto front archive +private: + SearchSpace ss; + + Population pop; ///< population of programs + Selection selector; ///< selection algorithm + Evaluation evaluator; ///< evaluation code + Variation variator; ///< variation operators + Selection survivor; ///< survival algorithm + + Log_Stats stats; ///< runtime stats + + Timer timer; ///< start time of training + + bool is_fitted; ///< keeps track of whether fit was called. + + void init(); + + /// set flag indicating whether fit has been called + inline void set_is_fitted(bool f){is_fitted=f;} +}; + +// Only stuff to make new predictions or call fit again +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine, params, best_ind, archive); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine,params, best_ind, archive); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine,params, best_ind, archive); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine,params, best_ind, archive); + +} // Brush +#endif diff --git a/src/eval/evaluation.cpp b/src/eval/evaluation.cpp new file mode 100644 index 00000000..c365150d --- /dev/null +++ b/src/eval/evaluation.cpp @@ -0,0 +1,97 @@ +#include "evaluation.h" + +namespace Brush{ +namespace Eval{ + + +// fitness of population +template +void Evaluation::update_fitness(Population& pop, + int island, + const Dataset& data, + const Parameters& params, + bool fit, + bool validation + ) +{ + auto indices = pop.get_island_indexes(island); + + for (unsigned i = 0; i& ind = *pop.individuals.at(indices.at(i)).get(); // we are modifying it, so operator[] wont work + + bool pass = false; + + if (pass) + { + ind.fitness.loss = MAX_FLT; + ind.fitness.loss_v = MAX_FLT; + ind.error = MAX_FLT*VectorXf::Ones(data.y.size()); + } + else + { + // assign weights to individual + if (fit && ind.get_is_fitted() == false) + { + ind.program.fit(data); + } + + assign_fit(ind, data, params, validation); + } + } +} + +// assign loss to program +template +void Evaluation::assign_fit(Individual& ind, const Dataset& data, + const Parameters& params, bool val) +{ + VectorXf errors; + using PT = ProgramType; + + Dataset train = data.get_training_data(); + float f = S.score(ind, train, errors, params); + + float f_v = f; + if (data.use_validation) { + Dataset validation = data.get_validation_data(); + f_v = S.score(ind, validation, errors, params); + } + + // TODO: implement the class weights and use it here (and on errors) + + ind.set_objectives(params.objectives); + + // we will always set all values for fitness (regardless of being used). + // this will make sure the information is calculated and ready to be used + // regardless of how the program is set to run. + ind.error = errors; + ind.fitness.set_loss(f); + ind.fitness.set_loss_v(f_v); + ind.fitness.set_size(ind.get_size()); + ind.fitness.set_complexity(ind.get_complexity()); + ind.fitness.set_depth(ind.get_depth()); + + vector values; + values.resize(0); + + for (const auto& n : ind.get_objectives()) + { + if (n.compare("error")==0) + values.push_back(val ? f_v : f); + else if (n.compare("complexity")==0) + values.push_back(ind.program.complexity()); + else if (n.compare("size")==0) + values.push_back(ind.program.size()); + else if (n.compare("depth")==0) + values.push_back(ind.program.depth()); + else + HANDLE_ERROR_THROW(n+" is not a known objective"); + } + + // will use inner attributes to set the fitness object + ind.fitness.set_values(values); +} + +} // Pop +} // Brush \ No newline at end of file diff --git a/src/eval/evaluation.h b/src/eval/evaluation.h new file mode 100644 index 00000000..e03dc9f5 --- /dev/null +++ b/src/eval/evaluation.h @@ -0,0 +1,93 @@ + +#ifndef EVALUATION_H +#define EVALUATION_H + +#include + +#include "../vary/search_space.h" +#include "../ind/individual.h" +#include "../data/data.h" +#include "scorer.h" +#include "../pop/population.h" + +using std::string; + +namespace Brush { + +using namespace Pop; + +namespace Eval { + +template +/** + * @class Evaluation + * @brief Class for evaluating the fitness of individuals in a population. + */ +class Evaluation { +public: + Scorer S; + /** + * @brief Constructor for Evaluation class. + * @details Initializes the scorer based on the program type. + */ + Evaluation(){ + // TODO: make eval update loss_v accordingly, and set to th same as train loss if there is no batch or no validation + + string scorer; + if ( (T == Brush::ProgramType::MulticlassClassifier) + || (T == Brush::ProgramType::Representer) ) + scorer = "multi_log"; + else if (T == Brush::ProgramType::BinaryClassifier) + scorer = "log"; + else + scorer = "mse"; + + this->S.set_scorer(scorer); + }; + ~Evaluation(){}; + + /** + * @brief Set the scorer for evaluation. + * @param scorer The scorer to be set. + */ + void set_scorer(string scorer){this->S.set_scorer(scorer);}; + + /** + * @brief Get the current scorer. + * @return The current scorer. + */ + string get_scorer(){return this->S.get_scorer();}; + + /** + * @brief Update the fitness of individuals in a population. + * @param pop The population to update. + * @param island The island index. + * @param data The dataset for evaluation. + * @param params The parameters for evaluation. + * @param fit Flag indicating whether to update fitness. + * @param validation Flag indicating whether to perform validation. + */ + void update_fitness(Population& pop, + int island, + const Dataset& data, + const Parameters& params, + bool fit=true, + bool validation=false + ); + + /** + * @brief Assign fitness to an individual. + * @param ind The individual to assign fitness to. + * @param data The dataset for evaluation. + * @param params The parameters for evaluation. + * @param val Flag indicating whether it is validation fitness. + */ + void assign_fit(Individual& ind, const Dataset& data, + const Parameters& params, bool val=false); + + // representation program (TODO: implement) +}; + +} //selection +} //brush +#endif diff --git a/src/eval/metrics.cpp b/src/eval/metrics.cpp new file mode 100644 index 00000000..60de8c8a --- /dev/null +++ b/src/eval/metrics.cpp @@ -0,0 +1,181 @@ +#include "metrics.h" + +namespace Brush { +namespace Eval { + +/* Scoring functions */ + +/// mean squared error +float mse(const VectorXf& y, const VectorXf& yhat, VectorXf& loss, + const vector& class_weights) +{ + loss = (yhat - y).array().pow(2); + return loss.mean(); +} + +VectorXf log_loss(const VectorXf& y, const VectorXf& predict_proba, + const vector& class_weights) +{ + float eps = pow(10,-10); + + VectorXf loss; + + float sum_weights = 0; + loss.resize(y.rows()); + for (unsigned i = 0; i < y.rows(); ++i) + { + if (predict_proba(i) < eps || 1 - predict_proba(i) < eps) + // clip probabilities since log loss is undefined for predict_proba=0 or predict_proba=1 + loss(i) = -(y(i)*log(eps) + (1-y(i))*log(1-eps)); + else + loss(i) = -(y(i)*log(predict_proba(i)) + (1-y(i))*log(1-predict_proba(i))); + if (loss(i)<0) + std::runtime_error("loss(i)= " + to_string(loss(i)) + + ". y = " + to_string(y(i)) + ", predict_proba(i) = " + + to_string(predict_proba(i))); + + if (!class_weights.empty()) + { + loss(i) = loss(i) * class_weights.at(y(i)); + sum_weights += class_weights.at(y(i)); + } + } + + if (sum_weights > 0) + loss = loss.array() / sum_weights * y.size(); // normalize weight contributions + + return loss; +} + +/// log loss +float mean_log_loss(const VectorXf& y, + const VectorXf& predict_proba, VectorXf& loss, + const vector& class_weights) +{ + loss = log_loss(y,predict_proba,class_weights); + return loss.mean(); +} + +float average_precision_score(const VectorXf& y, const VectorXf& predict_proba, + VectorXf& loss, + const vector& class_weights) { + + // get argsort of predict proba + vector argsort(predict_proba.size()); + iota(argsort.begin(), argsort.end(), 0); + sort(argsort.begin(), argsort.end(), [&](int i, int j) { + return predict_proba[i] > predict_proba[j]; + }); + + float ysum = 0; + if (!class_weights.empty()) + for (int i = 0; i < class_weights.size(); i++) { + ysum += y(i) * class_weights.at(y(i)); + } + else + ysum = y.sum(); + + // Calculate the precision and recall values + VectorXf precision(predict_proba.size()); + VectorXf recall(predict_proba.size()); + + float true_positives = 0; + float false_positives = 0; + float positives = 0; + + for (int i = 0; i < predict_proba.size(); i++) { + if (predict_proba[argsort[i]] >= 0.5 && y[argsort[i]] == 1) { + true_positives += 1; + } + else { + if (!class_weights.empty()) + false_positives = class_weights[y(argsort[i])]; + else + false_positives += 1; + } + positives = true_positives + false_positives; + + precision[i] = true_positives / (positives + 1); + recall[i] = ysum==0.0 ? 1.0 : true_positives/ysum; + } + + // Calculate the average precision score + float average_precision = 0; + float last_recall = 0; + + for (int i = 0; i < predict_proba.size(); i++) { + if (recall[i] != last_recall) { + loss[i] = precision[i] * (recall[i] - last_recall); + average_precision += loss[i]; + last_recall = recall[i]; + } + } + + return average_precision; +} + +// multinomial log loss +VectorXf multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba, + const vector& class_weights) +{ + // TODO: fix softmax and multiclassification, then implement this + VectorXf loss = VectorXf::Zero(y.rows()); + + // TODO: needs to be the index of unique elements + // get class labels + // vector uc = unique( ArrayXi(y.cast()) ); + + // float eps = pow(10,-10); + // float sum_weights = 0; + // for (unsigned i = 0; i < y.rows(); ++i) + // { + // for (const auto& c : uc) + // { + // // for specific class + // ArrayXf yhat = predict_proba.col(int(c)); + // /* std::cout << "class " << c << "\n"; */ + + // /* float yi = y(i) == c ? 1.0 : 0.0 ; */ + // /* std::cout << "yi: " << yi << ", yhat(" << i << "): " << yhat(i) ; */ + // if (y(i) == c) + // { + // if (yhat(i) < eps || 1 - yhat(i) < eps) + // { + // // clip probabilities since log loss is undefined for yhat=0 or yhat=1 + // loss(i) += -log(eps); + // } + // else + // { + // loss(i) += -log(yhat(i)); + // } + // /* std::cout << ", loss(" << i << ") = " << loss(i); */ + // } + // /* std::cout << "\n"; */ + // } + // if (!class_weights.empty()){ + // /* std::cout << "weights.at(y(" << i << ")): " << class_weights.at(y(i)) << "\n"; */ + // loss(i) = loss(i)*class_weights.at(y(i)); + // sum_weights += class_weights.at(y(i)); + // } + // } + // if (sum_weights > 0) + // loss = loss.array() / sum_weights * y.size(); + + /* cout << "loss.mean(): " << loss.mean() << "\n"; */ + /* cout << "loss.sum(): " << loss.sum() << "\n"; */ + return loss; +} + +float mean_multi_log_loss(const VectorXf& y, + const ArrayXXf& predict_proba, VectorXf& loss, + const vector& class_weights) +{ + loss = multi_log_loss(y, predict_proba, class_weights); + + /* std::cout << "loss: " << loss.transpose() << "\n"; */ + /* std::cout << "mean loss: " << loss.mean() << "\n"; */ + return loss.mean(); +} + +} // metrics +} // Brush \ No newline at end of file diff --git a/src/eval/metrics.h b/src/eval/metrics.h new file mode 100644 index 00000000..7a66f8e5 --- /dev/null +++ b/src/eval/metrics.h @@ -0,0 +1,90 @@ +#ifndef METRICS_H +#define METRICS_H + +#include "../data/data.h" + +namespace Brush { +/** + * @namespace Eval + * @brief Namespace containing scoring functions for evaluation metrics. + */ +namespace Eval { + +/* Scoring functions */ + +// regression ------------------------------------------------------------------ + +/** + * @brief Calculates the mean squared error between the predicted values and the true values. + * @param y The true values. + * @param yhat The predicted values. + * @param loss Reference to store the calculated losses for each sample. + * @param class_weights The optional class weights (not used for MSE). + * @return The mean squared error. + */ +float mse(const VectorXf& y, const VectorXf& yhat, VectorXf& loss, + const vector& class_weights=vector() ); + +// binary classification ------------------------------------------------------- + +/** + * @brief Calculates the log loss between the predicted probabilities and the true labels. + * @param y The true labels. + * @param predict_proba The predicted probabilities. + * @param class_weights The optional class weights. + * @return The log loss. + */ +VectorXf log_loss(const VectorXf& y, const VectorXf& predict_proba, + const vector& class_weights=vector()); + +/** + * @brief Calculates the mean log loss between the predicted probabilities and the true labels. + * @param y The true labels. + * @param predict_proba The predicted probabilities. + * @param loss Reference to store the calculated losses for each sample. + * @param class_weights The optional class weights. + * @return The mean log loss. + */ +float mean_log_loss(const VectorXf& y, const VectorXf& predict_proba, VectorXf& loss, + const vector& class_weights = vector()); + +/** + * @brief Calculates the average precision score between the predicted probabilities and the true labels. + * @param y The true labels. + * @param predict_proba The predicted probabilities. + * @param loss Reference to store the calculated losses for each sample. + * @param class_weights The optional class weights. + * @return The average precision score. + */ +float average_precision_score(const VectorXf& y, const VectorXf& predict_proba, + VectorXf& loss, + const vector& class_weights=vector()); + +// multiclass classification --------------------------------------------------- + +/** + * @brief Calculates the multinomial log loss between the predicted probabilities and the true labels. + * @param y The true labels. + * @param predict_proba The predicted probabilities. + * @param class_weights The optional class weights. + * @return The multinomial log loss. + */ +VectorXf multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba, + const vector& class_weights=vector()); + +/** + * @brief Calculates the mean multinomial log loss between the predicted probabilities and the true labels. + * @param y The true labels. + * @param predict_proba The predicted probabilities. + * @param loss Reference to store the calculated losses for each sample. + * @param class_weights The optional class weights. + * @return The mean multinomial log loss. + */ +float mean_multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba, + VectorXf& loss, + const vector& class_weights=vector()); + +} // metrics +} // Brush + +#endif \ No newline at end of file diff --git a/src/eval/scorer.h b/src/eval/scorer.h new file mode 100644 index 00000000..a47e4c9f --- /dev/null +++ b/src/eval/scorer.h @@ -0,0 +1,182 @@ +#ifndef SCORER_H +#define SCORER_H + +#include "metrics.h" +#include "../util/error.h" +#include "../types.h" + +// code to evaluate GP programs. +namespace Brush{ + +using namespace Pop; + +namespace Eval{ + + +template +class Scorer +{ + +using RetType = + typename std::conditional_t

>; + +typedef float (*funcPointer)(const VectorXf&, + const VectorXf&, + VectorXf&, + const vector&); +public: + // map the string into a function to be called when calculating the score + std::map score_hash; + string scorer; + + // TODO: add more scores, include them here, add to score_hash + Scorer(string scorer="mse") { + score_hash["mse"] = &mse; + + this->set_scorer(scorer); + }; + + void set_scorer(string scorer){ this->scorer = scorer; }; + string get_scorer(){return this->scorer; }; + + /* void set_scorer(string scorer); */ + float score(const VectorXf& y_true, const VectorXf& y_pred, + VectorXf& loss, const vector& w) + { + // loss is an array passed by reference to store each prediction (used in lexicase) + // weights are used to give more or less importance for a given sample. + // Every scorer must have the same function signature, but arent required to use all info + + if ( score_hash.find(this->scorer) == score_hash.end() ) + { + HANDLE_ERROR_THROW("Scoring function '" + this->scorer + "' not defined"); + return 0.0; + } + else + { + return score_hash.at(this->scorer)(y_true, y_pred, loss, w); + } + }; + + float score(Individual

& ind, Dataset& data, + VectorXf& loss, const Parameters& params) + { + RetType y_pred = ind.predict(data); + return score(data.y, y_pred, loss, params.class_weights); + } +}; + + +// TODO: improve this so we dont have a lot of different declarations +template + requires( P == PT::BinaryClassifier) +class Scorer

+{ + +using RetType = ArrayXf; + +typedef float (*funcPointer)(const VectorXf&, + const VectorXf&, + VectorXf&, + const vector&); +public: + // map the string into a function to be called when calculating the score + std::map score_hash; + string scorer; + + Scorer(string scorer="log") { + score_hash["log"] = &mean_log_loss; + score_hash["average_precision_score"] = &average_precision_score; + + this->set_scorer(scorer); + }; + + void set_scorer(string scorer){ this->scorer = scorer; }; + string get_scorer(){return this->scorer; }; + + /* void set_scorer(string scorer); */ + float score(const VectorXf& y_true, const VectorXf& y_pred, + VectorXf& loss, const vector& w) + { + if ( score_hash.find(this->scorer) == score_hash.end() ) + { + // not found + HANDLE_ERROR_THROW("Scoring function '" + this->scorer + + "' not defined"); + return 0.0; + } + else + { + // found + return score_hash.at(this->scorer)(y_true, y_pred, loss, w); + } + }; + + float score(Individual

& ind, Dataset& data, + VectorXf& loss, const Parameters& params) + { + RetType y_pred = ind.predict_proba(data); // .template cast(); + return score(data.y, y_pred, loss, params.class_weights); + } +}; + +template + requires(P == PT::MulticlassClassifier) +class Scorer

+{ + +using RetType = ArrayXXf; + +typedef float (*funcPointer)(const VectorXf&, + const ArrayXXf&, + VectorXf&, + const vector&); +public: + // map the string into a function to be called when calculating the score + std::map score_hash; + string scorer; + + Scorer(string scorer="multi_log") { + score_hash["multi_log"] = &mean_multi_log_loss; + + this->set_scorer(scorer); + }; + + void set_scorer(string scorer){ this->scorer = scorer; }; + string get_scorer(){return this->scorer; }; + + /* void set_scorer(string scorer); */ + float score(const VectorXf& y_true, const ArrayXXf& y_pred, + VectorXf& loss, const vector& w) + { + // loss is an array passed by reference to store each prediction (used in lexicase) + // weights are used to give more or less importance for a given sample. + // Every scorer must have the same function signature, but arent required to use all info + + if ( score_hash.find(this->scorer) == score_hash.end() ) + { + // not found + HANDLE_ERROR_THROW("Scoring function '" + this->scorer + + "' not defined"); + return 0.0; + } + else + { + // found + return score_hash.at(this->scorer)(y_true, y_pred, loss, w); + } + }; + + float score(Individual

& ind, Dataset& data, + VectorXf& loss, const Parameters& params) + { + RetType y_pred = ind.predict_proba(data); // .template cast(); + return score(data.y, y_pred, loss, params.class_weights); + } +}; + +} +} +#endif diff --git a/src/ind/fitness.cpp b/src/ind/fitness.cpp new file mode 100644 index 00000000..e3bd2d59 --- /dev/null +++ b/src/ind/fitness.cpp @@ -0,0 +1,73 @@ +#include "fitness.h" + +namespace Brush +{ + +void to_json(json &j, const Fitness &f) +{ + j = json{ + {"values", f.values}, + {"weights", f.weights}, + {"wvalues", f.wvalues}, + {"loss", f.loss}, + {"loss_v", f.loss_v}, + {"complexity", f.complexity}, + {"size", f.size}, + {"depth", f.depth}, + {"dcounter", f.dcounter}, + {"dominated", f.dominated}, + {"rank", f.rank}, + {"crowding_dist", f.crowding_dist} + }; +} + +void from_json(const json &j, Fitness& f) +{ + j.at("values").get_to( f.values ); + j.at("weights").get_to( f.weights ); + j.at("wvalues").get_to( f.wvalues ); + j.at("loss").get_to( f.loss ); + j.at("loss_v").get_to( f.loss_v ); + j.at("complexity").get_to( f.complexity ); + j.at("size").get_to( f.size ); + j.at("depth").get_to( f.depth ); + j.at("dcounter").get_to( f.dcounter ); + j.at("dominated").get_to( f.dominated ); + j.at("rank").get_to( f.rank ); + j.at("crowding_dist").get_to( f.crowding_dist ); +} + + +int Fitness::dominates(const Fitness& b) const +{ + int flag1 = 0, // to check if this has a better objective + flag2 = 0; // to check if b has a better objective + + // TODO: replace comparison of individual values by using the overloaded operators (here and in nsga2) + for (int i=0; i b.get_wvalues().at(i) + || std::isnan(b.get_wvalues().at(i)) + ) + flag1 = 1; + if (get_wvalues().at(i) < b.get_wvalues().at(i) + || std::isnan(get_wvalues().at(i)) + ) + flag2 = 1; + } + + // the proper way of comparing weighted values is considering everything as a maximization problem + // (this is like deap does, and our fitness is inspired by them) + if (flag1==1 && flag2==0) + // there is at least one smaller objective for this and none + // for b + return 1; + else if (flag1==0 && flag2==1) + // there is at least one smaller objective for b and none + // for this + return -1; + else + // no smaller objective or both have one smaller + return 0; +} + +} // Brush \ No newline at end of file diff --git a/src/ind/fitness.h b/src/ind/fitness.h new file mode 100644 index 00000000..6cabcf97 --- /dev/null +++ b/src/ind/fitness.h @@ -0,0 +1,197 @@ +#ifndef FITNESS_H +#define FITNESS_H + +#include +#include "../init.h" +#include "../util/utils.h" + +using namespace nlohmann; + +namespace Brush{ + +/** + * @brief Represents the fitness of an individual in the Brush namespace. + * + * The `Fitness` struct stores various attributes related to the fitness of an individual in the Brush namespace. + * It includes the aggregate loss score, aggregate validation loss score, complexity, size, depth, dominance counter, + * dominated individuals, Pareto front rank, crowding distance on the Pareto front, weighted values, and weights. + * + * The struct provides getter and setter methods for accessing and modifying these attributes. + * It also includes methods for calculating the hash value, setting values, clearing values, checking validity, + * and performing comparison operations. + * + * Additionally, there are methods for converting the `Fitness` object to JSON format and vice versa. + */ +struct Fitness { + // the loss is used in evolutionary functions + + float loss; ///< aggregate loss score + float loss_v; ///< aggregate validation loss score + + unsigned int complexity; + unsigned int size; + unsigned int depth; + + // these can be different depending on the island the individual is + unsigned int dcounter; ///< number of individuals this dominates + vector dominated; ///< individual indices this dominates + unsigned int rank; ///< pareto front rank + float crowding_dist; ///< crowding distance on the Pareto front + + vector values; + vector weights; + + // weighted values + vector wvalues; + + void set_dominated(vector& dom){ dominated=dom; }; + vector get_dominated() const { return dominated; }; + + void set_loss(float f){ loss=f; }; + float get_loss() const { return loss; }; + + void set_loss_v(float f_v){ loss_v=f_v; }; + float get_loss_v() const { return loss_v; }; + + void set_size(unsigned int new_s){ size=new_s; }; + unsigned int get_size() const { return size; }; + + void set_complexity(unsigned int new_c){ complexity=new_c; }; + unsigned int get_complexity() const { return complexity; }; + + void set_depth(unsigned int new_d){ depth=new_d; }; + unsigned int get_depth() const { return depth; }; + + void set_dcounter(unsigned int d){ dcounter=d; }; + unsigned int get_dcounter() const { return dcounter; }; + + void set_rank(unsigned r){ rank=r; }; + size_t get_rank() const { return rank; }; + + void set_crowding_dist(float cd){ crowding_dist=cd; }; + float get_crowding_dist() const { return crowding_dist; }; + + // Constructor with initializer list for weights + Fitness(const vector& w={}) : values(), wvalues(), weights(w) { + dcounter = 0; + set_rank(0); + set_crowding_dist(0); + dominated.resize(0); + } + + // Hash function (deap requires individuals (and fitness by induction) + // to be hashable) + size_t hash() const { + std::size_t h = std::hash>{}(wvalues); + return h; + } + + void set_weights(vector& w) { + weights = w; + } + vector get_weights() const { + return weights; + } + vector get_values() const { + return values; + } + vector get_wvalues() const { + return wvalues; + } + + // Method to set values + void set_values(vector& v) { + if (v.size() != weights.size()) { + throw std::length_error("Assigned values have not the same length than current values"); + } + + values.resize(0); + for (const auto& element : v) { + values.push_back(element); + } + + // Minimizing/maximizing problem: negative/positive weight, respectively. + wvalues.resize(weights.size()); + + // Perform element-wise multiplication + std::transform(v.begin(), v.end(), + weights.begin(), wvalues.begin(), + [](double a, double b) { + return a * b; + }); + } + + // Method to clear values + void clearValues() { + wvalues.clear(); + } + + bool valid() const { + return !wvalues.empty(); + } + + // Equality comparison + bool operator==(const Fitness& other) const { + return wvalues == other.wvalues; + } + + // Inequality comparison + bool operator!=(const Fitness& other) const { + return !(*this == other); + } + + // Less than comparison + bool operator<(const Fitness& other) const { + // because of the weights, every objective is a maximization problem + return !std::lexicographical_compare(wvalues.begin(), wvalues.end(), + other.wvalues.begin(), other.wvalues.end()); + } + + // Greater than comparison + bool operator>(const Fitness& other) const { + return other < *this; + } + + // Less than or equal to comparison + bool operator<=(const Fitness& other) const { + return !(other < *this); + } + + // Greater than or equal to comparison + bool operator>=(const Fitness& other) const { + return !(*this < other); + } + + // String representation + std::string toString() const { + if (valid()) { + string s = "Fitness("; + for (auto& v : values) + s += to_string(v) + " "; + return s+")"; + } else { + return "Fitness()"; + } + } + + // Representation for debugging + std::string repr() const { + if (valid()) { + string s = "Fitness("; + for (auto& v : values) + s += to_string(v) + " "; + return s+")"; + } else { + return "Fitness()"; + } + } + + /// set obj vector given a string of objective names + int dominates(const Fitness& b) const; +}; + +void to_json(json &j, const Fitness &f); +void from_json(const json &j, Fitness& f); + +} +#endif \ No newline at end of file diff --git a/src/ind/individual.cpp b/src/ind/individual.cpp new file mode 100644 index 00000000..a08668c0 --- /dev/null +++ b/src/ind/individual.cpp @@ -0,0 +1,8 @@ +#include "individual.h" + +namespace Brush{ +namespace Pop{ + + +} // Pop +} // Brush \ No newline at end of file diff --git a/src/ind/individual.h b/src/ind/individual.h new file mode 100644 index 00000000..aa030c58 --- /dev/null +++ b/src/ind/individual.h @@ -0,0 +1,173 @@ +#ifndef INDIVIDUAL_H +#define INDIVIDUAL_H + +#include "../program/program.h" +#include "fitness.h" + +#include + +using namespace nlohmann; + +namespace Brush{ +namespace Pop{ + +template +class Individual{ +public: // TODO: make these private (and work with nlohman json) + Program program; ///< executable data structure + + // store just info that we dont have a getter. size, depth, complexity: they can all be obtained with program. + + // error is the aggregation of error vector, and can be user sppecified + + // this flag is used to avoid re-fitting an individual. the program is_fitted_ flag is used to perform checks (like in predict with weights). They are two different things and I think I;ll keep this way (individual is just a container to keep program and fitness together) + bool is_fitted_ = false; + + // archive utility (and also keep track of evolution) (this is meaningful only + // if variation is done using the vary() function) + unsigned id; ///< tracking id + vector parent_id; ///< ids of parents + + VectorXf error; ///< training error (used in lexicase selectors) + + Fitness fitness; ///< aggregate fitness score + + vector objectives; ///< objectives for use with Pareto selection + + Individual() + { + objectives = {"error", "complexity"}; + id = 0; // unsigned + }; + + Individual(Program& prg) : Individual() { program = prg; }; + + void init(SearchSpace& ss, const Parameters& params) + { + program = ss.make_program>(params, 0, 0); + + // If different from zero, then the program is created with a fixed depth and size. + // If zero, it samples the value + // program = SS.make_program(params, params.max_depth, params.max_size); + }; + + // TODO: replace occurences of program.fit with these (also predict and predict_proba) + Individual &fit(const Dataset& data) { + program.fit(data); + this->is_fitted_ = true; + return *this; + }; + Individual &fit(const Ref& X, const Ref& y) + { + Dataset d(X,y); + return fit(d); + }; + + auto predict(const Dataset& data) { return program.predict(data); }; + auto predict(const Ref& X) + { + Dataset d(X); + return predict(d); + }; + + template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) + auto predict_proba(const Dataset &d) { return program.predict_proba(d); }; + template + requires((P == PT::BinaryClassifier) || (P == PT::MulticlassClassifier)) + auto predict_proba(const Ref& X) + { + Dataset d(X); + return predict_proba(d); + }; + + // just getters + bool get_is_fitted() const { return this->is_fitted_; }; + unsigned int get_size() const { return program.size(); }; + unsigned int get_depth() const { return program.depth(); }; + unsigned int get_complexity() const { return program.complexity(); }; + Program& get_program() { return program; }; + + string get_model(string fmt="compact", bool pretty=false) { + return program.get_model(fmt, pretty); }; + string get_dot_model(string extras="") { + return program.get_dot_model(extras); }; + + void set_fitness(Fitness &f) { fitness=f; }; + Fitness& get_fitness() { return fitness; }; + + void set_id(unsigned i){id = i;}; + void set_parents(const vector>& parents){ + parent_id.clear(); + for (const auto& p : parents) + parent_id.push_back(p.id); + }; /// set parent ids using parents + void set_parents(const vector& parents){ parent_id = parents; }; /// set parent ids using id values + + // TODO: USE setters and getters intead of accessing it directly + // template + // void Individual::set_objectives(const vector& objectives) + + // Static map for weights associated with strings. + // this will determine each fitness metric to be a min/max problem. + // generic error metric: by default log and multi_log if it is a + // classification problem, and MSE if it is a regression (so its always + // a minimization by default, thus "error" has weight -1.0) + inline static std::map weightsMap = { + {"complexity", -1.0}, + {"size", -1.0}, + {"mse", -1.0}, + {"log", -1.0}, + {"multi_log", -1.0}, + {"average_precision_score", +1.0}, + {"accuracy", +1.0}, + {"error", -1.0} + }; + + vector get_objectives() const { return objectives; }; + void set_objectives(vector objs){ + objectives=objs; + + vector weights; + weights.resize(0); + for (const auto& obj : objectives) { + auto it = weightsMap.find(obj); + if (it != weightsMap.end()) { + weights.push_back(it->second); + } else { + throw std::runtime_error( + "Unknown metric used as fitness. Value was " + obj); + } + } + + fitness.set_weights(weights); + }; +}; + + +// serialization for Individual +template +void to_json(json &j, const Individual &p) +{ + j = json{ + {"program", p.program}, + {"fitness", p.fitness}, + {"id", p.id}, + {"parent_id", p.parent_id}, + {"objectives", p.objectives} + }; +} + +template +void from_json(const json &j, Individual& p) +{// TODO: figure out if this works with private attributes and try to actually make them private (and use getters and setters) + j.at("program").get_to( p.program ); + j.at("fitness").get_to( p.fitness ); + j.at("id").get_to( p.id ); + j.at("parent_id").get_to( p.parent_id ); + j.at("objectives").get_to( p.objectives ); +} +} // Pop +} // Brush + +#endif diff --git a/src/params.cpp b/src/params.cpp deleted file mode 100644 index c785b6c3..00000000 --- a/src/params.cpp +++ /dev/null @@ -1,11 +0,0 @@ -/* Brush -copyright 2020 William La Cava -license: GNU/GPL v3 -*/ -#include "params.h" -namespace Brush -{ - nlohmann::json PARAMS; - void set_params(const ns::json& j) { PARAMS = j; } - ns::json get_params(){ return PARAMS;} -} diff --git a/src/params.h b/src/params.h index eeed65f0..2ac70594 100644 --- a/src/params.h +++ b/src/params.h @@ -5,13 +5,211 @@ license: GNU/GPL v3 #ifndef PARAMS_H #define PARAMS_H + #include "init.h" +#include "util/logger.h" + namespace ns = nlohmann; + namespace Brush { - extern ns::json PARAMS; - void set_params(const ns::json& j); - ns::json get_params(); + +struct Parameters +{ +public: + // by default, the rng generator will use any random seed if random_state is zero + int random_state = 0; + int verbosity = 0; + + // Evolutionary algorithm settings + string mode="regression"; + + unsigned int current_gen = 1; + + // termination criteria + int pop_size = 100; + int max_gens = 100; + int max_stall = 0; + int max_time = -1; + + unsigned int max_depth = 6; + unsigned int max_size = 50; + + vector objectives{"error","complexity"}; // error should be generic and deducted based on mode + + string sel = "lexicase"; //selection method + string surv = "nsga2"; //survival method + std::unordered_map functions; + int num_islands=5; + + // if we should save pareto front of the entire evolution (use_arch=true) + // or just the final population (use_arch=false) + bool use_arch=false; + bool val_from_arch=true; + + // variation + std::map mutation_probs = { + {"point", 0.167}, + {"insert", 0.167}, + {"delete", 0.167}, + {"subtree", 0.167}, + {"toggle_weight_on", 0.167}, + {"toggle_weight_off", 0.167} + }; + + float cx_prob=0.2; ///< cross rate for variation + float mig_prob = 0.05; + + string scorer_="mse"; ///< actual loss function used, determined by error + + vector classes; ///< class labels + vector class_weights; ///< weights for each class + vector sample_weights; ///< weights for each sample + + // for creating dataset from X and y in Engine::fit. Ignored if + // the uses uses an dataset + bool classification; + unsigned int n_classes; + + // validation partition + bool shuffle_split = false; + float validation_size = 0.75; + vector feature_names = {}; + float batch_size = 0.0; + + string load_population = ""; + string save_population = ""; + + string logfile = ""; + + int n_jobs = 1; ///< number of parallel jobs -1 use all threads; 0 use same as number of islands; positive number specify the amouut of threads + + Parameters(){}; + ~Parameters(){}; + + // TODO: use logger to log information. Make getters const + void set_verbosity(int new_verbosity){ Brush::Util::logger.set_log_level(new_verbosity); + verbosity = new_verbosity; }; + int get_verbosity(){ return verbosity; }; + + void set_random_state(int new_random_state){random_state = new_random_state; }; + int get_random_state(){ return random_state; }; + + void set_pop_size(int new_pop_size){ pop_size = new_pop_size; }; + int get_pop_size(){ return pop_size; }; + + void set_max_gens(int new_max_gens){ max_gens = new_max_gens; }; + int get_max_gens(){ return max_gens; }; + + void set_max_stall(int new_max_stall){ max_stall = new_max_stall; }; + int get_max_stall(){ return max_stall; }; + + void set_max_time(int new_max_time){ max_time = new_max_time; }; + int get_max_time(){ return max_time; }; + + void set_scorer_(string new_scorer_){ scorer_ = new_scorer_; }; + string get_scorer_(){ return scorer_; }; + + void set_load_population(string new_load_population){ load_population = new_load_population; }; + string get_load_population(){ return load_population; }; + + void set_save_population(string new_save_population){ save_population = new_save_population; }; + string get_save_population(){ return save_population; }; + + string get_logfile(){ return logfile; }; + void set_logfile(string s){ logfile=s; }; + + void set_current_gen(unsigned int gen){ current_gen = gen; }; + unsigned int get_current_gen(){ return current_gen; }; + + void set_num_islands(int new_num_islands){ num_islands = new_num_islands; }; + int get_num_islands(){ return num_islands; }; + + void set_max_depth(unsigned new_max_depth){ max_depth = new_max_depth; }; + unsigned get_max_depth() const { return max_depth; }; + + void set_n_jobs(int new_n_jobs){ n_jobs = new_n_jobs; }; + int get_n_jobs(){ return n_jobs; }; + + void set_max_size(unsigned new_max_size){ max_size = new_max_size; }; + unsigned get_max_size() const { return max_size; }; + + void set_objectives(vector new_objectives){ objectives = new_objectives; }; + vector get_objectives(){ return objectives; }; + + void set_sel(string new_sel){ sel = new_sel; }; + string get_sel(){ return sel; }; + + void set_surv(string new_surv){ surv = new_surv; }; + string get_surv(){ return surv; }; + + void set_cx_prob(float new_cx_prob){ cx_prob = new_cx_prob; }; + float get_cx_prob(){ return cx_prob; }; + + void set_mig_prob(float new_mig_prob){ mig_prob = new_mig_prob; }; + float get_mig_prob(){ return mig_prob; }; + + void set_use_arch(bool new_use_arch){ use_arch = new_use_arch; }; + bool get_use_arch(){ return use_arch; }; + + void set_val_from_arch(bool new_val_from_arch){ val_from_arch = new_val_from_arch; }; + bool get_val_from_arch(){ return val_from_arch; }; + + void set_classification(bool c){ classification = c; }; + bool get_classification(){ return classification; }; + + void set_shuffle_split(bool shuff){ shuffle_split = shuff; }; + bool get_shuffle_split(){ return shuffle_split; }; + + void set_n_classes(unsigned int new_n_classes){ n_classes = new_n_classes; }; + unsigned int get_n_classes(){ return n_classes; }; + + void set_validation_size(float s){ validation_size = s; }; + float get_validation_size(){ return validation_size; }; + + void set_feature_names(vector vn){ feature_names = vn; }; + vector get_feature_names(){ return feature_names; }; + + void set_batch_size(float c){ batch_size = c; }; + float get_batch_size(){ return batch_size; }; + + void set_mutation_probs(std::map new_mutation_probs){ mutation_probs = new_mutation_probs; }; + std::map get_mutation_probs(){ return mutation_probs; }; + + void set_functions(std::unordered_map new_functions){ functions = new_functions; }; + std::unordered_map get_functions(){ return functions; }; +}; + +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Parameters, + verbosity, + random_state, + pop_size, + max_gens, + max_stall, + max_time, + scorer_, + load_population, + save_population, + logfile, + current_gen, + num_islands, + max_depth, + n_jobs, + max_size, + objectives, + sel, + surv, + cx_prob, + mig_prob, + classification, + n_classes, + validation_size, + feature_names, + batch_size, + mutation_probs, + functions +); + } // Brush #endif diff --git a/src/pop/archive.cpp b/src/pop/archive.cpp new file mode 100644 index 00000000..4eb5ebf9 --- /dev/null +++ b/src/pop/archive.cpp @@ -0,0 +1,137 @@ +#include "archive.h" + +namespace Brush { +namespace Pop { + +template +Archive::Archive(): selector(true) {}; + +template +void Archive::set_objectives(vector objectives) +{ + this->sort_complexity = in(objectives, std::string("complexity")); +} + +// sorting etc --- all done using fitness class (easier to compare regardless of obj func) +template +bool Archive::sortComplexity(const Individual& lhs, + const Individual& rhs) +{ + // TODO: use getters for all info in fitness (instead of directly accessing them?). + // other option would be having the getters and setters to use iin pybind11, but + // in cpp we do it directly (we know how to manipulate this thing, but users may not, + // so these setters could do some validation to justify its existence). + + return lhs.fitness.complexity < rhs.fitness.complexity; +} + +template +bool Archive::sortObj1(const Individual& lhs, + const Individual& rhs) +{ + // sort based on index (we can have more than 2 obj in brush implementation) + // obs: because of the weights, every objective is a maximization problem + // when comparing weighted values (which should be the right way of doing it) + // the bigger the better. the weights allow us to use different min/max metrics + // without having to deal with this particular details + + float lhs_obj1 = lhs.fitness.wvalues.at(0); + float rhs_obj1 = rhs.fitness.wvalues.at(0); + + return lhs_obj1 > rhs_obj1; +} + +template +bool Archive::sameFitComplexity(const Individual& lhs, + const Individual& rhs) +{ + // fitness' operator== is overloaded to compare wvalues. + // we also check complexity equality to avoid the case where the user + // did not specified complexity as one of the objectives + return (lhs.fitness == rhs.fitness && + lhs.fitness.complexity == rhs.fitness.complexity); +} + +template +bool Archive::sameObjectives(const Individual& lhs, + const Individual& rhs) +{ + return (lhs.fitness == rhs.fitness); + +} + +template +void Archive::init(Population& pop) +{ + // TODO: copy the population to a new vector (instead of changing inplace). + // also, fix this in update function + + individuals.resize(0); + + // dealing with islands --> fast nds for each island + for (int island =0; island< pop.num_islands; ++island) { + vector indices = pop.get_island_indexes(island); + + selector.fast_nds(pop, indices); + } + + // OBS: fast_nds will change all individual fitness inplace. + // It will update the values for dcounter, rank, and dominated individuals. + + // TODO: fix this way of getting pareto front (the pareto front of different islands combined will not necessarily be the final pareto front). Also fix this in update + + /* vector front = this->sorted_front(); */ + for (int island =0; island< pop.num_islands; ++island) { + auto indices = pop.get_island_indexes(island); + + for (unsigned i = 0; isort_complexity) + std::sort(individuals.begin(),individuals.end(), &sortComplexity); + else + std::sort(individuals.begin(),individuals.end(), &sortObj1); + +} + +template +void Archive::update(Population& pop, const Parameters& params) +{ + individuals.resize(0); // clear archive + + // refill archive with new pareto fronts (one pareto front for each island!) + for (int island =0; island< pop.num_islands; ++island) { + vector indices = pop.get_island_indexes(island); + + // TODO: can i just call fast nds with all indexes in indices? + vector> front = selector.fast_nds(pop, indices); + for (const auto& i : front[0]) + { + individuals.push_back( *pop.individuals.at(i) ); + } + } + + if (this->sort_complexity) + std::sort(individuals.begin(), individuals.end(), &sortComplexity); + else + std::sort(individuals.begin(), individuals.end(), &sortObj1); + + /* auto it = std::unique(individuals.begin(),individuals.end(), &sameFitComplexity); */ + auto it = std::unique(individuals.begin(),individuals.end(), + &sameObjectives); + + individuals.resize(std::distance(individuals.begin(),it)); +} + +} // Pop +} // Brush \ No newline at end of file diff --git a/src/pop/archive.h b/src/pop/archive.h new file mode 100644 index 00000000..a4105ede --- /dev/null +++ b/src/pop/archive.h @@ -0,0 +1,113 @@ +#ifndef ARCHIVE_H +#define ARCHIVE_H + +#include "../ind/individual.h" + +///< nsga2 selection operator for getting the front +#include "../selection/nsga2.h" + +namespace Brush{ + +using namespace Sel; + +namespace Pop{ + +/** + * @brief The Archive struct represents a collection of individual programs. + * + * The Archive struct is used to store individual programs in a collection. It provides + * functionality for initializing, updating, and sorting the archive based on complexity + * or objectives. The archive can be operated on by a single thread. + * + * @tparam T The program type. + */ +template +struct Archive +{ + vector> individuals; ///< individual programs in the archive + bool sort_complexity; ///< whether to sort archive by complexity + NSGA2 selector; ///< using NSGA2 in survival mode (nsga2 does not implement selection) + + /** + * @brief Default constructor for the Archive struct. + */ + Archive(); + + /** + * @brief Initializes the archive with individuals from a population. + * @param pop The population from which to initialize the archive. + */ + void init(Population& pop); + + /** + * @brief Updates the archive with individuals from a population. + * @param pop The population from which to update the archive. + * @param params The parameters for the update. + */ + void update(Population& pop, const Parameters& params); + + /** + * @brief Sets the objectives for the archive. + * + * This function sets the objectives for the archive. The objectives are used for + * sorting the archive. + * + * @param objectives The objectives to set for the archive. + */ + void set_objectives(vector objectives); + + /** + * @brief Sorts the population in increasing complexity. + * + * This static function is used to sort the population in increasing complexity. + * It is used as a comparison function for sorting algorithms. + * + * @param lhs The left-hand side individual to compare. + * @param rhs The right-hand side individual to compare. + */ + static bool sortComplexity(const Individual& lhs, const Individual& rhs); + + /** + * @brief Sorts the population by the first objective. + * + * This static function is used to sort the population by the first objective. + * It is used as a comparison function for sorting algorithms. + * + * @param lhs The left-hand side individual to compare. + * @param rhs The right-hand side individual to compare. + */ + static bool sortObj1(const Individual& lhs, const Individual& rhs); + + /** + * @brief Checks if two individuals have the same fitness complexity. + * + * This static function is used to check if two individuals have the same fitness complexity. + * It is used as a comparison function for finding duplicates in the population. + * + * @param lhs The left-hand side individual to compare. + * @param rhs The right-hand side individual to compare. + */ + static bool sameFitComplexity(const Individual& lhs, const Individual& rhs); + + /** + * @brief Checks if two individuals have the same objectives. + * + * This static function is used to check if two individuals have the same objectives. + * It is used as a comparison function for finding duplicates in the population. + * + * @param lhs The left-hand side individual to compare. + * @param rhs The right-hand side individual to compare. + */ + static bool sameObjectives(const Individual& lhs, const Individual& rhs); +}; + +//serialization +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Archive, individuals, sort_complexity); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Archive, individuals, sort_complexity); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Archive, individuals, sort_complexity); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Archive, individuals, sort_complexity); + +} // Pop +} // Brush + +#endif diff --git a/src/pop/population.cpp b/src/pop/population.cpp new file mode 100644 index 00000000..300e5e78 --- /dev/null +++ b/src/pop/population.cpp @@ -0,0 +1,393 @@ +#include "population.h" + +namespace Brush{ +namespace Pop{ + +template +Population::Population() +{ + individuals.resize(0); + mig_prob = 0.0; + pop_size = 0; + num_islands = 0; +} + + +template +void Population::init(vector>& new_individuals, const Parameters& params) +{ + if (new_individuals.size() != params.pop_size + && new_individuals.size() != 2*params.pop_size ) { + throw std::runtime_error("Individual vector has different number of individuals than pop_size. popsize is "+to_string(params.pop_size)+", number of individuals is " + to_string(new_individuals.size())); + } + + this->mig_prob = params.mig_prob; + this->pop_size = params.pop_size; + this->num_islands=params.num_islands; + + island_indexes.resize(num_islands); + + // If the assert fails, execution stops, but for completeness, you can also throw an exception + size_t p = pop_size; + + individuals.resize(2*p); + std::fill(individuals.begin(), individuals.end(), nullptr); + + for (int i=0; i>(new_individuals.at(j)); + } +} + +template +void Population::init(SearchSpace& ss, const Parameters& params) +{ + this->mig_prob = params.mig_prob; + this->pop_size = params.pop_size; + this->num_islands=params.num_islands; + + // Tuples with start and end indexes for each island. Number of individuals + // in each island can slightly differ if num_islands is not a divisor of p (popsize) + island_indexes.resize(num_islands); + + size_t p = pop_size; // population size + + for (int i=0; i>(); + individuals.at(i)->init(ss, params); + individuals.at(i)->set_objectives(params.objectives); + + // second half is space to the offspring (but we dont initialize them) + individuals.at(p+i) = nullptr; + } +} + +template +void Population::save(string filename) +{ + std::ofstream out; + if (!filename.empty()) + out.open(filename); + else + out.open("population.json"); + + json j; + to_json(j, *this); + out << j ; + out.close(); + logger.log("Saved population to file " + filename, 1); +} + +template +void Population::load(string filename) +{ + std::ifstream indata; + indata.open(filename); + if (!indata.good()) + HANDLE_ERROR_THROW("Invalid input file " + filename + "\n"); + + std::string line; + indata >> line; + + json j = json::parse(line); + from_json(j, *this); + + logger.log("Loaded population from " + filename + " of size = " + + to_string(this->size()),1); + + indata.close(); +} + +/// update individual vector size and island indexes +template +void Population::add_offspring_indexes(int island) +{ + size_t p = pop_size; // population size. prep_offspring slots will douple the population, adding the new expressions into the islands + + // this is going to be tricky (pay attention to delta and p use) + size_t idx_start = std::floor(island*p/num_islands); + size_t idx_end = std::floor((island+1)*p/num_islands); + + auto delta = idx_end - idx_start; // island size + + // inserting indexes of the offspring + island_indexes.at(island).resize(island_indexes.at(island).size() + delta); + iota( + island_indexes.at(island).begin() + delta, island_indexes.at(island).end(), + p+idx_start); + + // Im keeping the offspring and parents in the same population object, because we + // have operations that require them together (archive, hall of fame.) + // The downside is having to be aware that islands will create offsprings + // intercalated with other islands +} + +template +void Population::update(vector> survivors) +{ + // this is the step that should end up cutting off half of the population + vector> new_pop; + new_pop.resize(0); + for (int j=0; jindividuals.resize(0); + for (auto ind : new_pop) + { + // making hard copies of the individuals + json ind_copy = ind; + + // this will fill just half of the pop + individuals.push_back( + std::make_shared>(ind_copy) ); + } + + assert(individuals.size() == pop_size + && " number of new individuals is different from pop size"); + + for (int i=0; i< pop_size; ++i) + { + // second half is space to the offspring (but we dont initialize them) + individuals.push_back(nullptr); + } +} + +template +string Population::print_models(string sep) +{ + // not printing the island each individual belongs to + string output = ""; + + for (int j=0; j& ind = *individuals.at(island_indexes.at(j).at(k)).get(); + output += ind.get_model() + sep; + } + } + return output; +} + +template +vector> Population::sorted_front(unsigned rank) +{ + // this is used to migration and update archive at the end of a generation. expect islands without offspring + + /* Returns individuals on the Pareto front, sorted by increasign complexity. */ + vector> pf_islands; + pf_islands.resize(num_islands); + + for (int j=0;j pf; + + for (int i=0; ifitness.rank == rank) + pf.push_back(i); + } + + std::sort(pf.begin(),pf.end(),SortComplexity(*this)); + auto it = std::unique(pf.begin(),pf.end(),SameFitComplexity(*this)); + + pf.resize(std::distance(pf.begin(),it)); + pf_islands.at(j) = pf; + } + + return pf_islands; +} + +template +vector Population::hall_of_fame(unsigned rank) +{ + // TODO: hall of fame should unify all pareto fronts by doing a new fast_nds. + // TODO: use hall of fame instead of re-implmementing this feature in + // archive init and update functions + + // this is used to migration and update archive at the end of a generation. + // Thiis function expects islands without offspring + + vector pf(0); + + for (int j=0;jfitness.rank == rank) + pf.push_back(indices.at(i)); + } + } + std::sort(pf.begin(),pf.end(),SortComplexity(*this)); + + auto it = std::unique(pf.begin(),pf.end(),SameFitComplexity(*this)); + + pf.resize(std::distance(pf.begin(),it)); + + return pf; +} + +template +void Population::migrate() +{ + // changes where island points to by shuffling it + + if (num_islands==1) + return; // skipping. this only work because update is fixing island indexes + + // This method is not thread safe (as it is now) + vector> new_island_indexes; + new_island_indexes.resize(num_islands); + + // std::cout << "Looping" << std::endl; + for (int island=0; island other_islands(num_islands-1); + iota(other_islands.begin(), other_islands.end(), 0); + + // skipping current island + auto it = other_islands.begin(); + std::advance(it, island); + for (;it != other_islands.end(); ++it) { + ++(*it); + } + + // picking other island + int other_island = *r.select_randomly( + other_islands.begin(), + other_islands.end()); + + migrating_idx = *r.select_randomly( + island_indexes.at(other_island).begin(), + island_indexes.at(other_island).end()); + + new_island_indexes.at(island).push_back(migrating_idx); + } + else + { + new_island_indexes.at(island).push_back(indices.at(i)); + } + } + } + + // making hard copies (so the next generation starts with islands that does not share individuals + // this is particularly important to avoid multiple threads assigning different rank/crowdist/dcounter + // or different fitness) + + // std::cout << "starting to consolidate pop" << std::endl; + vector> new_pop; + new_pop.resize(0); + for (int j=0; jindividuals.resize(0); + for (auto ind : new_pop) + { + // making hard copies of the individuals + json ind_copy = ind; + + // this will fill just half of the pop + individuals.push_back( + std::make_shared>(ind_copy) ); + } + for (int i=0; i< pop_size; ++i) + { + // second half is space to the offspring (but we dont initialize them) + individuals.push_back(nullptr); + } +} + +} // Pop +} // Brush diff --git a/src/pop/population.h b/src/pop/population.h new file mode 100644 index 00000000..6871c6e8 --- /dev/null +++ b/src/pop/population.h @@ -0,0 +1,98 @@ +#ifndef POPULATION_H +#define POPULATION_H + +#include "../util/utils.h" +#include "../util/error.h" +#include "../ind/individual.h" + +namespace Brush { +namespace Pop { + +template +class Population{ +public: + size_t pop_size; + int num_islands; + float mig_prob; + + vector>> individuals; + vector> island_indexes; + + Population(); + ~Population(){}; + + /// initialize population of programs with a starting model and/or from file + void init(SearchSpace& ss, const Parameters& params); + + // initialize based on list of individuals + void init(vector>& individuals, const Parameters& params); + + // save serialized population + void save(string filename); + // load serialized population + void load(string filename); + + /// returns population size (the effective size of the individuals) + int size() { return individuals.size(); }; + + vector get_island_indexes(int island){ return island_indexes.at(island); }; + + /// update individual vector size, distributing the expressions in num_islands + void add_offspring_indexes(int island); + + /// reduce programs to the indices in survivors. Not thread safe,as it removes elements + void update(vector> survivors); + + /// setting and getting from individuals vector (will ignore islands) + const Individual& operator [](size_t i) const {return *individuals.at(i);} + const Individual& operator [](size_t i) {return *individuals.at(i);} + + /// return population equations. + string print_models(string sep="\n"); + + /// return complexity-sorted Pareto front indices for each island + vector> sorted_front(unsigned rank=1); + + // pareto front ignoring island divisions + vector hall_of_fame(unsigned rank=1); + + // perform a migration in the population. Individuals from sorted front or hall of fame will replace others by the + // probability set in parameters. Expects a population without offspring + void migrate(); + + /// Sort each island in increasing complexity. This is not thread safe. I should set complexities of the whole population before calling it, and use get_complexity instead + struct SortComplexity + { + Population& pop; + SortComplexity(Population& p): pop(p){} + bool operator()(size_t i, size_t j) + { + return pop[i].get_complexity() < pop[j].get_complexity(); + } + }; + + /// check for same fitness and complexity to filter uniqueness. + struct SameFitComplexity + { + Population & pop; + SameFitComplexity(Population& p): pop(p){} + bool operator()(size_t i, size_t j) + { + return pop[i].get_complexity() == pop[j].get_complexity(); + } + }; +}; + +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE( + Population, individuals, island_indexes, pop_size, num_islands); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE( + Population, individuals, island_indexes, pop_size, num_islands); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE( + Population, individuals, island_indexes, pop_size, num_islands); +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE( + Population, individuals, island_indexes, pop_size, num_islands); + +}// Pop +}// Brush + +#endif diff --git a/src/program/functions.h b/src/program/functions.h index ff5acc8f..c334afe5 100644 --- a/src/program/functions.h +++ b/src/program/functions.h @@ -167,6 +167,7 @@ namespace Brush template inline auto operator()(const TimeSeries& t) { return t.prod(); } }; + /* sum */ template<> struct Function @@ -182,6 +183,28 @@ namespace Brush inline auto operator()(const TimeSeries& t) { return t.sum(); } }; + /* OffsetSum */ + template<> + struct Function + { + // just add with a constant (definition is like identity) + template + inline auto operator()(const T& t) { + return t; + } + + // n-ary version + // template + // inline auto operator()(const T& t) { return t.rowwise().sum(); } + + // inline auto operator()(ArrayXXb t) { + // return (t.rowwise().count().cast ()); + // } + + // template + // inline auto operator()(const TimeSeries& t) { return t.sum(); } + }; + template<> struct Function { @@ -202,7 +225,6 @@ namespace Brush t.row(i).maxCoeff(&idx(i)); return idx; } - }; template<> @@ -403,14 +425,71 @@ namespace Brush return this->softmax(t); } - // template - // inline auto operator()(const Array& first, const Ts& ... inputs) - // { - // auto output = Stack(first, inputs...); - // return this->softmax(output); - // } + // template + // inline auto operator()(const Array& first, const Ts& ... inputs) + // { + // auto output = Stack(first, inputs...); + // return this->softmax(output); + // } }; + /* logical and -- mul with boolean inputs */ + template<> + struct Function + { + template + inline auto operator()(const ArrayBase& t1, const ArrayBase& t2) { + return t1 && t2; + } + template requires same_as + inline auto operator()(const ArrayBase& t1, const ArrayBase& t2) { + // ArrayXb t1_bool(t1.size()); + // for (int i = 0; i< t1.size(); ++i) + // t1_bool(i) = t1(i).a; + + // ArrayXb t2_bool(t2.size()); + // for (int i = 0; i< t2.size(); ++i) + // t2_bool(i) = t2(i).a; + + // return (t1_bool || t2_bool).cast(); + return t1 * t2; + } + }; + + /* logical or -- add with boolean inputs */ + template<> + struct Function + { + template + inline auto operator()(const ArrayBase& t1, const ArrayBase& t2) { + return t1 || t2; + } + template requires same_as + inline auto operator()(const ArrayBase& t1, const ArrayBase& t2) { + return t1 + t2; + } + }; + + /* logical not -- negate the input */ + template<> + struct Function + { + template + inline auto operator()(const ArrayBase& t) { + return !t; + } + template requires same_as + inline auto operator()(const ArrayBase& t) { + auto trues = ArrayXb::Constant(t.size(), true); + return (t - trues); + + // for (size_t i = 0; i < t.size(); ++i) { + // t.at(i).a = !t.at(i).a; + // } + + // return t; + } + }; } // Brush #endif diff --git a/src/program/node.cpp b/src/program/node.cpp index 2c632249..23a7be82 100644 --- a/src/program/node.cpp +++ b/src/program/node.cpp @@ -31,8 +31,19 @@ auto Node::get_name(bool include_weight) const noexcept -> std::string { return fmt::format("{:.2f}", W); } + else if (Is(node_type)) + { + if (include_weight) + return fmt::format("{:.2f}*{}", W, feature); + + return feature; + } + else if (Is(node_type)){ + return fmt::format("{}+Sum", W); + } else if (is_weighted && include_weight) return fmt::format("{:.2f}*{}",W,name); + return name; } @@ -49,6 +60,16 @@ string Node::get_model(const vector& children) const noexcept ); } else if (Is(node_type)){ + if (arg_types.at(0) == DataType::ArrayB) + { + // booleans dont use thresholds (they are used directly as mask in split) + return fmt::format("If({},{},{})", + children.at(0), + children.at(1), + children.at(2) + ); + } + // integers or floating points (they have a threshold) return fmt::format("If({}>{:.2f},{},{})", children.at(0), W, @@ -56,6 +77,18 @@ string Node::get_model(const vector& children) const noexcept children.at(2) ); } + else if (Is(node_type)){ + // weight is part of the model + string args = fmt::format("{},", W); + + for (int i = 0; i < children.size(); ++i){ + args += children.at(i); + if (i < children.size()-1) + args += ","; + } + + return fmt::format("Sum({})", args); + } else{ string args = ""; for (int i = 0; i < children.size(); ++i){ @@ -125,6 +158,7 @@ void init_node_with_default_signature(Node& node) NT::Sqrtabs, NT::Square, NT::Logistic, + NT::OffsetSum, // unary version NT::CustomUnaryOp >(n)) { @@ -139,15 +173,29 @@ void init_node_with_default_signature(Node& node) NT::SplitBest, NT::CustomSplit >(n)) - { + { node.set_signature>(); + } + else if (Is< + NT::And, + NT::Or + >(n)) + { + node.set_signature>(); } + // else if (Is< + // NT::Not + // >(n)) + // { + // node.set_signature>(); + // } else if (Is< NT::Min, NT::Max, NT::Mean, NT::Median, NT::Sum, + // NT::OffsetSum, // n-ary version NT::Prod, NT::Softmax >(n)) @@ -199,8 +247,6 @@ void from_json(const json &j, Node& p) if (j.contains("prob_change")) j.at("prob_change").get_to(p.prob_change); - else - p.prob_change=1.0; // if node has a ret_type and arg_types, get them. if not we need to make diff --git a/src/program/node.h b/src/program/node.h index cf4541ef..a6265f31 100644 --- a/src/program/node.h +++ b/src/program/node.h @@ -39,7 +39,6 @@ using Brush::Data::Dataset; namespace Brush{ -// TODO: should I move this declaration to another place? template inline auto Isnt(DataType dt) -> bool { return !((dt == T) || ...); } @@ -238,8 +237,8 @@ struct Node { // getters and setters //TODO revisit float get_prob_change() const { return fixed ? 0.0 : this->prob_change;}; - void set_prob_change(float w){ if (!fixed) this->prob_change = w;}; - float get_prob_keep() const { return 1-this->prob_change;}; + void set_prob_change(float w){ this->prob_change = w;}; + float get_prob_keep() const { return fixed ? 1.0 : 1.0-this->prob_change;}; inline void set_feature(string f){ feature = f; }; inline string get_feature() const { return feature; }; @@ -264,14 +263,15 @@ template inline auto Isnt(NodeType nt) -> bool { return !((nt == T) || ...); } inline auto IsLeaf(NodeType nt) noexcept -> bool { - return Is(nt); + return Is(nt); } inline auto IsCommutative(NodeType nt) noexcept -> bool { return Is(nt); + NodeType::Max + >(nt); } inline auto IsDifferentiable(NodeType nt) noexcept -> bool { @@ -281,7 +281,10 @@ inline auto IsDifferentiable(NodeType nt) noexcept -> bool { NodeType::Before, NodeType::After, NodeType::During, - NodeType::Count + NodeType::Count, + NodeType::And, + NodeType::Or, + NodeType::Not >(nt); } template @@ -294,7 +297,10 @@ inline auto IsWeighable() noexcept -> bool { NodeType::During, NodeType::Count, NodeType::SplitOn, - NodeType::SplitBest + NodeType::SplitBest, + NodeType::And, + NodeType::Or, + NodeType::Not >(NT); } inline auto IsWeighable(NodeType nt) noexcept -> bool { @@ -306,7 +312,10 @@ inline auto IsWeighable(NodeType nt) noexcept -> bool { NodeType::During, NodeType::Count, NodeType::SplitOn, - NodeType::SplitBest + NodeType::SplitBest, + NodeType::And, + NodeType::Or, + NodeType::Not >(nt); } diff --git a/src/program/nodetype.cpp b/src/program/nodetype.cpp index b58302a7..de7a6668 100644 --- a/src/program/nodetype.cpp +++ b/src/program/nodetype.cpp @@ -31,10 +31,10 @@ std::map NodeNameType = { {"Pow", NodeType::Pow}, {"Logistic", NodeType::Logistic}, - // logic; not sure these will make it in - // {"And", NodeType::And}, - // {"Or", NodeType::Or}, - // {"Not", NodeType::Not}, + // logic + {"And", NodeType::And}, + {"Or", NodeType::Or}, + {"Not", NodeType::Not}, // {"Xor", NodeType::Xor}, // decision (same) @@ -51,6 +51,7 @@ std::map NodeNameType = { {"Median", NodeType::Median}, {"Count", NodeType::Count}, {"Sum", NodeType::Sum}, + {"OffsetSum", NodeType::OffsetSum}, {"Prod", NodeType::Prod}, {"ArgMax", NodeType::ArgMax}, @@ -67,13 +68,14 @@ std::map NodeNameType = { {"SplitOn", NodeType::SplitOn}, // leaves + {"MeanLabel", NodeType::MeanLabel}, {"Constant", NodeType::Constant}, {"Terminal", NodeType::Terminal}, // custom {"CustomUnaryOp", NodeType::CustomUnaryOp}, {"CustomBinaryOp", NodeType::CustomBinaryOp}, - {"CustomSplit", NodeType::CustomSplit}, + {"CustomSplit", NodeType::CustomSplit} }; std::map NodeTypeName = Util::reverse_map(NodeNameType); diff --git a/src/program/nodetype.h b/src/program/nodetype.h index b1e9ce4f..14b0d2e0 100644 --- a/src/program/nodetype.h +++ b/src/program/nodetype.h @@ -28,7 +28,8 @@ using Brush::Data::TimeSeriesf; namespace Brush { -enum class NodeType : uint64_t { +enum class NodeType : uint64_t { // Each node type must have a complexity + // in operator_complexities@tree_node.cpp // Unary Abs = 1UL << 0UL, Acos = 1UL << 1UL, @@ -49,57 +50,74 @@ enum class NodeType : uint64_t { Sqrt = 1UL << 16UL, Sqrtabs = 1UL << 17UL, Square = 1UL << 18UL, - Logistic = 1UL << 19UL, + Logistic = 1UL << 19UL, // used as root for classification trees + // timing masks Before = 1UL << 20UL, After = 1UL << 21UL, During = 1UL << 22UL, + // Reducers Min = 1UL << 23UL, Max = 1UL << 24UL, Mean = 1UL << 25UL, Median = 1UL << 26UL, - Sum = 1UL << 27UL, - Prod = 1UL << 28UL, + Prod = 1UL << 27UL, + Sum = 1UL << 28UL, + OffsetSum = 1UL << 29UL, // Sum with weight as one of its arguments + // Transformers - Softmax = 1UL << 29UL, + Softmax = 1UL << 30UL, // used as root for multiclf trees + // Binary - Add = 1UL << 30UL, - Sub = 1UL << 31UL, - Mul = 1UL << 32UL, - Div = 1UL << 33UL, - Pow = 1UL << 34UL, + Add = 1UL << 31UL, + Sub = 1UL << 32UL, + Mul = 1UL << 33UL, + Div = 1UL << 34UL, + Pow = 1UL << 35UL, + //split - SplitBest = 1UL << 35UL, - SplitOn = 1UL << 36UL, + SplitBest = 1UL << 36UL, + SplitOn = 1UL << 37UL, + // these ones change type /* Equals = 1UL << 39UL, */ /* LessThan = 1UL << 40UL, */ /* GreaterThan = 1UL << 41UL, */ /* Leq = 1UL << 42UL, */ /* Geq = 1UL << 43UL, */ - // leaves - Constant = 1UL << 37UL, - Terminal = 1UL << 38UL, - ArgMax = 1UL << 39UL, - Count = 1UL << 40UL, - // custom - CustomUnaryOp = 1UL << 41UL, - CustomBinaryOp = 1UL << 42UL, - CustomSplit = 1UL << 43UL + // boolean - // And = 1UL << 37UL, - // Or = 1UL << 38UL, + And = 1UL << 38UL, + Or = 1UL << 39UL, + Not = 1UL << 40UL, // Xor = 1UL << 39UL, - // Not = 1UL << 19UL, + + // leaves (must be the last ones in this enum) + MeanLabel = 1UL << 41UL, + Constant = 1UL << 42UL, + Terminal = 1UL << 43UL, + + // TODO: implement operators below and move them before leaves + ArgMax = 1UL << 44UL, + Count = 1UL << 45UL, + + // custom + CustomUnaryOp = 1UL << 46UL, + CustomBinaryOp = 1UL << 47UL, + CustomSplit = 1UL << 48UL }; using UnderlyingNodeType = std::underlying_type_t; struct NodeTypes { // magic number keeping track of the number of different node types - static constexpr size_t Count = 39; - static constexpr size_t OpCount = Count-2; + + // index of last available node visible to search_space + static constexpr size_t Count = 44; + + // subtracting leaves (leaving just the ops into this) + static constexpr size_t OpCount = Count-3; // returns the index of the given type in the NodeType enum static auto GetIndex(NodeType type) -> size_t @@ -165,10 +183,10 @@ NLOHMANN_JSON_SERIALIZE_ENUM( NodeType, { {NodeType::Pow,"Pow" }, {NodeType::Logistic,"Logistic" }, - // logic; not sure these will make it in - // {NodeType::And,"And" }, - // {NodeType::Or,"Or" }, - // {NodeType::Not,"Not" }, + // logic + {NodeType::And,"And" }, + {NodeType::Or,"Or" }, + {NodeType::Not,"Not" }, // {NodeType::Xor,"Xor" }, // decision (same) @@ -185,6 +203,7 @@ NLOHMANN_JSON_SERIALIZE_ENUM( NodeType, { {NodeType::Median,"Median" }, {NodeType::Count,"Count" }, {NodeType::Sum,"Sum" }, + {NodeType::OffsetSum,"OffsetSum" }, {NodeType::Prod,"Prod" }, {NodeType::ArgMax,"ArgMax" }, @@ -201,13 +220,14 @@ NLOHMANN_JSON_SERIALIZE_ENUM( NodeType, { {NodeType::SplitOn,"SplitOn" }, // leaves + {NodeType::MeanLabel,"MeanLabel" }, {NodeType::Constant,"Constant" }, {NodeType::Terminal,"Terminal" }, // custom {NodeType::CustomUnaryOp,"CustomUnaryOp" }, {NodeType::CustomBinaryOp,"CustomBinaryOp" }, - {NodeType::CustomSplit,"CustomSplit" }, + {NodeType::CustomSplit,"CustomSplit" } }) #endif @@ -255,6 +275,7 @@ static constexpr bool UnaryOp = is_in_v; template @@ -265,6 +286,7 @@ static constexpr bool BinaryOp = is_in_v; + template static constexpr bool AssociativeBinaryOp = is_in_v; + // // TODO: make this work // template // concept Transformer = requires(NT n, size_t ArgCount) diff --git a/src/program/operator.h b/src/program/operator.h index 195afc6a..2abceeba 100644 --- a/src/program/operator.h +++ b/src/program/operator.h @@ -226,6 +226,22 @@ struct Operator return this->apply(inputs); }; + // overloaded version for offset sum + template + requires is_in_v + RetType eval(const Dataset& d, TreeNode& tn, const W** weights=nullptr) const + { + auto inputs = get_kids(d, tn, weights); + if constexpr (is_one_of_v) + { + if (tn.data.get_is_weighted()) + { + auto w = util::get_weight(tn, weights); + return this->apply(inputs) + w; + } + } + return this->apply(inputs); + }; }; ////////////////////////////////////////////////////////////////////////////////// @@ -303,6 +319,38 @@ struct Operator else return RetType::Constant(d.get_n_samples(), d.get_n_features(), w); }; + +}; + +//////////////////////////////////////////////////////////////////////////// +// MeanLabel overload +template +struct Operator +{ + using RetType = typename S::RetType; + using W = typename S::WeightType; + + RetType fit(const Dataset& d, TreeNode& tn) const { + tn.data.W = d.y.mean(); + return predict(d, tn); + }; + + template + RetType predict(const Dataset& d, TreeNode& tn, const W** weights=nullptr) const + { + Scalar w = util::get_weight(tn, weights); + if constexpr (N == 1) + return RetType::Constant(d.get_n_samples(), w); + else + return RetType::Constant(d.get_n_samples(), d.get_n_features(), w); + }; + + RetType eval(const Dataset& d, TreeNode& tn, const W** weights=nullptr) const { + if constexpr (Fit) + return fit(d,tn); + else + return predict(d,tn,weights); + }; }; //////////////////////////////////////////////////////////////////////////// diff --git a/src/program/optimizer/weight_optimizer.h b/src/program/optimizer/weight_optimizer.h index 9b727fae..e7afbd35 100644 --- a/src/program/optimizer/weight_optimizer.h +++ b/src/program/optimizer/weight_optimizer.h @@ -74,9 +74,9 @@ struct ResidualEvaluator { size_t numParameters_; // cache the number of parameters in the tree }; +// TODO: see this struct and try to understand how to make non-templated classes struct WeightOptimizer { - /// @brief Update program weights using non-linear least squares. /// @tparam PT the program type /// @param program the program @@ -86,6 +86,7 @@ struct WeightOptimizer { if (program.get_n_weights() == 0) return; + // fmt::print("number of weights: {}\n",program.get_n_weights()); auto init_weights = program.get_weights(); diff --git a/src/program/program.h b/src/program/program.h index d1330cc2..a311603d 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -18,10 +18,11 @@ license: GNU/GPL v3 #include "../init.h" #include "tree_node.h" #include "node.h" -#include "../search_space.h" +#include "../vary/search_space.h" #include "../params.h" #include "../util/utils.h" #include "functions.h" +// #include "../variation.h" // #include "weight_optimizer.h" @@ -36,10 +37,6 @@ namespace Brush { typedef tree::pre_order_iterator Iter; typedef tree::post_order_iterator PostIter; -struct Fitness { - vector values; - bool valid; -}; using PT = ProgramType; // for unsupervised learning, classification and regression. @@ -60,6 +57,7 @@ template struct Program std::conditional_t>>>; + /// the type of output from the tree object using TreeType = std::conditional_t struct Program /// whether fit has been called bool is_fitted_; + /// fitness - Fitness fitness; + // Fitness fitness; /// the underlying tree tree Tree; @@ -82,26 +81,28 @@ template struct Program SSref = std::optional>{s}; } + Program copy() { return Program(*this); } + inline void set_search_space(const std::reference_wrapper s) { SSref = std::optional>{s}; } + /// @brief count the complexity of the program. + /// @return int complexity. + int complexity() const{ + auto head = Tree.begin(); + + return head.node->get_complexity(); + } + /// @brief count the tree size of the program, including the weights in weighted nodes. /// @param include_weight whether to include the node's weight in the count. /// @return int number of nodes. int size(bool include_weight=true) const{ - int acc = 0; - - std::for_each(Tree.begin(), Tree.end(), - [include_weight, &acc](auto& node){ - ++acc; // the node operator or terminal - - if (include_weight && node.get_is_weighted()==true) - acc += 2; // weight and multiplication, if enabled - }); - - return acc; + auto head = Tree.begin(); + + return head.node->get_size(include_weight); } /// @brief count the size of a given subtree, optionally including the @@ -111,26 +112,7 @@ template struct Program /// @return int number of nodes. int size_at(Iter& top, bool include_weight=true) const{ - int acc = 0; - - // inspired in tree.hh size. First create two identical iterators - Iter it=top, eit=top; - - // Then make the second one point to the next sibling - eit.skip_children(); - ++eit; - - // calculate tree size for each node until reach next sibling - while(it!=eit) { - ++acc; // counting the node operator/terminal - - if (include_weight && it.node->data.get_is_weighted()==true) - acc += 2; // weight and multiplication, if enabled - - ++it; - } - - return acc; + return top.node->get_size(include_weight); } /// @brief count the tree depth of the program. The depth is not influenced by weighted nodes. @@ -343,7 +325,7 @@ template struct Program * @param pretty currently unused. * @return string the model in string form. */ - string get_model(string fmt="compact", bool pretty=false) + string get_model(string fmt="compact", bool pretty=false) const { auto head = Tree.begin(); if (fmt=="tree") @@ -359,7 +341,7 @@ template struct Program * @param extras extra code passed to the beginning of the dot code. * @return string the model in dot language. */ - string get_dot_model(string extras="") + string get_dot_model(string extras="") const { // TODO: make the node names their hash or index, and the node label the nodetype name. // ref: https://stackoverflow.com/questions/10579041/graphviz-create-new-node-with-this-same-label#10579155 @@ -381,7 +363,6 @@ template struct Program const auto& parent = iter.node; // const auto& parent_data = iter.node->data; - string parent_id = get_id(parent); // if (Is(parent_data.node_type)) // parent_id = parent_data.get_name(false); @@ -390,7 +371,6 @@ template struct Program // } // // parent_id = parent_id.substr(2); - // if the first node is weighted, make a dummy output node so that the // first node's weight can be shown if (i==0 && parent->data.get_is_weighted()) @@ -401,16 +381,18 @@ template struct Program parent_id, parent->data.W ); - } // add the node - bool is_constant = Is(parent->data.node_type); + bool is_constant = Is(parent->data.node_type); string node_label = parent->data.get_name(is_constant); if (Is(parent->data.node_type)){ node_label = fmt::format("{}>{:.2f}?", parent->data.get_feature(), parent->data.W); } + if (Is(parent->data.node_type)){ + node_label = fmt::format("Add"); + } out += fmt::format("\"{}\" [label=\"{}\"];\n", parent_id, node_label); // add edges to the node's children @@ -426,7 +408,8 @@ template struct Program // string kid_id = fmt::format("{}",fmt::ptr(kid)); // kid_id = kid_id.substr(2); - if (kid->data.get_is_weighted() && Isnt(kid->data.node_type)){ + if (kid->data.get_is_weighted() + && Isnt(kid->data.node_type)){ edge_label = fmt::format("{:.2f}",kid->data.W); } @@ -459,7 +442,6 @@ template struct Program head_label, tail_label ); - } else{ out += fmt::format("\"{}\" -> \"{}\" [label=\"{}\"];\n", @@ -470,27 +452,28 @@ template struct Program } kid = kid->next_sibling; } + + // adding the offset as the last child + if (Is(parent->data.node_type)){ + // drawing the edge + out += fmt::format("\"{}\" -> \"{}\" [label=\"\"];\n", + parent_id, + parent_id+"Offset" + ); + + // drawing the node + out += fmt::format("\"{}\" [label=\"{}\"];\n", + parent_id+"Offset", + parent->data.W + ); + } + ++i; } out += "}\n"; return out; } - //////////////////////////////////////////////////////////////////////////// - // Mutation & Crossover - - /// @brief convenience wrapper for :cpp:func:`variation:mutate()` in variation.h - /// @return a mutated version of this program - std::optional> mutate() const; - - /** - * @brief convenience wrapper for :cpp:func:`variation:cross` in variation.h - * - * @param other another program to cross with this one. - * @return a new version of this and the other program - */ - std::optional> cross(Program other) const; - /// @brief turns program tree into a linear program. /// @return a vector of nodes encoding the program in reverse polish notation vector linearize() const { @@ -505,6 +488,7 @@ template struct Program //////////////////////////////////////////////////////////////////////////////// // weight optimization #include "optimizer/weight_optimizer.h" +// #include "../variation.h" namespace Brush{ template @@ -517,22 +501,6 @@ void Program::update_weights(const Dataset& d) WO.update((*this), d); }; -//////////////////////////////////////////////////////////////////////////////// -// mutation and crossover -#include "../variation.h" -template -std::optional> Program::mutate() const -{ - return variation::mutate(*this, this->SSref.value().get()); -}; - -/// swaps subtrees between this and other (note the pass by copy) -template -std::optional> Program::cross(Program other) const -{ - return variation::cross(*this, other); -}; - //////////////////////////////////////////////////////////////////////////////// // serialization @@ -552,4 +520,6 @@ void from_json(const json &j, Program& p) }//namespace Brush + + #endif diff --git a/src/program/signatures.h b/src/program/signatures.h index a46e58a9..12ff9319 100644 --- a/src/program/signatures.h +++ b/src/program/signatures.h @@ -201,6 +201,13 @@ struct Signatures; }; +template<> +struct Signatures{ + using type = std::tuple< + Signature + >; +}; + template struct Signatures; }; -// template -// struct Signatures>>{ -// using type = std::tuple< -// Signature, -// Signature -// >; -// }; - -// template<> -// struct Signatures { -// using type = std::tuple< -// Signature, -// Signature -// >; -// }; +template +struct Signatures>>{ + using type = std::tuple< + Signature + >; + }; + +template<> +struct Signatures{ + using type = std::tuple< + Signature + >; + }; template struct Signatures>>{ // using type = std::tuple< // Signature, @@ -300,7 +305,7 @@ struct Signatures, Signature - >; + >;// TODO: should I implement compatibility with integers? using naryTuple = NarySignatures_t; @@ -361,22 +366,25 @@ struct Signatures{ Signature, Signature, Signature, + Signature, - Signature - /* Signature, */ - /* Signature, */ - /* Signature, */ - /* Signature */ + Signature, + Signature, + + Signature, + Signature, + Signature >; }; - template <> - struct Signatures +template <> +struct Signatures { using unaryTuple = std::tuple< Signature >; using naryTuple = NarySignatures_t; using type = decltype(std::tuple_cat(unaryTuple(), naryTuple())); }; + } // namespace Brush #endif \ No newline at end of file diff --git a/src/program/split.h b/src/program/split.h index b7078738..9b937ea8 100644 --- a/src/program/split.h +++ b/src/program/split.h @@ -181,6 +181,7 @@ namespace Split{ } } // namespace Split + //////////////////////////////////////////////////////////////////////////////// // Split operator overload template diff --git a/src/program/tree_node.cpp b/src/program/tree_node.cpp index 0e4dfcd3..2a186418 100644 --- a/src/program/tree_node.cpp +++ b/src/program/tree_node.cpp @@ -37,9 +37,7 @@ string TreeNode::get_tree_model(bool pretty, string offset) const if (sib != nullptr) child_outputs += "\n"; } - /* if (pretty) */ - /* return op_name + child_outputs; */ - /* else */ + return data.get_name() + child_outputs; }; //////////////////////////////////////////////////////////////////////////////// @@ -75,4 +73,132 @@ void from_json(const json &j, tree &t) stack.push_back(subtree); } t = stack.back(); -} \ No newline at end of file +} + +unordered_map operator_complexities = { + // Unary + {NodeType::Abs , 3}, + {NodeType::Acos , 5}, + {NodeType::Asin , 5}, + {NodeType::Atan , 5}, + {NodeType::Cos , 5}, + {NodeType::Cosh , 5}, + {NodeType::Sin , 5}, + {NodeType::Sinh , 5}, + {NodeType::Tan , 5}, + {NodeType::Tanh , 5}, + {NodeType::Ceil , 4}, + {NodeType::Floor , 4}, + {NodeType::Exp , 4}, + {NodeType::Log , 4}, + {NodeType::Logabs , 12}, + {NodeType::Log1p , 8}, + {NodeType::Sqrt , 4}, + {NodeType::Sqrtabs , 4}, + {NodeType::Square , 3}, + {NodeType::Logistic, 3}, + {NodeType::OffsetSum, 2}, + + // timing masks + {NodeType::Before, 3}, + {NodeType::After , 3}, + {NodeType::During, 3}, + + // Reducers + {NodeType::Min , 3}, + {NodeType::Max , 3}, + {NodeType::Mean , 3}, + {NodeType::Median , 3}, + {NodeType::Sum , 2}, + {NodeType::Prod , 3}, + + // Transformers + {NodeType::Softmax, 4}, + + // Binary + {NodeType::Add, 2}, + {NodeType::Sub, 2}, + {NodeType::Mul, 3}, + {NodeType::Div, 4}, + {NodeType::Pow, 5}, + + //split + {NodeType::SplitBest, 4}, + {NodeType::SplitOn , 4}, + + // boolean + {NodeType::And, 2}, + {NodeType::Or , 2}, + {NodeType::Not, 2}, + + // leaves + {NodeType::MeanLabel, 1}, + {NodeType::Constant , 1}, + {NodeType::Terminal , 2}, + {NodeType::ArgMax , 5}, + {NodeType::Count , 3}, + + // custom + {NodeType::CustomUnaryOp , 5}, + {NodeType::CustomBinaryOp, 5}, + {NodeType::CustomSplit , 5} +}; + +int TreeNode::get_complexity() const +{ + int node_complexity = operator_complexities.at(data.node_type); + int children_complexity_sum = 0; // acumulator for children complexities + + auto child = first_child; + for(int i = 0; i < data.get_arg_count(); ++i) + { + children_complexity_sum += child->get_complexity(); + child = child->next_sibling; + } + + // avoid multiplication by zero if the node is a terminal + children_complexity_sum = max(children_complexity_sum, 1); + + // include the `w` and `*` if the node is weighted (and it is not a constant or mean label) + if (data.get_is_weighted() + && !(Is(data.node_type) + || (Is(data.node_type) + || Is(data.node_type)) ) + ) + return operator_complexities.at(NodeType::Mul)*( + operator_complexities.at(NodeType::Constant) + + node_complexity*(children_complexity_sum) + ); + + return node_complexity*(children_complexity_sum); +}; + +int TreeNode::get_size(bool include_weight) const +{ + int acc = 1; // the node operator or terminal + + // SplitBest has an optimizable decision tree consisting of 3 nodes + // (terminal, arithmetic comparison, value) that needs to be taken + // into account. Split on will have an random decision tree that can + // have different sizes, but will also have the arithmetic comparison + // and a value. + if (Is(data.node_type)) + acc += 3; + else if (Is(data.node_type)) + acc += 2; + + if ( (include_weight && data.get_is_weighted()==true) + && Isnt(data.node_type) ) + // Taking into account the weight and multiplication, if enabled. + // weighted constants still count as 1 (simpler than constant terminals) + acc += 2; + + auto child = first_child; + for(int i = 0; i < data.get_arg_count(); ++i) + { + acc += child->get_size(include_weight); + child = child->next_sibling; + } + + return acc; +}; diff --git a/src/program/tree_node.h b/src/program/tree_node.h index 81836137..dc50f00a 100644 --- a/src/program/tree_node.h +++ b/src/program/tree_node.h @@ -49,6 +49,9 @@ class tree_node_ { // size: 5*4=20 bytes (on 32 bit arch), can be reduced string get_model(bool pretty=false) const; string get_tree_model(bool pretty=false, string offset="") const; + + int get_complexity() const; + int get_size(bool include_weight=true) const; }; using TreeNode = class tree_node_; diff --git a/src/selection/lexicase.cpp b/src/selection/lexicase.cpp new file mode 100644 index 00000000..30373412 --- /dev/null +++ b/src/selection/lexicase.cpp @@ -0,0 +1,175 @@ +#include "lexicase.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; +using namespace Sel; + +template +Lexicase::Lexicase(bool surv) +{ + this->name = "lexicase"; + this->survival = surv; +} + +template +vector Lexicase::select(Population& pop, int island, + const Parameters& params) +{ + // this one can be executed in parallel because it is just reading the errors. This + // method assumes that the expressions have been fitted previously, and their respective + // error vectors are filled + + auto island_pool = pop.get_island_indexes(island); + + // if this is first generation, just return indices to pop + if (params.current_gen==0) + return island_pool; + + //< number of samples + unsigned int N = pop.individuals.at(island_pool.at(0))->error.size(); + + //< number of individuals + unsigned int P = island_pool.size(); + + // define epsilon + ArrayXf epsilon = ArrayXf::Zero(N); + + // if output is continuous, use epsilon lexicase + if (!params.classification || params.scorer_.compare("log")==0 + || params.scorer_.compare("multi_log")==0) + { + // for each sample, calculate epsilon + for (int i = 0; ierror(i); + } + epsilon(i) = mad(case_errors); + } + } + assert(epsilon.size() == N); + + // selection pool + vector starting_pool; + for (int i = 0; i < island_pool.size(); ++i) + { + starting_pool.push_back(island_pool[i]); + } + assert(starting_pool.size() == P); + + vector selected(P,0); // selected individuals + + for (unsigned int i = 0; i cases; // cases (samples) + if (params.classification && !params.class_weights.empty()) + { + // for classification problems, weight case selection + // by class weights + vector choices(N); + std::iota(choices.begin(), choices.end(),0); + + vector sample_weights = params.sample_weights; + + for (unsigned i = 0; i choice_indices(N-i); + std::iota(choice_indices.begin(),choice_indices.end(),0); + + size_t idx = *r.select_randomly( + choice_indices.begin(), choice_indices.end(), + sample_weights.begin(), sample_weights.end()); + + cases.push_back(choices.at(idx)); + choices.erase(choices.begin() + idx); + + sample_weights.erase(sample_weights.begin() + idx); + } + } + else + { // otherwise, choose cases randomly + cases.resize(N); + std::iota(cases.begin(),cases.end(),0); + r.shuffle(cases.begin(),cases.end()); // shuffle cases + } + vector pool = starting_pool; // initial pool + vector winner; // winners + + bool pass = true; // checks pool size and number of cases + unsigned int h = 0; // case count + + float epsilon_threshold; + + while(pass){ // main loop + epsilon_threshold = 0; + + winner.resize(0); // winners + // minimum error on case + float minfit = std::numeric_limits::max(); + + // get minimum + for (size_t j = 0; jerror(cases[h]) < minfit) + minfit = pop.individuals.at(pool[j])->error(cases[h]); + + // criteria to stay in pool + epsilon_threshold = minfit+epsilon[cases[h]]; + + // select best + for (size_t j = 0; jerror(cases[h]) + <= epsilon_threshold) + winner.push_back(pool[j]); + + ++h; // next case + // only keep going if needed + pass = (winner.size()>1 && h= cases.size()) + winner.push_back(*r.select_randomly( + pool.begin(), pool.end()) ); + else + pass = true; + } + else + pool = winner; // reduce pool to remaining individuals + } + + assert(winner.size()>0); + + //if more than one winner, pick randomly + selected.at(i) = *r.select_randomly( + winner.begin(), winner.end() ); + + // cout << "parallel end index " + to_string(i) << endl; + } + + if (selected.size() != island_pool.size()) + { + // std::cout << "selected: " ; + // for (auto s: selected) std::cout << s << " "; std::cout << "\n"; + HANDLE_ERROR_THROW("Lexicase did not select correct number of \ + parents"); + } + + return selected; +} + +template +vector Lexicase::survive(Population& pop, int island, + const Parameters& params) +{ + /* Lexicase survival */ + HANDLE_ERROR_THROW("Lexicase survival not implemented"); + return vector(); +} + +} +} diff --git a/src/selection/lexicase.h b/src/selection/lexicase.h new file mode 100644 index 00000000..9613bfcb --- /dev/null +++ b/src/selection/lexicase.h @@ -0,0 +1,38 @@ +#ifndef LEXICASE_H +#define LEXICASE_H + +#include "selection_operator.h" +#include "../util/utils.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; +using namespace Sel; + + +/*! +* @class Lexicase +* @brief Lexicase selection operator. +*/ + +template +class Lexicase : public SelectionOperator +{ +public: + Lexicase(bool surv=false); + ~Lexicase(){}; + + /// function returns a set of selected indices from pop + vector select(Population& pop, int island, + const Parameters& p); + + /// lexicase survival + vector survive(Population& pop, int island, + const Parameters& p); +}; + +} // Sel +} // Brush +#endif \ No newline at end of file diff --git a/src/selection/nsga2.cpp b/src/selection/nsga2.cpp new file mode 100644 index 00000000..50ca00f8 --- /dev/null +++ b/src/selection/nsga2.cpp @@ -0,0 +1,248 @@ +#include "nsga2.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; +using namespace Sel; + +template +NSGA2::NSGA2(bool surv) +{ + this->name = "nsga2"; + this->survival = surv; +} + +template +size_t NSGA2::tournament(Population& pop, size_t i, size_t j) const +{ + // gets two individuals and compares them. i and j bhould be within island range + const Individual& ind1 = pop[i]; + const Individual& ind2 = pop[j]; + + int flag = ind1.fitness.dominates(ind2.fitness); + + if (flag == 1) // ind1 dominates ind2 + return i; + else if (flag == -1) // ind2 dominates ind1 + return j; + else if (ind1.fitness.crowding_dist > ind2.fitness.crowding_dist) + return i; + else if (ind2.fitness.crowding_dist > ind1.fitness.crowding_dist) + return j; + else + return i; +} + +template +vector NSGA2::select(Population& pop, int island, + const Parameters& params) +{ + // tournament selection. TODO: move this to tournament selection file, and throw not implemented error in nsga. + auto island_pool = pop.get_island_indexes(island); + + // if this is first generation, just return indices to pop + if (params.current_gen==0) + return island_pool; + + // i am not sure if I need this update of rank and crowding distance (bc first generation is ignored by if above, and the other generations will always have individuals that went through survival, which already calculates this information. TODO: in the final algorithm, I need to make sure this is correct) + auto front = fast_nds(pop, island_pool); + for (size_t i = 0; i< front.size(); i++) + { + crowding_distance(pop, front, i); + } + + vector selected(0); + for (int i = 0; i < island_pool.size(); ++i) // selecting based on island_pool size + { + size_t winner = tournament(pop, + *r.select_randomly(island_pool.begin(), island_pool.end()), + *r.select_randomly(island_pool.begin(), island_pool.end())); + + selected.push_back(winner); + } + return selected; +} + +template +vector NSGA2::survive(Population& pop, int island, + const Parameters& params) +{ + size_t idx_start = std::floor(island*params.pop_size/params.num_islands); + size_t idx_end = std::floor((island+1)*params.pop_size/params.num_islands); + + auto original_size = idx_end - idx_start; // original island size (survive must be called with an island with offfspring) + + auto island_pool = pop.get_island_indexes(island); + + // fast non-dominated sort + auto front = fast_nds(pop, island_pool); + + // Push back selected individuals until full + vector selected; + selected.resize(0); + + int i = 0; + while ( + i < front.size() + && ( selected.size() + front.at(i).size() < original_size ) + ) + { + std::vector& Fi = front.at(i); // indices in front i + + crowding_distance(pop, front, i); // calculate crowding in Fi + + for (int j = 0; j < Fi.size(); ++j) // Pt+1 = Pt+1 U Fi + selected.push_back(Fi.at(j)); + + ++i; + } + + // fmt::print("crowding distance\n"); + crowding_distance(pop, front, i); // calculate crowding in final front to include + std::sort(front.at(i).begin(),front.at(i).end(),sort_n(pop)); + + // fmt::print("adding last front)\n"); + const int extra = original_size - selected.size(); + for (int j = 0; j < extra; ++j) // Pt+1 = Pt+1 U Fi[1:N-|Pt+1|] + selected.push_back(front.at(i).at(j)); + + // fmt::print("returning\n"); + return selected; +} + +template +vector> NSGA2::fast_nds(Population& pop, vector& island_pool) +{ + // this will update pareto dominance attributes in fitness class + // based on the population + + //< the Pareto fronts + vector> front; + + front.resize(1); + front.at(0).clear(); + + for (int i = 0; i < island_pool.size(); ++i) { + + std::vector dom; + int dcount = 0; + + auto p = pop.individuals.at(island_pool[i]); + + for (int j = 0; j < island_pool.size(); ++j) { + + const Individual& q = pop[island_pool[j]]; + + int compare = p->fitness.dominates(q.fitness); + if (compare == 1) { // p dominates q + //p.dominated.push_back(j); + dom.push_back(island_pool[j]); + } else if (compare == -1) { // q dominates p + //p.dcounter += 1; + dcount += 1; + } + } + p->fitness.dcounter = dcount; + p->fitness.dominated.clear(); + p->fitness.dominated = dom; // dom will have values already referring to island indexes + + if (p->fitness.dcounter == 0) { + // fmt::print("pushing {}...\n", island_pool[i]); + p->fitness.set_rank(1); + // front will have values already referring to island indexes + front.at(0).push_back(island_pool[i]); + } + + } + + // fmt::print("First front size {}...\n", front.at(0).size()); + + // using OpenMP can have different orders in the front.at(0) + // so let's sort it so that the algorithm is deterministic + // given a seed + std::sort(front.at(0).begin(), front.at(0).end()); + + int fi = 1; + while (front.at(fi-1).size() > 0) { + std::vector& fronti = front.at(fi-1); + std::vector Q; + for (int i = 0; i < fronti.size(); ++i) { + + const Individual& p = pop[fronti.at(i)]; + + // iterating over dominated individuals + for (int j = 0; j < p.fitness.dominated.size() ; ++j) { + // fmt::print("decreased counter of ind {} for {} to {} \n", j, p.fitness.dominated.at(j), pop.individuals.at(p.fitness.dominated.at(j))->fitness.dcounter); + + auto q = pop.individuals.at(p.fitness.dominated.at(j)); + + // fmt::print("decreased counter \n"); + q->fitness.dcounter -= 1; + + if (q->fitness.dcounter == 0) { + // fmt::print("updated counter for ind {} \n", j); + + q->fitness.set_rank(fi+1); + Q.push_back(p.fitness.dominated.at(j)); + } + } + } + + front.push_back(Q); + + fi += 1; + } + return front; +} + +template +void NSGA2::crowding_distance(Population& pop, vector>& front, int fronti) +{ + + // fmt::print("inside crowding distance for front {}...\n", fronti); + + std::vector F = front.at(fronti); + if (F.size() == 0 ){ + // fmt::print("empty front\n"); + return; + } + + const int fsize = F.size(); + // fmt::print("front size is {}...\n", fsize); + + for (int i = 0; i < fsize; ++i) + pop.individuals.at(F.at(i))->fitness.crowding_dist = 0; + + // fmt::print("reseted crowding distance for individuals in this front\n"); + + const int limit = pop.individuals.at(0)->fitness.get_wvalues().size(); + // fmt::print("limit is {}\n", limit); + + for (int m = 0; m < limit; ++m) { + // fmt::print("m {}\n", m); + + std::sort(F.begin(), F.end(), comparator_obj(pop,m)); + + // in the paper dist=INF for the first and last, in the code + // this is only done to the first one or to the two first when size=2 + pop.individuals.at(F.at(0))->fitness.crowding_dist = std::numeric_limits::max(); + if (fsize > 1) + pop.individuals.at(F.at(fsize-1))->fitness.crowding_dist = std::numeric_limits::max(); + + for (int i = 1; i < fsize-1; ++i) + { + if (pop.individuals.at(F.at(i))->fitness.crowding_dist != std::numeric_limits::max()) + { // crowd over obj + // TODO: this could be improved + pop.individuals.at(F.at(i))->fitness.crowding_dist += + (pop.individuals.at(F.at(i+1))->fitness.get_wvalues().at(m) - pop.individuals.at(F.at(i-1))->fitness.get_wvalues().at(m)) + / (pop.individuals.at(F.at(fsize-1))->fitness.get_wvalues().at(m) - pop.individuals.at(F.at(0))->fitness.get_wvalues().at(m)); + } + } + } +} + +} // selection +} // Brush \ No newline at end of file diff --git a/src/selection/nsga2.h b/src/selection/nsga2.h new file mode 100644 index 00000000..f883d832 --- /dev/null +++ b/src/selection/nsga2.h @@ -0,0 +1,82 @@ +#ifndef NSGA2_H +#define NSGA2_H + +#include "selection_operator.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; +using namespace Sel; + +template +class NSGA2 : public SelectionOperator +{ +public: + // should operate only on a given island index + /** NSGA-II based selection and survival methods. */ + + // if any of the islands have overlapping indexes, parallel access and modification should be ok (because i dont increase or decrease pop size, not change island ranges inside selection) + + NSGA2(bool surv=false); + ~NSGA2(){}; + + /// selection according to the survival scheme of NSGA-II + vector select(Population& pop, int island, + const Parameters& p); + + /// survival according to the survival scheme of NSGA-II + vector survive(Population& pop, int island, + const Parameters& p); + + //< Fast non-dominated sorting + vector> fast_nds(Population&, vector&); + + // front cannot be an attribute because selection will be executed in different threads for different islands (this is a modificationf rom original FEAT code that I got inspiration) + + //< crowding distance of a front i + void crowding_distance(Population&, vector>&, int); + + private: + /// sort based on rank, breaking ties with crowding distance + struct sort_n + { + const Population& pop; ///< population address + + sort_n(const Population& population) : pop(population) {}; + + bool operator() (int i, int j) { + // TODO: Improve operator[], and decrease use of pop.individuals.at(). Also, decrease number of auto declarations + auto ind1 = pop.individuals[i]; + auto ind2 = pop.individuals[j]; + + if (ind1->fitness.get_rank() < ind2->fitness.get_rank()) + return true; + else if (ind1->fitness.get_rank() == ind2->fitness.get_rank() && + ind1->fitness.crowding_dist > ind2->fitness.crowding_dist) + return true; + return false; + }; + }; + + /// sort based on objective m + struct comparator_obj + { + const Population& pop; ///< population address + int m; ///< objective index + + comparator_obj(const Population& population, int index) + : pop(population), m(index) {}; + + // because of the weighted values, every objective is a maximization problem + bool operator() (int i, int j) { + return pop[i].fitness.get_wvalues()[m] > pop[j].fitness.get_wvalues()[m]; }; + }; + + size_t tournament(Population& pop, size_t i, size_t j) const; +}; + +} // selection +} // Brush +#endif \ No newline at end of file diff --git a/src/selection/selection.cpp b/src/selection/selection.cpp new file mode 100644 index 00000000..f1097d02 --- /dev/null +++ b/src/selection/selection.cpp @@ -0,0 +1,66 @@ +#include "selection.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; + +template +Selection::Selection() +{ + this->type = "nsga2"; + this->survival = false; + this->set_operator(); +} + + +template +Selection::Selection(string type, bool survival) +{ + /*! + * set type of selection operator. + */ + this->type = type; + this->survival = survival; + this->set_operator(); +} + +template +void Selection::set_operator() +{ + if (this->type == "nsga2") + pselector = new NSGA2(survival); + else if (this->type == "lexicase") + pselector = new Lexicase(survival); + else + HANDLE_ERROR_THROW("Undefined Selection Operator " + this->type + "\n"); + +} + +/// return type of selectionoperator +template +string Selection::get_type(){ return pselector->name; } + +/// set type of selectionoperator +template +void Selection::set_type(string in){ type = in; set_operator();} + +/// perform selection +template +vector Selection::select(Population& pop, int island, + const Parameters& params) +{ + return pselector->select(pop, island, params); +} + +/// perform survival +template +vector Selection::survive(Population& pop, int island, + const Parameters& params) +{ + return pselector->survive(pop, island, params); +} + +} // Sel +} // Brush diff --git a/src/selection/selection.h b/src/selection/selection.h new file mode 100644 index 00000000..2ab6c344 --- /dev/null +++ b/src/selection/selection.h @@ -0,0 +1,52 @@ +/* Brush +copyright 2020 William La Cava +license: GNU/GPL v3 +*/ + +#ifndef SELECTION_H +#define SELECTION_H + +#include "selection_operator.h" +#include "nsga2.h" +#include "lexicase.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; + +/*! +* @class Selection +* @brief interfaces with selection operators. +*/ +template +struct Selection +{ +public: + SelectionOperator* pselector; // TODO: THIS SHOULD BE A SHARED POINTER + string type; + bool survival; + + Selection(); + ~Selection(){}; + Selection(string type, bool survival); + + void set_operator(); + + /// return type of selectionoperator + string get_type(); + void set_type(string); + + /// perform selection. selection uses a pop that has no offspring space + vector select(Population& pop, int island, + const Parameters& params); + + /// perform survival. uses a pop with offspring space + vector survive(Population& pop, int island, + const Parameters& params); +}; + +} // Sel +} // Brush +#endif \ No newline at end of file diff --git a/src/selection/selection_operator.cpp b/src/selection/selection_operator.cpp new file mode 100644 index 00000000..b0c628ca --- /dev/null +++ b/src/selection/selection_operator.cpp @@ -0,0 +1,29 @@ +#include "selection_operator.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; + +template +SelectionOperator::~SelectionOperator(){}; + +template +vector SelectionOperator::select(Population& pop, int island, + const Parameters& p) +{ + HANDLE_ERROR_THROW("Undefined select() operation"); + return vector(); +}; + +template +vector SelectionOperator::survive(Population& pop, int island, + const Parameters& p) +{ + HANDLE_ERROR_THROW("Undefined select() operation"); + return vector(); +}; + +} // selection +} // Brush \ No newline at end of file diff --git a/src/selection/selection_operator.h b/src/selection/selection_operator.h new file mode 100644 index 00000000..6bf824b0 --- /dev/null +++ b/src/selection/selection_operator.h @@ -0,0 +1,62 @@ +#ifndef SELECTION_OPERATOR_H +#define SELECTION_OPERATOR_H + +// virtual class. selection must be made with static methods + +// #include "../init.h" +// #include "../data/data.h" +// #include "../types.h" +// #include "../params.h" +#include "../pop/population.h" + +namespace Brush { +namespace Sel { + +using namespace Brush; +using namespace Pop; + +/*! + * @class SelectionOperator + * @brief base class for selection operators. + */ +template +/** + * @brief The SelectionOperator class represents a base class for selection operators in a genetic algorithm. + * + * This class provides common functionality and interface for selection operators. + */ +class SelectionOperator +{ +public: + bool survival; /**< Flag indicating whether the selection operator is used for survival selection. */ + string name; /**< The name of the selection operator. */ + + /** + * @brief Destructor for the SelectionOperator class. + */ + virtual ~SelectionOperator(); + + /** + * @brief Selects individuals from the population based on the selection operator's strategy. + * + * @param pop The population from which to select individuals. + * @param island The index of the island in a parallel genetic algorithm. + * @param p The parameters for the selection operator. + * @return A vector of indices representing the selected individuals. + */ + virtual vector select(Population& pop, int island, const Parameters& p); + + /** + * @brief Applies the selection operator to determine which individuals survive in the population. + * + * @param pop The population in which to apply the survival selection. + * @param island The index of the island in a parallel genetic algorithm. + * @param p The parameters for the selection operator. + * @return A vector of indices representing the surviving individuals. + */ + virtual vector survive(Population& pop, int island, const Parameters& p); +}; + +} // selection +} // Brush +#endif diff --git a/src/types.h b/src/types.h index 5badc481..a4415389 100644 --- a/src/types.h +++ b/src/types.h @@ -80,6 +80,24 @@ typedef Program ClassifierProgram; typedef Program MulticlassClassifierProgram; typedef Program RepresenterProgram; +//////////////////////////////////////////////////////////////////////////////// +// Individual +namespace Pop { + template class Individual; +} +typedef Pop::Individual RegressorIndividual; +typedef Pop::Individual ClassifierIndividual; +typedef Pop::Individual MulticlassClassifierIndividual; +typedef Pop::Individual RepresenterIndividual; + +//////////////////////////////////////////////////////////////////////////////// +// Engine +using PT = ProgramType; +template class Engine; +typedef Engine RegressorEngine; +typedef Engine ClassifierEngine; +typedef Engine MulticlassClassifierEngine; +typedef Engine RepresenterEngine; //////////////////////////////////////////////////////////////////////////////// // Data diff --git a/src/util/error.h b/src/util/error.h index 5fe29d36..96911acf 100644 --- a/src/util/error.h +++ b/src/util/error.h @@ -21,9 +21,9 @@ namespace Brush{ namespace Util { ///prints error to stderr and returns void HandleErrorNoThrow(string err, const char *file, int line ); - #define HANDLE_ERROR_THROW( err ) (Brush::Util::HandleErrorThrow( err, __FILE__, __LINE__ )) - #define HANDLE_WARNING( err ) (Brush::Util::HandleErrorNoThrow( err, __FILE__, __LINE__ )) - + // TODO: have more errors }} +#define HANDLE_ERROR_THROW( err ) (Util::HandleErrorThrow( err, __FILE__, __LINE__ )) +#define HANDLE_WARNING( err ) (Util::HandleErrorNoThrow( err, __FILE__, __LINE__ )) #endif diff --git a/src/util/logger.h b/src/util/logger.h index 4351d36d..ae04c794 100644 --- a/src/util/logger.h +++ b/src/util/logger.h @@ -10,40 +10,55 @@ license: GNU/GPL v3 using namespace std; namespace Brush { +namespace Util{ + +/*! + * @class Logger + * @brief Defines a multi level static logger. + */ +class Logger +{ +public: + + /*! + * @brief Initializes the logger instance. + * @return A pointer to the logger instance. + */ + static Logger* initLogger(); + + /*! + * @brief Destroys the logger instance. + */ + static void destroy(); - namespace Util{ + /*! + * @brief Sets the log level. + * @param verbosity The log level to be set. + */ + void set_log_level(int& verbosity); - ////////////////////////////////////////////////////////////////////////////////// Declarations - - /*! - * @class Logger - * @brief Defines a multi level static logger. - */ + /*! + * @brief Gets the current log level. + * @return The current log level. + */ + int get_log_level(); + + /*! + * @brief Prints a log message with verbosity control. + * @param m The log message to be printed. + * @param v The verbosity level of the log message. + * @param sep The separator to be used between log messages. + * @return The formatted log message. + */ + string log(string m, int v, string sep="\n") const; + +private: + int verbosity; //!< The current log level. + static Logger* instance; //!< The singleton instance of the logger. +}; - class Logger - { - public: - - static Logger* initLogger(); - - static void destroy(); +static Logger &logger = *Logger::initLogger(); - void set_log_level(int& verbosity); - - int get_log_level(); - - /// print message with verbosity control. - string log(string m, int v, string sep="\n") const; - - private: - - int verbosity; - - static Logger* instance; - - }; - - static Logger &logger = *Logger::initLogger(); - } +} } #endif diff --git a/src/util/rnd.cpp b/src/util/rnd.cpp index ac95b699..bb8a9fa6 100644 --- a/src/util/rnd.cpp +++ b/src/util/rnd.cpp @@ -17,8 +17,8 @@ namespace Brush { namespace Util{ * the number of available cores. */ - //cout << "Max threads are " < split(ArrayXf& v, ArrayXb& mask) */ diff --git a/src/util/utils.h b/src/util/utils.h index 4cc9a35c..f767e653 100644 --- a/src/util/utils.h +++ b/src/util/utils.h @@ -27,6 +27,54 @@ using namespace std; * @brief namespace containing various utility functions */ +// serializing vector of shared ptr: https://github.com/nlohmann/json/discussions/2377 +// (used in population.h, which has a shared_ptr vector) +namespace nlohmann +{ +template +struct adl_serializer> +{ + static void to_json(json& j, const std::shared_ptr& opt) + { + if (opt) + { + j = *opt; + } + else + { + j = nullptr; + } + } + + static void from_json(const json& j, std::shared_ptr& opt) + { + if (j.is_null()) + { + opt = nullptr; + } + else + { + opt.reset(new T(j.get())); + } + } +}; +} + +// to overload operators and compare our individuals, we need to be able to +// serialize vectors. +// this is intended to be used with DEAP (so our brush individuals +// can be hashed and compared to each other in python side) +template <> +struct std::hash> { + std::size_t operator()(const std::vector& v) const { + std::size_t seed = v.size(); + for (const auto& elem : v) { + seed ^= std::hash{}(elem) + 0x9e3779b9 + (seed << 6) + (seed >> 2); + } + return seed; + } +}; + // namespace std // { @@ -350,29 +398,49 @@ struct Log_Stats { vector generation; vector time; + vector best_score; vector best_score_v; vector med_score; - vector med_loss_v; + vector med_score_v; + vector med_size; vector med_complexity; - vector med_num_params; - vector med_dim; - + vector max_size; + vector max_complexity; + void update(int index, float timer_count, + float bst_score, float bst_score_v, float md_score, - float md_loss_v, + float md_score_v, + unsigned md_size, unsigned md_complexity, - unsigned md_num_params, - unsigned md_dim); + unsigned mx_size, + unsigned mx_complexity + ); }; typedef struct Log_Stats Log_stats; +NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Log_Stats, + generation, + time, + + best_score, + best_score_v, + med_score, + med_score_v, + + med_size, + med_complexity, + max_size, + max_complexity +); + /// limits the output to finite real numbers template std::enable_if_t, T> diff --git a/src/variation.h b/src/variation.h deleted file mode 100644 index dcfd8288..00000000 --- a/src/variation.h +++ /dev/null @@ -1,406 +0,0 @@ -/* Brush - -copyright 2020 William La Cava -license: GNU/GPL v3 -*/ -#ifndef VARIATION_H -#define VARIATION_H - -// #include "search_space.h" -// #include "program/program.h" -// #include "program/tree_node.h" -// #include "node.h" - -#include - -// namespace Brush{ - -// typedef tree::pre_order_iterator Iter; - -//////////////////////////////////////////////////////////////////////////// -// Mutation & Crossover - - -/** - * @brief Namespace for variation functions like crossover and mutation. - * - */ -namespace variation { - -typedef tree::pre_order_iterator Iter; - -/// @brief replace node with same typed node -/// @param Tree the program tree -/// @param spot an iterator to the node that is being mutated -/// @param SS the search space to sample a node like `spot` -/// @return boolean indicating the success (true) or fail (false) of the operation -inline bool point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) -{ - // cout << "point mutation\n"; - - // get_node_like will sample a similar node based on node_map_weights or - // terminal_weights, and maybe will return a Node. - std::optional newNode = SS.get_node_like(spot.node->data); - - if (!newNode) // newNode == std::nullopt - return false; - - // if optional contains a Node, we access its contained value - Tree.replace(spot, *newNode); - - return true; -} - -/// @brief insert a node with spot as a child -/// @param Tree the program tree -/// @param spot an iterator to the node that is being mutated -/// @param SS the search space to sample a node like `spot` -/// @return boolean indicating the success (true) or fail (false) of the operation -inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) -{ - // cout << "insert mutation\n"; - auto spot_type = spot.node->data.ret_type; - - // pick a random compatible node to insert (with probabilities given by - // node_map_weights). The `-1` represents the node being inserted. - // Ideally, it should always find at least one match (the same node - // used as a reference when calling the function). However, we have a - // size restriction, which will be relaxed here (just as it is in the PTC2 - // algorithm). This mutation can create a new expression that exceeds the - // maximum size by the highest arity among the operators. - std::optional n = SS.sample_op_with_arg(spot_type, spot_type, true, - PARAMS["max_size"].get()-Tree.size()-1); - - if (!n) // there is no operator with compatible arguments - return false; - - // make node n wrap the subtree at the chosen spot - auto parent_node = Tree.wrap(spot, *n); - - // now fill the arguments of n appropriately - bool spot_filled = false; - for (auto a: (*n).arg_types) - { - if (spot_filled) - { - // if spot is in its child position, append children. - // TODO: reminding that sample_terminal may fail as well - auto opt = SS.sample_terminal(a); - - if (!opt) - return false; - - Tree.append_child(parent_node, opt.value()); - } - // if types match, treat this spot as filled by the spot node - else if (a == spot_type) - spot_filled = true; - // otherwise, add siblings before spot node - else { - auto opt = SS.sample_terminal(a); - - if (!opt) - return false; - - Tree.insert(spot, opt.value()); - } - } - - return true; -} - -/// @brief delete subtree and replace it with a terminal of the same return type -/// @param Tree the program tree -/// @param spot an iterator to the node that is being mutated -/// @param SS the search space to sample a node like `spot` -/// @return boolean indicating the success (true) or fail (false) of the operation -inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) -{ - // cout << "delete mutation\n"; - - // sample_terminal will sample based on terminal_weights. If it succeeds, - // then the new terminal will be in `opt.value()` - auto opt = SS.sample_terminal(spot.node->data.ret_type); - - if (!opt) // there is no terminal with compatible arguments - return false; - - Tree.erase_children(spot); - - Tree.replace(spot, opt.value()); - - return true; -}; - -/// @brief toggle the node's weight ON. -/// @param Tree the program tree -/// @param spot an iterator to the node that is being mutated -/// @param SS the search space (unused) -/// @return boolean indicating the success (true) or fail (false) of the operation -inline bool toggle_weight_on_mutation(tree& Tree, Iter spot, const SearchSpace& SS) -{ - if (spot.node->data.get_is_weighted()==true // cant turn on whats already on - || !IsWeighable(spot.node->data.ret_type)) // does not accept weights (e.g. boolean) - return false; // false indicates that mutation failed and should return std::nullopt - - spot.node->data.set_is_weighted(true); - return true; -} - -/// @brief toggle the node's weight OFF. -/// @param Tree the program tree -/// @param spot an iterator to the node that is being mutated -/// @param SS the search space (unused) -/// @return boolean indicating the success (true) or fail (false) of the operation -inline bool toggle_weight_off_mutation(tree& Tree, Iter spot, const SearchSpace& SS) -{ - if (spot.node->data.get_is_weighted()==false) - return false; - - spot.node->data.set_is_weighted(false); - return true; -} - -/// @brief replaces the subtree rooted in `spot` -/// @param Tree the program tree -/// @param spot an iterator to the node that is being mutated -/// @param SS the search space to generate a compatible subtree -/// @return boolean indicating the success (true) or fail (false) of the operation -inline bool subtree_mutation(tree& Tree, Iter spot, const SearchSpace& SS) -{ - auto spot_type = spot.node->data.ret_type; - auto max_size = PARAMS["max_size"].get() - (Tree.size() - Tree.size(spot)); - auto max_depth = PARAMS["max_depth"].get() - (Tree.depth(spot)); - - // sample subtree uses PTC2, which operates on depth and size of the tree - // (and not on the program!). we shoudn't care for weights here - auto subtree = SS.sample_subtree(spot.node->data, max_depth, max_size); - - if (!subtree) // there is no terminal with compatible arguments - return false; - - // if optional contains a Node, we access its contained value - Tree.erase_children(spot); - Tree.replace(spot, subtree.value().begin()); - - return true; -} - -/** - * @brief Stochastically mutate a program. - * - * Types of mutation: - * - * - point mutation changes a single node. - * - insertion mutation inserts a node as the parent of an existing node, and fills in the other arguments. - * - deletion mutation deletes a node. - * - subtree mutation inserts a new subtree into the program. - * - toggle_weight_on mutation turns a node's weight ON. - * - toggle_weight_off mutation turns a node's weight OFF. - * - * Every mutation has a probability (weight) based on global parameters. The - * spot where the mutation will take place is sampled based on attribute - * `get_prob_change` of each node in the tree. Inside each type of mutation, - * when a new node is inserted, it is sampled based on `terminal_weights`. - * - * Due to the stochastic behavior, and the several sampling steps, it may come to - * a case where the search space does not hold any possible modification to do in - * the program. In this case, the method returns `std::nullopt` (and has overloads - * so it can be used in a boolean context). - * - * If the mutation succeeds, the mutated program can be accessed through the - * `.value()` attribute of the `std::optional`. - * - * This means that, if you use the mutation as `auto opt = mutate(parent, SS)`, - * either `opt==false` or `opt.value()` contains the child program. - * - * @tparam T program type - * @param parent the program to be mutated - * @param SS a search space - * @return `std::optional` that may contain the child program of type `T` - */ -template -std::optional> mutate(const Program& parent, const SearchSpace& SS) -{ - // all mutation validation and setup should be done here. Specific mutaiton - // functions are intended to work on the program tree thus cannot access - // program functions and attributes. - Program child(parent); - - // choose location by weighted sampling of program - vector weights(child.Tree.size()); - std::transform(child.Tree.begin(), child.Tree.end(), - weights.begin(), - [](const auto& n){ return n.get_prob_change(); } - ); - - auto options = PARAMS["mutation_options"].get>(); - - if (std::all_of(weights.begin(), weights.end(), [](const auto& w) { - return w<=0.0; - })) - { // There is no spot that has a probability to be selected - return std::nullopt; - } - - auto spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), - weights.begin(), weights.end()); - - if (std::all_of(options.begin(), options.end(), [](const auto& kv) { - return kv.second<=0.0; - })) - { // No mutation can be successfully applied to this solution - return std::nullopt; - } - - // choose a valid mutation option - string choice = r.random_choice(options); - - // std::cout << "mutation configuration (choice was " << choice << "):" << std::endl; - // for (const auto& [k, v] : options) - // std::cout << " - " << k << " : " << v << std::endl; - - // Every mutation here works inplace, so they return bool instead of - // std::optional to indicare the result of their manipulation over the - // program tree. Here we call the mutation function and return the result - using MutationFunc = std::function&, Iter, const SearchSpace&)>; - - std::map mutations{ - {"insert", insert_mutation}, - {"delete", delete_mutation}, - {"point", point_mutation}, - {"subtree", subtree_mutation}, - {"toggle_weight_on", toggle_weight_on_mutation}, - {"toggle_weight_off", toggle_weight_off_mutation} - }; - - // Try to find the mutation function based on the choice - auto it = mutations.find(choice); - if (it == mutations.end()) { - std::string msg = fmt::format("{} not a valid mutation choice", choice); - HANDLE_ERROR_THROW(msg); - } - - // apply the mutation and check if it succeeded - bool success = it->second(child.Tree, spot, SS); - - if (success - && ( (child.size() <= PARAMS["max_size"].get() ) - && (child.depth() <= PARAMS["max_depth"].get()) )){ - - return child; - } else { - return std::nullopt; - } -}; - -/** - * @brief Stochastically swaps subtrees between root and other, returning a new program. - * - * The spot where the cross will take place in the `root` parent is sampled - * based on attribute `get_prob_change` of each node in the tree. After selecting - * the cross spot, the program will iterate through the `other` parent searching - * for all compatible sub-trees to replace. - * - * Due to the stochastic behavior, it may come to a case where there is no - * candidate to replace the spot node. In this case, the method returns - * `std::nullopt` (and has overloads so it can be used in a boolean context). - * - * If the cross succeeds, the child program can be accessed through the - * `.value()` attribute of the `std::optional`. - * - * This means that, if you use the cross as `auto opt = mutate(parent, SS)`, - * either `opt==false` or `opt.value()` contains the child. - * - * @tparam T the program type - * @param root the root parent - * @param other the donating parent - * @return `std::optional` that may contain the child program of type `T` - */ -template -std::optional> cross(const Program& root, const Program& other) -{ - /* subtree crossover between this and other, producing new Program */ - // choose location by weighted sampling of program - // TODO: why doesn't this copy the search space reference to child? - Program child(root); - - // pick a subtree to replace - vector child_weights(child.Tree.size()); - std::transform(child.Tree.begin(), child.Tree.end(), - child_weights.begin(), - [](const auto& n){ return n.get_prob_change(); } - ); - - if (std::all_of(child_weights.begin(), child_weights.end(), [](const auto& w) { - return w<=0.0; - })) - { // There is no spot that has a probability to be selected - return std::nullopt; - } - - auto child_spot = r.select_randomly(child.Tree.begin(), - child.Tree.end(), - child_weights.begin(), - child_weights.end() - ); - - auto child_ret_type = child_spot.node->data.ret_type; - - auto allowed_size = PARAMS["max_size"].get() - - ( child.size() - child.size_at(child_spot) ); - auto allowed_depth = PARAMS["max_depth"].get() - - ( child.depth_to_reach(child_spot) ); - - // pick a subtree to insert. Selection is based on other_weights - vector other_weights(other.Tree.size()); - - // iterator to get the size of subtrees inside transform - auto other_iter = other.Tree.begin(); - - // lambda function to check feasibility of solution and increment the iterator - const auto check_and_incrm = [other, &other_iter, allowed_size, allowed_depth]() -> bool { - int s = other.size_at( other_iter ); - int d = other.depth_at( other_iter ); - - std::advance(other_iter, 1); - return (s <= allowed_size) && (d <= allowed_depth); - }; - - std::transform(other.Tree.begin(), other.Tree.end(), - other_weights.begin(), - [child_ret_type, check_and_incrm](const auto& n){ - // need to pick a node that has a matching output type to the child_spot. - // also need to check if swaping this node wouldn't exceed max_size - if (check_and_incrm() && (n.ret_type == child_ret_type)) - return n.get_prob_change(); - else - // setting the weight to zero to indicate a non-feasible crossover point - return float(0.0); - } - ); - - bool matching_spots_found = false; - for (const auto& w: other_weights) - { - matching_spots_found = w > 0.0; - - if (matching_spots_found) { - auto other_spot = r.select_randomly( - other.Tree.begin(), - other.Tree.end(), - other_weights.begin(), - other_weights.end() - ); - - // fmt::print("other_spot : {}\n",other_spot.node->data); - // swap subtrees at child_spot and other_spot - child.Tree.move_ontop(child_spot, other_spot); - return child; - } - } - - return std::nullopt; -}; -} //namespace variation -#endif \ No newline at end of file diff --git a/src/search_space.cpp b/src/vary/search_space.cpp similarity index 53% rename from src/search_space.cpp rename to src/vary/search_space.cpp index 4ea0b518..95a9cf0b 100644 --- a/src/search_space.cpp +++ b/src/vary/search_space.cpp @@ -1,15 +1,17 @@ #include "search_space.h" -#include "program/program.h" -#include +#include "../program/program.h" // TODO: dont import this header here namespace Brush{ float calc_initial_weight(const ArrayXf& value, const ArrayXf& y) { + // OBS: only for terminals! + // weights are initialized as the slope of the z-score of x and y. - // If y has different length from X, we get a core dump here. + // If y has different length from X, we get a core dump in this function. + // That is why Dataset makes a check for this // TODO: need to make SS (or Datasaet) check for this when loading the data vector dtypes = {'f', 'f'}; @@ -29,14 +31,24 @@ float calc_initial_weight(const ArrayXf& value, const ArrayXf& y) float prob_change = std::abs(slope(data.col(0).array() , // x=variable data.col(1).array() )); // y=target + // having a minimum feature weight if it was not set to zero + if (std::abs(prob_change)<1e-4) + prob_change = 1e-1; + + // prob_change will evaluate to nan if variance(x)==0. Features with + // zero variance should not be used (as they behave just like a constant). + if (std::isnan(prob_change)) + prob_change = 0.0; + return prob_change; } /// @brief generate terminals from the dataset features and random constants. /// @param d a dataset +/// @param weights_init whether the terminal prob_change should be estimated from correlations with the target value /// @return a vector of nodes -vector generate_terminals(const Dataset& d) +vector generate_terminals(const Dataset& d, const bool weights_init) { vector terminals; int i = 0; @@ -57,43 +69,46 @@ vector generate_terminals(const Dataset& d) float prob_change = 1.0; // default value - // if the value can be casted to float array, we can calculate slope - if (std::holds_alternative(value)) + if (d.y.size()>0 && weights_init) { - prob_change = calc_initial_weight(std::get(value), d.y); - } - else if (std::holds_alternative(value)) - { - // for each variable we create a one-vs-all binary variable, then - // calculate slope. Final value will be the average of slopes - - auto tmp = std::get(value); - - //get number of unique values - std::map uniqueMap; - for(int i = 0; i < tmp.size(); i++) - uniqueMap[(float)tmp(i)] = true; - - ArrayXf slopes = ArrayXf::Ones(uniqueMap.size()); - int slopesIterator = 0; - for (const auto& pair : uniqueMap) + // if the value can be casted to float array, we can calculate slope + if (std::holds_alternative(value) && d.y.size()>0) { - auto one_vs_all = ArrayXf::Ones(tmp.size()).array() * (tmp.array()==pair.first).cast(); - - slopes[slopesIterator++] = calc_initial_weight(one_vs_all, d.y); + prob_change = calc_initial_weight(std::get(value), d.y); + } + else if (std::holds_alternative(value)) + { + // for each variable we create a one-vs-all binary variable, then + // calculate slope. Final value will be the average of slopes + + auto tmp = std::get(value); + + //get number of unique values + std::map uniqueMap; + for(int i = 0; i < tmp.size(); i++) + uniqueMap[(float)tmp(i)] = true; + + ArrayXf slopes = ArrayXf::Ones(uniqueMap.size()); + int slopesIterator = 0; + for (const auto& pair : uniqueMap) + { + auto one_vs_all = ArrayXf::Ones(tmp.size()).array() * (tmp.array()==pair.first).cast(); + + slopes[slopesIterator++] = calc_initial_weight(one_vs_all, d.y); + } + + prob_change = slopes.mean(); + } + else if (std::holds_alternative(value)) + { + auto tmp = std::get(value).template cast(); + prob_change = calc_initial_weight(tmp, d.y); + } + else + { + auto msg = fmt::format("Brush coudn't calculate the initial weight of variable {}\n",feature_name); + HANDLE_ERROR_THROW(msg); } - - prob_change = slopes.mean(); - } - else if (std::holds_alternative(value)) - { - auto tmp = std::get(value).template cast(); - prob_change = calc_initial_weight(tmp, d.y); - } - else - { - auto msg = fmt::format("Brush coudn't calculate the initial weight of variable {}\n",feature_name); - HANDLE_ERROR_THROW(msg); } n.set_prob_change( prob_change ); @@ -120,18 +135,25 @@ vector generate_terminals(const Dataset& d) return sum / count; }; - auto cXf = Node(NodeType::Constant, Signature{}, true, "C"); - cXf.set_prob_change(signature_avg(cXf.ret_type)); + // constants for each type + auto cXf = Node(NodeType::Constant, Signature{}, true, "Cf"); + float floats_avg_weights = signature_avg(cXf.ret_type); + cXf.set_prob_change(floats_avg_weights); terminals.push_back(cXf); - auto cXi = Node(NodeType::Constant, Signature{}, true, "C"); + auto cXi = Node(NodeType::Constant, Signature{}, true, "Ci"); cXi.set_prob_change(signature_avg(cXi.ret_type)); terminals.push_back(cXi); - auto cXb = Node(NodeType::Constant, Signature{}, false, "C"); + auto cXb = Node(NodeType::Constant, Signature{}, false, "Cb"); cXb.set_prob_change(signature_avg(cXb.ret_type)); terminals.push_back(cXb); + // mean label node + auto meanlabel = Node(NodeType::MeanLabel, Signature{}, true, "MeanLabel"); + meanlabel.set_prob_change(floats_avg_weights); + terminals.push_back(meanlabel); + return terminals; }; @@ -141,7 +163,8 @@ void SearchSpace::print() const { std::cout << fmt::format("{}\n", *this) << std::flush; } -void SearchSpace::init(const Dataset& d, const unordered_map& user_ops) +void SearchSpace::init(const Dataset& d, const unordered_map& user_ops, + bool weights_init) { // fmt::print("constructing search space...\n"); this->node_map.clear(); @@ -158,11 +181,43 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user // create nodes based on data types terminal_types = d.unique_data_types; - vector terminals = generate_terminals(d); + vector terminals = generate_terminals(d, weights_init); + // If it is a classification problem, we need to add the fixed root nodes + // (logistic for binary classification, softmax for multiclassification). + // Sometimes, the user may not specify these two nodes as candidates when + // sampling functions, so we check if they are already in the terminal set, and + // we add them with zero prob if they are not. They need to be in the func set + // when calling GenerateNodeMap, so the search_space will contain all the hashes + // and signatures for them (and they can be used only in program root). + // TODO: fix softmax and add it here + + // Copy the original map using the copy constructor + std::unordered_map extended_user_ops(user_ops); + + if (d.classification) + { + // Convert ArrayXf to std::vector for compatibility with std::set + std::vector vec(d.y.data(), d.y.data() + d.y.size()); + + std::set unique_classes(vec.begin(), vec.end()); + + // We need some ops in the search space so we can have the logit and offset + if (user_ops.find("OffsetSum") == user_ops.end()) + extended_user_ops.insert({"OffsetSum", 0.0f}); + + if (unique_classes.size()==2 && (user_ops.find("Logistic") == user_ops.end())) { + extended_user_ops.insert({"Logistic", 0.0f}); + } + else if (user_ops.find("Softmax") == user_ops.end()) { + extended_user_ops.insert({"Softmax", 0.0f}); + } + } + /* fmt::print("generate nodetype\n"); */ - GenerateNodeMap(user_ops, d.unique_data_types, + GenerateNodeMap(extended_user_ops, d.unique_data_types, std::make_index_sequence()); + // map terminals /* fmt::print("looping through terminals...\n"); */ for (const auto& term : terminals) @@ -199,13 +254,19 @@ std::optional> SearchSpace::sample_subtree(Node root, int max_d, int terminal_weights.at(root.ret_type).end())) ) return std::nullopt; + auto Tree = tree(); + auto spot = Tree.insert(Tree.begin(), root); + // we should notice the difference between size of a PROGRAM and a TREE. // program count weights in its size, while the TREE structure dont. Wenever // using size of a program/tree, make sure you use the function from the correct class - return PTC2(root, max_d, max_size); + PTC2(Tree, spot, max_d, max_size); + + return Tree; }; -tree SearchSpace::PTC2(Node root, int max_d, int max_size) const +tree& SearchSpace::PTC2(tree& Tree, + tree::iterator spot, int max_d, int max_size) const { // PTC2 is agnostic of program type @@ -215,56 +276,61 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const // parameters, the real maximum size that can occur is `max_size` plus the // highest operator arity, and the real maximum depth is `max_depth` plus one. - auto Tree = tree(); - - /* fmt::print("building program with max size {}, max depth {}",max_size,max_d); */ - // Queue of nodes that need children vector> queue; - /* cout << "chose " << n.name << endl; */ - // auto spot = Tree.set_head(n); - /* cout << "inserting...\n"; */ - auto spot = Tree.insert(Tree.begin(), root); // node depth int d = 1; // current tree size int s = 1; + + Node root = spot.node->data; + + // updating size accordingly to root node + if (Is(root.node_type)) + s += 3; + else if (Is(root.node_type)) + s += 2; + + if ( root.get_is_weighted()==true + && Isnt(root.node_type) ) + s += 2; + //For each argument position a of n, Enqueue(a; g) for (auto a : root.arg_types) { - /* cout << "queing a node of type " << DataTypeName[a] << endl; */ + // cout << "queing a node of type " << DataTypeName[a] << endl; auto child_spot = Tree.append_child(spot); queue.push_back(make_tuple(child_spot, a, d)); } + int max_arity = 4; + Node n; // Now we actually start the PTC2 procedure to create the program tree - /* cout << "queue size: " << queue.size() << endl; */ - /* cout << "entering first while loop...\n"; */ - while ( 3*(queue.size()-1) + s < max_size && queue.size() > 0) + while ( queue.size() + s < max_size && queue.size() > 0) { + // including the queue size in the max_size, since each element in queue + // can grow up exponentially + // by default, terminals are weighted (counts as 3 nodes in program size). // since every spot in queue has potential to be a terminal, we multiply // its size by 3. Subtracting one due to the fact that this loop will // always insert a non terminal (which by default has weights off). // this way, we can have PTC2 working properly. - /* cout << "queue size: " << queue.size() << endl; */ + // cout << "queue size: " << queue.size() << endl; auto [qspot, t, d] = RandomDequeue(queue); - /* cout << "current depth: " << d << endl; */ - if (d == max_d) + // cout << "current depth: " << d << endl; + if (d >= max_d || s >= max_size) { - // choose terminal of matching type - /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ - // qspot = sample_terminal(t); - // Tree.replace(qspot, sample_terminal(t)); - // Tree.append_child(qspot, sample_terminal(t)); - auto opt = sample_terminal(t); - while (!opt) - opt = sample_terminal(t); + + // if it returned optional, then there's nothing to sample based on weights. + // We'll force sampling again with uniform probs + if (!opt) + opt = sample_terminal(t, true); // If we successfully get a terminal, use it n = opt.value(); @@ -274,14 +340,19 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const else { //choose a nonterminal of matching type - /* cout << "getting op of type " << DataTypeName[t] << endl; */ auto opt = sample_op(t); - /* cout << "chose " << n.name << endl; */ - // TreeIter new_spot = Tree.append_child(qspot, n); - // qspot = n; - while (!opt) - opt = sample_op(t); + if (!opt) { // there is no operator for this node. sample a terminal instead + opt = sample_terminal(t); + } + + if (!opt) { // no operator nor terminal. weird. + auto msg = fmt::format("Failed to sample operator AND terminal of data type {} during PTC2.\n", DataTypeName[t]); + HANDLE_ERROR_THROW(msg); + + // queue.push_back(make_tuple(qspot, t, d)); + // continue; + } n = opt.value(); @@ -290,8 +361,6 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const // For each arg of n, add to queue for (auto a : n.arg_types) { - /* cout << "queing a node of type " << DataTypeName[a] << endl; */ - // queue.push_back(make_tuple(new_spot, a, d+1)); auto child_spot = Tree.append_child(newspot); queue.push_back(make_tuple(child_spot, a, d+1)); @@ -300,64 +369,55 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const // increment is different based on node weights ++s; - if (n.get_is_weighted()) + + if (Is(n.node_type)) + s += 3; + else if (Is(n.node_type)) s += 2; - /* cout << "current tree size: " << s << endl; */ + if ( n.get_is_weighted()==true + && Isnt(n.node_type) ) + s += 2; } - /* cout << "entering second while loop...\n"; */ + while (queue.size() > 0) { if (queue.size() == 0) break; - /* cout << "queue size: " << queue.size() << endl; */ - auto [qspot, t, d] = RandomDequeue(queue); - /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ - // Tree.append_child(qspot, sample_terminal(t)); - // qspot = sample_terminal(t); - // auto newspot = Tree.replace(qspot, sample_terminal(t)); - auto opt = sample_terminal(t); - while (!opt) { - opt = sample_terminal(t); - } + if (!opt) + opt = sample_terminal(t, true); n = opt.value(); auto newspot = Tree.replace(qspot, n); } - - /* cout << "final tree:\n" */ - /* << Tree.begin().node->get_model() << "\n" */ - /* << Tree.begin().node->get_tree_model(true) << endl; */ - /* << Tree.get_model() << "\n" */ - /* << Tree.get_model(true) << endl; // pretty */ - return Tree; }; -RegressorProgram SearchSpace::make_regressor(int max_d, int max_size) +// TODO: stop using params as a default argument and actually pass it (also update tests) +RegressorProgram SearchSpace::make_regressor(int max_d, int max_size, const Parameters& params) { - return make_program(max_d, max_size); + return make_program(params, max_d, max_size); }; -ClassifierProgram SearchSpace::make_classifier(int max_d, int max_size) +ClassifierProgram SearchSpace::make_classifier(int max_d, int max_size, const Parameters& params) { - return make_program(max_d, max_size); + return make_program(params, max_d, max_size); }; MulticlassClassifierProgram SearchSpace::make_multiclass_classifier( - int max_d, int max_size) + int max_d, int max_size, const Parameters& params) { - return make_program(max_d, max_size); + return make_program(params, max_d, max_size); }; -RepresenterProgram SearchSpace::make_representer(int max_d, int max_size) +RepresenterProgram SearchSpace::make_representer(int max_d, int max_size, const Parameters& params) { - return make_program(max_d, max_size); + return make_program(params, max_d, max_size); }; } //Brush diff --git a/src/search_space.h b/src/vary/search_space.h similarity index 78% rename from src/search_space.h rename to src/vary/search_space.h index ac751a65..0697fbae 100644 --- a/src/search_space.h +++ b/src/vary/search_space.h @@ -5,16 +5,18 @@ license: GNU/GPL v3 #ifndef SEARCHSPACE_H #define SEARCHSPACE_H //internal includes -#include "init.h" -#include "program/node.h" -#include "program/nodetype.h" -#include "program/tree_node.h" +#include "../init.h" +#include "../program/node.h" +#include "../program/nodetype.h" +#include "../program/tree_node.h" // #include "program/program.h" -#include "util/utils.h" -#include "util/rnd.h" -#include "params.h" +#include "../util/error.h" +#include "../util/utils.h" +#include "../util/rnd.h" +#include "../params.h" #include #include +#include /* Defines the search space of Brush. * The search spaces consists of nodes and their accompanying probability @@ -45,7 +47,7 @@ using TreeIter = tree::pre_order_iterator; // enum class ProgramType: uint32_t; // template struct ProgramTypeEnum; -vector generate_terminals(const Dataset& d); +vector generate_terminals(const Dataset& d, const bool weights_init); //////////////////////////////////////////////////////////////////////////////// @@ -144,45 +146,47 @@ struct SearchSpace * */ template - PT make_program(int max_d=0, int max_size=0); + PT make_program(const Parameters& params, int max_d=0, int max_size=0); /// @brief Makes a random regressor program. Convenience wrapper for @ref make_program /// @param max_d max depth of the program /// @param max_size max size of the program /// @return a regressor program - RegressorProgram make_regressor(int max_d = 0, int max_size = 0); + RegressorProgram make_regressor(int max_d = 0, int max_size = 0, const Parameters& params=Parameters()); /// @brief Makes a random classifier program. Convenience wrapper for @ref make_program /// @param max_d max depth of the program /// @param max_size max size of the program /// @return a classifier program - ClassifierProgram make_classifier(int max_d = 0, int max_size = 0); + ClassifierProgram make_classifier(int max_d = 0, int max_size = 0, const Parameters& params=Parameters()); /// @brief Makes a random multiclass classifier program. Convenience wrapper for @ref make_program /// @param max_d max depth of the program /// @param max_size max size of the program /// @return a multiclass classifier program - MulticlassClassifierProgram make_multiclass_classifier(int max_d = 0, int max_size = 0); + MulticlassClassifierProgram make_multiclass_classifier(int max_d = 0, int max_size = 0, const Parameters& params=Parameters()); /// @brief Makes a random representer program. Convenience wrapper for @ref make_program /// @param max_d max depth of the program /// @param max_size max size of the program /// @return a representer program - RepresenterProgram make_representer(int max_d = 0, int max_size = 0); + RepresenterProgram make_representer(int max_d = 0, int max_size = 0, const Parameters& params=Parameters()); SearchSpace() = default; /// @brief Construct a search space /// @param d A dataset containing terminal definitions /// @param user_ops Optional user-provided dictionary of operators with their probability of being chosen - SearchSpace(const Dataset& d, const unordered_map& user_ops = {}){ - init(d,user_ops); + /// @param weights_init whether the terminal prob_change should be estimated from correlations with the target value + SearchSpace(const Dataset& d, const unordered_map& user_ops = {}, bool weights_init = true){ + init(d,user_ops,weights_init); } /// @brief Called by the constructor to initialize the search space /// @param d A dataset containing terminal definitions /// @param user_ops Optional user-provided dictionary of operators with their probability of being chosen - void init(const Dataset& d, const unordered_map& user_ops = {}); + /// @param weights_init whether the terminal prob_change should be estimated from correlations with the target value + void init(const Dataset& d, const unordered_map& user_ops = {}, bool weights_init = true); /// @brief check if a return type is in the node map /// @param R data type @@ -312,7 +316,7 @@ struct SearchSpace /// @brief Get a random terminal /// @return `std::optional` that may contain a terminal Node. - std::optional sample_terminal() const + std::optional sample_terminal(bool force_return=false) const { //TODO: match terminal args_type (probably '{}' or something?) // make a separate terminal_map @@ -320,17 +324,24 @@ struct SearchSpace // We'll make terminal types to have its weights proportional to the // DataTypes Weights they hold vector data_type_weights(terminal_weights.size()); - std::transform( - terminal_weights.begin(), - terminal_weights.end(), - data_type_weights.begin(), - [](const auto& tw){ - return std::reduce(tw.second.begin(), tw.second.end()); } - ); - - if (!has_solution_space(data_type_weights.begin(), - data_type_weights.end())) - return std::nullopt; + if (force_return) + { + std::fill(data_type_weights.begin(), data_type_weights.end(), 1.0f); + } + else + { + std::transform( + terminal_weights.begin(), + terminal_weights.end(), + data_type_weights.begin(), + [](const auto& tw){ + return std::reduce(tw.second.begin(), tw.second.end()); } + ); + + if (!has_solution_space(data_type_weights.begin(), + data_type_weights.end())) + return std::nullopt; + } // If we got this far, then it is garanteed that we'll return something // The match take into account datatypes with non-zero weights @@ -341,16 +352,32 @@ struct SearchSpace data_type_weights.end() ); - return *r.select_randomly( - match.second.begin(), match.second.end(), - terminal_weights.at(match.first).begin(), - terminal_weights.at(match.first).end() - ); + // theres always a constant of each data type + vector match_weights(match.second.size()); + if (force_return) + { + std::fill(match_weights.begin(), match_weights.end(), 1.0f); + } + else + { + std::transform( + terminal_weights.at(match.first).begin(), + terminal_weights.at(match.first).end(), + match_weights.begin(), + [](const auto& w){ return w; }); + + if (!has_solution_space(match_weights.begin(), + match_weights.end())) + return std::nullopt; + } + + return *r.select_randomly(match.second.begin(), match.second.end(), + match_weights.begin(), match_weights.end()); }; /// @brief Get a random terminal with return type `R` /// @return `std::optional` that may contain a terminal Node of type `R`. - std::optional sample_terminal(DataType R) const + std::optional sample_terminal(DataType R, bool force_return=false) const { // should I keep doing this check? // if (terminal_map.find(R) == terminal_map.end()){ @@ -358,16 +385,33 @@ struct SearchSpace // HANDLE_ERROR_THROW(msg); // } + // If there's at least one constant for every data type, its always possible to force sample_terminal to return something + // TODO: try to combine with above function - if ( (terminal_map.find(R) == terminal_map.end()) - || (!has_solution_space(terminal_weights.at(R).begin(), - terminal_weights.at(R).end())) ) + vector match_weights(terminal_weights.at(R).size()); + if (force_return) + { + std::fill(match_weights.begin(), match_weights.end(), 1.0f); + } + else + { + std::transform( + terminal_weights.at(R).begin(), + terminal_weights.at(R).end(), + match_weights.begin(), + [](const auto& w){ return w; } + ); + + if ( (terminal_map.find(R) == terminal_map.end()) + || (!has_solution_space(match_weights.begin(), + match_weights.end())) ) return std::nullopt; - + } + return *r.select_randomly(terminal_map.at(R).begin(), - terminal_map.at(R).end(), - terminal_weights.at(R).begin(), - terminal_weights.at(R).end()); + terminal_map.at(R).end(), + match_weights.begin(), + match_weights.end()); }; /// @brief get an operator matching return type `ret`. @@ -376,6 +420,8 @@ struct SearchSpace std::optional sample_op(DataType ret) const { // check(ret); + if (node_map.find(ret) == node_map.end()) + return std::nullopt; //TODO: match terminal args_type (probably '{}' or something?) auto ret_match = node_map.at(ret); @@ -408,6 +454,8 @@ struct SearchSpace std::optional sample_op(NodeType type, DataType R) { // check(R); + if (node_map.find(R) == node_map.end()) + return std::nullopt; auto ret_match = node_map.at(R); @@ -501,7 +549,7 @@ struct SearchSpace /// @return `std::optional` that may contain a Node std::optional get_node_like(Node node) const { - if (Is(node.node_type)){ + if (Is(node.node_type)){ return sample_terminal(node.ret_type); } @@ -531,10 +579,10 @@ struct SearchSpace void print() const; private: - tree PTC2(Node root, int max_d, int max_size) const; + tree& PTC2(tree& Tree, tree::iterator root, int max_d, int max_size) const; template - requires (!is_in_v) + requires (!is_in_v) static constexpr std::optional CreateNode( const auto& unique_data_types, bool use_all, @@ -558,12 +606,13 @@ struct SearchSpace const vector& unique_data_types ) { - bool use_all = user_ops.size() == 0; auto name = NodeTypeName[NT]; - //TODO: address this (whether weights should be included by default) - // bool weighted = (IsWeighable() && is_same_v); + bool weighted = false; + if (Is(NT)) // this has to have weights on by default + weighted = true; + auto n_maybe = CreateNode(unique_data_types, use_all, weighted); if (n_maybe){ @@ -588,7 +637,7 @@ struct SearchSpace const vector& unique_data_types ) { - if (Is(NT)) + if (Is(NT)) return; bool use_all = user_ops.size() == 0; auto name = NodeTypeName.at(NT); @@ -629,67 +678,76 @@ T RandomDequeue(std::vector& Q) }; template -P SearchSpace::make_program(int max_d, int max_size) +P SearchSpace::make_program(const Parameters& params, int max_d, int max_size) { - if (max_d == 0) - max_d = PARAMS["max_depth"].get(); - if (max_size == 0) - max_size = r.rnd_int(1, PARAMS["max_size"].get()); + // this is what makes `make_program` create uniformly distributed + // individuals to feed initial population + if (max_d < 1) + max_d = r.rnd_int(1, params.max_depth); + if (max_size < 1) + max_size = r.rnd_int(1, params.max_size); DataType root_type = DataTypeEnum::value; ProgramType program_type = P::program_type; // ProgramType program_type = ProgramTypeEnum::value; + // Tree is pre-filled with some fixed nodes depending on program type auto Tree = tree(); - if (max_size == 1) + + // building the tree for each program case. Then, we give the spot to PTC2, + // and it will fill the rest of the tree + tree::iterator spot; + + // building the root node for each program case + if (P::program_type == ProgramType::BinaryClassifier) { - // auto root = Tree.insert(Tree.begin(), sample_terminal(root_type)); + Node node_logit = get(NodeType::Logistic, DataType::ArrayF, Signature()); + node_logit.set_prob_change(0.0); + node_logit.fixed=true; + auto spot_logit = Tree.insert(Tree.begin(), node_logit); - // We can only have a terminal here, but the terminal must be compatible - auto opt = sample_terminal(root_type); + if (true) { // Logistic(Add(Constant, <>)). + Node node_offset = get(NodeType::OffsetSum, DataType::ArrayF, Signature()); + node_offset.set_prob_change(0.0); + node_offset.fixed=true; - if (!opt){ - auto msg = fmt::format("Program with size=1 could not be created. " - "The search space does not contain any terminal with data type {}./n", - root_type); - HANDLE_ERROR_THROW(msg); + auto spot_offset = Tree.append_child(spot_logit); + + spot = Tree.replace(spot_offset, node_offset); + } + else { // If false, then model will be Logistic(<>) + spot = spot_logit; } - - Tree.insert(Tree.begin(), opt.value()); } - else {// Our program can (and will) be grater than 1 node - - // building the root node for each program case. We give the root, and it - // fills the rest of the tree + else if (P::program_type == ProgramType::MulticlassClassifier) + { + Node node_softmax = get(NodeType::Softmax, DataType::MatrixF, Signature()); + node_softmax.set_prob_change(0.0); + node_softmax.fixed=true; + + spot = Tree.insert(Tree.begin(), node_softmax); + } + else // regression or representer --- sampling any candidate op or terminal + { Node root; - // building the root node for each program case - if (P::program_type == ProgramType::BinaryClassifier) - { - root = get(NodeType::Logistic, DataType::ArrayF, Signature()); - root.set_prob_change(0.0); - root.fixed=true; + std::optional opt=std::nullopt; - } - else if (P::program_type == ProgramType::MulticlassClassifier) - { - root = get(NodeType::Softmax, DataType::MatrixF, Signature()); - root.set_prob_change(0.0); - root.fixed=true; - } - else { - // we start with a non-terminal (can be replaced inside PTC2 though, if max_size==1) - auto opt = sample_op(root_type); - while (!opt) { - opt = sample_op(root_type); - } - root = opt.value(); - } - - Tree = PTC2(root, max_d, max_size); + if (max_size>1 && max_d>1) + opt = sample_op(root_type); + + if (!opt) // if failed, then we dont have any operator to use as root... + opt = sample_terminal(root_type, true); + + root = opt.value(); + + spot = Tree.insert(Tree.begin(), root); } - return P(*this,Tree); + // max_d-1 because we always pick the root before calling ptc2 + PTC2(Tree, spot, max_d-1, max_size); // change inplace + + return P(*this, Tree); }; extern SearchSpace SS; diff --git a/src/vary/variation.cpp b/src/vary/variation.cpp new file mode 100644 index 00000000..3e75182b --- /dev/null +++ b/src/vary/variation.cpp @@ -0,0 +1,641 @@ +#include "variation.h" + +namespace Brush { +namespace Var { + +/// @brief replace node with same typed node +/// @param prog the program +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to sample a node like `spot` +/// @return boolean indicating the success (true) or fail (false) of the operation +class PointMutation : public MutationBase +{ +public: + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params) + { + // get_node_like will sample a similar node based on node_map_weights or + // terminal_weights, and maybe will return a Node. + optional newNode = SS.get_node_like(spot.node->data); + + if (!newNode) // overload to check if newNode == nullopt + return false; + + // if optional contains a Node, we access its contained value + Tree.replace(spot, *newNode); + + return true; + } +}; + +/// @brief insert a node with spot as a child +/// @param prog the program +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to sample a node like `spot` +/// @return boolean indicating the success (true) or fail (false) of the operation +class InsertMutation : public MutationBase +{ +public: + static auto find_spots(tree& Tree, const SearchSpace& SS, + const Parameters& params) + { + vector weights; + + if (Tree.size() < params.get_max_size()) { + Iter iter = Tree.begin(); + std::transform(Tree.begin(), Tree.end(), std::back_inserter(weights), + [&](const auto& n){ + size_t d = 1+Tree.depth(iter); + std::advance(iter, 1); + + // check if SS holds an operator to avoid failing `check` in sample_op_with_arg + if ((d >= params.get_max_depth()) + || (SS.node_map.find(n.ret_type) == SS.node_map.end())) { + return 0.0f; + } + else { + return n.get_prob_change(); + } + }); + } + else { + // fill the vector with zeros, since we're already at max_size + weights.resize(Tree.size()); + std::fill(weights.begin(), weights.end(), 0.0f); + } + + return weights; + } + + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params) + { + auto spot_type = spot.node->data.ret_type; + + // pick a random compatible node to insert (with probabilities given by + // node_map_weights). The `-1` represents the node being inserted. + // Ideally, it should always find at least one match (the same node + // used as a reference when calling the function). However, we have a + // size restriction, which will be relaxed here (just as it is in the PTC2 + // algorithm). This mutation can create a new expression that exceeds the + // maximum size by the highest arity among the operators. + std::optional n = SS.sample_op_with_arg( + spot_type, spot_type, true, params.max_size-Tree.size()-1); + + if (!n) // there is no operator with compatible arguments + return false; + + // make node n wrap the subtree at the chosen spot + auto parent_node = Tree.wrap(spot, *n); + + // now fill the arguments of n appropriately + bool spot_filled = false; + for (auto a: (*n).arg_types) + { + if (spot_filled) + { + // if spot is in its child position, append children. + auto opt = SS.sample_terminal(a); + + if (!opt) + return false; + + Tree.append_child(parent_node, opt.value()); + } + // if types match, treat this spot as filled by the spot node + else if (a == spot_type) + spot_filled = true; + // otherwise, add siblings before spot node + else { + auto opt = SS.sample_terminal(a); + + if (!opt) + return false; + + Tree.insert(spot, opt.value()); + } + } + + return true; + } +}; + +/// @brief delete subtree and replace it with a terminal of the same return type +/// @param prog the program +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to sample a node like `spot` +/// @return boolean indicating the success (true) or fail (false) of the operation +class DeleteMutation : public MutationBase +{ +public: + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params) + { + // sample_terminal will sample based on terminal_weights. If it succeeds, + // then the new terminal will be in `opt.value()` + auto opt = SS.sample_terminal(spot.node->data.ret_type); + + if (!opt) // there is no terminal with compatible arguments + return false; + + Tree.erase_children(spot); + + Tree.replace(spot, opt.value()); + + return true; + } +}; + +/// @brief toggle the node's weight ON +/// @param prog the program +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space (unused) +/// @return boolean indicating the success (true) or fail (false) of the operation +class ToggleWeightOnMutation : public MutationBase +{ +public: + static auto find_spots(tree& Tree, const SearchSpace& SS, + const Parameters& params) + { + vector weights(Tree.size()); + + if (Tree.size() < params.max_size) { + std::transform(Tree.begin(), Tree.end(), weights.begin(), + [&](const auto& n){ + // some nodetypes must always have a weight + if (Is(n.node_type)) + return 0.0f; + + // only weighted nodes can be toggled off + if (!n.get_is_weighted() + && IsWeighable(n.ret_type)) + { + return n.get_prob_change(); + } + else + return 0.0f; + }); + } + else { + // fill the vector with zeros, since we're already at max_size + std::fill(weights.begin(), weights.end(), 0.0f); + } + + return weights; + } + + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params) + { + if (spot.node->data.get_is_weighted()==true // cant turn on whats already on + || !IsWeighable(spot.node->data.ret_type)) // does not accept weights (e.g. boolean) + return false; // false indicates that mutation failed and should return std::nullopt + + spot.node->data.set_is_weighted(true); + return true; + } +}; + +/// @brief toggle the node's weight OFF +/// @param prog the program +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space (unused) +/// @return boolean indicating the success (true) or fail (false) of the operation +class ToggleWeightOffMutation : public MutationBase +{ +public: + static auto find_spots(tree& Tree, const SearchSpace& SS, + const Parameters& params) + { + vector weights(Tree.size()); + + std::transform(Tree.begin(), Tree.end(), weights.begin(), + [&](const auto& n){ + // some nodetypes must always have a weight + if (Is(n.node_type)) + return 0.0f; + + if (n.get_is_weighted() + && IsWeighable(n.ret_type)) + return n.get_prob_change(); + else + return 0.0f; + }); + + return weights; + } + + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params) + { + // cout << "toggle_weight_off mutation\n"; + + if (spot.node->data.get_is_weighted()==false) + return false; + + spot.node->data.set_is_weighted(false); + return true; + } +}; + +/// @brief replaces the subtree rooted in `spot` +/// @param prog the program +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to generate a compatible subtree +/// @return boolean indicating the success (true) or fail (false) of the operation +class SubtreeMutation : public MutationBase +{ +public: + static auto find_spots(tree& Tree, const SearchSpace& SS, + const Parameters& params) + { + vector weights; + + auto node_map = SS.node_map; + + if (Tree.size() < params.max_size) { + Iter iter = Tree.begin(); + std::transform(Tree.begin(), Tree.end(), std::back_inserter(weights), + [&](const auto& n){ + size_t d = 1+Tree.depth(iter); + std::advance(iter, 1); + + // we need to make sure there's some node to start the subtree + if ((d >= params.max_depth) + || (SS.node_map.find(n.ret_type) == SS.node_map.end()) + || (SS.node_map.find(n.ret_type) == SS.node_map.end()) ) + return 0.0f; + else + return n.get_prob_change(); + }); + } + else { + weights.resize(Tree.size()); + std::fill(weights.begin(), weights.end(), 0.0f); + } + + return weights; + } + + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params) + { + // check if we exceeded the size/depth constrains (without subtracting, + // to avoid overflow cases if the user sets max_size smaller than arity + // of smallest operator. The overflow would happen when calculating d and + // s in the following lines, to choose the PTC2 limits) + if ( params.max_size <= (Tree.size() - Tree.size(spot)) + || params.max_depth <= Tree.depth(spot) ) + return false; + + auto spot_type = spot.node->data.ret_type; + + // d and s must be compatible with PTC2 --- they should be based on + // tree structure, not program structure + size_t d = params.max_depth - Tree.depth(spot); + size_t s = params.max_size - (Tree.size() - Tree.size(spot)); + + s = r.rnd_int(1, s); + + // sample subtree uses PTC2, which operates on depth and size of the tree + // (and not on the program!). we shoudn't care for weights here + auto subtree = SS.sample_subtree(spot.node->data, d, s); + + if (!subtree) // there is no terminal with compatible arguments + return false; + + // if optional contains a Node, we access its contained value + Tree.erase_children(spot); + Tree.replace(spot, subtree.value().begin()); + + return true; + } +}; + +/** + * @brief Stochastically swaps subtrees between root and other, returning a new program. + * + * The spot where the cross will take place in the `root` parent is sampled + * based on attribute `get_prob_change` of each node in the tree. After selecting + * the cross spot, the program will iterate through the `other` parent searching + * for all compatible sub-trees to replace. + * + * Due to the stochastic behavior, it may come to a case where there is no + * candidate to replace the spot node. In this case, the method returns + * `std::nullopt` (and has overloads so it can be used in a boolean context). + * + * If the cross succeeds, the child program can be accessed through the + * `.value()` attribute of the `std::optional`. + * TODO: update this documentation (it doesnt take the program but the individual. also update mutation documentation) + * This means that, if you use the cross as `auto opt = mutate(parent, SS)`, + * either `opt==false` or `opt.value()` contains the child. + * + * @tparam T the program type + * @param root the root parent + * @param other the donating parent + * @return `std::optional` that may contain the child program of type `T` + */ +template +std::optional> Variation::cross( + const Individual& mom, const Individual& dad) +{ + /* subtree crossover between this and other, producing new Program */ + // choose location by weighted sampling of program + // TODO: why doesn't this copy the search space reference to child? + Program child(mom.program); + + // pick a subtree to replace + vector child_weights(child.Tree.size()); + auto child_iter = child.Tree.begin(); + std::transform(child.Tree.begin(), child.Tree.end(), child_weights.begin(), + [&](const auto& n){ + auto s_at = child.size_at(child_iter); + auto d_at = child.depth_to_reach(child_iter); + + std::advance(child_iter, 1); + + if (s_at other(dad.program); + + int attempts = 0; + while (++attempts <= 3) + { + auto child_spot = r.select_randomly(child.Tree.begin(), + child.Tree.end(), + child_weights.begin(), + child_weights.end() + ); + + auto child_ret_type = child_spot.node->data.ret_type; + + auto allowed_size = parameters.max_size - + ( child.size() - child.size_at(child_spot) ); + auto allowed_depth = parameters.max_depth - + ( child.depth_to_reach(child_spot) ); + + vector other_weights(other.Tree.size()); + + // iterator to get the size of subtrees inside transform + auto other_iter = other.Tree.begin(); + + // lambda function to check feasibility of solution and increment the iterator + const auto check_and_incrm = [other, &other_iter, allowed_size, allowed_depth]() -> bool { + int s = other.size_at( other_iter ); + int d = other.depth_at( other_iter ); + + std::advance(other_iter, 1); + return (s <= allowed_size) && (d <= allowed_depth); + }; + + std::transform(other.Tree.begin(), other.Tree.end(), + other_weights.begin(), + [child_ret_type, check_and_incrm](const auto& n){ + // need to pick a node that has a matching output type to the child_spot. + // also need to check if swaping this node wouldn't exceed max_size + if (check_and_incrm() && (n.ret_type == child_ret_type)) + return n.get_prob_change(); + else + // setting the weight to zero to indicate a non-feasible crossover point + return 0.0f; + } + ); + + bool matching_spots_found = false; + for (const auto& w: other_weights) + { + // we found at least one weight that is non-zero + matching_spots_found = w > 0.0; + + if (matching_spots_found) { + auto other_spot = r.select_randomly( + other.Tree.begin(), + other.Tree.end(), + other_weights.begin(), + other_weights.end() + ); + + // fmt::print("other_spot : {}\n",other_spot.node->data); + // swap subtrees at child_spot and other_spot + child.Tree.move_ontop(child_spot, other_spot); + + Individual ind(child); + ind.set_objectives(mom.get_objectives()); // it will have an invalid fitness + + return ind; + } + } + } + + return std::nullopt; +} + +/** + * @brief Stochastically mutate a program. + * + * Types of mutation: + * + * - point mutation changes a single node. + * - insertion mutation inserts a node as the parent of an existing node, and fills in the other arguments. + * - deletion mutation deletes a node. + * - subtree mutation inserts a new subtree into the program. + * - toggle_weight_on mutation turns a node's weight ON. + * - toggle_weight_off mutation turns a node's weight OFF. + * + * Every mutation has a probability (weight) based on global parameters. The + * spot where the mutation will take place is sampled based on attribute + * `get_prob_change` of each node in the tree. Inside each type of mutation, + * when a new node is inserted, it is sampled based on `terminal_weights`. + * + * Due to the stochastic behavior, and the several sampling steps, it may come to + * a case where the search space does not hold any possible modification to do in + * the program. In this case, the method returns `std::nullopt` (and has overloads + * so it can be used in a boolean context). + * + * If the mutation succeeds, the mutated program can be accessed through the + * `.value()` attribute of the `std::optional`. + * + * This means that, if you use the mutation as `auto opt = mutate(parent, SS)`, + * either `opt==false` or `opt.value()` contains the child program. + * + * @tparam T program type + * @param parent the program to be mutated + * @param SS a search space + * @return `std::optional` that may contain the child program of type `T` + */ +template +std::optional> Variation::mutate(const Individual& parent) +{ + auto options = parameters.mutation_probs; + + bool all_zero = true; + for (auto &it : parameters.mutation_probs) { + if (it.second > 0.0) { + all_zero = false; + break; + } + } + + if (all_zero) + { // No mutation can be successfully applied to this solution + return std::nullopt; + } + + Program child(parent.program); + + int attempts = 0; + while(++attempts <= 3) + { + // choose a valid mutation option + string choice = r.random_choice(parameters.mutation_probs); + + vector weights; + + // choose location by weighted sampling of program + if (choice == "point") + weights = PointMutation::find_spots(child.Tree, search_space, parameters); + else if (choice == "insert") + weights = InsertMutation::find_spots(child.Tree, search_space, parameters); + else if (choice == "delete") + weights = DeleteMutation::find_spots(child.Tree, search_space, parameters); + else if (choice == "subtree") + weights = SubtreeMutation::find_spots(child.Tree, search_space, parameters); + else if (choice == "toggle_weight_on") + weights = ToggleWeightOnMutation::find_spots(child.Tree, search_space, parameters); + else if (choice == "toggle_weight_off") + weights = ToggleWeightOffMutation::find_spots(child.Tree, search_space, parameters); + else { + std::string msg = fmt::format("{} not a valid mutation choice", choice); + HANDLE_ERROR_THROW(msg); + } + + if (std::all_of(weights.begin(), weights.end(), [](const auto& w) { + return w<=0.0; + })) + { // There is no spot that has a probability to be selected + continue; + } + + // apply the mutation and check if it succeeded + auto spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), + weights.begin(), weights.end()); + + // Every mutation here works inplace, so they return bool instead of + // std::optional to indicare the result of their manipulation over the + // program tree. Here we call the mutation function and return the result + + bool success; + if (choice == "point") + success = PointMutation::mutate(child.Tree, spot, search_space, parameters); + else if (choice == "insert") + success = InsertMutation::mutate(child.Tree, spot, search_space, parameters); + else if (choice == "delete") + success = DeleteMutation::mutate(child.Tree, spot, search_space, parameters); + else if (choice == "subtree") + success = SubtreeMutation::mutate(child.Tree, spot, search_space, parameters); + else if (choice == "toggle_weight_on") + success = ToggleWeightOnMutation::mutate(child.Tree, spot, search_space, parameters); + else // it must be"toggle_weight_off" + success = ToggleWeightOffMutation::mutate(child.Tree, spot, search_space, parameters); + + // std::cout << "returning" << std::endl; + if (success + && ( (child.size() <= parameters.max_size) + && (child.depth() <= parameters.max_depth) )){ + + Individual ind(child); + ind.set_objectives(parent.get_objectives()); // it will have an invalid fitness + + return ind; + } else { + continue; + } + } + + return std::nullopt; +} + +template +void Variation::vary(Population& pop, int island, + const vector& parents) +{ + auto indices = pop.get_island_indexes(island); + + for (unsigned i = 0; i> opt=std::nullopt; // new individual + + const Individual& mom = pop[ + *r.select_randomly(parents.begin(), parents.end())]; + + vector> ind_parents; + if ( r() < parameters.cx_prob) // crossover + { + const Individual& dad = pop[ + *r.select_randomly(parents.begin(), parents.end())]; + + opt = cross(mom, dad); + ind_parents = {mom, dad}; + } + else // mutation + { + opt = mutate(mom); + ind_parents = {mom}; + } + + // this assumes that islands do not share indexes before doing variation + unsigned id = parameters.current_gen*parameters.pop_size+indices.at(i); + + // mutation and crossover already perform 3 attempts. If it fails, we just fill with a random individual + if (opt) // variation worked, lets keep this + { + Individual ind = opt.value(); + + ind.is_fitted_ = false; + ind.set_id(id); + ind.set_parents(ind_parents); + + assert(ind.program.size()>0); + pop.individuals.at(indices.at(i)) = std::make_shared>(ind); + } + else { // no optional value was returned + Individual new_ind; + + // creating a new random individual + new_ind.init(search_space, parameters); + new_ind.set_objectives(mom.get_objectives()); // it will have an invalid fitness + new_ind.set_id(id); + new_ind.is_fitted_ = false; + + pop.individuals.at(indices.at(i)) = std::make_shared>(new_ind); + } + } +} + +} //namespace Var +} //namespace Brush diff --git a/src/vary/variation.h b/src/vary/variation.h new file mode 100644 index 00000000..2f3bced4 --- /dev/null +++ b/src/vary/variation.h @@ -0,0 +1,124 @@ +/* Brush + +copyright 2020 William La Cava +license: GNU/GPL v3 +*/ +#ifndef VARIATION_H +#define VARIATION_H + +#include "../pop/population.h" + +#include +#include + +using namespace Brush::Pop; + +/** + * @brief Namespace for variation functions like crossover and mutation. + * + */ +namespace Brush { +namespace Var { + +class MutationBase { +public: + using Iter = tree::pre_order_iterator; + + static auto find_spots(tree& Tree, const SearchSpace& SS, + const Parameters& params) + { + vector weights(Tree.size()); + + // by default, mutation can happen anywhere, based on node weights + std::transform(Tree.begin(), Tree.end(), weights.begin(), + [&](const auto& n){ return n.get_prob_change();}); + + // Should have same size as prog.Tree.size, even if all weights <= 0.0 + return weights; + } + + static auto mutate(tree& Tree, Iter spot, const SearchSpace& SS, + const Parameters& params); +}; + +/*! + * @class Variation + * @brief Class representing the variation operators in Brush. + * + * The Variation class is responsible for performing individual-level variations + * and handling the variation of a population in Brush. It contains methods for + * crossing individuals, mutating individuals, and varying a population. + */ +template +class Variation { +public: + /** + * @brief Default constructor. + */ + Variation() = default; + + /** + * @brief Constructor that initializes the Variation object with parameters and search space. + * + * @param params The parameters for the variation operator. + * @param ss The search space for the variation operator. + */ + Variation(Parameters& params, SearchSpace& ss) + : parameters(params) + , search_space(ss) + {}; + + /** + * @brief Destructor. + */ + ~Variation() {}; + + /** + * @brief Initializes the Variation object with parameters and search space. + * + * @param params The parameters for the variation operator. + * @param ss The search space for the variation operator. + */ + void init(Parameters& params, SearchSpace& ss){ + this->parameters = params; + this->search_space = ss; + }; + + /** + * @brief Performs crossover operation on two individuals. + * + * @param mom The first parent individual. + * @param dad The second parent individual. + * @return An optional containing the offspring individual if the crossover + * is successful, or an empty optional otherwise. + */ + std::optional> cross(const Individual& mom, + const Individual& dad); + + /** + * @brief Performs mutation operation on an individual. + * + * @param parent The parent individual. + * @return An optional containing the mutated individual if the mutation is + * successful, or an empty optional otherwise. + */ + std::optional> mutate(const Individual& parent); + + /** + * @brief Handles variation of a population. + * + * @param pop The population to be varied. + * @param island The island index. + * @param parents The indices of the parent individuals. + * @param p The parameters for the variation operator. + */ + void vary(Population& pop, int island, const vector& parents); + +private: + SearchSpace search_space; // The search space for the variation operator. + Parameters parameters; // The parameters for the variation operator +}; + +} //namespace Var +} //namespace Brush +#endif \ No newline at end of file diff --git a/tests/cpp/test_brush.cpp b/tests/cpp/test_brush.cpp new file mode 100644 index 00000000..da489231 --- /dev/null +++ b/tests/cpp/test_brush.cpp @@ -0,0 +1,157 @@ +#include "testsHeader.h" + +#include "../../src/vary/search_space.h" +#include "../../src/program/program.h" +// #include "../../src/program/dispatch_table.h" +#include "../../src/data/io.h" +#include "../../src/engine.h" +#include "../../src/engine.cpp" +#include "../../src/selection/selection.h" +#include "../../src/selection/selection_operator.h" +#include "../../src/selection/nsga2.h" +#include "../../src/selection/lexicase.h" +#include "../../src/eval/evaluation.h" +#include "../../src/pop/archive.h" +#include "../../src/pop/population.h" + +// TODO: omg i need to figure out why my code only works if i import basically the whole stuff +#include "../../src/selection/selection.cpp" +#include "../../src/selection/selection_operator.cpp" +#include "../../src/selection/nsga2.cpp" +#include "../../src/selection/lexicase.cpp" +#include "../../src/eval/evaluation.cpp" +#include "../../src/pop/archive.cpp" +#include "../../src/pop/population.cpp" + +// TODO: test predict from archive +// TODO: rename it to test_engine + +// TODO: test serialization of archive (get archive and save to json) + +// TODO: test logger, verbose, print stats, etc. +TEST(Engine, EngineWorks) +{ + MatrixXf X(10,2); + ArrayXf y(10); + X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, + 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, + + 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, + 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; + + y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, + 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; + + Dataset data(X,y); + + Parameters params; + params.set_pop_size(100); + params.set_max_gens(10); + params.set_mig_prob(0.0); + + // TODO: archive tests + + // TODO: test termination criterion --- max stall, generations, time + + params.set_verbosity(2); // TODO: verbosity tests + + // checking if validation size works + params.set_validation_size(0.2); + + std::cout << "n jobs = 1" << std::endl; + params.set_n_jobs(1); + Brush::RegressorEngine est5(params); + est5.run(data); // this will not use validation size from parameters + std::cout << "best individual using run(data)" << std::endl; + std::cout << est5.best_ind.program.get_model() << std::endl; + + est5.fit(X, y); // this will use validation size from parameters + std::cout << "best individual using fit(X, y)" << std::endl; + std::cout << est5.best_ind.program.get_model() << std::endl; + + std::cout << "n jobs = 2" << std::endl; + params.set_n_jobs(2); + Brush::RegressorEngine est2(params); + est2.run(data); + + std::cout << "n jobs = -1" << std::endl; + params.set_n_jobs(-1); + Brush::RegressorEngine est3(params); + est3.run(data); + + std::cout << "n jobs = 0" << std::endl; + params.set_n_jobs(0); + Brush::RegressorEngine est4(params); + est4.run(data); + + std::cout << "testing migration" << std::endl; + + params.set_pop_size(10); + params.set_max_gens(10); + params.set_mig_prob(0.5); + + // just to see if nothing breaks + params.set_use_arch(true); + + std::cout << "n jobs = 1" << std::endl; + params.set_n_jobs(1); + Brush::RegressorEngine est6(params); + est6.run(data); + + std::cout << "n jobs = 2" << std::endl; + params.set_logfile("./tests/cpp/__logfile.csv"); // TODO: test classification and regression and save log so we can inspect it + params.set_n_jobs(2); + Brush::RegressorEngine est7(params); + est7.run(data); + params.set_logfile(""); + + std::cout << "n jobs = -1" << std::endl; + params.set_n_jobs(-1); + Brush::RegressorEngine est8(params); + est8.run(data); + + std::cout << "n jobs = 0" << std::endl; + params.set_n_jobs(0); + Brush::RegressorEngine est9(params); + est9.run(data); + + // when popsize is not divisible by num_islands + std::cout << "popsize not divisible by num_islands" << std::endl; + params.set_pop_size(15); + params.set_max_gens(10); + params.set_num_islands(4); // fewer individuals in one island + params.set_n_jobs(1); + Brush::RegressorEngine est_not_div1(params); + est_not_div1.run(data); + + // TODO: use logger in the tests + std::cout << "popsize not divisible by num_islands" << std::endl; + params.set_pop_size(10); + params.set_max_gens(10); + params.set_num_islands(3); // extra individuals in one island + params.set_n_jobs(1); + Brush::RegressorEngine est_not_div2(params); + est_not_div2.run(data); + + // TODO: validation loss +} + + +TEST(Engine, ClassificationEngineWorks) +{ + // TODO: test regression and multiclassifier . add some asserts here + Dataset data = Data::read_csv("docs/examples/datasets/d_analcatdata_aids.csv", "target"); + + ASSERT_TRUE(data.classification); + + Parameters params; + params.set_pop_size(100); + params.set_max_gens(10); + params.set_mig_prob(0.0); + params.set_scorer_("log"); + + params.set_verbosity(2); + + Brush::ClassifierEngine est(params); + est.run(data); +} \ No newline at end of file diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index 09893c2c..705830a6 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -1,7 +1,5 @@ #include "testsHeader.h" -#include "../../src/search_space.h" -#include "../../src/program/program.h" -#include "../../src/program/dispatch_table.h" + TEST(Data, ErrorHandling) { @@ -27,11 +25,7 @@ TEST(Data, ErrorHandling) TEST(Data, MixedVariableTypes) { - // We need to set at least the mutation options (and respective - // probabilities) in order to call PRG.predict() - PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight_on", 0.125}, {"toggle_weight_off", 0.125} - }; + Parameters params; MatrixXf X(5,3); X << 0 , 1, 0 , // binary with integer values @@ -46,31 +40,39 @@ TEST(Data, MixedVariableTypes) y << 6.1, 7.7, -4.2; // y = x_0 + x_1 + x_2 - unordered_map user_ops = { - {"Add", 1}, - {"Sub", 1}, - {"SplitOn", 1} + params.functions = { + {"Add", 0.5}, + {"Sub", 0.5}, + // a boolean operator + {"And", 1.0}, + {"Or", 1.0}, + // operator that takes boolean as argument + {"SplitOn", 1.0} }; Dataset dt(X, y); SearchSpace SS; - SS.init(dt, user_ops); + SS.init(dt, params.functions); dt.print(); SS.print(); - for (int d = 1; d < 5; ++d) - for (int s = 1; s < 5; ++s) + for (size_t d = 5; d < 10; ++d) + for (size_t s = 5; s < 20; ++s) { - - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; - - RegressorProgram PRG = SS.make_regressor(d, s); fmt::print( "=================================================\n" - "Tree model for depth = {}, size= {}: {}\n", - d, s, PRG.get_model("compact", true) + "depth={}, size={}. ", d, s + ); + + params.max_size = s; + params.max_depth = d; + + // TODO: update all calls of make_ to use params + RegressorProgram PRG = SS.make_regressor(0, 0, params); + + fmt::print( + "Tree model: {}\n", PRG.get_model("compact", true) ); // visualizing detailed information for the model @@ -81,17 +83,21 @@ TEST(Data, MixedVariableTypes) n.name, n.node_type, n.get_feature(), n.sig_hash, n.ret_type, typeid(n.ret_type).name()); }); - std::cout << std::endl; fmt::print( "PRG fit\n"); PRG.fit(dt); + fmt::print( "PRG predict\n"); ArrayXf y_pred = PRG.predict(dt); fmt::print( "y_pred: {}\n", y_pred); // creating and fitting a child - auto opt = PRG.mutate(); + Variation variator = Variation(params, SS); + + Individual IND(PRG); + + std::optional> opt = variator.mutate(IND); if (!opt){ fmt::print("Mutation failed to create a child\n"); @@ -99,13 +105,22 @@ TEST(Data, MixedVariableTypes) else { auto Child = opt.value(); - fmt::print("Child model: {}\n", Child.get_model("compact", true)); + fmt::print("Child program model: {}\n", Child.program.get_model("compact", true)); fmt::print( "Child fit\n"); Child.fit(dt); + fmt::print( "Child predict\n"); ArrayXf y_pred_child = Child.predict(dt); - fmt::print( "y_pred: {}\n", y_pred); + fmt::print( "y_pred: {}\n", y_pred_child); + + // should be the same as the fit and predict above + fmt::print( "Child program fit\n"); + Child.program.fit(dt); + + fmt::print( "Child program predict\n"); + ArrayXf y_pred_child_program = Child.program.predict(dt); + fmt::print( "y_pred: {}\n", y_pred_child_program); } } diff --git a/tests/cpp/test_evaluation.cpp b/tests/cpp/test_evaluation.cpp new file mode 100644 index 00000000..db71c641 --- /dev/null +++ b/tests/cpp/test_evaluation.cpp @@ -0,0 +1 @@ +// write a test for different metrics \ No newline at end of file diff --git a/tests/cpp/test_individuals.cpp b/tests/cpp/test_individuals.cpp new file mode 100644 index 00000000..5b3e5df6 --- /dev/null +++ b/tests/cpp/test_individuals.cpp @@ -0,0 +1,3 @@ +// TODO: test predict, predict proba, fit. + +// TODO: test parent_id and id \ No newline at end of file diff --git a/tests/cpp/test_optimization.cpp b/tests/cpp/test_optimization.cpp index 857ea47d..b7c6fdfd 100644 --- a/tests/cpp/test_optimization.cpp +++ b/tests/cpp/test_optimization.cpp @@ -1,5 +1,5 @@ #include "testsHeader.h" -#include "../../src/search_space.h" +#include "../../src/vary/search_space.h" #include "../../src/program/program.h" #include "../../src/program/dispatch_table.h" #include "../../src/data/io.h" @@ -66,7 +66,7 @@ TEST_P(OptimizerTest, OptimizeWeightsWorksCorrectly) { fmt::print( "weights: {}\n", learned_weights); // calculating the MSE - float mse = (data.y - y_pred).square().mean(); + float mse_error = (data.y - y_pred).square().mean(); ASSERT_TRUE(data.y.isApprox(y_pred, 1e-3)) << "Not all predictions " "are close to the correct values. Predictions are\n" << y_pred << @@ -75,7 +75,7 @@ TEST_P(OptimizerTest, OptimizeWeightsWorksCorrectly) { ASSERT_TRUE(check_fit(learned_weights)) << "Check of learned weights " "didn't pass. Learned weights are\n" << learned_weights << std::endl; - ASSERT_TRUE(mse <= 1e-3) << "The MSE " << mse << "obtained after fitting " + ASSERT_TRUE(mse_error <= 1e-3) << "The MSE " << mse_error << "obtained after fitting " "the expression is not smaller than threshold of 1e-3" << std::endl; } diff --git a/tests/cpp/test_params.cpp b/tests/cpp/test_params.cpp index e69de29b..f1a07f32 100644 --- a/tests/cpp/test_params.cpp +++ b/tests/cpp/test_params.cpp @@ -0,0 +1,44 @@ +#include "testsHeader.h" + +using namespace Brush::Pop; +using namespace Brush::Sel; +using namespace Brush::Eval; +using namespace Brush::Sel; + +TEST(Params, ParamsTests) +{ + + Parameters params; + + params.set_max_size(12); + ASSERT_EQ(params.max_size, 12); + ASSERT_EQ(params.get_max_size(), 12); + + params.set_max_depth(4); + ASSERT_EQ(params.max_depth, 4); + ASSERT_EQ(params.get_max_depth(), 4); + + params.set_max_depth(6); + ASSERT_EQ(params.max_depth, 6); + ASSERT_EQ(params.get_max_depth(), 6); + + params.set_objectives({"fitness","complexity"}); + ASSERT_EQ(params.get_objectives().size(), 2); + ASSERT_STREQ(params.get_objectives()[0].c_str(), "fitness"); + ASSERT_STREQ(params.get_objectives()[1].c_str(), "complexity"); + + // TODO: implement logger and verbosity and make this work + // string str1 = "Hello\n"; + // string str2 = logger.log("Hello", 0); + // ASSERT_STREQ(str1.c_str(), str2.c_str()); + + // str2 = logger.log("Hello", 2); + // ASSERT_STREQ(str1.c_str(), str2.c_str()); + + // str2 = logger.log("Hello", 3); + // ASSERT_STREQ(str1.c_str(), str2.c_str()); + + // ft.params.set_verbosity(2); + // ASSERT_EQ(ft.params.verbosity, 2); + // ASSERT_STREQ("", logger.log("Hello", 3).c_str()); +} diff --git a/tests/cpp/test_population.cpp b/tests/cpp/test_population.cpp new file mode 100644 index 00000000..7a78d3f1 --- /dev/null +++ b/tests/cpp/test_population.cpp @@ -0,0 +1,149 @@ +#include "testsHeader.h" + +#include "../../src/ind/individual.cpp" +#include "../../src/pop/population.cpp" // TODO: figure out if thats ok to include cpps instead of headers +#include "../../src/eval/evaluation.cpp" +#include "../../src/selection/nsga2.cpp" +#include "../../src/selection/lexicase.cpp" +#include "../../src/selection/selection_operator.cpp" +#include "../../src/selection/selection.cpp" + +using namespace Brush::Pop; +using namespace Brush::Sel; +using namespace Brush::Eval; +using namespace Brush::Sel; + +TEST(Population, PopulationTests) +{ + // works with even and uneven pop sizes. (TODO: PARAMETERIZE this test to do it with even and uneven, and single individual pop) + + MatrixXf X(4,2); + VectorXf y(4); + + X << 0,1, + 0.47942554,0.87758256, + 0.84147098, 0.54030231, + 0.99749499, 0.0707372; + y << 3.0, 3.59159876, 3.30384889, 2.20720158; + + fmt::print("Initializing all classes;\n"); + Dataset data(X,y); + + SearchSpace SS; + SS.init(data); + + Parameters params; + params.pop_size = 20; // small pop just for tests + Population pop = Population(); + + // aux classes (they are not tested in-depth in this file) + Evaluation evaluator = Evaluation(); + Selection selector = Selection(params.sel, false); + Selection survivor = Selection(params.surv, true); + Variation variator = Variation(params, SS); + + selector.set_operator(); + survivor.set_operator(); + + // size, all individuals were initialized + ASSERT_TRUE(pop.size() == pop.individuals.size() + && pop.size() == 0); //before initialization, it should be empty + + fmt::print("Initializing individuals in the population:\n"); + pop.init(SS, params); + + fmt::print("pop.size() {}, pop.individuals.size() {}, params.pop_size, {}", + pop.size(), pop.individuals.size(), params.pop_size); + ASSERT_TRUE(pop.size() == pop.individuals.size() + && pop.size()/2 == params.pop_size); // now we have a population. + // Its size is actually the double, + // but the real value goes just up to the middle (no offspring was initialized) + + // TODO: put a lot of asserts here between the steps + + for (int i=0; i> survivors(pop.num_islands); + + fmt::print("Fitting individuals\n"); // this must be done in one thread (or implement mutex), because we can have multiple islands pointing to same individuals + for (int j=0; j parents = selector.select(pop, j, params); + ASSERT_TRUE(parents.size() > 0); + + fmt::print("Preparing offspring\n"); + pop.add_offspring_indexes(j); + + // variation applied to population + fmt::print("Variations for island {}\n", j); + variator.vary(pop, j, parents); + + fmt::print("fitting {}\n", j); // at this step, we know that theres only one pointer to each individual being fitted, so we can perform it in parallel + evaluator.update_fitness(pop, j, data, params, true, true); + + fmt::print("survivors {}\n", j); + auto island_survivors = survivor.survive(pop, j, params); + survivors.at(j) = island_survivors; + } + + fmt::print("Updating and migrating\n"); + pop.update(survivors); + fmt::print("Migrating\n"); + pop.migrate(); + + fmt::print("Printing generation {} population:\n", i); + for (int i=0; i DXtree; for (int d = 1; d < 10; ++d) for (int s = 1; s < 10; ++s) { - RegressorProgram PRG = SS.make_regressor(d, s); + params.max_size = s; + params.max_depth = d; + + RegressorProgram PRG = SS.make_regressor(0, 0, params); fmt::print( "=================================================\n" - "Tree model for depth = {}, size= {}: {}\n" - "=================================================\n", + "Tree model for depth = {}, size= {}: {}\n", d, s, PRG.get_model("compact", true) ); + + auto clone = PRG.copy(); + fmt::print( + "Copy of the original model: {}\n" + "=================================================\n", + clone.get_model("compact", true) + ); + + ASSERT_TRUE( PRG.get_model("compact", true)==clone.get_model("compact", true) ); + + fmt::print("Models have the same representation\n"); + + // weights didnt changed + vector PRG_weights(PRG.Tree.size()); + std::transform(PRG.Tree.begin(), PRG.Tree.end(), PRG_weights.begin(), + [&](const auto& n){ return n.get_prob_change();}); + + vector clone_weights(clone.Tree.size()); + std::transform(clone.Tree.begin(), clone.Tree.end(), clone_weights.begin(), + [&](const auto& n){ return n.get_prob_change();}); + + ASSERT_TRUE( PRG_weights.size()==clone_weights.size() ); + fmt::print("Models have the same number of node weights\n"); + + for (size_t i=0; i(); RegressorProgram newPRG = PRGjson; json newPRGjson = newPRG; + fmt::print( "json of loaded model: {}\n", newPRGjson.dump(2)); fmt::print("Initial Model: {}\n",PRG.get_model("compact", true)); fmt::print("Loaded Model: {}\n",newPRG.get_model("compact", true)); + ASSERT_TRUE( std::equal(PRG.Tree.begin(), PRG.Tree.end(), newPRG.Tree.begin()) ); @@ -174,19 +246,21 @@ TEST(Operators, ProgramSizeAndDepthPARAMS) Dataset data(X,y); + Parameters params; + SearchSpace SS; SS.init(data); - for (int d = 1; d < 10; ++d) + for (int d = 1; d < 6; ++d) { - for (int s = 1; s < 10; ++s) + for (int s = 10; s < 20; ++s) { - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; + params.max_size = s; + params.max_depth = d; fmt::print("d={},s={}\n",d,s); fmt::print("make_regressor\n"); - RegressorProgram PRG = SS.make_regressor(0, 0); + RegressorProgram PRG = SS.make_regressor(0, 0, params); fmt::print( "depth = {}, size= {}\n" diff --git a/tests/cpp/test_search_space.cpp b/tests/cpp/test_search_space.cpp index 02eaaf19..7777e4cb 100644 --- a/tests/cpp/test_search_space.cpp +++ b/tests/cpp/test_search_space.cpp @@ -1,10 +1,12 @@ #include "testsHeader.h" -#include "../../src/search_space.h" +#include "../../src/vary/search_space.h" #include "../../src/program/program.h" #include "../../src/program/dispatch_table.h" TEST(SearchSpace, Initialization) { + float minimum_prob = 1e-1f; // minimum probability of changing + ArrayXf y(4); y << 3.00000, 3.59876, 7.18622, 15.19294; @@ -40,14 +42,13 @@ TEST(SearchSpace, Initialization) // dtable_predict.print(); // manually calculated. last value is the avg of prev values - ArrayXf expected_weights_Xf(4); // 4 elements (x3, x4, x5 and c) - expected_weights_Xf << 0.80240685, 0.19270448, 0.5994426, 0.531518; - + ArrayXf expected_weights_Xf(4); // 5 elements (x3, x4, x5, c, meanLabel) + expected_weights_Xf << 0.80240685, 0.19270448, 0.5994426, 0.531518, 0.531518; + auto actual_weights_f = SS.terminal_weights.at(DataType::ArrayF); Eigen::Map actual_weights_Xf(actual_weights_f.data(), actual_weights_f.size()); ASSERT_TRUE(expected_weights_Xf.isApprox(actual_weights_Xf)); - ArrayXf expected_weights_Xi(2); // 2 elements (x2 and c) expected_weights_Xi << 0.2736814, 0.2736814; @@ -57,7 +58,6 @@ TEST(SearchSpace, Initialization) ASSERT_TRUE(expected_weights_Xi.isApprox(actual_weights_Xi)); - ArrayXf expected_weights_Xb(2); // 2 elements (x0 and c) expected_weights_Xb << 0.8117065, 0.8117065; diff --git a/tests/cpp/test_selection.cpp b/tests/cpp/test_selection.cpp new file mode 100644 index 00000000..e69de29b diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index d0eb9bcf..6209237e 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -1,22 +1,127 @@ #include "testsHeader.h" -#include "../../src/search_space.h" -#include "../../src/program/program.h" -#include "../../src/program/dispatch_table.h" -#include "../../src/data/io.h" -TEST(Operators, InsertMutationWorks) +TEST(Variation, FixedRootDoesntChange) { - // TODO: this tests could be parameterized. + Parameters params; + + MatrixXf X(10,2); + ArrayXf y(10); + X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, + 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, + + 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, + 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; + + y << 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0; + + Dataset data(X,y); + + SearchSpace SS; + SS.init(data); + + auto logistic_hash = Signature().hash(); + + // TODO: use these values for d and s in all tests (not 1, 1 for example) + for (int d = 3; d < 6; ++d) + { + for (int s = 10; s < 50; ++s) + { + params.max_size = s; + params.max_depth = d; + + Variation variator = Variation(params, SS); + + int successes = 0; + for (int attempt = 0; attempt < 10; ++attempt) + { + // different program types changes how predict works (and the rettype of predict) + ClassifierProgram PRG = SS.make_classifier(0, 0, params); + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model 1: {}\n", + d, s, + PRG.get_model("compact", true) + ); + + Node root = *(PRG.Tree.begin()); + + ASSERT_TRUE(root.node_type == NodeType::Logistic); + ASSERT_TRUE(root.ret_type == DataType::ArrayF); + ASSERT_TRUE(root.sig_hash == logistic_hash); + ASSERT_TRUE(root.get_prob_change()==0.0); + ASSERT_TRUE(root.fixed==true); + + Individual IND(PRG); + auto opt_mutation = variator.mutate(IND); + + if (opt_mutation) + { + successes += 1; + auto Mut_Child = opt_mutation.value(); + fmt::print("After mutation : {}\n", + Mut_Child.program.get_model("compact", true)); + + Node mut_child_root = *(Mut_Child.program.Tree.begin()); + + ASSERT_TRUE(mut_child_root.node_type == NodeType::Logistic); + ASSERT_TRUE(mut_child_root.ret_type == DataType::ArrayF); + ASSERT_TRUE(mut_child_root.sig_hash == logistic_hash); + ASSERT_TRUE(mut_child_root.get_prob_change()==0.0); + ASSERT_TRUE(mut_child_root.fixed==true); + } + + ClassifierProgram PRG2 = SS.make_classifier(0, 0, params); + + Individual IND2(PRG2); + auto opt_cx = variator.cross(IND, IND2); + + if (opt_cx) + { + successes += 1; + auto CX_Child = opt_cx.value(); + fmt::print("After crossover: {}\n", + CX_Child.program.get_model("compact", true)); + + Node cx_child_root = *(CX_Child.program.Tree.begin()); + + ASSERT_TRUE(cx_child_root.node_type == NodeType::Logistic); + ASSERT_TRUE(cx_child_root.ret_type == DataType::ArrayF); + ASSERT_TRUE(cx_child_root.sig_hash == logistic_hash); + ASSERT_TRUE(cx_child_root.get_prob_change()==0.0); + ASSERT_TRUE(cx_child_root.fixed==true); + } + + // root remained unchanged + ASSERT_TRUE(root.node_type == NodeType::Logistic); + ASSERT_TRUE(root.ret_type == DataType::ArrayF); + ASSERT_TRUE(root.sig_hash == logistic_hash); + ASSERT_TRUE(root.get_prob_change()==0.0); + ASSERT_TRUE(root.fixed==true); + } + ASSERT_TRUE(successes > 0); + } + } +} + +TEST(Variation, InsertMutationWorks) +{ + // TODO: this tests could be parameterized (one type of mutation each). // To understand design implementation of this test, check Mutation test - PARAMS["mutation_options"] = { - {"point", 0.0}, {"insert", 1.0}, {"delete", 0.0}, {"subtree", 0.0}, {"toggle_weight_on", 0.0}, {"toggle_weight_off", 0.0} + Parameters params; + params.mutation_probs = { + {"point", 0.0}, + {"insert", 1.0}, + {"delete", 0.0}, + {"subtree", 0.0}, + {"toggle_weight_on", 0.0}, + {"toggle_weight_off", 0.0} }; // retrieving the options to check if everything was set right std::cout << "Initial mutation configuration" << std::endl; - auto options = PARAMS["mutation_options"].get>(); - for (const auto& [k, v] : options) + for (const auto& [k, v] : params.mutation_probs) std::cout << k << " : " << v << std::endl; MatrixXf X(10,2); @@ -35,20 +140,19 @@ TEST(Operators, InsertMutationWorks) SearchSpace SS; SS.init(data); + Variation variator = Variation(params, SS); + int successes = 0; for (int attempt = 0; attempt < 100; ++attempt) - { - // we need to have big values here so the mutation will work - // (when the xmen child exceeds the maximum limits, mutation returns - // std::nullopt) - PARAMS["max_size"] = 20; - PARAMS["max_depth"] = 10; - - fmt::print("d={},s={}\n", PARAMS["max_depth"].get(), PARAMS["max_size"].get()); + { + params.max_size = 50; + params.max_depth = 6; + + fmt::print("d={},s={}\n", params.max_depth, params.max_size); fmt::print("make_regressor\n"); // creating a "small" program (with a plenty amount of space to insert stuff) - RegressorProgram PRG = SS.make_regressor(5, 5); + RegressorProgram PRG = SS.make_regressor(5, 5, params); fmt::print("PRG.fit(data);\n"); PRG.fit(data); @@ -56,7 +160,12 @@ TEST(Operators, InsertMutationWorks) // applying mutation and checking if the optional result is non-empty fmt::print("auto Child = PRG.mutate();\n"); - auto opt = PRG.mutate(); // We should assume that it will be always the insert mutation + + // We should assume that it will be always the insert mutation + + Individual IND(PRG); + + auto opt = variator.mutate(IND); if (opt){ successes += 1; @@ -66,9 +175,9 @@ TEST(Operators, InsertMutationWorks) "depth = {}, size= {}\n" "Initial Model: {}\n" "Mutated Model: {}\n", - PARAMS["max_depth"].get(), PARAMS["max_size"].get(), - PRG.get_model("compact", true), - Child.get_model("compact", true) + params.max_depth, params.max_size, + IND.program.get_model("compact", true), + Child.program.get_model("compact", true) ); fmt::print("child fit\n"); @@ -76,49 +185,43 @@ TEST(Operators, InsertMutationWorks) y_pred = Child.predict(data); // since we successfully inserted a node, this should be always true - ASSERT_TRUE(Child.size() > PRG.size()); + ASSERT_TRUE(Child.program.size() > IND.program.size()); // maybe the insertion spot was a shorter branch than the maximum // depth. At least, xmen depth should be equal to its parent - ASSERT_TRUE(Child.depth() >= PRG.depth()); + ASSERT_TRUE(Child.program.depth() >= IND.program.depth()); } // lets also see if it always fails when the child exceeds the maximum limits - PARAMS["max_size"] = PRG.size(); - PARAMS["max_depth"] = PRG.depth(); + variator.parameters.set_max_depth(IND.program.depth()); + variator.parameters.set_max_size(IND.program.size()); - auto opt2 = PRG.mutate(); - if (opt2){ // This shoudl't happen. We'll print then error + auto opt2 = variator.mutate(IND); + if (opt2){ // This shoudl't happen. We'll print the error auto Child2 = opt2.value(); std::cout << "Fail failed. Mutation weights:" << std::endl; - auto options2 = PARAMS["mutation_options"].get>(); - for (const auto& [k, v] : options2) + for (const auto& [k, v] : params.mutation_probs) std::cout << k << " : " << v << std::endl; fmt::print( - "=================================================\n" - "depth = {}, size= {}\n" + "max depth = {}, max size= {}\n" "Initial Model: {}\n" - "Mutated Model: {}\n", - PARAMS["max_depth"].get(), PARAMS["max_size"].get(), - PRG.get_model("compact", true), - Child2.get_model("compact", true) + "Mutated Model: {}\n" + "=================================================\n", + params.max_depth, params.max_size, + IND.program.get_model("compact", true), + Child2.program.get_model("compact", true) ); - ASSERT_TRUE(opt2==std::nullopt); + ASSERT_TRUE(opt2==std::nullopt); // this will fail, so we can see the log } } ASSERT_TRUE(successes > 0); } -TEST(Operators, Mutation) +TEST(Variation, Mutation) { - // test mutation - // TODO: set random seed - - PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight_on", 0.125}, {"toggle_weight_off", 0.125} - }; + Parameters params; MatrixXf X(10,2); ArrayXf y(10); @@ -136,26 +239,42 @@ TEST(Operators, Mutation) SearchSpace SS; SS.init(data); - for (int d = 1; d < 10; ++d) + int successes = 0; + for (int d = 1; d < 6; ++d) { - int successes = 0; - for (int s = 1; s < 10; ++s) + for (int s = 10; s < 20; ++s) { + params.max_size = s; + params.max_depth = d; + + Variation variator = Variation(params, SS); + fmt::print("d={},s={}\n",d,s); fmt::print("make_regressor\n"); // if we set max_size and max_depth to zero, it will use the // values in the global PARAMS. Otherwise, it will respect the // values passed as argument. - RegressorProgram PRG = SS.make_regressor(d, s); + RegressorProgram PRG = SS.make_regressor(0, 0, params); fmt::print("PRG.fit(data);\n"); PRG.fit(data); + + // saving a string representation + auto PRG_model = PRG.get_model("compact", true); + + fmt::print( + "=================================================\n" + "Original model (BEFORE MUTATION) 1: {}\n", + PRG.get_model("compact", true) + ); ArrayXf y_pred = PRG.predict(data); // applying mutation and checking if the optional result is non-empty fmt::print("auto Child = PRG.mutate();\n"); - auto opt = PRG.mutate(); + + Individual IND(PRG); + auto opt = variator.mutate(IND); if (!opt){ fmt::print( @@ -164,7 +283,7 @@ TEST(Operators, Mutation) "Initial Model: {}\n" "Mutation failed to create a child", d, s, - PRG.get_model("compact", true) + IND.program.get_model("compact", true) ); } else { @@ -176,25 +295,26 @@ TEST(Operators, Mutation) "Initial Model: {}\n" "Mutated Model: {}\n", d, s, - PRG.get_model("compact", true), - Child.get_model("compact", true) + IND.program.get_model("compact", true), + Child.program.get_model("compact", true) ); fmt::print("child fit\n"); Child.fit(data); y_pred = Child.predict(data); + + // no collateral effect (parent still the same) + ASSERT_TRUE(PRG_model == IND.program.get_model("compact", true)); } } - // since x1 and x2 have same type, we shoudn't get fails - ASSERT_TRUE(successes > 0); } + // since x1 and x2 have same type, we shoudn't get fails + ASSERT_TRUE(successes > 0); } -TEST(Operators, MutationSizeAndDepthLimit) +TEST(Variation, MutationSizeAndDepthLimit) { - PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight_on", 0.125}, {"toggle_weight_off", 0.125} - }; + Parameters params; MatrixXf X(10,2); ArrayXf y(10); @@ -211,18 +331,21 @@ TEST(Operators, MutationSizeAndDepthLimit) SearchSpace SS; SS.init(data); + + // prod operator --> arity 4: prod(T1, T2, T3) + // split best --> arity 6: if(terminal > value, T_case_true, T_case_false) + int max_arity = 6; - // split operator --> arity 3 - // prod operator --> arity 4 - int max_arity = 4; - - for (int d = 5; d < 15; ++d) + int successes = 0; + for (int d = 1; d < 6; ++d) { - int successes = 0; for (int s = 5; s < 15; ++s) { - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; + params.max_size = s; + params.max_depth = d; + + // creating and fitting a child + Variation variator = Variation(params, SS); fmt::print("d={},s={}\n",d,s); fmt::print("make_regressor\n"); @@ -230,11 +353,12 @@ TEST(Operators, MutationSizeAndDepthLimit) // Enforcing that the parents does not exceed max_size by // taking into account the highest arity of the function nodes; // and the max_depth+1 that PTC2 can generate - RegressorProgram PRG = SS.make_regressor(d-1, s - max_arity); + RegressorProgram PRG = SS.make_regressor(0, 0, params); auto PRG_model = PRG.get_model("compact", true); - auto opt = PRG.mutate(); + Individual IND(PRG); + auto opt = variator.mutate(IND); if (!opt){ fmt::print( @@ -243,7 +367,7 @@ TEST(Operators, MutationSizeAndDepthLimit) "Initial Model: {}\n" "Mutation failed to create a child", d, s, - PRG.get_model("compact", true) + IND.program.get_model("compact", true) ); } else { @@ -263,31 +387,33 @@ TEST(Operators, MutationSizeAndDepthLimit) "Mutated depth: {}\n" "Mutated size : {}\n", d, s, - PRG.get_model("compact", true), - Child.get_model("compact", true), - Child.depth(), - Child.size() + IND.program.get_model("compact", true), + Child.program.get_model("compact", true), + Child.program.depth(), + Child.program.size() ); // Original didn't change - ASSERT_TRUE(PRG_model == PRG.get_model("compact", true)); + ASSERT_TRUE(PRG_model == IND.program.get_model("compact", true)); - ASSERT_TRUE(Child.size() > 0); - ASSERT_TRUE(Child.size() <= s); + ASSERT_TRUE(Child.program.size() > 0); + ASSERT_TRUE(Child.program.size() <= s); - ASSERT_TRUE(Child.size() > 0); - ASSERT_TRUE(Child.size() <= s); + ASSERT_TRUE(Child.program.size() > 0); + ASSERT_TRUE(Child.program.size() <= s); - ASSERT_TRUE(Child.depth() >= 0); - ASSERT_TRUE(Child.depth() <= d); + ASSERT_TRUE(Child.program.depth() >= 0); + ASSERT_TRUE(Child.program.depth() <= d); } } - ASSERT_TRUE(successes > 0); } + ASSERT_TRUE(successes > 0); } -TEST(Operators, Crossover) +TEST(Variation, Crossover) { + Parameters params; + MatrixXf X(10,2); ArrayXf y(10); X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, @@ -304,15 +430,23 @@ TEST(Operators, Crossover) SearchSpace SS; SS.init(data); - for (int d = 1; d < 10; ++d) + int successes = 0; + for (int d = 2; d < 6; ++d) { - int successes = 0; - for (int s = 1; s < 10; ++s) + for (int s = 5; s < 15; ++s) { - RegressorProgram PRG1 = SS.make_regressor(d, s); - RegressorProgram PRG2 = SS.make_regressor(d, s); + params.max_size = s; + params.max_depth = d; + Variation variator = Variation(params, SS); + + RegressorProgram PRG1 = SS.make_regressor(d, 0, params); PRG1.fit(data); + auto PRG1_model = PRG1.get_model("compact", true); + + RegressorProgram PRG2 = SS.make_regressor(d, 0, params); PRG2.fit(data); + auto PRG2_model = PRG2.get_model("compact", true); + fmt::print( "=================================================\n" @@ -327,7 +461,10 @@ TEST(Operators, Crossover) ArrayXf y_pred = PRG1.predict(data); fmt::print("cross one\n"); - auto opt = PRG1.cross(PRG2); + Individual IND1(PRG1); + Individual IND2(PRG2); + auto opt = variator.cross(IND1, IND2); + if (!opt){ fmt::print( "=================================================\n" @@ -336,8 +473,8 @@ TEST(Operators, Crossover) "Original model 2: {}\n", "Crossover failed to create a child", d, s, - PRG1.get_model("compact", true), - PRG2.get_model("compact", true) + IND1.program.get_model("compact", true), + IND2.program.get_model("compact", true) ); } else { @@ -346,24 +483,30 @@ TEST(Operators, Crossover) fmt::print( "Original model 1 after cross: {}\n" "Original model 2 after cross: {}\n", - PRG1.get_model("compact", true), - PRG2.get_model("compact", true) + IND1.program.get_model("compact", true), + IND2.program.get_model("compact", true) ); fmt::print( "Crossed Model: {}\n" "=================================================\n", - Child.get_model("compact", true) + Child.program.get_model("compact", true) ); Child.fit(data); auto child_pred1 = Child.predict(data); + + // no collateral effect (parent still the same) + ASSERT_TRUE(PRG1_model == IND1.program.get_model("compact", true)); + ASSERT_TRUE(PRG2_model == IND2.program.get_model("compact", true)); } } - ASSERT_TRUE(successes > 0); } + ASSERT_TRUE(successes > 0); } -TEST(Operators, CrossoverSizeAndDepthLimit) +TEST(Variation, CrossoverSizeAndDepthLimit) { + Parameters params; + MatrixXf X(10,2); ArrayXf y(10); X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, @@ -380,22 +523,23 @@ TEST(Operators, CrossoverSizeAndDepthLimit) SearchSpace SS; SS.init(data); - // split operator --> arity 3 - // prod operator --> arity 4 - int max_arity = 4; + // prod operator --> arity 4: prod(T1, T2, T3) + // split best --> arity 6: if(terminal > value, T_case_true, T_case_false) + int max_arity = 6; - for (int d = 5; d < 15; ++d) + int successes = 0; + for (int d = 1; d < 6; ++d) { - int successes = 0; for (int s = 5; s < 15; ++s) { - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; + params.max_size = s; + params.max_depth = d; + Variation variator = Variation(params, SS); // Enforcing that the parents does not exceed max_size by // taking into account the highest arity of the function nodes - RegressorProgram PRG1 = SS.make_regressor(d-1, s-max_arity); - RegressorProgram PRG2 = SS.make_regressor(d-1, s-max_arity); + RegressorProgram PRG1 = SS.make_regressor(0, 0, params); + RegressorProgram PRG2 = SS.make_regressor(0, 0, params); auto PRG1_model = PRG1.get_model("compact", true); auto PRG2_model = PRG2.get_model("compact", true); @@ -415,7 +559,9 @@ TEST(Operators, CrossoverSizeAndDepthLimit) ); fmt::print("cross\n"); - auto opt = PRG1.cross(PRG2); + Individual IND1(PRG1); + Individual IND2(PRG2); + auto opt = variator.cross(IND1, IND2); if (!opt){ fmt::print("Crossover failed to create a child" @@ -429,22 +575,22 @@ TEST(Operators, CrossoverSizeAndDepthLimit) "Child Model depth: {}\n" "Child Model size : {}\n" "=================================================\n", - Child.get_model("compact", true), - Child.depth(), Child.size() + Child.program.get_model("compact", true), + Child.program.depth(), Child.program.size() ); // Original didn't change - ASSERT_TRUE(PRG1_model == PRG1.get_model("compact", true)); - ASSERT_TRUE(PRG2_model == PRG2.get_model("compact", true)); + ASSERT_TRUE(PRG1_model == IND1.program.get_model("compact", true)); + ASSERT_TRUE(PRG2_model == IND2.program.get_model("compact", true)); // Child is within restrictions - ASSERT_TRUE(Child.size() > 0); - ASSERT_TRUE(Child.size() <= s); + ASSERT_TRUE(Child.program.size() > 0); + ASSERT_TRUE(Child.program.size() <= s + 3*max_arity); - ASSERT_TRUE(Child.depth() >= 0); - ASSERT_TRUE(Child.depth() <= d); + ASSERT_TRUE(Child.program.depth() >= 0); + ASSERT_TRUE(Child.program.depth() <= d); } } - ASSERT_TRUE(successes > 0); } + ASSERT_TRUE(successes > 0); } \ No newline at end of file diff --git a/tests/cpp/testsHeader.h b/tests/cpp/testsHeader.h index 093f867a..63c9ea9b 100644 --- a/tests/cpp/testsHeader.h +++ b/tests/cpp/testsHeader.h @@ -26,9 +26,27 @@ using std::stof; #include #include "../../src/init.h" +#include "../../src/params.h" #include "../../src/data/data.h" #include "../../src/program/operator.h" +#include "../../src/program/dispatch_table.h" +#include "../../src/program/program.h" +#include "../../src/ind/individual.h" +#include "../../src/vary/search_space.h" +#include "../../src/params.h" +#include "../../src/vary/variation.h" +#include "../../src/selection/selection.h" +#include "../../src/selection/selection_operator.h" +#include "../../src/selection/nsga2.h" +#include "../../src/selection/lexicase.h" +#include "../../src/eval/evaluation.h" +#include "../../src/eval/metrics.h" +#include "../../src/eval/scorer.h" +#include "../../src/engine.h" +#include "../../src/vary/variation.cpp" // TODO: is this ok? (otherwise I would have to create a test separated file, or move the implementation to the header) + using namespace Brush; using namespace Brush::Data; +using namespace Brush::Var; #endif diff --git a/tests/python/test_brush.py b/tests/python/test_brush.py deleted file mode 100644 index 5e38898f..00000000 --- a/tests/python/test_brush.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python3 -import brush -import pytest -import numpy as np -import pandas as pd -from pmlb import fetch_data -from sklearn.utils import resample - -import traceback -import logging - -@pytest.fixture -def brush_args(): - return dict( - max_gen=10, - pop_size=20, - max_size=50, - max_depth=6, - mutation_options = {"point":0.25, "insert": 0.5, "delete": 0.25}, - ) - -@pytest.fixture -def classification_setup(): - df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') - X = df.drop(columns='target') - y = df['target'] - - return brush.BrushClassifier, X, y - -@pytest.fixture -def multiclass_classification_setup(): - df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') - X = df.drop(columns='target') - y = df['target'] - - return brush.BrushClassifier, X, y - -@pytest.fixture -def regression_setup(): - df = pd.read_csv('docs/examples/datasets/d_enc.csv') - X = df.drop(columns='label') - y = df['label'] - - return brush.BrushRegressor, X, y - -@pytest.mark.parametrize('setup', ['classification_setup', 'regression_setup']) -def test_fit(setup, brush_args, request): - """Testing common utilities related to fitting and generic brush estimator. - """ - - Estimator, X, y = request.getfixturevalue(setup) - - try: - est = Estimator(**brush_args) - est.fit(X, y) - - print('score:',est.score(X,y)) - - except Exception as e: - pytest.fail(f"Unexpected Exception caught: {e}") - logging.error(traceback.format_exc()) - - -# def test_random_state(): # TODO: make it work -# test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) -# test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], -# [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T - -# est1 = brush.BrushRegressor(random_state=42).fit(test_X, test_y) -# est2 = brush.BrushRegressor(random_state=42).fit(test_X, test_y) - -# assert est1.best_estimator_.get_model() == est2.best_estimator_.get_model(), \ -# "random state failed to generate same results" \ No newline at end of file diff --git a/tests/python/test_deap_api.py b/tests/python/test_deap_api.py new file mode 100644 index 00000000..6d09ca70 --- /dev/null +++ b/tests/python/test_deap_api.py @@ -0,0 +1,225 @@ +#!/usr/bin/env python3 +import pybrush +import pytest +import numpy as np +import pandas as pd +from pmlb import fetch_data +from sklearn.utils import resample + +import traceback +import logging + +# TODO: prototyping_with_brush.ipynb or something like that +@pytest.fixture +def brush_args(): + return dict( + max_gens=10, + pop_size=20, + max_size=50, + max_depth=6, + cx_prob= 1/7, + num_islands=1, + mutation_probs = {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, + "toggle_weight_on":1/6, "toggle_weight_off":1/6}, + ) + +@pytest.fixture +def DEAP_classification_setup(): + df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') + X = df.drop(columns='target') + y = df['target'] + + return pybrush.DeapClassifier, X, y + +@pytest.fixture +def DEAP_multiclass_classification_setup(): + df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') + X = df.drop(columns='target') + y = df['target'] + + return pybrush.DeapClassifier, X, y + +@pytest.fixture +def DEAP_regression_setup(): + df = pd.read_csv('docs/examples/datasets/d_enc.csv') + X = df.drop(columns='label') + y = df['label'] + + return pybrush.DeapRegressor, X, y + + +@pytest.fixture +def BRUSH_classification_setup(): + df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') + X = df.drop(columns='target') + y = df['target'] + + return pybrush.BrushClassifier, X, y + +@pytest.fixture +def BRUSH_multiclass_classification_setup(): + df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv') + X = df.drop(columns='target') + y = df['target'] + + return pybrush.BrushClassifier, X, y + +@pytest.fixture +def BRUSH_regression_setup(): + df = pd.read_csv('docs/examples/datasets/d_enc.csv') + X = df.drop(columns='label') + y = df['label'] + + return pybrush.BrushRegressor, X, y + + +@pytest.mark.parametrize('setup,algorithm', + [('DEAP_classification_setup', 'nsga2island'), + ('DEAP_classification_setup', 'nsga2' ), + ('DEAP_classification_setup', 'gaisland' ), + ('DEAP_classification_setup', 'ga' ), + ('DEAP_regression_setup', 'nsga2island'), + ('DEAP_regression_setup', 'nsga2' ), + ('DEAP_regression_setup', 'gaisland' ), + ('DEAP_regression_setup', 'ga' ), + + ('BRUSH_classification_setup', 'nsga2island'), + ('BRUSH_regression_setup', 'nsga2island') + ]) +def test_fit(setup, algorithm, brush_args, request): + """Testing common utilities related to fitting and generic brush estimator. + """ + + Estimator, X, y = request.getfixturevalue(setup) + + brush_args["algorithm"] = algorithm + try: + est = Estimator(**brush_args) + est.fit(X, y) + + print('score:',est.score(X,y)) + + except Exception as e: + pytest.fail(f"Unexpected Exception caught: {e}") + logging.error(traceback.format_exc()) + + +@pytest.mark.parametrize('setup', + [('DEAP_classification_setup'), + ('DEAP_multiclass_classification_setup'), + ('BRUSH_classification_setup'), + ('BRUSH_multiclass_classification_setup'), + ]) +def test_predict_proba(setup, brush_args, request): + + Estimator, X, y = request.getfixturevalue(setup) + + est = Estimator(**brush_args) + est.fit(X, y) + + y_prob = est.predict_proba(X) + + assert len(y_prob.shape) == 2, "predict_proba should be 2-dimensional" + assert y_prob.shape[1] >= 2, \ + "every class should have its own column (even for binary clf)" + + +# @pytest.mark.parametrize('setup,num_islands', +# [('DEAP_classification_setup', 1), +# ('DEAP_regression_setup', 1), +# ('BRUSH_classification_setup', 1), +# ('BRUSH_regression_setup', 1), + +# ('DEAP_classification_setup', -1), +# ('DEAP_regression_setup', -1), +# ('BRUSH_classification_setup', -1), +# ('BRUSH_regression_setup', -1), + +# ('DEAP_classification_setup', 2), +# ('DEAP_regression_setup', 2), +# ('BRUSH_classification_setup', 2), +# ('BRUSH_regression_setup', 2)]) +# def test_num_islands(setup, num_islands, brush_args, request): +# Estimator, X, y = request.getfixturevalue(setup) + +# brush_args["algorithm"] = 'nsga2island' +# brush_args["num_islands"] = num_islands +# try: +# est = Estimator(**brush_args) +# est.fit(X, y) + +# print('score:', est.score(X,y)) + +# except Exception as e: +# pytest.fail(f"Unexpected Exception caught: {e}") +# logging.error(traceback.format_exc()) + + +# TODO: make this test for BRUSH_classification (it does not use toolbox) +@pytest.mark.parametrize('setup,fixed_node', [ + ('DEAP_classification_setup', 'Logistic'), + # ('DEAP_multiclass_classification_setup', 'Softmax'), + ]) +def test_fixed_nodes(setup, fixed_node, brush_args, request): + # Classification has a fixed root that should not change after mutation or crossover + + Estimator, X, y = request.getfixturevalue(setup) + + est = Estimator(**brush_args) + est.fit(X, y) # Calling fit to make it create the setup toolbox and variation functions + + for i in range(10): + # Initial population + pop = est.toolbox_.population(n=100) + pop_models = [] + for p in pop: + pop_models.append(p.program.get_model()) + assert p.program.get_model().startswith(fixed_node), \ + (f"An individual for {setup} was criated without {fixed_node} " + + f"node on root. Model was {p.ind.get_model()}") + + # Clones + clones = [est.toolbox_.Clone(p) for p in pop] + for c in clones: + assert c.program.get_model().startswith(fixed_node), \ + (f"An individual for {setup} was cloned without {fixed_node} " + + f"node on root. Model was {c.ind.get_model()}") + + # Mutation + xmen = [est.toolbox_.mutate(c) for c in clones] + xmen = [x for x in xmen if x is not None] + assert len(xmen) > 0, "Mutation didn't worked for any individual" + for x in xmen: + assert x.program.get_model().startswith(fixed_node), \ + (f"An individual for {setup} was mutated without {fixed_node} " + + f"node on root. Model was {x.ind.get_model()}") + + # Crossover + cxmen = [] + [cxmen.append(est.toolbox_.mate(c1, c2)) + for (c1, c2) in zip(clones[::2], clones[1::2])] + cxmen = [x for x in cxmen if x is not None] + assert len(cxmen) > 0, "Crossover didn't worked for any individual" + for cx in cxmen: + assert cx.program.get_model().startswith(fixed_node), \ + (f"An individual for {setup} was crossovered without {fixed_node} " + + f"node on root. Model was {cx.ind.get_model()}") + + # Originals still the same + for p, p_original_model in zip(pop, pop_models): + assert p.program.get_model() == p_original_model, \ + "Variation operator changed the original model." + + + +# TODO: make this work (i need to make each island (thread) use its own random generator) +# def test_random_state(): +# test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) +# test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], +# [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T + +# est1 = pybrush.BrushRegressor(random_state=42).fit(test_X, test_y) +# est2 = pybrush.BrushRegressor(random_state=42).fit(test_X, test_y) + +# assert est1.best_estimator_.program.get_model() == est2.best_estimator_.program.get_model(), \ +# "random state failed to generate same results" \ No newline at end of file diff --git a/tests/python/test_optimization.py b/tests/python/test_optimization.py index 06cb0339..7eab2743 100644 --- a/tests/python/test_optimization.py +++ b/tests/python/test_optimization.py @@ -1,6 +1,5 @@ #!/usr/bin/env python3 -import brush import pytest import numpy as np import pandas as pd diff --git a/tests/python/test_params.py b/tests/python/test_params.py index 03d08bc4..22c6f568 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -6,90 +6,91 @@ import numpy as np -def test_param_random_state(): - # Check if make_regressor, mutation and crossover will create the same expressions - test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) - test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], - [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T +# TODO; get this to work again +# def test_param_random_state(): +# # Check if make_regressor, mutation and crossover will create the same expressions +# test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) +# test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], +# [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T - data = _brush.Dataset(test_X, test_y) - SS = _brush.SearchSpace(data) +# data = _brush.Dataset(test_X, test_y) +# SS = _brush.SearchSpace(data) - _brush.set_random_state(123) +# _brush.set_random_state(123) - first_run = [] - for d in range(1,4): - for s in range(1,20): - prg = SS.make_regressor(d, s) - prg = prg.mutate() +# first_run = [] +# for d in range(1,4): +# for s in range(1,20): +# prg = SS.make_regressor(d, s) +# prg = prg.mutate() - if prg != None: prg = prg.cross(prg) - if prg != None: first_run.append(prg.get_model()) +# if prg != None: prg = prg.cross(prg) +# if prg != None: first_run.append(prg.get_model()) - assert len(first_run) > 0, "either mutation or crossover is always failing" +# assert len(first_run) > 0, "either mutation or crossover is always failing" - _brush.set_random_state(123) +# _brush.set_random_state(123) - second_run = [] - for d in range(1,4): - for s in range(1,20): - prg = SS.make_regressor(d, s) - prg = prg.mutate() +# second_run = [] +# for d in range(1,4): +# for s in range(1,20): +# prg = SS.make_regressor(d, s) +# prg = prg.mutate() - if prg != None: prg = prg.cross(prg) - if prg != None: second_run.append(prg.get_model()) +# if prg != None: prg = prg.cross(prg) +# if prg != None: second_run.append(prg.get_model()) - assert len(second_run) > 0, "either mutation or crossover is always failing" - - for fr, sr in zip(first_run, second_run): - assert fr==sr, "random state failed to generate same expressions" - - -def _change_and_wait(config): - "Will change the mutation weights to set only the `index` to 1, then wait " - "`seconts` to retrieve the _brush PARAMS and print weight values" - index, seconds = config - - # Sample configuration - params = { - 'verbosity': False, - 'pop_size' : 100, - 'max_gen' : 100, - 'max_depth': 5, - 'max_size' : 50, - 'mutation_options': {'point' : 0.0, - 'insert' : 0.0, - 'delete' : 0.0, - 'subtree' : 0.0, - 'toggle_weight_on' : 0.0, - 'toggle_weight_off': 0.0} - } - - # We need to guarantee order to use the index correctly - mutations = ['point', 'insert', 'delete', 'subtree', 'toggle_weight_on', 'toggle_weight_off'] - - for i, m in enumerate(mutations): - params['mutation_options'][m] = 0 if i != index else 1.0 - - print(f"(Thread id {index}{seconds}) Setting mutation {mutations[index]} to 1 and wait {seconds} seconds") - - _brush.set_params(params) - time.sleep(seconds) +# assert len(second_run) > 0, "either mutation or crossover is always failing" + +# for fr, sr in zip(first_run, second_run): +# assert fr==sr, "random state failed to generate same expressions" + + +# def _change_and_wait(config): +# "Will change the mutation weights to set only the `index` to 1, then wait " +# "`seconts` to retrieve the _brush PARAMS and print weight values" +# index, seconds = config + +# # Sample configuration +# params = { +# 'verbosity': False, +# 'pop_size' : 100, +# 'gens' : 100, +# 'max_depth': 5, +# 'max_size' : 50, +# 'mutation_probs': {'point' : 0.0, +# 'insert' : 0.0, +# 'delete' : 0.0, +# 'subtree' : 0.0, +# 'toggle_weight_on' : 0.0, +# 'toggle_weight_off': 0.0} +# } + +# # We need to guarantee order to use the index correctly +# mutations = ['point', 'insert', 'delete', 'subtree', 'toggle_weight_on', 'toggle_weight_off'] + +# for i, m in enumerate(mutations): +# params['mutation_probs'][m] = 0 if i != index else 1.0 + +# print(f"(Thread id {index}{seconds}) Setting mutation {mutations[index]} to 1 and wait {seconds} seconds") + +# _brush.set_params(params) +# time.sleep(seconds) - print(f"(Thread id {index}{seconds}) Retrieving PARAMS: {_brush.get_params()['mutation_options']}") +# print(f"(Thread id {index}{seconds}) Retrieving PARAMS: {_brush.get_params()['mutation_probs']}") - assert params['mutation_options']==_brush.get_params()['mutation_options'], \ - f"(Thread id {index}{seconds}) BRUSH FAILED TO KEEP SEPARATE INSTANCES OF `PARAMS` BETWEEN MULTIPLE THREADS" +# assert params['mutation_probs']==_brush.get_params()['mutation_probs'], \ +# f"(Thread id {index}{seconds}) BRUSH FAILED TO KEEP SEPARATE INSTANCES OF `PARAMS` BETWEEN MULTIPLE THREADS" -def test_global_PARAMS_sharing(): - print("By default, all threads starts with all mutations having weight zero.") +# def test_global_PARAMS_sharing(): +# print("By default, all threads starts with all mutations having weight zero.") - scale = 0.25 # Scale the time of each thread (for human manual checking) - - # Checking if brush's PARAMS can be modified inside a pool without colateral effects. - # Each configuration will start in the same order as they are listed, but they - # will finish in different times. They are all modifying the brush's PARAMS. - Pool(processes=3).map(_change_and_wait, [(0, 3*scale), - (1, 1*scale), - (2, 2*scale)]) +# scale = 0.25 # Scale the time of each thread (for human manual checking) + +# # Checking if brush's PARAMS can be modified inside a pool without colateral effects. +# # Each configuration will start in the same order as they are listed, but they +# # will finish in different times. They are all modifying the brush's PARAMS. +# Pool(processes=3).map(_change_and_wait, [(0, 3*scale), +# (1, 1*scale), +# (2, 2*scale)]) \ No newline at end of file diff --git a/tests/python/test_program.py b/tests/python/test_program.py index 78356bee..e1933c18 100644 --- a/tests/python/test_program.py +++ b/tests/python/test_program.py @@ -87,22 +87,22 @@ def test_json_regressor(): #assert all(round(i,4) == round(j, 4) for i,j in zip(learned_weights, true_weights)) np.allclose(learned_weights, true_weights, atol=1e-4) -# def test_serialization(): -# data = _brush.read_csv("docs/examples/datasets/d_2x1_plus_3x2.csv","target") -# SS = _brush.SearchSpace(data) +def test_serialization(): + data = _brush.read_csv("docs/examples/datasets/d_2x1_plus_3x2.csv","target") + SS = _brush.SearchSpace(data) -# for d in range(1,4): -# for s in range(1, 20): -# prg = SS.make_regressor(d, s) -# prg.fit(data) -# print(f"Initial Model:", prg.get_model()) -# y_pred = prg.predict(data) -# pgr_pickle = pickle.dumps(prg) + for d in range(1,4): + for s in range(1, 20): + prg = SS.make_regressor(d, s) + prg.fit(data) + print(f"Initial Model:", prg.get_model()) + y_pred = prg.predict(data) + pgr_pickle = pickle.dumps(prg) -# new_pgr = pickle.loads(pgr_pickle) -# new_pgr.fit(data) -# print(f"Loaded Model:", new_pgr.get_model()) -# new_y_pred = new_pgr.predict(data) + new_pgr = pickle.loads(pgr_pickle) + #new_pgr.fit(data) + print(f"Loaded Model:", new_pgr.get_model()) + new_y_pred = new_pgr.predict(data) -# assert prg.get_model() == new_pgr.get_model() -# assert np.allclose(new_y_pred, y_pred, atol=1e-3) \ No newline at end of file + assert prg.get_model() == new_pgr.get_model() + assert np.allclose(new_y_pred, y_pred, atol=1e-3, equal_nan=True) \ No newline at end of file