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Ravin Kohli: [RELEASE] Release v0.2 (#448)
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| # Sphinx build info version 1 | ||
| # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. | ||
| config: 970eb458f32b434db48854a3317b2f05 | ||
| config: 29bd076c4adc563a4d280f931be44bec | ||
| tags: 645f666f9bcd5a90fca523b33c5a78b7 |
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master/_downloads/250b72c5b7995e4f210c161ca8b36741/example_plot_over_time.py
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| """ | ||
| ============================== | ||
| Plot the Performance over Time | ||
| ============================== | ||
| Auto-Pytorch uses SMAC to fit individual machine learning algorithms | ||
| and then ensembles them together using `Ensemble Selection | ||
| <https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf>`_. | ||
| The following examples shows how to plot both the performance | ||
| of the individual models and their respective ensemble. | ||
| Additionally, as we are compatible with matplotlib, | ||
| you can input any args or kwargs that are compatible with ax.plot. | ||
| In the case when you would like to create multipanel visualization, | ||
| please input plt.Axes obtained from matplotlib.pyplot.subplots. | ||
| """ | ||
| import warnings | ||
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| import numpy as np | ||
| import pandas as pd | ||
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| from sklearn import model_selection | ||
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| import matplotlib.pyplot as plt | ||
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| from autoPyTorch.api.tabular_classification import TabularClassificationTask | ||
| from autoPyTorch.utils.results_visualizer import PlotSettingParams | ||
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| warnings.simplefilter(action='ignore', category=UserWarning) | ||
| warnings.simplefilter(action='ignore', category=FutureWarning) | ||
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| ############################################################################ | ||
| # Task Definition | ||
| # =============== | ||
| n_samples, dim = 100, 2 | ||
| X = np.random.random((n_samples, dim)) * 2 - 1 | ||
| y = ((X ** 2).sum(axis=-1) < 2 / np.pi).astype(np.int32) | ||
| print(y) | ||
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| X, y = pd.DataFrame(X), pd.DataFrame(y) | ||
| X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y) | ||
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| ############################################################################ | ||
| # API Instantiation and Searching | ||
| # =============================== | ||
| api = TabularClassificationTask(seed=42) | ||
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| api.search(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, | ||
| optimize_metric='accuracy', total_walltime_limit=120, func_eval_time_limit_secs=10) | ||
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| ############################################################################ | ||
| # Create Setting Parameters Object | ||
| # ================================ | ||
| metric_name = 'accuracy' | ||
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| params = PlotSettingParams( | ||
| xscale='log', | ||
| xlabel='Runtime', | ||
| ylabel='Accuracy', | ||
| title='Toy Example', | ||
| figname='example_plot_over_time.png', | ||
| savefig_kwargs={'bbox_inches': 'tight'}, | ||
| show=False # If you would like to show, make it True and set figname=None | ||
| ) | ||
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| ############################################################################ | ||
| # Plot with the Specified Setting Parameters | ||
| # ========================================== | ||
| # _, ax = plt.subplots() <=== You can feed it to post-process the figure. | ||
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| # You might need to run `export DISPLAY=:0.0` if you are using non-GUI based environment. | ||
| api.plot_perf_over_time( | ||
| metric_name=metric_name, | ||
| plot_setting_params=params, | ||
| marker='*', | ||
| markersize=10 | ||
| ) |
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master/_downloads/83b132442a977189d27fd09560b2bd34/example_single_configuration.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib inline" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "\n# Fit a single configuration\n*Auto-PyTorch* searches for the best combination of machine learning algorithms\nand their hyper-parameter configuration for a given task.\nThis example shows how one can fit one of these pipelines, both, with a user defined\nconfiguration, and a randomly sampled one form the configuration space.\nThe pipelines that Auto-PyTorch fits are compatible with Scikit-Learn API. You can\nget further documentation about Scikit-Learn models here: <https://scikit-learn.org/stable/getting_started.html`>_\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\nimport tempfile as tmp\nimport warnings\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sklearn.datasets\nimport sklearn.metrics\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask\nfrom autoPyTorch.datasets.resampling_strategy import HoldoutValTypes" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Data Loading\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "X, y = sklearn.datasets.fetch_openml(data_id=3, return_X_y=True, as_frame=True)\nX_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X, y, test_size=0.5, random_state=3\n)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define an estimator\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "estimator = TabularClassificationTask(\n resampling_strategy=HoldoutValTypes.holdout_validation,\n resampling_strategy_args={'val_share': 0.5},\n)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Get a configuration of the pipeline for current dataset\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "dataset = estimator.get_dataset(X_train=X_train,\n y_train=y_train,\n X_test=X_test,\n y_test=y_test,\n dataset_name='kr-vs-kp')\nconfiguration = estimator.get_search_space(dataset).get_default_configuration()\n\nprint(\"Passed Configuration:\", configuration)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Fit the configuration\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "pipeline, run_info, run_value, dataset = estimator.fit_pipeline(dataset=dataset,\n configuration=configuration,\n budget_type='epochs',\n budget=5,\n run_time_limit_secs=75\n )\n\n# The fit_pipeline command also returns a named tuple with the pipeline constraints\nprint(run_info)\n\n# The fit_pipeline command also returns a named tuple with train/test performance\nprint(run_value)\n\n# This object complies with Scikit-Learn Pipeline API.\n# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\nprint(pipeline.named_steps)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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