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‎master/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 29bd076c4adc563a4d280f931be44bec
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config: 81b1e868fc54c75351118afeb42f47f5
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎master/_downloads/38ebc52de63d1626596d1647c695c721/example_tabular_regression.ipynb

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},
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"outputs": [],
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"source": [
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"api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='r2',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50,\n)"
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"api.search(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test.copy(),\n y_test=y_test.copy(),\n optimize_metric='r2',\n total_walltime_limit=300,\n func_eval_time_limit_secs=50,\n dataset_name=\"Boston\"\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Print the final ensemble performance\n\n"
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"## Print the final ensemble performance before refit\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"y_pred = api.predict(X_test)\n\n# Rescale the Neural Network predictions into the original target range\nscore = api.score(y_pred, y_test)\n\nprint(score)\n# Print the final ensemble built by AutoPyTorch\nprint(api.show_models())\n\n# Print statistics from search\nprint(api.sprint_statistics())"
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"y_pred = api.predict(X_test)\nscore = api.score(y_pred, y_test)\nprint(score)\n\n# Print statistics from search\nprint(api.sprint_statistics())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Refit the models on the full dataset.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"api.refit(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test,\n y_test=y_test,\n dataset_name=\"Boston\",\n total_walltime_limit=500,\n run_time_limit_secs=50\n # you can change the resampling strategy to\n # for example, CrossValTypes.k_fold_cross_validation\n # to fit k fold models and have a voting classifier\n # resampling_strategy=CrossValTypes.k_fold_cross_validation\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Print the final ensemble performance after refit\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"y_pred = api.predict(X_test)\nscore = api.score(y_pred, y_test)\nprint(score)\n\n# Print the final ensemble built by AutoPyTorch\nprint(api.show_models())"
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]
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}
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],

‎master/_downloads/3b0b756ccfcac69e6a1673e56f2f543f/example_visualization.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Visualizing the Results\n\nAuto-Pytorch uses SMAC to fit individual machine learning algorithms\nand then ensembles them together using `Ensemble Selection\n<https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf>`_.\n\nThe following examples shows how to visualize both the performance\nof the individual models and their respective ensemble.\n\nAdditionally, as we are compatible with scikit-learn,\nwe show how to further interact with `Scikit-Learn Inspection\n<https://scikit-learn.org/stable/inspection.html>`_ support.\n"
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"\n# Visualizing the Results\n\nAuto-Pytorch uses SMAC to fit individual machine learning algorithms\nand then ensembles them together using [Ensemble Selection](https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf).\n\nThe following examples shows how to visualize both the performance\nof the individual models and their respective ensemble.\n\nAdditionally, as we are compatible with scikit-learn,\nwe show how to further interact with [Scikit-Learn Inspection](https://scikit-learn.org/stable/inspection.html) support.\n"
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]
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},
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{

‎master/_downloads/6ee656697d20c490e1d49bdbfb69d108/example_tabular_classification.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Tabular Classification\n\nThe following example shows how to fit a sample classification model\nwith AutoPyTorch\n"
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"\n# Tabular Classification\n\nThe following example shows how to fit a simple classification ensemble\nwith AutoPyTorch and refit the found ensemble.\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"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.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask"
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"import os\nimport tempfile as tmp\nimport warnings\n\nfrom autoPyTorch.datasets.resampling_strategy import CrossValTypes\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.model_selection\n\nfrom autoPyTorch.api.tabular_classification import TabularClassificationTask"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Print the final ensemble performance\n\n"
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"## Print the final ensemble performance before refit\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"y_pred = api.predict(X_test)\nscore = api.score(y_pred, y_test)\nprint(score)\n# Print the final ensemble built by AutoPyTorch\nprint(api.show_models())\n\n# Print statistics from search\nprint(api.sprint_statistics())"
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"y_pred = api.predict(X_test)\nscore = api.score(y_pred, y_test)\nprint(score)\n\n# Print statistics from search\nprint(api.sprint_statistics())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Refit the models on the full dataset.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"api.refit(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test,\n y_test=y_test,\n dataset_name=\"Australian\",\n # you can change the resampling strategy to\n # for example, CrossValTypes.k_fold_cross_validation\n # to fit k fold models and have a voting classifier\n # resampling_strategy=CrossValTypes.k_fold_cross_validation\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Print the final ensemble performance after refit\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"y_pred = api.predict(X_test)\nscore = api.score(y_pred, y_test)\nprint(score)\n\n# Print the final ensemble built by AutoPyTorch\nprint(api.show_models())"
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]
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}
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],

‎master/_downloads/8410e1dea0ba7de106ac5e2a04cee84d/example_plot_over_time.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"\n# Plot the Performance over Time\n\nAuto-Pytorch uses SMAC to fit individual machine learning algorithms\nand then ensembles them together using `Ensemble Selection\n<https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf>`_.\n\nThe following examples shows how to plot both the performance\nof the individual models and their respective ensemble.\n\nAdditionally, as we are compatible with matplotlib,\nyou can input any args or kwargs that are compatible with ax.plot.\nIn the case when you would like to create multipanel visualization,\nplease input plt.Axes obtained from matplotlib.pyplot.subplots.\n"
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"\n# Plot the Performance over Time\n\nAuto-Pytorch uses SMAC to fit individual machine learning algorithms\nand then ensembles them together using [Ensemble Selection](https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf).\n\nThe following examples shows how to plot both the performance\nof the individual models and their respective ensemble.\n\nAdditionally, as we are compatible with matplotlib,\nyou can input any args or kwargs that are compatible with ax.plot.\nIn the case when you would like to create multipanel visualization,\nplease input plt.Axes obtained from matplotlib.pyplot.subplots.\n"
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},
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{

‎master/_downloads/bbd6ad27117588060207b551c10df82e/example_tabular_regression.py

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optimize_metric='r2',
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dataset_name="Boston"
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)
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############################################################################
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# Print the final ensemble performance
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# ====================================
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# Print the final ensemble performance before refit
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# =================================================
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y_pred = api.predict(X_test)
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# Rescale the Neural Network predictions into the original target range
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score = api.score(y_pred, y_test)
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print(score)
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# Print the final ensemble built by AutoPyTorch
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print(api.show_models())
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# Print statistics from search
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print(api.sprint_statistics())
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###########################################################################
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# Refit the models on the full dataset.
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# =====================================
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api.refit(
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X_train=X_train,
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y_train=y_train,
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X_test=X_test,
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y_test=y_test,
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dataset_name="Boston",
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total_walltime_limit=500,
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run_time_limit_secs=50
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# you can change the resampling strategy to
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# for example, CrossValTypes.k_fold_cross_validation
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# to fit k fold models and have a voting classifier
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# resampling_strategy=CrossValTypes.k_fold_cross_validation
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)
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############################################################################
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# Print the final ensemble performance after refit
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# ================================================
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y_pred = api.predict(X_test)
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score = api.score(y_pred, y_test)
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print(score)
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# Print the final ensemble built by AutoPyTorch
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print(api.show_models())

‎master/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎master/_downloads/eaf3a203570ee37a60e53304131a24a3/example_tabular_classification.py

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Tabular Classification
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======================
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The following example shows how to fit a sample classification model
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with AutoPyTorch
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The following example shows how to fit a simple classification ensemble
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with AutoPyTorch and refit the found ensemble.
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"""
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import tempfile as tmp
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import warnings
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from autoPyTorch.datasets.resampling_strategy import CrossValTypes
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os.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()
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os.environ['OMP_NUM_THREADS'] = '1'
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############################################################################
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# Print the final ensemble performance
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# Print the final ensemble performance before refit
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# =================================================
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y_pred = api.predict(X_test)
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print(score)
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###########################################################################
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# Refit the models on the full dataset.
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# =====================================
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api.refit(
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X_train=X_train,
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y_train=y_train,
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y_test=y_test,
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dataset_name="Australian",
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# you can change the resampling strategy to
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# for example, CrossValTypes.k_fold_cross_validation
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# to fit k fold models and have a voting classifier
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# resampling_strategy=CrossValTypes.k_fold_cross_validation
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)
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############################################################################
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# Print the final ensemble performance after refit
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# ================================================
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y_pred = api.predict(X_test)
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score = api.score(y_pred, y_test)
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print(score)
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# Print the final ensemble built by AutoPyTorch
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print(api.show_models())

‎master/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

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‎master/_images/sphx_glr_example_plot_over_time_001.png

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‎master/_modules/autoPyTorch/api/tabular_classification.html

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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>autoPyTorch.api.tabular_classification &#8212; AutoPyTorch 0.2 documentation</title>
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<title>autoPyTorch.api.tabular_classification &#8212; AutoPyTorch 0.2.1 documentation</title>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.1.1.<br/>
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‎master/_modules/autoPyTorch/api/tabular_regression.html

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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.0.2.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.1.1.<br/>
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‎master/_modules/autoPyTorch/api/time_series_forecasting.html

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<title>autoPyTorch.api.time_series_forecasting &#8212; AutoPyTorch 0.2 documentation</title>
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<title>autoPyTorch.api.time_series_forecasting &#8212; AutoPyTorch 0.2.1 documentation</title>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.1.1.<br/>
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‎master/_modules/autoPyTorch/pipeline/components/base_choice.html

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<title>autoPyTorch.pipeline.components.base_choice &#8212; AutoPyTorch 0.2.1 documentation</title>
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&copy; Copyright 2014-2022, Machine Learning Professorship Freiburg.<br/>
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