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| """ | ||
| File: _legacy.py | ||
| Author: Panyi Dong | ||
| GitHub: https://github.com/PanyiDong/ | ||
| Mathematics Department, University of Illinois at Urbana-Champaign (UIUC) | ||
| Project: My_AutoML | ||
| Latest Version: 0.2.0 | ||
| Relative Path: /My_AutoML/_balancing/_legacy.py | ||
| File Created: Friday, 8th April 2022 9:19:36 pm | ||
| Author: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| Last Modified: Friday, 8th April 2022 9:19:56 pm | ||
| Modified By: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| MIT License | ||
| Copyright (c) 2022 - 2022, Panyi Dong | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
|
|
||
| from ._over_sampling import SimpleRandomOverSampling, Smote | ||
| from ._under_sampling import ( | ||
| SimpleRandomUnderSampling, | ||
| TomekLink, | ||
| EditedNearestNeighbor, | ||
| CondensedNearestNeighbor, | ||
| OneSidedSelection, | ||
| CNN_TomekLink, | ||
| ) | ||
| from ._mixed_sampling import Smote_TomekLink, Smote_ENN | ||
| from My_AutoML._base import no_processing | ||
|
|
||
| balancings = { | ||
| "no_processing": no_processing, | ||
| "SimpleRandomOverSampling": SimpleRandomOverSampling, | ||
| "SimpleRandomUnderSampling": SimpleRandomUnderSampling, | ||
| "TomekLink": TomekLink, | ||
| "EditedNearestNeighbor": EditedNearestNeighbor, | ||
| "CondensedNearestNeighbor": CondensedNearestNeighbor, | ||
| "OneSidedSelection": OneSidedSelection, | ||
| "CNN_TomekLink": CNN_TomekLink, | ||
| "Smote": Smote, | ||
| "Smote_TomekLink": Smote_TomekLink, | ||
| "Smote_ENN": Smote_ENN, | ||
| } |
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| """ | ||
| File: _legacy.py | ||
| Author: Panyi Dong | ||
| GitHub: https://github.com/PanyiDong/ | ||
| Mathematics Department, University of Illinois at Urbana-Champaign (UIUC) | ||
| Project: My_AutoML | ||
| Latest Version: 0.2.0 | ||
| Relative Path: /My_AutoML/_encoding/_legacy.py | ||
| File Created: Friday, 8th April 2022 9:20:03 pm | ||
| Author: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| Last Modified: Friday, 8th April 2022 9:20:14 pm | ||
| Modified By: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| MIT License | ||
| Copyright (c) 2022 - 2022, Panyi Dong | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
|
|
||
| from ._encoding import DataEncoding | ||
|
|
||
| encoders = {"DataEncoding": DataEncoding} |
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| """ | ||
| File: _legacy.py | ||
| Author: Panyi Dong | ||
| GitHub: https://github.com/PanyiDong/ | ||
| Mathematics Department, University of Illinois at Urbana-Champaign (UIUC) | ||
| Project: My_AutoML | ||
| Latest Version: 0.2.0 | ||
| Relative Path: /My_AutoML/_feature_selection/_legacy.py | ||
| File Created: Friday, 8th April 2022 9:20:27 pm | ||
| Author: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| Last Modified: Friday, 8th April 2022 9:20:55 pm | ||
| Modified By: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| MIT License | ||
| Copyright (c) 2022 - 2022, Panyi Dong | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
|
|
||
| from ._base import RBFSampler | ||
| from ._advance import FeatureFilter, ASFFS, GeneticAlgorithm | ||
| from My_AutoML._base import no_processing | ||
|
|
||
| import autosklearn | ||
| import autosklearn.pipeline.components.feature_preprocessing | ||
|
|
||
| feature_selections = { | ||
| "no_processing": no_processing, | ||
| # "LDASelection": LDASelection, | ||
| # "PCA_FeatureSelection": PCA_FeatureSelection, | ||
| "RBFSampler": RBFSampler, | ||
| "FeatureFilter": FeatureFilter, | ||
| "ASFFS": ASFFS, | ||
| "GeneticAlgorithm": GeneticAlgorithm, | ||
| # 'densifier' : autosklearn.pipeline.components.feature_preprocessing.densifier.Densifier, # from autosklearn | ||
| "extra_trees_preproc_for_classification": autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_classification.ExtraTreesPreprocessorClassification, | ||
| "extra_trees_preproc_for_regression": autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_regression.ExtraTreesPreprocessorRegression, | ||
| # "fast_ica": autosklearn.pipeline.components.feature_preprocessing.fast_ica.FastICA, | ||
| # "feature_agglomeration": autosklearn.pipeline.components.feature_preprocessing.feature_agglomeration.FeatureAgglomeration, | ||
| # "kernel_pca": autosklearn.pipeline.components.feature_preprocessing.kernel_pca.KernelPCA, | ||
| # "kitchen_sinks": autosklearn.pipeline.components.feature_preprocessing.kitchen_sinks.RandomKitchenSinks, | ||
| "liblinear_svc_preprocessor": autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor.LibLinear_Preprocessor, | ||
| # "nystroem_sampler": autosklearn.pipeline.components.feature_preprocessing.nystroem_sampler.Nystroem, | ||
| # "pca": autosklearn.pipeline.components.feature_preprocessing.pca.PCA, | ||
| "polynomial": autosklearn.pipeline.components.feature_preprocessing.polynomial.PolynomialFeatures, | ||
| # "random_trees_embedding": autosklearn.pipeline.components.feature_preprocessing.random_trees_embedding.RandomTreesEmbedding, | ||
| # 'select_percentile' : autosklearn.pipeline.components.feature_preprocessing.select_percentile.SelectPercentileBase, | ||
| "select_percentile_classification": autosklearn.pipeline.components.feature_preprocessing.select_percentile_classification.SelectPercentileClassification, | ||
| "select_percentile_regression": autosklearn.pipeline.components.feature_preprocessing.select_percentile_regression.SelectPercentileRegression, | ||
| "select_rates_classification": autosklearn.pipeline.components.feature_preprocessing.select_rates_classification.SelectClassificationRates, | ||
| "select_rates_regression": autosklearn.pipeline.components.feature_preprocessing.select_rates_regression.SelectRegressionRates, | ||
| "truncatedSVD": autosklearn.pipeline.components.feature_preprocessing.truncatedSVD.TruncatedSVD, | ||
| } |
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| """ | ||
| File: _legacy.py | ||
| Author: Panyi Dong | ||
| GitHub: https://github.com/PanyiDong/ | ||
| Mathematics Department, University of Illinois at Urbana-Champaign (UIUC) | ||
| Project: My_AutoML | ||
| Latest Version: 0.2.0 | ||
| Relative Path: /My_AutoML/_imputation/_legacy.py | ||
| File Created: Friday, 8th April 2022 9:21:08 pm | ||
| Author: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| Last Modified: Friday, 8th April 2022 9:21:33 pm | ||
| Modified By: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| MIT License | ||
| Copyright (c) 2022 - 2022, Panyi Dong | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
|
|
||
| from ._base import SimpleImputer, DummyImputer, JointImputer | ||
| from ._multiple import ExpectationMaximization, KNNImputer, MissForestImputer, MICE | ||
| from ._nn import GAIN | ||
|
|
||
| imputers = { | ||
| "SimpleImputer": SimpleImputer, | ||
| # 'DummyImputer' : DummyImputer, | ||
| "JointImputer": JointImputer, | ||
| "ExpectationMaximization": ExpectationMaximization, | ||
| "KNNImputer": KNNImputer, | ||
| "MissForestImputer": MissForestImputer, | ||
| "MICE": MICE, | ||
| "GAIN": GAIN, | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| File: _legacy.py | ||
| Author: Panyi Dong | ||
| GitHub: https://github.com/PanyiDong/ | ||
| Mathematics Department, University of Illinois at Urbana-Champaign (UIUC) | ||
| Project: My_AutoML | ||
| Latest Version: 0.2.0 | ||
| Relative Path: /My_AutoML/_model/_legacy.py | ||
| File Created: Friday, 8th April 2022 9:18:08 pm | ||
| Author: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| Last Modified: Friday, 8th April 2022 9:18:53 pm | ||
| Modified By: Panyi Dong (panyid2@illinois.edu) | ||
| ----- | ||
| MIT License | ||
| Copyright (c) 2022 - 2022, Panyi Dong | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
|
|
||
| import autosklearn.pipeline.components.classification | ||
| import autosklearn.pipeline.components.regression | ||
|
|
||
| #################################################################################################### | ||
| # classifiers | ||
|
|
||
| classifiers = { | ||
| # classification models from autosklearn | ||
| "AdaboostClassifier": autosklearn.pipeline.components.classification.adaboost.AdaboostClassifier, | ||
| "BernoulliNB": autosklearn.pipeline.components.classification.bernoulli_nb.BernoulliNB, | ||
| "DecisionTree": autosklearn.pipeline.components.classification.decision_tree.DecisionTree, | ||
| "ExtraTreesClassifier": autosklearn.pipeline.components.classification.extra_trees.ExtraTreesClassifier, | ||
| "GaussianNB": autosklearn.pipeline.components.classification.gaussian_nb.GaussianNB, | ||
| "GradientBoostingClassifier": autosklearn.pipeline.components.classification.gradient_boosting.GradientBoostingClassifier, | ||
| "KNearestNeighborsClassifier": autosklearn.pipeline.components.classification.k_nearest_neighbors.KNearestNeighborsClassifier, | ||
| "LDA": autosklearn.pipeline.components.classification.lda.LDA, | ||
| "LibLinear_SVC": autosklearn.pipeline.components.classification.liblinear_svc.LibLinear_SVC, | ||
| "LibSVM_SVC": autosklearn.pipeline.components.classification.libsvm_svc.LibSVM_SVC, | ||
| "MLPClassifier": autosklearn.pipeline.components.classification.mlp.MLPClassifier, | ||
| "MultinomialNB": autosklearn.pipeline.components.classification.multinomial_nb.MultinomialNB, | ||
| "PassiveAggressive": autosklearn.pipeline.components.classification.passive_aggressive.PassiveAggressive, | ||
| "QDA": autosklearn.pipeline.components.classification.qda.QDA, | ||
| "RandomForest": autosklearn.pipeline.components.classification.random_forest.RandomForest, | ||
| "SGD": autosklearn.pipeline.components.classification.sgd.SGD, | ||
| } | ||
|
|
||
| # regressors | ||
| regressors = { | ||
| # regression models from autosklearn | ||
| "AdaboostRegressor": autosklearn.pipeline.components.regression.adaboost.AdaboostRegressor, | ||
| "ARDRegression": autosklearn.pipeline.components.regression.ard_regression.ARDRegression, | ||
| "DecisionTree": autosklearn.pipeline.components.regression.decision_tree.DecisionTree, | ||
| "ExtraTreesRegressor": autosklearn.pipeline.components.regression.extra_trees.ExtraTreesRegressor, | ||
| "GaussianProcess": autosklearn.pipeline.components.regression.gaussian_process.GaussianProcess, | ||
| "GradientBoosting": autosklearn.pipeline.components.regression.gradient_boosting.GradientBoosting, | ||
| "KNearestNeighborsRegressor": autosklearn.pipeline.components.regression.k_nearest_neighbors.KNearestNeighborsRegressor, | ||
| "LibLinear_SVR": autosklearn.pipeline.components.regression.liblinear_svr.LibLinear_SVR, | ||
| "LibSVM_SVR": autosklearn.pipeline.components.regression.libsvm_svr.LibSVM_SVR, | ||
| "MLPRegressor": autosklearn.pipeline.components.regression.mlp.MLPRegressor, | ||
| "RandomForest": autosklearn.pipeline.components.regression.random_forest.RandomForest, | ||
| "SGD": autosklearn.pipeline.components.regression.sgd.SGD, | ||
| } | ||
|
|
||
|
|
||
| """ | ||
| LibSVM_SVR, MLP and SGD have problems of requiring inverse_transform | ||
| of StandardScaler while having 1D array | ||
| https://github.com/automl/auto-sklearn/issues/1297 | ||
| problem solved | ||
| """ |
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