├── .flake8 ├── .github ├── CODE_OF_CONDUCT.md ├── ISSUE_TEMPLATE │ ├── BUG_REPORT.md │ ├── DOCUMENTATION_IMPROVEMENT.md │ ├── FEATURE_REQUEST.md │ ├── OTHER.md │ ├── USAGE_QUESTION.md │ └── config.yml ├── PULL_REQUEST_TEMPLATE.md └── workflows │ ├── linter.yml │ ├── publish.yaml │ └── python-package-conda.yml ├── .gitignore ├── .pep8speaks.yml ├── .pre-commit-config.yaml ├── LICENSE-BSD3.txt ├── LICENSE-CC-BY.txt ├── MANIFEST.in ├── README.md ├── docs ├── custom_theme │ ├── 404.html │ ├── __init__.py │ ├── base.html │ ├── content.html │ ├── css │ │ ├── base.css │ │ ├── bootstrap-custom.css │ │ ├── bootstrap-custom.min.css │ │ ├── cinder.css │ │ ├── font-awesome-4.0.3.css │ │ └── highlight.css │ ├── fonts │ │ ├── fontawesome-webfont.eot │ │ ├── fontawesome-webfont.svg │ │ ├── fontawesome-webfont.ttf │ │ └── fontawesome-webfont.woff │ ├── img │ │ ├── favicon.ico │ │ ├── grid1.png │ │ ├── grid10.png │ │ ├── grid11.png │ │ ├── grid12.png │ │ ├── grid13.png │ │ ├── grid14.png │ │ ├── grid15.png │ │ ├── grid16.png │ │ ├── grid17.png │ │ ├── grid18.png │ │ ├── grid19.png │ │ ├── grid2.png │ │ ├── grid20.png │ │ ├── grid3.png │ │ ├── grid4.png │ │ ├── grid5.png │ │ ├── grid6.png │ │ ├── grid7.png │ │ ├── grid8.png │ │ └── grid9.png │ ├── js │ │ ├── base.js │ │ ├── bootstrap-3.0.3.min.js │ │ ├── highlight.pack.js │ │ └── jquery-1.10.2.min.js │ ├── main.html │ ├── mkdocs_theme.yml │ ├── nav-sub.html │ ├── nav.html │ └── toc.html ├── favicon.ico ├── ipynb2markdown.py ├── make_api.py ├── make_userguide.py ├── md2pdf.py ├── mdx_math.py ├── mdx_math_setup.py ├── mkdocs.yml ├── pdftemplate.tex └── sources │ ├── CHANGELOG.md │ ├── CONTRIBUTING.md │ ├── Code-of-Conduct.md │ ├── USER-GUIDE-INDEX.md │ ├── USER_GUIDE_INDEX.md │ ├── cite.md │ ├── contributors.md │ ├── discuss.md │ ├── extra.css │ ├── img │ ├── contributing │ │ ├── fork.png │ │ ├── new_issue.png │ │ └── pull_request.png │ ├── ensemble_decision_regions_2d.png │ ├── logo.jpg │ ├── logo.pdf │ └── logo.png │ ├── index.md │ ├── installation.md │ ├── license.md │ ├── mathjaxhelper.js │ └── user_guide │ ├── classifier │ ├── Adaline.ipynb │ ├── Adaline_files │ │ ├── Adaline_15_0.png │ │ ├── Adaline_16_0.png │ │ ├── Adaline_17_1.png │ │ ├── Adaline_17_3.png │ │ ├── Adaline_18_0.png │ │ ├── Adaline_18_1.png │ │ ├── Adaline_18_3.png │ │ ├── Adaline_19_1.png │ │ ├── Adaline_19_2.png │ │ ├── Adaline_20_1.png │ │ ├── Adaline_20_2.png │ │ ├── Adaline_20_3.png │ │ ├── Adaline_21_1.png │ │ ├── Adaline_21_2.png │ │ ├── Adaline_22_1.png │ │ ├── Adaline_22_2.png │ │ ├── Adaline_24_1.png │ │ ├── Adaline_24_2.png │ │ └── adaline_schematic.png │ ├── EnsembleVoteClassifier.ipynb │ ├── EnsembleVoteClassifier_files │ │ ├── EnsembleVoteClassifier_24_0.png │ │ ├── EnsembleVoteClassifier_24_1.png │ │ ├── EnsembleVoteClassifier_25_0.png │ │ ├── EnsembleVoteClassifier_26_0.png │ │ ├── EnsembleVoteClassifier_27_0.png │ │ ├── majority_voting.png │ │ └── voting.png │ ├── LogisticRegression.ipynb │ ├── LogisticRegression_files │ │ ├── LogisticRegression_43_1.png │ │ ├── LogisticRegression_43_2.png │ │ ├── LogisticRegression_44_1.png │ │ ├── LogisticRegression_44_2.png │ │ ├── LogisticRegression_46_1.png │ │ ├── LogisticRegression_46_2.png │ │ ├── LogisticRegression_49_1.png │ │ ├── LogisticRegression_49_2.png │ │ ├── LogisticRegression_50_1.png │ │ ├── LogisticRegression_50_2.png │ │ ├── LogisticRegression_52_1.png │ │ ├── LogisticRegression_52_2.png │ │ ├── LogisticRegression_55_1.png │ │ ├── LogisticRegression_55_2.png │ │ ├── logistic_cost.png │ │ ├── logistic_function.png │ │ └── logistic_regression_schematic.png │ ├── MultiLayerPerceptron.ipynb │ ├── MultiLayerPerceptron_files │ │ ├── MultiLayerPerceptron_23_0.png │ │ ├── MultiLayerPerceptron_24_0.png │ │ ├── MultiLayerPerceptron_25_0.png │ │ ├── MultiLayerPerceptron_26_0.png │ │ ├── MultiLayerPerceptron_27_0.png │ │ ├── MultiLayerPerceptron_28_1.png │ │ ├── MultiLayerPerceptron_29_1.png │ │ ├── MultiLayerPerceptron_31_0.png │ │ ├── MultiLayerPerceptron_31_1.png │ │ ├── MultiLayerPerceptron_32_0.png │ │ ├── MultiLayerPerceptron_34_0.png │ │ ├── MultiLayerPerceptron_36_0.png │ │ ├── MultiLayerPerceptron_37_0.png │ │ ├── MultiLayerPerceptron_39_0.png │ │ ├── MultiLayerPerceptron_42_1.png │ │ ├── MultiLayerPerceptron_43_1.png │ │ └── MultiLayerPerceptron_45_1.png │ ├── NeuralNetMLP_files │ │ ├── NeuralNetMLP_26_1.png │ │ ├── NeuralNetMLP_27_0.png │ │ ├── NeuralNetMLP_28_1.png │ │ ├── NeuralNetMLP_30_0.png │ │ ├── NeuralNetMLP_33_0.png │ │ ├── NeuralNetMLP_36_0.png │ │ ├── NeuralNetMLP_38_0.png │ │ ├── NeuralNetMLP_43_0.png │ │ ├── NeuralNetMLP_49_1.png │ │ ├── logistic_function.png │ │ └── neuralnet_mlp_1.png │ ├── OneRClassifier.ipynb │ ├── Perceptron.ipynb │ ├── Perceptron_files │ │ ├── Perceptron_22_1.png │ │ ├── Perceptron_22_3.png │ │ ├── Perceptron_23_1.png │ │ ├── Perceptron_23_3.png │ │ ├── Perceptron_25_1.png │ │ ├── Perceptron_25_3.png │ │ └── perceptron_schematic.png │ ├── SoftmaxRegression.ipynb │ ├── SoftmaxRegression_files │ │ ├── SoftmaxRegression_31_1.png │ │ ├── SoftmaxRegression_31_2.png │ │ ├── SoftmaxRegression_32_1.png │ │ ├── SoftmaxRegression_32_2.png │ │ ├── SoftmaxRegression_33_0.png │ │ ├── SoftmaxRegression_33_1.png │ │ ├── SoftmaxRegression_34_0.png │ │ ├── SoftmaxRegression_34_1.png │ │ ├── SoftmaxRegression_34_2.png │ │ ├── SoftmaxRegression_37_0.png │ │ ├── SoftmaxRegression_37_1.png │ │ ├── SoftmaxRegression_38_0.png │ │ ├── SoftmaxRegression_38_1.png │ │ ├── SoftmaxRegression_39_0.png │ │ ├── SoftmaxRegression_39_1.png │ │ ├── SoftmaxRegression_40_0.png │ │ ├── SoftmaxRegression_40_1.png │ │ ├── SoftmaxRegression_44_0.png │ │ ├── SoftmaxRegression_44_1.png │ │ ├── logistic_regression_schematic.png │ │ └── softmax_schematic_1.png │ ├── StackingCVClassifier.ipynb │ ├── StackingCVClassifier_files │ │ ├── StackingCVClassifier_13_0.png │ │ ├── StackingCVClassifier_14_0.png │ │ ├── StackingCVClassifier_16_0.png │ │ ├── StackingCVClassifier_30_0.png │ │ ├── StackingCVClassifier_32_0.png │ │ ├── stacking_cv_algorithm.png │ │ └── stacking_cv_classification_overview.png │ ├── StackingCVRegressor_files │ │ └── stacking_cv_regressor_overview.png │ ├── StackingClassifier.ipynb │ └── StackingClassifier_files │ │ ├── StackingClassifier_13_0.png │ │ ├── StackingClassifier_14_0.png │ │ ├── StackingClassifier_16_0.png │ │ ├── StackingClassifier_17_0.png │ │ ├── StackingClassifier_31_0.png │ │ ├── StackingClassifier_33_0.png │ │ ├── StackingClassifier_37_0.png │ │ ├── StackingClassifier_39_0.png │ │ ├── stacking_algorithm.png │ │ └── stackingclassification_overview.png │ ├── cluster │ ├── Kmeans.ipynb │ └── Kmeans_files │ │ ├── Kmeans_10_0.png │ │ ├── Kmeans_11_0.png │ │ ├── Kmeans_13_0.png │ │ ├── Kmeans_14_0.png │ │ ├── Kmeans_15_0.png │ │ └── Kmeans_17_0.png │ ├── data │ ├── autompg_data.ipynb │ ├── boston_housing_data.ipynb │ ├── iris_data.ipynb │ ├── loadlocal_mnist.ipynb │ ├── make_multiplexer_dataset.ipynb │ ├── make_multiplexer_dataset_data_files │ │ ├── 6bit_multiplexer.pdf │ │ └── 6bit_multiplexer.png │ ├── mnist_data.ipynb │ ├── mnist_data_files │ │ ├── mnist_data_10_0.png │ │ ├── mnist_data_11_0.png │ │ └── mnist_data_13_0.png │ ├── three_blobs_data.ipynb │ ├── three_blobs_data_files │ │ ├── three_blobs_data_10_0.png │ │ ├── three_blobs_data_11_0.png │ │ ├── three_blobs_data_12_0.png │ │ ├── three_blobs_data_13_0.png │ │ └── three_blobs_data_14_0.png │ └── wine_data.ipynb │ ├── evaluate │ ├── BootstrapOutOfBag.ipynb │ ├── BootstrapOutOfBag_files │ │ ├── BootstrapOutOfBag_16_0.png │ │ └── bootstrap_concept.png │ ├── GroupTimeSeriesSplit.ipynb │ ├── GroupTimeSeriesSplit_files │ │ ├── GroupTimeSeriesSplit_22_0.png │ │ ├── GroupTimeSeriesSplit_24_0.png │ │ ├── GroupTimeSeriesSplit_30_0.png │ │ └── GroupTimeSeriesSplit_36_0.png │ ├── PredefinedHoldoutSplit.ipynb │ ├── RandomHoldoutSplit.ipynb │ ├── accuracy_score.ipynb │ ├── bias_variance_decomp.ipynb │ ├── bias_variance_decomp_files │ │ ├── high-bias-plot.png │ │ ├── image-20181029010428686.png │ │ └── varianceplot.png │ ├── bootstrap.ipynb │ ├── bootstrap_point632_score.ipynb │ ├── cochrans_q.ipynb │ ├── combined_ftest_5x2cv.ipynb │ ├── confusion_matrix.ipynb │ ├── confusion_matrix_files │ │ ├── confusion_matrix_1.png │ │ ├── confusion_matrix_12_0.png │ │ ├── confusion_matrix_13_0.png │ │ ├── confusion_matrix_14_0.png │ │ ├── confusion_matrix_15_0.png │ │ ├── confusion_matrix_16_0.png │ │ ├── confusion_matrix_17_0.png │ │ ├── confusion_matrix_19_0.png │ │ ├── confusion_matrix_21_0.png │ │ ├── confusion_matrix_22_0.png │ │ └── confusion_matrix_24_0.png │ ├── create_counterfactual.ipynb │ ├── create_counterfactual_files │ │ ├── create_counterfactual_16_0.png │ │ ├── create_counterfactual_18_0.png │ │ ├── create_counterfactual_21_0.png │ │ ├── create_counterfactual_21_1.png │ │ ├── create_counterfactual_21_2.png │ │ ├── create_counterfactual_21_3.png │ │ ├── create_counterfactual_21_4.png │ │ └── create_counterfactual_24_1.png │ ├── feature_importance_permutation.ipynb │ ├── feature_importance_permutation_files │ │ ├── feature_importance_permutation_17_0.png │ │ ├── feature_importance_permutation_23_0.png │ │ ├── feature_importance_permutation_27_0.png │ │ ├── feature_importance_permutation_32_0.png │ │ ├── feature_importance_permutation_35_0.png │ │ ├── feature_importance_permutation_52_0.png │ │ └── feature_importance_permutation_57_0.png │ ├── ftest.ipynb │ ├── lift_score.ipynb │ ├── lift_score_files │ │ └── lift_cm_1.png │ ├── mcnemar.ipynb │ ├── mcnemar_files │ │ ├── mcnemar_scenario_a.png │ │ └── mcnemar_scenario_b.png │ ├── mcnemar_table.ipynb │ ├── mcnemar_table_files │ │ ├── mcnemar_contingency_table.png │ │ ├── mcnemar_contingency_table_ex1.png │ │ ├── mcnemar_table_13_0.png │ │ └── mcnemar_table_14_0.png │ ├── mcnemar_tables.ipynb │ ├── mcnemar_tables_files │ │ ├── mcnemar_contingency_table.png │ │ ├── mcnemar_contingency_table_ex1.png │ │ ├── mcnemar_table_13_0.png │ │ ├── mcnemar_table_14_0.png │ │ └── mcnemar_tables_13_0.png │ ├── paired_ttest_5x2cv.ipynb │ ├── paired_ttest_kfold_cv.ipynb │ ├── paired_ttest_resampled.ipynb │ ├── permutation_test.ipynb │ ├── proportion_difference.ipynb │ ├── scoring.ipynb │ └── scoring_files │ │ ├── conf_mat_binary-1.png │ │ ├── conf_mat_multi-1.png │ │ └── multi-ex-1.png │ ├── feature_extraction │ ├── LinearDiscriminantAnalysis.ipynb │ ├── LinearDiscriminantAnalysis_files │ │ ├── LinearDiscriminantAnalysis_11_0.png │ │ ├── LinearDiscriminantAnalysis_12_0.png │ │ ├── LinearDiscriminantAnalysis_14_0.png │ │ ├── LinearDiscriminantAnalysis_15_0.png │ │ ├── LinearDiscriminantAnalysis_16_0.png │ │ └── LinearDiscriminantAnalysis_18_0.png │ ├── PrincipalComponentAnalysis.ipynb │ ├── PrincipalComponentAnalysis_files │ │ ├── PrincipalComponentAnalysis_11_0.png │ │ ├── PrincipalComponentAnalysis_12_0.png │ │ ├── PrincipalComponentAnalysis_14_0.png │ │ ├── PrincipalComponentAnalysis_15_0.png │ │ ├── PrincipalComponentAnalysis_16_0.png │ │ ├── PrincipalComponentAnalysis_17_0.png │ │ ├── PrincipalComponentAnalysis_18_0.png │ │ ├── PrincipalComponentAnalysis_19_0.png │ │ ├── PrincipalComponentAnalysis_20_0.png │ │ ├── PrincipalComponentAnalysis_21_0.png │ │ ├── PrincipalComponentAnalysis_22_0.png │ │ ├── PrincipalComponentAnalysis_24_0.png │ │ ├── PrincipalComponentAnalysis_26_0.png │ │ ├── PrincipalComponentAnalysis_33_0.png │ │ ├── PrincipalComponentAnalysis_35_0.png │ │ ├── PrincipalComponentAnalysis_37_0.png │ │ └── PrincipalComponentAnalysis_39_0.png │ ├── RBFKernelPCA.ipynb │ └── RBFKernelPCA_files │ │ ├── RBFKernelPCA_15_0.png │ │ ├── RBFKernelPCA_16_0.png │ │ ├── RBFKernelPCA_17_0.png │ │ ├── RBFKernelPCA_18_0.png │ │ ├── RBFKernelPCA_20_0.png │ │ ├── RBFKernelPCA_22_0.png │ │ ├── RBFKernelPCA_23_0.png │ │ ├── RBFKernelPCA_24_0.png │ │ ├── RBFKernelPCA_25_0.png │ │ ├── RBFKernelPCA_27_0.png │ │ ├── RBFKernelPCA_28_0.png │ │ ├── RBFKernelPCA_29_0.png │ │ ├── RBFKernelPCA_31_0.png │ │ ├── RBFKernelPCA_32_0.png │ │ ├── RBFKernelPCA_33_0.png │ │ ├── RBFKernelPCA_34_0.png │ │ ├── RBFKernelPCA_35_0.png │ │ ├── RBFKernelPCA_36_0.png │ │ ├── RBFKernelPCA_37_0.png │ │ ├── linear_vs_nonlinear.png │ │ └── pca_1.png │ ├── feature_selection │ ├── ColumnSelector.ipynb │ ├── ExhaustiveFeatureSelector.ipynb │ ├── ExhaustiveFeatureSelector_files │ │ ├── ExhaustiveFeatureSelector_15_0.png │ │ ├── ExhaustiveFeatureSelector_16_0.png │ │ ├── ExhaustiveFeatureSelector_18_0.png │ │ ├── ExhaustiveFeatureSelector_19_0.png │ │ ├── ExhaustiveFeatureSelector_22_0.png │ │ └── ExhaustiveFeatureSelector_23_0.png │ ├── SequentialFeatureSelector.ipynb │ └── SequentialFeatureSelector_files │ │ ├── SequentialFeatureSelector_39_1.png │ │ ├── SequentialFeatureSelector_40_1.png │ │ ├── SequentialFeatureSelector_42_0.png │ │ ├── SequentialFeatureSelector_42_1.png │ │ ├── SequentialFeatureSelector_43_1.png │ │ ├── SequentialFeatureSelector_44_1.png │ │ ├── SequentialFeatureSelector_46_0.png │ │ ├── SequentialFeatureSelector_47_0.png │ │ ├── SequentialFeatureSelector_49_1.png │ │ ├── SequentialFeatureSelector_50_1.png │ │ ├── SequentialFeatureSelector_52_0.png │ │ ├── SequentialFeatureSelector_53_0.png │ │ ├── SequentialFeatureSelector_55_0.png │ │ ├── SequentialFeatureSelector_69_1.png │ │ ├── SequentialFeatureSelector_69_2.png │ │ ├── SequentialFeatureSelector_70_1.png │ │ ├── SequentialFeatureSelector_70_2.png │ │ ├── SequentialFeatureSelector_74_1.png │ │ ├── SequentialFeatureSelector_75_1.png │ │ ├── SequentialFeatureSelector_76_1.png │ │ ├── SequentialFeatureSelector_77_1.png │ │ ├── SequentialFeatureSelector_80_1.png │ │ ├── SequentialFeatureSelector_81_1.png │ │ ├── SequentialFeatureSelector_82_1.png │ │ ├── feature_groups.jpeg │ │ ├── feature_groups.key │ │ └── sbs-gpcr2020.png │ ├── file_io │ ├── data_find_filegroups │ │ ├── dir_1 │ │ │ ├── file_1.log │ │ │ ├── file_2.log │ │ │ └── file_3.log │ │ ├── dir_2 │ │ │ ├── file_1.csv │ │ │ ├── file_2.csv │ │ │ └── file_3.csv │ │ └── dir_3 │ │ │ ├── file_1.txt │ │ │ ├── file_2.txt │ │ │ └── file_3.txt │ ├── find_filegroups.ipynb │ └── find_files.ipynb │ ├── frequent_patterns │ ├── apriori.ipynb │ ├── association_rules.ipynb │ ├── fpgrowth.ipynb │ ├── fpmax.ipynb │ └── hmine.ipynb │ ├── math │ ├── num_combinations.ipynb │ ├── num_permutations.ipynb │ ├── vectorspace_dimensionality.ipynb │ └── vectorspace_orthonormalization.ipynb │ ├── plotting │ ├── category_scatter.ipynb │ ├── category_scatter_files │ │ ├── category_scatter_10_0.png │ │ ├── category_scatter_11_0.png │ │ ├── category_scatter_13_0.png │ │ ├── category_scatter_14_0.png │ │ ├── category_scatter_15_0.png │ │ └── category_scatter_9_0.png │ ├── checkerboard_plot.ipynb │ ├── checkerboard_plot_files │ │ ├── checkerboard_plot_10_0.png │ │ ├── checkerboard_plot_11_0.png │ │ ├── checkerboard_plot_8_0.png │ │ └── checkerboard_plot_9_0.png │ ├── ecdf.ipynb │ ├── ecdf_files │ │ ├── ecdf_10_0.png │ │ ├── ecdf_12_0.png │ │ └── ecdf_8_0.png │ ├── enrichment_plot.ipynb │ ├── enrichment_plot_files │ │ ├── enrichment_plot_10_0.png │ │ ├── enrichment_plot_11_0.png │ │ └── enrichment_plot_12_0.png │ ├── heatmap.ipynb │ ├── heatmap_files │ │ ├── heatmap_10_0.png │ │ ├── heatmap_11_0.png │ │ ├── heatmap_13_0.png │ │ └── heatmap_8_0.png │ ├── plot_confusion_matrix.ipynb │ ├── plot_confusion_matrix_files │ │ ├── plot_confusion_matrix_10_0.png │ │ ├── plot_confusion_matrix_11_0.png │ │ ├── plot_confusion_matrix_11_1.png │ │ ├── plot_confusion_matrix_12_0.png │ │ ├── plot_confusion_matrix_13_0.png │ │ ├── plot_confusion_matrix_13_1.png │ │ ├── plot_confusion_matrix_14_0.png │ │ ├── plot_confusion_matrix_15_0.png │ │ ├── plot_confusion_matrix_15_1.png │ │ ├── plot_confusion_matrix_16_0.png │ │ ├── plot_confusion_matrix_19_0.png │ │ ├── plot_confusion_matrix_21_0.png │ │ ├── plot_confusion_matrix_24_0.png │ │ ├── plot_confusion_matrix_26_0.png │ │ ├── plot_confusion_matrix_9_0.png │ │ └── plot_confusion_matrix_9_1.png │ ├── plot_decision_regions.ipynb │ ├── plot_decision_regions_files │ │ ├── plot_decision_regions_11_0.png │ │ ├── plot_decision_regions_11_1.png │ │ ├── plot_decision_regions_12_0.png │ │ ├── plot_decision_regions_13_0.png │ │ ├── plot_decision_regions_14_0.png │ │ ├── plot_decision_regions_15_0.png │ │ ├── plot_decision_regions_16_0.png │ │ ├── plot_decision_regions_17_0.png │ │ ├── plot_decision_regions_19_0.png │ │ ├── plot_decision_regions_19_1.png │ │ ├── plot_decision_regions_20_0.png │ │ ├── plot_decision_regions_21_0.png │ │ ├── plot_decision_regions_21_1.png │ │ ├── plot_decision_regions_22_0.png │ │ ├── plot_decision_regions_23_0.png │ │ ├── plot_decision_regions_23_1.png │ │ ├── plot_decision_regions_24_0.png │ │ ├── plot_decision_regions_25_0.png │ │ ├── plot_decision_regions_25_1.png │ │ ├── plot_decision_regions_26_0.png │ │ ├── plot_decision_regions_27_0.png │ │ ├── plot_decision_regions_27_1.png │ │ ├── plot_decision_regions_29_0.png │ │ ├── plot_decision_regions_29_1.png │ │ ├── plot_decision_regions_31_0.png │ │ ├── plot_decision_regions_34_0.png │ │ ├── plot_decision_regions_38_0.png │ │ ├── plot_decision_regions_40_0.png │ │ ├── plot_decision_regions_43_0.png │ │ ├── plot_decision_regions_46_0.png │ │ ├── plot_decision_regions_52_0.png │ │ ├── plot_decision_regions_52_1.png │ │ ├── plot_decision_regions_6_0.png │ │ ├── plot_decision_regions_7_0.png │ │ ├── plot_decision_regions_8_0.png │ │ └── plot_decision_regions_9_0.png │ ├── plot_learning_curves.ipynb │ ├── plot_learning_curves_files │ │ ├── plot_learning_curves_6_0.png │ │ ├── plot_learning_curves_7_0.png │ │ ├── plot_learning_curves_8_0.png │ │ └── plot_learning_curves_9_0.png │ ├── plot_linear_regression.ipynb │ ├── plot_linear_regression_files │ │ ├── plot_linear_regression_11_0.png │ │ ├── plot_linear_regression_8_0.png │ │ └── plot_linear_regression_9_0.png │ ├── plot_pca_correlated_circle_files │ │ └── plot_pca_correlated_circle_6_0.png │ ├── plot_pca_correlation_graph.ipynb │ ├── plot_pca_correlation_graph_files │ │ ├── plot_pca_correlation_graph_6_0.png │ │ └── plot_pca_correlation_graph_7_0.png │ ├── plot_sequential_feature_selection.ipynb │ ├── plot_sequential_feature_selection_files │ │ ├── plot_sequential_feature_selection_10_1.png │ │ ├── plot_sequential_feature_selection_7_0.png │ │ ├── plot_sequential_feature_selection_7_1.png │ │ └── plot_sequential_feature_selection_8_1.png │ ├── scatter_hist.ipynb │ ├── scatter_hist_files │ │ ├── scatter_hist_11_0.png │ │ ├── scatter_hist_8_0.png │ │ ├── scatter_hist_dataframe.png │ │ └── scatter_hist_numpyarray.png │ ├── scatterplotmatrix.ipynb │ ├── scatterplotmatrix_files │ │ ├── scatterplotmatrix_10_0.png │ │ └── scatterplotmatrix_8_0.png │ ├── stacked_barplot.ipynb │ └── stacked_barplot_files │ │ ├── stacked_barplot_10_0.png │ │ ├── stacked_barplot_11_0.png │ │ └── stacked_barplot_13_0.png │ ├── preprocessing │ ├── CopyTransformer.ipynb │ ├── DenseTransformer.ipynb │ ├── MeanCenterer.ipynb │ ├── TransactionEncoder.ipynb │ ├── minmax_scaling.ipynb │ ├── one-hot_encoding.ipynb │ ├── shuffle_arrays_unison.ipynb │ └── standardize.ipynb │ ├── regressor │ ├── LinearRegression.ipynb │ ├── LinearRegression_files │ │ ├── LinearRegression_13_1.png │ │ ├── LinearRegression_14_1.png │ │ ├── LinearRegression_15_1.png │ │ ├── LinearRegression_15_2.png │ │ ├── LinearRegression_16_0.png │ │ ├── LinearRegression_16_1.png │ │ ├── LinearRegression_16_2.png │ │ ├── LinearRegression_17_0.png │ │ ├── LinearRegression_17_1.png │ │ ├── LinearRegression_17_2.png │ │ ├── LinearRegression_18_0.png │ │ ├── LinearRegression_18_1.png │ │ ├── LinearRegression_19_0.png │ │ ├── LinearRegression_19_1.png │ │ ├── LinearRegression_19_2.png │ │ ├── LinearRegression_20_0.png │ │ ├── LinearRegression_20_1.png │ │ ├── LinearRegression_21_0.png │ │ ├── LinearRegression_21_1.png │ │ ├── LinearRegression_22_0.png │ │ ├── LinearRegression_22_1.png │ │ ├── LinearRegression_22_2.png │ │ ├── LinearRegression_23_0.png │ │ ├── LinearRegression_23_1.png │ │ ├── LinearRegression_24_0.png │ │ ├── LinearRegression_24_1.png │ │ ├── LinearRegression_25_0.png │ │ ├── LinearRegression_25_1.png │ │ ├── LinearRegression_26_0.png │ │ ├── LinearRegression_27_1.png │ │ ├── LinearRegression_28_0.png │ │ ├── LinearRegression_28_1.png │ │ ├── LinearRegression_29_0.png │ │ └── simple_regression.png │ ├── StackingCVRegressor.ipynb │ ├── StackingCVRegressor_files │ │ └── stacking_cv_regressor_overview.png │ ├── StackingRegressor.ipynb │ └── StackingRegressor_files │ │ ├── StackingRegressor_11_1.png │ │ ├── StackingRegressor_12_1.png │ │ ├── StackingRegressor_14_1.png │ │ ├── StackingRegressor_16_1.png │ │ ├── StackingRegressor_17_1.png │ │ ├── StackingRegressor_18_1.png │ │ ├── StackingRegressor_19_1.png │ │ └── stackingregression_overview.png │ ├── text │ ├── generalize_names.ipynb │ ├── generalize_names_duplcheck.ipynb │ └── tokenizer.ipynb │ └── utils │ └── Counter.ipynb ├── environment.yml ├── files.txt ├── mlxtend ├── __init__.py ├── _base │ ├── __init__.py │ ├── _base_model.py │ ├── _classifier.py │ ├── _cluster.py │ ├── _iterative_model.py │ ├── _multiclass.py │ ├── _multilayer.py │ ├── _regressor.py │ └── tests │ │ ├── test_base_model.py │ │ ├── test_classifier.py │ │ ├── test_cluster.py │ │ ├── test_iterative_model.py │ │ ├── test_multiclass.py │ │ ├── test_multilayer.py │ │ └── test_regressor.py ├── classifier │ ├── __init__.py │ ├── _base_classification.py │ ├── adaline.py │ ├── ensemble_vote.py │ ├── logistic_regression.py │ ├── multilayerperceptron.py │ ├── oner.py │ ├── perceptron.py │ ├── softmax_regression.py │ ├── stacking_classification.py │ ├── stacking_cv_classification.py │ └── tests │ │ ├── test_adaline.py │ │ ├── test_ensemble_vote_classifier.py │ │ ├── test_logistic_regression.py │ │ ├── test_multilayerperceptron.py │ │ ├── test_oner.py │ │ ├── test_perceptron.py │ │ ├── test_softmax_regression.py │ │ ├── test_stacking_classifier.py │ │ └── test_stacking_cv_classifier.py ├── cluster │ ├── __init__.py │ ├── kmeans.py │ └── tests │ │ └── test_kmeans.py ├── data │ ├── __init__.py │ ├── autompg.py │ ├── boston_housing.py │ ├── data │ │ ├── autompg.csv.gz │ │ ├── boston_housing.csv │ │ ├── iris.csv.gz │ │ ├── mnist_5k.csv.gz │ │ ├── three_blobs.csv.gz │ │ └── wine.csv │ ├── iris.py │ ├── local_mnist.py │ ├── mnist.py │ ├── multiplexer.py │ ├── tests │ │ ├── test_datasets.py │ │ ├── test_iris.py │ │ └── test_multiplexer.py │ ├── three_blobs.py │ └── wine.py ├── evaluate │ ├── __init__.py │ ├── accuracy.py │ ├── bias_variance_decomp.py │ ├── bootstrap.py │ ├── bootstrap_outofbag.py │ ├── bootstrap_point632.py │ ├── cochrans_q.py │ ├── confusion_matrix.py │ ├── counterfactual.py │ ├── f_test.py │ ├── feature_importance.py │ ├── holdout.py │ ├── lift_score.py │ ├── mcnemar.py │ ├── permutation.py │ ├── proportion_difference.py │ ├── scoring.py │ ├── tests │ │ ├── test_accuracy.py │ │ ├── test_bias_variance_decomp.py │ │ ├── test_bootstrap.py │ │ ├── test_bootstrap_outofbag.py │ │ ├── test_bootstrap_point632.py │ │ ├── test_cochran_q.py │ │ ├── test_combined_ftest_5x2cv.py │ │ ├── test_confusion_matrix.py │ │ ├── test_counterfactual.py │ │ ├── test_f_test.py │ │ ├── test_feature_importance.py │ │ ├── test_holdout.py │ │ ├── test_lift_score.py │ │ ├── test_mcnemar_table.py │ │ ├── test_mcnemar_tables.py │ │ ├── test_mcnemar_test.py │ │ ├── test_paired_ttest_5x2cv.py │ │ ├── test_paired_ttest_kfold.py │ │ ├── test_paired_ttest_resampled.py │ │ ├── test_permutation.py │ │ ├── test_proportion_difference.py │ │ ├── test_scoring.py │ │ └── test_time_series.py │ ├── time_series.py │ └── ttest.py ├── externals │ ├── __init__.py │ ├── adjust_text.py │ ├── estimator_checks.py │ ├── name_estimators.py │ ├── pyprind │ │ ├── __init__.py │ │ ├── generator_factory.py │ │ ├── prog_class.py │ │ ├── progbar.py │ │ └── progpercent.py │ └── signature_py27.py ├── feature_extraction │ ├── __init__.py │ ├── base.py │ ├── linear_discriminant_analysis.py │ ├── principal_component_analysis.py │ ├── rbf_kernel_pca.py │ └── tests │ │ ├── test_base.py │ │ ├── test_kernel_pca.py │ │ ├── test_linear_discriminant_analysis.py │ │ └── test_principal_component_analysis.py ├── feature_selection │ ├── __init__.py │ ├── column_selector.py │ ├── exhaustive_feature_selector.py │ ├── sequential_feature_selector.py │ ├── tests │ │ ├── test_column_selector.py │ │ ├── test_exhaustive_feature_selector.py │ │ ├── test_sequential_feature_selector.py │ │ ├── test_sequential_feature_selector_feature_groups.py │ │ └── test_sequential_feature_selector_fixed_features.py │ └── utilities.py ├── file_io │ ├── __init__.py │ ├── find_filegroups.py │ └── find_files.py ├── frequent_patterns │ ├── __init__.py │ ├── apriori.py │ ├── association_rules.py │ ├── fpcommon.py │ ├── fpgrowth.py │ ├── fpmax.py │ ├── hmine.py │ └── tests │ │ ├── test_apriori.py │ │ ├── test_association_rules.py │ │ ├── test_fpbase.py │ │ ├── test_fpgrowth.py │ │ ├── test_fpmax.py │ │ └── test_hmine.py ├── math │ ├── __init__.py │ ├── counting.py │ ├── linalg.py │ └── tests │ │ ├── test_counting.py │ │ └── test_linalg.py ├── plotting │ ├── __init__.py │ ├── checkerboard.py │ ├── decision_regions.py │ ├── ecdf.py │ ├── enrichment_plot.py │ ├── heatmap.py │ ├── learning_curves.py │ ├── pca_correlation_graph.py │ ├── plot_confusion_matrix.py │ ├── plot_linear_regression.py │ ├── plot_sequential_feature_selection.py │ ├── remove_chartjunk.py │ ├── scatter.py │ ├── scatter_hist.py │ ├── scatterplotmatrix.py │ ├── stacked_barplot.py │ └── tests │ │ ├── data │ │ └── data.csv │ │ ├── test_checkerboard.py │ │ ├── test_decision_regions.py │ │ ├── test_ecdf.py │ │ ├── test_heatmap.py │ │ ├── test_learning_curves.py │ │ ├── test_pca_corr_graph.py │ │ └── test_scatter_hist.py ├── preprocessing │ ├── __init__.py │ ├── copy_transformer.py │ ├── dense_transformer.py │ ├── mean_centering.py │ ├── onehot.py │ ├── scaling.py │ ├── shuffle.py │ ├── tests │ │ ├── test__scaling__minmax_scaling.py │ │ ├── test__scaling__standardizing.py │ │ ├── test_copy_transformer.py │ │ ├── test_dense_transformer.py │ │ ├── test_mean_centering.py │ │ ├── test_onehot.py │ │ ├── test_shuffle_shuffle_arrays_unison.py │ │ └── test_transactionencoder.py │ └── transactionencoder.py ├── regressor │ ├── __init__.py │ ├── linear_regression.py │ ├── stacking_cv_regression.py │ ├── stacking_regression.py │ └── tests │ │ ├── test_linear_regression.py │ │ ├── test_stacking_cv_regression.py │ │ └── test_stacking_regression.py ├── text │ ├── __init__.py │ ├── names.py │ ├── tests │ │ ├── data_names.py │ │ ├── test_generalize_names.py │ │ ├── test_generalize_names_duplcheck.py │ │ └── test_tokenizer.py │ └── tokenizer.py └── utils │ ├── __init__.py │ ├── base_compostion.py │ ├── checking.py │ ├── counter.py │ ├── testing.py │ └── tests │ ├── test_checking_inputs.py │ ├── test_counter.py │ └── test_testing.py ├── oryx-build-commands.txt ├── paper.bib ├── paper.md ├── pyproject.toml ├── requirements-test.txt ├── requirements.lock └── requirements.txt /.flake8: -------------------------------------------------------------------------------- 1 | [flake8] 2 | ignore = E203, E266, E501, W503, F403, F401, E402, C901 3 | max-line-length = 89 4 | max-complexity = 18 5 | select = B,C,E,F,W,T4,B9 6 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/DOCUMENTATION_IMPROVEMENT.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Documentation improvement 3 | about: Issues or confusions regarding the documentation 4 | title: '' 5 | labels: Documentation 6 | assignees: '' 7 | 8 | --- 9 | 10 | 14 | 15 | 16 | #### Describe the documentation issue 17 | 18 | 21 | 22 | #### Suggest a potential improvement or addition 23 | 24 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest a new algorithm, enhancement to an existing algorithm, etc. 4 | title: '' 5 | labels: New Feature 6 | assignees: '' 7 | 8 | --- 9 | 10 | 14 | 15 | 16 | #### Describe the workflow you want to enable 17 | 18 | #### Describe your proposed solution 19 | 20 | #### Describe alternatives you've considered, if relevant 21 | 22 | #### Additional context -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/OTHER.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Other 3 | about: Issues that don't fall into any of the other categories 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/USAGE_QUESTION.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Usage question 3 | about: If you have a usage question 4 | title: '' 5 | labels: Question 6 | assignees: '' 7 | 8 | --- 9 | 10 | 14 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | blank_issues_enabled: false -------------------------------------------------------------------------------- /.github/workflows/linter.yml: -------------------------------------------------------------------------------- 1 | name: Lint using isort, black, and flake8 2 | 3 | on: [pull_request] 4 | 5 | jobs: 6 | formatting: 7 | runs-on: ubuntu-latest 8 | steps: 9 | - uses: actions/checkout@v4 10 | - name: Set up Python 11 | uses: actions/setup-python@v5 12 | with: 13 | python-version: '3.x' 14 | - name: Install isort, black, and flake8 15 | run: | 16 | python -m pip install --upgrade pip 17 | pip install isort black flake8 18 | - name: Run isort and black 19 | run: | 20 | isort -p mlxtend --check --diff --line-length 88 --multi-line 3 --py 39 --profile black mlxtend/* 21 | black --check --diff mlxtend/* 22 | # exit-zero treats all errors as warnings. 23 | flake8 . --config=.flake8 --count --exit-zero --statistics -------------------------------------------------------------------------------- /.github/workflows/publish.yaml: -------------------------------------------------------------------------------- 1 | # To create a release, create a tag and push it to GitHub: 2 | #git tag -a "v0.0.1-beta" -m "beta version testing" 3 | #git push --tags 4 | # https://dev.to/iamtekson/publish-package-to-pypi-and-release-new-version-using-github-actions-108k 5 | name: Publish MLxtend to PyPI 6 | 7 | on: 8 | push: 9 | tags: 10 | - "v*" 11 | jobs: 12 | build-n-publish: 13 | name: Build and publish to PyPI 14 | runs-on: ubuntu-latest 15 | environment: 16 | name: pypi 17 | url: https://pypi.org/p/mlxtend 18 | permissions: 19 | id-token: write 20 | 21 | steps: 22 | - name: Checkout source 23 | uses: actions/checkout@v4 24 | 25 | - name: Set up Python 26 | uses: actions/setup-python@v5 27 | with: 28 | python-version: "3.x" 29 | 30 | - name: Build source and wheel distributions 31 | run: | 32 | python -m pip install --upgrade build twine 33 | pip install importlib_metadata==7.2.1 34 | python -m build 35 | twine check --strict dist/* 36 | - name: Publish distribution to PyPI 37 | uses: pypa/gh-action-pypi-publish@release/v1 38 | with: 39 | user: __token__ 40 | password: ${{ secrets.PYPI_API_TOKEN }} -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Test Suites 2 | htmlcov 3 | .coverage* 4 | .pytest_cache/ 5 | 6 | # Documentation builds 7 | docs/site/ 8 | docs/mlxtend.pdf 9 | 10 | # Temporary documentation files 11 | docs/sources/user_guide/*.md 12 | docs/sources/user_guide/*/*.md 13 | #docs/sources/user_guide/*/*/*.png 14 | #docs/sources/user_guide/*.png 15 | #docs/sources/user_guide/*/*.png 16 | #docs/sources/user_guide/*/*/*.png 17 | docs/sources/api_modules/ 18 | docs/sources/api_subpackages/ 19 | 20 | # PyPI 21 | build/ 22 | dist/ 23 | MANIFEST 24 | 25 | # Temporary files 26 | .coverage 27 | .ipynb_checkpoints 28 | 29 | *.sqlite 30 | gmon.out 31 | __pycache__ 32 | *.pyc 33 | *.o 34 | *.so 35 | *.gcno 36 | *.swp 37 | *.egg-info 38 | *.egg 39 | *~ 40 | build 41 | dist 42 | lib/test 43 | doc/_build 44 | *env 45 | *ENV 46 | .DS_store 47 | .idea 48 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/PyCQA/isort 3 | rev: 5.12.0 4 | hooks: 5 | - id: isort 6 | name: Format imports 7 | args: ["--project", "mlxtend", "--line-length", " 88", "--multi-line", "3", "--py", "39", "--profile", "black", "mlxtend/*black"] 8 | 9 | - repo: https://github.com/psf/black 10 | rev: 23.1.0 11 | hooks: 12 | - id: black 13 | name: Format code 14 | 15 | - repo: https://github.com/PyCQA/flake8 16 | rev: 6.0.0 17 | hooks: 18 | - id: flake8 19 | name: Check PEP8 20 | args: ["--config", ".flake8"] -------------------------------------------------------------------------------- /LICENSE-CC-BY.txt: -------------------------------------------------------------------------------- 1 | mlxtend documentation figures (c) by Sebastian Raschka 2 | 3 | mlxtend documentation figures are licensed under a 4 | Creative Commons Attribution 4.0 International License. 5 | 6 | . 7 | 8 | You are free to: 9 | ================ 10 | 11 | Share — copy and redistribute the material in any medium or format 12 | Adapt — remix, transform, and build upon the material 13 | for any purpose, even commercially. 14 | The licensor cannot revoke these 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6 | Any theme customisations should override this file to redefine blocks defined in 7 | the various templates. The custom theme should only need to define a main.html 8 | which `{% extends "base.html" %}` and defines various blocks which will replace 9 | the blocks defined in base.html and its included child templates. 10 | #} -------------------------------------------------------------------------------- /docs/custom_theme/mkdocs_theme.yml: -------------------------------------------------------------------------------- 1 | cinder_theme: true -------------------------------------------------------------------------------- /docs/custom_theme/nav-sub.html: -------------------------------------------------------------------------------- 1 | {% if not nav_item.children %} 2 |
  • 3 | {{ nav_item.title }} 4 |
    • 5 | {% else %} 6 |
  • 7 | {{ nav_item.title }} 8 | 13 |
    • 14 | {% endif %} 15 | -------------------------------------------------------------------------------- /docs/custom_theme/toc.html: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /docs/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rasbt/mlxtend/29e2fc4ac70d95a9e65852434f979fac25aaeeea/docs/favicon.ico -------------------------------------------------------------------------------- /docs/make_userguide.py: -------------------------------------------------------------------------------- 1 | # API generator script 2 | # 3 | # Sebastian Raschka 2014-2024 4 | # mlxtend Machine Learning Library Extensions 5 | # 6 | # Author: Sebastian Raschka 7 | # 8 | # License: BSD 3 clause 9 | 10 | 11 | import os.path 12 | 13 | import yaml 14 | 15 | s = "# User Guide Index" 16 | 17 | yml_cont = open("mkdocs.yml", "r") 18 | usr_gd = yaml.load(yml_cont)["pages"][1]["User Guide"] 19 | for dct in usr_gd[1:]: 20 | subpk = list(dct.keys())[0] 21 | s += "\n\n## `%s`" % subpk 22 | for obj in dct[subpk]: 23 | bsname = os.path.basename(obj).split(".md")[0] 24 | s += "\n- [%s](%s)" % (bsname, obj) 25 | 26 | usr_gd_file = os.path.join("sources", "USER_GUIDE_INDEX.md") 27 | 28 | with open(usr_gd_file, "w") as f: 29 | f.write(s) 30 | -------------------------------------------------------------------------------- /docs/sources/cite.md: -------------------------------------------------------------------------------- 1 | # Citing mlxtend 2 | 3 | If you use mlxtend as part of your workflow in a scientific publication, please consider citing the mlxtend repository with the following DOI: 4 | 5 | [![DOI](https://joss.theoj.org/papers/10.21105/joss.00638/status.svg)](https://doi.org/10.21105/joss.00638) 6 | 7 | - Raschka, Sebastian (2018) [MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack](https://rasbt.github.io/mlxtend/). 8 | J Open Source Softw 3(24). 9 | 10 | ``` 11 | @article{raschkas_2018_mlxtend, 12 | author = {Sebastian Raschka}, 13 | title = {MLxtend: Providing machine learning and data science 14 | utilities and extensions to Python’s 15 | scientific computing stack}, 16 | journal = {The Journal of Open Source Software}, 17 | volume = {3}, 18 | number = {24}, 19 | month = apr, 20 | year = 2018, 21 | publisher = {The Open Journal}, 22 | doi = {10.21105/joss.00638}, 23 | url = {https://joss.theoj.org/papers/10.21105/joss.00638} 24 | } 25 | ``` 26 | -------------------------------------------------------------------------------- /docs/sources/contributors.md: -------------------------------------------------------------------------------- 1 | # Contributors 2 | 3 | For the current list of contributors to mlxtend, please see the GitHub contributor page at [https://github.com/rasbt/mlxtend/graphs/contributors](https://github.com/rasbt/mlxtend/graphs/contributors ). 4 | -------------------------------------------------------------------------------- /docs/sources/discuss.md: -------------------------------------------------------------------------------- 1 | # Discuss 2 | 3 | [![Discuss](https://img.shields.io/badge/discuss-google_group-blue.svg)](https://groups.google.com/forum/#!forum/mlxtend) 4 | 5 | Any questions or comments about mlxtend? Join the mlxtend mailing list on Google Groups! 6 | 7 | -------------------------------------------------------------------------------- /docs/sources/extra.css: -------------------------------------------------------------------------------- 1 | body { 2 | background-image: none; 3 | background-color: #ffffff; 4 | } 5 | 6 | div.col-md-9 img { 7 | border: none; 8 | } 9 | 10 | h1, h2, h3, h4, h5, h6 { 11 | line-height: 120%; 12 | } 13 | 14 | h1, h2 { 15 | margin-top:80px; 16 | margin-bottom:30px; 17 | } 18 | 19 | h3, h4 { 20 | margin-top:40px; 21 | margin-bottom:20px; 22 | } 23 | 24 | h5, h6 { 25 | margin-top:30px; 26 | margin-bottom:10px; 27 | } 28 | 29 | img { 30 | max-width: 90%; 31 | height: auto; 32 | } 33 | -------------------------------------------------------------------------------- /docs/sources/img/contributing/fork.png: -------------------------------------------------------------------------------- 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12 | - setuptools>=59.4.0 13 | - pip: 14 | - markdown>=3.3.6 15 | -------------------------------------------------------------------------------- /mlxtend/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | __version__ = "0.23.4" 8 | -------------------------------------------------------------------------------- /mlxtend/_base/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from ._base_model import _BaseModel 8 | from ._classifier import _Classifier 9 | from ._cluster import _Cluster 10 | from ._iterative_model import _IterativeModel 11 | from ._multiclass import _MultiClass 12 | from ._multilayer import _MultiLayer 13 | from ._regressor import _Regressor 14 | 15 | __all__ = [ 16 | "_BaseModel", 17 | "_Cluster", 18 | "_Classifier", 19 | "_Regressor", 20 | "_IterativeModel", 21 | "_MultiClass", 22 | "_MultiLayer", 23 | ] 24 | -------------------------------------------------------------------------------- /mlxtend/_base/_multiclass.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # Base MultiClass (MultiClass Parent Class) 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | import numpy as np 10 | 11 | 12 | class _MultiClass(object): 13 | def __init__(self): 14 | pass 15 | 16 | def _one_hot(self, y, n_labels, dtype): 17 | """Returns a matrix where each sample in y is represented 18 | as a row, and each column represents the class label in 19 | the one-hot encoding scheme. 20 | 21 | Example: 22 | 23 | y = np.array([0, 1, 2, 3, 4, 2]) 24 | mc = _BaseMultiClass() 25 | mc._one_hot(y=y, n_labels=5, dtype='float') 26 | 27 | np.array([[1., 0., 0., 0., 0.], 28 | [0., 1., 0., 0., 0.], 29 | [0., 0., 1., 0., 0.], 30 | [0., 0., 0., 1., 0.], 31 | [0., 0., 0., 0., 1.], 32 | [0., 0., 1., 0., 0.]]) 33 | 34 | """ 35 | mat = np.zeros((len(y), n_labels)) 36 | for i, val in enumerate(y): 37 | mat[i, val] = 1 38 | return mat.astype(dtype) 39 | -------------------------------------------------------------------------------- /mlxtend/_base/tests/test_base_model.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import numpy as np 8 | 9 | from mlxtend._base import _BaseModel 10 | from mlxtend.utils import assert_raises 11 | 12 | 13 | class BlankModel(_BaseModel): 14 | def __init__(self, print_progress=0, random_seed=1): 15 | self.print_progress = print_progress 16 | self.random_seed = random_seed 17 | 18 | 19 | def test_init(): 20 | est = BlankModel(print_progress=0, random_seed=1) 21 | assert hasattr(est, "print_progress") 22 | assert hasattr(est, "random_seed") 23 | 24 | 25 | def test_check_array_1(): 26 | X = np.array([1, 2, 3]) 27 | est = BlankModel() 28 | assert_raises( 29 | ValueError, 30 | "X must be a 2D array. Try X[:, numpy.newaxis]", 31 | est._check_arrays, 32 | X, 33 | ) 34 | 35 | 36 | def test_check_array_2(): 37 | X = list([[1], [2], [3]]) 38 | est = BlankModel(print_progress=0, random_seed=1) 39 | 40 | assert_raises(ValueError, "X must be a numpy array", est._check_arrays, X) 41 | 42 | 43 | def test_check_array_3(): 44 | X = np.array([[1], [2], [3]]) 45 | est = BlankModel(print_progress=0, random_seed=1) 46 | est._check_arrays(X) 47 | -------------------------------------------------------------------------------- /mlxtend/classifier/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .adaline import Adaline 8 | from .ensemble_vote import EnsembleVoteClassifier 9 | from .logistic_regression import LogisticRegression 10 | from .multilayerperceptron import MultiLayerPerceptron 11 | from .oner import OneRClassifier 12 | from .perceptron import Perceptron 13 | from .softmax_regression import SoftmaxRegression 14 | from .stacking_classification import StackingClassifier 15 | from .stacking_cv_classification import StackingCVClassifier 16 | 17 | __all__ = [ 18 | "Adaline", 19 | "EnsembleVoteClassifier", 20 | "LogisticRegression", 21 | "MultiLayerPerceptron", 22 | "OneRClassifier", 23 | "Perceptron", 24 | "SoftmaxRegression", 25 | "StackingClassifier", 26 | "StackingCVClassifier", 27 | ] 28 | -------------------------------------------------------------------------------- /mlxtend/cluster/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .kmeans import Kmeans 8 | 9 | __all__ = ["Kmeans"] 10 | -------------------------------------------------------------------------------- /mlxtend/data/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .autompg import autompg_data 8 | from .boston_housing import boston_housing_data 9 | from .iris import iris_data 10 | from .local_mnist import loadlocal_mnist 11 | from .mnist import mnist_data 12 | from .multiplexer import make_multiplexer_dataset 13 | from .three_blobs import three_blobs_data 14 | from .wine import wine_data 15 | 16 | __all__ = [ 17 | "iris_data", 18 | "wine_data", 19 | "autompg_data", 20 | "loadlocal_mnist", 21 | "mnist_data", 22 | "boston_housing_data", 23 | "three_blobs_data", 24 | "make_multiplexer_dataset", 25 | ] 26 | -------------------------------------------------------------------------------- /mlxtend/data/data/autompg.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rasbt/mlxtend/29e2fc4ac70d95a9e65852434f979fac25aaeeea/mlxtend/data/data/autompg.csv.gz -------------------------------------------------------------------------------- /mlxtend/data/data/iris.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rasbt/mlxtend/29e2fc4ac70d95a9e65852434f979fac25aaeeea/mlxtend/data/data/iris.csv.gz -------------------------------------------------------------------------------- /mlxtend/data/data/mnist_5k.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rasbt/mlxtend/29e2fc4ac70d95a9e65852434f979fac25aaeeea/mlxtend/data/data/mnist_5k.csv.gz -------------------------------------------------------------------------------- /mlxtend/data/data/three_blobs.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rasbt/mlxtend/29e2fc4ac70d95a9e65852434f979fac25aaeeea/mlxtend/data/data/three_blobs.csv.gz -------------------------------------------------------------------------------- /mlxtend/data/local_mnist.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # A function for fetching the open-source MNIST dataset. 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | import struct 10 | 11 | import numpy as np 12 | 13 | 14 | def loadlocal_mnist(images_path, labels_path): 15 | """Read MNIST from ubyte files. 16 | 17 | Parameters 18 | ---------- 19 | images_path : str 20 | path to the test or train MNIST ubyte file 21 | labels_path : str 22 | path to the test or train MNIST class labels file 23 | 24 | Returns 25 | -------- 26 | images : [n_samples, n_pixels] numpy.array 27 | Pixel values of the images. 28 | labels : [n_samples] numpy array 29 | Target class labels 30 | 31 | Examples 32 | ----------- 33 | For usage examples, please see 34 | https://rasbt.github.io/mlxtend/user_guide/data/loadlocal_mnist/ 35 | 36 | """ 37 | with open(labels_path, "rb") as lbpath: 38 | magic, n = struct.unpack(">II", lbpath.read(8)) 39 | labels = np.fromfile(lbpath, dtype=np.uint8) 40 | with open(images_path, "rb") as imgpath: 41 | magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16)) 42 | images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784) 43 | 44 | return images, labels 45 | -------------------------------------------------------------------------------- /mlxtend/data/mnist.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # A function for loading the open-source MNIST. 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | import os 10 | 11 | import numpy as np 12 | 13 | this_dir, this_filename = os.path.split(__file__) 14 | DATA_PATH = os.path.join(this_dir, "data", "mnist_5k.csv.gz") 15 | 16 | 17 | def mnist_data(): 18 | """5000 samples from the MNIST handwritten digits dataset. 19 | 20 | Data Source : https://yann.lecun.com/exdb/mnist/ 21 | 22 | Returns 23 | -------- 24 | X, y : [n_samples, n_features], [n_class_labels] 25 | X is the feature matrix with 5000 image samples as rows, 26 | each row consists of 28x28 pixels that were unrolled into 27 | 784 pixel feature vectors. 28 | y contains the 10 unique class labels 0-9. 29 | 30 | Examples 31 | ----------- 32 | For usage examples, please see 33 | https://rasbt.github.io/mlxtend/user_guide/data/mnist_data/ 34 | 35 | """ 36 | tmp = np.genfromtxt(fname=DATA_PATH, delimiter=",") 37 | X, y = tmp[:, :-1], tmp[:, -1] 38 | y = y.astype(int) 39 | return X, y 40 | -------------------------------------------------------------------------------- /mlxtend/data/three_blobs.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # A function for loading a sample dataset for clustering evaluations 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | import os 10 | 11 | import numpy as np 12 | 13 | this_dir, this_filename = os.path.split(__file__) 14 | DATA_PATH = os.path.join(this_dir, "data", "three_blobs.csv.gz") 15 | 16 | 17 | def three_blobs_data(): 18 | """A random dataset of 3 2D blobs for clustering. 19 | 20 | Number of samples : 150 21 | Suggested labels : {0, 1, 2}, distribution: [50, 50, 50] 22 | 23 | Returns 24 | -------- 25 | X, y : [n_samples, n_features], [n_cluster_labels] 26 | X is the feature matrix with 159 samples as rows 27 | and 2 feature columns. 28 | y is a 1-dimensional array of the 3 suggested cluster labels 0, 1, 2 29 | 30 | Examples 31 | ----------- 32 | For usage examples, please see 33 | https://rasbt.github.io/mlxtend/user_guide/data/three_blobs_data 34 | 35 | """ 36 | tmp = np.genfromtxt(fname=DATA_PATH, delimiter=",") 37 | X, y = tmp[:, :-1], tmp[:, -1] 38 | y = y.astype(int) 39 | return X, y 40 | -------------------------------------------------------------------------------- /mlxtend/evaluate/tests/test_bootstrap.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import numpy as np 8 | 9 | from mlxtend.evaluate import bootstrap 10 | from mlxtend.utils import assert_raises 11 | 12 | 13 | def test_defaults(): 14 | rng = np.random.RandomState(123) 15 | x = rng.normal(loc=5.0, size=100) 16 | original, std_err, ci_bounds = bootstrap(x, func=np.mean, seed=123) 17 | assert round(original, 2) == 5.03 18 | assert round(std_err, 2) == 0.11 19 | assert round(ci_bounds[0], 2) == 4.80 20 | assert round(ci_bounds[1], 2) == 5.26 21 | 22 | 23 | def test_fail_ci(): 24 | msg = "ci must be in range (0, 1)" 25 | assert_raises( 26 | AttributeError, msg, bootstrap, np.array([1, 2, 3]), np.mean, 1000, 95 27 | ) 28 | 29 | 30 | def test_fail_func(): 31 | def f(x): 32 | return np.array([0, 1]) 33 | 34 | msg = "ci must be in range (0, 1)" 35 | assert_raises(AttributeError, msg, bootstrap, np.array([1, 2, 3]), f, 1000, 95) 36 | -------------------------------------------------------------------------------- /mlxtend/evaluate/tests/test_bootstrap_outofbag.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import numpy as np 8 | 9 | from mlxtend.evaluate import BootstrapOutOfBag 10 | from mlxtend.utils import assert_raises 11 | 12 | 13 | def test_defaults(): 14 | oob = BootstrapOutOfBag() 15 | results = list(oob.split(np.array([1, 2, 3, 4, 5]))) 16 | assert len(results) == 200 17 | 18 | 19 | def test_splits(): 20 | oob = BootstrapOutOfBag(n_splits=3, random_seed=123) 21 | results = list(oob.split(np.array([1, 2, 3, 4, 5]))) 22 | assert len(results) == 3 23 | assert np.array_equal(results[0][0], np.array([2, 4, 2, 1, 3])) 24 | assert np.array_equal(results[0][1], np.array([0])) 25 | assert np.array_equal(results[-1][0], np.array([1, 1, 0, 0, 1])) 26 | assert np.array_equal(results[-1][1], np.array([2, 3, 4])) 27 | 28 | 29 | def test_invalid_splits(): 30 | assert_raises( 31 | ValueError, "Number of splits must be greater than 1.", BootstrapOutOfBag, 0 32 | ) 33 | 34 | 35 | def test_get_n_splits(): 36 | oob = BootstrapOutOfBag(n_splits=3, random_seed=123) 37 | assert oob.n_splits == 3 38 | -------------------------------------------------------------------------------- /mlxtend/evaluate/tests/test_confusion_matrix.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import numpy as np 8 | from numpy.testing import assert_array_equal 9 | 10 | from mlxtend.evaluate import confusion_matrix 11 | 12 | 13 | def test_multiclass(): 14 | y_targ = [1, 1, 1, 0, 0, 2, 0, 3] 15 | y_pred = [1, 0, 1, 0, 0, 2, 1, 3] 16 | x = np.array([[2, 1, 0, 0], [1, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) 17 | y = confusion_matrix(y_targ, y_pred, binary=False, positive_label=1) 18 | assert_array_equal(x, y) 19 | 20 | 21 | def test_binary(): 22 | y_targ = [1, 1, 1, 0, 0, 2, 0, 3] 23 | y_pred = [1, 0, 1, 0, 0, 2, 1, 3] 24 | x = np.array([[4, 1], [1, 2]]) 25 | y = confusion_matrix(y_targ, y_pred, binary=True, positive_label=1) 26 | assert_array_equal(x, y) 27 | -------------------------------------------------------------------------------- /mlxtend/externals/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | -------------------------------------------------------------------------------- /mlxtend/externals/name_estimators.py: -------------------------------------------------------------------------------- 1 | from collections import defaultdict 2 | 3 | # The :mod:`sklearn.pipeline` module implements utilities to build a composite 4 | # estimator, as a chain of transforms and estimators. 5 | # Author: Edouard Duchesnay 6 | # Gael Varoquaux 7 | # Virgile Fritsch 8 | # Alexandre Gramfort 9 | # Lars Buitinck 10 | # Licence: BSD 11 | 12 | 13 | def _name_estimators(estimators): 14 | """Generate names for estimators.""" 15 | 16 | names = [type(estimator).__name__.lower() for estimator in estimators] 17 | namecount = defaultdict(int) 18 | for _, name in zip(estimators, names): 19 | namecount[name] += 1 20 | 21 | for k, v in list(namecount.items()): 22 | if v == 1: 23 | del namecount[k] 24 | 25 | for i in reversed(range(len(estimators))): 26 | name = names[i] 27 | if name in namecount: 28 | names[i] += "-%d" % namecount[name] 29 | namecount[name] -= 1 30 | 31 | return list(zip(names, estimators)) 32 | -------------------------------------------------------------------------------- /mlxtend/externals/pyprind/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Sebastian Raschka 2014-2017 3 | Python Progress Indicator Utility 4 | 5 | Author: Sebastian Raschka 6 | License: BSD 3 clause 7 | 8 | Contributors: https://github.com/rasbt/pyprind/graphs/contributors 9 | Code Repository: https://github.com/rasbt/pyprind 10 | PyPI: https://pypi.python.org/pypi/PyPrind 11 | """ 12 | 13 | from .generator_factory import prog_bar, prog_percent 14 | from .progbar import ProgBar 15 | from .progpercent import ProgPercent 16 | 17 | __version__ = "2.11.2" 18 | -------------------------------------------------------------------------------- /mlxtend/externals/pyprind/generator_factory.py: -------------------------------------------------------------------------------- 1 | # PyPrind 2 | # Author: Sebastian Raschka 3 | # Contributors: https://github.com/rasbt/pyprind/graphs/contributors 4 | # License: BSD 3 clause 5 | # Code Repository: https://github.com/rasbt/pyprind 6 | # PyPI: https://pypi.python.org/pypi/PyPrind 7 | 8 | from .progbar import ProgBar 9 | from .progpercent import ProgPercent 10 | 11 | 12 | def generator_factory(mother_class): 13 | def generator_progress(iteritem, iterations=None, *args, **kw): 14 | if iterations is None: 15 | iterations = len(iteritem) 16 | assert iterations 17 | mbar = mother_class(iterations, *args, **kw) 18 | for item in iteritem: 19 | yield item 20 | mbar.update() 21 | 22 | return generator_progress 23 | 24 | 25 | prog_percent = generator_factory(ProgPercent) 26 | prog_bar = generator_factory(ProgBar) 27 | -------------------------------------------------------------------------------- /mlxtend/feature_extraction/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .linear_discriminant_analysis import LinearDiscriminantAnalysis 8 | from .principal_component_analysis import PrincipalComponentAnalysis 9 | from .rbf_kernel_pca import RBFKernelPCA 10 | 11 | __all__ = ["PrincipalComponentAnalysis", "LinearDiscriminantAnalysis", "RBFKernelPCA"] 12 | -------------------------------------------------------------------------------- /mlxtend/feature_extraction/base.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # Base Classifier (Classifier Parent Class) 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | import numpy as np 10 | 11 | 12 | class _BaseFeatureExtractor(object): 13 | """Parent Class for Feature Extractors""" 14 | 15 | def __init__(self): 16 | pass 17 | 18 | def _check_arrays(self, X, y=None): 19 | if isinstance(X, list): 20 | raise ValueError("X must be a numpy array") 21 | if not len(X.shape) == 2: 22 | raise ValueError("X must be a 2D array. Try X[:,numpy.newaxis]") 23 | try: 24 | if y is None: 25 | return 26 | except AttributeError: 27 | if not len(y.shape) == 1: 28 | raise ValueError("y must be a 1D array.") 29 | 30 | if not isinstance(y, np.ndarray): 31 | raise ValueError("y must be a numpy array") 32 | 33 | if y.shape[0] != X.shape[0]: 34 | raise ValueError("X and y must contain the same number of samples") 35 | -------------------------------------------------------------------------------- /mlxtend/feature_extraction/tests/test_base.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import numpy as np 8 | import pytest 9 | 10 | from mlxtend.feature_extraction.base import _BaseFeatureExtractor 11 | 12 | X, y = np.array([[1, 2], [4, 5], [3, 9]]), np.array([1, 2, 3]) 13 | base = _BaseFeatureExtractor() 14 | 15 | 16 | def test_X_array_pass(): 17 | base._check_arrays(X=X) 18 | 19 | 20 | def test_X_y_array_pass(): 21 | base._check_arrays(X=X, y=y) 22 | 23 | 24 | def test_1D_X(): 25 | with pytest.raises(ValueError): 26 | base._check_arrays(X=X[1]) 27 | 28 | 29 | def test_X_int_y(): 30 | with pytest.raises(ValueError): 31 | base._check_arrays(X=X, y=y[1]) 32 | 33 | 34 | def test_X_short_y(): 35 | print(y[1:].shape) 36 | print(X.shape) 37 | with pytest.raises(ValueError): 38 | base._check_arrays(X=X, y=y[1:]) 39 | -------------------------------------------------------------------------------- /mlxtend/feature_selection/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .column_selector import ColumnSelector 8 | from .exhaustive_feature_selector import ExhaustiveFeatureSelector 9 | from .sequential_feature_selector import SequentialFeatureSelector 10 | 11 | __all__ = ["ColumnSelector", "SequentialFeatureSelector", "ExhaustiveFeatureSelector"] 12 | -------------------------------------------------------------------------------- /mlxtend/file_io/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | 8 | from .find_filegroups import find_filegroups 9 | from .find_files import find_files 10 | 11 | __all__ = ["find_files", "find_filegroups"] 12 | -------------------------------------------------------------------------------- /mlxtend/frequent_patterns/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .apriori import apriori 8 | from .association_rules import association_rules 9 | from .fpgrowth import fpgrowth 10 | from .fpmax import fpmax 11 | from .hmine import hmine 12 | 13 | __all__ = ["apriori", "association_rules", "fpgrowth", "fpmax", "hmine"] 14 | -------------------------------------------------------------------------------- /mlxtend/math/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .counting import factorial, num_combinations, num_permutations 8 | from .linalg import vectorspace_dimensionality, vectorspace_orthonormalization 9 | 10 | __all__ = [ 11 | "num_combinations", 12 | "num_permutations", 13 | "factorial", 14 | "vectorspace_orthonormalization", 15 | "vectorspace_dimensionality", 16 | ] 17 | -------------------------------------------------------------------------------- /mlxtend/math/tests/test_counting.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | 8 | from mlxtend.math import factorial, num_combinations, num_permutations 9 | 10 | 11 | def test_factorial(): 12 | assert factorial(1) == 1 13 | assert factorial(3) == 6 14 | 15 | 16 | def test_num_combinations(): 17 | assert num_combinations(n=20, k=8, with_replacement=False) == 125970 18 | assert num_combinations(n=20, k=8, with_replacement=True) == 2220075 19 | print(num_combinations(n=300, k=10)) 20 | assert num_combinations(n=300, k=10, with_replacement=False) == 1398320233241701770 21 | 22 | 23 | def test_num_permutations(): 24 | assert num_permutations(n=20, k=8, with_replacement=False) == 5079110400 25 | assert num_permutations(n=20, k=8, with_replacement=True) == 25600000000 26 | -------------------------------------------------------------------------------- /mlxtend/plotting/tests/data/data.csv: -------------------------------------------------------------------------------- 1 | ,label,x,y 2 | 0,class1,10.0,8.04 3 | 1,class1,8.0,6.95 4 | 2,class1,13.2,7.58 5 | 3,class1,9.0,8.81 6 | 4,class1,11.0,8.33 7 | 5,class1,14.0,3.0 8 | 6,class1,6.0,7.7 9 | 7,class1,4.0,4.26 10 | 8,class1,12.4,10.84 11 | 9,class1,7.0,4.82 12 | 10,class1,5.0,5.7 13 | 11,class2,10.0,9.3 14 | 12,class2,1.0,8.5 15 | 13,class2,14.3,7.8 16 | 14,class2,9.0,8.77 17 | 15,class2,11.0,9.26 18 | 16,class2,14.0,8.1 19 | 17,class2,6.0,6.13 20 | 18,class2,4.0,3.1 21 | 19,class2,12.0,9.13 22 | 20,class2,7.0,7.26 23 | 21,class2,5.0,4.74 24 | 22,class3,10.0,7.46 25 | 23,class3,8.0,6.77 26 | 24,class3,13.0,12.74 27 | 25,class3,9.0,7.11 28 | 26,class3,11.0,7.81 29 | 27,class3,14.0,8.84 30 | 28,class3,6.0,6.08 31 | 29,class3,4.0,5.39 32 | 30,class3,12.0,9.08 33 | 31,class3,7.0,6.42 34 | 32,class3,2.0,5.73 35 | 33,class4,8.0,6.58 36 | 34,class4,4.3,5.76 37 | 35,class4,4.7,7.71 38 | 36,class4,7.8,8.84 39 | 37,class4,9.5,8.47 40 | 38,class4,8.8,7.04 41 | 39,class4,8.6,5.25 42 | 40,class4,19.0,12.5 43 | 41,class4,8.0,5.56 44 | 42,class4,8.0,7.91 45 | 43,class4,8.0,6.89 46 | -------------------------------------------------------------------------------- /mlxtend/plotting/tests/test_checkerboard.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import matplotlib.pyplot as plt 8 | import numpy as np 9 | 10 | from mlxtend.plotting import checkerboard_plot 11 | 12 | plt.switch_backend("agg") 13 | 14 | 15 | def test_runs(): 16 | ary = np.random.random((6, 4)) 17 | checkerboard_plot( 18 | ary, 19 | col_labels=["abc", "def", "ghi", "jkl"], 20 | row_labels=["sample %d" % i for i in range(1, 6)], 21 | cell_colors=["skyblue", "whitesmoke"], 22 | font_colors=["black", "black"], 23 | figsize=(5, 5), 24 | ) 25 | -------------------------------------------------------------------------------- /mlxtend/plotting/tests/test_ecdf.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from mlxtend.data import iris_data 8 | from mlxtend.plotting import ecdf 9 | 10 | 11 | def test_threshold(): 12 | X, y = iris_data() 13 | ax, threshold, count = ecdf(x=X[:, 0], x_label="sepal length (cm)", percentile=0.8) 14 | assert threshold == 6.5 15 | assert count == 120 16 | -------------------------------------------------------------------------------- /mlxtend/plotting/tests/test_heatmap.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import matplotlib.pyplot as plt 8 | import numpy as np 9 | import pytest 10 | 11 | from mlxtend.plotting import heatmap 12 | 13 | plt.switch_backend("agg") 14 | 15 | 16 | def test_defaults(): 17 | heatmap(np.random.random((10, 5))) 18 | 19 | 20 | def test_wrong_column_name_number(): 21 | with pytest.raises(AssertionError) as excinfo: 22 | heatmap(np.random.random((10, 5)), column_names=["a", "b", "c"]) 23 | assert excinfo.value.message == ( 24 | "len(column_names) (got 3)" 25 | " should be equal to number of" 26 | " rows in the input " 27 | " array (expect 5)." 28 | ) 29 | 30 | 31 | def test_wrong_row_name_number(): 32 | with pytest.raises(AssertionError) as excinfo: 33 | heatmap(np.random.random((10, 5)), row_names=["a", "b", "c"]) 34 | assert excinfo.value.message == ( 35 | "len(column_names) (got 3)" 36 | " should be equal to number of" 37 | " rows in the input " 38 | " array (expect 10)." 39 | ) 40 | -------------------------------------------------------------------------------- /mlxtend/plotting/tests/test_scatter_hist.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | from mlxtend.data import iris_data 4 | from mlxtend.plotting import scatter_hist 5 | 6 | X, y = iris_data() 7 | df = pd.DataFrame(X) 8 | df.columns = [ 9 | "sepal length [cm]", 10 | "sepal width [cm]", 11 | "petal length [cm]", 12 | "petal width [cm]", 13 | ] 14 | 15 | 16 | def test_pass_data_as_dataframe(): 17 | scatter_hist(df["sepal length [cm]"], df["sepal width [cm]"]) 18 | 19 | 20 | def test_pass_data_as_numpy_array(): 21 | scatter_hist(X[:, 0], X[:, 1]) 22 | -------------------------------------------------------------------------------- /mlxtend/preprocessing/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .copy_transformer import CopyTransformer 8 | from .dense_transformer import DenseTransformer 9 | from .mean_centering import MeanCenterer 10 | from .onehot import one_hot 11 | from .scaling import minmax_scaling, standardize 12 | from .shuffle import shuffle_arrays_unison 13 | from .transactionencoder import TransactionEncoder 14 | 15 | __all__ = [ 16 | "MeanCenterer", 17 | "shuffle_arrays_unison", 18 | "CopyTransformer", 19 | "minmax_scaling", 20 | "standardize", 21 | "DenseTransformer", 22 | "one_hot", 23 | "TransactionEncoder", 24 | ] 25 | -------------------------------------------------------------------------------- /mlxtend/preprocessing/tests/test_mean_centering.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | 8 | import numpy as np 9 | import pytest 10 | 11 | from mlxtend.preprocessing import MeanCenterer 12 | 13 | 14 | def test_fitting_error(): 15 | X1 = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) 16 | mc = MeanCenterer() 17 | with pytest.raises(AttributeError): 18 | mc.transform(X1) 19 | 20 | 21 | def test_array_mean_centering(): 22 | X1 = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) 23 | X1_out = np.array([[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]) 24 | mc = MeanCenterer() 25 | assert mc.fit_transform(X1).all() == X1_out.all() 26 | 27 | 28 | def test_list_mean_centering(): 29 | X2 = [1.0, 2.0, 3.0] 30 | X2_out = np.array([-1.0, 0.0, 1.0]) 31 | mc = MeanCenterer() 32 | assert mc.fit_transform(X2).all().all() == X2_out.all() 33 | -------------------------------------------------------------------------------- /mlxtend/preprocessing/tests/test_shuffle_shuffle_arrays_unison.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | 8 | import numpy as np 9 | 10 | from mlxtend.preprocessing import shuffle_arrays_unison 11 | 12 | 13 | def test_shuffle_arrays_unison(): 14 | X1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) 15 | y1 = np.array([1, 2, 3]) 16 | 17 | X2, y2 = shuffle_arrays_unison(arrays=[X1, y1], random_seed=3) 18 | 19 | assert X2.all() == np.array([[4, 5, 6], [1, 2, 3], [7, 8, 9]]).all() 20 | assert y2.all() == np.array([2, 1, 3]).all() 21 | -------------------------------------------------------------------------------- /mlxtend/regressor/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .linear_regression import LinearRegression 8 | from .stacking_cv_regression import StackingCVRegressor 9 | from .stacking_regression import StackingRegressor 10 | 11 | __all__ = ["LinearRegression", "StackingRegressor", "StackingCVRegressor"] 12 | -------------------------------------------------------------------------------- /mlxtend/text/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | import sys 8 | 9 | if sys.version_info >= (3, 0): 10 | from .names import generalize_names 11 | from .names import generalize_names_duplcheck 12 | 13 | from .tokenizer import tokenizer_emoticons, tokenizer_words_and_emoticons 14 | 15 | __all__ = [ 16 | "generalize_names", 17 | "generalize_names_duplcheck", 18 | "tokenizer_words_and_emoticons", 19 | "tokenizer_emoticons", 20 | ] 21 | -------------------------------------------------------------------------------- /mlxtend/text/tests/test_generalize_names_duplcheck.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | if sys.version_info < (3, 0): 4 | from nose.plugins.skip import SkipTest 5 | 6 | raise SkipTest 7 | 8 | from io import StringIO 9 | 10 | import pandas as pd 11 | from data_names import csv 12 | 13 | from mlxtend.text import generalize_names, generalize_names_duplcheck 14 | 15 | 16 | def test_generalize_names_duplcheck(): 17 | df = pd.read_csv(StringIO(csv)) 18 | 19 | # duplicates before 20 | dupl = any(df["Name"].apply(generalize_names).duplicated()) 21 | assert dupl is True 22 | 23 | # no duplicates 24 | df_new = generalize_names_duplcheck(df=df, col_name="Name") 25 | no_dupl = any(df_new["Name"].duplicated()) 26 | assert no_dupl is False 27 | -------------------------------------------------------------------------------- /mlxtend/text/tests/test_tokenizer.py: -------------------------------------------------------------------------------- 1 | from mlxtend.text import tokenizer_emoticons, tokenizer_words_and_emoticons 2 | 3 | 4 | def test_tokenizer_words_and_emoticons_1(): 5 | assert tokenizer_words_and_emoticons("This :) is :( a test :-)!") == [ 6 | "this", 7 | "is", 8 | "a", 9 | "test", 10 | ":)", 11 | ":(", 12 | ":-)", 13 | ] 14 | 15 | 16 | def test_tokenizer_words_and_emoticons_2(): 17 | assert tokenizer_emoticons("This :) is :( a test :-)!") == [":)", ":(", ":-)"] 18 | -------------------------------------------------------------------------------- /mlxtend/text/tokenizer.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # Functions for tokenizing text data. 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | import re 10 | 11 | 12 | def tokenizer_words_and_emoticons(text): 13 | """Convert text to lowercase words and emoticons. 14 | 15 | Examples 16 | ----------- 17 | >>> tokenizer_words_and_emoticons('This :) is :( a test :-)!') 18 | ['this', 'is', 'a', 'test', ':)', ':(', ':-)'] 19 | 20 | For more usage examples, please see 21 | https://rasbt.github.io/mlxtend/user_guide/text/tokenizer_words_and_emoticons/ 22 | 23 | """ 24 | text = re.sub(r"<[^>]*>", "", text) 25 | emoticons = re.findall(r"(?::|;|=)(?:-)?(?:\)|\(|D|P)", text) 26 | text = re.sub(r"[\W]+", " ", text.lower()) + " ".join(emoticons) 27 | return text.split() 28 | 29 | 30 | def tokenizer_emoticons(text): 31 | """Return emoticons from text 32 | 33 | Examples 34 | ----------- 35 | >>> tokenizer_emoticons('This :) is :( a test :-)!') 36 | [':)', ':(', ':-)'] 37 | 38 | For usage examples, please see 39 | https://rasbt.github.io/mlxtend/user_guide/text/tokenizer_emoticons/ 40 | 41 | """ 42 | text = re.sub(r"<[^>]*>", "", text) 43 | emoticons = re.findall(r"(?::|;|=)(?:-)?(?:\)|\(|D|P)", text) 44 | return emoticons 45 | -------------------------------------------------------------------------------- /mlxtend/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from .checking import check_Xy, format_kwarg_dictionaries 8 | from .counter import Counter 9 | from .testing import assert_raises 10 | 11 | __all__ = ["Counter", "assert_raises", "check_Xy", "format_kwarg_dictionaries"] 12 | -------------------------------------------------------------------------------- /mlxtend/utils/testing.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # 4 | # A counter class for printing the progress of an iterator. 5 | # Author: Sebastian Raschka 6 | # 7 | # License: BSD 3 clause 8 | 9 | 10 | def assert_raises(exception_type, message, func, *args, **kwargs): 11 | """Check that an exception is raised with a specific message 12 | 13 | Parameters 14 | ---------- 15 | exception_type : exception 16 | The exception that should be raised 17 | message : str (default: None) 18 | The error message that should be raised. Ignored if False or None. 19 | func : callable 20 | The function that raises the exception 21 | *args : positional arguments to `func`. 22 | **kwargs : keyword arguments to `func` 23 | 24 | """ 25 | try: 26 | func(*args, **kwargs) 27 | except exception_type as e: 28 | error_message = str(e) 29 | if message and message not in error_message: 30 | raise AssertionError( 31 | "Error message differs from the expected" 32 | " string: %r. Got error message: %r" % (message, error_message) 33 | ) 34 | else: 35 | raise AssertionError("%s not raised." % exception_type.__name__) 36 | -------------------------------------------------------------------------------- /mlxtend/utils/tests/test_counter.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from mlxtend.utils import Counter 8 | 9 | 10 | def test_counter(): 11 | cnt = Counter() 12 | for i in range(20): 13 | cnt.update() 14 | assert cnt.curr_iter == 20 15 | -------------------------------------------------------------------------------- /mlxtend/utils/tests/test_testing.py: -------------------------------------------------------------------------------- 1 | # Sebastian Raschka 2014-2024 2 | # mlxtend Machine Learning Library Extensions 3 | # Author: Sebastian Raschka 4 | # 5 | # License: BSD 3 clause 6 | 7 | from mlxtend.utils import assert_raises 8 | 9 | 10 | def test_without_message(): 11 | def my_func(): 12 | raise AttributeError 13 | 14 | assert_raises(AttributeError, func=my_func, message=None) 15 | 16 | 17 | def test_with_message(): 18 | def my_func(): 19 | raise AttributeError("Failed") 20 | 21 | assert_raises(AttributeError, func=my_func, message="Failed") 22 | -------------------------------------------------------------------------------- /oryx-build-commands.txt: -------------------------------------------------------------------------------- 1 | PlatformWithVersion=Python 2 | BuildCommands=conda env create --file environment.yml --prefix ./venv --quiet 3 | -------------------------------------------------------------------------------- /requirements-test.txt: -------------------------------------------------------------------------------- 1 | isort 2 | joblib 3 | dlib 4 | scikit-image 5 | imageio 6 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | scipy>=1.2.1 2 | numpy>=1.16.2 3 | pandas>=0.24.2 4 | scikit-learn>=1.3.1 5 | matplotlib>=3.0.0 6 | joblib>=0.13.2 --------------------------------------------------------------------------------