├── .gitignore ├── FAQ.md ├── LICENSE ├── Makefile ├── README.md ├── air-gapped_installations ├── README.md └── load_custom_recipe.py ├── data ├── GroupAgg.py ├── airlines.py ├── airlines_joined_data_flights_in_out.py ├── airlines_joined_data_flights_in_out_regression.py ├── airlines_multiple.py ├── any_env.py ├── any_env2.py ├── any_env3.py ├── any_env4.py ├── audio_to_image.py ├── bigquery_sharded_data_pull.py ├── catchallenge.py ├── covidtracking_daily_by_states.py ├── create_transactional_data_or_convert_to_iid.py ├── creditcard.py ├── data_template.py ├── data_test.py ├── data_test_multiple.py ├── databases │ ├── Readme.md │ └── create_dataset_from_mongodb_collection.py ├── elasticsearch_dataset.py ├── feature_selection.py ├── feature_selection_backward.py ├── feature_selection_bidirectional.py ├── feature_selection_exhaustive.py ├── feature_selection_forward.py ├── generate_random_int_columns.py ├── ieee_data_puddle.py ├── image_cropper.py ├── imdb_datasets.py ├── kaggle_bosch.py ├── kaggle_ieee_fraud.py ├── kaggle_m5.py ├── keywords_data.py ├── load_json_from_zip.py ├── load_sas7bdat.py ├── load_sas7bdat_from_zip.py ├── load_xml_from_zip.py ├── marketbasketanalysis.py ├── mnist.py ├── mozilla_deepspeech_wav2txt.py ├── nlp │ ├── README.md │ ├── sentiment_score.py │ ├── sentiment_score_vader.py │ ├── text_summarization.py │ ├── tokenize_chinese.py │ ├── tokenize_japanese.py │ ├── topic_modeling.py │ └── twitter_preprocessing_recipe.py ├── nytimes_covid19_cases_deaths_by_counties.py ├── nytimes_covid19_cases_deaths_by_states.py ├── nytimes_covid19_cases_deaths_us.py ├── owid_covid19_cases_deaths_by_countries.py ├── rowwise_aggregates_by_column_groups.py ├── seattle_rain_modify.py ├── seattle_rain_upload.py ├── ts_fill_n_cluster.py ├── two_sigma_rental.py ├── unsupervised │ ├── DBScanClustering.py │ ├── IsolationForestRecipe.py │ ├── KMeansClustering.py │ ├── Kmeans_with_adjusted_mutual_info_score.py │ ├── Kmeans_with_fowlkes_mallows_score.py │ ├── Kmeans_with_homogeneity_score.py │ └── KmodesRecipe.py ├── video_to_image.py ├── wav2txt.py └── weather_missing.csv ├── explainers ├── README.md ├── doc │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.md │ ├── MLI_BYORS_DEVELOPER_GUIDE.md │ ├── api │ │ └── mli-byor-api-ref-1-9-2-html.zip │ └── images │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.choose-explainer.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.gom.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.launch-mli.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.mli-byor-tab-home.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.mli-byor-upload-recipe.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.morris-done.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.morris-gom.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.morris-logs.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.new-interpretation.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.open-logs.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.overview.png │ │ ├── CREATING_CUSTOM_EXPLAINER_WITH_MLI_BYOR.set-ds-choose-explainer.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.chart-search-row.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.custom-explainer-uml.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.debug-task-mgr.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.dt-template.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.example-hello-get-ui.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.explainer-explanation-format.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.explainer-life-cycle.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.explainer-meta.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.featimp-template.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.get-explanations-ui.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.markdown-template.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.params-in-listing.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.params-value.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.pd-ice-template.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.scatter-template.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.shapley-summary.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.snapshots-1.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.snapshots-2.png │ │ ├── MLI_BYORS_DEVELOPER_GUIDE.snapshots-3.png │ │ └── MLI_BYORS_DEVELOPER_GUIDE.summary-shapley-local.png ├── explainers │ ├── ale_explainer.py │ ├── examples │ │ ├── example_compatibility_check_explainer.py │ │ ├── example_custom_explanation_explainer.py │ │ ├── example_dai_metadata_explainer.py │ │ ├── example_eda_explainer.py │ │ ├── example_hello_world_explainer.py │ │ ├── example_logging_explainer.py │ │ ├── example_params_explainer.py │ │ ├── example_persistence_explainer.py │ │ └── example_score_explainer.py │ ├── morris_sensitivity_explainer.py │ └── templates │ │ ├── template_dt_explainer.py │ │ ├── template_featimp_explainer.py │ │ ├── template_md_explainer.py │ │ ├── template_md_featimp_summary_explainer.py │ │ ├── template_md_vega_explainer.py │ │ ├── template_pd_explainer.py │ │ └── template_scatter_plot_explainer.py └── notebooks │ └── mli-byor.ipynb ├── gen-readme.py ├── how_to_write_a_recipe ├── ExampleLogTransformer.py ├── README.md ├── beamercolorthemeowl.sty ├── main.pdf └── main.tex ├── individuals ├── credit_card.py ├── indiv_test1.py ├── indiv_test2.py ├── individual_template.py ├── paribas_basemodel0.py └── walmart_lags.py ├── livecode ├── README.md ├── add_columns_with_rand_values.py ├── aggregate_and_augment_one_to_many.py ├── aggregate_columns_with_groupby.py ├── balance_data.py ├── bind_2_datasets.py ├── bind_X_and_Y.py ├── bind_X_and_target_y.py ├── bind_n_datasets.py ├── bootstrap_time_series.py ├── cast_columns_to_numeric.py ├── compute_rowwise_stats_by_column_groups.py ├── compute_shift_diff_per_column.py ├── compute_stats_by_groups_per_column.py ├── concat_columns.py ├── create_time_interval_partition.py ├── delete_columns.py ├── delete_rows.py ├── drop_duplicates.py ├── extract_header_data.py ├── fill_ts.py ├── filter_columns_by_types.py ├── find_mli_rowids.py ├── gen-readme.py ├── impute_X.py ├── insert_unique_id.py ├── join_X_left_outer_Y.py ├── map_target_to_binary_outcome.py ├── melt_X.py ├── melt_to_time_series.py ├── parse_string_to_datetime.py ├── pivot_X.py ├── rename_column_names.py ├── sample_X.py ├── shift_time_series.py ├── split_and_transpose_string.py ├── split_by_datetime.py ├── split_by_time_horizon.py ├── split_dataset_by_partition_column.py └── transform_features.py ├── models ├── .DS_Store ├── LICENSE ├── algorithms │ ├── adaboost.py │ ├── autogluon.py │ ├── calibratedClassifier.py │ ├── catboost.py │ ├── catboost_regression_uncertanity.py │ ├── datatable_glm.py │ ├── extra_trees.py │ ├── extremeClassifier.py │ ├── h2o-3-gbm-poisson.py │ ├── h2o-3-models.py │ ├── h2o-glm-poisson.py │ ├── kernel_naive_bayes.py │ ├── knearestneighbour.py │ ├── lda.py │ ├── libfm_fastfm.py │ ├── linear_svm.py │ ├── logistic_regression.py │ ├── nusvm.py │ ├── quantile_forest.py │ └── random_forest.py ├── custom_loss │ ├── lightgbm_quantile_regression.py │ ├── lightgbm_train_valid_score_gap.py │ ├── lightgbm_tweedie.py │ ├── lightgbm_with_custom_loss.py │ └── xgboost_with_custom_loss.py ├── mli │ ├── fairness │ │ ├── model_fairxgb.py │ │ └── reweighing_recipe.py │ ├── model_decision_tree_linear_combo.py │ ├── model_ebm.py │ ├── model_gam.py │ ├── model_skopes_rules.py │ └── model_xnn.py ├── mock_gpu_test_model.py ├── mock_tf2_test.py ├── mock_tf_test.py ├── model_template.py ├── monotonic │ └── monotonic_models.py ├── nlp │ ├── portuguese_bert.py │ ├── text_binary_count_logistic.py │ ├── text_tfidf_model.py │ └── text_tfidf_model_continuous.py ├── timeseries │ ├── autoarima_parallel.py │ ├── exponential_smoothing.py │ ├── fb_prophet.py │ ├── fb_prophet_parallel.py │ ├── historic_mean.py │ ├── nixtla_arimax.py │ ├── nixtla_ces.py │ ├── nixtla_ets.py │ ├── nixtla_theta.py │ └── ts_rnn.py ├── unsupervised │ ├── TextKMeansIsolationForest.py │ ├── TextSentiment.py │ ├── aggregator.py │ ├── dbscan.py │ ├── isolation_forest_custom_scorer.py │ ├── lda.py │ ├── local_outlier_factor.py │ ├── pytod_lof.py │ └── rapids_kmeans.py └── uplift │ ├── GLMTransformedOutcome.py │ ├── LightGBMTransformedOutcome.py │ └── XGBoostTransformedOutcome.py ├── recipes └── amazon.py ├── reference ├── DriverlessAI_BYOR.drawio └── DriverlessAI_BYOR.png ├── scorers ├── .DS_Store ├── LICENSE ├── classification │ ├── binary │ │ ├── average_mcc.py │ │ ├── balanced_accuracy.py │ │ ├── brier_loss.py │ │ ├── cost.py │ │ ├── cost_access_to_data.py │ │ ├── cost_smooth.py │ │ ├── fair_auc.py │ │ ├── kolmogorov_smirnov.py │ │ ├── logloss_with_costs.py │ │ ├── marketing_campaign.py │ │ └── profit.py │ ├── f3_score.py │ ├── f4_score.py │ ├── multiclass │ │ ├── hamming_loss.py │ │ ├── linear_weighted_kappa.py │ │ ├── map@k.py │ │ └── quadratic_weighted_kappa.py │ └── probF.py ├── huber_loss.py ├── regression │ ├── WAPE_scorer.py │ ├── asymmetric_mae.py │ ├── auuc.py │ ├── cosh_loss.py │ ├── explained_variance.py │ ├── gamma_deviance.py │ ├── largest_error.py │ ├── log_mae.py │ ├── mean_absolute_scaled_error.py │ ├── mean_squared_log_error.py │ ├── median_absolute_error.py │ ├── pearson_correlation.py │ ├── poisson_deviance.py │ ├── quantile_loss.py │ ├── r2_by_tgc.py │ ├── rmse_with_x.py │ ├── top_decile.py │ └── tweedie_deviance.py └── scorer_template.py └── transformers ├── LICENSE ├── anomaly └── isolation_forest.py ├── augmentation ├── germany_landers_holidays.py ├── holidays_this_week.py ├── ipaddress_features.py ├── is_ramadan.py ├── singapore_public_holidays.py ├── usairportcode_origin_dest.py ├── usairportcode_origin_dest_geo_features.py ├── uszipcode_features_database.py └── uszipcode_features_light.py ├── datetime ├── datetime_diff_transformer.py ├── datetime_encoder_transformer.py └── days_until_dec2020.py ├── executables ├── README.md ├── data │ ├── calc.exe │ └── putty.exe ├── pe_data_directory_features.py ├── pe_exports_features.py ├── pe_general_features.py ├── pe_header_features.py ├── pe_imports_features.py ├── pe_normalized_byte_count.py └── pe_section_characteristics.py ├── generic ├── count_missing_values_transformer.py ├── missing_flag_transformer.py └── specific_column_transformer.py ├── geospatial ├── README.md ├── geodesic.py └── myhaversine.py ├── hierarchical ├── firstNCharCVTE.py └── log_scale_target_encoding.py ├── how_to_debug_transformer.py ├── how_to_test_from_py_client.py ├── image ├── image_ocr_transformer.py └── image_url_transformer.py ├── nlp ├── README.md ├── continuous_TextTransformer.py ├── fuzzy_text_similarity_transformers.py ├── text_binary_count_transformer.py ├── text_char_tfidf_count_transformers.py ├── text_embedding_similarity_transformers.py ├── text_lang_detect_transformer.py ├── text_meta_transformers.py ├── text_named_entities_transformer.py ├── text_pos_tagging_transformer.py ├── text_preprocessing_transformer.py ├── text_readability_transformers.py ├── text_sentiment_transformer.py ├── text_similarity_transformers.py ├── text_spelling_correction_transformers.py ├── text_topic_modeling_transformer.py ├── text_url_summary_transformer.py └── vader_text_sentiment_transformer.py ├── numeric ├── boxcox_transformer.py ├── clusterdist_all.py ├── count_negative_values_transformer.py ├── count_positive_values_transformer.py ├── exp_diff_transformer.py ├── factor_analysis_transformer.py ├── log_transformer.py ├── ohe.py ├── pca_transformer.py ├── product.py ├── random_transformer.py ├── round_transformer.py ├── square_root_transformer.py ├── sum.py ├── truncated_svd_all.py └── yeojohnson_transformer.py ├── outliers ├── h2o3-dl-anomaly.py ├── quantile_winsorizer.py └── twosigma_winsorizer.py ├── recommendations └── matrixfactorization.py ├── signal_processing ├── README.md └── signal_processing.py ├── speech ├── README.md ├── audio_MFCC_transformer.py ├── azure_speech_to_text.py └── data │ ├── sample01.wav │ ├── sample02.wav │ ├── sample03.wav │ └── sample04.wav ├── string ├── simple_grok_parser.py ├── 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