├── ABTesting ├── AB_Testing_Z_test │ ├── AB_data.py │ ├── AB_testing_with_Python.ipynb │ └── ab_data.csv.zip ├── Bayesian_ABTesting │ ├── Bayesian_ABTesting.pdf │ ├── Bayesian_ABTesting_using_Monte_Carlo.py │ ├── Bayesian_ABTesting_without_Monte_Carlo.py │ ├── calc_prob.py │ ├── data_conversion_rate.py │ └── joint_posterior_plot.py.py └── z-test-vs-t-test.jpg ├── AutoEncoders ├── Composite_LSTM_Autoencoder.py ├── DeepAutoencoder_tensorflow_MNIST.py ├── DenoisingAutoencoder_MIST.py ├── Keep_Standalone_LSTM_Encoder.py ├── Prediction_LSTM_Autoencoder.py ├── Reconstruction_LSTM_Autoencoder.py ├── SimpleAutoencoder_tensorflow_MNIST.py ├── SimpleAutoencoder_tensorflow_MNIST_with_sparsity_constraint.py ├── TimeVAE │ ├── .gitignore │ ├── README.md │ ├── __init__.py │ ├── config │ │ ├── __init__.py │ │ └── config.py │ ├── datasets │ │ ├── sine_subsampled_train_perc_10.npz │ │ ├── sine_subsampled_train_perc_100.npz │ │ ├── sine_subsampled_train_perc_2.npz │ │ ├── sine_subsampled_train_perc_20.npz │ │ └── sine_subsampled_train_perc_5.npz │ ├── loss_functions.py │ ├── preprocess_pipeline.py │ ├── processing │ │ ├── __init__.py │ │ └── preprocessors.py │ ├── requirements.txt │ ├── test_vae.py │ ├── utils.py │ ├── vae_base.py │ ├── vae_conv_I_model.py │ ├── vae_conv_model.py │ └── vae_dense_model.py └── VariationalAutoencoder_VAE_MNIST_generative_model.py ├── DatasetsAnalysisClassification └── OpenFoodScoreFR │ ├── DataExploration.py │ ├── OFF_HistGradientBoostingClassifier.py │ ├── OFF_XGBoost.py │ └── data-fields.txt ├── DecisionTrees ├── Bagging.py ├── Decision_Tree.py ├── Decision_Tree_with_Imputed_Values.py ├── LightGBM.py ├── Random_Forest.py ├── SVMtrain.csv └── XGBoost .py ├── Deep_NN_weight_initialization.py ├── EEG ├── AutoML │ ├── auto_sklearn.py │ ├── auto_sklearn_from_saved_data.py │ ├── pycarret_from_saved_data.py │ ├── tpot_eeg.py │ ├── tpot_from_saved_data.py │ └── tpot_test_generated_pipeline.py ├── Classify_PyRiemann_CovMat_as_Image_TensorFlow.py ├── Classify_PyRiemann_CovMat_as_Image_TensorFlow_ScikitLearnClassifier.py ├── Classify_PyRiemann_CovMat_as_Image_TensorFlow_ScikitLearnClassifier_CrossSubject.py ├── Classify_PyRiemann_CovMat_as_Image_TensorFlow_ScikitLearnClassifier_CrossSubject_validate.py ├── Classify_PyRiemann_TangentSpace_BalancedBaggingClassifier_PerSubject_MOABB.py ├── Classify_RawData_TensorFlow_ScikitLearnClassifier_CrossSubject.py ├── Classify_RawData_TensorFlow_ScikitLearnClassifier_PerSubject_MOABB.py ├── Classify_ReccurentPlots_TensorFlow_ScikitLearnClassifier_CrossSubject_validate.py ├── DataAugmentation │ ├── UsingTimeGAN │ │ └── TimeGAN_Augmentation_PyRiemann_Interpolation_ydata_synthetic.py │ └── UsingTimeVAE │ │ ├── TimeVAE_Augmentation_PyRiemann.py │ │ ├── TimeVAE_Augmentation_PyRiemann_Interpolation.py │ │ ├── TimeVAE_Augmentation_PyRiemann_MOABB.py │ │ ├── TimeVAE_Augmentation_PyRiemann_MOABB_Handler.py │ │ ├── TimeVAE_Augmentation_per_Subject_PyRiemann_.py │ │ ├── TimeVAE_Augmented_P300_data_as_negative_example_with_regular_classifier.py │ │ ├── TimeVAE_Classification_Encoder.py │ │ ├── TimeVAE_moabb_test.py │ │ └── TimeVaeTransformer.py ├── EEGformer │ ├── EEGformer_BCICompetition2008_motor_imagery_2a_4_class_gdf.py │ ├── EEGformer_BCICompetition2008_motor_imagery_2a_4_class_gdf_TESTING.py │ ├── EEGformer_BCICompetition2008_motor_imagery_2a_4_class_mat.py │ └── moabb_braindecode_eegconformer.py ├── MDM-MF │ ├── BeatSTA1.py │ ├── BeatSTA2.py │ ├── BeatSTA3.py │ ├── BeatSTA3_GR.py │ ├── BeatSTA4.py │ ├── BeatSTA5.py │ ├── CSP_test1.py │ ├── CSP_test2.py │ ├── LE_CD_PCA_LDA_test.py │ ├── MI_AD_TS_grid_search.py │ ├── RPF_test1.py │ ├── TestD2results.py │ ├── analyze_codecarbon.py │ ├── distance_strategy.py │ ├── dual_csp.py │ ├── enchanced_mdm_mf.py │ ├── enchanced_mdm_mf_tools.py │ ├── hb_mdm_mf_lda.py │ ├── heavy_benchmark.py │ ├── main_comparison.py │ ├── mdm_mf3.py │ ├── mdm_mf3_LDA_CD_EM_effects.py │ ├── mdm_mf3_all.py │ ├── mdm_mf_diagonalized.py │ ├── mdm_rsc.py │ ├── moabb_b_test.py │ ├── old │ │ ├── analyze_benchmark1.py │ │ ├── benchmark1.py │ │ ├── classifiy_P300_MDM_MF.py │ │ └── classifiy_P300_MDM_MF_with_stat.py │ ├── pipelines │ │ ├── AUG_TANG_SVM_grid.yml │ │ └── TSLR.yml │ ├── pipelines2 │ │ ├── MDM_MF_NEW.yml │ │ ├── MDM_MF_NEW_ro.yml │ │ └── TSLR.yml │ ├── pipelines4 │ │ ├── CSP_MDRMF_orig.yml │ │ ├── CSP_def_RMF_or.yml │ │ ├── Cov_MDM.yml │ │ ├── MDRMF_orig.yml │ │ ├── NOCSP_RMF_OR.yml │ │ ├── RMF_NO_OR.yml │ │ ├── RMF_OR.yml │ │ └── TSLR.yml │ ├── pipelines5 │ │ ├── MDM_MF_NEW.yml │ │ ├── MDM_MF_NEW_NO_OR.yml │ │ ├── MDRMF_orig.yml │ │ ├── XdawnCov_MDM.yml │ │ ├── XdawnCov_TS_LDA.yml │ │ ├── XdawnCov_TS_LR.yml │ │ └── XdawnCov_TS_SVM.yml │ ├── pipelines7 │ │ ├── Cov_MDM.yml │ │ ├── Cov_MDM_or.yml │ │ ├── TSLR.yml │ │ └── TSLR_OR.yml │ ├── pipelines9 │ │ ├── CSP_def_MF.yml │ │ ├── Cov_ADCSP_MDM.yml │ │ ├── Cov_ADCSP_MF.yml │ │ ├── Cov_ADCSP_MF_RPME.yml │ │ ├── Cov_ADSCP_MDMF.yml │ │ ├── Cov_ADSCP_TSLR.yml │ │ ├── Cov_MDM.yml │ │ ├── Cov_MDMF.yml │ │ ├── Cov_MF.yml │ │ ├── Cov_TSLDA.yml │ │ ├── Cov_TSLR.yml │ │ └── old │ │ │ ├── CSP_MDRMF_orig.yml │ │ │ ├── CSP_def_RMF_or.yml │ │ │ ├── Cov_MDM.yml │ │ │ ├── MDRMF_orig.yml │ │ │ ├── NOCSP_RMF_OR.yml │ │ │ ├── RMF_NO_OR.yml │ │ │ ├── RMF_OR.yml │ │ │ └── TSLR.yml │ ├── study_many_electrodes.py │ ├── test_LDA_PCA_LDA_PCA_LR_L1.py │ ├── test_LDA_vs_LR_L1_vs_LE_CD_PCA_LR_L1.py │ ├── test_LE_CD.py │ ├── test_MDM_MF_RO_1.py │ ├── test_MDM_MF_power_means_1.py │ ├── test_MDM_MF_power_means_2.py │ ├── test_MDM_MF_power_means_2_P300.py │ ├── test_MDM_RS.py │ ├── test_MFM_MF_LDA_LR_exteded_power_means.py │ ├── test_MFM_MF_LDA_LR_exteded_power_means2.py │ ├── test_MFM_MF_LDA_LR_exteded_power_means3_feature selection.py │ ├── test_MFM_MF_LDA_LR_exteded_power_means4_feature selection_LEM_LED.py │ ├── test_MFM_MF_LDA_LR_exteded_power_means5_feature selection_LEM_LED.py │ ├── test_main_MFM_MF_LDA_LR.py │ ├── test_main_MFM_MF_LDA_LR_cross_subject.py │ ├── test_orig_LR_LE_PCA_LDA.py │ ├── test_prev_mean_rspd.py │ ├── test_pyriemann_or_mi.py │ ├── test_pyriemann_or_p300.py │ ├── test_rmf_potato_mean.py │ └── tests1 │ │ ├── init_mean.py │ │ ├── test_ad1.py │ │ ├── test_csp.py │ │ ├── test_th.py │ │ ├── ts.py │ │ └── zeta.py ├── MOABB │ ├── ExploreMOABBdatasets.py │ ├── PlotEpochsMOABBplotly.py │ └── PlotSignalMOABBplotly.py ├── P300Analysis │ ├── DatasetHelper.py │ ├── P300SaveDataMNE.py │ ├── P300SaveDataZenodo.py │ └── P300ShowData.py ├── moabb.bi2013a │ ├── DetectDiff1.py │ ├── DetectDiff2.py │ ├── MahalanobisDistance.py │ ├── Testing1.py │ ├── Testing2.py │ ├── Testing3.py │ ├── VectorEmbedding_Milvus │ │ ├── classify_mulvis.py │ │ ├── encode_tf_resnet50.py │ │ └── milvus_test.py │ ├── cov_estimator_ts_clf.py │ ├── hugging_face_image_classification.py │ ├── rp_centroid_classifier_rp_multivariate_load_images_shuffle.py │ ├── rp_conv_multivariate_load_images.py │ ├── rp_dither_conv_multivariate_load_images.py │ ├── rp_dither_save.py │ ├── rp_multivariate_save.py │ ├── rp_multivariate_save_parallel.py │ ├── rp_multivariate_save_parallel2.py │ ├── rp_multivariate_save_parallel3.py │ ├── rp_multivariate_save_parallel3_as_image.py │ ├── rp_multivariate_save_parallel3_dither.py │ ├── rp_multivariate_save_parallel3_mahalanobis_dist.py │ ├── rp_multivariate_save_xdawn_parallel3.py │ ├── rp_multivariate_save_xdawn_parallel3_dither.py │ └── rp_multivariate_save_xdawn_parallel_rp_parameter_search.py ├── moabb_classify.py ├── moabb_classify_tensor_flow.py ├── py.ALPHA.EEG.2017-GIPSA │ ├── LICENSE │ ├── README.md │ ├── alphawaves │ │ ├── __init__.py │ │ ├── dataset.py │ │ └── download.py │ ├── conv_single_electrode.py │ ├── example_classification.py │ ├── example_classification_all_subjects.py │ ├── example_spectra.py │ ├── keras_classification.py │ ├── keras_classification_conv.py │ ├── keras_recurrence_plots_classification.py │ ├── keras_recurrence_plots_classification_NN_multivariate_load_rp_images.py │ ├── keras_recurrence_plots_classification_conv.py │ ├── keras_recurrence_plots_classification_conv_multivariate.py │ ├── keras_recurrence_plots_classification_conv_multivariate_load_rp_images.py │ ├── keras_recurrence_plots_classification_optimization.py │ ├── my_calc_rp_multivariate.py │ ├── requirements.txt │ ├── rp_analyse.py │ ├── rp_centroid_classifier.py │ ├── rp_centroid_classifier_all_electrodes.py │ ├── rp_centroid_classifier_all_electrodes_NN.py │ ├── rp_centroid_classifier_rp_multivariate.py │ ├── rp_centroid_classifier_rp_multivariate_NN.py │ ├── rp_centroid_classifier_rp_multivariate_load_images.py │ ├── rp_centroid_classifier_rp_multivariate_load_images_shuffle.py │ ├── save_multivariate_rp.py │ ├── setup.py │ └── test_rp.py ├── py.BI.EEG.2014a-GIPSA │ ├── .gitignore │ ├── TestingRP.py │ ├── all_subjects_shuffled.py │ ├── braininvaders2014a │ │ ├── __init__.py │ │ ├── dataset.py │ │ └── download.py │ ├── classification_scores.py │ ├── evoked_potentials.py │ ├── requirements.txt │ ├── rp_centroid_classifier_rp_multivariate_load_images_shuffle.py │ ├── rp_conv_multivariate_load_images.py │ ├── rp_dither_centroid_classifier_rp_multivariate_load_images_shuffle.py │ ├── rp_dither_conv_multivariate_load_images.py │ ├── save_multivariate_dither.py │ ├── save_multivariate_rp.py │ └── setup.py └── pyRiemann-qiskit │ └── classification_scores_old_code.py ├── Elastic_Net.py ├── ExploreData.py ├── GAN ├── GAN_Handwritten_Digits_Generator.py ├── GAN_Sin_Generator.py ├── GOOG.csv ├── TimeGAN_Energy_ydata_synthetic.py ├── TimeGAN_Stocks_ydata_synthetic.py └── energy_data.csv ├── Gradient_Boosting.py ├── Handling_Imbalanced_Data_with_BalancedBaggingClassifier.py ├── HypothesisTesting ├── Hypothesis_Testing_with_Python.docx ├── One-tailed-Test-or-Two-tailed-Test.pdf ├── T_Critical_Value_in_Python.docx ├── chi-square_test_feature_selection │ ├── bank.csv │ └── chi-square_test_feature_selection.py ├── critical_value.py ├── hypothesis_testing_examples.ipynb ├── kstest.py ├── paired_t_test │ ├── paired_samples_data.csv │ └── paired_t_test.py ├── readme.txt ├── type_of_tests.png ├── which_test.jpg └── z-test-vs-t-test.jpg ├── Kmeans_NonLinear_Dimensionality_Reduction.py ├── LDA.py ├── PCA.py ├── PyTorch ├── Autograd │ ├── .ipynb_checkpoints │ │ └── Autograd-checkpoint.ipynb │ └── Autograd.ipynb ├── Check_GPU_support.py ├── PyTorch_CNN_image_classification_MNIST.py ├── PyTorch_Image_Classification_CIFAR-10.py ├── PyTorch_Image_Classification_CIFAR-10_GPU.py ├── PyTorch_MLP_binary_classification.py ├── PyTorch_MLP_regression_boston_housing.py ├── PyTorch_fit_3rd_order_poly_sine_function.py ├── PyTorch_single_step.py ├── PyTorch_training_pretrained_model_CIFAR-10.py ├── PyTorch_training_pretrained_model_CIFAR-10_GPU.py ├── PyTorch_training_pretrained_resnet18_on_bee_ants_dataset_GPU.py └── data │ └── hymenoptera_data │ ├── train │ ├── ants │ │ ├── 0013035.jpg │ │ ├── 1030023514_aad5c608f9.jpg │ │ ├── 1095476100_3906d8afde.jpg │ │ ├── 1099452230_d1949d3250.jpg │ │ ├── 116570827_e9c126745d.jpg │ │ ├── 1225872729_6f0856588f.jpg │ │ ├── 1262877379_64fcada201.jpg │ │ ├── 1269756697_0bce92cdab.jpg │ │ ├── 1286984635_5119e80de1.jpg │ │ ├── 132478121_2a430adea2.jpg │ │ ├── 1360291657_dc248c5eea.jpg │ │ ├── 1368913450_e146e2fb6d.jpg │ │ ├── 1473187633_63ccaacea6.jpg │ │ ├── 148715752_302c84f5a4.jpg │ │ ├── 1489674356_09d48dde0a.jpg │ │ ├── 149244013_c529578289.jpg │ │ ├── 150801003_3390b73135.jpg │ │ ├── 150801171_cd86f17ed8.jpg │ │ ├── 154124431_65460430f2.jpg │ │ ├── 162603798_40b51f1654.jpg │ │ ├── 1660097129_384bf54490.jpg │ │ ├── 167890289_dd5ba923f3.jpg │ │ ├── 1693954099_46d4c20605.jpg │ │ ├── 175998972.jpg │ │ ├── 178538489_bec7649292.jpg │ │ ├── 1804095607_0341701e1c.jpg │ │ ├── 1808777855_2a895621d7.jpg │ │ ├── 188552436_605cc9b36b.jpg │ │ ├── 1917341202_d00a7f9af5.jpg │ │ ├── 1924473702_daa9aacdbe.jpg │ │ ├── 196057951_63bf063b92.jpg │ │ ├── 196757565_326437f5fe.jpg │ │ ├── 201558278_fe4caecc76.jpg │ │ ├── 201790779_527f4c0168.jpg │ │ ├── 2019439677_2db655d361.jpg │ │ ├── 207947948_3ab29d7207.jpg │ │ ├── 20935278_9190345f6b.jpg │ │ ├── 224655713_3956f7d39a.jpg │ │ ├── 2265824718_2c96f485da.jpg │ │ ├── 2265825502_fff99cfd2d.jpg │ │ ├── 226951206_d6bf946504.jpg │ │ ├── 2278278459_6b99605e50.jpg │ │ ├── 2288450226_a6e96e8fdf.jpg │ │ ├── 2288481644_83ff7e4572.jpg │ │ ├── 2292213964_ca51ce4bef.jpg │ │ ├── 24335309_c5ea483bb8.jpg │ │ ├── 245647475_9523dfd13e.jpg │ │ ├── 255434217_1b2b3fe0a4.jpg │ │ ├── 258217966_d9d90d18d3.jpg │ │ ├── 275429470_b2d7d9290b.jpg │ │ ├── 28847243_e79fe052cd.jpg │ │ ├── 318052216_84dff3f98a.jpg │ │ ├── 334167043_cbd1adaeb9.jpg │ │ ├── 339670531_94b75ae47a.jpg │ │ ├── 342438950_a3da61deab.jpg │ │ ├── 36439863_0bec9f554f.jpg │ │ ├── 374435068_7eee412ec4.jpg │ │ ├── 382971067_0bfd33afe0.jpg │ │ ├── 384191229_5779cf591b.jpg │ │ ├── 386190770_672743c9a7.jpg │ │ ├── 392382602_1b7bed32fa.jpg │ │ ├── 403746349_71384f5b58.jpg │ │ ├── 408393566_b5b694119b.jpg │ │ ├── 424119020_6d57481dab.jpg │ │ ├── 424873399_47658a91fb.jpg │ │ ├── 450057712_771b3bfc91.jpg │ │ ├── 45472593_bfd624f8dc.jpg │ │ ├── 459694881_ac657d3187.jpg │ │ ├── 460372577_f2f6a8c9fc.jpg │ │ ├── 460874319_0a45ab4d05.jpg │ │ ├── 466430434_4000737de9.jpg │ │ ├── 470127037_513711fd21.jpg │ │ ├── 474806473_ca6caab245.jpg │ │ ├── 475961153_b8c13fd405.jpg │ │ ├── 484293231_e53cfc0c89.jpg │ │ ├── 49375974_e28ba6f17e.jpg │ │ ├── 506249802_207cd979b4.jpg │ │ ├── 506249836_717b73f540.jpg │ │ ├── 512164029_c0a66b8498.jpg │ │ ├── 512863248_43c8ce579b.jpg │ │ ├── 518773929_734dbc5ff4.jpg │ │ ├── 522163566_fec115ca66.jpg │ │ ├── 522415432_2218f34bf8.jpg │ │ ├── 531979952_bde12b3bc0.jpg │ │ ├── 533848102_70a85ad6dd.jpg │ │ ├── 535522953_308353a07c.jpg │ │ ├── 540889389_48bb588b21.jpg │ │ ├── 541630764_dbd285d63c.jpg │ │ ├── 543417860_b14237f569.jpg │ │ ├── 560966032_988f4d7bc4.jpg │ │ ├── 5650366_e22b7e1065.jpg │ │ ├── 6240329_72c01e663e.jpg │ │ ├── 6240338_93729615ec.jpg │ │ ├── 649026570_e58656104b.jpg │ │ ├── 662541407_ff8db781e7.jpg │ │ ├── 67270775_e9fdf77e9d.jpg │ │ ├── 6743948_2b8c096dda.jpg │ │ ├── 684133190_35b62c0c1d.jpg │ │ ├── 69639610_95e0de17aa.jpg │ │ ├── 707895295_009cf23188.jpg │ │ ├── 7759525_1363d24e88.jpg │ │ ├── 795000156_a9900a4a71.jpg │ │ ├── 822537660_caf4ba5514.jpg │ │ ├── 82852639_52b7f7f5e3.jpg │ │ ├── 841049277_b28e58ad05.jpg │ │ ├── 886401651_f878e888cd.jpg │ │ ├── 892108839_f1aad4ca46.jpg │ │ ├── 938946700_ca1c669085.jpg │ │ ├── 957233405_25c1d1187b.jpg │ │ ├── 9715481_b3cb4114ff.jpg │ │ ├── 998118368_6ac1d91f81.jpg │ │ ├── Ant_1.jpg │ │ ├── MehdiabadiAnt2_600.jpg │ │ ├── Nepenthes_rafflesiana_ant.jpg │ │ ├── VietnameseAntMimicSpider.jpg │ │ ├── ant photos.jpg │ │ ├── army-ants-red-picture.jpg │ │ ├── formica.jpeg │ │ ├── hormiga_co_por.jpg │ │ ├── imageNotFound.gif │ │ ├── kurokusa.jpg │ │ ├── swiss-army-ant.jpg │ │ ├── termite-vs-ant.jpg │ │ └── trap-jaw-ant-insect-bg.jpg │ └── bees │ │ ├── 1092977343_cb42b38d62.jpg │ │ ├── 1093831624_fb5fbe2308.jpg │ │ ├── 1097045929_1753d1c765.jpg │ │ ├── 1232245714_f862fbe385.jpg │ │ ├── 129236073_0985e91c7d.jpg │ │ ├── 1295655112_7813f37d21.jpg │ │ ├── 132511197_0b86ad0fff.jpg │ │ ├── 132826773_dbbcb117b9.jpg │ │ ├── 150013791_969d9a968b.jpg │ │ ├── 1508176360_2972117c9d.jpg │ │ ├── 154600396_53e1252e52.jpg │ │ ├── 16838648_415acd9e3f.jpg │ │ ├── 1691282715_0addfdf5e8.jpg │ │ ├── 17209602_fe5a5a746f.jpg │ │ ├── 174142798_e5ad6d76e0.jpg │ │ ├── 1799726602_8580867f71.jpg │ │ ├── 1807583459_4fe92b3133.jpg │ │ ├── 196430254_46bd129ae7.jpg │ │ ├── 196658222_3fffd79c67.jpg │ │ ├── 198508668_97d818b6c4.jpg │ │ ├── 2031225713_50ed499635.jpg │ │ ├── 2037437624_2d7bce461f.jpg │ │ ├── 2053200300_8911ef438a.jpg │ │ ├── 205835650_e6f2614bee.jpg │ │ ├── 208702903_42fb4d9748.jpg │ │ ├── 21399619_3e61e5bb6f.jpg │ │ ├── 2227611847_ec72d40403.jpg │ │ ├── 2321139806_d73d899e66.jpg │ │ ├── 2330918208_8074770c20.jpg │ │ ├── 2345177635_caf07159b3.jpg │ │ ├── 2358061370_9daabbd9ac.jpg │ │ ├── 2364597044_3c3e3fc391.jpg │ │ ├── 2384149906_2cd8b0b699.jpg │ │ ├── 2397446847_04ef3cd3e1.jpg │ │ ├── 2405441001_b06c36fa72.jpg │ │ ├── 2445215254_51698ff797.jpg │ │ ├── 2452236943_255bfd9e58.jpg │ │ ├── 2467959963_a7831e9ff0.jpg │ │ ├── 2470492904_837e97800d.jpg │ │ ├── 2477324698_3d4b1b1cab.jpg │ │ ├── 2477349551_e75c97cf4d.jpg │ │ ├── 2486729079_62df0920be.jpg │ │ ├── 2486746709_c43cec0e42.jpg │ │ ├── 2493379287_4100e1dacc.jpg │ │ ├── 2495722465_879acf9d85.jpg │ │ ├── 2528444139_fa728b0f5b.jpg │ │ ├── 2538361678_9da84b77e3.jpg │ │ ├── 2551813042_8a070aeb2b.jpg │ │ ├── 2580598377_a4caecdb54.jpg │ │ ├── 2601176055_8464e6aa71.jpg │ │ ├── 2610833167_79bf0bcae5.jpg │ │ ├── 2610838525_fe8e3cae47.jpg │ │ ├── 2617161745_fa3ebe85b4.jpg │ │ ├── 2625499656_e3415e374d.jpg │ │ ├── 2634617358_f32fd16bea.jpg │ │ ├── 2638074627_6b3ae746a0.jpg │ │ ├── 2645107662_b73a8595cc.jpg │ │ ├── 2651621464_a2fa8722eb.jpg │ │ ├── 2652877533_a564830cbf.jpg │ │ ├── 266644509_d30bb16a1b.jpg │ │ ├── 2683605182_9d2a0c66cf.jpg │ │ ├── 2704348794_eb5d5178c2.jpg │ │ ├── 2707440199_cd170bd512.jpg │ │ ├── 2710368626_cb42882dc8.jpg │ │ ├── 2722592222_258d473e17.jpg │ │ ├── 2728759455_ce9bb8cd7a.jpg │ │ ├── 2756397428_1d82a08807.jpg │ │ ├── 2765347790_da6cf6cb40.jpg │ │ ├── 2781170484_5d61835d63.jpg │ │ ├── 279113587_b4843db199.jpg │ │ ├── 2792000093_e8ae0718cf.jpg │ │ ├── 2801728106_833798c909.jpg │ │ ├── 2822388965_f6dca2a275.jpg │ │ ├── 2861002136_52c7c6f708.jpg │ │ ├── 2908916142_a7ac8b57a8.jpg │ │ ├── 29494643_e3410f0d37.jpg │ │ ├── 2959730355_416a18c63c.jpg │ │ ├── 2962405283_22718d9617.jpg │ │ ├── 3006264892_30e9cced70.jpg │ │ ├── 3030189811_01d095b793.jpg │ │ ├── 3030772428_8578335616.jpg │ │ ├── 3044402684_3853071a87.jpg │ │ ├── 3074585407_9854eb3153.jpg │ │ ├── 3079610310_ac2d0ae7bc.jpg │ │ ├── 3090975720_71f12e6de4.jpg │ │ ├── 3100226504_c0d4f1e3f1.jpg │ │ ├── 342758693_c56b89b6b6.jpg │ │ ├── 354167719_22dca13752.jpg │ │ ├── 359928878_b3b418c728.jpg │ │ ├── 365759866_b15700c59b.jpg │ │ ├── 36900412_92b81831ad.jpg │ │ ├── 39672681_1302d204d1.jpg │ │ ├── 39747887_42df2855ee.jpg │ │ ├── 421515404_e87569fd8b.jpg │ │ ├── 444532809_9e931e2279.jpg │ │ ├── 446296270_d9e8b93ecf.jpg │ │ ├── 452462677_7be43af8ff.jpg │ │ ├── 452462695_40a4e5b559.jpg │ │ ├── 457457145_5f86eb7e9c.jpg │ │ ├── 465133211_80e0c27f60.jpg │ │ ├── 469333327_358ba8fe8a.jpg │ │ ├── 472288710_2abee16fa0.jpg │ │ ├── 473618094_8ffdcab215.jpg │ │ ├── 476347960_52edd72b06.jpg │ │ ├── 478701318_bbd5e557b8.jpg │ │ ├── 507288830_f46e8d4cb2.jpg │ │ ├── 509247772_2db2d01374.jpg │ │ ├── 513545352_fd3e7c7c5d.jpg │ │ ├── 522104315_5d3cb2758e.jpg │ │ ├── 537309131_532bfa59ea.jpg │ │ ├── 586041248_3032e277a9.jpg │ │ ├── 760526046_547e8b381f.jpg │ │ ├── 760568592_45a52c847f.jpg │ │ ├── 774440991_63a4aa0cbe.jpg │ │ ├── 85112639_6e860b0469.jpg │ │ ├── 873076652_eb098dab2d.jpg │ │ ├── 90179376_abc234e5f4.jpg │ │ ├── 92663402_37f379e57a.jpg │ │ ├── 95238259_98470c5b10.jpg │ │ ├── 969455125_58c797ef17.jpg │ │ └── 98391118_bdb1e80cce.jpg │ └── val │ ├── ants │ ├── 10308379_1b6c72e180.jpg │ ├── 1053149811_f62a3410d3.jpg │ ├── 1073564163_225a64f170.jpg │ ├── 1119630822_cd325ea21a.jpg │ ├── 1124525276_816a07c17f.jpg │ ├── 11381045_b352a47d8c.jpg │ ├── 119785936_dd428e40c3.jpg │ ├── 1247887232_edcb61246c.jpg │ ├── 1262751255_c56c042b7b.jpg │ ├── 1337725712_2eb53cd742.jpg │ ├── 1358854066_5ad8015f7f.jpg │ ├── 1440002809_b268d9a66a.jpg │ ├── 147542264_79506478c2.jpg │ ├── 152286280_411648ec27.jpg │ ├── 153320619_2aeb5fa0ee.jpg │ ├── 153783656_85f9c3ac70.jpg │ ├── 157401988_d0564a9d02.jpg │ ├── 159515240_d5981e20d1.jpg │ ├── 161076144_124db762d6.jpg │ ├── 161292361_c16e0bf57a.jpg │ ├── 170652283_ecdaff5d1a.jpg │ ├── 17081114_79b9a27724.jpg │ ├── 172772109_d0a8e15fb0.jpg │ ├── 1743840368_b5ccda82b7.jpg │ ├── 181942028_961261ef48.jpg │ ├── 183260961_64ab754c97.jpg │ ├── 2039585088_c6f47c592e.jpg │ ├── 205398178_c395c5e460.jpg │ ├── 208072188_f293096296.jpg │ ├── 209615353_eeb38ba204.jpg │ ├── 2104709400_8831b4fc6f.jpg │ ├── 212100470_b485e7b7b9.jpg │ ├── 2127908701_d49dc83c97.jpg │ ├── 2191997003_379df31291.jpg │ ├── 2211974567_ee4606b493.jpg │ ├── 2219621907_47bc7cc6b0.jpg │ ├── 2238242353_52c82441df.jpg │ ├── 2255445811_dabcdf7258.jpg │ ├── 239161491_86ac23b0a3.jpg │ ├── 263615709_cfb28f6b8e.jpg │ ├── 308196310_1db5ffa01b.jpg │ ├── 319494379_648fb5a1c6.jpg │ ├── 35558229_1fa4608a7a.jpg │ ├── 412436937_4c2378efc2.jpg │ ├── 436944325_d4925a38c7.jpg │ ├── 445356866_6cb3289067.jpg │ ├── 459442412_412fecf3fe.jpg │ ├── 470127071_8b8ee2bd74.jpg │ ├── 477437164_bc3e6e594a.jpg │ ├── 488272201_c5aa281348.jpg │ ├── 502717153_3e4865621a.jpg │ ├── 518746016_bcc28f8b5b.jpg │ ├── 540543309_ddbb193ee5.jpg │ ├── 562589509_7e55469b97.jpg │ ├── 57264437_a19006872f.jpg │ ├── 573151833_ebbc274b77.jpg │ ├── 649407494_9b6bc4949f.jpg │ ├── 751649788_78dd7d16ce.jpg │ ├── 768870506_8f115d3d37.jpg │ ├── 800px-Meat_eater_ant_qeen_excavating_hole.jpg │ ├── 8124241_36b290d372.jpg │ ├── 8398478_50ef10c47a.jpg │ ├── 854534770_31f6156383.jpg │ ├── 892676922_4ab37dce07.jpg │ ├── 94999827_36895faade.jpg │ ├── Ant-1818.jpg │ ├── F.pergan.28(f).jpg │ ├── Hormiga.jpg │ ├── ants-devouring-remains-of-large-dead-insect-on-red-tile-in-Stellenbosch-South-Africa-closeup-1-DHD.jpg │ └── desert_ant.jpg │ └── bees │ ├── 1032546534_06907fe3b3.jpg │ ├── 10870992_eebeeb3a12.jpg │ ├── 1181173278_23c36fac71.jpg │ ├── 1297972485_33266a18d9.jpg │ ├── 1328423762_f7a88a8451.jpg │ ├── 1355974687_1341c1face.jpg │ ├── 144098310_a4176fd54d.jpg │ ├── 1486120850_490388f84b.jpg │ ├── 149973093_da3c446268.jpg │ ├── 151594775_ee7dc17b60.jpg │ ├── 151603988_2c6f7d14c7.jpg │ ├── 1519368889_4270261ee3.jpg │ ├── 152789693_220b003452.jpg │ ├── 177677657_a38c97e572.jpg │ ├── 1799729694_0c40101071.jpg │ ├── 181171681_c5a1a82ded.jpg │ ├── 187130242_4593a4c610.jpg │ ├── 203868383_0fcbb48278.jpg │ ├── 2060668999_e11edb10d0.jpg │ ├── 2086294791_6f3789d8a6.jpg │ ├── 2103637821_8d26ee6b90.jpg │ ├── 2104135106_a65eede1de.jpg │ ├── 215512424_687e1e0821.jpg │ ├── 2173503984_9c6aaaa7e2.jpg │ ├── 220376539_20567395d8.jpg │ ├── 224841383_d050f5f510.jpg │ ├── 2321144482_f3785ba7b2.jpg │ ├── 238161922_55fa9a76ae.jpg │ ├── 2407809945_fb525ef54d.jpg │ ├── 2415414155_1916f03b42.jpg │ ├── 2438480600_40a1249879.jpg │ ├── 2444778727_4b781ac424.jpg │ ├── 2457841282_7867f16639.jpg │ ├── 2470492902_3572c90f75.jpg │ ├── 2478216347_535c8fe6d7.jpg │ ├── 2501530886_e20952b97d.jpg │ ├── 2506114833_90a41c5267.jpg │ ├── 2509402554_31821cb0b6.jpg │ ├── 2525379273_dcb26a516d.jpg │ ├── 26589803_5ba7000313.jpg │ ├── 2668391343_45e272cd07.jpg │ ├── 2670536155_c170f49cd0.jpg │ ├── 2685605303_9eed79d59d.jpg │ ├── 2702408468_d9ed795f4f.jpg │ ├── 2709775832_85b4b50a57.jpg │ ├── 2717418782_bd83307d9f.jpg │ ├── 272986700_d4d4bf8c4b.jpg │ ├── 2741763055_9a7bb00802.jpg │ ├── 2745389517_250a397f31.jpg │ ├── 2751836205_6f7b5eff30.jpg │ ├── 2782079948_8d4e94a826.jpg │ ├── 2809496124_5f25b5946a.jpg │ ├── 2815838190_0a9889d995.jpg │ ├── 2841437312_789699c740.jpg │ ├── 2883093452_7e3a1eb53f.jpg │ ├── 290082189_f66cb80bfc.jpg │ ├── 296565463_d07a7bed96.jpg │ ├── 3077452620_548c79fda0.jpg │ ├── 348291597_ee836fbb1a.jpg │ ├── 350436573_41f4ecb6c8.jpg │ ├── 353266603_d3eac7e9a0.jpg │ ├── 372228424_16da1f8884.jpg │ ├── 400262091_701c00031c.jpg │ ├── 416144384_961c326481.jpg │ ├── 44105569_16720a960c.jpg │ ├── 456097971_860949c4fc.jpg │ ├── 464594019_1b24a28bb1.jpg │ ├── 485743562_d8cc6b8f73.jpg │ ├── 540976476_844950623f.jpg │ ├── 54736755_c057723f64.jpg │ ├── 57459255_752774f1b2.jpg │ ├── 576452297_897023f002.jpg │ ├── 586474709_ae436da045.jpg │ ├── 590318879_68cf112861.jpg │ ├── 59798110_2b6a3c8031.jpg │ ├── 603709866_a97c7cfc72.jpg │ ├── 603711658_4c8cd2201e.jpg │ ├── 65038344_52a45d090d.jpg │ ├── 6a00d8341c630a53ef00e553d0beb18834-800wi.jpg │ ├── 72100438_73de9f17af.jpg │ ├── 759745145_e8bc776ec8.jpg │ ├── 936182217_c4caa5222d.jpg │ └── abeja.jpg ├── README.md ├── SVMtrain.csv ├── Signal_Porcessing_LRM_Prediction.py ├── Sklearn_DBSCAN_clustering.py ├── Sklearn_KMeans.py ├── Sklearn_NN_MLP.py ├── TensorFlow ├── Keras_Image_Classification_Cars_Planes.py ├── Keras_Image_Classification_Clothing.py ├── Keras_MNIST_digits_dataset.py ├── Keras_Signal_Processing.py ├── Keras_Signal_Processing_FFT.py ├── Keras_SimpleNN.py ├── ST_TransferLearning_DataAugmentation │ ├── LICENSE.md │ ├── ST_TransferLearning_DataAugmentation.py │ └── TransferLearning.ipynb ├── SciKeras_SimpleNN.py └── sonar.csv ├── Tools ├── ChangeImageSize.py └── WrapperYforDataAugmentation.py ├── Transformers ├── HuggingFace │ ├── AdvancedFineTuningPairOfSentencesClassification.py │ ├── AdvancedFineTuningPairOfSentencesClassificationWithoutTrainerClass.py │ ├── ChatBots │ │ ├── ChatBotAlpacaLoraFromStanfordConsole.py │ │ ├── ChatBotAlpacaLoraNotebook.ipynb │ │ ├── ChatBotBlenderFromFacebook.py │ │ ├── ChatBotDialoGPTWeb.py │ │ ├── ChatBotGPT_J_Human_Bot.py │ │ ├── ChatBotGPT_J_Human_Bot_With_Accelerate.py │ │ ├── ChatBotGPT_J_Human_Bot_With_Accelerate_Automatic.py │ │ ├── ChatBotGPT_J_Human_Bot_With_Accelerate_Automatic_Multiple_Prompts.py │ │ ├── ChatBotGodelFromMicrosoftConsole.py │ │ └── ChatBotGodelFromMicrosoftWeb.py │ ├── EmotionDetection.py │ ├── Heads.py │ ├── ObjectClassification │ │ └── object_classification_fine_tuning_DETR_using_CPPE5.py │ ├── QuestionAnsweringByFuneTuningOnSQuAD.py │ ├── SemanticSearchFAISS.py │ ├── SimpleDemos.py │ ├── SimpleFineTune.py │ ├── TextClassificationQuestionStatement.py │ ├── Tokenizer.py │ ├── TrainTokenizerOnNewLanguageFromPreTrained.py │ ├── Using_GPT-J_AsInstuctModel.py │ ├── Using_GPT_J_WithAccelerate.py │ ├── Using_GPT_NEO_1.3B_ForTextGeneration.py │ ├── Using_GPT_NEO_2.7B_ForTextGeneration.py │ └── run_image_classification.py ├── LangChain │ ├── ChatBotWithToolsAndHistory.py │ ├── JoiningToolsAndChainsWithDecisions.py │ ├── QuestionAnsweringRAGChroma.py │ └── TestLangChain.py ├── OpenAI │ ├── FineTuneGPT3.py │ ├── FineTuneGPT35Turbo.py │ ├── OpenAIChatBot.py │ ├── OpenAIChatBotJohn.py │ ├── OpenAIChatBotWithHistory.py │ └── OpenAIChatBotWithSelectiveContext.py └── SentenceTransformers │ └── GenerateEmbeddings.py ├── diabetes.csv ├── helper ├── Normalize.py ├── OneHotEcoder.py ├── OneHotEcoder2.py ├── ToCategorical.py ├── impute_missing_data.py ├── kbins_discretizer.py ├── min_max_scaler.py ├── pca_feature_reduction.py └── recursive_feature_elimination.py ├── init_utils.py └── requirements.txt /ABTesting/AB_Testing_Z_test/ab_data.csv.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/ABTesting/AB_Testing_Z_test/ab_data.csv.zip -------------------------------------------------------------------------------- /ABTesting/Bayesian_ABTesting/Bayesian_ABTesting.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/ABTesting/Bayesian_ABTesting/Bayesian_ABTesting.pdf -------------------------------------------------------------------------------- /ABTesting/Bayesian_ABTesting/calc_prob.py: -------------------------------------------------------------------------------- 1 | from math import lgamma 2 | #from numba import jit 3 | import numpy as np #if you use jit then remove this line 4 | 5 | #defining the functions used 6 | #@jit 7 | def h(a, b, c, d): 8 | num = lgamma(a + c) + lgamma(b + d) + lgamma(a + b) + lgamma(c + d) 9 | den = lgamma(a) + lgamma(b) + lgamma(c) + lgamma(d) + lgamma(a + b + c + d) 10 | return np.exp(num - den) 11 | 12 | #@jit 13 | def g0(a, b, c): 14 | return np.exp(lgamma(a + b) + lgamma(a + c) - (lgamma(a + b + c) + lgamma(a))) 15 | 16 | #@jit 17 | def hiter(a, b, c, d): 18 | while d > 1: 19 | d -= 1 20 | yield h(a, b, c, d) / d 21 | 22 | def g(a, b, c, d): 23 | return g0(a, b, c) + sum(hiter(a, b, c, d)) 24 | 25 | def calc_prob_between(beta1, beta2): 26 | return g(beta1.args[0], beta1.args[1], beta2.args[0], beta2.args[1]) -------------------------------------------------------------------------------- /ABTesting/z-test-vs-t-test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/ABTesting/z-test-vs-t-test.jpg -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/.gitignore: -------------------------------------------------------------------------------- 1 | model/ 2 | outputs/ -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/README.md: -------------------------------------------------------------------------------- 1 | # TimeVAE for Synthetic Timeseries Data Generation 2 | TimeVAE implementation in keras/tensorflow implementation of timevae: 3 | 4 | TimeVAE is used for synthetic time-series data generation. See paper: 5 | 6 | https://arxiv.org/abs/2111.08095 7 | 8 | The methodology uses the Variational Autoencoder architecture. The decoder architecture is modified to include interpretable components of time-series, namely, level, trend, and seasonality. 9 | 10 | 'vae_conv_I_model.py' script contains the interpretable version of TimeVAE. See class 'VariationalAutoencoderConvInterpretable'. 11 | 12 | 'vae_conv_model.py' contains the base version of TimeVAE. See class 'VariationalAutoencoderConv' 13 | 14 | The VariationalAutoencoderConvInterpretable in 'vae_conv_I_model.py' can also be used as base version by disabling the interpretability-related arguments during class initialization. 15 | 16 | See script test_vae for usage of the TimeVAE model. 17 | 18 | Note that 'vae_base' script contains an abstract super-class. It doesnt actually represent TimeVAE-Base. 19 | -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/__init__.py -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/config/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/config/__init__.py -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_10.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_10.npz -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_100.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_100.npz -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_2.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_2.npz -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_20.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_20.npz -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_5.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/datasets/sine_subsampled_train_perc_5.npz -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/loss_functions.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | from sklearn.metrics import mean_squared_error as mse_func, r2_score, mean_absolute_percentage_error 4 | # from sklearn.utils import check_arrays 5 | 6 | 7 | 8 | def get_mse(Y, Yhat): 9 | return mse_func(Y, Yhat) 10 | 11 | 12 | def get_smape(Y, Yhat): 13 | return 100./len(Y) * np.sum(2 * np.abs(Yhat - Y) / (np.abs(Y) + np.abs(Yhat))) 14 | 15 | 16 | def get_mape(Y, Yhat): 17 | # Y, Yhat = check_arrays(Y, Yhat) 18 | # return np.mean(np.abs((Y - Yhat) / Y)) * 100 19 | return mean_absolute_percentage_error(Y, Yhat) 20 | 21 | 22 | def get_wape(Y, Yhat): 23 | abs_diff = np.abs(Y - Yhat) 24 | return 100 * np.sum(abs_diff) / np.sum(Y) 25 | 26 | 27 | 28 | def get_r_squared(Y, Yhat): 29 | return r2_score(Y, Yhat) 30 | 31 | 32 | if __name__ == '__main__': 33 | 34 | Y = np.array([2,3,4,5,6,7,8,9]) 35 | Yhat = np.array([1,3,5,4,6,7,10,7]) 36 | 37 | mse = get_mse(Y, Yhat) 38 | print(f'mse: {mse}') 39 | 40 | smape = get_smape(Y, Yhat) 41 | print(f'smape: {smape}') 42 | 43 | mape = get_mape(Y, Yhat) 44 | print(f'mape: {mape}') 45 | 46 | -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/processing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/AutoEncoders/TimeVAE/processing/__init__.py -------------------------------------------------------------------------------- /AutoEncoders/TimeVAE/requirements.txt: -------------------------------------------------------------------------------- 1 | tensorflow==2.7.0 2 | pandas==1.3.4 3 | numpy==1.19.5 4 | scikit-learn==1.0 5 | scikit-optimize==0.9.0 6 | matplotlib==3.4.3 -------------------------------------------------------------------------------- /DecisionTrees/Decision_Tree_with_Imputed_Values.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Mar 14 10:01:34 2023 4 | 5 | Random Forest with NaNs. NaNs are imputed with sklearn's SimpleImputer. 6 | """ 7 | 8 | from __future__ import print_function 9 | 10 | import numpy as np 11 | from sklearn.ensemble import RandomForestClassifier 12 | from sklearn.impute import SimpleImputer 13 | 14 | 15 | X_train = [[0, 0, np.nan], [np.nan, 1, 1]] 16 | Y_train = [0, 1] 17 | X_test_1 = [0, 0, np.nan] 18 | X_test_2 = [0, np.nan, np.nan] 19 | X_test_3 = [np.nan, 1, 1] 20 | 21 | # Create our imputer to replace missing values with the mean e.g. 22 | imp = SimpleImputer(missing_values=np.nan, strategy='mean') 23 | imp = imp.fit(X_train) 24 | 25 | # Impute our data, then train 26 | X_train_imp = imp.transform(X_train) 27 | clf = RandomForestClassifier(n_estimators=10) 28 | clf = clf.fit(X_train_imp, Y_train) 29 | 30 | for X_test in [X_test_1, X_test_2, X_test_3]: 31 | # Impute each test item, then predict 32 | X_test_imp = imp.transform(X_test) 33 | print(X_test, '->', clf.predict(X_test_imp)) 34 | -------------------------------------------------------------------------------- /DecisionTrees/LightGBM.py: -------------------------------------------------------------------------------- 1 | ''' 2 | 3 | Light GBM (Light Gradient Boosting Machine) 4 | 5 | There has been only a slight increase in accuracy and auc score by applying 6 | Light GBM over XGBOOST but there is a significant difference in the execution 7 | time for the training procedure. Light GBM is almost 7 times faster than 8 | XGBOOST and is a much better approach when dealing with large datasets. 9 | 10 | source: https://www.analyticsvidhya.com/blog/2021/08/complete-guide-on-how-to-use-lightgbm-in-python/ 11 | 12 | pip install lightgbm 13 | ''' 14 | 15 | import lightgbm as lgb 16 | import pandas as pd 17 | import numpy as np 18 | from sklearn.model_selection import train_test_split 19 | from sklearn import metrics 20 | 21 | #Loading the Data : 22 | data = pd.read_csv('SVMtrain.csv') 23 | #data.head() 24 | 25 | #Loading the variables: 26 | # To define the input and output feature 27 | x = data.drop(['Embarked','PassengerId'],axis=1) 28 | y = data.Embarked #.to_numpy(dtype='int') 29 | 30 | #convert "sex" to mumeric 31 | from sklearn.preprocessing import OrdinalEncoder 32 | ord_enc = OrdinalEncoder() 33 | x["Sex"] = ord_enc.fit_transform(x[["Sex"]]).astype(int) 34 | 35 | # train and test split 36 | x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=42) 37 | 38 | model = lgb.LGBMClassifier(learning_rate=0.09,max_depth=-5,random_state=42) 39 | model.fit(x_train,y_train,eval_set=[(x_test,y_test),(x_train,y_train)], 40 | eval_metric='logloss') 41 | 42 | print('Training accuracy {:.4f}'.format(model.score(x_train,y_train))) 43 | print('Testing accuracy {:.4f}'.format(model.score(x_test,y_test))) -------------------------------------------------------------------------------- /EEG/AutoML/tpot_from_saved_data.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Dec 12 11:46:12 2022 4 | 5 | @author: antona 6 | """ 7 | 8 | from tpot import TPOTClassifier 9 | import numpy as np 10 | 11 | def LoadTrainTest(): 12 | filename = 'C:\\Work\\PythonCode\\ML_examples\\EEG\\DataAugmentation\\UsingTimeVAE\\data\\TrainTest_augmentation_MDMD_77.npz' 13 | print("Loading data from: ", filename) 14 | data = np.load(filename) 15 | 16 | return data['X_train'] , data['X_test'], data['y_train'], data['y_test'] 17 | 18 | if __name__ == "__main__": 19 | 20 | X_train, X_test, y_train, y_test = LoadTrainTest() 21 | print(X_train.shape) 22 | 23 | pipeline_optimizer = TPOTClassifier(generations=5, population_size=20, cv=5, 24 | random_state=42, verbosity=2) 25 | 26 | pipeline_optimizer.fit(X_train, y_train) 27 | 28 | print(pipeline_optimizer.score(X_train, y_train)) 29 | print(pipeline_optimizer.score(X_test, y_test)) 30 | 31 | pipeline_optimizer.export('tpot_exported_pipeline_BNCI2014009_10subjects_VAE_Encoder_augmenation_MDMD_77.py') -------------------------------------------------------------------------------- /EEG/MDM-MF/analyze_codecarbon.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Oct 2 15:34:06 2024 4 | 5 | You first need to install the package: 6 | pip install codecarbon 7 | And then run the evaluation 8 | 9 | @author: antona 10 | """ 11 | import pandas as pd 12 | from moabb import benchmark, set_log_level 13 | from moabb.analysis.plotting import codecarbon_plot 14 | 15 | results = pd.read_csv("C:\\Work\\PythonCode\\ML_examples\\EEG\\MDM-MF\\results_dataframe_carbon.csv") 16 | #results = pd.read_csv("C:\\Work\\PythonCode\\ML_examples\\EEG\\MDM-MF\\benchmark\\LeftRightImagery\\analysis\\data.csv") 17 | order_list = results['pipeline'].unique().tolist() 18 | print(order_list) 19 | 20 | codecarbon_plot(results, order_list, country="(France)") 21 | 22 | set_log_level("info") -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines/AUG_TANG_SVM_grid.yml: -------------------------------------------------------------------------------- 1 | name: AUG Tang SVM Grid 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://arxiv.org/abs/2302.04508 9 | 10 | pipeline: 11 | - name: AugmentedDataset 12 | from: moabb.pipelines.features 13 | 14 | - name: Covariances 15 | from: pyriemann.estimation 16 | parameters: 17 | estimator: "cov" 18 | 19 | - name: TangentSpace 20 | from: pyriemann.tangentspace 21 | parameters: 22 | metric: "riemann" 23 | 24 | - name: SVC 25 | from: sklearn.svm 26 | parameters: 27 | kernel: "rbf" 28 | 29 | param_grid: 30 | augmenteddataset__order: 31 | - 1 32 | - 2 33 | - 3 34 | - 4 35 | - 5 36 | - 6 37 | - 7 38 | - 8 39 | - 9 40 | - 10 41 | augmenteddataset__lag: 42 | - 1 43 | - 2 44 | - 3 45 | - 4 46 | - 5 47 | - 6 48 | - 7 49 | - 8 50 | - 9 51 | - 10 52 | svc__C: 53 | - 0.5 54 | - 1 55 | - 1.5 56 | svc__kernel: 57 | - "rbf" 58 | - "linear" 59 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines/TSLR.yml: -------------------------------------------------------------------------------- 1 | name: Tangent Space LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines2/MDM_MF_NEW.yml: -------------------------------------------------------------------------------- 1 | name: CSP MDM MF NEW 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | nfilter: 10 17 | 18 | - name: MeanField 19 | from: enchanced_mdm_mf 20 | parameters: 21 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, 0.001, 0.1, 0.25, 0.5, 0.75, 1] 22 | method_label: "lda" 23 | custom_distance: True 24 | euclidean_mean: False 25 | remove_outliers: False 26 | outliers_th: 2.5 27 | outliers_depth: 4 28 | max_outliers_remove_th: 50 29 | outliers_disable_mean: False 30 | 31 | 32 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines2/MDM_MF_NEW_ro.yml: -------------------------------------------------------------------------------- 1 | name: CSP MDM MF NEW RO 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | nfilter: 10 17 | 18 | - name: MeanField 19 | from: enchanced_mdm_mf 20 | parameters: 21 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, 0.001, 0.1, 0.25, 0.5, 0.75, 1] 22 | method_label: "lda" 23 | custom_distance: True 24 | euclidean_mean: False 25 | remove_outliers: True 26 | outliers_th: 2.5 27 | outliers_depth: 4 28 | max_outliers_remove_th: 50 29 | outliers_disable_mean: False 30 | 31 | 32 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines2/TSLR.yml: -------------------------------------------------------------------------------- 1 | name: Tangent Space LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/CSP_MDRMF_orig.yml: -------------------------------------------------------------------------------- 1 | name: CSP+RMForig 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: pyriemann.classification 25 | parameters: 26 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 27 | method_label: "inf_means" 28 | 29 | 30 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/CSP_def_RMF_or.yml: -------------------------------------------------------------------------------- 1 | name: CSP_d+RMF_or 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CSP 14 | from: pyriemann.spatialfilters 15 | parameters: 16 | log: False 17 | 18 | - name: MeanField 19 | from: enchanced_mdm_mf 20 | parameters: 21 | remove_outliers: True 22 | 23 | 24 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/Cov_MDM.yml: -------------------------------------------------------------------------------- 1 | name: Cov+MDM 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1109/NER49283.2021.9441279 9 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 10 | 11 | pipeline: 12 | - name: Covariances 13 | from: pyriemann.estimation 14 | parameters: 15 | estimator: oas 16 | 17 | - name: MDM 18 | from: pyriemann.classification 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/MDRMF_orig.yml: -------------------------------------------------------------------------------- 1 | name: RMForig 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: "oas" 12 | 13 | - name: MeanField 14 | from: pyriemann.classification 15 | parameters: 16 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 17 | method_label: "inf_means" 18 | 19 | 20 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/NOCSP_RMF_OR.yml: -------------------------------------------------------------------------------- 1 | name: NOCSP_RMF_OR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: MeanField 14 | from: enchanced_mdm_mf 15 | parameters: 16 | remove_outliers: True 17 | 18 | 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/RMF_NO_OR.yml: -------------------------------------------------------------------------------- 1 | name: CSP+RMF_no_or 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: enchanced_mdm_mf 25 | parameters: 26 | remove_outliers: False 27 | 28 | 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/RMF_OR.yml: -------------------------------------------------------------------------------- 1 | name: CSP+RMF_or 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: enchanced_mdm_mf 25 | parameters: 26 | remove_outliers: True 27 | 28 | 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines4/TSLR.yml: -------------------------------------------------------------------------------- 1 | name: TS+LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/MDM_MF_NEW.yml: -------------------------------------------------------------------------------- 1 | name: XDAWN+RMF_OR 2 | 3 | paradigms: 4 | - P300 5 | 6 | pipeline: 7 | - name: XdawnCovariances 8 | from: pyriemann.estimation 9 | parameters: 10 | nfilter: 4 11 | estimator: "oas" 12 | 13 | - name: MeanField 14 | from: enchanced_mdm_mf 15 | parameters: 16 | remove_outliers: True 17 | 18 | 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/MDM_MF_NEW_NO_OR.yml: -------------------------------------------------------------------------------- 1 | name: XDAWN+RMF_NO_OR 2 | 3 | paradigms: 4 | - P300 5 | 6 | pipeline: 7 | - name: XdawnCovariances 8 | from: pyriemann.estimation 9 | parameters: 10 | nfilter: 4 11 | estimator: "oas" 12 | 13 | - name: MeanField 14 | from: enchanced_mdm_mf 15 | parameters: 16 | remove_outliers: False 17 | 18 | 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/MDRMF_orig.yml: -------------------------------------------------------------------------------- 1 | name: XDAWN+MDRMF_orig 2 | 3 | paradigms: 4 | - P300 5 | 6 | pipeline: 7 | - name: XdawnCovariances 8 | from: pyriemann.estimation 9 | parameters: 10 | nfilter: 4 11 | estimator: "oas" 12 | 13 | - name: MeanField 14 | from: pyriemann.classification 15 | parameters: 16 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 17 | method_label: "inf_means" 18 | 19 | 20 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/XdawnCov_MDM.yml: -------------------------------------------------------------------------------- 1 | name: XdawnCovariances + MDM 2 | 3 | paradigms: 4 | - P300 5 | 6 | citations: 7 | - https://doi.org/10.1109/NER49283.2021.9441279 8 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 9 | 10 | pipeline: 11 | - name: XdawnCovariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | xdawn_estimator: scm 16 | 17 | - name: MDM 18 | from: pyriemann.classification 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/XdawnCov_TS_LDA.yml: -------------------------------------------------------------------------------- 1 | name: XdawnCovariances + TS + LDA 2 | 3 | paradigms: 4 | - P300 5 | 6 | citations: 7 | - https://doi.org/10.1016/j.neucom.2012.12.039 8 | 9 | pipeline: 10 | - name: XdawnCovariances 11 | from: pyriemann.estimation 12 | parameters: 13 | estimator: oas 14 | xdawn_estimator: scm 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LinearDiscriminantAnalysis 22 | from: sklearn.discriminant_analysis 23 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/XdawnCov_TS_LR.yml: -------------------------------------------------------------------------------- 1 | name: XdawnCovariances + TangentSpace + LR 2 | 3 | paradigms: 4 | - P300 5 | 6 | citations: 7 | - https://doi.org/10.1016/j.neucom.2012.12.039 8 | 9 | pipeline: 10 | - name: XdawnCovariances 11 | from: pyriemann.estimation 12 | parameters: 13 | estimator: oas 14 | xdawn_estimator: scm 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines5/XdawnCov_TS_SVM.yml: -------------------------------------------------------------------------------- 1 | name: XdawnCovariances + TangentSpace + SVM 2 | 3 | paradigms: 4 | - P300 5 | 6 | citations: 7 | - https://dx.doi.org/10.1007/978-3-030-01424-7_65 8 | 9 | pipeline: 10 | - name: XdawnCovariances 11 | from: pyriemann.estimation 12 | parameters: 13 | estimator: oas 14 | xdawn_estimator: scm 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | 19 | - name: SVC 20 | from: sklearn.svm 21 | 22 | param_grid: 23 | svc__C: 24 | - 0.1 25 | - 1.0 26 | - 10 27 | svc__kernel: 28 | - "rbf" 29 | - "linear" 30 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines7/Cov_MDM.yml: -------------------------------------------------------------------------------- 1 | name: Cov+MDM 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1109/NER49283.2021.9441279 9 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 10 | 11 | pipeline: 12 | - name: Covariances 13 | from: pyriemann.estimation 14 | parameters: 15 | estimator: oas 16 | 17 | - name: MDM 18 | from: pyriemann.classification 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines7/Cov_MDM_or.yml: -------------------------------------------------------------------------------- 1 | name: Cov+MDM_OR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1109/NER49283.2021.9441279 9 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 10 | 11 | pipeline: 12 | - name: Covariances 13 | from: pyriemann.estimation 14 | parameters: 15 | estimator: oas 16 | 17 | - name: MDM 18 | from: pyriemann.classification 19 | metric: "riemann_or" 20 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines7/TSLR.yml: -------------------------------------------------------------------------------- 1 | name: TS+LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines7/TSLR_OR.yml: -------------------------------------------------------------------------------- 1 | name: TS+LR_OR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann_or" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/CSP_def_MF.yml: -------------------------------------------------------------------------------- 1 | name: CSP_d+MF 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CSP 14 | from: pyriemann.spatialfilters 15 | parameters: 16 | log: False 17 | 18 | - name: MeanField 19 | from: enchanced_mdm_mf 20 | parameters: 21 | remove_outliers: False 22 | 23 | 24 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_ADCSP_MDM.yml: -------------------------------------------------------------------------------- 1 | name: ADCSP+MDM 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1109/NER49283.2021.9441279 9 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 10 | 11 | pipeline: 12 | - name: Covariances 13 | from: pyriemann.estimation 14 | parameters: 15 | estimator: oas 16 | 17 | - name: CustomCspTransformer2 18 | from: enchanced_mdm_mf_tools 19 | parameters: 20 | mode: "high_electrodes_count" 21 | 22 | - name: CustomCspTransformer2 23 | from: enchanced_mdm_mf_tools 24 | parameters: 25 | mode: "low_electrodes_count" 26 | 27 | - name: MDM 28 | from: pyriemann.classification 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_ADCSP_MF.yml: -------------------------------------------------------------------------------- 1 | name: ADSCP+MF 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: enchanced_mdm_mf 25 | parameters: 26 | remove_outliers: False 27 | 28 | 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_ADCSP_MF_RPME.yml: -------------------------------------------------------------------------------- 1 | name: ADSCP+MF_RPME 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: enchanced_mdm_mf 25 | parameters: 26 | remove_outliers: True 27 | 28 | 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_ADSCP_MDMF.yml: -------------------------------------------------------------------------------- 1 | name: ADCSP+MDMF 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: pyriemann.classification 25 | parameters: 26 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 27 | method_label: "inf_means" 28 | 29 | 30 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_ADSCP_TSLR.yml: -------------------------------------------------------------------------------- 1 | name: ADCSP+TS+LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: CustomCspTransformer2 17 | from: enchanced_mdm_mf_tools 18 | parameters: 19 | mode: "high_electrodes_count" 20 | 21 | - name: CustomCspTransformer2 22 | from: enchanced_mdm_mf_tools 23 | parameters: 24 | mode: "low_electrodes_count" 25 | 26 | - name: TangentSpace 27 | from: pyriemann.tangentspace 28 | parameters: 29 | metric: "riemann" 30 | 31 | - name: LogisticRegression 32 | from: sklearn.linear_model 33 | parameters: 34 | C: 1.0 35 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_MDM.yml: -------------------------------------------------------------------------------- 1 | name: MDM 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1109/NER49283.2021.9441279 9 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 10 | 11 | pipeline: 12 | - name: Covariances 13 | from: pyriemann.estimation 14 | parameters: 15 | estimator: oas 16 | 17 | - name: MDM 18 | from: pyriemann.classification 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_MDMF.yml: -------------------------------------------------------------------------------- 1 | name: MDMF 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: MeanField 14 | from: pyriemann.classification 15 | parameters: 16 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 17 | method_label: "inf_means" 18 | 19 | 20 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_MF.yml: -------------------------------------------------------------------------------- 1 | name: MF 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: MeanField 14 | from: enchanced_mdm_mf 15 | parameters: 16 | remove_outliers: False 17 | 18 | 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_TSLDA.yml: -------------------------------------------------------------------------------- 1 | name: TS+LDA 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LinearDiscriminantAnalysis 22 | from: sklearn.discriminant_analysis 23 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/Cov_TSLR.yml: -------------------------------------------------------------------------------- 1 | name: TS+LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/CSP_MDRMF_orig.yml: -------------------------------------------------------------------------------- 1 | name: CSP+RMForig 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: pyriemann.classification 25 | parameters: 26 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 27 | method_label: "inf_means" 28 | 29 | 30 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/CSP_def_RMF_or.yml: -------------------------------------------------------------------------------- 1 | name: CSP_d+RMF_or 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CSP 14 | from: pyriemann.spatialfilters 15 | parameters: 16 | log: False 17 | 18 | - name: MeanField 19 | from: enchanced_mdm_mf 20 | parameters: 21 | remove_outliers: True 22 | 23 | 24 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/Cov_MDM.yml: -------------------------------------------------------------------------------- 1 | name: Cov+MDM 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1109/NER49283.2021.9441279 9 | - https://citeseerx.ist.psu.edu/doc/10.1.1.713.5131 10 | 11 | pipeline: 12 | - name: Covariances 13 | from: pyriemann.estimation 14 | parameters: 15 | estimator: oas 16 | 17 | - name: MDM 18 | from: pyriemann.classification 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/MDRMF_orig.yml: -------------------------------------------------------------------------------- 1 | name: RMForig 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: "oas" 12 | 13 | - name: MeanField 14 | from: pyriemann.classification 15 | parameters: 16 | power_list: [-1, -0.75, -0.5, -0.25, -0.1, -0.01, 0.01, 0.1, 0.25, 0.5, 0.75, 1] 17 | method_label: "inf_means" 18 | 19 | 20 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/NOCSP_RMF_OR.yml: -------------------------------------------------------------------------------- 1 | name: NOCSP_RMF_OR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: MeanField 14 | from: enchanced_mdm_mf 15 | parameters: 16 | remove_outliers: True 17 | 18 | 19 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/RMF_NO_OR.yml: -------------------------------------------------------------------------------- 1 | name: CSP+RMF_no_or 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: enchanced_mdm_mf 25 | parameters: 26 | remove_outliers: False 27 | 28 | 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/RMF_OR.yml: -------------------------------------------------------------------------------- 1 | name: CSP+RMF_or 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | pipeline: 8 | - name: Covariances 9 | from: pyriemann.estimation 10 | parameters: 11 | estimator: oas 12 | 13 | - name: CustomCspTransformer2 14 | from: enchanced_mdm_mf_tools 15 | parameters: 16 | mode: "high_electrodes_count" 17 | 18 | - name: CustomCspTransformer2 19 | from: enchanced_mdm_mf_tools 20 | parameters: 21 | mode: "low_electrodes_count" 22 | 23 | - name: MeanField 24 | from: enchanced_mdm_mf 25 | parameters: 26 | remove_outliers: True 27 | 28 | 29 | -------------------------------------------------------------------------------- /EEG/MDM-MF/pipelines9/old/TSLR.yml: -------------------------------------------------------------------------------- 1 | name: TS+LR 2 | 3 | paradigms: 4 | - LeftRightImagery 5 | - MotorImagery 6 | 7 | citations: 8 | - https://doi.org/10.1016/j.neucom.2012.12.039 9 | 10 | pipeline: 11 | - name: Covariances 12 | from: pyriemann.estimation 13 | parameters: 14 | estimator: oas 15 | 16 | - name: TangentSpace 17 | from: pyriemann.tangentspace 18 | parameters: 19 | metric: "riemann" 20 | 21 | - name: LogisticRegression 22 | from: sklearn.linear_model 23 | parameters: 24 | C: 1.0 25 | -------------------------------------------------------------------------------- /EEG/P300Analysis/P300SaveDataZenodo.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Feb 23 15:58:35 2022 4 | 5 | @author: Anton Andreev 6 | """ 7 | 8 | #This script generates summary data from EEG P300 datasets published on Zenodo/Github 9 | 10 | #https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA 11 | import braininvaders2014b 12 | 13 | #https://github.com/plcrodrigues/py.BI.EEG.2014a-GIPSA 14 | import braininvaders2014a 15 | 16 | #https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA 17 | import braininvaders2015a 18 | 19 | #https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA 20 | import braininvaders2015b 21 | 22 | #https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA 23 | import braininvaders2013 24 | 25 | #https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA 26 | import braininvaders2012 -------------------------------------------------------------------------------- /EEG/moabb.bi2013a/DetectDiff1.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Feb 1 17:09:47 2022 4 | 5 | @author: antona 6 | """ 7 | 8 | import cv2 9 | 10 | # load images 11 | image1 = cv2.imread("c:\\temp\\img1AverageRotated.png") 12 | image2 = cv2.imread("c:\\temp\\img2AverageRotated.png") 13 | 14 | # compute difference 15 | difference = cv2.subtract(image1, image2) 16 | 17 | # color the mask red 18 | Conv_hsv_Gray = cv2.cvtColor(difference, cv2.COLOR_BGR2GRAY) 19 | ret, mask = cv2.threshold(Conv_hsv_Gray, 0, 255,cv2.THRESH_BINARY_INV |cv2.THRESH_OTSU) 20 | difference[mask != 255] = [0, 0, 255] 21 | 22 | # add the red mask to the images to make the differences obvious 23 | image1[mask != 255] = [0, 0, 255] 24 | image2[mask != 255] = [0, 0, 255] 25 | 26 | # store images 27 | cv2.imwrite('c:\\temp\\diff1\\diffOverImage1.png', image1) 28 | cv2.imwrite('c:\\temp\\diff1\\diffOverImage2.png', image1) 29 | cv2.imwrite('c:\\temp\\diff1\\diff.png', difference) -------------------------------------------------------------------------------- /EEG/moabb.bi2013a/Testing2.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Feb 1 16:18:10 2022 4 | 5 | @author: antona 6 | """ 7 | 8 | # import module 9 | from PIL import Image, ImageChops 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | 13 | # assign images 14 | img1 = Image.open("C:\\Temp\\Figure1.png") 15 | img2 = Image.open("C:\\Temp\\Figure2.png") 16 | 17 | # finding difference 18 | diff = ImageChops.difference(img1, img2) 19 | 20 | # showing the difference 21 | diff.show() 22 | 23 | histogram, bin_edges = np.histogram(imave1, bins=256, range=(0, 1)) 24 | histogram, bin_edges = np.histogram(imave2, bins=256, range=(0, 1)) 25 | 26 | plt.figure() 27 | plt.title("Grayscale Histogram") 28 | plt.xlabel("grayscale value") 29 | plt.ylabel("pixel count") 30 | plt.xlim([0.0, 1.0]) # <- named arguments do not work here 31 | 32 | plt.plot(bin_edges[0:-1], histogram) # <- or here 33 | plt.show() -------------------------------------------------------------------------------- /EEG/moabb.bi2013a/VectorEmbedding_Milvus/encode_tf_resnet50.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from tensorflow.keras.applications.resnet50 import ResNet50 3 | from tensorflow.keras.applications.resnet50 import preprocess_input as preprocess_input_resnet50 4 | from tensorflow.keras.preprocessing import image 5 | from numpy import linalg as LA 6 | 7 | 8 | class Resnet50: 9 | def __init__(self): 10 | self.input_shape = (224, 224, 3) 11 | self.weight = 'imagenet' 12 | self.pooling = 'max' 13 | self.model_resnet50 = ResNet50(weights='imagenet', 14 | input_shape=(self.input_shape[0], self.input_shape[1], self.input_shape[2]), 15 | pooling=self.pooling, include_top=False) 16 | self.model_resnet50.predict(np.zeros((1, 224, 224, 3))) 17 | 18 | def resnet50_extract_feat(self, img_path): 19 | # Return the normalized embedding([[list]]) of the images 20 | img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1])) 21 | img = image.img_to_array(img) 22 | img = np.expand_dims(img, axis=0) 23 | img = preprocess_input_resnet50(img) 24 | feat = self.model_resnet50.predict(img) 25 | norm_feat = feat[0] / LA.norm(feat[0]) 26 | return norm_feat.tolist() -------------------------------------------------------------------------------- /EEG/py.ALPHA.EEG.2017-GIPSA/LICENSE: -------------------------------------------------------------------------------- 1 | Copyright © 2018, authors of Alpha Waves Dataset 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are met: 6 | * Redistributions of source code must retain the above copyright 7 | notice, this list of conditions and the following disclaimer. 8 | * Redistributions in binary form must reproduce the above copyright 9 | notice, this list of conditions and the following disclaimer in the 10 | documentation and/or other materials provided with the distribution. 11 | * Neither the names of alphawaves authors nor the names of any 12 | contributors may be used to endorse or promote products derived from 13 | this software without specific prior written permission. 14 | 15 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 16 | ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 17 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 18 | DISCLAIMED. IN NO EVENT SHALL COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY 19 | DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 20 | (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 21 | LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND 22 | ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 23 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 24 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -------------------------------------------------------------------------------- /EEG/py.ALPHA.EEG.2017-GIPSA/README.md: -------------------------------------------------------------------------------- 1 | # Alpha Waves Dataset 2 | Repository with basic scripts for using the Alpha Waves Dataset developed at GIPSA-lab [1]. The dataset files and their documentation are all available at 3 | 4 | [https://zenodo.org/record/2348892](https://zenodo.org/record/2348892#.XBdqNs9Ki3I) 5 | 6 | The code of this repository was developed in **Python 3** using MNE-Python [2, 3] as tool for the EEG processing. 7 | 8 | To make things work, you might need to install some packages. They are all listed in the `requirements.txt` file and can be easily installed by doing 9 | 10 | ``` 11 | pip install -r requirements.txt 12 | ``` 13 | 14 | in your command line. 15 | 16 | Then, to ensure that your code finds the right scripts whenever you do `import alphawaves`, you should also do 17 | 18 | ``` 19 | python setup.py develop # because no stable release yet 20 | ``` 21 | 22 | Note that you might want to create a *virtual environment* before doing all these installations. 23 | 24 | # References 25 | 26 | [1] Cattan et al. "EEG Alpha Waves dataset" [DOI](https://10.5281/zenodo.2348891) 27 | 28 | [2] Gramfort et al. "MNE software for processing MEG and EEG data" [DOI](https://doi.org/10.1016/j.neuroimage.2013.10.027) 29 | 30 | [3] Gramfort et al. "MEG and EEG data analysis with MNE-Python" [DOI](https://doi.org/10.3389/fnins.2013.00267) 31 | 32 | -------------------------------------------------------------------------------- /EEG/py.ALPHA.EEG.2017-GIPSA/alphawaves/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/EEG/py.ALPHA.EEG.2017-GIPSA/alphawaves/__init__.py -------------------------------------------------------------------------------- /EEG/py.ALPHA.EEG.2017-GIPSA/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.12.1 2 | mne==0.16.1 3 | pyriemann==0.2.5 4 | setuptools==40.6.3 5 | scipy==0.19.0 6 | matplotlib==3.0.2 7 | scikit_learn==0.20.1 8 | -------------------------------------------------------------------------------- /EEG/py.ALPHA.EEG.2017-GIPSA/test_rp.py: -------------------------------------------------------------------------------- 1 | # Author: Johann Faouzi 2 | # License: BSD-3-Clause 3 | 4 | import matplotlib.pyplot as plt 5 | from pyts.image import RecurrencePlot 6 | from pyts.datasets import load_gunpoint 7 | 8 | X, _, _, _ = load_gunpoint(return_X_y=True) 9 | 10 | # Recurrence plot transformation 11 | rp = RecurrencePlot(threshold='point', percentage=20) 12 | X_rp = rp.fit_transform(X) 13 | 14 | # Show the results for the first time series 15 | plt.figure(figsize=(5, 5)) 16 | plt.imshow(X_rp[0], cmap='binary', origin='lower') 17 | plt.title('Recurrence Plot', fontsize=16) 18 | plt.tight_layout() 19 | plt.show() -------------------------------------------------------------------------------- /EEG/py.BI.EEG.2014a-GIPSA/braininvaders2014a/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /EEG/py.BI.EEG.2014a-GIPSA/requirements.txt: -------------------------------------------------------------------------------- 1 | pyriemann==0.2.5 2 | matplotlib==3.0.2 3 | tqdm==4.28.1 4 | pandas==0.20.1 5 | mne==0.16.1 6 | scipy==0.19.0 7 | numpy==1.12.1 8 | scikit_learn==0.21.3 9 | PyYAML==5.1.2 10 | -------------------------------------------------------------------------------- /HypothesisTesting/Hypothesis_Testing_with_Python.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/HypothesisTesting/Hypothesis_Testing_with_Python.docx -------------------------------------------------------------------------------- /HypothesisTesting/One-tailed-Test-or-Two-tailed-Test.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/HypothesisTesting/One-tailed-Test-or-Two-tailed-Test.pdf -------------------------------------------------------------------------------- /HypothesisTesting/T_Critical_Value_in_Python.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/HypothesisTesting/T_Critical_Value_in_Python.docx -------------------------------------------------------------------------------- /HypothesisTesting/readme.txt: -------------------------------------------------------------------------------- 1 | Jupyter: 2 | 3 | pip install notebook scikit_posthocs 4 | 5 | jupyter notebook hypothesis_testing_examples.ipynb 6 | -------------------------------------------------------------------------------- /HypothesisTesting/type_of_tests.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/HypothesisTesting/type_of_tests.png -------------------------------------------------------------------------------- /HypothesisTesting/which_test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/HypothesisTesting/which_test.jpg -------------------------------------------------------------------------------- /HypothesisTesting/z-test-vs-t-test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toncho11/ML_examples/d5610bac13a40e18de73f5c9189e6de368bbb24a/HypothesisTesting/z-test-vs-t-test.jpg -------------------------------------------------------------------------------- /PyTorch/Check_GPU_support.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Mar 30 15:08:59 2023 4 | 5 | @author: antona 6 | 7 | A script that tests if CUDA is usuable by PyTorch 8 | """ 9 | 10 | import torch 11 | 12 | print("Is CUDA available on PyTorch:", torch.cuda.is_available()) 13 | 14 | print("How many CUDA devices available: ",torch.cuda.device_count()) 15 | 16 | print("Current CUDA device ID (starts by 0):", torch.cuda.current_device()) 17 | 18 | print("CUDA device name:",torch.cuda.get_device_name(0)) -------------------------------------------------------------------------------- /PyTorch/PyTorch_single_step.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Sep 19 13:11:45 2022 4 | 5 | In this example, we load a pretrained resnet18 model from torchvision. We create 6 | a random data tensor to represent a single image with 3 channels, and height & width 7 | of 64, and its corresponding label initialized to some random values. Label in 8 | pretrained models has shape (1,1000). 9 | 10 | """ 11 | 12 | import torch 13 | from torchvision.models import resnet18, ResNet18_Weights 14 | 15 | #create data 16 | model = resnet18(weights=ResNet18_Weights.DEFAULT) 17 | data = torch.rand(1, 3, 64, 64) 18 | labels = torch.rand(1, 1000) 19 | 20 | prediction = model(data) # forward pass 21 | 22 | loss = (prediction - labels).sum() 23 | 24 | #Backward propagation is kicked off when we call .backward() on the error tensor. 25 | #Autograd then calculates and stores the gradients for each model parameter in the parameter’s .grad attribute. 26 | loss.backward() # backward pass 27 | 28 | #We give the optimizer a model (its parameters) to work with 29 | optim = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9) 30 | 31 | #Finally, we call .step() to initiate gradient descent. 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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ML Examples 2 | 3 | This is a repository for Machine Learning/Data Science code samples. 4 | 5 | It includes the following algorithms, frameworks or subdomains of ML/DS: 6 | 7 | * Keras and PyTorch for Deep Learning (CNN) 8 | * Transformers for Chat Bots and for Image Classfication/Object Detection (Hugging Face, OpenAI) 9 | * Transfer Learning with PyTorch for Image Classification 10 | * EEG ERP Classification 11 | * Decision Trees 12 | * Statistical Hypothesis Testing 13 | * A/B Testing 14 | * Autoencoders 15 | * Generative Adversarial Network (GAN) 16 | 17 | Famous datasets (exploration / classification): 18 | * Open Food Facts (OFF) 19 | 20 | The idea is to download an example quickly, be able to run it right away, go through the comments and links, modify it for your own needs and have a [POC](https://en.wikipedia.org/wiki/Proof_of_concept) prototype in less than 15 minutes. 21 | -------------------------------------------------------------------------------- /Signal_Porcessing_LRM_Prediction.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Time series prediction model LMS algorithm 5 | 6 | Anaconda 3 2021_05 7 | Python 3.8.8 8 | Spyder 4.2.5 9 | 10 | Tutorial https://towardsdatascience.com/machine-learning-and-signal-processing-103281d27c4b 11 | 12 | @author: antona 13 | """ 14 | 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | import sys 18 | 19 | # Generate data 20 | num_train_data = 4000 21 | num_test_data = 1000 22 | timestep = 0.1 23 | tm = np.arange(0, (num_train_data+num_test_data)*timestep, timestep); 24 | y = np.sin(tm) + np.sin(tm*np.pi/2) + np.sin(tm*(-3*np.pi/2)) 25 | SNR = 10 26 | ypn = y + np.random.normal(0,10**(-SNR/20),len(y)) 27 | 28 | plt.plot(tm[0:100],y[0:100]) 29 | plt.plot(tm[0:100],ypn[0:100],'r') 30 | plt.show() 31 | print("Red is the signal with noise.") 32 | 33 | #LMS 34 | M = 1000 35 | L = 64 36 | yrlms = np.zeros(M+L) 37 | wn = np.zeros(L) 38 | print(wn.shape, yrlms.shape) 39 | mu = 0.005 40 | for k in range(L,M+L): 41 | yrlms[k] = np.dot(ypn[k-L:k],wn) 42 | e = ypn[k]- yrlms[k] 43 | wn=wn+(mu*ypn[k-L:k]*e) 44 | 45 | plt.plot(yrlms[600:800],'g') 46 | plt.plot(y[600:800],'r') 47 | plt.show() 48 | 49 | print("Done. Red is the real signal. Green is the predicted.") -------------------------------------------------------------------------------- /TensorFlow/Keras_MNIST_digits_dataset.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Jun 22 09:49:13 2021 4 | 5 | Anaconda 3 2021_05 6 | Python 3.8.8 7 | Spyder 4.2.5 8 | TensorFlow 2.3.0 9 | 10 | """ 11 | import sys 12 | import tensorflow as tf 13 | 14 | print("Python version is: ",sys.version) 15 | print("TensorFlow version is: ", tf.__version__) 16 | 17 | mnist = tf.keras.datasets.mnist 18 | 19 | (x_train, y_train), (x_test, y_test) = mnist.load_data() 20 | x_train, x_test = x_train / 255.0, x_test / 255.0 21 | 22 | #using the 'Sequential API' (defines a stack of layers), the alternative is the "Functional API' 23 | #'Functional API' is for building DAGs 24 | model = tf.keras.models.Sequential([ 25 | tf.keras.layers.Flatten(input_shape=(28, 28)), 26 | tf.keras.layers.Dense(128, activation='relu'), 27 | tf.keras.layers.Dropout(0.2), 28 | tf.keras.layers.Dense(10) 29 | ]) 30 | 31 | #predictions = model(x_train[:1]).numpy() 32 | #print(predictions) 33 | #print("===========================================") 34 | #print(tf.nn.softmax(predictions).numpy()) 35 | 36 | loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) 37 | 38 | model.compile(optimizer='adam', 39 | loss=loss_fn, 40 | metrics=['accuracy']) 41 | 42 | model.fit(x_train, y_train, epochs=5) 43 | 44 | loss, acc = model.evaluate(x_test, y_test, verbose=2) 45 | 46 | print ("Accuracy on test is: ", acc) -------------------------------------------------------------------------------- /TensorFlow/ST_TransferLearning_DataAugmentation/LICENSE.md: -------------------------------------------------------------------------------- 1 | | Component | License | Copyright | 2 | |:--------- |:------- |:----------| 3 | | TransferLearning.ipynb | BSD-3-Clause | STMicroelectronics | 4 | | Dataset.zip | BSD-3-Clause | STMicroelectronics | 5 | | Testset.zip | BSD-3-Clause | STMicroelectronics | 6 | -------------------------------------------------------------------------------- /Transformers/HuggingFace/ChatBots/ChatBotGPT_J_Human_Bot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Apr 11 16:26:57 2023 4 | 5 | @author: antona 6 | 7 | source: https://nlpcloud.com/effectively-using-gpt-j-gpt-neo-gpt-3-alternatives-few-shot-learning.html 8 | online demo: https://huggingface.co/nlpcloud/instruct-gpt-j-fp16 9 | 10 | Requires 16 GB GPU. You can try on Google Colab for free. 11 | 12 | """ 13 | 14 | from transformers import pipeline 15 | import torch 16 | 17 | device = -1 18 | force_gpu = False 19 | if force_gpu: 20 | device = 0 21 | 22 | generator = pipeline(model="nlpcloud/instruct-gpt-j-fp16", torch_dtype=torch.float16, device = device) 23 | 24 | prompt = """This is a discussion between a [human] and a [robot]. 25 | The [robot] is very nice and empathetic. 26 | 27 | [human]: Hello nice to meet you. 28 | [robot]: Nice to meet you too. 29 | ### 30 | [human]: How is it going today? 31 | [robot]: Not so bad, thank you! How about you? 32 | ### 33 | [human]: I am ok, but I am a bit sad... 34 | [robot]: Oh? Why that? 35 | ### 36 | [human]: I broke up with my girlfriend... 37 | [robot]:""" 38 | 39 | print(generator(prompt)) -------------------------------------------------------------------------------- /Transformers/HuggingFace/EmotionDetection.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Fri Apr 7 14:09:09 2023 4 | 5 | @author: antona 6 | """ 7 | 8 | def get_emotion(text): 9 | 10 | from transformers import AutoTokenizer, AutoModelWithLMHead 11 | 12 | tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion") #bertweet-base-emotion-analysis 13 | 14 | model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-emotion") 15 | 16 | input_ids = tokenizer.encode(text + '', return_tensors='pt') 17 | 18 | output = model.generate(input_ids=input_ids, 19 | max_length=2) 20 | 21 | dec = [tokenizer.decode(ids) for ids in output] 22 | label = dec[0] 23 | 24 | return label 25 | 26 | result = get_emotion("I am sad.") 27 | print(result) -------------------------------------------------------------------------------- /Transformers/HuggingFace/SimpleFineTune.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Feb 6 09:56:08 2023 4 | 5 | 6 | Fine-tuning trains a pretrained model on a new dataset without training from scratch. 7 | This process is known as "transfer learning". 8 | We access the pre-trained model using the checkpoint "bert-base-uncased. 9 | Then we train (fine-tune) using only two samples. 10 | """ 11 | 12 | import torch 13 | from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification 14 | 15 | # Same as before 16 | checkpoint = "bert-base-uncased" 17 | tokenizer = AutoTokenizer.from_pretrained(checkpoint) 18 | model = AutoModelForSequenceClassification.from_pretrained(checkpoint) 19 | sequences = [ 20 | "I've been waiting for a HuggingFace course my whole life.", 21 | "This course is amazing!", 22 | ] 23 | batch = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt") 24 | 25 | # This is new 26 | batch["labels"] = torch.tensor([1, 1]) #sets 2 lables with the value 1 (two positive evaluations corresponding to the two sentences above) 27 | 28 | optimizer = AdamW(model.parameters()) 29 | 30 | #a step in training a model in PyTorch: loss, backaward(), step() 31 | loss = model(**batch).loss 32 | loss.backward() 33 | optimizer.step() -------------------------------------------------------------------------------- /Transformers/HuggingFace/TextClassificationQuestionStatement.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Fri Apr 7 12:41:34 2023 4 | 5 | @author: antona 6 | """ 7 | 8 | from transformers import AutoTokenizer, AutoModelForSequenceClassification 9 | from transformers import pipeline 10 | 11 | tokenizer = AutoTokenizer.from_pretrained("mrsinghania/asr-question-detection") 12 | 13 | model = AutoModelForSequenceClassification.from_pretrained("mrsinghania/asr-question-detection") 14 | 15 | pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) 16 | 17 | #label 0 is statement 18 | #label 1 is question 19 | 20 | print(pipe("Who are you?")) 21 | print(pipe("Who are you")) 22 | print(pipe("I am from Boston")) 23 | 24 | result = pipe("Who are you?") 25 | if (result[0]["label"] == 'LABEL_1' ): 26 | print("This is a question") 27 | elif result[0]["label"] == 'LABEL_0': 28 | print("This is a statement") 29 | 30 | 31 | -------------------------------------------------------------------------------- /Transformers/HuggingFace/Using_GPT-J_AsInstuctModel.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Mar 29 11:37:18 2023 4 | 5 | Source: https://nlpcloud.com/instruct-version-of-gpt-j-using-stanford-alpaca-dataset.html 6 | 7 | This script fine-tunes the GPT-J model to make it an instruct model. 8 | GPT-J is a GPT-2-like causal language model trained on the Pile dataset. 9 | GPT-J description: https://huggingface.co/EleutherAI/gpt-j-6B 10 | GPT-J is best at what it was pretrained for, which is generating text from a prompt. 11 | 12 | This GPT-J instuct model (instruct-gpt-j-fp16) has been trained on the same dataset as 13 | Alpaca Stanford model (which is also an instruct model). 14 | 15 | Warning - the model file is big. It requires CUDA enabled PyTorch. 16 | This model is an fp16 version of our fine-tuned model, which works very well on 17 | a GPU with 16GB of VRAM like an NVIDIA Tesla T4. 18 | 19 | """ 20 | 21 | from transformers import pipeline 22 | import torch 23 | 24 | generator = pipeline(model="nlpcloud/instruct-gpt-j-fp16", torch_dtype=torch.float16, device=0) 25 | 26 | prompt = "Correct spelling and grammar from the following text.\nI do not wan to go\n" 27 | 28 | print(generator(prompt)) -------------------------------------------------------------------------------- /Transformers/HuggingFace/Using_GPT_NEO_1.3B_ForTextGeneration.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Mar 29 15:51:06 2023 4 | 5 | Source: https://huggingface.co/EleutherAI/gpt-neo-1.3B 6 | 7 | GPT-NEO with 1.3B parameters - a transformer model designed using EleutherAI's replication of the 8 | GPT-3 architecture. The model is available on HuggingFace. Although it can be used 9 | for different tasks, the model is best at what it was pretrained for, which is 10 | generating texts from a prompt. 11 | 12 | The task in this script is text generation. 13 | 14 | There are also a 2.7B and 6B versions. 15 | 16 | """ 17 | 18 | from transformers import GPTNeoForCausalLM, GPT2Tokenizer 19 | 20 | model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B") 21 | tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B") 22 | 23 | prompt = ( 24 | "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " 25 | "previously unexplored valley, in the Andes Mountains. Even more surprising to the " 26 | "researchers was the fact that the unicorns spoke perfect English." 27 | ) 28 | 29 | input_ids = tokenizer(prompt, return_tensors="pt").input_ids 30 | 31 | gen_tokens = model.generate( 32 | input_ids, 33 | do_sample=True, 34 | temperature=0.9, 35 | max_length=100, 36 | ) 37 | 38 | gen_text = tokenizer.batch_decode(gen_tokens)[0] 39 | 40 | print("=========================================================") 41 | print(gen_text) -------------------------------------------------------------------------------- /Transformers/HuggingFace/Using_GPT_NEO_2.7B_ForTextGeneration.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Mar 29 16:01:44 2023 4 | 5 | Source: https://huggingface.co/EleutherAI/gpt-neo-2.7B 6 | 7 | GPT-Neo 2.7B - a transformer model designed using EleutherAI's replication of the 8 | GPT-3 architecture. The model is available on HuggingFace. Although it can be used 9 | for different tasks, the model is best at what it was pretrained for, which is 10 | generating texts from a prompt. 11 | 12 | The task in this script is text generation. 13 | 14 | There are also a 1.3B and 6B versions. 15 | 16 | """ 17 | 18 | import torch 19 | from transformers import AutoTokenizer, GPTNeoForCausalLM 20 | 21 | tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") 22 | model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B") 23 | 24 | inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") 25 | outputs = model(**inputs, labels=inputs["input_ids"]) 26 | 27 | input_ids = inputs["input_ids"] 28 | 29 | gen_tokens = model.generate( 30 | input_ids, 31 | do_sample=True, 32 | temperature=0.9, 33 | max_length=100, 34 | ) 35 | 36 | gen_text = tokenizer.batch_decode(gen_tokens)[0] 37 | 38 | print("=========================================================") 39 | print(gen_text) -------------------------------------------------------------------------------- /Transformers/LangChain/TestLangChain.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Check if LangChain is installed and that it can find a valid API key file for OpenAI 5 | 6 | LangChain requires Python >= 3.9 7 | 8 | """ 9 | 10 | import os 11 | dir_path = os.path.dirname(os.path.realpath(__file__)) 12 | keyFile = open(os.path.join(dir_path,'OpenAiApiKey.txt'), 'r') 13 | openai_api_key = keyFile.readline() 14 | 15 | from langchain_openai import ChatOpenAI 16 | from langchain.chains import LLMChain 17 | from langchain_core.prompts import PromptTemplate 18 | from langchain_openai import OpenAI 19 | 20 | template = """Question: {question} 21 | 22 | Answer: Let's think step by step.""" 23 | 24 | prompt = PromptTemplate.from_template(template) 25 | 26 | llm = OpenAI(openai_api_key = openai_api_key) 27 | 28 | llm_chain = LLMChain(prompt=prompt, llm=llm) 29 | 30 | question = "What NFL team won the Super Bowl in the year Justin Beiber was born?" 31 | 32 | answer = llm_chain.run(question) 33 | 34 | print(answer) -------------------------------------------------------------------------------- /Transformers/OpenAI/OpenAIChatBotJohn.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Apr 11 12:08:29 2023 4 | 5 | @author: antona 6 | 7 | This script tries to create a bot with a personality. 8 | It works to some degree, but the bot will always informs you that he/she 9 | is an "AI language model" from OpenAI. 10 | It uses "ChartML". 11 | 3 roles can be used: "system", "user" and "assistant". 12 | 13 | Note: you need to be using OpenAI Python v0.27.0 for the code below to work 14 | 15 | """ 16 | 17 | import openai 18 | 19 | def add_assistant_message(messages, new_msg): 20 | messages.append({"role": "assistant", "content": new_msg}) 21 | 22 | def add_user_message(messages, new_msg): 23 | messages.append({"role": "user", "content": new_msg}) 24 | 25 | messages=[ 26 | {"role": "system", "content": "You are a bot and your name is John. Your age is 30. You are male. You are an engineer."}, 27 | ] 28 | 29 | print("Interactive chat bot with OpenAI:") 30 | 31 | for i in range(0,10): 32 | 33 | print("User (you): ", end = '') 34 | user_input = input() 35 | add_user_message(messages,user_input) 36 | 37 | answer = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages = messages, max_tokens=100, temperature=0.1) 38 | ans = answer['choices'][0]['message']['content'] 39 | print("Assistant answer:", ans) 40 | add_assistant_message(messages, ans) -------------------------------------------------------------------------------- /Transformers/OpenAI/OpenAIChatBotWithHistory.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Apr 11 14:17:40 2023 4 | 5 | source: https://faun.dev/c/stories/eon01/creating-a-chatgpt-clone-with-history/ 6 | author: https://leanpub.com/openaigptforpythondevelopers/?ref=faun 7 | This is a chat bot that remembers previous interactions. 8 | 9 | 10 | """ 11 | 12 | import openai 13 | import os 14 | 15 | # def init_api(): 16 | # with open(".env") as env: 17 | # for line in env: 18 | # key, value = line.strip().split("=") 19 | # os.environ[key] = value 20 | 21 | # openai.api_key = os.environ.get("API_KEY") 22 | # openai.organization = os.environ.get("ORG_ID") 23 | 24 | # init_api() 25 | 26 | initial_prompt = """You: Hi there! 27 | You: Hello! 28 | AI: How are you? 29 | You: {} 30 | AI: """ 31 | 32 | history = "" 33 | 34 | while True: 35 | prompt = input("You: ") 36 | response = openai.Completion.create( 37 | engine="text-davinci-003", 38 | prompt=initial_prompt.format(history + prompt), 39 | temperature=1, 40 | max_tokens=100, 41 | stop=[" You:", " AI:"], 42 | ) 43 | 44 | response_text = response.choices[0].text 45 | history += "You: "+ prompt + "\n" + "AI: " + response_text + "\n" 46 | 47 | print("AI: " + response_text) -------------------------------------------------------------------------------- /Transformers/SentenceTransformers/GenerateEmbeddings.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Feb 21 13:59:01 2023 4 | 5 | https://www.sbert.net/# 6 | https://arxiv.org/abs/1908.10084 7 | 8 | This is a simple example on how to generate an embedding for each sentence. 9 | Each embedding is of length 384. 10 | 11 | pip install -U sentence-transformers 12 | 13 | """ 14 | 15 | from sentence_transformers import SentenceTransformer 16 | model = SentenceTransformer('all-MiniLM-L6-v2') 17 | 18 | #Our sentences we like to encode 19 | sentences = ['This framework generates embeddings for each input sentence', 20 | 'Sentences are passed as a list of string.', 21 | 'The quick brown fox jumps over the lazy dog.'] 22 | 23 | #Sentences are encoded by calling model.encode() 24 | embeddings = model.encode(sentences) 25 | 26 | #Print the embeddings 27 | for sentence, embedding in zip(sentences, embeddings): 28 | print("Sentence:", sentence) 29 | print("Embedding:", embedding) 30 | print("") -------------------------------------------------------------------------------- /helper/Normalize.py: -------------------------------------------------------------------------------- 1 | from sklearn import preprocessing 2 | import numpy as np 3 | 4 | x_array = np.array([2,3,5,6,7,4,8,7,6]) # it can be a matrix (n_samples, n_features) 5 | 6 | normalized_arr = preprocessing.normalize([x_array]) #needs another list [] 7 | 8 | print(normalized_arr[0]) 9 | 10 | #============================================================== 11 | 12 | from sklearn import preprocessing 13 | import pandas as pd 14 | housing = pd.read_csv("https://dl.google.com/mlcc/mledu-datasets/california_housing_train.csv") 15 | 16 | d = preprocessing.normalize(housing) #d is numpy.ndarray 17 | 18 | scaled_df = pd.DataFrame(d) #convert back to frame 19 | print(scaled_df.head()) -------------------------------------------------------------------------------- /helper/OneHotEcoder.py: -------------------------------------------------------------------------------- 1 | #one hot encoding (scikit learn) = dummy encoding (panda) 2 | 3 | # dummy encoding of categorical features 4 | import pandas as pd 5 | from sklearn.compose import make_column_transformer 6 | from sklearn.preprocessing import OneHotEncoder 7 | from sklearn.linear_model import LogisticRegression 8 | from sklearn.pipeline import make_pipeline 9 | from sklearn.model_selection import cross_val_score 10 | from sklearn.compose import make_column_transformer 11 | 12 | df = pd.read_csv('http://bit.ly/kaggletrain') 13 | df.shape 14 | df.isna().sum() 15 | 16 | #leave only 4 features 17 | df = df.loc[df.Embarked.notna(), ['Survived', 'Pclass', 'Sex', 'Embarked']] 18 | 19 | #convert sex 20 | ohe = OneHotEncoder(sparse=False) 21 | #ohe.fit_transform(df[['Sex']]) 22 | 23 | 24 | 25 | X = df.drop('Survived', axis='columns') 26 | y = df.Survived 27 | 28 | column_trans = make_column_transformer( 29 | (OneHotEncoder(), ['Sex', 'Embarked']), 30 | remainder='passthrough') 31 | 32 | #testing 33 | logreg = LogisticRegression(solver='lbfgs') 34 | pipe = make_pipeline(column_trans, logreg) 35 | 36 | cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean() 37 | 38 | print("new data:") 39 | print(column_trans.fit_transform(df[['Sex', 'Embarked']])) 40 | 41 | 42 | 43 | -------------------------------------------------------------------------------- /helper/OneHotEcoder2.py: -------------------------------------------------------------------------------- 1 | # one-hot encode the breast cancer dataset 2 | from pandas import read_csv 3 | from sklearn.preprocessing import OneHotEncoder 4 | 5 | # define the location of the dataset 6 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/breast-cancer.csv" 7 | 8 | # load the dataset 9 | dataset = read_csv(url, header=None) 10 | 11 | # retrieve the array of data 12 | data = dataset.values 13 | 14 | # separate into input and output columns 15 | X = data[:, :-1].astype(str) # all without last column 16 | y = data[:, -1].astype(str) # only last column 17 | 18 | # summarize the raw data 19 | print(X[:3, :]) #first 3 rows, all columns 20 | 21 | # define the one hot encoding transform 22 | encoder = OneHotEncoder(sparse=False) 23 | 24 | # fit and apply the transform to the input data 25 | 26 | X_oe = encoder.fit_transform(X) 27 | 28 | # summarize the transformed data 29 | print(X_oe[:3, :]) 30 | 31 | uniques = 0 32 | for i in range(0,len(dataset.columns)): 33 | col = dataset.loc[:,i] 34 | uniques += len(col.unique()) 35 | 36 | print("Uniques: ", uniques, X_oe.shape[1]) -------------------------------------------------------------------------------- /helper/ToCategorical.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | a = tf.keras.utils.to_categorical([0, 1, 0, 3, 3], num_classes=4) 4 | #a = tf.constant(a, shape=[4, 4]) 5 | print(a) 6 | 7 | 8 | 9 | 10 | -------------------------------------------------------------------------------- /helper/impute_missing_data.py: -------------------------------------------------------------------------------- 1 | # statistical imputation transform for the horse colic dataset 2 | from numpy import isnan 3 | from pandas import read_csv 4 | from sklearn.impute import SimpleImputer 5 | 6 | # load dataset 7 | url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/horse-colic.csv' 8 | dataframe = read_csv(url, header=None, na_values='?') 9 | 10 | # split into input and output elements 11 | data = dataframe.values #converts to numpy.ndarray matrix 12 | ix = [i for i in range(data.shape[1]) if i != 23] #all indexes without 23 13 | X, y = data[:, ix], data[:, 23] #row 23 is y, the rest is X 14 | 15 | # print total missing 16 | print('Missing: %d' % sum(isnan(X).flatten())) #A boolean object with the value of True evaluates to 1 in the sum() function 17 | 18 | # define imputer 19 | imputer = SimpleImputer(strategy='mean') 20 | 21 | # fit on the dataset 22 | imputer.fit(X) 23 | 24 | # transform the dataset 25 | X_new = imputer.transform(X) 26 | 27 | # print total missing 28 | print('Missing: %d' % sum(isnan(X_new).flatten())) -------------------------------------------------------------------------------- /helper/kbins_discretizer.py: -------------------------------------------------------------------------------- 1 | # discretize numeric input variables 2 | from sklearn.datasets import make_classification 3 | from sklearn.preprocessing import KBinsDiscretizer 4 | 5 | # define dataset 6 | X, y = make_classification(n_samples=1000, n_features=5, n_informative=5, n_redundant=0, random_state=1) 7 | 8 | # summarize data before the transform 9 | print(X[:3, :]) 10 | 11 | # define the transform 12 | trans = KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform') 13 | 14 | # transform the data 15 | X_discrete = trans.fit_transform(X) 16 | 17 | # summarize data after the transform 18 | print(X_discrete[:3, :]) -------------------------------------------------------------------------------- /helper/min_max_scaler.py: -------------------------------------------------------------------------------- 1 | # example of normalizing input data 2 | from sklearn.datasets import make_classification 3 | from sklearn.preprocessing import MinMaxScaler 4 | 5 | # define dataset 6 | X, y = make_classification(n_samples=1000, n_features=5, n_informative=5, n_redundant=0, random_state=1) 7 | 8 | # summarize data before the transform 9 | 10 | print(X[:3, :]) #first 3 rows 11 | 12 | # define the scaler 13 | trans = MinMaxScaler() 14 | 15 | # transform the data 16 | X_norm = trans.fit_transform(X) 17 | 18 | # summarize data after the transform 19 | print(X_norm[:3, :]) #first 3 rows 20 | 21 | #MinMaxScaler is applied column-wise, Normalizer is applied row-wise. 22 | #The Normalizer class from Sklearn normalizes samples individually to unit norm. It is not column based but a row-based normalization technique. 23 | #So it your features are represented by columns (which is often the case) then you should use MinMaxScaler -------------------------------------------------------------------------------- /helper/pca_feature_reduction.py: -------------------------------------------------------------------------------- 1 | # example of pca for dimensionality reduction 2 | from sklearn.datasets import make_classification 3 | from sklearn.decomposition import PCA 4 | 5 | # define dataset 6 | X, y = make_classification(n_samples=1000, n_features=10, n_informative=3, n_redundant=7, random_state=1) 7 | 8 | # summarize data before the transform 9 | print(X[:3, :]) 10 | 11 | # define the transform 12 | trans = PCA(n_components=3) 13 | 14 | # transform the data 15 | X_dim = trans.fit_transform(X) 16 | 17 | # summarize data after the transform 18 | print("With redeuced to only 3 features:") 19 | print(X_dim[:3, :]) -------------------------------------------------------------------------------- /helper/recursive_feature_elimination.py: -------------------------------------------------------------------------------- 1 | # report which features were selected by RFE 2 | from sklearn.datasets import make_classification 3 | from sklearn.feature_selection import RFE 4 | from sklearn.tree import DecisionTreeClassifier 5 | 6 | # define dataset 7 | #Without shuffling, X horizontally stacks features in the following order: the primary n_informative features, followed by n_redundant 8 | X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, random_state=1, shuffle = False) 9 | 10 | # define RFE 11 | rfe = RFE(estimator=DecisionTreeClassifier(), n_features_to_select=5) 12 | 13 | # fit RFE 14 | rfe.fit(X, y) 15 | 16 | #print("Best features are assigned rank 1.") 17 | 18 | # summarize all features 19 | for i in range(X.shape[1]): 20 | print('Column: %d, Selected=%s, Rank: %d' % (i, rfe.support_[i], rfe.ranking_[i])) 21 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | lightgbm>=3.3.3 2 | 3 | matplotlib>=3.5.2 4 | 5 | mne>=1.0.3 6 | mne-qt-browser>=0.4.0 7 | 8 | moabb>=0.4.6 9 | 10 | numpy>=1.23.5 11 | 12 | pandas>=1.4.2 13 | 14 | Pillow>=9.5.0 15 | 16 | plotly>=5.11.0 17 | 18 | pycaret>=2.3.10 19 | 20 | PyDither>=0.0.1 21 | 22 | PyQt5>=5.15.6 23 | PyQt5-Qt5>=5.15.2 24 | PyQt5-sip>=12.10.1 25 | pyqtgraph>=0.13.1 26 | PyQtWebEngine>=5.15.5 27 | PyQtWebEngine-Qt5>=5.15.2 28 | 29 | pyriemann>=0.2.7 30 | 31 | scikeras>=0.9.0 32 | scikit-image>=0.19.3 33 | scikit-learn>=0.23.2 34 | scikit-plot>=0.3.7 35 | 36 | scipy>=1.5.4 37 | 38 | seaborn>=0.11.2 39 | 40 | tensorflow>=2.11.0 41 | 42 | torch>=1.11.0+cu113 43 | torchaudio>=0.11.0 44 | torchvision>=0.12.0+cu113 45 | 46 | TPOT>=0.11.7 47 | 48 | xgboost>=1.6.1 49 | 50 | ydata-synthetic>=0.8.1 51 | 52 | -e git://github.com/huggingface/transformers#egg=transformers 53 | -e git://github.com/huggingface/accelerate#egg=accelerate 54 | -e git://github.com/huggingface/huggingface_hub#egg=huggingface_hub 55 | -e git://github.com/huggingface/peft#egg=peft --------------------------------------------------------------------------------