aufraeumen branch
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@ -17,9 +17,8 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"df= pd.read_parquet(r\"C:\\Users\\micha\\FAUbox\\WS2526_Fahrsimulator_MSY (Celina Korzer)\\AU_dataset\\output_windowed.parquet\")\n",
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"df= pd.read_parquet(r\" \")\n",
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"print(df.shape)\n",
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"print(df.shape)"
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"\n"
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]
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]
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@ -107,7 +107,8 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"dataset_path = Path(r\"data-paulusjafahrsimulator-gpu/new_datasets/combined_dataset_25hz.parquet\")"
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"dataset_path = Path(r\"data-paulusjafahrsimulator-gpu/new_datasets/combined_dataset_25hz.parquet\")\n",
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"# dataset_path = Path(r\"/home/jovyan/data-paulusjafahrsimulator-gpu/new_datasets/120s_combined_dataset_25hz.parquet\")"
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]
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@ -475,7 +476,7 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"normalizer_path=Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/normalizer.pkl')"
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"normalizer_path=Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/normalizer_min_max_global.pkl')"
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]
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]
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{
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@ -494,7 +495,7 @@
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"print(len(eye_cols))\n",
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"print(len(eye_cols))\n",
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"all_signal_columns = face_au_cols+eye_cols\n",
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"all_signal_columns = face_au_cols+eye_cols\n",
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"print(len(all_signal_columns))\n",
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"print(len(all_signal_columns))\n",
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"normalizer = fit_normalizer(train_df, all_signal_columns, method='standard', scope='subject')\n",
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"normalizer = fit_normalizer(train_df, all_signal_columns, method='minmax', scope='global')\n",
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"save_normalizer(normalizer, normalizer_path )"
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"save_normalizer(normalizer, normalizer_path )"
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]
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]
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},
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},
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@ -691,10 +692,10 @@
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"model = build_intermediate_fusion_autoencoder(\n",
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"model = build_intermediate_fusion_autoencoder(\n",
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" input_dim_mod1=len(face_au_cols),\n",
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" input_dim_mod1=len(face_au_cols),\n",
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" input_dim_mod2=len(eye_cols),\n",
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" input_dim_mod2=len(eye_cols),\n",
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" encoder_hidden_dim_mod1=15, # individuell\n",
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" encoder_hidden_dim_mod1=12, # individuell\n",
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" encoder_hidden_dim_mod2=10, # individuell\n",
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" encoder_hidden_dim_mod2=8, # individuell\n",
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" latent_dim=8,\n",
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" latent_dim=4,\n",
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" dropout_rate=0.3, # einstellbar\n",
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" dropout_rate=0.7, # einstellbar\n",
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" neg_slope=0.1,\n",
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" neg_slope=0.1,\n",
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" weight_decay=1e-3\n",
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" weight_decay=1e-3\n",
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")\n",
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")\n",
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@ -708,7 +709,7 @@
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" \"recon_modality_1\": 1.0,\n",
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" \"recon_modality_1\": 1.0,\n",
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" \"recon_modality_2\": 1.0,\n",
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" \"recon_modality_2\": 1.0,\n",
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" },\n",
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" },\n",
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" optimizer=tf.keras.optimizers.Adam(1e-2)\n",
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" optimizer=tf.keras.optimizers.Adam(1e-3)\n",
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" \n",
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" \n",
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")\n",
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")\n",
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"\n",
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"\n",
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@ -739,7 +740,7 @@
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" \"recon_modality_1\": 1.0,\n",
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" \"recon_modality_1\": 1.0,\n",
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" \"recon_modality_2\": 1.0,\n",
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" \"recon_modality_2\": 1.0,\n",
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" },\n",
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" },\n",
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" optimizer=tf.keras.optimizers.Adam(1e-5),\n",
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" optimizer=tf.keras.optimizers.Adam(1e-4),\n",
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")\n",
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")\n",
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"model.fit(\n",
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"model.fit(\n",
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" x=[X_face, X_eye],\n",
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" x=[X_face, X_eye],\n",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"encoder_save_path =Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/encoder_6_deep.keras')\n",
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"encoder_save_path =Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/encoder_8_deep.keras')\n",
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"encoder.save(encoder_save_path)"
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"encoder.save(encoder_save_path)"
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]
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]
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},
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},
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" return get_radius_from_arrays(nu, X_face, X_eye)\n",
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" return get_radius_from_arrays(nu, X_face, X_eye)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"nu = 0.05\n",
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"nu = 0.25\n",
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"\n",
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"\n",
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"train_dataset = tf.data.Dataset.from_tensor_slices((X_face, X_eye)).shuffle(64).batch(64)\n",
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"train_dataset = tf.data.Dataset.from_tensor_slices((X_face, X_eye)).shuffle(64).batch(64)\n",
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"# train_dataset = tf.data.Dataset.from_tensor_slices((X_face, X_eye))\n",
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"# train_dataset = tf.data.Dataset.from_tensor_slices((X_face, X_eye))\n",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"deep_svdd_save_path =Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/deep_svdd_05.keras')\n",
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"deep_svdd_save_path =Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/deep_svdd_06.keras')\n",
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"deep_svdd_net.save(deep_svdd_save_path)"
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"deep_svdd_net.save(deep_svdd_save_path)"
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]
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]
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},
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},
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"test_predictions = (test_scores > 0).astype(int)\n"
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"test_predictions = (test_scores > 0).astype(int)\n"
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]
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "575dddcf",
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"metadata": {},
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"outputs": [],
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"source": [
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"normal_acc = np.mean(test_predictions[y_test == 0] == 0)\n",
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"anomaly_acc = np.mean(test_predictions[y_test == 1] == 1)\n",
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"print(f'Accuracy on Test set: {accuracy_score(y_test, test_predictions)}')"
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]
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"# SET\n",
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"# SET\n",
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"threshold_mad = 100\n",
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"threshold_mad = 5\n",
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"column_praefix ='AU'\n",
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"column_praefix ='AU'\n",
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"\n",
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"\n",
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"au_columns = [col for col in df.columns if col.startswith(column_praefix)]\n",
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"au_columns = [col for col in df.columns if col.startswith(column_praefix)]\n",
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"cleaned_df = mad_outlier_removal(df,columns=au_columns, threshold=threshold_mad)\n",
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"cleaned_df = mad_outlier_removal.mad_outlier_removal(df,columns=au_columns, threshold=threshold_mad)\n",
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"print(cleaned_df.shape)\n",
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"print(cleaned_df.shape)\n",
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"print(df.shape)"
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"print(df.shape)"
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]
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a6c1732",
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"metadata": {},
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"source": [
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"#### TO DO\n",
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" * pipeline aus Autoencoder und SVM\n",
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" * group k fold\n",
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" * AE überpüfen, loss dokumentieren"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "877309d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"### Variational Autoencoder with Classifier Head\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"from tensorflow.keras import layers, Model\n",
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"from sklearn.model_selection import GroupKFold\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.metrics import (\n",
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" accuracy_score, precision_score, recall_score, f1_score, \n",
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" roc_auc_score, confusion_matrix, classification_report\n",
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")\n",
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"import matplotlib.pyplot as plt\n",
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"from collections import defaultdict\n",
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"\n",
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"# ============================================================================\n",
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"# 1. CREATE LABELS\n",
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"# ============================================================================\n",
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"\n",
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"# Low workload: baseline + n-back level 1,4\n",
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"low_all = cleaned_df[\n",
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" ((cleaned_df[\"PHASE\"] == \"baseline\") |\n",
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" ((cleaned_df[\"STUDY\"] == \"n-back\") & (cleaned_df[\"PHASE\"] != \"baseline\") & (cleaned_df[\"LEVEL\"].isin([1,4]))))\n",
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"].copy()\n",
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"low_all['label'] = 0\n",
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"print(f\"Low workload samples: {low_all.shape[0]}\")\n",
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"\n",
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"# High workload n-back: level 2,3,5,6\n",
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"high_nback = cleaned_df[\n",
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" (cleaned_df[\"STUDY\"]==\"n-back\") &\n",
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" (cleaned_df[\"LEVEL\"].isin([2, 3, 5, 6])) &\n",
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" (cleaned_df[\"PHASE\"].isin([\"train\", \"test\"]))\n",
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"].copy()\n",
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"high_nback['label'] = 1\n",
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"print(f\"High n-back samples: {high_nback.shape[0]}\")\n",
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"\n",
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"# High workload k-drive\n",
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"high_kdrive = cleaned_df[\n",
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" (cleaned_df[\"STUDY\"] == \"k-drive\") & (cleaned_df[\"PHASE\"] != \"baseline\")\n",
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"].copy()\n",
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"high_kdrive['label'] = 1\n",
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"print(f\"High k-drive samples: {high_kdrive.shape[0]}\")\n",
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"\n",
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"# Combine all high workload\n",
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"high_all = pd.concat([high_nback, high_kdrive])\n",
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"print(f\"Total high workload samples: {high_all.shape[0]}\")\n",
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"\n",
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"# Complete labeled dataset\n",
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"labeled_df = pd.concat([low_all, high_all]).reset_index(drop=True)\n",
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"print(f\"\\nTotal labeled samples: {labeled_df.shape[0]}\")\n",
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"print(f\"Class distribution:\\n{labeled_df['label'].value_counts()}\")\n",
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"\n",
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"# ============================================================================\n",
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"# 2. TRAIN/TEST SPLIT BY SUBJECTS\n",
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"# ============================================================================\n",
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"\n",
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"train_df = labeled_df[labeled_df['subjectID'].isin(training_subjects)].copy()\n",
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"test_df = labeled_df[labeled_df['subjectID'].isin(test_subjects)].copy()\n",
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"\n",
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"print(f\"\\nTraining subjects: {training_subjects}\")\n",
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"print(f\"Test subjects: {test_subjects}\")\n",
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"print(f\"Train samples: {train_df.shape[0]}, Test samples: {test_df.shape[0]}\")\n",
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"\n",
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"# Extract features and labels\n",
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"au_columns = [col for col in labeled_df.columns if col.startswith('AU')]\n",
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"print(f\"\\nUsing {len(au_columns)} AU features: {au_columns}\")\n",
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"\n",
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"X_train = train_df[au_columns].values\n",
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"y_train = train_df['label'].values\n",
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"groups_train = train_df['subjectID'].values\n",
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"\n",
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"X_test = test_df[au_columns].values\n",
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"y_test = test_df['label'].values\n",
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"\n",
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"# Normalize features\n",
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"scaler = StandardScaler()\n",
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"X_train_scaled = scaler.fit_transform(X_train)\n",
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"X_test_scaled = scaler.transform(X_test)\n",
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"\n",
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"print(f\"\\nTrain class distribution: {np.bincount(y_train)}\")\n",
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"print(f\"Test class distribution: {np.bincount(y_test)}\")\n",
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"\n",
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"# ============================================================================\n",
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"# 3. VAE WITH CLASSIFIER HEAD MODEL\n",
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"# ============================================================================\n",
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"\n",
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"class Sampling(layers.Layer):\n",
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" \"\"\"Reparameterization trick for VAE\"\"\"\n",
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" def call(self, inputs):\n",
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" z_mean, z_log_var = inputs\n",
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" batch = tf.shape(z_mean)[0]\n",
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" dim = tf.shape(z_mean)[1]\n",
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" epsilon = tf.random.normal(shape=(batch, dim))\n",
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" return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n",
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"\n",
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"def build_vae_classifier(input_dim, latent_dim, encoder_dims=[32, 16], \n",
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" decoder_dims=[16, 32], classifier_dims=[16]):\n",
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" \"\"\"\n",
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" Build VAE with classifier head\n",
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" \n",
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" Args:\n",
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" input_dim: Number of input features (20 AUs)\n",
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" latent_dim: Dimension of latent space (2-5)\n",
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" encoder_dims: Hidden layer sizes for encoder\n",
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" decoder_dims: Hidden layer sizes for decoder\n",
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" classifier_dims: Hidden layer sizes for classifier\n",
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" \"\"\"\n",
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" \n",
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" # ---- ENCODER ----\n",
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" encoder_inputs = keras.Input(shape=(input_dim,), name='encoder_input')\n",
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" x = encoder_inputs\n",
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" \n",
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" for i, dim in enumerate(encoder_dims):\n",
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" x = layers.Dense(dim, activation='relu', name=f'encoder_dense_{i}')(x)\n",
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" x = layers.BatchNormalization(name=f'encoder_bn_{i}')(x)\n",
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" x = layers.Dropout(0.2, name=f'encoder_dropout_{i}')(x)\n",
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" \n",
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" z_mean = layers.Dense(latent_dim, name='z_mean')(x)\n",
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" z_log_var = layers.Dense(latent_dim, name='z_log_var')(x)\n",
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" z = Sampling()([z_mean, z_log_var])\n",
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" \n",
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" encoder = Model(encoder_inputs, [z_mean, z_log_var, z], name='encoder')\n",
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" \n",
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" # ---- DECODER ----\n",
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" latent_inputs = keras.Input(shape=(latent_dim,), name='latent_input')\n",
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" x = latent_inputs\n",
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" \n",
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" for i, dim in enumerate(decoder_dims):\n",
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" x = layers.Dense(dim, activation='relu', name=f'decoder_dense_{i}')(x)\n",
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" x = layers.BatchNormalization(name=f'decoder_bn_{i}')(x)\n",
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" \n",
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" decoder_outputs = layers.Dense(input_dim, activation='linear', name='decoder_output')(x)\n",
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" decoder = Model(latent_inputs, decoder_outputs, name='decoder')\n",
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" \n",
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" # ---- CLASSIFIER HEAD ----\n",
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" x = latent_inputs\n",
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" for i, dim in enumerate(classifier_dims):\n",
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" x = layers.Dense(dim, activation='relu', name=f'classifier_dense_{i}')(x)\n",
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||||||
|
" x = layers.Dropout(0.3, name=f'classifier_dropout_{i}')(x)\n",
|
||||||
|
" \n",
|
||||||
|
" classifier_output = layers.Dense(1, activation='sigmoid', name='classifier_output')(x)\n",
|
||||||
|
" classifier = Model(latent_inputs, classifier_output, name='classifier')\n",
|
||||||
|
" \n",
|
||||||
|
" # ---- FULL MODEL ----\n",
|
||||||
|
" inputs = keras.Input(shape=(input_dim,), name='vae_input')\n",
|
||||||
|
" z_mean, z_log_var, z = encoder(inputs)\n",
|
||||||
|
" reconstructed = decoder(z)\n",
|
||||||
|
" classification = classifier(z)\n",
|
||||||
|
" \n",
|
||||||
|
" model = Model(inputs, [reconstructed, classification], name='vae_classifier')\n",
|
||||||
|
" \n",
|
||||||
|
" return model, encoder, decoder, classifier\n",
|
||||||
|
"\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"# 4. CUSTOM TRAINING LOOP WITH COMBINED LOSS\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"\n",
|
||||||
|
"class VAEClassifier(keras.Model):\n",
|
||||||
|
" def __init__(self, encoder, decoder, classifier, **kwargs):\n",
|
||||||
|
" super().__init__(**kwargs)\n",
|
||||||
|
" self.encoder = encoder\n",
|
||||||
|
" self.decoder = decoder\n",
|
||||||
|
" self.classifier = classifier\n",
|
||||||
|
" self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n",
|
||||||
|
" self.reconstruction_loss_tracker = keras.metrics.Mean(name=\"reconstruction_loss\")\n",
|
||||||
|
" self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n",
|
||||||
|
" self.classification_loss_tracker = keras.metrics.Mean(name=\"classification_loss\")\n",
|
||||||
|
" self.accuracy_tracker = keras.metrics.BinaryAccuracy(name=\"accuracy\")\n",
|
||||||
|
" \n",
|
||||||
|
" @property\n",
|
||||||
|
" def metrics(self):\n",
|
||||||
|
" return [\n",
|
||||||
|
" self.total_loss_tracker,\n",
|
||||||
|
" self.reconstruction_loss_tracker,\n",
|
||||||
|
" self.kl_loss_tracker,\n",
|
||||||
|
" self.classification_loss_tracker,\n",
|
||||||
|
" self.accuracy_tracker,\n",
|
||||||
|
" ]\n",
|
||||||
|
" \n",
|
||||||
|
" def train_step(self, data):\n",
|
||||||
|
" x, y = data\n",
|
||||||
|
" \n",
|
||||||
|
" with tf.GradientTape() as tape:\n",
|
||||||
|
" # Forward pass\n",
|
||||||
|
" z_mean, z_log_var, z = self.encoder(x, training=True)\n",
|
||||||
|
" reconstruction = self.decoder(z, training=True)\n",
|
||||||
|
" classification = self.classifier(z, training=True)\n",
|
||||||
|
" \n",
|
||||||
|
" # Reconstruction loss (MSE)\n",
|
||||||
|
" reconstruction_loss = tf.reduce_mean(\n",
|
||||||
|
" keras.losses.mse(x, reconstruction))\n",
|
||||||
|
" \n",
|
||||||
|
" # KL divergence loss\n",
|
||||||
|
" kl_loss = -0.5 * tf.reduce_mean(\n",
|
||||||
|
" tf.reduce_sum(\n",
|
||||||
|
" 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var),\n",
|
||||||
|
" axis=1\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" \n",
|
||||||
|
" # Classification loss (binary crossentropy)\n",
|
||||||
|
" # Classification loss (binary crossentropy)\n",
|
||||||
|
" classification_loss = tf.reduce_mean(\n",
|
||||||
|
" keras.losses.binary_crossentropy(tf.expand_dims(y, -1), classification)\n",
|
||||||
|
" )\n",
|
||||||
|
" \n",
|
||||||
|
" # Combined loss with weights\n",
|
||||||
|
" total_loss = reconstruction_loss + kl_loss + classification_loss\n",
|
||||||
|
" \n",
|
||||||
|
" # Backpropagation\n",
|
||||||
|
" grads = tape.gradient(total_loss, self.trainable_weights)\n",
|
||||||
|
" self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
|
||||||
|
" \n",
|
||||||
|
" # Update metrics\n",
|
||||||
|
" self.total_loss_tracker.update_state(total_loss)\n",
|
||||||
|
" self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
|
||||||
|
" self.kl_loss_tracker.update_state(kl_loss)\n",
|
||||||
|
" self.classification_loss_tracker.update_state(classification_loss)\n",
|
||||||
|
" self.accuracy_tracker.update_state(y, classification)\n",
|
||||||
|
" \n",
|
||||||
|
" return {\n",
|
||||||
|
" \"total_loss\": self.total_loss_tracker.result(),\n",
|
||||||
|
" \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
|
||||||
|
" \"kl_loss\": self.kl_loss_tracker.result(),\n",
|
||||||
|
" \"classification_loss\": self.classification_loss_tracker.result(),\n",
|
||||||
|
" \"accuracy\": self.accuracy_tracker.result(),\n",
|
||||||
|
" }\n",
|
||||||
|
" \n",
|
||||||
|
" def test_step(self, data):\n",
|
||||||
|
" x, y = data\n",
|
||||||
|
" \n",
|
||||||
|
" z_mean, z_log_var, z = self.encoder(x, training=False)\n",
|
||||||
|
" reconstruction = self.decoder(z, training=False)\n",
|
||||||
|
" classification = self.classifier(z, training=False)\n",
|
||||||
|
" \n",
|
||||||
|
" # Reconstruction loss (MSE)\n",
|
||||||
|
" reconstruction_loss = tf.reduce_mean(\n",
|
||||||
|
" keras.losses.mse(x, reconstruction))\n",
|
||||||
|
" kl_loss = -0.5 * tf.reduce_mean(\n",
|
||||||
|
" tf.reduce_sum(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), axis=1)\n",
|
||||||
|
" )\n",
|
||||||
|
" # Classification loss (binary crossentropy)\n",
|
||||||
|
" classification_loss = tf.reduce_mean(\n",
|
||||||
|
" keras.losses.binary_crossentropy(tf.expand_dims(y, -1), classification)\n",
|
||||||
|
" )\n",
|
||||||
|
" total_loss = reconstruction_loss + kl_loss + classification_loss\n",
|
||||||
|
" \n",
|
||||||
|
" self.total_loss_tracker.update_state(total_loss)\n",
|
||||||
|
" self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
|
||||||
|
" self.kl_loss_tracker.update_state(kl_loss)\n",
|
||||||
|
" self.classification_loss_tracker.update_state(classification_loss)\n",
|
||||||
|
" self.accuracy_tracker.update_state(y, classification)\n",
|
||||||
|
" \n",
|
||||||
|
" return {\n",
|
||||||
|
" \"total_loss\": self.total_loss_tracker.result(),\n",
|
||||||
|
" \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
|
||||||
|
" \"kl_loss\": self.kl_loss_tracker.result(),\n",
|
||||||
|
" \"classification_loss\": self.classification_loss_tracker.result(),\n",
|
||||||
|
" \"accuracy\": self.accuracy_tracker.result(),\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"# 5. GROUP K-FOLD CROSS-VALIDATION WITH GRID SEARCH\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"\n",
|
||||||
|
"# Hyperparameter grid\n",
|
||||||
|
"param_grid = {\n",
|
||||||
|
" 'latent_dim': [2, 5],\n",
|
||||||
|
" 'encoder_dims': [[32, 16], [64, 32]],\n",
|
||||||
|
" 'learning_rate': [0.001, 0.005],\n",
|
||||||
|
" 'batch_size': [32, 64],\n",
|
||||||
|
"}\n",
|
||||||
|
"\n",
|
||||||
|
"# Generate all combinations\n",
|
||||||
|
"from itertools import product\n",
|
||||||
|
"keys = param_grid.keys()\n",
|
||||||
|
"values = param_grid.values()\n",
|
||||||
|
"param_combinations = [dict(zip(keys, v)) for v in product(*values)]\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"\\nTotal hyperparameter combinations: {len(param_combinations)}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Group K-Fold setup\n",
|
||||||
|
"n_splits = 5\n",
|
||||||
|
"gkf = GroupKFold(n_splits=n_splits)\n",
|
||||||
|
"\n",
|
||||||
|
"# Store results\n",
|
||||||
|
"cv_results = []\n",
|
||||||
|
"\n",
|
||||||
|
"# Grid search with cross-validation\n",
|
||||||
|
"for idx, params in enumerate(param_combinations):\n",
|
||||||
|
" print(f\"\\n{'='*80}\")\n",
|
||||||
|
" print(f\"Testing combination {idx+1}/{len(param_combinations)}: {params}\")\n",
|
||||||
|
" print(f\"{'='*80}\")\n",
|
||||||
|
" \n",
|
||||||
|
" fold_results = []\n",
|
||||||
|
" \n",
|
||||||
|
" for fold, (train_idx, val_idx) in enumerate(gkf.split(X_train_scaled, y_train, groups_train)):\n",
|
||||||
|
" print(f\"\\nFold {fold+1}/{n_splits}\")\n",
|
||||||
|
" \n",
|
||||||
|
" X_fold_train, X_fold_val = X_train_scaled[train_idx], X_train_scaled[val_idx]\n",
|
||||||
|
" y_fold_train, y_fold_val = y_train[train_idx], y_train[val_idx]\n",
|
||||||
|
" \n",
|
||||||
|
" # Build model\n",
|
||||||
|
" model, encoder, decoder, classifier = build_vae_classifier(\n",
|
||||||
|
" input_dim=len(au_columns),\n",
|
||||||
|
" latent_dim=params['latent_dim'],\n",
|
||||||
|
" encoder_dims=params['encoder_dims'],\n",
|
||||||
|
" decoder_dims=list(reversed(params['encoder_dims'])),\n",
|
||||||
|
" classifier_dims=[16]\n",
|
||||||
|
" )\n",
|
||||||
|
" \n",
|
||||||
|
" vae_classifier = VAEClassifier(encoder, decoder, classifier)\n",
|
||||||
|
" vae_classifier.compile(optimizer=keras.optimizers.Adam(params['learning_rate']))\n",
|
||||||
|
" \n",
|
||||||
|
" # Early stopping\n",
|
||||||
|
" early_stop = keras.callbacks.EarlyStopping(\n",
|
||||||
|
" monitor='val_total_loss',\n",
|
||||||
|
" patience=10,\n",
|
||||||
|
" restore_best_weights=True,\n",
|
||||||
|
" mode='min'\n",
|
||||||
|
" )\n",
|
||||||
|
" \n",
|
||||||
|
" # Train\n",
|
||||||
|
" history = vae_classifier.fit(\n",
|
||||||
|
" X_fold_train, y_fold_train,\n",
|
||||||
|
" validation_data=(X_fold_val, y_fold_val),\n",
|
||||||
|
" epochs=60,\n",
|
||||||
|
" batch_size=params['batch_size'],\n",
|
||||||
|
" callbacks=[early_stop],\n",
|
||||||
|
" verbose=0\n",
|
||||||
|
" )\n",
|
||||||
|
" \n",
|
||||||
|
" # Evaluate on validation fold\n",
|
||||||
|
" z_mean_val, _, _ = encoder.predict(X_fold_val, verbose=0)\n",
|
||||||
|
" y_pred_proba = classifier.predict(z_mean_val, verbose=0).flatten()\n",
|
||||||
|
" y_pred = (y_pred_proba > 0.5).astype(int)\n",
|
||||||
|
" \n",
|
||||||
|
" fold_metrics = {\n",
|
||||||
|
" 'accuracy': accuracy_score(y_fold_val, y_pred),\n",
|
||||||
|
" 'precision': precision_score(y_fold_val, y_pred, zero_division=0),\n",
|
||||||
|
" 'recall': recall_score(y_fold_val, y_pred, zero_division=0),\n",
|
||||||
|
" 'f1': f1_score(y_fold_val, y_pred, zero_division=0),\n",
|
||||||
|
" 'roc_auc': roc_auc_score(y_fold_val, y_pred_proba),\n",
|
||||||
|
" 'final_recon_loss': history.history['val_reconstruction_loss'][-1],\n",
|
||||||
|
" 'final_kl_loss': history.history['val_kl_loss'][-1],\n",
|
||||||
|
" 'final_class_loss': history.history['val_classification_loss'][-1],\n",
|
||||||
|
" }\n",
|
||||||
|
" \n",
|
||||||
|
" fold_results.append(fold_metrics)\n",
|
||||||
|
" print(f\" Accuracy: {fold_metrics['accuracy']:.4f}, F1: {fold_metrics['f1']:.4f}, AUC: {fold_metrics['roc_auc']:.4f}\")\n",
|
||||||
|
" \n",
|
||||||
|
" # Clear session to free memory\n",
|
||||||
|
" keras.backend.clear_session()\n",
|
||||||
|
" \n",
|
||||||
|
" # Average across folds\n",
|
||||||
|
" avg_results = {\n",
|
||||||
|
" 'params': params,\n",
|
||||||
|
" 'mean_accuracy': np.mean([r['accuracy'] for r in fold_results]),\n",
|
||||||
|
" 'std_accuracy': np.std([r['accuracy'] for r in fold_results]),\n",
|
||||||
|
" 'mean_f1': np.mean([r['f1'] for r in fold_results]),\n",
|
||||||
|
" 'std_f1': np.std([r['f1'] for r in fold_results]),\n",
|
||||||
|
" 'mean_roc_auc': np.mean([r['roc_auc'] for r in fold_results]),\n",
|
||||||
|
" 'std_roc_auc': np.std([r['roc_auc'] for r in fold_results]),\n",
|
||||||
|
" 'mean_recon_loss': np.mean([r['final_recon_loss'] for r in fold_results]),\n",
|
||||||
|
" 'mean_kl_loss': np.mean([r['final_kl_loss'] for r in fold_results]),\n",
|
||||||
|
" 'mean_class_loss': np.mean([r['final_class_loss'] for r in fold_results]),\n",
|
||||||
|
" 'fold_results': fold_results\n",
|
||||||
|
" }\n",
|
||||||
|
" \n",
|
||||||
|
" cv_results.append(avg_results)\n",
|
||||||
|
" \n",
|
||||||
|
" print(f\"\\nMean CV Accuracy: {avg_results['mean_accuracy']:.4f} ± {avg_results['std_accuracy']:.4f}\")\n",
|
||||||
|
" print(f\"Mean CV F1: {avg_results['mean_f1']:.4f} ± {avg_results['std_f1']:.4f}\")\n",
|
||||||
|
" print(f\"Mean CV AUC: {avg_results['mean_roc_auc']:.4f} ± {avg_results['std_roc_auc']:.4f}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"# 6. SELECT BEST MODEL AND EVALUATE ON TEST SET\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"\n",
|
||||||
|
"# Find best hyperparameters based on mean F1 score\n",
|
||||||
|
"best_idx = np.argmax([r['mean_f1'] for r in cv_results])\n",
|
||||||
|
"best_params = cv_results[best_idx]['params']\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"\\n{'='*80}\")\n",
|
||||||
|
"print(\"BEST HYPERPARAMETERS (based on CV F1 score):\")\n",
|
||||||
|
"print(f\"{'='*80}\")\n",
|
||||||
|
"for key, value in best_params.items():\n",
|
||||||
|
" print(f\"{key}: {value}\")\n",
|
||||||
|
"print(f\"\\nCV Performance:\")\n",
|
||||||
|
"print(f\" Accuracy: {cv_results[best_idx]['mean_accuracy']:.4f} ± {cv_results[best_idx]['std_accuracy']:.4f}\")\n",
|
||||||
|
"print(f\" F1 Score: {cv_results[best_idx]['mean_f1']:.4f} ± {cv_results[best_idx]['std_f1']:.4f}\")\n",
|
||||||
|
"print(f\" ROC-AUC: {cv_results[best_idx]['mean_roc_auc']:.4f} ± {cv_results[best_idx]['std_roc_auc']:.4f}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Train final model on all training data\n",
|
||||||
|
"print(f\"\\n{'='*80}\")\n",
|
||||||
|
"print(\"TRAINING FINAL MODEL ON ALL TRAINING DATA\")\n",
|
||||||
|
"print(f\"{'='*80}\")\n",
|
||||||
|
"\n",
|
||||||
|
"final_model, final_encoder, final_decoder, final_classifier = build_vae_classifier(\n",
|
||||||
|
" input_dim=len(au_columns),\n",
|
||||||
|
" latent_dim=best_params['latent_dim'],\n",
|
||||||
|
" encoder_dims=best_params['encoder_dims'],\n",
|
||||||
|
" decoder_dims=list(reversed(best_params['encoder_dims'])),\n",
|
||||||
|
" classifier_dims=[16]\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"final_vae_classifier = VAEClassifier(final_encoder, final_decoder, final_classifier)\n",
|
||||||
|
"final_vae_classifier.compile(optimizer=keras.optimizers.Adam(best_params['learning_rate']))\n",
|
||||||
|
"\n",
|
||||||
|
"final_history = final_vae_classifier.fit(\n",
|
||||||
|
" X_train_scaled, y_train,\n",
|
||||||
|
" validation_split=0.2,\n",
|
||||||
|
" epochs=100,\n",
|
||||||
|
" batch_size=best_params['batch_size'],\n",
|
||||||
|
" callbacks=[keras.callbacks.EarlyStopping(monitor='val_total_loss', patience=15, restore_best_weights=True, mode='min')],\n",
|
||||||
|
" verbose=1\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# Evaluate on held-out test set\n",
|
||||||
|
"print(f\"\\n{'='*80}\")\n",
|
||||||
|
"print(\"EVALUATION ON HELD-OUT TEST SET\")\n",
|
||||||
|
"print(f\"{'='*80}\")\n",
|
||||||
|
"\n",
|
||||||
|
"z_mean_test, _, _ = final_encoder.predict(X_test_scaled, verbose=0)\n",
|
||||||
|
"y_test_pred_proba = final_classifier.predict(z_mean_test, verbose=0).flatten()\n",
|
||||||
|
"y_test_pred = (y_test_pred_proba > 0.5).astype(int)\n",
|
||||||
|
"\n",
|
||||||
|
"test_metrics = {\n",
|
||||||
|
" 'accuracy': accuracy_score(y_test, y_test_pred),\n",
|
||||||
|
" 'precision': precision_score(y_test, y_test_pred),\n",
|
||||||
|
" 'recall': recall_score(y_test, y_test_pred),\n",
|
||||||
|
" 'f1': f1_score(y_test, y_test_pred),\n",
|
||||||
|
" 'roc_auc': roc_auc_score(y_test, y_test_pred_proba),\n",
|
||||||
|
"}\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\nTest Set Performance:\")\n",
|
||||||
|
"for metric, value in test_metrics.items():\n",
|
||||||
|
" print(f\" {metric.capitalize()}: {value:.4f}\")\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\nConfusion Matrix:\")\n",
|
||||||
|
"print(confusion_matrix(y_test, y_test_pred))\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\nClassification Report:\")\n",
|
||||||
|
"print(classification_report(y_test, y_test_pred, target_names=['Low Workload', 'High Workload']))\n",
|
||||||
|
"\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"# 7. VISUALIZATION\n",
|
||||||
|
"# ============================================================================\n",
|
||||||
|
"\n",
|
||||||
|
"# Plot training history\n",
|
||||||
|
"fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
|
||||||
|
"\n",
|
||||||
|
"axes[0, 0].plot(final_history.history['reconstruction_loss'], label='Train')\n",
|
||||||
|
"axes[0, 0].plot(final_history.history['val_reconstruction_loss'], label='Val')\n",
|
||||||
|
"axes[0, 0].set_title('Reconstruction Loss')\n",
|
||||||
|
"axes[0, 0].set_xlabel('Epoch')\n",
|
||||||
|
"axes[0, 0].set_ylabel('Loss')\n",
|
||||||
|
"axes[0, 0].legend()\n",
|
||||||
|
"axes[0, 0].grid(True)\n",
|
||||||
|
"\n",
|
||||||
|
"axes[0, 1].plot(final_history.history['kl_loss'], label='Train')\n",
|
||||||
|
"axes[0, 1].plot(final_history.history['val_kl_loss'], label='Val')\n",
|
||||||
|
"axes[0, 1].set_title('KL Divergence Loss')\n",
|
||||||
|
"axes[0, 1].set_xlabel('Epoch')\n",
|
||||||
|
"axes[0, 1].set_ylabel('Loss')\n",
|
||||||
|
"axes[0, 1].legend()\n",
|
||||||
|
"axes[0, 1].grid(True)\n",
|
||||||
|
"\n",
|
||||||
|
"axes[1, 0].plot(final_history.history['classification_loss'], label='Train')\n",
|
||||||
|
"axes[1, 0].plot(final_history.history['val_classification_loss'], label='Val')\n",
|
||||||
|
"axes[1, 0].set_title('Classification Loss')\n",
|
||||||
|
"axes[1, 0].set_xlabel('Epoch')\n",
|
||||||
|
"axes[1, 0].set_ylabel('Loss')\n",
|
||||||
|
"axes[1, 0].legend()\n",
|
||||||
|
"axes[1, 0].grid(True)\n",
|
||||||
|
"\n",
|
||||||
|
"axes[1, 1].plot(final_history.history['accuracy'], label='Train')\n",
|
||||||
|
"axes[1, 1].plot(final_history.history['val_accuracy'], label='Val')\n",
|
||||||
|
"axes[1, 1].set_title('Classification Accuracy')\n",
|
||||||
|
"axes[1, 1].set_xlabel('Epoch')\n",
|
||||||
|
"axes[1, 1].set_ylabel('Accuracy')\n",
|
||||||
|
"axes[1, 1].legend()\n",
|
||||||
|
"axes[1, 1].grid(True)\n",
|
||||||
|
"\n",
|
||||||
|
"plt.tight_layout()\n",
|
||||||
|
"plt.show()\n",
|
||||||
|
"\n",
|
||||||
|
"# Visualize latent space (if 2D or 3D)\n",
|
||||||
|
"if best_params['latent_dim'] == 2:\n",
|
||||||
|
" z_mean_train, _, _ = final_encoder.predict(X_train_scaled, verbose=0)\n",
|
||||||
|
" \n",
|
||||||
|
" plt.figure(figsize=(10, 8))\n",
|
||||||
|
" scatter = plt.scatter(z_mean_train[:, 0], z_mean_train[:, 1], \n",
|
||||||
|
" c=y_train, cmap='RdYlBu', alpha=0.6, edgecolors='k')\n",
|
||||||
|
" plt.colorbar(scatter, label='Workload (0=Low, 1=High)')\n",
|
||||||
|
" plt.xlabel('Latent Dimension 1')\n",
|
||||||
|
" plt.ylabel('Latent Dimension 2')\n",
|
||||||
|
" plt.title('2D Latent Space Representation (Training Data)')\n",
|
||||||
|
" plt.grid(True, alpha=0.3)\n",
|
||||||
|
" plt.show()\n",
|
||||||
|
" \n",
|
||||||
|
" # Test set latent space\n",
|
||||||
|
" plt.figure(figsize=(10, 8))\n",
|
||||||
|
" scatter = plt.scatter(z_mean_test[:, 0], z_mean_test[:, 1], \n",
|
||||||
|
" c=y_test, cmap='RdYlBu', alpha=0.6, edgecolors='k')\n",
|
||||||
|
" plt.colorbar(scatter, label='Workload (0=Low, 1=High)')\n",
|
||||||
|
" plt.xlabel('Latent Dimension 1')\n",
|
||||||
|
" plt.ylabel('Latent Dimension 2')\n",
|
||||||
|
" plt.title('2D Latent Space Representation (Test Data)')\n",
|
||||||
|
" plt.grid(True, alpha=0.3)\n",
|
||||||
|
" plt.show()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\n\" + \"=\"*80)\n",
|
||||||
|
"print(\"TRAINING COMPLETE!\")\n",
|
||||||
|
"print(\"=\"*80)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "79bcfc58",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"### Save Trained VAE Classifier Model\n",
|
||||||
|
"from pathlib import Path\n",
|
||||||
|
"from datetime import datetime\n",
|
||||||
|
"\n",
|
||||||
|
"# Define save path\n",
|
||||||
|
"model_dir = Path(\"/home/jovyan/data-paulusjafahrsimulator-gpu/trained_models\")\n",
|
||||||
|
"model_dir.mkdir(parents=True, exist_ok=True)\n",
|
||||||
|
"\n",
|
||||||
|
"timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
|
||||||
|
"model_path = model_dir / f\"vae_classifier_{timestamp}.keras\"\n",
|
||||||
|
"\n",
|
||||||
|
"# Save the complete model\n",
|
||||||
|
"final_vae_classifier.save(model_path)\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Model saved to: {model_path}\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "d700e517",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "30d8d100",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"### Plot Confusion Matrix for Final Model\n",
|
||||||
|
"from sklearn.metrics import ConfusionMatrixDisplay\n",
|
||||||
|
"x = Path(\"/home/jovyan/data-paulusjafahrsimulator-gpu/trained_models/vae_classifier_20251210_230121.keras\")\n",
|
||||||
|
"# Load the saved model\n",
|
||||||
|
"print(f\"Loading model from: {x}\")\n",
|
||||||
|
"# loaded_vae_classifier = tf.keras.models.load_model(x)\n",
|
||||||
|
"loaded_vae_classifier = final_vae_classifier\n",
|
||||||
|
"print(\"✓ Model loaded successfully!\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Extract encoder and classifier from loaded model\n",
|
||||||
|
"loaded_encoder = loaded_vae_classifier.encoder\n",
|
||||||
|
"loaded_classifier = loaded_vae_classifier.classifier\n",
|
||||||
|
"\n",
|
||||||
|
"# Get predictions on test set\n",
|
||||||
|
"z_mean_test, _, _ = loaded_encoder.predict(X_test_scaled, verbose=0)\n",
|
||||||
|
"y_test_pred_proba = loaded_classifier.predict(z_mean_test, verbose=0).flatten()\n",
|
||||||
|
"y_test_pred = (y_test_pred_proba > 0.5).astype(int)\n",
|
||||||
|
"\n",
|
||||||
|
"# Create and plot confusion matrix\n",
|
||||||
|
"cm = confusion_matrix(y_test, y_test_pred)\n",
|
||||||
|
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, \n",
|
||||||
|
" display_labels=['Low Workload', 'High Workload'])\n",
|
||||||
|
"\n",
|
||||||
|
"fig, ax = plt.subplots(figsize=(8, 6))\n",
|
||||||
|
"disp.plot(ax=ax, cmap='Blues', values_format='d')\n",
|
||||||
|
"plt.title('Confusion Matrix - Test Set (Loaded Model)')\n",
|
||||||
|
"plt.tight_layout()\n",
|
||||||
|
"plt.show()\n",
|
||||||
|
"\n",
|
||||||
|
"# Print metrics\n",
|
||||||
|
"print(f\"\\nTest Set Performance (Loaded Model):\")\n",
|
||||||
|
"print(f\" Accuracy: {accuracy_score(y_test, y_test_pred):.4f}\")\n",
|
||||||
|
"print(f\" Precision: {precision_score(y_test, y_test_pred):.4f}\")\n",
|
||||||
|
"print(f\" Recall: {recall_score(y_test, y_test_pred):.4f}\")\n",
|
||||||
|
"print(f\" F1 Score: {f1_score(y_test, y_test_pred):.4f}\")\n",
|
||||||
|
"print(f\" ROC-AUC: {roc_auc_score(y_test, y_test_pred_proba):.4f}\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "e826a998",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"TO DO\n",
|
||||||
|
" * autoencoder langsam anfangen mit 19 schichten\n",
|
||||||
|
" * dann AE und SVM mit hybridem training wie bei claude?!\n",
|
||||||
|
" * dataset aus eyetracking verwenden?"
|
||||||
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|||||||
@ -1,5 +1,7 @@
|
|||||||
from sklearn.preprocessing import MinMaxScaler, StandardScaler
|
import pickle
|
||||||
import pandas as pd
|
from sklearn.preprocessing import StandardScaler, MinMaxScaler
|
||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
|
||||||
def fit_normalizer(train_data, au_columns, method='standard', scope='global'):
|
def fit_normalizer(train_data, au_columns, method='standard', scope='global'):
|
||||||
"""
|
"""
|
||||||
@ -19,9 +21,8 @@ def fit_normalizer(train_data, au_columns, method='standard', scope='global'):
|
|||||||
Returns:
|
Returns:
|
||||||
--------
|
--------
|
||||||
dict
|
dict
|
||||||
Dictionary containing fitted scalers
|
Dictionary containing fitted scalers and statistics for new subjects
|
||||||
"""
|
"""
|
||||||
# Select scaler based on method
|
|
||||||
if method == 'standard':
|
if method == 'standard':
|
||||||
Scaler = StandardScaler
|
Scaler = StandardScaler
|
||||||
elif method == 'minmax':
|
elif method == 'minmax':
|
||||||
@ -30,19 +31,54 @@ def fit_normalizer(train_data, au_columns, method='standard', scope='global'):
|
|||||||
raise ValueError("method must be 'standard' or 'minmax'")
|
raise ValueError("method must be 'standard' or 'minmax'")
|
||||||
|
|
||||||
scalers = {}
|
scalers = {}
|
||||||
|
|
||||||
if scope == 'subject':
|
if scope == 'subject':
|
||||||
# Fit one scaler per subject
|
# Fit one scaler per subject
|
||||||
|
subject_stats = []
|
||||||
|
|
||||||
for subject in train_data['subjectID'].unique():
|
for subject in train_data['subjectID'].unique():
|
||||||
subject_mask = train_data['subjectID'] == subject
|
subject_mask = train_data['subjectID'] == subject
|
||||||
scaler = Scaler()
|
scaler = Scaler()
|
||||||
scaler.fit(train_data.loc[subject_mask, au_columns])
|
scaler.fit(train_data.loc[subject_mask, au_columns].values)
|
||||||
scalers[subject] = scaler
|
scalers[subject] = scaler
|
||||||
|
|
||||||
|
# Store statistics for averaging
|
||||||
|
if method == 'standard':
|
||||||
|
subject_stats.append({
|
||||||
|
'mean': scaler.mean_,
|
||||||
|
'std': scaler.scale_
|
||||||
|
})
|
||||||
|
elif method == 'minmax':
|
||||||
|
subject_stats.append({
|
||||||
|
'min': scaler.data_min_,
|
||||||
|
'max': scaler.data_max_
|
||||||
|
})
|
||||||
|
|
||||||
|
# Calculate average statistics for new subjects
|
||||||
|
if method == 'standard':
|
||||||
|
avg_mean = np.mean([s['mean'] for s in subject_stats], axis=0)
|
||||||
|
avg_std = np.mean([s['std'] for s in subject_stats], axis=0)
|
||||||
|
fallback_scaler = StandardScaler()
|
||||||
|
fallback_scaler.mean_ = avg_mean
|
||||||
|
fallback_scaler.scale_ = avg_std
|
||||||
|
fallback_scaler.var_ = avg_std ** 2
|
||||||
|
fallback_scaler.n_features_in_ = len(au_columns)
|
||||||
|
elif method == 'minmax':
|
||||||
|
avg_min = np.mean([s['min'] for s in subject_stats], axis=0)
|
||||||
|
avg_max = np.mean([s['max'] for s in subject_stats], axis=0)
|
||||||
|
fallback_scaler = MinMaxScaler()
|
||||||
|
fallback_scaler.data_min_ = avg_min
|
||||||
|
fallback_scaler.data_max_ = avg_max
|
||||||
|
fallback_scaler.data_range_ = avg_max - avg_min
|
||||||
|
fallback_scaler.scale_ = 1.0 / fallback_scaler.data_range_
|
||||||
|
fallback_scaler.min_ = -avg_min * fallback_scaler.scale_
|
||||||
|
fallback_scaler.n_features_in_ = len(au_columns)
|
||||||
|
|
||||||
|
scalers['_fallback'] = fallback_scaler
|
||||||
|
|
||||||
elif scope == 'global':
|
elif scope == 'global':
|
||||||
# Fit one scaler for all subjects
|
# Fit one scaler for all subjects
|
||||||
scaler = Scaler()
|
scaler = Scaler()
|
||||||
scaler.fit(train_data[au_columns])
|
scaler.fit(train_data[au_columns].values)
|
||||||
scalers['global'] = scaler
|
scalers['global'] = scaler
|
||||||
|
|
||||||
else:
|
else:
|
||||||
@ -50,7 +86,7 @@ def fit_normalizer(train_data, au_columns, method='standard', scope='global'):
|
|||||||
|
|
||||||
return {'scalers': scalers, 'method': method, 'scope': scope}
|
return {'scalers': scalers, 'method': method, 'scope': scope}
|
||||||
|
|
||||||
def apply_normalizer(data, au_columns, normalizer_dict):
|
def apply_normalizer(data, columns, normalizer_dict):
|
||||||
"""
|
"""
|
||||||
Apply fitted normalization scalers to data.
|
Apply fitted normalization scalers to data.
|
||||||
|
|
||||||
@ -71,28 +107,70 @@ def apply_normalizer(data, au_columns, normalizer_dict):
|
|||||||
normalized_data = data.copy()
|
normalized_data = data.copy()
|
||||||
scalers = normalizer_dict['scalers']
|
scalers = normalizer_dict['scalers']
|
||||||
scope = normalizer_dict['scope']
|
scope = normalizer_dict['scope']
|
||||||
|
normalized_data[columns] = normalized_data[columns].astype(np.float64)
|
||||||
|
|
||||||
if scope == 'subject':
|
if scope == 'subject':
|
||||||
# Apply per-subject normalization
|
# Apply per-subject normalization
|
||||||
for subject in data['subjectID'].unique():
|
for subject in data['subjectID'].unique():
|
||||||
subject_mask = data['subjectID'] == subject
|
subject_mask = data['subjectID'] == subject
|
||||||
|
|
||||||
# Use the subject's scaler if available, otherwise use a fitted scaler from training
|
# Use the subject's scaler if available, otherwise use fallback
|
||||||
if subject in scalers:
|
if subject in scalers:
|
||||||
scaler = scalers[subject]
|
scaler = scalers[subject]
|
||||||
else:
|
else:
|
||||||
# For new subjects not seen in training, use the first available scaler
|
# Use averaged scaler for new subjects
|
||||||
# (This is a fallback - ideally all test subjects should be in training for subject-level normalization)
|
scaler = scalers['_fallback']
|
||||||
print(f"Warning: Subject {subject} not found in training data. Using fallback scaler.")
|
print(f"Info: Subject {subject} not in training data. Using averaged scaler from training subjects.")
|
||||||
scaler = list(scalers.values())[0]
|
|
||||||
|
|
||||||
normalized_data.loc[subject_mask, au_columns] = scaler.transform(
|
normalized_data.loc[subject_mask, columns] = scaler.transform(
|
||||||
data.loc[subject_mask, au_columns]
|
data.loc[subject_mask, columns].values
|
||||||
)
|
)
|
||||||
|
|
||||||
elif scope == 'global':
|
elif scope == 'global':
|
||||||
# Apply global normalization
|
# Apply global normalization
|
||||||
scaler = scalers['global']
|
scaler = scalers['global']
|
||||||
normalized_data[au_columns] = scaler.transform(data[au_columns])
|
normalized_data[columns] = scaler.transform(data[columns].values)
|
||||||
|
|
||||||
return normalized_data
|
return normalized_data
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def save_normalizer(normalizer_dict, filepath):
|
||||||
|
"""
|
||||||
|
Save fitted normalizer to disk.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
-----------
|
||||||
|
normalizer_dict : dict
|
||||||
|
Dictionary containing fitted scalers from fit_normalizer()
|
||||||
|
filepath : str
|
||||||
|
Path to save the normalizer (e.g., 'normalizer.pkl')
|
||||||
|
"""
|
||||||
|
# Create directory if it does not exist
|
||||||
|
dirpath = os.path.dirname(filepath)
|
||||||
|
if dirpath:
|
||||||
|
os.makedirs(dirpath, exist_ok=True)
|
||||||
|
|
||||||
|
with open(filepath, 'wb') as f:
|
||||||
|
pickle.dump(normalizer_dict, f)
|
||||||
|
|
||||||
|
print(f"Normalizer saved to {filepath}")
|
||||||
|
|
||||||
|
def load_normalizer(filepath):
|
||||||
|
"""
|
||||||
|
Load fitted normalizer from disk.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
-----------
|
||||||
|
filepath : str
|
||||||
|
Path to the saved normalizer file
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
--------
|
||||||
|
dict
|
||||||
|
Dictionary containing fitted scalers
|
||||||
|
"""
|
||||||
|
with open(filepath, 'rb') as f:
|
||||||
|
normalizer_dict = pickle.load(f)
|
||||||
|
print(f"Normalizer loaded from {filepath}")
|
||||||
|
return normalizer_dict
|
||||||
Loading…
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Reference in New Issue
Block a user