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update CNNs

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Celina Korzer 2026-03-01 18:31:42 +01:00
parent e69000fbd8
commit f19dde3f9a
2 changed files with 46 additions and 33 deletions
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53
model_training/CNN/CNN_crossVal_EarlyFusion_Test_Eval.ipynb
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@ -180,11 +180,16 @@
"gss = GroupShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
"train_idx, test_idx = next(gss.split(X, y, groups))\n",
"\n",
"feature_columns_train, feature_columns_test = X[train_idx], X[test_idx]\n",
"#feature_columns_train, feature_columns_test = X[train_idx], X[test_idx]\n",
"X_train, X_test = X[train_idx], X[test_idx]\n",
"y_train, y_test = y[train_idx], y[test_idx]\n",
"groups_train, groups_test = groups[train_idx], groups[test_idx]\n",
"\n",
"print(\"Train:\", len(y_train), \" | Test:\", len(y_test))"
"print(\"Train:\", len(y_train), \" | Test:\", len(y_test))\n",
"print(\"Train:\", len(X_train), \" | Test:\", len(X_test))\n",
"print(train_idx)\n",
"print(test_idx)\n",
"print(np.intersect1d(train_idx,test_idx))"
]
},
{
@ -247,9 +252,9 @@
"fold_subjects = []\n",
"all_conf_matrices = []\n",
"\n",
"for fold, (train_idx, val_idx) in enumerate(gkf.split(X, y, groups)):\n",
" train_subjects = np.unique(groups[train_idx]) \n",
" val_subjects = np.unique(groups[val_idx]) \n",
"for fold, (tr_idx, val_idx) in enumerate(gkf.split(X_train, y_train, groups_train)):\n",
" train_subjects = np.unique(groups_train[tr_idx]) \n",
" val_subjects = np.unique(groups_train[val_idx]) \n",
" fold_subjects.append({\"Fold\": fold+1, \n",
" \"Train_Subjects\": train_subjects, \n",
" \"Val_Subjects\": val_subjects}) \n",
@ -259,23 +264,17 @@
" print(\"Val-Subjects:\", val_subjects) \n",
"\n",
" #Split\n",
" X_train, X_val = X[train_idx], X[val_idx] \n",
" y_train, y_val = y[train_idx], y[val_idx] # Normalisierung pro Fold \n",
" X_tr, X_val = X_train[tr_idx], X_train[val_idx] \n",
" y_tr, y_val = y_train[tr_idx], y_train[val_idx] # Normalisierung pro Fold \n",
"\n",
" #Normalisierung pro Fold\n",
" scaler = StandardScaler() \n",
" X_train = scaler.fit_transform(X_train.reshape(len(X_train), -1)).reshape(X_train.shape) \n",
" X_tr = scaler.fit_transform(X_tr.reshape(len(X_tr), -1)).reshape(X_tr.shape) \n",
" X_val = scaler.transform(X_val.reshape(len(X_val), -1)).reshape(X_val.shape) \n",
"\n",
" # Plausibilitäts-Check \n",
" print(\"Train Mittelwerte (erste 5 Features):\", X_train.mean(axis=0)[:5]) \n",
" print(\"Train Std (erste 5 Features):\", X_train.std(axis=0)[:5]) \n",
" print(\"Val Mittelwerte (erste 5 Features):\", X_val.mean(axis=0)[:5]) \n",
" print(\"Val Std (erste 5 Features):\", X_val.std(axis=0)[:5]) \n",
"\n",
" # Modell \n",
" model = build_model(input_shape=(len(feature_columns_train),1), lr=1e-4) \n",
" model.summary() \n",
" model = build_model(input_shape=(len(feature_columns),1), lr=1e-4) \n",
" #model.summary() \n",
"\n",
" callbacks = [ \n",
" tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10, restore_best_weights=True), \n",
@ -283,9 +282,9 @@
" ] \n",
"\n",
" history = model.fit( \n",
" X_train, y_train, \n",
" X_tr, y_tr, \n",
" validation_data=(X_val, y_val), \n",
" epochs=100, \n",
" epochs=50,\n",
" batch_size=16, \n",
" callbacks=callbacks, \n",
" verbose=0 \n",
@ -373,14 +372,14 @@
"outputs": [],
"source": [
"scaler_final = StandardScaler() \n",
"X_scaled = scaler_final.fit_transform(feature_columns_train.reshape(len(feature_columns_train), -1)).reshape(feature_columns_train.shape) \n",
"X_train_scaled = scaler_final.fit_transform( X_train.reshape(len(X_train), -1) ).reshape(X_train.shape)\n",
"\n",
"final_model = build_model(input_shape=(len(feature_columns_train),1), lr=1e-4) \n",
"final_model.summary() \n",
"final_model = build_model(input_shape=(len(feature_columns),1), lr=1e-4) \n",
"#final_model.summary() \n",
"\n",
"final_model.fit( \n",
" X_scaled, y_train, \n",
" epochs=150, \n",
" X_train_scaled, y_train,\n",
" epochs=50, \n",
" batch_size=16, \n",
" verbose=1 \n",
")"
@ -401,8 +400,8 @@
"metadata": {},
"outputs": [],
"source": [
"# final_model.save(\"cnn_crossVal_EarlyFusion_V2.keras\") \n",
"# joblib.dump(scaler_final, \"scaler_crossVal_EarlyFusion_V2.joblib\") \n",
"final_model.save(\"cnn_crossVal_EarlyFusion_V2_0103.keras\") \n",
"joblib.dump(scaler_final, \"scaler_crossVal_EarlyFusion_V2_0103.joblib\") \n",
"\n",
"# print(\"Finales Modell und Scaler gespeichert als 'cnn_crossVal_EarlyFusion_V2.keras' und 'scaler_crossVal_EarlyFusion_V2.joblib'\")"
]
@ -458,8 +457,8 @@
"source": [
"# Preprocessing Testdaten \n",
"X_test_scaled = scaler.transform( \n",
" feature_columns_test.reshape(len(feature_columns_test), -1) \n",
").reshape(feature_columns_test.shape) \n",
" X_test.reshape(len(X_test), -1) \n",
").reshape(X_test.shape) \n",
"\n",
"# Vorhersagen \n",
"y_prob_test = model.predict(X_test_scaled).flatten() \n",

24
model_training/CNN/CNN_crossVal_HybridFusion_Test_Eval.ipynb
View File

@ -140,8 +140,10 @@
"data = data.dropna(subset=au_columns + eye_columns + [\"label\"]) \n",
"\n",
"# Arrays \n",
"print(data[au_columns].shape)\n",
"X_au = data[au_columns].values[..., np.newaxis] \n",
"X_eye = data[eye_columns].values\n",
"print(X_au.shape)\n",
"y = data[\"label\"].values \n",
"groups = data[\"subjectID\"].values"
]
@ -169,7 +171,9 @@
"y_train, y_test = y[train_idx], y[test_idx]\n",
"groups_train, groups_test = groups[train_idx], groups[test_idx]\n",
"\n",
"print(\"Train:\", len(y_train), \" | Test:\", len(y_test))"
"print(\"Train:\", len(y_train), \" | Test:\", len(y_test))\n",
"print(np.unique(groups_test))\n",
"print(np.unique(groups_train))"
]
},
{
@ -235,9 +239,19 @@
"cv_histories = [] \n",
"cv_results = [] \n",
"all_conf_matrices = [] \n",
"fold_subjects = []\n",
"\n",
"for fold, (tr_idx, va_idx) in enumerate(gkf.split(X_au_train, y_train, groups_train)): \n",
" print(f\"\\n===== FOLD {fold+1} =====\") \n",
" \n",
" train_subjects = np.unique(groups_train[tr_idx]) \n",
" val_subjects = np.unique(groups_train[va_idx]) \n",
" fold_subjects.append({\"Fold\": fold+1, \n",
" \"Train_Subjects\": train_subjects, \n",
" \"Val_Subjects\": val_subjects}) \n",
" \n",
" print(f\"\\n--- Fold {fold+1} ---\") \n",
" print(\"Train-Subjects:\", train_subjects) \n",
" print(\"Val-Subjects:\", val_subjects) \n",
" \n",
" X_tr_au, X_va_au = X_au_train[tr_idx], X_au_train[va_idx] \n",
" X_tr_eye, X_va_eye = X_eye_train[tr_idx], X_eye_train[va_idx] \n",
@ -361,9 +375,9 @@
"metadata": {},
"outputs": [],
"source": [
"model_cv.save(\"hybrid_fusion_model_Test_group_split.keras\") \n",
"joblib.dump(scaler_au, \"scaler_au_Test_group_split.joblib\") \n",
"joblib.dump(scaler_eye, \"scaler_eye_Test_group_split.joblib\") \n",
"model_cv.save(\"hybrid_fusion_model_Test_group_split_0103.keras\") \n",
"joblib.dump(scaler_au, \"scaler_au_Test_group_split_0103.joblib\") \n",
"joblib.dump(scaler_eye, \"scaler_eye_Test_group_split_0103.joblib\") \n",
"\n",
"print(\"Finales Modell gespeichert.\")"
]