CNN with test-split

model_training/CNN/CNN_crossVal_EarlyFusion.ipynb

@@ -1706,18 +1706,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
   }
  },
  "nbformat": 4,

model_training/CNN/CNN_crossVal_EarlyFusion_Filter.ipynb

@@ -1756,7 +1756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "5a09f80c",
    "metadata": {},
    "outputs": [
@@ -1815,10 +1815,10 @@
     "plt.show()\n",
     "\n",
     "\n",
-    "print(\"Precision:\", precision_score(y, y_pred_final)) \n",
-    "print(\"Recall:\", recall_score(y, y_pred_final)) \n",
-    "print(\"F1‑Score:\", f1_score(y, y_pred_final)) \n",
-    "print(\"Balanced Accuracy:\", balanced_accuracy_score(y, y_pred_final))"
+    "#print(\"Precision:\", precision_score(y, y_pred_final)) \n",
+    "#print(\"Recall:\", recall_score(y, y_pred_final)) \n",
+    "#print(\"F1‑Score:\", f1_score(y, y_pred_final)) \n",
+    "#print(\"Balanced Accuracy:\", balanced_accuracy_score(y, y_pred_final))"
    ]
   },
   {
@@ -1921,18 +1921,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
   }
  },
  "nbformat": 4,

model_training/CNN/CNN_crossVal_HybridFusion.ipynb

@@ -20,8 +20,9 @@
     "import matplotlib.pyplot as plt\n",
     "import random \n",
     "import joblib \n",
+    "import seaborn as sns\n",
     "from pathlib import Path \n",
-    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, balanced_accuracy_score\n",
     "\n",
     "from sklearn.model_selection import GroupKFold \n",
     "from sklearn.preprocessing import StandardScaler \n",
@@ -1215,12 +1216,30 @@
     "    cm = confusion_matrix(y_val, y_pred)\n",
     "    all_conf_matrices.append(cm) \n",
     "    \n",
+    "    #Heatmap \n",
+    "    plt.figure(figsize=(6,5)) \n",
+    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
+    "                xticklabels=[\"Pred 0\", \"Pred 1\"], \n",
+    "                yticklabels=[\"True 0\", \"True 1\"]) \n",
+    "    plt.title(f\"Konfusionsmatrix – Fold {fold+1}\") \n",
+    "    plt.xlabel(\"Predicted\") \n",
+    "    plt.ylabel(\"True\") \n",
+    "    plt.show()\n",
+    "    \n",
     "    print(f\"Konfusionsmatrix Fold {fold+1}:\\n{cm}\\n\") \n",
     "    \n",
     "# Aggregierte Matrix \n",
     "agg_cm = sum(all_conf_matrices) \n",
     "print(\"Aggregierte Konfusionsmatrix über alle Folds:\") \n",
-    "print(agg_cm)\n"
+    "print(agg_cm)\n",
+    "plt.figure(figsize=(6,5))\n",
+    "sns.heatmap(agg_cm, annot=True, fmt=\"d\", cmap=\"Purples\",\n",
+    "            xticklabels=[\"Pred 0\", \"Pred 1\"],\n",
+    "            yticklabels=[\"True 0\", \"True 1\"])\n",
+    "plt.title(\"Aggregierte Konfusionsmatrix – alle Folds\")\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"True\")\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -1640,6 +1659,60 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "445fe4c8",
+   "metadata": {},
+   "source": [
+    "Evalutation auf allen Trainingsdaten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a14a3bda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Preprocessing der Trainingsdaten\n",
+    "X_au_train_final = scaler_au_final.transform(\n",
+    "    X_au_train.reshape(len(X_au_train), -1)\n",
+    ").reshape(X_au_train.shape)\n",
+    "\n",
+    "X_eye_train_final = scaler_eye_final.transform(X_eye_train)\n",
+    "\n",
+    "# 2. Vorhersagen\n",
+    "y_prob_final = final_model.predict([X_au_train_final, X_eye_train_final]).flatten()\n",
+    "y_pred_final = (y_prob_final > 0.5).astype(int)\n",
+    "\n",
+    "# 3. Metriken\n",
+    "loss, acc, auc_val = final_model.evaluate(\n",
+    "    [X_au_train_final, X_eye_train_final], y_train, verbose=0\n",
+    ")\n",
+    "\n",
+    "print(f\"Final Loss: {loss:.4f}\")\n",
+    "print(f\"Final Accuracy: {acc:.4f}\")\n",
+    "print(f\"Final AUC: {auc_val:.4f}\")\n",
+    "\n",
+    "# 4. Konfusionsmatrix (Heatmap)\n",
+    "cm_final = confusion_matrix(y_train, y_pred_final)\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "sns.heatmap(cm_final, annot=True, fmt=\"d\", cmap=\"Oranges\",\n",
+    "            xticklabels=[\"Pred 0\", \"Pred 1\"],\n",
+    "            yticklabels=[\"True 0\", \"True 1\"])\n",
+    "plt.title(\"Konfusionsmatrix – Finales Hybrid-Modell (Trainingsdaten)\")\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"True\")\n",
+    "plt.show()\n",
+    "\n",
+    "# 5. Weitere Metriken\n",
+    "#print(\"Precision:\", precision_score(y_train, y_pred_final))\n",
+    "#print(\"Recall:\", recall_score(y_train, y_pred_final))\n",
+    "#print(\"F1‑Score:\", f1_score(y_train, y_pred_final))\n",
+    "#print(\"Balanced Accuracy:\", balanced_accuracy_score(y_train, y_pred_final))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7c7f9cc4",