feat(notebooks): Add confusion matrix plotting loop for Sensor 1 models

code/notebooks/stft.ipynb

@@ -718,6 +718,31 @@
     "    print(f\"{name} on sensor1: Accuracy = {res['accuracy']:.2f}%\")\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from joblib import load\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "for i in results_sensor1:\n",
+    "    model = load(f\"D:/thesis/models/sensor1/{i['model']}.joblib\")\n",
+    "    y_pred = model.predict(x_test1)\n",
+    "    cm = confusion_matrix(y_test, y_pred) # -> ndarray\n",
+    "\n",
+    "    # get the class labels\n",
+    "    labels = model.classes_\n",
+    "\n",
+    "    # Plot\n",
+    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
+    "    disp.plot(cmap=plt.cm.Blues)  # You can change colormap\n",
+    "    plt.title(f\"{i['model']} Sensor A CM Training\")\n",
+    "    plt.show()\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -855,7 +880,8 @@
     "from sklearn.metrics import accuracy_score, classification_report\n",
     "# 4. Validate on Dataset B\n",
     "from joblib import load\n",
-    "svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
+    "# svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
+    "svm_model = load('D:/thesis/models/sensor1/SVM with StandardScaler and PCA.joblib')\n",
     "y_pred_svm = svm_model.predict(X1b)\n",
     "\n",
     "# 5. Evaluate\n",