From 3ef656dd289fc40ddd7e9da90ca6617a0f7b1052 Mon Sep 17 00:00:00 2001
From: nuluh <dam.ar@outlook.com>
Date: Tue, 26 Aug 2025 08:24:04 +0700
Subject: [PATCH] refactor(notebooks): remove redundant confusion matrix code
 for Sensor A and update reporting for Sensor B

---
 code/notebooks/stft.ipynb | 32 ++++++++++++++++++++------------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/code/notebooks/stft.ipynb b/code/notebooks/stft.ipynb
index b4b46db..03c4e5f 100644
--- a/code/notebooks/stft.ipynb
+++ b/code/notebooks/stft.ipynb
@@ -862,17 +862,19 @@
     "import matplotlib.pyplot as plt\n",
     "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
     "\n",
+    "# Create a figure with subplots\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
     "\n",
-    "cm = confusion_matrix(y, y_pred_svm_1) # -> ndarray\n",
+    "# Calculate confusion matrix\n",
+    "cm_A = confusion_matrix(y, y_pred_svm_1)\n",
     "\n",
-    "# get the class labels\n",
+    "# Get class labels\n",
     "labels = svm_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(\"Confusion Matrix of Sensor A Test on Dataset B\")\n",
-    "plt.show()"
+    "# Plot confusion matrix in first subplot\n",
+    "disp_A = ConfusionMatrixDisplay(confusion_matrix=cm_A, display_labels=labels)\n",
+    "disp_A.plot(ax=axes[0], cmap=plt.cm.Blues)\n",
+    "axes[0].set_title(\"Sensor A\")"
    ]
   },
   {
@@ -888,20 +890,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "svm_model = load('D:/thesis/models/sensor2/SVM.joblib')\n",
-    "# svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
+    "# svm_model = load('D:/thesis/models/sensor2/SVM.joblib')\n",
+    "svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
     "y_pred_svm_2 = svm_model.predict(X2b)\n",
     "\n",
     "# 5. Evaluate\n",
-    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_2))\n",
-    "print(classification_report(y, y_pred_svm_2))"
+    "\n",
+    "df = pd.DataFrame(classification_report(y, y_pred_svm_2, output_dict=True)).T\n",
+    "# Round numbers nicely and move 'accuracy' into a row that fits your desired layout\n",
+    "df_rounded = df.round(2)\n",
+    "\n",
+    "# Export to LaTeX\n",
+    "latex_table = df_rounded.to_latex(index=True, float_format=\"%.2f\", caption=\"Classification report on Dataset B\", label=\"tab:clf_report_auto\")\n",
+    "print(latex_table)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Confusion Matrix Sensor B"
+    "#### Confusion Matrix Sensor A and B"
    ]
   },
   {