From 3cbef17b0c40cc25082447fe0324524b2d7a2840 Mon Sep 17 00:00:00 2001
From: nuluh <dam.ar@outlook.com>
Date: Mon, 28 Jul 2025 05:20:10 +0700
Subject: [PATCH 1/2] feat(model_selection): add timing for model training and
 validation processes

---
 code/src/ml/model_selection.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/code/src/ml/model_selection.py b/code/src/ml/model_selection.py
index 5243536..2c10474 100644
--- a/code/src/ml/model_selection.py
+++ b/code/src/ml/model_selection.py
@@ -117,11 +117,20 @@ def train_and_evaluate_model(
     result = {"model": model_name, "sensor": sensor_label, "success": False}
 
     try:
+        import time
+        start_time = time.time()
+
         # Train the model
         model.fit(x_train, y_train)
 
+        result["elapsed_time_training"] = time.time() - start_time
         try:
+            # Predict on the test set (validation)
+            start_time = time.time()
+    
             y_pred = model.predict(x_test)
+
+            result["elapsed_time_validation"] = time.time() - start_time
             result["y_pred"] = y_pred  # Convert to numpy array
         except Exception as e:
             result["error"] = f"Prediction error: {str(e)}"

From 3e2b153d11fa600c44dee0c86f9b7c18bc869ab9 Mon Sep 17 00:00:00 2001
From: nuluh <dam.ar@outlook.com>
Date: Mon, 28 Jul 2025 05:22:24 +0700
Subject: [PATCH 2/2] refactor(stft): comment out unused imports and update SVM
 model loading for consistency

---
 code/notebooks/stft.ipynb | 38 ++++++++++++++++++++++++++------------
 1 file changed, 26 insertions(+), 12 deletions(-)

diff --git a/code/notebooks/stft.ipynb b/code/notebooks/stft.ipynb
index ceb9176..0f62cb6 100644
--- a/code/notebooks/stft.ipynb
+++ b/code/notebooks/stft.ipynb
@@ -636,11 +636,11 @@
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.decomposition import PCA\n",
-    "from xgboost import XGBClassifier\n",
-    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "# from xgboost import XGBClassifier\n",
+    "# from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
+    "# from sklearn.tree import DecisionTreeClassifier\n",
+    "# from sklearn.neighbors import KNeighborsClassifier\n",
+    "# from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
     "from sklearn.neural_network import MLPClassifier\n",
     "\n"
    ]
@@ -669,11 +669,11 @@
     "    #     StandardScaler(),\n",
     "    #     SVC(kernel='rbf', probability=True)\n",
     "    # ),\n",
-    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    #                                     StandardScaler(),\n",
-    "    #                                     PCA(n_components=10),\n",
-    "    #                                     SVC(kernel='rbf')\n",
-    "    #                                     ),\n",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "                                        StandardScaler(),\n",
+    "                                        PCA(n_components=10),\n",
+    "                                        SVC(kernel='rbf', probability=True)\n",
+    "                                        ),\n",
     "\n",
     "    # \"XGBoost\": XGBClassifier()\n",
     "    \"MLPClassifier\": make_pipeline(\n",
@@ -818,8 +818,22 @@
    "source": [
     "# 4. Sensor A Validate on Dataset B\n",
     "from joblib import load\n",
-    "svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
-    "y_pred_svm_1 = svm_model.predict(X1b)"
+    "svm_model = load('D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib')\n",
+    "y_pred_svm_1 = svm_model.predict_proba(X1b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Set NumPy to display full decimal values\n",
+    "np.set_printoptions(suppress=True, precision=6)  # Suppress scientific notation, set precision to 6 decimals\n",
+    "\n",
+    "y_pred_svm_1[1]"
    ]
   },
   {