refactor(stft): comment out unused imports and update SVM model loading for consistency

[EXP] Alterntive Undamage Case Data

.gitignore

@@ -2,4 +2,17 @@
 data/**/*.csv
 .venv/
 *.pyc
-*.egg-info/
+*.egg-info/
+
+# Latex
+*.aux
+*.log
+*.out
+*.toc
+*.bbl
+*.blg
+*.fdb_latexmk
+*.fls
+*.synctex.gz
+*.dvi
+

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 xgboost import XGBClassifier\n",
-    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
+    "# from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
+    "# from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "# from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\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",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    #                                     StandardScaler(),\n",
+    "                                        StandardScaler(),\n",
-    "    #                                     PCA(n_components=10),\n",
+    "                                        PCA(n_components=10),\n",
-    "    #                                     SVC(kernel='rbf')\n",
+    "                                        SVC(kernel='rbf', probability=True)\n",
-    "    #                                     ),\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",
+    "svm_model = load('D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib')\n",
-    "y_pred_svm_1 = svm_model.predict(X1b)"
+    "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]"
    ]
   },
   {

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)}"