feat(notebooks): training model with new alternative undamaged (label 0) data

code/notebooks/stft.ipynb

@@ -443,6 +443,37 @@
     "plt.pcolormesh(ready_data2a[0].transpose(), cmap='jet', vmax=0.03, vmin=0.0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ready_data1b = []\n",
+    "for file in os.listdir('D:/thesis/data/converted/raw_B/sensor1'):\n",
+    "    ready_data1b.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw_B/sensor1', file), skiprows=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dpi\n",
+    "plt.figure(dpi=300)  # Set figure size and DPI\n",
+    "plt.pcolormesh(ready_data1b[0].iloc[:22,:].transpose(), cmap='jet', vmax=0.03, vmin=0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(ready_data1b[0])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -660,8 +691,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X1a.iloc[-1,:]\n",
-    "# y[2565]"
+    "# X1a.iloc[-1,:]\n",
+    "y[2564]"
    ]
   },
   {
@@ -717,6 +748,7 @@
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.decomposition import PCA\n",
+    "from xgboost import XGBClassifier\n",
     "# Define models for sensor1\n",
     "models_sensor1 = {\n",
     "    # \"Random Forest\": RandomForestClassifier(),\n",
@@ -724,14 +756,14 @@
     "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
     "    # \"KNN\": KNeighborsClassifier(),\n",
     "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
-    "    \"SVM\": SVC(),\n",
-    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    StandardScaler(),\n",
-    "    PCA(n_components=10),\n",
-    "    SVC(kernel='rbf')\n",
-    "    ),\n",
+    "    # \"SVM\": SVC(),\n",
+    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "    # StandardScaler(),\n",
+    "    # PCA(n_components=10),\n",
+    "    # SVC(kernel='rbf')\n",
+    "    # ),\n",
     "\n",
-    "    # \"XGBoost\": XGBClassifier()\n",
+    "    \"XGBoost\": XGBClassifier()\n",
     "}\n",
     "\n",
     "results_sensor1 = []\n",