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Exp/74 exp cross dataset validation #107

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nuluh merged 3 commits from exp/74-exp-cross-dataset-validation into dev 2025-08-28 05:09:16 +00:00
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1 changed files with 100 additions and 52 deletions
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@@ -287,8 +287,8 @@
"source": [
"# Define output directories for each sensor exported data\n",
"output_dirs = {\n",
" 'sensor1': os.path.join(damage_base_path, 'sensor1'),\n",
" 'sensor2': os.path.join(damage_base_path, 'sensor2')\n",
" 'sensorA': os.path.join(damage_base_path, 'sensorA'),\n",
" 'sensorB': os.path.join(damage_base_path, 'sensorB')\n",
"}"
]
},
@@ -305,7 +305,7 @@
"\n",
"with multiprocessing.Pool() as pool:\n",
" # Process each DAMAGE_X case in parallel\n",
" pool.map(process_damage_case, range(num_damage_cases), Fs, window_size, hop_size, output_dirs)"
" pool.map(process_damage_case, range(num_damage_cases))"
]
},
{
@@ -789,8 +789,8 @@
"source": [
"from src.ml.model_selection import create_ready_data\n",
"\n",
"X1b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor1') # sensor A\n",
"X2b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor2') # sensor B"
"X1b, y1 = create_ready_data('D:/thesis/data/converted/raw_B/sensor1') # sensor A\n",
"X2b, y2 = create_ready_data('D:/thesis/data/converted/raw_B/sensor2') # sensor B"
]
},
{
@@ -807,6 +807,17 @@
"#### Sensor A"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Rename first column using proper pandas method\n",
"X1b = X1b.rename(columns={X1b.columns[0]: \"Freq_0.00\"})\n",
"X2b = X2b.rename(columns={X2b.columns[0]: \"Freq_0.00\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -815,8 +826,27 @@
"source": [
"# 4. Sensor A Validate on Dataset B\n",
"from joblib import load\n",
"svm_model = load('D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib')\n",
"y_pred_svm_1 = svm_model.predict_proba(X1b)"
"from sklearn.svm import SVC\n",
"svm_model: SVC = load('D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"time_taken = np.array([])\n",
"for i in range(5): # Run multiple times to get an average time\n",
" start_time = time.time()\n",
" y_pred_svm_1 = svm_model.predict(X1b)\n",
" end_time = time.time()\n",
" time_taken = np.append(time_taken, end_time - start_time)\n",
"\n",
"print(time_taken)\n",
"print(time_taken.mean())\n"
]
},
{
@@ -828,9 +858,7 @@
"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]"
"np.set_printoptions(suppress=True, precision=6) # Suppress scientific notation, set precision to 6 decimals"
]
},
{
@@ -842,39 +870,14 @@
"from sklearn.metrics import accuracy_score, classification_report\n",
"\n",
"# 5. Evaluate\n",
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_1))\n",
"print(classification_report(y, y_pred_svm_1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Confusion Matrix Sensor A"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
"print(\"Accuracy on Dataset B:\", accuracy_score(y1, y_pred_svm_1))\n",
"df = pd.DataFrame(classification_report(y1, y_pred_svm_1, 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",
"# Create a figure with subplots\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
"\n",
"# Calculate confusion matrix\n",
"cm_A = confusion_matrix(y, y_pred_svm_1)\n",
"\n",
"# Get class labels\n",
"labels = svm_model.classes_\n",
"\n",
"# 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\")"
"# 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)"
]
},
{
@@ -892,11 +895,19 @@
"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",
"y_pred_svm_2 = svm_model.predict(X2b)\n",
"\n",
"y_pred_svm_2 = svm_model.predict(X2b)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 5. Evaluate\n",
"import pandas as pd\n",
"\n",
"df = pd.DataFrame(classification_report(y, y_pred_svm_2, output_dict=True)).T\n",
"df = pd.DataFrame(classification_report(y2, 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",
@@ -920,17 +931,54 @@
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
"import numpy as np\n",
"\n",
"# Create a fresh figure with subplots\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
"\n",
"cm = confusion_matrix(y, y_pred_svm_2) # -> ndarray\n",
"# Plot confusion matrix for Sensor A\n",
"cm_A = confusion_matrix(y, y_pred_svm_1)\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\")\n",
"\n",
"# get the class labels\n",
"labels = svm_model.classes_\n",
"# Plot confusion matrix for Sensor B\n",
"cm_B = confusion_matrix(y, y_pred_svm_2)\n",
"disp_B = ConfusionMatrixDisplay(confusion_matrix=cm_B, display_labels=labels)\n",
"disp_B.plot(ax=axes[1], cmap=plt.cm.Blues)\n",
"axes[1].set_title(\"Sensor B\")\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 B Test on Dataset B\")\n",
"# Find and modify colorbars to show max values\n",
"# The colorbars are typically the 3rd and 4th axes in the figure\n",
"for i, (cbar_idx, cm) in enumerate(zip([2, 3], [cm_A, cm_B])):\n",
" if cbar_idx < len(fig.axes):\n",
" cbar_ax = fig.axes[cbar_idx]\n",
" \n",
" # Get max value from the confusion matrix\n",
" max_val = cm.max()\n",
" \n",
" # Create a new set of ticks with reasonable spacing and ending with max_val\n",
" # For example, if max is around 2560, create ticks: [0, 500, 1000, 1500, 2000, 2560]\n",
" tick_interval = 500\n",
" new_ticks = list(range(0, int(max_val), tick_interval))\n",
" if np.isclose(new_ticks[-1], max_val, rtol=0.05):\n",
" new_ticks[-1] = max_val \n",
" else:\n",
" new_ticks.extend([max_val])\n",
" # Set the new ticks\n",
" cbar_ax.set_yticks(new_ticks)\n",
" \n",
" # Format tick labels as integers\n",
" # cbar_ax.set_yticklabels([f\"{int(t)}\" if t.is_integer() else f\"{t:.1f}\" for t in new_ticks])\n",
"\n",
"# Set SVG font rendering for better PDF output\n",
"plt.rcParams['svg.fonttype'] = 'none'\n",
"\n",
"# Adjust layout\n",
"plt.tight_layout()\n",
"\n",
"# Save and show\n",
"plt.savefig(\"output.svg\")\n",
"plt.show()"
]
},