Spaces:
Sleeping
Sleeping
File size: 16,796 Bytes
de7c20a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Calibrate Injury Likelihood Mapping\n",
"\n",
"This notebook calibrates the injury likelihood mapping for the injury risk prediction system. We use the training dataset (`Refined_Sports_Injury_Dataset.csv`) to map the model's predicted probabilities to true injury probabilities.\n",
"\n",
"## Objectives\n",
"- Load the dataset and trained models.\n",
"- Preprocess the data consistently with the training pipeline.\n",
"- Use the models to predict probabilities on a calibration set.\n",
"- Fit a logistic regression model to map predicted probabilities to true injury probabilities.\n",
"- Save the calibration model for use in `predict.py`."
],
"metadata": {
"id": "v4eUbEr3u9Rm"
}
},
{
"cell_type": "code",
"source": [
"# Import libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import joblib\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import brier_score_loss\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Set random seed\n",
"np.random.seed(42)"
],
"metadata": {
"id": "Y_dhli46u9Ro"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ---------------------- 1. Load Data and Models ----------------------\n",
"# Load the dataset\n",
"data_path = \"/content/Refined_Sports_Injury_Dataset.csv\"\n",
"try:\n",
" df = pd.read_csv(data_path)\n",
"except FileNotFoundError:\n",
" raise FileNotFoundError(f\"Dataset file not found at {data_path}. Please ensure the file exists.\")\n",
"print(\"Dataset loaded. Shape:\", df.shape)\n",
"\n",
"# Load the trained models with error handling\n",
"model_dir = \"/content/model\"\n",
"try:\n",
" rf_model = joblib.load(f\"{model_dir}/rf_injury_model.pkl\")\n",
"except Exception as e:\n",
" raise FileNotFoundError(f\"Failed to load RandomForest model: {str(e)}. Ensure RandomForest.ipynb has been run successfully to generate {model_dir}/rf_injury_model.pkl.\")\n",
"\n",
"try:\n",
" xgb_model = joblib.load(f\"{model_dir}/xgboost_injury_model.pkl\")\n",
"except Exception as e:\n",
" raise FileNotFoundError(f\"Failed to load XGBoost model: {str(e)}. Ensure XGBOOST.ipynb has been run successfully to generate {model_dir}/xgboost_injury_model.pkl.\")\n",
"\n",
"print(\"Models loaded.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NPDVk8WLu9Rp",
"outputId": "303e0a70-70b3-4796-d887-0b845c219e9a"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset loaded. Shape: (10000, 18)\n",
"Models loaded.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ---------------------- 2. Preprocess Data ----------------------\n",
"# Encode categorical columns (consistent with training notebooks)\n",
"gender_mapping = {\"Male\": 0, \"Female\": 1}\n",
"experience_mapping = {\"Beginner\": 0, \"Intermediate\": 1, \"Advanced\": 2, \"Professional\": 3}\n",
"injury_type_mapping = {\"None\": 0, \"Sprain\": 1, \"Ligament Tear\": 2, \"Tendonitis\": 3, \"Strain\": 4, \"Fracture\": 5}\n",
"\n",
"# Handle NaN values in Previous_Injury_Type\n",
"df[\"Previous_Injury_Type\"] = df[\"Previous_Injury_Type\"].fillna(\"None\")\n",
"\n",
"df[\"Gender\"] = df[\"Gender\"].map(gender_mapping).fillna(0).astype(int)\n",
"df[\"Sport_Type\"] = df[\"Sport_Type\"].astype(\"category\").cat.codes\n",
"df[\"Experience_Level\"] = df[\"Experience_Level\"].map(experience_mapping).fillna(0).astype(int)\n",
"df[\"Previous_Injury_Type\"] = df[\"Previous_Injury_Type\"].map(injury_type_mapping).fillna(0).astype(int)\n",
"\n",
"# Replace 0 with 0.1 in Total_Weekly_Training_Hours\n",
"df[\"Total_Weekly_Training_Hours\"] = df[\"Total_Weekly_Training_Hours\"].replace(0, 0.1)\n",
"\n",
"# Create derived features\n",
"df[\"Intensity_Ratio\"] = df[\"High_Intensity_Training_Hours\"] / df[\"Total_Weekly_Training_Hours\"]\n",
"df[\"Recovery_Per_Training\"] = df[\"Recovery_Time_Between_Sessions\"] / df[\"Total_Weekly_Training_Hours\"]\n",
"\n",
"# Create Injury_Occurred column probabilistically based on Injury_Risk_Level\n",
"print(\"Injury_Risk_Level distribution:\\n\", df[\"Injury_Risk_Level\"].value_counts())\n",
"\n",
"# Define probabilities of injury occurrence based on risk level\n",
"injury_probabilities = {\n",
" \"High\": 0.95, # 95% chance of injury\n",
" \"Medium\": 0.5, # 50% chance of injury\n",
" \"Low\": 0.05 # 5% chance of injury\n",
"}\n",
"\n",
"# Generate Injury_Occurred using random sampling based on Injury_Risk_Level\n",
"df[\"Injury_Occurred\"] = df[\"Injury_Risk_Level\"].apply(\n",
" lambda x: np.random.binomial(1, injury_probabilities[x])\n",
")\n",
"\n",
"# Check the distribution of Injury_Occurred\n",
"print(\"Injury_Occurred distribution (full dataset):\\n\", df[\"Injury_Occurred\"].value_counts())\n",
"\n",
"# Ensure both classes are present\n",
"if len(df[\"Injury_Occurred\"].unique()) < 2:\n",
" raise ValueError(\"Injury_Occurred contains only one class after probabilistic assignment. Adjust probabilities or dataset.\")\n",
"\n",
"# Define features\n",
"features = [\n",
" \"Age\", \"Gender\", \"Sport_Type\", \"Experience_Level\", \"Flexibility_Score\",\n",
" \"Total_Weekly_Training_Hours\", \"High_Intensity_Training_Hours\", \"Strength_Training_Frequency\",\n",
" \"Recovery_Time_Between_Sessions\", \"Training_Load_Score\", \"Sprint_Speed\", \"Endurance_Score\",\n",
" \"Agility_Score\", \"Fatigue_Level\", \"Previous_Injury_Count\", \"Previous_Injury_Type\",\n",
" \"Intensity_Ratio\", \"Recovery_Per_Training\"\n",
"]\n",
"\n",
"# Prepare features and target\n",
"X = df[features]\n",
"y_outcome = df[\"Injury_Occurred\"]\n",
"\n",
"print(\"Features prepared:\", features)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rEUcpksxu9Rq",
"outputId": "70eee7a9-b2f5-4d3c-a668-504cd0c9dd81"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Injury_Risk_Level distribution:\n",
" Injury_Risk_Level\n",
"Medium 6016\n",
"Low 2827\n",
"High 1157\n",
"Name: count, dtype: int64\n",
"Injury_Occurred distribution (full dataset):\n",
" Injury_Occurred\n",
"0 5801\n",
"1 4199\n",
"Name: count, dtype: int64\n",
"Features prepared: ['Age', 'Gender', 'Sport_Type', 'Experience_Level', 'Flexibility_Score', 'Total_Weekly_Training_Hours', 'High_Intensity_Training_Hours', 'Strength_Training_Frequency', 'Recovery_Time_Between_Sessions', 'Training_Load_Score', 'Sprint_Speed', 'Endurance_Score', 'Agility_Score', 'Fatigue_Level', 'Previous_Injury_Count', 'Previous_Injury_Type', 'Intensity_Ratio', 'Recovery_Per_Training']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ---------------------- 3. Split Data for Calibration ----------------------\n",
"# Split data into training and calibration sets\n",
"X_train, X_calib, y_train_outcome, y_calib_outcome = train_test_split(\n",
" X, y_outcome, test_size=0.2, stratify=y_outcome, random_state=42\n",
")\n",
"\n",
"print(\"Calibration set size:\", X_calib.shape)\n",
"print(\"Calibration Injury_Occurred distribution:\\n\", y_calib_outcome.value_counts())\n",
"\n",
"# Check if y_calib_outcome has both classes\n",
"if len(y_calib_outcome.unique()) < 2:\n",
" raise ValueError(\"Calibration set contains only one class in Injury_Occurred. Cannot proceed with calibration.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kbGVsNq4u9Rr",
"outputId": "732a5e13-f52b-4001-ee03-71a1b929fac7"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Calibration set size: (2000, 18)\n",
"Calibration Injury_Occurred distribution:\n",
" Injury_Occurred\n",
"0 1160\n",
"1 840\n",
"Name: count, dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ---------------------- 4. Predict Probabilities with Ensemble ----------------------\n",
"# Use the ensemble (average of RandomForest and XGBoost) to predict probabilities\n",
"rf_probs = rf_model.predict_proba(X_calib)\n",
"xgb_probs = xgb_model.predict_proba(X_calib)\n",
"\n",
"# Average the probabilities (same as in predict.py)\n",
"avg_probs = (rf_probs + xgb_probs) / 2\n",
"\n",
"# Get the predicted class and confidence\n",
"predicted_classes = np.argmax(avg_probs, axis=1)\n",
"confidences = np.max(avg_probs, axis=1)\n",
"\n",
"print(\"Sample of predicted confidences:\\n\", confidences[:5])\n",
"print(\"Sample of predicted classes:\\n\", predicted_classes[:5])\n",
"\n",
"# Inspect the relationship between predicted classes and Injury_Occurred\n",
"calib_df = pd.DataFrame({\n",
" \"Predicted_Class\": predicted_classes,\n",
" \"Injury_Occurred\": y_calib_outcome\n",
"})\n",
"print(\"Distribution of Injury_Occurred by Predicted Class:\\n\", calib_df.groupby(\"Predicted_Class\")[\"Injury_Occurred\"].value_counts())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-Qyc_ZYOu9Rr",
"outputId": "10e3592e-c51c-49cb-9ff4-6e157701811e"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sample of predicted confidences:\n",
" [0.98814357 0.9881264 0.97794891 0.97335743 0.97383904]\n",
"Sample of predicted classes:\n",
" [2 2 2 1 1]\n",
"Distribution of Injury_Occurred by Predicted Class:\n",
" Predicted_Class Injury_Occurred\n",
"0 1 216\n",
" 0 14\n",
"1 0 540\n",
" 1 40\n",
"2 0 606\n",
" 1 584\n",
"Name: count, dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ---------------------- 5. Map Probabilities to Injury Probabilities ----------------------\n",
"# Use the raw probabilities as features for calibration\n",
"calib_data = pd.DataFrame({\n",
" \"prob_high\": avg_probs[:, 0], # Probability of High risk\n",
" \"prob_low\": avg_probs[:, 1], # Probability of Low risk\n",
" \"prob_medium\": avg_probs[:, 2] # Probability of Medium risk\n",
"})\n",
"\n",
"# Fit logistic regression with regularization\n",
"lr_calib = LogisticRegression(max_iter=1000, penalty='l2', C=1.0)\n",
"lr_calib.fit(calib_data, y_calib_outcome)\n",
"\n",
"# Predict calibrated probabilities\n",
"calibrated_probs = lr_calib.predict_proba(calib_data)[:, 1]\n",
"\n",
"# Evaluate calibration using Brier score\n",
"brier_score = brier_score_loss(y_calib_outcome, calibrated_probs)\n",
"print(f\"Brier Score (lower is better): {brier_score:.4f}\")\n",
"\n",
"# Inspect the mapping\n",
"calib_results = pd.DataFrame({\n",
" \"Predicted_Class\": predicted_classes,\n",
" \"Confidence\": confidences,\n",
" \"Calibrated_Probability\": calibrated_probs,\n",
" \"True_Injury_Occurred\": y_calib_outcome\n",
"})\n",
"print(\"Average Calibrated Probability by Predicted Class:\\n\", calib_results.groupby(\"Predicted_Class\")[\"Calibrated_Probability\"].mean())\n",
"\n",
"# Plot calibration curve\n",
"plt.figure(figsize=(8, 6))\n",
"plt.scatter(confidences, calibrated_probs, alpha=0.5)\n",
"plt.plot([0, 1], [0, 1], 'k--', label=\"Perfectly Calibrated\")\n",
"plt.xlabel(\"Original Confidence (Ensemble)\")\n",
"plt.ylabel(\"Calibrated Injury Probability (P(Injury_Occurred=1))\")\n",
"plt.title(\"Calibration Curve: Confidence vs. Injury Probability\")\n",
"plt.legend()\n",
"plt.savefig(f\"{model_dir}/calibration_curve.png\")\n",
"plt.close()\n",
"\n",
"print(f\"Calibration curve saved to {model_dir}/calibration_curve.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oITFxyA4u9Rs",
"outputId": "98d1b529-b387-43ee-a7a8-9a7244701dd3"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Brier Score (lower is better): 0.1734\n",
"Average Calibrated Probability by Predicted Class:\n",
" Predicted_Class\n",
"0 0.923188\n",
"1 0.075402\n",
"2 0.490621\n",
"Name: Calibrated_Probability, dtype: float64\n",
"Calibration curve saved to /content/model/calibration_curve.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ---------------------- 6. Save Calibration Model ----------------------\n",
"# Save the logistic regression model for use in predict.py\n",
"import os\n",
"os.makedirs(model_dir, exist_ok=True)\n",
"joblib.dump(lr_calib, f\"{model_dir}/likelihood_calibrator.pkl\")\n",
"print(f\"Calibration model saved to {model_dir}/likelihood_calibrator.pkl\")\n",
"print(\"Note: You are running this in Google Colab. The file is saved to /content/model/likelihood_calibrator.pkl.\")\n",
"print(\"Please download it and move it to C:/Users/amrHa/Desktop/final 3/deployment/model/ for deployment.\")\n",
"print(\"Alternatively, if running locally, update model_dir to 'C:/Users/amrHa/Desktop/final 3/deployment/model'.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "S8IY2YCZu9Rs",
"outputId": "4585d640-eb1b-49d2-ec60-2727704b2de5"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Calibration model saved to /content/model/likelihood_calibrator.pkl\n",
"Note: You are running this in Google Colab. The file is saved to /content/model/likelihood_calibrator.pkl.\n",
"Please download it and move it to C:/Users/amrHa/Desktop/final 3/deployment/model/ for deployment.\n",
"Alternatively, if running locally, update model_dir to 'C:/Users/amrHa/Desktop/final 3/deployment/model'.\n"
]
}
]
}
]
} |