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1957862 | 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 | """Script to train and save the diabetes prediction model."""
import sys
from pathlib import Path
from sklearn.metrics import classification_report
# Add the src directory to the Python path
sys.path.append(str(Path(__file__).parent))
from src.diabetes_prediction.data.processor import DataProcessor
from src.diabetes_prediction.ml.model import DiabetesPredictor
from src.diabetes_prediction.config.settings import DATA_PATH, MODEL_PATH
def main():
"""Train and save the diabetes prediction model."""
print("Starting model training...")
# Initialize data processor and load data
print("Loading and preprocessing data...")
processor = DataProcessor(DATA_PATH)
X, y = processor.preprocess_data()
# Split data into train and test sets
X_train, X_test, y_train, y_test = processor.train_test_split()
# Train the model
print("Training model...")
predictor = DiabetesPredictor()
predictor.train(X_train, y_train)
# Evaluate the model
print("\nModel evaluation:")
metrics = predictor.evaluate(X_test, y_test)
report = metrics['classification_report']
print(f"Accuracy: {metrics['accuracy']:.4f}")
print(f"ROC-AUC: {metrics['roc_auc']:.4f}")
print(f"Precision (Class 1): {report['1']['precision']:.4f}")
print(f"Recall (Class 1): {report['1']['recall']:.4f}")
print(f"F1-Score (Class 1): {report['1']['f1-score']:.4f}")
print(f"\nClassification Report:")
print(classification_report(y_test, predictor.predict(X_test)))
# Save the model
print(f"\nSaving model to {MODEL_PATH}...")
predictor.save()
print("Model training and saving completed successfully!")
if __name__ == "__main__":
main()
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