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import os
import pandas as pd
import matplotlib
matplotlib.use('Agg') # Use non-interactive backend
import matplotlib.pyplot as plt
from src.utils.data_generator import generate_loan_data
from src.models.loan_recovery_model import LoanRecoveryModel
def train_and_save_model(data_path=None, model_type='random_forest', tune_hyperparameters=False):
"""
Train a loan recovery model and save it to disk.
Parameters:
-----------
data_path : str, optional
Path to the loan data CSV file, by default None
If None, generates synthetic data
model_type : str, optional
Type of model to train, by default 'random_forest'
tune_hyperparameters : bool, optional
Whether to tune hyperparameters, by default False
Returns:
--------
dict
Dictionary containing model performance metrics
"""
# Create directories if they don't exist
os.makedirs('data', exist_ok=True)
os.makedirs('models', exist_ok=True)
# Load or generate data
if data_path and os.path.exists(data_path):
print(f"Loading data from {data_path}")
data = pd.read_csv(data_path)
else:
print("Generating synthetic loan data")
data = generate_loan_data(n_samples=1000)
# Save generated data
data_path = 'data/loan_data.csv'
data.to_csv(data_path, index=False)
print(f"Saved generated data to {data_path}")
# Print data summary
print(f"\nData shape: {data.shape}")
print(f"Recovery rate: {data['recovery_status'].mean() * 100:.2f}%")
# Train model
print(f"\nTraining {model_type} model...")
model = LoanRecoveryModel(model_type=model_type)
metrics = model.train(data, tune_hyperparameters=tune_hyperparameters)
# Print performance metrics
print("\nModel Performance:")
print(f"Accuracy: {metrics['accuracy']:.4f}")
print(f"ROC AUC: {metrics['roc_auc']:.4f}")
print("\nClassification Report:")
for label, values in metrics['classification_report'].items():
if label in ['0', '1']:
label_name = 'Not Recovered' if label == '0' else 'Recovered'
print(f"{label_name}:")
print(f" Precision: {values['precision']:.4f}")
print(f" Recall: {values['recall']:.4f}")
print(f" F1-score: {values['f1-score']:.4f}")
# Save model
model_path = f"models/loan_recovery_{model_type}.pkl"
model.save_model(model_path)
print(f"\nSaved model to {model_path}")
# Plot feature importance if available
if 'feature_importance' in metrics:
fig = model.plot_feature_importance(top_n=10)
fig_path = f"models/feature_importance_{model_type}.png"
fig.savefig(fig_path)
plt.close(fig)
print(f"Saved feature importance plot to {fig_path}")
return metrics
if __name__ == "__main__":
# Train only Random Forest model
print(f"\n{'='*50}")
print(f"Training Random Forest Model")
print(f"{'='*50}")
train_and_save_model(model_type='random_forest', tune_hyperparameters=True)
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