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import pandas as pd
import os
from models.loan_model import LoanApprovalModel
def test_trained_model():
# Check if model exists
model_path = 'models/loan_model.joblib'
if not os.path.exists(model_path):
print(f"Error: Trained model not found at {model_path}")
print("Please run 'python src/train_model.py' first to train the model")
return
# Load the trained model
print(f"Loading trained model from {model_path}...")
model = LoanApprovalModel.load(model_path)
# Print available categories for categorical features
print("\nAvailable categories in the trained model:")
for col, encoder in model.label_encoders.items():
print(f"{col}: {list(encoder.classes_)}")
# Test with a good application (likely to be approved)
good_application = {
'no_of_dependents': 2,
'education': ' Graduate', # Note the space before Graduate to match dataset format
'self_employed': ' No', # Note the space before No to match dataset format
'income_annum': 8000000,
'loan_amount': 15000000,
'loan_term': 10,
'cibil_score': 750,
'residential_assets_value': 10000000,
'commercial_assets_value': 5000000,
'luxury_assets_value': 20000000,
'bank_asset_value': 7000000
}
# Test with a risky application (likely to be rejected)
risky_application = {
'no_of_dependents': 5,
'education': ' Not Graduate', # Note the space before Not Graduate
'self_employed': ' Yes', # Note the space before Yes
'income_annum': 2000000,
'loan_amount': 15000000,
'loan_term': 20,
'cibil_score': 350,
'residential_assets_value': 2000000,
'commercial_assets_value': 1000000,
'luxury_assets_value': 5000000,
'bank_asset_value': 1000000
}
# Make predictions
print("\nTesting with a good application:")
good_result = model.predict(good_application)
print(f"Prediction: {'Approved' if good_result['prediction'] == 1 else 'Rejected'}")
print(f"Confidence: {good_result['probability']:.2%}")
print(f"Explanation: {good_result['explanation']['text']}")
print("\nTop 3 factors:")
for i, factor in enumerate(good_result['feature_importance'][:3]):
print(f"{i+1}. {factor['feature']}: {factor['importance']:.4f}")
print("\nTesting with a risky application:")
risky_result = model.predict(risky_application)
print(f"Prediction: {'Approved' if risky_result['prediction'] == 1 else 'Rejected'}")
print(f"Confidence: {risky_result['probability']:.2%}")
print(f"Explanation: {risky_result['explanation']['text']}")
print("\nTop 3 factors:")
for i, factor in enumerate(risky_result['feature_importance'][:3]):
print(f"{i+1}. {factor['feature']}: {factor['importance']:.4f}")
if __name__ == "__main__":
test_trained_model() |