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import json
import joblib
import pandas as pd
from typing import Dict
from src.tests.config import MODEL_PATH, FEATURES_PATH
class CreditScorePredictor:
def __init__(self):
self.model = None
self.features = None
self._model_loaded = False
def load_model(self):
if not self._model_loaded:
self.model = joblib.load(MODEL_PATH)
with open(FEATURES_PATH, 'r') as f:
self.features = json.load(f)
self._model_loaded = True
def predict(self, features_dict: Dict[str, float]) -> str:
# Ensure model is loaded
self.load_model()
df = pd.DataFrame([features_dict])
# Ensure correct feature order
df = df[
self.features['all_features']
]
# Get prediction
pred_class = self.model.predict(df)[0]
# Map to credit score labels
credit_labels = {0: 'Poor', 1: 'Standard', 2: 'Good'}
prediction = credit_labels.get(pred_class, 'Unknown')
return prediction
def predict_proba(self, features_dict: Dict[str, float]) -> Dict[str, float]:
# Ensure model is loaded
self.load_model()
df = pd.DataFrame([features_dict])
# Ensure correct feature order
df = df[self.features['all_features']]
# Get prediction probabilities
proba = self.model.predict_proba(df)[0]
# Map to credit score labels
credit_labels = {0: 'Poor', 1: 'Standard', 2: 'Good'}
probabilities = {
credit_labels[i]: float(proba[i]) for i in range(len(proba))
}
return probabilities
def get_feature_names(self):
# Ensure model is loaded to get feature names
self.load_model()
return self.features['all_features']
def get_top_features(self, n=10):
# Ensure model is loaded
self.load_model()
# Top 10 most important features based on model evaluation
top_features = [
'Credit_Mix_Ordinal',
'Outstanding_Debt',
'Delay_from_due_date',
'Payment_of_Min_Amount_Yes',
'Changed_Credit_Limit',
'Credit_Utilization_Ratio',
'Monthly_Balance',
'Num_Bank_Accounts',
'Num_Credit_Inquiries',
'Annual_Income'
]
return top_features[:n]
predictor = CreditScorePredictor()
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