Update src/tests/inference.py

src/tests/inference.py

@@ -1,85 +1,85 @@
-import json
-import joblib
-import pandas as pd
-from typing import Dict
-from 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()
+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()