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ccfd_1.0_decision-tree.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fbdba46e3c71e148e877b8d61b5f66afad69087e521cdf8b8b694affdeb3374
+size 155243

ccfd_1.0_random-forest.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d30529b90df0f17fa7396347c4230061093ab45c200307b0ce65fb3f5288e12b
+size 43463794

ccfd_1.0_xg-boost.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e53f13355a23ff71608e94bbd3af142a30b8535b76fe78e96433c9202e5debd4
+size 5746222

handler.py

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+import os
+import joblib # Use joblib to load files saved with joblib.dump
+import pandas as pd
+from typing import Dict, Any, List
+
+# Define the models and the version based on the training script output
+class EndpointHandler:
+    VERSION = "1.0"
+
+    # Maps user-friendly aliases to the EXACT filenames from your training script
+    MODEL_MAP = {
+        "decision_tree": "classifier_FULL_Decision_Tree.pkl",
+        "random_forest": "classifier_FULL_Random_Forest.pkl",
+        "xgboost": "classifier_FULL_XGBoost.pkl",
+    }
+    
+    # List of all features the model expects (is_fraud is excluded as it's the target)
+    EXPECTED_FEATURES = [
+        "cc_num", "merchant", "category", "amt", "gender", "state", "zip",
+        "lat", "long", "city_pop", "job", "unix_time", "merch_lat", 
+        "merch_long", "age", "trans_hour", "trans_day", "trans_month", 
+        "trans_weekday", "distance"
+    ]
+    
+    def __init__(self, path="."):
+        """Loads all three Pipeline objects using joblib."""
+        self.models = {}
+        print(f"Server starting up for version: {self.VERSION}")
+        
+        for alias, filename in self.MODEL_MAP.items():
+            model_path = os.path.join(path, filename)
+            try:
+                # Use joblib.load for files saved with joblib.dump
+                self.models[alias] = joblib.load(model_path)
+                print(f"✅ Pipeline loaded for {alias}")
+            except Exception as e:
+                # Note: Errors here are often due to package version mismatch
+                print(f"❌ Error loading {filename}. Check scikit-learn/xgboost versions: {e}")
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Handles the API request, selects the model, and performs inference."""
+        
+        inputs = data.get("inputs", {})
+        target_model_alias = inputs.get("model_name")
+        requested_version = inputs.get("model_version")
+        features_list = inputs.get("features")
+        
+        base_response = {"server_version": self.VERSION}
+
+        # 1. Validation Checks
+        if requested_version != self.VERSION:
+            return {
+                **base_response,
+                "error": f"Requested version '{requested_version}' does not match server version '{self.VERSION}'."
+            }
+
+        if not target_model_alias or target_model_alias not in self.models:
+            return {
+                **base_response,
+                "error": f"Model '{target_model_alias}' not found. Available: {list(self.MODEL_MAP.keys())}",
+            }
+        
+        if not features_list:
+            return {
+                **base_response,
+                "error": "No transaction features provided in the 'features' list."
+            }
+
+        # 2. Prepare Data
+        model_pipeline = self.models[target_model_alias]
+        
+        try:
+            # Convert list of dicts to DataFrame and ensure column order matches training data
+            df_features = pd.DataFrame(features_list)
+            
+            # CRITICAL: Reindex to ensure the columns are in the exact order the pipeline expects
+            if set(df_features.columns) != set(self.EXPECTED_FEATURES):
+                return {
+                    **base_response,
+                    "error": "Input features do not match expected features. Check column names."
+                }
+            df_features = df_features[self.EXPECTED_FEATURES]
+
+        except Exception as e:
+            return {
+                **base_response,
+                "error": f"Data preparation failed. Ensure JSON fields match all expected features: {str(e)}"
+            }
+
+        # 3. Predict Probability
+        try:
+            # predict_proba runs the ColumnTransformer (preprocessing) and then the classifier
+            # We take the probability of the positive class (Fraud=1), which is column [:, 1]
+            probabilities = model_pipeline.predict_proba(df_features)[:, 1]
+
+            return {
+                **base_response,
+                "model_used": target_model_alias,
+                "model_version": requested_version,
+                "prediction_probabilities": probabilities.tolist(),
+            }
+        except Exception as e:
+            return {
+                **base_response,
+                "error": f"Prediction execution failed. This may indicate a data type mismatch: {str(e)}",
+            }

requirements.txt

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+pandas
+scikit-learn
+xgboost
+joblib