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predictor.py

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+
+import joblib
+import json
+import numpy as np
+import pandas as pd
+from catboost import CatBoostClassifier
+
+class RelationshipPredictor:
+    """
+    Relationship Longevity Predictor
+    
+    Predicts compatibility between two individuals based on their 
+    personal profiles, values, and interests.
+    
+    Returns:
+        - compatibility_score (0-1): Predicted probability of successful match
+        - prediction: "High Compatibility" / "Moderate Compatibility" / "Low Compatibility"
+        - key_factors: Top factors driving the prediction
+    """
+    
+    def __init__(self, model_dir="./"):
+        self.xgb = joblib.load(f"{model_dir}/xgboost_model.joblib")
+        self.lgb = joblib.load(f"{model_dir}/lightgbm_model.joblib")
+        self.cat = CatBoostClassifier()
+        self.cat.load_model(f"{model_dir}/catboost_model.cbm")
+        self.feature_cols = joblib.load(f"{model_dir}/feature_columns.joblib")
+        
+        with open(f"{model_dir}/ensemble_config.json") as f:
+            self.config = json.load(f)
+    
+    def predict(self, person_a: dict, person_b: dict) -> dict:
+        """
+        Predict relationship compatibility between two people.
+        
+        Args:
+            person_a: Dict with keys like age, race, interests, personality scores
+            person_b: Dict with same structure
+            
+        Returns:
+            Dict with compatibility_score, prediction label, and key factors
+        """
+        # Build feature vector from the two profiles
+        features = self._build_features(person_a, person_b)
+        
+        # Ensemble prediction
+        xgb_prob = self.xgb.predict_proba(features)[:, 1][0]
+        lgb_prob = self.lgb.predict_proba(features)[:, 1][0]
+        cat_prob = self.cat.predict_proba(features)[:, 1][0]
+        
+        w = self.config['weights']
+        score = w['xgboost'] * xgb_prob + w['lightgbm'] * lgb_prob + w['catboost'] * cat_prob
+        
+        if score >= 0.7:
+            label = "High Compatibility"
+        elif score >= 0.4:
+            label = "Moderate Compatibility" 
+        else:
+            label = "Low Compatibility"
+        
+        return {
+            'compatibility_score': round(float(score), 4),
+            'prediction': label,
+            'individual_models': {
+                'xgboost': round(float(xgb_prob), 4),
+                'lightgbm': round(float(lgb_prob), 4),
+                'catboost': round(float(cat_prob), 4),
+            }
+        }
+    
+    def _build_features(self, a, b):
+        """Build engineered feature vector from two person profiles."""
+        # This would map raw profile inputs to the trained feature space
+        # Implementation depends on the input format
+        raise NotImplementedError(
+            "Implement feature mapping based on your input format. "
+            "See feature_columns.joblib for required features."
+        )
+
+# Usage example:
+# predictor = RelationshipPredictor("./model_output")
+# result = predictor.predict(person_a_profile, person_b_profile)