recommendation_layer.py

"""
Product Recommendation Layer - ML-Based Ranking System
Enhances grocery selection with ML probability scoring + price normalization

This module adds intelligent ranking on top of ML inference:
- Extracts ML probability scores for diet suitability
- Computes normalized price scores (lower price = higher score)
- Combines both using weighted aggregation for final recommendation strength
"""

import numpy as np
import pandas as pd
from pathlib import Path


def compute_recommendation_scores(products_df: pd.DataFrame, 
                                   ml_probabilities: np.ndarray,
                                   target_diet_idx: int,
                                   diet_weight: float = 0.6,
                                   price_weight: float = 0.4) -> pd.DataFrame:
    """
    Compute recommendation scores combining ML suitability and price affordability.
    
    Score Formula:
        final_score = (diet_weight × diet_proba) + (price_weight × price_score)
        
    Args:
        products_df: DataFrame with product information
        ml_probabilities: Shape (n_products, 5) - probability matrix from model.predict_proba()
        target_diet_idx: Index of target diet in probability matrix
        diet_weight: Weight for ML diet suitability (default 0.6 = 60%)
        price_weight: Weight for price affordability (default 0.4 = 40%)
    
    Returns:
        DataFrame with added 'diet_proba', 'price_score', 'recommendation_score' columns
    
    Rationale:
        - diet_proba (60%): ML model's confidence in diet suitability
        - price_score (40%): Affordability factor (inverse normalized price)
        - Weighted combination ensures both factors influence ranking
    """
    
    # Step 1: Extract diet probability for target diet type
    # This represents ML model's confidence that product suits the diet
    diet_proba = ml_probabilities[:, target_diet_idx]
    
    # Step 2: Compute normalized price score
    # Lower price → higher score (inverted and normalized to [0, 1])
    price_min = products_df['price'].min()
    price_max = products_df['price'].max()
    price_range = price_max - price_min
    if price_range == 0:
        price_normalized = np.zeros(len(products_df), dtype=float)
    else:
        price_normalized = (products_df['price'] - price_min) / price_range
    price_score = 1 - price_normalized  # Invert: lower price gets higher score
    
    # Step 3: Compute weighted final recommendation score
    # Combines ML suitability with affordability
    recommendation_score = (diet_weight * diet_proba) + (price_weight * price_score)
    
    # Add scores to dataframe
    result_df = products_df.copy()
    result_df['diet_proba'] = diet_proba
    result_df['price_score'] = price_score
    result_df['recommendation_score'] = recommendation_score
    
    return result_df


def recommend_products(products_df: pd.DataFrame,
                       budget: float,
                       family_size: int,
                       max_items: int = None) -> tuple:
    """
    Select and rank products based on recommendation scores within budget.
    
    Strategy:
        1. Sort by recommendation_score (descending) - best recommendations first
        2. Iterate through ranked products
        3. Select items until budget is exhausted
        4. Adjust selection for family size
    
    Args:
        products_df: DataFrame with recommendation_score column
        budget: Budget constraint (PKR)
        family_size: Number of family members
        max_items: Maximum items to select (auto-calculated if None)
    
    Returns:
        (selected_items, total_cost) - tuple with selected products and total spent
    
    Why this ranking approach:
        - ML probability ensures diet compatibility
        - Price score ensures affordability
        - Combined score balances both factors
        - Top-ranked products offer best value for the diet type
    """
    
    # Calculate target items based on family size
    if max_items is None:
        max_items = max(10, min(32, 8 + family_size * 2))

    # Higher family size should consume more budget by increasing per-item quantity.
    quantity_multiplier = max(1, int(round(family_size / 3)))

    # Larger households should aim for higher budget utilization.
    target_utilization = min(0.65 + (family_size * 0.03), 0.95)
    
    # Sort by recommendation score (descending) - highest recommendations first
    # This ensures most suitable AND affordable items are selected first
    ranked_products = products_df.sort_values(
        by='recommendation_score',
        ascending=False
    )
    
    selected_items = []
    total_cost = 0.0
    
    # Iterate through ranked products and select within budget
    for idx, (_, product) in enumerate(ranked_products.iterrows()):
        price = float(product['price'])
        quantity = quantity_multiplier
        item_cost = price * quantity
        
        # Check if adding this item stays within budget
        if total_cost + item_cost <= budget:
            selected_items.append({
                'rank': idx + 1,  # Ranking position
                'product': product['product'],
                'category': product['category'],
                'quantity': quantity,
                'price_per_unit': price,
                'total_cost': float(item_cost),
                'diet_proba': float(product['diet_proba']),  # ML confidence
                'price_score': float(product['price_score']),  # Affordability
                'recommendation_score': float(product['recommendation_score'])  # Final score
            })
            total_cost += item_cost
        
        # Stop if reached target items or budget threshold
        if len(selected_items) >= max_items or total_cost >= budget * target_utilization:
            break
    
    return selected_items, total_cost


def get_recommendation_summary(selected_items: list, budget: float) -> dict:
    """
    Generate summary statistics for recommendation results.
    
    Returns:
        Dictionary with ranking and score statistics
    """
    if not selected_items:
        return {}
    
    recommendation_scores = [item['recommendation_score'] for item in selected_items]
    diet_probas = [item['diet_proba'] for item in selected_items]
    price_scores = [item['price_score'] for item in selected_items]
    
    return {
        'avg_recommendation_score': float(np.mean(recommendation_scores)),
        'avg_diet_proba': float(np.mean(diet_probas)),
        'avg_price_score': float(np.mean(price_scores)),
        'best_score': float(max(recommendation_scores)),
        'worst_score': float(min(recommendation_scores)),
        'score_std_dev': float(np.std(recommendation_scores))
    }


# Example usage (for testing):
if __name__ == "__main__":
    import joblib
    from data_adapter import build_app_dataset_from_pakistan_csv
    
    # Load model and data
    MODEL = joblib.load("final_model_1.pkl")
    ENCODERS = joblib.load("final_model_1_encoders.pkl")
    DATASET = build_app_dataset_from_pakistan_csv(Path("Pakistan_Food_Prices_2025.csv"))
    
    # Encode features
    category_encoded = ENCODERS['category'].transform(DATASET['category'])
    price_min = ENCODERS['price_min']
    price_max = ENCODERS['price_max']
    price_normalized = (DATASET['price'] - price_min) / (price_max - price_min)
    
    # Get ML predictions
    X = np.column_stack([category_encoded, price_normalized])
    probabilities = MODEL.predict_proba(X)
    
    # Get target diet index
    target_diet = "Keto"
    diet_idx = list(ENCODERS['diet'].classes_).index(target_diet)
    
    # Compute recommendation scores
    scored_products = compute_recommendation_scores(DATASET, probabilities, diet_idx)
    
    # Recommend products
    selected, total_cost = recommend_products(scored_products, budget=5000, family_size=3)
    
    print(f"\nRecommended {len(selected)} items for {target_diet} diet:")
    print(f"Total Cost: {total_cost} PKR\n")
    
    for item in selected[:5]:
        print(f"  {item['product']}")
        print(f"    Score: {item['recommendation_score']:.4f} " +
              f"(Diet: {item['diet_proba']:.2%}, Price: {item['price_score']:.2%})")