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config.py

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+"""
+Configuration file for Recipe Generation RL System
+Designed to be HRM-ready with hierarchical constraint support
+"""
+
+# ====================
+# Dataset Configuration
+# ====================
+DATA_DIR = "data/FoodData_Central_foundation_food_csv_2024-10-31/"
+RAW_FOOD_FILE = "food.csv"
+RAW_NUTRIENT_FILE = "food_nutrient.csv"
+PROCESSED_INGREDIENT_FILE = "data/ingredients_processed.csv"
+
+# Nutrients to track (aligned with USDA nutrient IDs)
+# Foundation Foods uses Atwater Specific Factors for energy
+NUTRIENT_IDS = {
+    'calories': 2048,      # Energy (Atwater Specific Factors) (kcal)
+    'protein': 1003,       # Protein (g)
+    'sodium': 1093,        # Sodium (mg)
+    'carbs': 1005,         # Carbohydrates (g)
+    'fat': 1004,           # Total fat (g)
+}
+
+# ====================
+# Recipe Generation Configuration
+# ====================
+MAX_INGREDIENTS_PER_RECIPE = 10
+MIN_INGREDIENTS_PER_RECIPE = 3
+INGREDIENT_POOL_SIZE = 500  # Top N most common ingredients
+INGREDIENT_SERVING_SIZE_G = 50  # Grams per ingredient (scaled from 100g USDA data)
+
+# ====================
+# Health Constraints (Daily targets)
+# ====================
+# These can be overridden by user profiles or HRM high-level policy
+DEFAULT_CONSTRAINTS = {
+    'calories': {'min': 400, 'max': 800, 'target': 600},
+    'protein': {'min': 15, 'max': 50, 'target': 30},
+    'sodium': {'min': 0, 'max': 800, 'target': 500},
+    'carbs': {'min': 30, 'max': 100, 'target': 60},
+    'fat': {'min': 10, 'max': 30, 'target': 20},
+}
+
+# User profile templates
+USER_PROFILES = {
+    'standard': DEFAULT_CONSTRAINTS,
+    'low_sodium': {
+        'calories': {'min': 400, 'max': 800, 'target': 600},
+        'protein': {'min': 20, 'max': 50, 'target': 35},
+        'sodium': {'min': 0, 'max': 400, 'target': 300},
+        'carbs': {'min': 30, 'max': 100, 'target': 60},
+        'fat': {'min': 10, 'max': 30, 'target': 20},
+    },
+    'high_protein': {
+        'calories': {'min': 500, 'max': 900, 'target': 700},
+        'protein': {'min': 40, 'max': 70, 'target': 50},
+        'sodium': {'min': 0, 'max': 800, 'target': 500},
+        'carbs': {'min': 20, 'max': 80, 'target': 50},
+        'fat': {'min': 15, 'max': 35, 'target': 25},
+    },
+    'low_carb': {
+        'calories': {'min': 400, 'max': 800, 'target': 600},
+        'protein': {'min': 25, 'max': 50, 'target': 35},
+        'sodium': {'min': 0, 'max': 800, 'target': 500},
+        'carbs': {'min': 10, 'max': 40, 'target': 25},
+        'fat': {'min': 20, 'max': 40, 'target': 30},
+    }
+}
+
+# ====================
+# RL Configuration (Phase 1: Single Agent)
+# ====================
+RL_CONFIG = {
+    'algorithm': 'PPO',  # or 'DQN'
+    'learning_rate': 3e-4,
+    'gamma': 0.99,
+    'batch_size': 64,
+    'n_steps': 2048,
+    'n_epochs': 10,
+    'total_timesteps': 100000,
+    'ent_coef': 0.01,  # Entropy coefficient for exploration
+}
+
+# ====================
+# Reward Configuration (HRM-Ready)
+# ====================
+REWARD_WEIGHTS = {
+    # Low-level rewards (immediate)
+    'constraint_satisfaction': 20.0,  # Increased reward for meeting all constraints
+    'nutrient_balance': 2.0,  # Increased for better balance
+    'diversity_bonus': 5.0,  # Increased to encourage variety
+    'ingredient_repeat_penalty': -20.0,  # Much stronger penalty for repetition
+    'constraint_violation_penalty': -15.0,  # Stronger penalty
+    
+    # High-level rewards (HRM Phase 2)
+    'weekly_target_bonus': 10.0,  # Weekly aggregate constraint bonus
+    'long_term_health_stability': 5.0,  # Stability across week
+}
+
+# Lambda for hierarchical reward shaping
+# Phase 1 (baseline): λ = 0
+# Phase 2 (HRM): λ > 0
+LAMBDA_HIERARCHICAL = 0.5  # PHASE 2 ACTIVATED
+
+# ====================
+# HRM Configuration (Phase 2 - ACTIVE)
+# ====================
+HRM_ENABLED = True  # PHASE 2 ACTIVATED
+
+HRM_CONFIG = {
+    'planning_horizon': 7,  # Weekly planning
+    'high_level_update_freq': 7,  # Update meta-policy every N recipes
+    'weekly_targets': {
+        'calories': 4200,  # 600 * 7
+        'protein': 210,
+        'sodium': 3500,
+        'carbs': 420,
+        'fat': 140,
+    }
+}
+
+# ====================
+# Environment Configuration
+# ====================
+ENV_CONFIG = {
+    'normalize_observations': True,
+    'normalize_rewards': False,
+    'recipe_history_length': 10,  # For diversity tracking
+    'done_action': True,  # Allow agent to terminate recipe early
+}
+
+# ====================
+# Evaluation Metrics
+# ====================
+EVAL_METRICS = [
+    'constraint_compliance_rate',
+    'nutrient_balance_score',
+    'recipe_diversity',
+    'average_ingredients_per_recipe',
+    'reward_per_episode',
+]
+
+# ====================
+# Paths
+# ====================
+import os
+PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
+MODEL_SAVE_DIR = os.path.join(PROJECT_ROOT, "models/saved/")
+TENSORBOARD_LOG_DIR = os.path.join(PROJECT_ROOT, "logs/tensorboard/")
+EVAL_RESULTS_DIR = os.path.join(PROJECT_ROOT, "eval/results/")