# ================================================================
# TRAINING SERVICE CONFIGURATION
# services/training/app/core/config.py
# ================================================================

"""
Training service configuration
ML model training and management
"""

from shared.config.base import BaseServiceSettings
import os

class TrainingSettings(BaseServiceSettings):
    """Training service specific settings"""
    
    # Service Identity
    APP_NAME: str = "Training Service"
    SERVICE_NAME: str = "training-service"
    DESCRIPTION: str = "Machine learning model training service"
    
    # Database Configuration
    DATABASE_URL: str = os.getenv("TRAINING_DATABASE_URL",
                                  "postgresql+asyncpg://training_user:training_pass123@training-db:5432/training_db")
    
    # Redis Database (dedicated for training cache)
    REDIS_DB: int = 1
    
    # ML Model Storage
    MODEL_STORAGE_PATH: str = os.getenv("MODEL_STORAGE_PATH", "/app/models")
    MODEL_BACKUP_ENABLED: bool = os.getenv("MODEL_BACKUP_ENABLED", "true").lower() == "true"
    MODEL_VERSIONING_ENABLED: bool = os.getenv("MODEL_VERSIONING_ENABLED", "true").lower() == "true"
    
    # Training Configuration
    MAX_TRAINING_TIME_MINUTES: int = int(os.getenv("MAX_TRAINING_TIME_MINUTES", "30"))
    MAX_CONCURRENT_TRAINING_JOBS: int = int(os.getenv("MAX_CONCURRENT_TRAINING_JOBS", "3"))
    MIN_TRAINING_DATA_DAYS: int = int(os.getenv("MIN_TRAINING_DATA_DAYS", "30"))
    TRAINING_BATCH_SIZE: int = int(os.getenv("TRAINING_BATCH_SIZE", "1000"))
    
    # Prophet Specific Configuration
    PROPHET_SEASONALITY_MODE: str = os.getenv("PROPHET_SEASONALITY_MODE", "additive")
    PROPHET_CHANGEPOINT_PRIOR_SCALE: float = float(os.getenv("PROPHET_CHANGEPOINT_PRIOR_SCALE", "0.05"))
    PROPHET_SEASONALITY_PRIOR_SCALE: float = float(os.getenv("PROPHET_SEASONALITY_PRIOR_SCALE", "10.0"))
    PROPHET_HOLIDAYS_PRIOR_SCALE: float = float(os.getenv("PROPHET_HOLIDAYS_PRIOR_SCALE", "10.0"))
    
    # Spanish Holiday Integration
    ENABLE_SPANISH_HOLIDAYS: bool = True
    ENABLE_MADRID_HOLIDAYS: bool = True
    ENABLE_CUSTOM_HOLIDAYS: bool = os.getenv("ENABLE_CUSTOM_HOLIDAYS", "true").lower() == "true"
    
    # Data Processing
    DATA_PREPROCESSING_ENABLED: bool = True
    OUTLIER_DETECTION_ENABLED: bool = os.getenv("OUTLIER_DETECTION_ENABLED", "true").lower() == "true"
    SEASONAL_DECOMPOSITION_ENABLED: bool = os.getenv("SEASONAL_DECOMPOSITION_ENABLED", "true").lower() == "true"
    
    # Model Validation
    CROSS_VALIDATION_ENABLED: bool = os.getenv("CROSS_VALIDATION_ENABLED", "true").lower() == "true"
    VALIDATION_SPLIT_RATIO: float = float(os.getenv("VALIDATION_SPLIT_RATIO", "0.2"))
    MIN_MODEL_ACCURACY: float = float(os.getenv("MIN_MODEL_ACCURACY", "0.7"))
    
    # Distributed Training (for future scaling)
    DISTRIBUTED_TRAINING_ENABLED: bool = os.getenv("DISTRIBUTED_TRAINING_ENABLED", "false").lower() == "true"
    TRAINING_WORKER_COUNT: int = int(os.getenv("TRAINING_WORKER_COUNT", "1"))
    
    PROPHET_DAILY_SEASONALITY: bool = True
    PROPHET_WEEKLY_SEASONALITY: bool = True
    PROPHET_YEARLY_SEASONALITY: bool = True
    PROPHET_SEASONALITY_MODE: str = "additive"

settings = TrainingSettings()