Improve base config

services/training/app/core/config.py

@@ -1,53 +1,65 @@
+# ================================================================
+# 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
-from pydantic_settings import BaseSettings
-from typing import List
 
-class Settings(BaseSettings):
-    """Application settings"""
+class TrainingSettings(BaseServiceSettings):
+    """Training service specific settings"""
     
-    # Basic settings
+    # Service Identity
     APP_NAME: str = "Training Service"
-    VERSION: str = "1.0.0"
-    DEBUG: bool = os.getenv("DEBUG", "False").lower() == "true"
-    LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
+    SERVICE_NAME: str = "training-service"
+    DESCRIPTION: str = "Machine learning model training service"
     
-    # Database settings
-    DATABASE_URL: str = os.getenv("DATABASE_URL", "postgresql+asyncpg://training_user:training_pass123@training-db:5432/training_db")
+    # Database Configuration
+    DATABASE_URL: str = os.getenv("TRAINING_DATABASE_URL",
+                                  "postgresql+asyncpg://training_user:training_pass123@training-db:5432/training_db")
     
-    # Redis settings
-    REDIS_URL: str = os.getenv("REDIS_URL", "redis://redis:6379/1")
+    # Redis Database (dedicated for training cache)
+    REDIS_DB: int = 1
     
-    # RabbitMQ settings
-    RABBITMQ_URL: str = os.getenv("RABBITMQ_URL", "amqp://bakery:forecast123@rabbitmq:5672/")
-    
-    # Service URLs
-    AUTH_SERVICE_URL: str = os.getenv("AUTH_SERVICE_URL", "http://auth-service:8000")
-    DATA_SERVICE_URL: str = os.getenv("DATA_SERVICE_URL", "http://data-service:8000")
-    
-    # ML Settings
+    # 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 Settings
+    # Prophet Specific Configuration
     PROPHET_SEASONALITY_MODE: str = os.getenv("PROPHET_SEASONALITY_MODE", "additive")
-    PROPHET_DAILY_SEASONALITY: bool = os.getenv("PROPHET_DAILY_SEASONALITY", "true").lower() == "true"
-    PROPHET_WEEKLY_SEASONALITY: bool = os.getenv("PROPHET_WEEKLY_SEASONALITY", "true").lower() == "true"
-    PROPHET_YEARLY_SEASONALITY: bool = os.getenv("PROPHET_YEARLY_SEASONALITY", "true").lower() == "true"
+    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"))
     
-    # CORS
-    CORS_ORIGINS: str = os.getenv("CORS_ORIGINS", "http://localhost:3000,http://localhost:3001")
+    # 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"
     
-    @property
-    def CORS_ORIGINS_LIST(self) -> List[str]:
-        """Get CORS origins as list"""
-        return [origin.strip() for origin in self.CORS_ORIGINS.split(",")]
+    # 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"
     
-    class Config:
-        env_file = ".env"
+    # 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"))
 
-settings = Settings()
+settings = TrainingSettings()