REFACTOR external service and improve websocket training

services/training/app/core/config.py

@@ -41,25 +41,16 @@ class TrainingSettings(BaseServiceSettings):
     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
@@ -79,6 +70,8 @@ class TrainingSettings(BaseServiceSettings):
     PROPHET_DAILY_SEASONALITY: bool = True
     PROPHET_WEEKLY_SEASONALITY: bool = True
     PROPHET_YEARLY_SEASONALITY: bool = True
-    PROPHET_SEASONALITY_MODE: str = "additive"
 
-settings = TrainingSettings()
+    # Throttling settings for parallel training to prevent heartbeat blocking
+    MAX_CONCURRENT_TRAININGS: int = int(os.getenv("MAX_CONCURRENT_TRAININGS", "3"))
+
+settings = TrainingSettings()

services/training/app/core/constants.py

@@ -0,0 +1,97 @@
+"""
+Training Service Constants
+Centralized constants to avoid magic numbers throughout the codebase
+"""
+
+# Data Validation Thresholds
+MIN_DATA_POINTS_REQUIRED = 30
+RECOMMENDED_DATA_POINTS = 90
+MAX_ZERO_RATIO_ERROR = 0.9  # 90% zeros = error
+HIGH_ZERO_RATIO_WARNING = 0.7  # 70% zeros = warning
+MAX_ZERO_RATIO_INTERMITTENT = 0.8  # Products with >80% zeros are intermittent
+MODERATE_SPARSITY_THRESHOLD = 0.6  # 60% zeros = moderate sparsity
+
+# Training Time Periods (in days)
+MIN_NON_ZERO_DAYS = 30  # Minimum days with non-zero sales
+DATA_QUALITY_DAY_THRESHOLD_LOW = 90
+DATA_QUALITY_DAY_THRESHOLD_HIGH = 365
+MAX_TRAINING_RANGE_DAYS = 730  # 2 years
+MIN_TRAINING_RANGE_DAYS = 30
+
+# Product Classification Thresholds
+HIGH_VOLUME_MEAN_SALES = 10.0
+HIGH_VOLUME_ZERO_RATIO = 0.3
+MEDIUM_VOLUME_MEAN_SALES = 5.0
+MEDIUM_VOLUME_ZERO_RATIO = 0.5
+LOW_VOLUME_MEAN_SALES = 2.0
+LOW_VOLUME_ZERO_RATIO = 0.7
+
+# Hyperparameter Optimization
+OPTUNA_TRIALS_HIGH_VOLUME = 30
+OPTUNA_TRIALS_MEDIUM_VOLUME = 25
+OPTUNA_TRIALS_LOW_VOLUME = 20
+OPTUNA_TRIALS_INTERMITTENT = 15
+OPTUNA_TIMEOUT_SECONDS = 600
+
+# Prophet Uncertainty Sampling
+UNCERTAINTY_SAMPLES_SPARSE_MIN = 100
+UNCERTAINTY_SAMPLES_SPARSE_MAX = 200
+UNCERTAINTY_SAMPLES_LOW_MIN = 150
+UNCERTAINTY_SAMPLES_LOW_MAX = 300
+UNCERTAINTY_SAMPLES_MEDIUM_MIN = 200
+UNCERTAINTY_SAMPLES_MEDIUM_MAX = 500
+UNCERTAINTY_SAMPLES_HIGH_MIN = 300
+UNCERTAINTY_SAMPLES_HIGH_MAX = 800
+
+# MAPE Calculation
+MAPE_LOW_VOLUME_THRESHOLD = 2.0
+MAPE_MEDIUM_VOLUME_THRESHOLD = 5.0
+MAPE_CALCULATION_MIN_THRESHOLD = 0.5
+MAPE_CALCULATION_MID_THRESHOLD = 1.0
+MAPE_MAX_CAP = 200.0  # Cap MAPE at 200%
+MAPE_MEDIUM_CAP = 150.0
+
+# Baseline MAPE estimates for improvement calculation
+BASELINE_MAPE_VERY_SPARSE = 80.0
+BASELINE_MAPE_SPARSE = 60.0
+BASELINE_MAPE_HIGH_VOLUME = 25.0
+BASELINE_MAPE_MEDIUM_VOLUME = 35.0
+BASELINE_MAPE_LOW_VOLUME = 45.0
+IMPROVEMENT_SIGNIFICANCE_THRESHOLD = 0.8  # Only claim improvement if MAPE < 80% of baseline
+
+# Cross-validation
+CV_N_SPLITS = 2
+CV_MIN_VALIDATION_DAYS = 7
+
+# Progress tracking
+PROGRESS_DATA_PREPARATION_START = 0
+PROGRESS_DATA_PREPARATION_END = 45
+PROGRESS_MODEL_TRAINING_START = 45
+PROGRESS_MODEL_TRAINING_END = 85
+PROGRESS_FINALIZATION_START = 85
+PROGRESS_FINALIZATION_END = 100
+
+# HTTP Client Configuration
+HTTP_TIMEOUT_DEFAULT = 30.0  # seconds
+HTTP_TIMEOUT_LONG_RUNNING = 60.0  # for training data fetches
+HTTP_MAX_RETRIES = 3
+HTTP_RETRY_BACKOFF_FACTOR = 2.0
+
+# WebSocket Configuration
+WEBSOCKET_PING_TIMEOUT = 60.0  # seconds
+WEBSOCKET_ACTIVITY_WARNING_THRESHOLD = 90.0  # seconds
+WEBSOCKET_CONSUMER_HEARTBEAT_INTERVAL = 10.0  # seconds
+
+# Synthetic Data Generation
+SYNTHETIC_TEMP_DEFAULT = 50.0
+SYNTHETIC_TEMP_VARIATION = 100.0
+SYNTHETIC_TRAFFIC_DEFAULT = 50.0
+SYNTHETIC_TRAFFIC_VARIATION = 100.0
+
+# Model Storage
+MODEL_FILE_EXTENSION = ".pkl"
+METADATA_FILE_EXTENSION = ".json"
+
+# Data Quality Scoring
+MIN_QUALITY_SCORE = 0.1
+MAX_QUALITY_SCORE = 1.0

services/training/app/core/database.py

@@ -15,8 +15,16 @@ from app.core.config import settings
 
 logger = structlog.get_logger()
 
-# Initialize database manager using shared infrastructure
-database_manager = DatabaseManager(settings.DATABASE_URL)
+# Initialize database manager with connection pooling configuration
+database_manager = DatabaseManager(
+    settings.DATABASE_URL,
+    pool_size=settings.DB_POOL_SIZE,
+    max_overflow=settings.DB_MAX_OVERFLOW,
+    pool_timeout=settings.DB_POOL_TIMEOUT,
+    pool_recycle=settings.DB_POOL_RECYCLE,
+    pool_pre_ping=settings.DB_POOL_PRE_PING,
+    echo=settings.DB_ECHO
+)
 
 # Alias for convenience - matches the existing interface
 get_db = database_manager.get_db