Improve training code 4

services/training/app/models/training_models.py

@@ -0,0 +1,80 @@
+# services/training/app/models/training_models.py
+"""
+Database models for trained ML models
+"""
+
+from sqlalchemy import Column, String, DateTime, Float, Integer, Text, Boolean, JSON
+from sqlalchemy.ext.declarative import declarative_base
+from datetime import datetime
+import uuid
+
+Base = declarative_base()
+
+class TrainedModel(Base):
+    __tablename__ = "trained_models"
+    
+    # Primary identification
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    tenant_id = Column(String, nullable=False, index=True)
+    product_name = Column(String, nullable=False, index=True)
+    
+    # Model information
+    model_type = Column(String, default="prophet_optimized")
+    model_version = Column(String, default="1.0")
+    job_id = Column(String, nullable=False)
+    
+    # File storage
+    model_path = Column(String, nullable=False)  # Path to the .pkl file
+    metadata_path = Column(String)  # Path to metadata JSON
+    
+    # Training metrics
+    mape = Column(Float)
+    mae = Column(Float)
+    rmse = Column(Float)
+    r2_score = Column(Float)
+    training_samples = Column(Integer)
+    
+    # Hyperparameters and features
+    hyperparameters = Column(JSON)  # Store optimized parameters
+    features_used = Column(JSON)    # List of regressor columns
+    
+    # Model status
+    is_active = Column(Boolean, default=True)
+    is_production = Column(Boolean, default=False)
+    
+    # Timestamps
+    created_at = Column(DateTime, default=datetime.utcnow)
+    last_used_at = Column(DateTime)
+    
+    # Training data info
+    training_start_date = Column(DateTime)
+    training_end_date = Column(DateTime)
+    data_quality_score = Column(Float)
+    
+    # Additional metadata
+    notes = Column(Text)
+    created_by = Column(String)  # User who triggered training
+    
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "tenant_id": self.tenant_id,
+            "product_name": self.product_name,
+            "model_type": self.model_type,
+            "model_version": self.model_version,
+            "model_path": self.model_path,
+            "mape": self.mape,
+            "mae": self.mae,
+            "rmse": self.rmse,
+            "r2_score": self.r2_score,
+            "training_samples": self.training_samples,
+            "hyperparameters": self.hyperparameters,
+            "features_used": self.features_used,
+            "is_active": self.is_active,
+            "is_production": self.is_production,
+            "created_at": self.created_at.isoformat() if self.created_at else None,
+            "last_used_at": self.last_used_at.isoformat() if self.last_used_at else None,
+            "training_start_date": self.training_start_date.isoformat() if self.training_start_date else None,
+            "training_end_date": self.training_end_date.isoformat() if self.training_end_date else None,
+            "data_quality_score": self.data_quality_score
+        }

services/training/app/schemas/training.py

@@ -25,23 +25,6 @@ class TrainingJobRequest(BaseModel):
     products: Optional[List[str]] = Field(None, description="Specific products to train (if None, trains all)")
     start_date: Optional[datetime] = Field(None, description="Start date for training data")
     end_date: Optional[datetime] = Field(None, description="End date for training data")
-    
-    # Prophet-specific parameters
-    seasonality_mode: str = Field("additive", description="Prophet seasonality mode", pattern="^(additive|multiplicative)$")
-    daily_seasonality: bool = Field(True, description="Enable daily seasonality")
-    weekly_seasonality: bool = Field(True, description="Enable weekly seasonality")
-    yearly_seasonality: bool = Field(True, description="Enable yearly seasonality")
-    
-    # Advanced configuration
-    force_retrain: bool = Field(False, description="Force retraining even if recent model exists")
-    parallel_training: bool = Field(True, description="Train products in parallel")
-    max_workers: int = Field(4, description="Maximum parallel workers", ge=1, le=10)
-    
-    @validator('seasonality_mode')
-    def validate_seasonality_mode(cls, v):
-        if v not in ['additive', 'multiplicative']:
-            raise ValueError('seasonality_mode must be either "additive" or "multiplicative"')
-        return v
 
 
 class SingleProductTrainingRequest(BaseModel):