Few fixes

services/training/app/models/training.py

@@ -0,0 +1,101 @@
+"""
+Training models
+"""
+
+from sqlalchemy import Column, String, Integer, DateTime, Text, JSON, Boolean, Float
+from sqlalchemy.dialects.postgresql import UUID
+from datetime import datetime
+import uuid
+
+from shared.database.base import Base
+
+class TrainingJob(Base):
+    """Training job model"""
+    __tablename__ = "training_jobs"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    status = Column(String(20), nullable=False, default="queued")  # queued, running, completed, failed
+    progress = Column(Integer, default=0)
+    current_step = Column(String(200))
+    requested_by = Column(UUID(as_uuid=True), nullable=False)
+    
+    # Timing
+    started_at = Column(DateTime, default=datetime.utcnow)
+    completed_at = Column(DateTime)
+    duration_seconds = Column(Integer)
+    
+    # Results
+    models_trained = Column(JSON)
+    metrics = Column(JSON)
+    error_message = Column(Text)
+    
+    # Metadata
+    training_data_from = Column(DateTime)
+    training_data_to = Column(DateTime)
+    total_data_points = Column(Integer)
+    products_count = Column(Integer)
+    
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+    
+    def __repr__(self):
+        return f"<TrainingJob(id={self.id}, tenant_id={self.tenant_id}, status={self.status})>"
+
+class TrainedModel(Base):
+    """Trained model information"""
+    __tablename__ = "trained_models"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    training_job_id = Column(UUID(as_uuid=True), nullable=False)
+    
+    # Model details
+    product_name = Column(String(100), nullable=False)
+    model_type = Column(String(50), nullable=False, default="prophet")
+    model_version = Column(String(20), nullable=False)
+    model_path = Column(String(500))  # Path to saved model file
+    
+    # Performance metrics
+    mape = Column(Float)  # Mean Absolute Percentage Error
+    rmse = Column(Float)  # Root Mean Square Error
+    mae = Column(Float)   # Mean Absolute Error
+    r2_score = Column(Float)  # R-squared score
+    
+    # Training details
+    training_samples = Column(Integer)
+    validation_samples = Column(Integer)
+    features_used = Column(JSON)
+    hyperparameters = Column(JSON)
+    
+    # Status
+    is_active = Column(Boolean, default=True)
+    last_used_at = Column(DateTime)
+    
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+    
+    def __repr__(self):
+        return f"<TrainedModel(id={self.id}, product={self.product_name}, tenant={self.tenant_id})>"
+
+class TrainingLog(Base):
+    """Training log entries"""
+    __tablename__ = "training_logs"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    training_job_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    
+    level = Column(String(10), nullable=False)  # DEBUG, INFO, WARNING, ERROR
+    message = Column(Text, nullable=False)
+    step = Column(String(100))
+    progress = Column(Integer)
+    
+    # Additional data
+    execution_time = Column(Float)  # Time taken for this step
+    memory_usage = Column(Float)    # Memory usage in MB
+    metadata = Column(JSON)         # Additional metadata
+    
+    created_at = Column(DateTime, default=datetime.utcnow)
+    
+    def __repr__(self):
+        return f"<TrainingLog(id={self.id}, level={self.level})>"