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