153 lines
6.0 KiB
Python
153 lines
6.0 KiB
Python
# services/training/app/models/training.py
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"""
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Database models for training service
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"""
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from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, JSON, Float
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from sqlalchemy.dialects.postgresql import UUID, ARRAY
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from shared.database.base import Base
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from datetime import datetime
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import uuid
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class ModelTrainingLog(Base):
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"""
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Table to track training job execution and status.
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Replaces the old Celery task tracking.
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"""
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__tablename__ = "model_training_logs"
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id = Column(Integer, primary_key=True, index=True)
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job_id = Column(String(255), unique=True, index=True, nullable=False)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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status = Column(String(50), nullable=False, default="pending") # pending, running, completed, failed, cancelled
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progress = Column(Integer, default=0) # 0-100 percentage
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current_step = Column(String(500), default="")
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# Timestamps
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start_time = Column(DateTime, default=datetime.now)
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end_time = Column(DateTime, nullable=True)
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# Configuration and results
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config = Column(JSON, nullable=True) # Training job configuration
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results = Column(JSON, nullable=True) # Training results
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error_message = Column(Text, nullable=True)
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# Metadata
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created_at = Column(DateTime, default=datetime.now)
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updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
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class TrainedModel(Base):
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"""
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Table to store information about trained models.
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"""
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__tablename__ = "trained_models"
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id = Column(Integer, primary_key=True, index=True)
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model_id = Column(String(255), unique=True, index=True, nullable=False)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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product_name = Column(String(255), index=True, nullable=False)
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# Model information
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model_type = Column(String(50), nullable=False, default="prophet") # prophet, arima, etc.
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model_path = Column(String(1000), nullable=False) # Path to stored model file
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version = Column(Integer, nullable=False, default=1)
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# Training information
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training_samples = Column(Integer, nullable=False, default=0)
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features = Column(ARRAY(String), nullable=True) # List of features used
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hyperparameters = Column(JSON, nullable=True) # Model hyperparameters
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training_metrics = Column(JSON, nullable=True) # Training performance metrics
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# Data period information
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data_period_start = Column(DateTime, nullable=True)
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data_period_end = Column(DateTime, nullable=True)
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# Status and metadata
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is_active = Column(Boolean, default=True, index=True)
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created_at = Column(DateTime, default=datetime.now)
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updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
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class ModelPerformanceMetric(Base):
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"""
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Table to track model performance over time.
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"""
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__tablename__ = "model_performance_metrics"
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id = Column(Integer, primary_key=True, index=True)
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model_id = Column(String(255), index=True, nullable=False)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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product_name = Column(String(255), index=True, nullable=False)
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# Performance metrics
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mae = Column(Float, nullable=True) # Mean Absolute Error
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mse = Column(Float, nullable=True) # Mean Squared Error
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rmse = Column(Float, nullable=True) # Root Mean Squared Error
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mape = Column(Float, nullable=True) # Mean Absolute Percentage Error
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r2_score = Column(Float, nullable=True) # R-squared score
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# Additional metrics
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accuracy_percentage = Column(Float, nullable=True)
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prediction_confidence = Column(Float, nullable=True)
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# Evaluation information
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evaluation_period_start = Column(DateTime, nullable=True)
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evaluation_period_end = Column(DateTime, nullable=True)
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evaluation_samples = Column(Integer, nullable=True)
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# Metadata
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measured_at = Column(DateTime, default=datetime.now)
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created_at = Column(DateTime, default=datetime.now)
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class TrainingJobQueue(Base):
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"""
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Table to manage training job queue and scheduling.
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"""
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__tablename__ = "training_job_queue"
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id = Column(Integer, primary_key=True, index=True)
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job_id = Column(String(255), unique=True, index=True, nullable=False)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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# Job configuration
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job_type = Column(String(50), nullable=False) # full_training, single_product, evaluation
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priority = Column(Integer, default=1) # Higher number = higher priority
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config = Column(JSON, nullable=True)
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# Scheduling information
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scheduled_at = Column(DateTime, nullable=True)
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started_at = Column(DateTime, nullable=True)
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estimated_duration_minutes = Column(Integer, nullable=True)
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# Status
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status = Column(String(50), nullable=False, default="queued") # queued, running, completed, failed
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retry_count = Column(Integer, default=0)
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max_retries = Column(Integer, default=3)
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# Metadata
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created_at = Column(DateTime, default=datetime.now)
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updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
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class ModelArtifact(Base):
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"""
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Table to track model files and artifacts.
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"""
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__tablename__ = "model_artifacts"
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id = Column(Integer, primary_key=True, index=True)
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model_id = Column(String(255), index=True, nullable=False)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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# Artifact information
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artifact_type = Column(String(50), nullable=False) # model_file, metadata, training_data, etc.
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file_path = Column(String(1000), nullable=False)
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file_size_bytes = Column(Integer, nullable=True)
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checksum = Column(String(255), nullable=True) # For file integrity
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# Storage information
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storage_location = Column(String(100), nullable=False, default="local") # local, s3, gcs, etc.
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compression = Column(String(50), nullable=True) # gzip, lz4, etc.
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# Metadata
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created_at = Column(DateTime, default=datetime.now)
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expires_at = Column(DateTime, nullable=True) # For automatic cleanup |