91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
# ================================================================
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# services/forecasting/app/models/forecasts.py
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# ================================================================
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"""
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Forecast models for the forecasting service
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"""
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from sqlalchemy import Column, String, Integer, Float, DateTime, Boolean, Text, JSON
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from sqlalchemy.dialects.postgresql import UUID
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from datetime import datetime, timezone
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import uuid
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from shared.database.base import Base
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class Forecast(Base):
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"""Forecast model for storing prediction results"""
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__tablename__ = "forecasts"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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inventory_product_id = Column(UUID(as_uuid=True), nullable=False, index=True) # Reference to inventory service
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product_name = Column(String(255), nullable=True, index=True) # Product name (optional - use inventory_product_id as reference)
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location = Column(String(255), nullable=False, index=True)
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# Forecast period
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forecast_date = Column(DateTime(timezone=True), nullable=False, index=True)
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created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
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# Prediction results
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predicted_demand = Column(Float, nullable=False)
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confidence_lower = Column(Float, nullable=False)
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confidence_upper = Column(Float, nullable=False)
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confidence_level = Column(Float, default=0.8)
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# Model information
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model_id = Column(String(255), nullable=False)
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model_version = Column(String(50), nullable=False)
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algorithm = Column(String(50), default="prophet")
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# Business context
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business_type = Column(String(50), default="individual") # individual or central_workshop
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day_of_week = Column(Integer, nullable=False)
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is_holiday = Column(Boolean, default=False)
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is_weekend = Column(Boolean, default=False)
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# External factors
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weather_temperature = Column(Float)
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weather_precipitation = Column(Float)
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weather_description = Column(String(100))
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traffic_volume = Column(Integer)
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# Metadata
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processing_time_ms = Column(Integer)
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features_used = Column(JSON)
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def __repr__(self):
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return f"<Forecast(id={self.id}, inventory_product_id={self.inventory_product_id}, date={self.forecast_date})>"
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class PredictionBatch(Base):
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"""Batch prediction requests"""
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__tablename__ = "prediction_batches"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
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# Batch information
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batch_name = Column(String(255), nullable=False)
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requested_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
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completed_at = Column(DateTime(timezone=True))
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# Status
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status = Column(String(50), default="pending") # pending, processing, completed, failed
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total_products = Column(Integer, default=0)
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completed_products = Column(Integer, default=0)
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failed_products = Column(Integer, default=0)
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# Configuration
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forecast_days = Column(Integer, default=7)
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business_type = Column(String(50), default="individual")
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# Results
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error_message = Column(Text)
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processing_time_ms = Column(Integer)
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cancelled_by = Column(String, nullable=True)
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def __repr__(self):
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return f"<PredictionBatch(id={self.id}, status={self.status})>"
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