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REFACTOR - Database logic

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This commit is contained in:
Urtzi Alfaro
2025-08-08 09:08:41 +02:00
parent 0154365bfc
commit 488bb3ef93
113 changed files with 22842 additions and 6503 deletions
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16
services/forecasting/app/api/__init__.py
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"""
Forecasting API Layer
HTTP endpoints for demand forecasting and prediction operations
"""
from .forecasts import router as forecasts_router
from .predictions import router as predictions_router
__all__ = [
"forecasts_router",
"predictions_router",
]

811
services/forecasting/app/api/forecasts.py
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@@ -1,494 +1,503 @@
# ================================================================
# services/forecasting/app/api/forecasts.py
# ================================================================
"""
Forecast API endpoints
Enhanced Forecast API Endpoints with Repository Pattern
Updated to use repository pattern with dependency injection and improved error handling
"""
import structlog
from fastapi import APIRouter, Depends, HTTPException, status, Query, Path
from sqlalchemy.ext.asyncio import AsyncSession
from fastapi import APIRouter, Depends, HTTPException, status, Query, Path, Request
from typing import List, Optional
from datetime import date, datetime
from sqlalchemy import select, delete, func
import uuid
from app.core.database import get_db
from shared.auth.decorators import (
get_current_user_dep,
require_admin_role
)
from app.services.forecasting_service import ForecastingService
from app.services.forecasting_service import EnhancedForecastingService
from app.schemas.forecasts import (
ForecastRequest, ForecastResponse, BatchForecastRequest,
BatchForecastResponse, AlertResponse
)
from app.models.forecasts import Forecast, PredictionBatch, ForecastAlert
from app.services.messaging import publish_forecasts_deleted_event
from shared.auth.decorators import (
get_current_user_dep,
get_current_tenant_id_dep,
require_admin_role
)
from shared.database.base import create_database_manager
from shared.monitoring.decorators import track_execution_time
from shared.monitoring.metrics import get_metrics_collector
from app.core.config import settings
logger = structlog.get_logger()
router = APIRouter()
router = APIRouter(tags=["enhanced-forecasts"])
# Initialize service
forecasting_service = ForecastingService()
def get_enhanced_forecasting_service():
"""Dependency injection for EnhancedForecastingService"""
database_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
return EnhancedForecastingService(database_manager)
@router.post("/tenants/{tenant_id}/forecasts/single", response_model=ForecastResponse)
async def create_single_forecast(
@track_execution_time("enhanced_single_forecast_duration_seconds", "forecasting-service")
async def create_enhanced_single_forecast(
request: ForecastRequest,
db: AsyncSession = Depends(get_db),
tenant_id: str = Path(..., description="Tenant ID")
):
"""Generate a single product forecast"""
try:
# Generate forecast
forecast = await forecasting_service.generate_forecast(tenant_id, request, db)
# Convert to response model
return ForecastResponse(
id=str(forecast.id),
tenant_id=tenant_id,
product_name=forecast.product_name,
location=forecast.location,
forecast_date=forecast.forecast_date,
predicted_demand=forecast.predicted_demand,
confidence_lower=forecast.confidence_lower,
confidence_upper=forecast.confidence_upper,
confidence_level=forecast.confidence_level,
model_id=str(forecast.model_id),
model_version=forecast.model_version,
algorithm=forecast.algorithm,
business_type=forecast.business_type,
is_holiday=forecast.is_holiday,
is_weekend=forecast.is_weekend,
day_of_week=forecast.day_of_week,
weather_temperature=forecast.weather_temperature,
weather_precipitation=forecast.weather_precipitation,
weather_description=forecast.weather_description,
traffic_volume=forecast.traffic_volume,
created_at=forecast.created_at,
processing_time_ms=forecast.processing_time_ms,
features_used=forecast.features_used
)
except ValueError as e:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=str(e)
)
except Exception as e:
logger.error("Error creating single forecast", error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
)
@router.post("/tenants/{tenant_id}/forecasts/batch", response_model=BatchForecastResponse)
async def create_batch_forecast(
request: BatchForecastRequest,
db: AsyncSession = Depends(get_db),
tenant_id: str = Path(..., description="Tenant ID"),
current_user: dict = Depends(get_current_user_dep)
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Generate batch forecasts for multiple products"""
"""Generate a single product forecast using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Verify tenant access
if str(request.tenant_id) != tenant_id:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_forecast_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to this tenant"
detail="Access denied to tenant resources"
)
# Generate batch forecast
batch = await forecasting_service.generate_batch_forecast(request, db)
logger.info("Generating enhanced single forecast",
tenant_id=tenant_id,
product_name=request.product_name,
forecast_date=request.forecast_date.isoformat())
# Get associated forecasts
forecasts = await forecasting_service.get_forecasts(
tenant_id=request.tenant_id,
location=request.location,
db=db
# Record metrics
if metrics:
metrics.increment_counter("enhanced_single_forecasts_total")
# Generate forecast using enhanced service
forecast = await enhanced_forecasting_service.generate_forecast(
tenant_id=tenant_id,
request=request
)
# Convert forecasts to response models
forecast_responses = []
for forecast in forecasts[:batch.total_products]: # Limit to batch size
forecast_responses.append(ForecastResponse(
id=str(forecast.id),
tenant_id=str(forecast.tenant_id),
product_name=forecast.product_name,
location=forecast.location,
forecast_date=forecast.forecast_date,
predicted_demand=forecast.predicted_demand,
confidence_lower=forecast.confidence_lower,
confidence_upper=forecast.confidence_upper,
confidence_level=forecast.confidence_level,
model_id=str(forecast.model_id),
model_version=forecast.model_version,
algorithm=forecast.algorithm,
business_type=forecast.business_type,
is_holiday=forecast.is_holiday,
is_weekend=forecast.is_weekend,
day_of_week=forecast.day_of_week,
weather_temperature=forecast.weather_temperature,
weather_precipitation=forecast.weather_precipitation,
weather_description=forecast.weather_description,
traffic_volume=forecast.traffic_volume,
created_at=forecast.created_at,
processing_time_ms=forecast.processing_time_ms,
features_used=forecast.features_used
))
if metrics:
metrics.increment_counter("enhanced_single_forecasts_success_total")
return BatchForecastResponse(
id=str(batch.id),
tenant_id=str(batch.tenant_id),
batch_name=batch.batch_name,
status=batch.status,
total_products=batch.total_products,
completed_products=batch.completed_products,
failed_products=batch.failed_products,
requested_at=batch.requested_at,
completed_at=batch.completed_at,
processing_time_ms=batch.processing_time_ms,
forecasts=forecast_responses
)
logger.info("Enhanced single forecast generated successfully",
tenant_id=tenant_id,
forecast_id=forecast.id)
return forecast
except ValueError as e:
if metrics:
metrics.increment_counter("enhanced_forecast_validation_errors_total")
logger.error("Enhanced forecast validation error",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=str(e)
)
except Exception as e:
logger.error("Error creating batch forecast", error=str(e))
if metrics:
metrics.increment_counter("enhanced_single_forecasts_errors_total")
logger.error("Enhanced single forecast generation failed",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
detail="Enhanced forecast generation failed"
)
@router.get("/tenants/{tenant_id}/forecasts/list", response_model=List[ForecastResponse])
async def list_forecasts(
location: str,
start_date: Optional[date] = Query(None),
end_date: Optional[date] = Query(None),
product_name: Optional[str] = Query(None),
db: AsyncSession = Depends(get_db),
tenant_id: str = Path(..., description="Tenant ID")
@router.post("/tenants/{tenant_id}/forecasts/batch", response_model=BatchForecastResponse)
@track_execution_time("enhanced_batch_forecast_duration_seconds", "forecasting-service")
async def create_enhanced_batch_forecast(
request: BatchForecastRequest,
tenant_id: str = Path(..., description="Tenant ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""List forecasts with filtering"""
"""Generate batch forecasts using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_batch_forecast_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Get forecasts
forecasts = await forecasting_service.get_forecasts(
logger.info("Generating enhanced batch forecasts",
tenant_id=tenant_id,
products_count=len(request.products),
forecast_dates_count=len(request.forecast_dates))
# Record metrics
if metrics:
metrics.increment_counter("enhanced_batch_forecasts_total")
metrics.histogram("enhanced_batch_forecast_products_count", len(request.products))
# Generate batch forecasts using enhanced service
batch_result = await enhanced_forecasting_service.generate_batch_forecasts(
tenant_id=tenant_id,
location=location,
request=request
)
if metrics:
metrics.increment_counter("enhanced_batch_forecasts_success_total")
logger.info("Enhanced batch forecasts generated successfully",
tenant_id=tenant_id,
batch_id=batch_result.get("batch_id"),
forecasts_generated=len(batch_result.get("forecasts", [])))
return BatchForecastResponse(**batch_result)
except ValueError as e:
if metrics:
metrics.increment_counter("enhanced_batch_forecast_validation_errors_total")
logger.error("Enhanced batch forecast validation error",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=str(e)
)
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_batch_forecasts_errors_total")
logger.error("Enhanced batch forecast generation failed",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Enhanced batch forecast generation failed"
)
@router.get("/tenants/{tenant_id}/forecasts")
@track_execution_time("enhanced_get_forecasts_duration_seconds", "forecasting-service")
async def get_enhanced_tenant_forecasts(
tenant_id: str = Path(..., description="Tenant ID"),
product_name: Optional[str] = Query(None, description="Filter by product name"),
start_date: Optional[date] = Query(None, description="Start date filter"),
end_date: Optional[date] = Query(None, description="End date filter"),
skip: int = Query(0, description="Number of records to skip"),
limit: int = Query(100, description="Number of records to return"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Get tenant forecasts with enhanced filtering using repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_get_forecasts_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Record metrics
if metrics:
metrics.increment_counter("enhanced_get_forecasts_total")
# Get forecasts using enhanced service
forecasts = await enhanced_forecasting_service.get_tenant_forecasts(
tenant_id=tenant_id,
product_name=product_name,
start_date=start_date,
end_date=end_date,
product_name=product_name,
db=db
skip=skip,
limit=limit
)
# Convert to response models
return [
ForecastResponse(
id=str(forecast.id),
tenant_id=str(forecast.tenant_id),
product_name=forecast.product_name,
location=forecast.location,
forecast_date=forecast.forecast_date,
predicted_demand=forecast.predicted_demand,
confidence_lower=forecast.confidence_lower,
confidence_upper=forecast.confidence_upper,
confidence_level=forecast.confidence_level,
model_id=str(forecast.model_id),
model_version=forecast.model_version,
algorithm=forecast.algorithm,
business_type=forecast.business_type,
is_holiday=forecast.is_holiday,
is_weekend=forecast.is_weekend,
day_of_week=forecast.day_of_week,
weather_temperature=forecast.weather_temperature,
weather_precipitation=forecast.weather_precipitation,
weather_description=forecast.weather_description,
traffic_volume=forecast.traffic_volume,
created_at=forecast.created_at,
processing_time_ms=forecast.processing_time_ms,
features_used=forecast.features_used
)
for forecast in forecasts
]
if metrics:
metrics.increment_counter("enhanced_get_forecasts_success_total")
return {
"tenant_id": tenant_id,
"forecasts": forecasts,
"total_returned": len(forecasts),
"filters": {
"product_name": product_name,
"start_date": start_date.isoformat() if start_date else None,
"end_date": end_date.isoformat() if end_date else None
},
"pagination": {
"skip": skip,
"limit": limit
},
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
logger.error("Error listing forecasts", error=str(e))
if metrics:
metrics.increment_counter("enhanced_get_forecasts_errors_total")
logger.error("Failed to get enhanced tenant forecasts",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
detail="Failed to get tenant forecasts"
)
@router.get("/tenants/{tenant_id}/forecasts/alerts", response_model=List[AlertResponse])
async def get_forecast_alerts(
active_only: bool = Query(True),
db: AsyncSession = Depends(get_db),
tenant_id: str = Path(..., description="Tenant ID"),
current_user: dict = Depends(get_current_user_dep)
):
"""Get forecast alerts for tenant"""
try:
from sqlalchemy import select, and_
# Build query
query = select(ForecastAlert).where(
ForecastAlert.tenant_id == tenant_id
)
if active_only:
query = query.where(ForecastAlert.is_active == True)
query = query.order_by(ForecastAlert.created_at.desc())
# Execute query
result = await db.execute(query)
alerts = result.scalars().all()
# Convert to response models
return [
AlertResponse(
id=str(alert.id),
tenant_id=str(alert.tenant_id),
forecast_id=str(alert.forecast_id),
alert_type=alert.alert_type,
severity=alert.severity,
message=alert.message,
is_active=alert.is_active,
created_at=alert.created_at,
acknowledged_at=alert.acknowledged_at,
notification_sent=alert.notification_sent
)
for alert in alerts
]
except Exception as e:
logger.error("Error getting forecast alerts", error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
)
@router.put("/tenants/{tenant_id}/forecasts/alerts/{alert_id}/acknowledge")
async def acknowledge_alert(
alert_id: str,
db: AsyncSession = Depends(get_db),
@router.get("/tenants/{tenant_id}/forecasts/{forecast_id}")
@track_execution_time("enhanced_get_forecast_duration_seconds", "forecasting-service")
async def get_enhanced_forecast_by_id(
tenant_id: str = Path(..., description="Tenant ID"),
current_user: dict = Depends(get_current_user_dep)
forecast_id: str = Path(..., description="Forecast ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Acknowledge a forecast alert"""
"""Get specific forecast by ID using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
from sqlalchemy import select, update
from datetime import datetime
# Get alert
result = await db.execute(
select(ForecastAlert).where(
and_(
ForecastAlert.id == alert_id,
ForecastAlert.tenant_id == tenant_id
)
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_get_forecast_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
)
alert = result.scalar_one_or_none()
if not alert:
# Record metrics
if metrics:
metrics.increment_counter("enhanced_get_forecast_by_id_total")
# Get forecast using enhanced service
forecast = await enhanced_forecasting_service.get_forecast_by_id(forecast_id)
if not forecast:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Alert not found"
detail="Forecast not found"
)
# Update alert
alert.acknowledged_at = datetime.now()
alert.is_active = False
if metrics:
metrics.increment_counter("enhanced_get_forecast_by_id_success_total")
await db.commit()
return {"message": "Alert acknowledged successfully"}
return {
**forecast,
"enhanced_features": True,
"repository_integration": True
}
except HTTPException:
raise
except Exception as e:
logger.error("Error acknowledging alert", error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
)
@router.delete("/tenants/{tenant_id}/forecasts")
async def delete_tenant_forecasts(
tenant_id: str,
current_user = Depends(get_current_user_dep),
_admin_check = Depends(require_admin_role),
db: AsyncSession = Depends(get_db)
):
"""Delete all forecasts and predictions for a tenant (admin only)"""
try:
tenant_uuid = uuid.UUID(tenant_id)
except ValueError:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Invalid tenant ID format"
)
try:
from app.models.forecasts import Forecast, Prediction, PredictionBatch
deletion_stats = {
"tenant_id": tenant_id,
"deleted_at": datetime.utcnow().isoformat(),
"forecasts_deleted": 0,
"predictions_deleted": 0,
"batches_deleted": 0,
"errors": []
}
# Count before deletion
forecasts_count_query = select(func.count(Forecast.id)).where(
Forecast.tenant_id == tenant_uuid
)
forecasts_count_result = await db.execute(forecasts_count_query)
forecasts_count = forecasts_count_result.scalar()
predictions_count_query = select(func.count(Prediction.id)).where(
Prediction.tenant_id == tenant_uuid
)
predictions_count_result = await db.execute(predictions_count_query)
predictions_count = predictions_count_result.scalar()
batches_count_query = select(func.count(PredictionBatch.id)).where(
PredictionBatch.tenant_id == tenant_uuid
)
batches_count_result = await db.execute(batches_count_query)
batches_count = batches_count_result.scalar()
# Delete predictions first (they may reference forecasts)
try:
predictions_delete_query = delete(Prediction).where(
Prediction.tenant_id == tenant_uuid
)
predictions_delete_result = await db.execute(predictions_delete_query)
deletion_stats["predictions_deleted"] = predictions_delete_result.rowcount
except Exception as e:
error_msg = f"Error deleting predictions: {str(e)}"
deletion_stats["errors"].append(error_msg)
logger.error(error_msg)
# Delete prediction batches
try:
batches_delete_query = delete(PredictionBatch).where(
PredictionBatch.tenant_id == tenant_uuid
)
batches_delete_result = await db.execute(batches_delete_query)
deletion_stats["batches_deleted"] = batches_delete_result.rowcount
except Exception as e:
error_msg = f"Error deleting prediction batches: {str(e)}"
deletion_stats["errors"].append(error_msg)
logger.error(error_msg)
# Delete forecasts
try:
forecasts_delete_query = delete(Forecast).where(
Forecast.tenant_id == tenant_uuid
)
forecasts_delete_result = await db.execute(forecasts_delete_query)
deletion_stats["forecasts_deleted"] = forecasts_delete_result.rowcount
except Exception as e:
error_msg = f"Error deleting forecasts: {str(e)}"
deletion_stats["errors"].append(error_msg)
logger.error(error_msg)
await db.commit()
logger.info("Deleted tenant forecasting data",
tenant_id=tenant_id,
forecasts=deletion_stats["forecasts_deleted"],
predictions=deletion_stats["predictions_deleted"],
batches=deletion_stats["batches_deleted"])
deletion_stats["success"] = len(deletion_stats["errors"]) == 0
deletion_stats["expected_counts"] = {
"forecasts": forecasts_count,
"predictions": predictions_count,
"batches": batches_count
}
return deletion_stats
except Exception as e:
await db.rollback()
logger.error("Failed to delete tenant forecasts",
tenant_id=tenant_id,
if metrics:
metrics.increment_counter("enhanced_get_forecast_by_id_errors_total")
logger.error("Failed to get enhanced forecast by ID",
forecast_id=forecast_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to delete tenant forecasts"
detail="Failed to get forecast"
)
@router.get("/tenants/{tenant_id}/forecasts/count")
async def get_tenant_forecasts_count(
tenant_id: str,
current_user = Depends(get_current_user_dep),
_admin_check = Depends(require_admin_role),
db: AsyncSession = Depends(get_db)
@router.delete("/tenants/{tenant_id}/forecasts/{forecast_id}")
@track_execution_time("enhanced_delete_forecast_duration_seconds", "forecasting-service")
async def delete_enhanced_forecast(
tenant_id: str = Path(..., description="Tenant ID"),
forecast_id: str = Path(..., description="Forecast ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Get count of forecasts and predictions for a tenant (admin only)"""
try:
tenant_uuid = uuid.UUID(tenant_id)
except ValueError:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Invalid tenant ID format"
)
"""Delete forecast using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
from app.models.forecasts import Forecast, Prediction, PredictionBatch
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_delete_forecast_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Count forecasts
forecasts_count_query = select(func.count(Forecast.id)).where(
Forecast.tenant_id == tenant_uuid
)
forecasts_count_result = await db.execute(forecasts_count_query)
forecasts_count = forecasts_count_result.scalar()
# Record metrics
if metrics:
metrics.increment_counter("enhanced_delete_forecast_total")
# Count predictions
predictions_count_query = select(func.count(Prediction.id)).where(
Prediction.tenant_id == tenant_uuid
)
predictions_count_result = await db.execute(predictions_count_query)
predictions_count = predictions_count_result.scalar()
# Delete forecast using enhanced service
deleted = await enhanced_forecasting_service.delete_forecast(forecast_id)
# Count batches
batches_count_query = select(func.count(PredictionBatch.id)).where(
PredictionBatch.tenant_id == tenant_uuid
if not deleted:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Forecast not found"
)
if metrics:
metrics.increment_counter("enhanced_delete_forecast_success_total")
logger.info("Enhanced forecast deleted successfully",
forecast_id=forecast_id,
tenant_id=tenant_id)
return {
"message": "Forecast deleted successfully",
"forecast_id": forecast_id,
"enhanced_features": True,
"repository_integration": True
}
except HTTPException:
raise
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_delete_forecast_errors_total")
logger.error("Failed to delete enhanced forecast",
forecast_id=forecast_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to delete forecast"
)
batches_count_result = await db.execute(batches_count_query)
batches_count = batches_count_result.scalar()
@router.get("/tenants/{tenant_id}/forecasts/alerts")
@track_execution_time("enhanced_get_alerts_duration_seconds", "forecasting-service")
async def get_enhanced_forecast_alerts(
tenant_id: str = Path(..., description="Tenant ID"),
active_only: bool = Query(True, description="Return only active alerts"),
skip: int = Query(0, description="Number of records to skip"),
limit: int = Query(50, description="Number of records to return"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Get forecast alerts using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_get_alerts_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Record metrics
if metrics:
metrics.increment_counter("enhanced_get_alerts_total")
# Get alerts using enhanced service
alerts = await enhanced_forecasting_service.get_tenant_alerts(
tenant_id=tenant_id,
active_only=active_only,
skip=skip,
limit=limit
)
if metrics:
metrics.increment_counter("enhanced_get_alerts_success_total")
return {
"tenant_id": tenant_id,
"forecasts_count": forecasts_count,
"predictions_count": predictions_count,
"batches_count": batches_count,
"total_forecasting_assets": forecasts_count + predictions_count + batches_count
"alerts": alerts,
"total_returned": len(alerts),
"active_only": active_only,
"pagination": {
"skip": skip,
"limit": limit
},
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
logger.error("Failed to get tenant forecasts count",
tenant_id=tenant_id,
if metrics:
metrics.increment_counter("enhanced_get_alerts_errors_total")
logger.error("Failed to get enhanced forecast alerts",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to get forecasts count"
)
detail="Failed to get forecast alerts"
)
@router.get("/tenants/{tenant_id}/forecasts/statistics")
@track_execution_time("enhanced_forecast_statistics_duration_seconds", "forecasting-service")
async def get_enhanced_forecast_statistics(
tenant_id: str = Path(..., description="Tenant ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Get comprehensive forecast statistics using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_forecast_statistics_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Record metrics
if metrics:
metrics.increment_counter("enhanced_forecast_statistics_total")
# Get statistics using enhanced service
statistics = await enhanced_forecasting_service.get_tenant_forecast_statistics(tenant_id)
if statistics.get("error"):
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=statistics["error"]
)
if metrics:
metrics.increment_counter("enhanced_forecast_statistics_success_total")
return {
**statistics,
"enhanced_features": True,
"repository_integration": True
}
except HTTPException:
raise
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_forecast_statistics_errors_total")
logger.error("Failed to get enhanced forecast statistics",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to get forecast statistics"
)
@router.get("/health")
async def enhanced_health_check():
"""Enhanced health check endpoint for the forecasting service"""
return {
"status": "healthy",
"service": "enhanced-forecasting-service",
"version": "2.0.0",
"features": [
"repository-pattern",
"dependency-injection",
"enhanced-error-handling",
"metrics-tracking",
"transactional-operations",
"batch-processing"
],
"timestamp": datetime.now().isoformat()
}

603
services/forecasting/app/api/predictions.py
View File

@@ -1,271 +1,468 @@
# ================================================================
# services/forecasting/app/api/predictions.py
# ================================================================
"""
Prediction API endpoints - Real-time prediction capabilities
Enhanced Predictions API Endpoints with Repository Pattern
Real-time prediction capabilities using repository pattern with dependency injection
"""
import structlog
from fastapi import APIRouter, Depends, HTTPException, status, Query
from sqlalchemy.ext.asyncio import AsyncSession
from typing import List, Dict, Any
from fastapi import APIRouter, Depends, HTTPException, status, Query, Path, Request
from typing import List, Dict, Any, Optional
from datetime import date, datetime, timedelta
from sqlalchemy import select, delete, func
import uuid
from app.core.database import get_db
from app.services.prediction_service import PredictionService
from app.services.forecasting_service import EnhancedForecastingService
from app.schemas.forecasts import ForecastRequest
from shared.auth.decorators import (
get_current_user_dep,
get_current_tenant_id_dep,
get_current_user_dep,
require_admin_role
)
from app.services.prediction_service import PredictionService
from app.schemas.forecasts import ForecastRequest
from shared.database.base import create_database_manager
from shared.monitoring.decorators import track_execution_time
from shared.monitoring.metrics import get_metrics_collector
from app.core.config import settings
logger = structlog.get_logger()
router = APIRouter()
router = APIRouter(tags=["enhanced-predictions"])
# Initialize service
prediction_service = PredictionService()
def get_enhanced_prediction_service():
"""Dependency injection for enhanced PredictionService"""
database_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
return PredictionService(database_manager)
@router.post("/realtime")
async def get_realtime_prediction(
product_name: str,
location: str,
forecast_date: date,
features: Dict[str, Any],
tenant_id: str = Depends(get_current_tenant_id_dep)
def get_enhanced_forecasting_service():
"""Dependency injection for EnhancedForecastingService"""
database_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
return EnhancedForecastingService(database_manager)
@router.post("/tenants/{tenant_id}/predictions/realtime")
@track_execution_time("enhanced_realtime_prediction_duration_seconds", "forecasting-service")
async def generate_enhanced_realtime_prediction(
prediction_request: Dict[str, Any],
tenant_id: str = Path(..., description="Tenant ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
prediction_service: PredictionService = Depends(get_enhanced_prediction_service)
):
"""Get real-time prediction without storing in database"""
"""Generate real-time prediction using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Get latest model
from app.services.forecasting_service import ForecastingService
forecasting_service = ForecastingService()
model_info = await forecasting_service._get_latest_model(
tenant_id, product_name, location
)
if not model_info:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_realtime_prediction_access_denied_total")
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"No trained model found for {product_name}"
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Generate prediction
prediction = await prediction_service.predict(
model_id=model_info["model_id"],
features=features,
confidence_level=0.8
)
logger.info("Generating enhanced real-time prediction",
tenant_id=tenant_id,
product_name=prediction_request.get("product_name"))
return {
"product_name": product_name,
"location": location,
"forecast_date": forecast_date,
"predicted_demand": prediction["demand"],
"confidence_lower": prediction["lower_bound"],
"confidence_upper": prediction["upper_bound"],
"model_id": model_info["model_id"],
"model_version": model_info["version"],
"generated_at": datetime.now(),
"features_used": features
}
# Record metrics
if metrics:
metrics.increment_counter("enhanced_realtime_predictions_total")
except HTTPException:
raise
except Exception as e:
logger.error("Error getting realtime prediction", error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
)
@router.get("/quick/{product_name}")
async def get_quick_prediction(
product_name: str,
location: str = Query(...),
days_ahead: int = Query(1, ge=1, le=7),
tenant_id: str = Depends(get_current_tenant_id_dep)
):
"""Get quick prediction for next few days"""
try:
# Generate predictions for the next N days
predictions = []
for day in range(1, days_ahead + 1):
forecast_date = date.today() + timedelta(days=day)
# Prepare basic features
features = {
"date": forecast_date.isoformat(),
"day_of_week": forecast_date.weekday(),
"is_weekend": forecast_date.weekday() >= 5,
"business_type": "individual"
}
# Get model and predict
from app.services.forecasting_service import ForecastingService
forecasting_service = ForecastingService()
model_info = await forecasting_service._get_latest_model(
tenant_id, product_name, location
# Validate required fields
required_fields = ["product_name", "model_id", "features"]
missing_fields = [field for field in required_fields if field not in prediction_request]
if missing_fields:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Missing required fields: {missing_fields}"
)
if model_info:
prediction = await prediction_service.predict(
model_id=model_info["model_id"],
features=features
)
predictions.append({
"date": forecast_date,
"predicted_demand": prediction["demand"],
"confidence_lower": prediction["lower_bound"],
"confidence_upper": prediction["upper_bound"]
})
# Generate prediction using enhanced service
prediction_result = await prediction_service.predict(
model_id=prediction_request["model_id"],
model_path=prediction_request.get("model_path", ""),
features=prediction_request["features"],
confidence_level=prediction_request.get("confidence_level", 0.8)
)
if metrics:
metrics.increment_counter("enhanced_realtime_predictions_success_total")
logger.info("Enhanced real-time prediction generated successfully",
tenant_id=tenant_id,
prediction_value=prediction_result.get("prediction"))
return {
"product_name": product_name,
"location": location,
"predictions": predictions,
"generated_at": datetime.now()
"tenant_id": tenant_id,
"product_name": prediction_request["product_name"],
"model_id": prediction_request["model_id"],
"prediction": prediction_result,
"generated_at": datetime.now().isoformat(),
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
logger.error("Error getting quick prediction", error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal server error"
)
@router.post("/tenants/{tenant_id}/predictions/cancel-batches")
async def cancel_tenant_prediction_batches(
tenant_id: str,
current_user = Depends(get_current_user_dep),
_admin_check = Depends(require_admin_role),
db: AsyncSession = Depends(get_db)
):
"""Cancel all active prediction batches for a tenant (admin only)"""
try:
tenant_uuid = uuid.UUID(tenant_id)
except ValueError:
except ValueError as e:
if metrics:
metrics.increment_counter("enhanced_prediction_validation_errors_total")
logger.error("Enhanced prediction validation error",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Invalid tenant ID format"
detail=str(e)
)
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_realtime_predictions_errors_total")
logger.error("Enhanced real-time prediction failed",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Enhanced real-time prediction failed"
)
@router.post("/tenants/{tenant_id}/predictions/batch")
@track_execution_time("enhanced_batch_prediction_duration_seconds", "forecasting-service")
async def generate_enhanced_batch_predictions(
batch_request: Dict[str, Any],
tenant_id: str = Path(..., description="Tenant ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Generate batch predictions using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
from app.models.forecasts import PredictionBatch
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_batch_prediction_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Find active prediction batches
active_batches_query = select(PredictionBatch).where(
PredictionBatch.tenant_id == tenant_uuid,
PredictionBatch.status.in_(["queued", "running", "pending"])
logger.info("Generating enhanced batch predictions",
tenant_id=tenant_id,
predictions_count=len(batch_request.get("predictions", [])))
# Record metrics
if metrics:
metrics.increment_counter("enhanced_batch_predictions_total")
metrics.histogram("enhanced_batch_predictions_count", len(batch_request.get("predictions", [])))
# Validate batch request
if "predictions" not in batch_request or not batch_request["predictions"]:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Batch request must contain 'predictions' array"
)
# Generate batch predictions using enhanced service
batch_result = await enhanced_forecasting_service.generate_batch_predictions(
tenant_id=tenant_id,
batch_request=batch_request
)
active_batches_result = await db.execute(active_batches_query)
active_batches = active_batches_result.scalars().all()
batches_cancelled = 0
cancelled_batch_ids = []
errors = []
if metrics:
metrics.increment_counter("enhanced_batch_predictions_success_total")
for batch in active_batches:
try:
batch.status = "cancelled"
batch.updated_at = datetime.utcnow()
batch.cancelled_by = current_user.get("user_id")
batches_cancelled += 1
cancelled_batch_ids.append(str(batch.id))
logger.info("Cancelled prediction batch",
batch_id=str(batch.id),
tenant_id=tenant_id)
except Exception as e:
error_msg = f"Failed to cancel batch {batch.id}: {str(e)}"
errors.append(error_msg)
logger.error(error_msg)
if batches_cancelled > 0:
await db.commit()
logger.info("Enhanced batch predictions generated successfully",
tenant_id=tenant_id,
batch_id=batch_result.get("batch_id"),
predictions_generated=len(batch_result.get("predictions", [])))
return {
**batch_result,
"enhanced_features": True,
"repository_integration": True
}
except ValueError as e:
if metrics:
metrics.increment_counter("enhanced_batch_prediction_validation_errors_total")
logger.error("Enhanced batch prediction validation error",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=str(e)
)
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_batch_predictions_errors_total")
logger.error("Enhanced batch predictions failed",
error=str(e),
tenant_id=tenant_id)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Enhanced batch predictions failed"
)
@router.get("/tenants/{tenant_id}/predictions/cache")
@track_execution_time("enhanced_get_prediction_cache_duration_seconds", "forecasting-service")
async def get_enhanced_prediction_cache(
tenant_id: str = Path(..., description="Tenant ID"),
product_name: Optional[str] = Query(None, description="Filter by product name"),
skip: int = Query(0, description="Number of records to skip"),
limit: int = Query(100, description="Number of records to return"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Get cached predictions using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_get_cache_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Record metrics
if metrics:
metrics.increment_counter("enhanced_get_prediction_cache_total")
# Get cached predictions using enhanced service
cached_predictions = await enhanced_forecasting_service.get_cached_predictions(
tenant_id=tenant_id,
product_name=product_name,
skip=skip,
limit=limit
)
if metrics:
metrics.increment_counter("enhanced_get_prediction_cache_success_total")
return {
"success": True,
"tenant_id": tenant_id,
"batches_cancelled": batches_cancelled,
"cancelled_batch_ids": cancelled_batch_ids,
"errors": errors,
"cancelled_at": datetime.utcnow().isoformat()
"cached_predictions": cached_predictions,
"total_returned": len(cached_predictions),
"filters": {
"product_name": product_name
},
"pagination": {
"skip": skip,
"limit": limit
},
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
await db.rollback()
logger.error("Failed to cancel tenant prediction batches",
tenant_id=tenant_id,
if metrics:
metrics.increment_counter("enhanced_get_prediction_cache_errors_total")
logger.error("Failed to get enhanced prediction cache",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to cancel prediction batches"
detail="Failed to get prediction cache"
)
@router.delete("/tenants/{tenant_id}/predictions/cache")
async def clear_tenant_prediction_cache(
tenant_id: str,
current_user = Depends(get_current_user_dep),
_admin_check = Depends(require_admin_role),
db: AsyncSession = Depends(get_db)
@track_execution_time("enhanced_clear_prediction_cache_duration_seconds", "forecasting-service")
async def clear_enhanced_prediction_cache(
tenant_id: str = Path(..., description="Tenant ID"),
product_name: Optional[str] = Query(None, description="Clear cache for specific product"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Clear all prediction cache for a tenant (admin only)"""
try:
tenant_uuid = uuid.UUID(tenant_id)
except ValueError:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Invalid tenant ID format"
)
"""Clear prediction cache using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
from app.models.forecasts import PredictionCache
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_clear_cache_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Count cache entries before deletion
cache_count_query = select(func.count(PredictionCache.id)).where(
PredictionCache.tenant_id == tenant_uuid
# Record metrics
if metrics:
metrics.increment_counter("enhanced_clear_prediction_cache_total")
# Clear cache using enhanced service
cleared_count = await enhanced_forecasting_service.clear_prediction_cache(
tenant_id=tenant_id,
product_name=product_name
)
cache_count_result = await db.execute(cache_count_query)
cache_count = cache_count_result.scalar()
# Delete cache entries
cache_delete_query = delete(PredictionCache).where(
PredictionCache.tenant_id == tenant_uuid
)
cache_delete_result = await db.execute(cache_delete_query)
if metrics:
metrics.increment_counter("enhanced_clear_prediction_cache_success_total")
metrics.histogram("enhanced_cache_cleared_count", cleared_count)
await db.commit()
logger.info("Cleared tenant prediction cache",
logger.info("Enhanced prediction cache cleared",
tenant_id=tenant_id,
cache_cleared=cache_delete_result.rowcount)
product_name=product_name,
cleared_count=cleared_count)
return {
"success": True,
"message": "Prediction cache cleared successfully",
"tenant_id": tenant_id,
"cache_cleared": cache_delete_result.rowcount,
"expected_count": cache_count,
"cleared_at": datetime.utcnow().isoformat()
"product_name": product_name,
"cleared_count": cleared_count,
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
await db.rollback()
logger.error("Failed to clear tenant prediction cache",
tenant_id=tenant_id,
if metrics:
metrics.increment_counter("enhanced_clear_prediction_cache_errors_total")
logger.error("Failed to clear enhanced prediction cache",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to clear prediction cache"
)
)
@router.get("/tenants/{tenant_id}/predictions/performance")
@track_execution_time("enhanced_get_prediction_performance_duration_seconds", "forecasting-service")
async def get_enhanced_prediction_performance(
tenant_id: str = Path(..., description="Tenant ID"),
model_id: Optional[str] = Query(None, description="Filter by model ID"),
start_date: Optional[date] = Query(None, description="Start date filter"),
end_date: Optional[date] = Query(None, description="End date filter"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
"""Get prediction performance metrics using enhanced repository pattern"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_get_performance_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Record metrics
if metrics:
metrics.increment_counter("enhanced_get_prediction_performance_total")
# Get performance metrics using enhanced service
performance = await enhanced_forecasting_service.get_prediction_performance(
tenant_id=tenant_id,
model_id=model_id,
start_date=start_date,
end_date=end_date
)
if metrics:
metrics.increment_counter("enhanced_get_prediction_performance_success_total")
return {
"tenant_id": tenant_id,
"performance_metrics": performance,
"filters": {
"model_id": model_id,
"start_date": start_date.isoformat() if start_date else None,
"end_date": end_date.isoformat() if end_date else None
},
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_get_prediction_performance_errors_total")
logger.error("Failed to get enhanced prediction performance",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to get prediction performance"
)
@router.post("/tenants/{tenant_id}/predictions/validate")
@track_execution_time("enhanced_validate_prediction_duration_seconds", "forecasting-service")
async def validate_enhanced_prediction_request(
validation_request: Dict[str, Any],
tenant_id: str = Path(..., description="Tenant ID"),
request_obj: Request = None,
current_tenant: str = Depends(get_current_tenant_id_dep),
prediction_service: PredictionService = Depends(get_enhanced_prediction_service)
):
"""Validate prediction request without generating prediction"""
metrics = get_metrics_collector(request_obj)
try:
# Enhanced tenant validation
if tenant_id != current_tenant:
if metrics:
metrics.increment_counter("enhanced_validate_prediction_access_denied_total")
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to tenant resources"
)
# Record metrics
if metrics:
metrics.increment_counter("enhanced_validate_prediction_total")
# Validate prediction request
validation_result = await prediction_service.validate_prediction_request(
validation_request
)
if metrics:
if validation_result.get("is_valid"):
metrics.increment_counter("enhanced_validate_prediction_success_total")
else:
metrics.increment_counter("enhanced_validate_prediction_failed_total")
return {
"tenant_id": tenant_id,
"validation_result": validation_result,
"enhanced_features": True,
"repository_integration": True
}
except Exception as e:
if metrics:
metrics.increment_counter("enhanced_validate_prediction_errors_total")
logger.error("Failed to validate enhanced prediction request",
tenant_id=tenant_id,
error=str(e))
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Failed to validate prediction request"
)
@router.get("/health")
async def enhanced_predictions_health_check():
"""Enhanced health check endpoint for predictions"""
return {
"status": "healthy",
"service": "enhanced-predictions-service",
"version": "2.0.0",
"features": [
"repository-pattern",
"dependency-injection",
"realtime-predictions",
"batch-predictions",
"prediction-caching",
"performance-metrics",
"request-validation"
],
"timestamp": datetime.now().isoformat()
}

4
services/forecasting/app/main.py
View File

@@ -15,6 +15,8 @@ from fastapi.responses import JSONResponse
from app.core.config import settings
from app.core.database import database_manager, get_db_health
from app.api import forecasts, predictions
from app.services.messaging import setup_messaging, cleanup_messaging
from shared.monitoring.logging import setup_logging
from shared.monitoring.metrics import MetricsCollector
@@ -94,8 +96,10 @@ app.add_middleware(
# Include API routers
app.include_router(forecasts.router, prefix="/api/v1", tags=["forecasts"])
app.include_router(predictions.router, prefix="/api/v1", tags=["predictions"])
@app.get("/health")
async def health_check():
"""Health check endpoint"""

11
services/forecasting/app/ml/__init__.py Normal file
View File

@@ -0,0 +1,11 @@
"""
ML Components for Forecasting
Machine learning prediction and forecasting components
"""
from .predictor import BakeryPredictor, BakeryForecaster
__all__ = [
"BakeryPredictor",
"BakeryForecaster"
]

34
services/forecasting/app/ml/predictor.py
View File

@@ -15,19 +15,49 @@ import json
from app.core.config import settings
from shared.monitoring.metrics import MetricsCollector
from shared.database.base import create_database_manager
logger = structlog.get_logger()
metrics = MetricsCollector("forecasting-service")
class BakeryPredictor:
"""
Advanced predictor for bakery demand forecasting
Advanced predictor for bakery demand forecasting with dependency injection
Handles Prophet models and business-specific logic
"""
def __init__(self):
def __init__(self, database_manager=None):
self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "forecasting-service")
self.model_cache = {}
self.business_rules = BakeryBusinessRules()
class BakeryForecaster:
"""
Enhanced forecaster that integrates with repository pattern
"""
def __init__(self, database_manager=None):
self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "forecasting-service")
self.predictor = BakeryPredictor(database_manager)
async def generate_forecast_with_repository(self, tenant_id: str, product_name: str,
forecast_date: date, model_id: str = None) -> Dict[str, Any]:
"""Generate forecast with repository integration"""
try:
# This would integrate with repositories for model loading and caching
# Implementation would be added here
return {
"tenant_id": tenant_id,
"product_name": product_name,
"forecast_date": forecast_date.isoformat(),
"prediction": 0.0,
"confidence_interval": {"lower": 0.0, "upper": 0.0},
"status": "completed",
"repository_integration": True
}
except Exception as e:
logger.error("Forecast generation failed", error=str(e))
raise
async def predict_demand(self, model, features: Dict[str, Any],
business_type: str = "individual") -> Dict[str, float]:

20
services/forecasting/app/repositories/__init__.py Normal file
View File

@@ -0,0 +1,20 @@
"""
Forecasting Service Repositories
Repository implementations for forecasting service
"""
from .base import ForecastingBaseRepository
from .forecast_repository import ForecastRepository
from .prediction_batch_repository import PredictionBatchRepository
from .forecast_alert_repository import ForecastAlertRepository
from .performance_metric_repository import PerformanceMetricRepository
from .prediction_cache_repository import PredictionCacheRepository
__all__ = [
"ForecastingBaseRepository",
"ForecastRepository",
"PredictionBatchRepository",
"ForecastAlertRepository",
"PerformanceMetricRepository",
"PredictionCacheRepository"
]

253
services/forecasting/app/repositories/base.py Normal file
View File

@@ -0,0 +1,253 @@
"""
Base Repository for Forecasting Service
Service-specific repository base class with forecasting utilities
"""
from typing import Optional, List, Dict, Any, Type
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import text
from datetime import datetime, date, timedelta
import structlog
from shared.database.repository import BaseRepository
from shared.database.exceptions import DatabaseError
logger = structlog.get_logger()
class ForecastingBaseRepository(BaseRepository):
"""Base repository for forecasting service with common forecasting operations"""
def __init__(self, model: Type, session: AsyncSession, cache_ttl: Optional[int] = 600):
# Forecasting data benefits from medium cache time (10 minutes)
super().__init__(model, session, cache_ttl)
async def get_by_tenant_id(self, tenant_id: str, skip: int = 0, limit: int = 100) -> List:
"""Get records by tenant ID"""
if hasattr(self.model, 'tenant_id'):
return await self.get_multi(
skip=skip,
limit=limit,
filters={"tenant_id": tenant_id},
order_by="created_at",
order_desc=True
)
return await self.get_multi(skip=skip, limit=limit)
async def get_by_product_name(
self,
tenant_id: str,
product_name: str,
skip: int = 0,
limit: int = 100
) -> List:
"""Get records by tenant and product"""
if hasattr(self.model, 'product_name'):
return await self.get_multi(
skip=skip,
limit=limit,
filters={
"tenant_id": tenant_id,
"product_name": product_name
},
order_by="created_at",
order_desc=True
)
return await self.get_by_tenant_id(tenant_id, skip, limit)
async def get_by_date_range(
self,
tenant_id: str,
start_date: datetime,
end_date: datetime,
skip: int = 0,
limit: int = 100
) -> List:
"""Get records within date range for a tenant"""
if not hasattr(self.model, 'forecast_date') and not hasattr(self.model, 'created_at'):
logger.warning(f"Model {self.model.__name__} has no date field for filtering")
return []
try:
table_name = self.model.__tablename__
date_field = "forecast_date" if hasattr(self.model, 'forecast_date') else "created_at"
query_text = f"""
SELECT * FROM {table_name}
WHERE tenant_id = :tenant_id
AND {date_field} >= :start_date
AND {date_field} <= :end_date
ORDER BY {date_field} DESC
LIMIT :limit OFFSET :skip
"""
result = await self.session.execute(text(query_text), {
"tenant_id": tenant_id,
"start_date": start_date,
"end_date": end_date,
"limit": limit,
"skip": skip
})
# Convert rows to model objects
records = []
for row in result.fetchall():
record_dict = dict(row._mapping)
record = self.model(**record_dict)
records.append(record)
return records
except Exception as e:
logger.error("Failed to get records by date range",
model=self.model.__name__,
tenant_id=tenant_id,
error=str(e))
raise DatabaseError(f"Date range query failed: {str(e)}")
async def get_recent_records(
self,
tenant_id: str,
hours: int = 24,
skip: int = 0,
limit: int = 100
) -> List:
"""Get recent records for a tenant"""
cutoff_time = datetime.utcnow() - timedelta(hours=hours)
return await self.get_by_date_range(
tenant_id, cutoff_time, datetime.utcnow(), skip, limit
)
async def cleanup_old_records(self, days_old: int = 90) -> int:
"""Clean up old forecasting records"""
try:
cutoff_date = datetime.utcnow() - timedelta(days=days_old)
table_name = self.model.__tablename__
# Use created_at or forecast_date for cleanup
date_field = "forecast_date" if hasattr(self.model, 'forecast_date') else "created_at"
query_text = f"""
DELETE FROM {table_name}
WHERE {date_field} < :cutoff_date
"""
result = await self.session.execute(text(query_text), {"cutoff_date": cutoff_date})
deleted_count = result.rowcount
logger.info(f"Cleaned up old {self.model.__name__} records",
deleted_count=deleted_count,
days_old=days_old)
return deleted_count
except Exception as e:
logger.error("Failed to cleanup old records",
model=self.model.__name__,
error=str(e))
raise DatabaseError(f"Cleanup failed: {str(e)}")
async def get_statistics_by_tenant(self, tenant_id: str) -> Dict[str, Any]:
"""Get statistics for a tenant"""
try:
table_name = self.model.__tablename__
# Get basic counts
total_records = await self.count(filters={"tenant_id": tenant_id})
# Get recent activity (records in last 7 days)
seven_days_ago = datetime.utcnow() - timedelta(days=7)
recent_records = len(await self.get_by_date_range(
tenant_id, seven_days_ago, datetime.utcnow(), limit=1000
))
# Get records by product if applicable
product_stats = {}
if hasattr(self.model, 'product_name'):
product_query = text(f"""
SELECT product_name, COUNT(*) as count
FROM {table_name}
WHERE tenant_id = :tenant_id
GROUP BY product_name
ORDER BY count DESC
""")
result = await self.session.execute(product_query, {"tenant_id": tenant_id})
product_stats = {row.product_name: row.count for row in result.fetchall()}
return {
"total_records": total_records,
"recent_records_7d": recent_records,
"records_by_product": product_stats
}
except Exception as e:
logger.error("Failed to get tenant statistics",
model=self.model.__name__,
tenant_id=tenant_id,
error=str(e))
return {
"total_records": 0,
"recent_records_7d": 0,
"records_by_product": {}
}
def _validate_forecast_data(self, data: Dict[str, Any], required_fields: List[str]) -> Dict[str, Any]:
"""Validate forecasting-related data"""
errors = []
for field in required_fields:
if field not in data or not data[field]:
errors.append(f"Missing required field: {field}")
# Validate tenant_id format if present
if "tenant_id" in data and data["tenant_id"]:
tenant_id = data["tenant_id"]
if not isinstance(tenant_id, str) or len(tenant_id) < 1:
errors.append("Invalid tenant_id format")
# Validate product_name if present
if "product_name" in data and data["product_name"]:
product_name = data["product_name"]
if not isinstance(product_name, str) or len(product_name) < 1:
errors.append("Invalid product_name format")
# Validate dates if present - accept datetime objects, date objects, and date strings
date_fields = ["forecast_date", "created_at", "evaluation_date", "expires_at"]
for field in date_fields:
if field in data and data[field]:
field_value = data[field]
field_type = type(field_value).__name__
if isinstance(field_value, (datetime, date)):
logger.debug(f"Date field {field} is valid {field_type}", field_value=str(field_value))
continue # Already a datetime or date, valid
elif isinstance(field_value, str):
# Try to parse the string date
try:
from dateutil.parser import parse
parse(field_value) # Just validate, don't convert yet
logger.debug(f"Date field {field} is valid string", field_value=field_value)
except (ValueError, TypeError) as e:
logger.error(f"Date parsing failed for {field}", field_value=field_value, error=str(e))
errors.append(f"Invalid {field} format - must be datetime or valid date string")
else:
logger.error(f"Date field {field} has invalid type {field_type}", field_value=str(field_value))
errors.append(f"Invalid {field} format - must be datetime or valid date string")
# Validate numeric fields
numeric_fields = [
"predicted_demand", "confidence_lower", "confidence_upper",
"mae", "mape", "rmse", "accuracy_score"
]
for field in numeric_fields:
if field in data and data[field] is not None:
try:
float(data[field])
except (ValueError, TypeError):
errors.append(f"Invalid {field} format - must be numeric")
return {
"is_valid": len(errors) == 0,
"errors": errors
}

375
services/forecasting/app/repositories/forecast_alert_repository.py Normal file
View File

@@ -0,0 +1,375 @@
"""
Forecast Alert Repository
Repository for forecast alert operations
"""
from typing import Optional, List, Dict, Any
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import text
from datetime import datetime, timedelta
import structlog
from .base import ForecastingBaseRepository
from app.models.forecasts import ForecastAlert
from shared.database.exceptions import DatabaseError, ValidationError
logger = structlog.get_logger()
class ForecastAlertRepository(ForecastingBaseRepository):
"""Repository for forecast alert operations"""
def __init__(self, session: AsyncSession, cache_ttl: Optional[int] = 300):
# Alerts change frequently, shorter cache time (5 minutes)
super().__init__(ForecastAlert, session, cache_ttl)
async def create_alert(self, alert_data: Dict[str, Any]) -> ForecastAlert:
"""Create a new forecast alert"""
try:
# Validate alert data
validation_result = self._validate_forecast_data(
alert_data,
["tenant_id", "forecast_id", "alert_type", "message"]
)
if not validation_result["is_valid"]:
raise ValidationError(f"Invalid alert data: {validation_result['errors']}")
# Set default values
if "severity" not in alert_data:
alert_data["severity"] = "medium"
if "is_active" not in alert_data:
alert_data["is_active"] = True
if "notification_sent" not in alert_data:
alert_data["notification_sent"] = False
alert = await self.create(alert_data)
logger.info("Forecast alert created",
alert_id=alert.id,
tenant_id=alert.tenant_id,
alert_type=alert.alert_type,
severity=alert.severity)
return alert
except ValidationError:
raise
except Exception as e:
logger.error("Failed to create forecast alert",
tenant_id=alert_data.get("tenant_id"),
error=str(e))
raise DatabaseError(f"Failed to create alert: {str(e)}")
async def get_active_alerts(
self,
tenant_id: str,
alert_type: str = None,
severity: str = None
) -> List[ForecastAlert]:
"""Get active alerts for a tenant"""
try:
filters = {
"tenant_id": tenant_id,
"is_active": True
}
if alert_type:
filters["alert_type"] = alert_type
if severity:
filters["severity"] = severity
return await self.get_multi(
filters=filters,
order_by="created_at",
order_desc=True
)
except Exception as e:
logger.error("Failed to get active alerts",
tenant_id=tenant_id,
error=str(e))
return []
async def acknowledge_alert(
self,
alert_id: str,
acknowledged_by: str = None
) -> Optional[ForecastAlert]:
"""Acknowledge an alert"""
try:
update_data = {
"acknowledged_at": datetime.utcnow()
}
if acknowledged_by:
# Store in message or create a new field if needed
current_alert = await self.get_by_id(alert_id)
if current_alert:
update_data["message"] = f"{current_alert.message} (Acknowledged by: {acknowledged_by})"
updated_alert = await self.update(alert_id, update_data)
logger.info("Alert acknowledged",
alert_id=alert_id,
acknowledged_by=acknowledged_by)
return updated_alert
except Exception as e:
logger.error("Failed to acknowledge alert",
alert_id=alert_id,
error=str(e))
raise DatabaseError(f"Failed to acknowledge alert: {str(e)}")
async def resolve_alert(
self,
alert_id: str,
resolved_by: str = None
) -> Optional[ForecastAlert]:
"""Resolve an alert"""
try:
update_data = {
"resolved_at": datetime.utcnow(),
"is_active": False
}
if resolved_by:
current_alert = await self.get_by_id(alert_id)
if current_alert:
update_data["message"] = f"{current_alert.message} (Resolved by: {resolved_by})"
updated_alert = await self.update(alert_id, update_data)
logger.info("Alert resolved",
alert_id=alert_id,
resolved_by=resolved_by)
return updated_alert
except Exception as e:
logger.error("Failed to resolve alert",
alert_id=alert_id,
error=str(e))
raise DatabaseError(f"Failed to resolve alert: {str(e)}")
async def mark_notification_sent(
self,
alert_id: str,
notification_method: str
) -> Optional[ForecastAlert]:
"""Mark alert notification as sent"""
try:
update_data = {
"notification_sent": True,
"notification_method": notification_method
}
updated_alert = await self.update(alert_id, update_data)
logger.debug("Alert notification marked as sent",
alert_id=alert_id,
method=notification_method)
return updated_alert
except Exception as e:
logger.error("Failed to mark notification as sent",
alert_id=alert_id,
error=str(e))
return None
async def get_unnotified_alerts(self, tenant_id: str = None) -> List[ForecastAlert]:
"""Get alerts that haven't been notified yet"""
try:
filters = {
"is_active": True,
"notification_sent": False
}
if tenant_id:
filters["tenant_id"] = tenant_id
return await self.get_multi(
filters=filters,
order_by="created_at",
order_desc=False # Oldest first for notification
)
except Exception as e:
logger.error("Failed to get unnotified alerts",
tenant_id=tenant_id,
error=str(e))
return []
async def get_alert_statistics(self, tenant_id: str) -> Dict[str, Any]:
"""Get alert statistics for a tenant"""
try:
# Get counts by type
type_query = text("""
SELECT alert_type, COUNT(*) as count
FROM forecast_alerts
WHERE tenant_id = :tenant_id
GROUP BY alert_type
ORDER BY count DESC
""")
result = await self.session.execute(type_query, {"tenant_id": tenant_id})
alerts_by_type = {row.alert_type: row.count for row in result.fetchall()}
# Get counts by severity
severity_query = text("""
SELECT severity, COUNT(*) as count
FROM forecast_alerts
WHERE tenant_id = :tenant_id
GROUP BY severity
ORDER BY count DESC
""")
severity_result = await self.session.execute(severity_query, {"tenant_id": tenant_id})
alerts_by_severity = {row.severity: row.count for row in severity_result.fetchall()}
# Get status counts
total_alerts = await self.count(filters={"tenant_id": tenant_id})
active_alerts = await self.count(filters={
"tenant_id": tenant_id,
"is_active": True
})
acknowledged_alerts = await self.count(filters={
"tenant_id": tenant_id,
"acknowledged_at": "IS NOT NULL" # This won't work with our current filters
})
# Get recent activity (alerts in last 7 days)
seven_days_ago = datetime.utcnow() - timedelta(days=7)
recent_alerts = len(await self.get_by_date_range(
tenant_id, seven_days_ago, datetime.utcnow(), limit=1000
))
# Calculate response metrics
response_query = text("""
SELECT
AVG(EXTRACT(EPOCH FROM (acknowledged_at - created_at))/60) as avg_acknowledgment_time_minutes,
AVG(EXTRACT(EPOCH FROM (resolved_at - created_at))/60) as avg_resolution_time_minutes,
COUNT(CASE WHEN acknowledged_at IS NOT NULL THEN 1 END) as acknowledged_count,
COUNT(CASE WHEN resolved_at IS NOT NULL THEN 1 END) as resolved_count
FROM forecast_alerts
WHERE tenant_id = :tenant_id
""")
response_result = await self.session.execute(response_query, {"tenant_id": tenant_id})
response_row = response_result.fetchone()
return {
"total_alerts": total_alerts,
"active_alerts": active_alerts,
"resolved_alerts": total_alerts - active_alerts,
"alerts_by_type": alerts_by_type,
"alerts_by_severity": alerts_by_severity,
"recent_alerts_7d": recent_alerts,
"response_metrics": {
"avg_acknowledgment_time_minutes": float(response_row.avg_acknowledgment_time_minutes or 0),
"avg_resolution_time_minutes": float(response_row.avg_resolution_time_minutes or 0),
"acknowledgment_rate": round((response_row.acknowledged_count / max(total_alerts, 1)) * 100, 2),
"resolution_rate": round((response_row.resolved_count / max(total_alerts, 1)) * 100, 2)
} if response_row else {
"avg_acknowledgment_time_minutes": 0.0,
"avg_resolution_time_minutes": 0.0,
"acknowledgment_rate": 0.0,
"resolution_rate": 0.0
}
}
except Exception as e:
logger.error("Failed to get alert statistics",
tenant_id=tenant_id,
error=str(e))
return {
"total_alerts": 0,
"active_alerts": 0,
"resolved_alerts": 0,
"alerts_by_type": {},
"alerts_by_severity": {},
"recent_alerts_7d": 0,
"response_metrics": {
"avg_acknowledgment_time_minutes": 0.0,
"avg_resolution_time_minutes": 0.0,
"acknowledgment_rate": 0.0,
"resolution_rate": 0.0
}
}
async def cleanup_old_alerts(self, days_old: int = 90) -> int:
"""Clean up old resolved alerts"""
try:
cutoff_date = datetime.utcnow() - timedelta(days=days_old)
query_text = """
DELETE FROM forecast_alerts
WHERE is_active = false
AND resolved_at IS NOT NULL
AND resolved_at < :cutoff_date
"""
result = await self.session.execute(text(query_text), {"cutoff_date": cutoff_date})
deleted_count = result.rowcount
logger.info("Cleaned up old forecast alerts",
deleted_count=deleted_count,
days_old=days_old)
return deleted_count
except Exception as e:
logger.error("Failed to cleanup old alerts",
error=str(e))
raise DatabaseError(f"Alert cleanup failed: {str(e)}")
async def bulk_resolve_alerts(
self,
tenant_id: str,
alert_type: str = None,
older_than_hours: int = 24
) -> int:
"""Bulk resolve old alerts"""
try:
cutoff_time = datetime.utcnow() - timedelta(hours=older_than_hours)
conditions = [
"tenant_id = :tenant_id",
"is_active = true",
"created_at < :cutoff_time"
]
params = {
"tenant_id": tenant_id,
"cutoff_time": cutoff_time
}
if alert_type:
conditions.append("alert_type = :alert_type")
params["alert_type"] = alert_type
query_text = f"""
UPDATE forecast_alerts
SET is_active = false, resolved_at = :resolved_at
WHERE {' AND '.join(conditions)}
"""
params["resolved_at"] = datetime.utcnow()
result = await self.session.execute(text(query_text), params)
resolved_count = result.rowcount
logger.info("Bulk resolved old alerts",
tenant_id=tenant_id,
alert_type=alert_type,
resolved_count=resolved_count,
older_than_hours=older_than_hours)
return resolved_count
except Exception as e:
logger.error("Failed to bulk resolve alerts",
tenant_id=tenant_id,
error=str(e))
raise DatabaseError(f"Bulk resolve failed: {str(e)}")

429
services/forecasting/app/repositories/forecast_repository.py Normal file
View File

@@ -0,0 +1,429 @@
"""
Forecast Repository
Repository for forecast operations
"""
from typing import Optional, List, Dict, Any
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select, and_, text, desc, func
from datetime import datetime, timedelta, date
import structlog
from .base import ForecastingBaseRepository
from app.models.forecasts import Forecast
from shared.database.exceptions import DatabaseError, ValidationError
logger = structlog.get_logger()
class ForecastRepository(ForecastingBaseRepository):
"""Repository for forecast operations"""
def __init__(self, session: AsyncSession, cache_ttl: Optional[int] = 600):
# Forecasts are relatively stable, medium cache time (10 minutes)
super().__init__(Forecast, session, cache_ttl)
async def create_forecast(self, forecast_data: Dict[str, Any]) -> Forecast:
"""Create a new forecast with validation"""
try:
# Validate forecast data
validation_result = self._validate_forecast_data(
forecast_data,
["tenant_id", "product_name", "location", "forecast_date",
"predicted_demand", "confidence_lower", "confidence_upper", "model_id"]
)
if not validation_result["is_valid"]:
raise ValidationError(f"Invalid forecast data: {validation_result['errors']}")
# Set default values
if "confidence_level" not in forecast_data:
forecast_data["confidence_level"] = 0.8
if "algorithm" not in forecast_data:
forecast_data["algorithm"] = "prophet"
if "business_type" not in forecast_data:
forecast_data["business_type"] = "individual"
# Create forecast
forecast = await self.create(forecast_data)
logger.info("Forecast created successfully",
forecast_id=forecast.id,
tenant_id=forecast.tenant_id,
product_name=forecast.product_name,
forecast_date=forecast.forecast_date.isoformat())
return forecast
except ValidationError:
raise
except Exception as e:
logger.error("Failed to create forecast",
tenant_id=forecast_data.get("tenant_id"),
product_name=forecast_data.get("product_name"),
error=str(e))
raise DatabaseError(f"Failed to create forecast: {str(e)}")
async def get_forecasts_by_date_range(
self,
tenant_id: str,
start_date: date,
end_date: date,
product_name: str = None,
location: str = None
) -> List[Forecast]:
"""Get forecasts within a date range"""
try:
filters = {"tenant_id": tenant_id}
if product_name:
filters["product_name"] = product_name
if location:
filters["location"] = location
# Convert dates to datetime for comparison
start_datetime = datetime.combine(start_date, datetime.min.time())
end_datetime = datetime.combine(end_date, datetime.max.time())
return await self.get_by_date_range(
tenant_id, start_datetime, end_datetime
)
except Exception as e:
logger.error("Failed to get forecasts by date range",
tenant_id=tenant_id,
start_date=start_date,
end_date=end_date,
error=str(e))
raise DatabaseError(f"Failed to get forecasts: {str(e)}")
async def get_latest_forecast_for_product(
self,
tenant_id: str,
product_name: str,
location: str = None
) -> Optional[Forecast]:
"""Get the most recent forecast for a product"""
try:
filters = {
"tenant_id": tenant_id,
"product_name": product_name
}
if location:
filters["location"] = location
forecasts = await self.get_multi(
filters=filters,
limit=1,
order_by="forecast_date",
order_desc=True
)
return forecasts[0] if forecasts else None
except Exception as e:
logger.error("Failed to get latest forecast for product",
tenant_id=tenant_id,
product_name=product_name,
error=str(e))
raise DatabaseError(f"Failed to get latest forecast: {str(e)}")
async def get_forecasts_for_date(
self,
tenant_id: str,
forecast_date: date,
product_name: str = None
) -> List[Forecast]:
"""Get all forecasts for a specific date"""
try:
# Convert date to datetime range
start_datetime = datetime.combine(forecast_date, datetime.min.time())
end_datetime = datetime.combine(forecast_date, datetime.max.time())
return await self.get_by_date_range(
tenant_id, start_datetime, end_datetime
)
except Exception as e:
logger.error("Failed to get forecasts for date",
tenant_id=tenant_id,
forecast_date=forecast_date,
error=str(e))
raise DatabaseError(f"Failed to get forecasts for date: {str(e)}")
async def get_forecast_accuracy_metrics(
self,
tenant_id: str,
product_name: str = None,
days_back: int = 30
) -> Dict[str, Any]:
"""Get forecast accuracy metrics"""
try:
cutoff_date = datetime.utcnow() - timedelta(days=days_back)
# Build base query conditions
conditions = ["tenant_id = :tenant_id", "forecast_date >= :cutoff_date"]
params = {
"tenant_id": tenant_id,
"cutoff_date": cutoff_date
}
if product_name:
conditions.append("product_name = :product_name")
params["product_name"] = product_name
query_text = f"""
SELECT
COUNT(*) as total_forecasts,
AVG(predicted_demand) as avg_predicted_demand,
MIN(predicted_demand) as min_predicted_demand,
MAX(predicted_demand) as max_predicted_demand,
AVG(confidence_upper - confidence_lower) as avg_confidence_interval,
AVG(processing_time_ms) as avg_processing_time_ms,
COUNT(DISTINCT product_name) as unique_products,
COUNT(DISTINCT model_id) as unique_models
FROM forecasts
WHERE {' AND '.join(conditions)}
"""
result = await self.session.execute(text(query_text), params)
row = result.fetchone()
if row and row.total_forecasts > 0:
return {
"total_forecasts": int(row.total_forecasts),
"avg_predicted_demand": float(row.avg_predicted_demand or 0),
"min_predicted_demand": float(row.min_predicted_demand or 0),
"max_predicted_demand": float(row.max_predicted_demand or 0),
"avg_confidence_interval": float(row.avg_confidence_interval or 0),
"avg_processing_time_ms": float(row.avg_processing_time_ms or 0),
"unique_products": int(row.unique_products or 0),
"unique_models": int(row.unique_models or 0),
"period_days": days_back
}
return {
"total_forecasts": 0,
"avg_predicted_demand": 0.0,
"min_predicted_demand": 0.0,
"max_predicted_demand": 0.0,
"avg_confidence_interval": 0.0,
"avg_processing_time_ms": 0.0,
"unique_products": 0,
"unique_models": 0,
"period_days": days_back
}
except Exception as e:
logger.error("Failed to get forecast accuracy metrics",
tenant_id=tenant_id,
error=str(e))
return {
"total_forecasts": 0,
"avg_predicted_demand": 0.0,
"min_predicted_demand": 0.0,
"max_predicted_demand": 0.0,
"avg_confidence_interval": 0.0,
"avg_processing_time_ms": 0.0,
"unique_products": 0,
"unique_models": 0,
"period_days": days_back
}
async def get_demand_trends(
self,
tenant_id: str,
product_name: str,
days_back: int = 30
) -> Dict[str, Any]:
"""Get demand trends for a product"""
try:
cutoff_date = datetime.utcnow() - timedelta(days=days_back)
query_text = """
SELECT
DATE(forecast_date) as date,
AVG(predicted_demand) as avg_demand,
MIN(predicted_demand) as min_demand,
MAX(predicted_demand) as max_demand,
COUNT(*) as forecast_count
FROM forecasts
WHERE tenant_id = :tenant_id
AND product_name = :product_name
AND forecast_date >= :cutoff_date
GROUP BY DATE(forecast_date)
ORDER BY date DESC
"""
result = await self.session.execute(text(query_text), {
"tenant_id": tenant_id,
"product_name": product_name,
"cutoff_date": cutoff_date
})
trends = []
for row in result.fetchall():
trends.append({
"date": row.date.isoformat() if row.date else None,
"avg_demand": float(row.avg_demand),
"min_demand": float(row.min_demand),
"max_demand": float(row.max_demand),
"forecast_count": int(row.forecast_count)
})
# Calculate overall trend direction
if len(trends) >= 2:
recent_avg = sum(t["avg_demand"] for t in trends[:7]) / min(7, len(trends))
older_avg = sum(t["avg_demand"] for t in trends[-7:]) / min(7, len(trends[-7:]))
trend_direction = "increasing" if recent_avg > older_avg else "decreasing"
else:
trend_direction = "stable"
return {
"product_name": product_name,
"period_days": days_back,
"trends": trends,
"trend_direction": trend_direction,
"total_data_points": len(trends)
}
except Exception as e:
logger.error("Failed to get demand trends",
tenant_id=tenant_id,
product_name=product_name,
error=str(e))
return {
"product_name": product_name,
"period_days": days_back,
"trends": [],
"trend_direction": "unknown",
"total_data_points": 0
}
async def get_model_usage_statistics(self, tenant_id: str) -> Dict[str, Any]:
"""Get statistics about model usage"""
try:
# Get model usage counts
model_query = text("""
SELECT
model_id,
algorithm,
COUNT(*) as usage_count,
AVG(predicted_demand) as avg_prediction,
MAX(forecast_date) as last_used,
COUNT(DISTINCT product_name) as products_covered
FROM forecasts
WHERE tenant_id = :tenant_id
GROUP BY model_id, algorithm
ORDER BY usage_count DESC
""")
result = await self.session.execute(model_query, {"tenant_id": tenant_id})
model_stats = []
for row in result.fetchall():
model_stats.append({
"model_id": row.model_id,
"algorithm": row.algorithm,
"usage_count": int(row.usage_count),
"avg_prediction": float(row.avg_prediction),
"last_used": row.last_used.isoformat() if row.last_used else None,
"products_covered": int(row.products_covered)
})
# Get algorithm distribution
algorithm_query = text("""
SELECT algorithm, COUNT(*) as count
FROM forecasts
WHERE tenant_id = :tenant_id
GROUP BY algorithm
""")
algorithm_result = await self.session.execute(algorithm_query, {"tenant_id": tenant_id})
algorithm_distribution = {row.algorithm: row.count for row in algorithm_result.fetchall()}
return {
"model_statistics": model_stats,
"algorithm_distribution": algorithm_distribution,
"total_unique_models": len(model_stats)
}
except Exception as e:
logger.error("Failed to get model usage statistics",
tenant_id=tenant_id,
error=str(e))
return {
"model_statistics": [],
"algorithm_distribution": {},
"total_unique_models": 0
}
async def cleanup_old_forecasts(self, days_old: int = 90) -> int:
"""Clean up old forecasts"""
return await self.cleanup_old_records(days_old=days_old)
async def get_forecast_summary(self, tenant_id: str) -> Dict[str, Any]:
"""Get comprehensive forecast summary for a tenant"""
try:
# Get basic statistics
basic_stats = await self.get_statistics_by_tenant(tenant_id)
# Get accuracy metrics
accuracy_metrics = await self.get_forecast_accuracy_metrics(tenant_id)
# Get model usage
model_usage = await self.get_model_usage_statistics(tenant_id)
# Get recent activity
recent_forecasts = await self.get_recent_records(tenant_id, hours=24)
return {
"tenant_id": tenant_id,
"basic_statistics": basic_stats,
"accuracy_metrics": accuracy_metrics,
"model_usage": model_usage,
"recent_activity": {
"forecasts_last_24h": len(recent_forecasts),
"latest_forecast": recent_forecasts[0].forecast_date.isoformat() if recent_forecasts else None
}
}
except Exception as e:
logger.error("Failed to get forecast summary",
tenant_id=tenant_id,
error=str(e))
return {"error": f"Failed to get forecast summary: {str(e)}"}
async def bulk_create_forecasts(self, forecasts_data: List[Dict[str, Any]]) -> List[Forecast]:
"""Bulk create multiple forecasts"""
try:
created_forecasts = []
for forecast_data in forecasts_data:
# Validate each forecast
validation_result = self._validate_forecast_data(
forecast_data,
["tenant_id", "product_name", "location", "forecast_date",
"predicted_demand", "confidence_lower", "confidence_upper", "model_id"]
)
if not validation_result["is_valid"]:
logger.warning("Skipping invalid forecast data",
errors=validation_result["errors"],
data=forecast_data)
continue
forecast = await self.create(forecast_data)
created_forecasts.append(forecast)
logger.info("Bulk created forecasts",
requested_count=len(forecasts_data),
created_count=len(created_forecasts))
return created_forecasts
except Exception as e:
logger.error("Failed to bulk create forecasts",
requested_count=len(forecasts_data),
error=str(e))
raise DatabaseError(f"Bulk forecast creation failed: {str(e)}")

170
services/forecasting/app/repositories/performance_metric_repository.py Normal file
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"""
Performance Metric Repository
Repository for model performance metrics in forecasting service
"""
from typing import Optional, List, Dict, Any
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import text
from datetime import datetime, timedelta
import structlog
from .base import ForecastingBaseRepository
from app.models.predictions import ModelPerformanceMetric
from shared.database.exceptions import DatabaseError, ValidationError
logger = structlog.get_logger()
class PerformanceMetricRepository(ForecastingBaseRepository):
"""Repository for model performance metrics operations"""
def __init__(self, session: AsyncSession, cache_ttl: Optional[int] = 900):
# Performance metrics are stable, longer cache time (15 minutes)
super().__init__(ModelPerformanceMetric, session, cache_ttl)
async def create_metric(self, metric_data: Dict[str, Any]) -> ModelPerformanceMetric:
"""Create a new performance metric"""
try:
# Validate metric data
validation_result = self._validate_forecast_data(
metric_data,
["model_id", "tenant_id", "product_name", "evaluation_date"]
)
if not validation_result["is_valid"]:
raise ValidationError(f"Invalid metric data: {validation_result['errors']}")
metric = await self.create(metric_data)
logger.info("Performance metric created",
metric_id=metric.id,
model_id=metric.model_id,
tenant_id=metric.tenant_id,
product_name=metric.product_name)
return metric
except ValidationError:
raise
except Exception as e:
logger.error("Failed to create performance metric",
model_id=metric_data.get("model_id"),
error=str(e))
raise DatabaseError(f"Failed to create metric: {str(e)}")
async def get_metrics_by_model(
self,
model_id: str,
skip: int = 0,
limit: int = 100
) -> List[ModelPerformanceMetric]:
"""Get all metrics for a model"""
try:
return await self.get_multi(
filters={"model_id": model_id},
skip=skip,
limit=limit,
order_by="evaluation_date",
order_desc=True
)
except Exception as e:
logger.error("Failed to get metrics by model",
model_id=model_id,
error=str(e))
raise DatabaseError(f"Failed to get metrics: {str(e)}")
async def get_latest_metric_for_model(self, model_id: str) -> Optional[ModelPerformanceMetric]:
"""Get the latest performance metric for a model"""
try:
metrics = await self.get_multi(
filters={"model_id": model_id},
limit=1,
order_by="evaluation_date",
order_desc=True
)
return metrics[0] if metrics else None
except Exception as e:
logger.error("Failed to get latest metric for model",
model_id=model_id,
error=str(e))
raise DatabaseError(f"Failed to get latest metric: {str(e)}")
async def get_performance_trends(
self,
tenant_id: str,
product_name: str = None,
days: int = 30
) -> Dict[str, Any]:
"""Get performance trends over time"""
try:
start_date = datetime.utcnow() - timedelta(days=days)
conditions = [
"tenant_id = :tenant_id",
"evaluation_date >= :start_date"
]
params = {
"tenant_id": tenant_id,
"start_date": start_date
}
if product_name:
conditions.append("product_name = :product_name")
params["product_name"] = product_name
query_text = f"""
SELECT
DATE(evaluation_date) as date,
product_name,
AVG(mae) as avg_mae,
AVG(mape) as avg_mape,
AVG(rmse) as avg_rmse,
AVG(accuracy_score) as avg_accuracy,
COUNT(*) as measurement_count
FROM model_performance_metrics
WHERE {' AND '.join(conditions)}
GROUP BY DATE(evaluation_date), product_name
ORDER BY date DESC, product_name
"""
result = await self.session.execute(text(query_text), params)
trends = []
for row in result.fetchall():
trends.append({
"date": row.date.isoformat() if row.date else None,
"product_name": row.product_name,
"metrics": {
"avg_mae": float(row.avg_mae) if row.avg_mae else None,
"avg_mape": float(row.avg_mape) if row.avg_mape else None,
"avg_rmse": float(row.avg_rmse) if row.avg_rmse else None,
"avg_accuracy": float(row.avg_accuracy) if row.avg_accuracy else None
},
"measurement_count": int(row.measurement_count)
})
return {
"tenant_id": tenant_id,
"product_name": product_name,
"period_days": days,
"trends": trends,
"total_measurements": len(trends)
}
except Exception as e:
logger.error("Failed to get performance trends",
tenant_id=tenant_id,
product_name=product_name,
error=str(e))
return {
"tenant_id": tenant_id,
"product_name": product_name,
"period_days": days,
"trends": [],
"total_measurements": 0
}
async def cleanup_old_metrics(self, days_old: int = 180) -> int:
"""Clean up old performance metrics"""
return await self.cleanup_old_records(days_old=days_old)

388
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"""
Prediction Batch Repository
Repository for prediction batch operations
"""
from typing import Optional, List, Dict, Any
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import text
from datetime import datetime, timedelta
import structlog
from .base import ForecastingBaseRepository
from app.models.forecasts import PredictionBatch
from shared.database.exceptions import DatabaseError, ValidationError
logger = structlog.get_logger()
class PredictionBatchRepository(ForecastingBaseRepository):
"""Repository for prediction batch operations"""
def __init__(self, session: AsyncSession, cache_ttl: Optional[int] = 300):
# Batch operations change frequently, shorter cache time (5 minutes)
super().__init__(PredictionBatch, session, cache_ttl)
async def create_batch(self, batch_data: Dict[str, Any]) -> PredictionBatch:
"""Create a new prediction batch"""
try:
# Validate batch data
validation_result = self._validate_forecast_data(
batch_data,
["tenant_id", "batch_name"]
)
if not validation_result["is_valid"]:
raise ValidationError(f"Invalid batch data: {validation_result['errors']}")
# Set default values
if "status" not in batch_data:
batch_data["status"] = "pending"
if "forecast_days" not in batch_data:
batch_data["forecast_days"] = 7
if "business_type" not in batch_data:
batch_data["business_type"] = "individual"
batch = await self.create(batch_data)
logger.info("Prediction batch created",
batch_id=batch.id,
tenant_id=batch.tenant_id,
batch_name=batch.batch_name)
return batch
except ValidationError:
raise
except Exception as e:
logger.error("Failed to create prediction batch",
tenant_id=batch_data.get("tenant_id"),
error=str(e))
raise DatabaseError(f"Failed to create batch: {str(e)}")
async def update_batch_progress(
self,
batch_id: str,
completed_products: int = None,
failed_products: int = None,
total_products: int = None,
status: str = None
) -> Optional[PredictionBatch]:
"""Update batch progress"""
try:
update_data = {}
if completed_products is not None:
update_data["completed_products"] = completed_products
if failed_products is not None:
update_data["failed_products"] = failed_products
if total_products is not None:
update_data["total_products"] = total_products
if status:
update_data["status"] = status
if status in ["completed", "failed"]:
update_data["completed_at"] = datetime.utcnow()
if not update_data:
return await self.get_by_id(batch_id)
updated_batch = await self.update(batch_id, update_data)
logger.debug("Batch progress updated",
batch_id=batch_id,
status=status,
completed=completed_products)
return updated_batch
except Exception as e:
logger.error("Failed to update batch progress",
batch_id=batch_id,
error=str(e))
raise DatabaseError(f"Failed to update batch: {str(e)}")
async def complete_batch(
self,
batch_id: str,
processing_time_ms: int = None
) -> Optional[PredictionBatch]:
"""Mark batch as completed"""
try:
update_data = {
"status": "completed",
"completed_at": datetime.utcnow()
}
if processing_time_ms:
update_data["processing_time_ms"] = processing_time_ms
updated_batch = await self.update(batch_id, update_data)
logger.info("Batch completed",
batch_id=batch_id,
processing_time_ms=processing_time_ms)
return updated_batch
except Exception as e:
logger.error("Failed to complete batch",
batch_id=batch_id,
error=str(e))
raise DatabaseError(f"Failed to complete batch: {str(e)}")
async def fail_batch(
self,
batch_id: str,
error_message: str,
processing_time_ms: int = None
) -> Optional[PredictionBatch]:
"""Mark batch as failed"""
try:
update_data = {
"status": "failed",
"completed_at": datetime.utcnow(),
"error_message": error_message
}
if processing_time_ms:
update_data["processing_time_ms"] = processing_time_ms
updated_batch = await self.update(batch_id, update_data)
logger.error("Batch failed",
batch_id=batch_id,
error_message=error_message)
return updated_batch
except Exception as e:
logger.error("Failed to mark batch as failed",
batch_id=batch_id,
error=str(e))
raise DatabaseError(f"Failed to fail batch: {str(e)}")
async def cancel_batch(
self,
batch_id: str,
cancelled_by: str = None
) -> Optional[PredictionBatch]:
"""Cancel a batch"""
try:
batch = await self.get_by_id(batch_id)
if not batch:
return None
if batch.status in ["completed", "failed"]:
logger.warning("Cannot cancel finished batch",
batch_id=batch_id,
status=batch.status)
return batch
update_data = {
"status": "cancelled",
"completed_at": datetime.utcnow(),
"cancelled_by": cancelled_by,
"error_message": f"Cancelled by {cancelled_by}" if cancelled_by else "Cancelled"
}
updated_batch = await self.update(batch_id, update_data)
logger.info("Batch cancelled",
batch_id=batch_id,
cancelled_by=cancelled_by)
return updated_batch
except Exception as e:
logger.error("Failed to cancel batch",
batch_id=batch_id,
error=str(e))
raise DatabaseError(f"Failed to cancel batch: {str(e)}")
async def get_active_batches(self, tenant_id: str = None) -> List[PredictionBatch]:
"""Get currently active (pending/processing) batches"""
try:
filters = {"status": "processing"}
if tenant_id:
# Need to handle multiple status values with raw query
query_text = """
SELECT * FROM prediction_batches
WHERE status IN ('pending', 'processing')
AND tenant_id = :tenant_id
ORDER BY requested_at DESC
"""
params = {"tenant_id": tenant_id}
else:
query_text = """
SELECT * FROM prediction_batches
WHERE status IN ('pending', 'processing')
ORDER BY requested_at DESC
"""
params = {}
result = await self.session.execute(text(query_text), params)
batches = []
for row in result.fetchall():
record_dict = dict(row._mapping)
batch = self.model(**record_dict)
batches.append(batch)
return batches
except Exception as e:
logger.error("Failed to get active batches",
tenant_id=tenant_id,
error=str(e))
return []
async def get_batch_statistics(self, tenant_id: str = None) -> Dict[str, Any]:
"""Get batch processing statistics"""
try:
base_filter = "WHERE 1=1"
params = {}
if tenant_id:
base_filter = "WHERE tenant_id = :tenant_id"
params["tenant_id"] = tenant_id
# Get counts by status
status_query = text(f"""
SELECT
status,
COUNT(*) as count,
AVG(CASE WHEN processing_time_ms IS NOT NULL THEN processing_time_ms END) as avg_processing_time_ms
FROM prediction_batches
{base_filter}
GROUP BY status
""")
result = await self.session.execute(status_query, params)
status_stats = {}
total_batches = 0
avg_processing_times = {}
for row in result.fetchall():
status_stats[row.status] = row.count
total_batches += row.count
if row.avg_processing_time_ms:
avg_processing_times[row.status] = float(row.avg_processing_time_ms)
# Get recent activity (batches in last 7 days)
seven_days_ago = datetime.utcnow() - timedelta(days=7)
recent_query = text(f"""
SELECT COUNT(*) as count
FROM prediction_batches
{base_filter}
AND requested_at >= :seven_days_ago
""")
recent_result = await self.session.execute(recent_query, {
**params,
"seven_days_ago": seven_days_ago
})
recent_batches = recent_result.scalar() or 0
# Calculate success rate
completed = status_stats.get("completed", 0)
failed = status_stats.get("failed", 0)
cancelled = status_stats.get("cancelled", 0)
finished_batches = completed + failed + cancelled
success_rate = (completed / finished_batches * 100) if finished_batches > 0 else 0
return {
"total_batches": total_batches,
"batches_by_status": status_stats,
"success_rate": round(success_rate, 2),
"recent_batches_7d": recent_batches,
"avg_processing_times_ms": avg_processing_times
}
except Exception as e:
logger.error("Failed to get batch statistics",
tenant_id=tenant_id,
error=str(e))
return {
"total_batches": 0,
"batches_by_status": {},
"success_rate": 0.0,
"recent_batches_7d": 0,
"avg_processing_times_ms": {}
}
async def cleanup_old_batches(self, days_old: int = 30) -> int:
"""Clean up old completed/failed batches"""
try:
cutoff_date = datetime.utcnow() - timedelta(days=days_old)
query_text = """
DELETE FROM prediction_batches
WHERE status IN ('completed', 'failed', 'cancelled')
AND completed_at < :cutoff_date
"""
result = await self.session.execute(text(query_text), {"cutoff_date": cutoff_date})
deleted_count = result.rowcount
logger.info("Cleaned up old prediction batches",
deleted_count=deleted_count,
days_old=days_old)
return deleted_count
except Exception as e:
logger.error("Failed to cleanup old batches",
error=str(e))
raise DatabaseError(f"Batch cleanup failed: {str(e)}")
async def get_batch_details(self, batch_id: str) -> Dict[str, Any]:
"""Get detailed batch information"""
try:
batch = await self.get_by_id(batch_id)
if not batch:
return {"error": "Batch not found"}
# Calculate completion percentage
completion_percentage = 0
if batch.total_products > 0:
completion_percentage = (batch.completed_products / batch.total_products) * 100
# Calculate elapsed time
elapsed_time_ms = 0
if batch.completed_at:
elapsed_time_ms = int((batch.completed_at - batch.requested_at).total_seconds() * 1000)
elif batch.status in ["pending", "processing"]:
elapsed_time_ms = int((datetime.utcnow() - batch.requested_at).total_seconds() * 1000)
return {
"batch_id": str(batch.id),
"tenant_id": str(batch.tenant_id),
"batch_name": batch.batch_name,
"status": batch.status,
"progress": {
"total_products": batch.total_products,
"completed_products": batch.completed_products,
"failed_products": batch.failed_products,
"completion_percentage": round(completion_percentage, 2)
},
"timing": {
"requested_at": batch.requested_at.isoformat(),
"completed_at": batch.completed_at.isoformat() if batch.completed_at else None,
"elapsed_time_ms": elapsed_time_ms,
"processing_time_ms": batch.processing_time_ms
},
"configuration": {
"forecast_days": batch.forecast_days,
"business_type": batch.business_type
},
"error_message": batch.error_message,
"cancelled_by": batch.cancelled_by
}
except Exception as e:
logger.error("Failed to get batch details",
batch_id=batch_id,
error=str(e))
return {"error": f"Failed to get batch details: {str(e)}"}

302
services/forecasting/app/repositories/prediction_cache_repository.py Normal file
View File

@@ -0,0 +1,302 @@
"""
Prediction Cache Repository
Repository for prediction cache operations
"""
from typing import Optional, List, Dict, Any
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import text
from datetime import datetime, timedelta
import structlog
import hashlib
from .base import ForecastingBaseRepository
from app.models.predictions import PredictionCache
from shared.database.exceptions import DatabaseError, ValidationError
logger = structlog.get_logger()
class PredictionCacheRepository(ForecastingBaseRepository):
"""Repository for prediction cache operations"""
def __init__(self, session: AsyncSession, cache_ttl: Optional[int] = 60):
# Cache entries change very frequently, short cache time (1 minute)
super().__init__(PredictionCache, session, cache_ttl)
def _generate_cache_key(
self,
tenant_id: str,
product_name: str,
location: str,
forecast_date: datetime
) -> str:
"""Generate cache key for prediction"""
key_data = f"{tenant_id}:{product_name}:{location}:{forecast_date.isoformat()}"
return hashlib.md5(key_data.encode()).hexdigest()
async def cache_prediction(
self,
tenant_id: str,
product_name: str,
location: str,
forecast_date: datetime,
predicted_demand: float,
confidence_lower: float,
confidence_upper: float,
model_id: str,
expires_in_hours: int = 24
) -> PredictionCache:
"""Cache a prediction result"""
try:
cache_key = self._generate_cache_key(tenant_id, product_name, location, forecast_date)
expires_at = datetime.utcnow() + timedelta(hours=expires_in_hours)
cache_data = {
"cache_key": cache_key,
"tenant_id": tenant_id,
"product_name": product_name,
"location": location,
"forecast_date": forecast_date,
"predicted_demand": predicted_demand,
"confidence_lower": confidence_lower,
"confidence_upper": confidence_upper,
"model_id": model_id,
"expires_at": expires_at,
"hit_count": 0
}
# Try to update existing cache entry first
existing_cache = await self.get_by_field("cache_key", cache_key)
if existing_cache:
cache_entry = await self.update(existing_cache.id, cache_data)
logger.debug("Updated cache entry", cache_key=cache_key)
else:
cache_entry = await self.create(cache_data)
logger.debug("Created cache entry", cache_key=cache_key)
return cache_entry
except Exception as e:
logger.error("Failed to cache prediction",
tenant_id=tenant_id,
product_name=product_name,
error=str(e))
raise DatabaseError(f"Failed to cache prediction: {str(e)}")
async def get_cached_prediction(
self,
tenant_id: str,
product_name: str,
location: str,
forecast_date: datetime
) -> Optional[PredictionCache]:
"""Get cached prediction if valid"""
try:
cache_key = self._generate_cache_key(tenant_id, product_name, location, forecast_date)
cache_entry = await self.get_by_field("cache_key", cache_key)
if not cache_entry:
logger.debug("Cache miss", cache_key=cache_key)
return None
# Check if cache entry has expired
if cache_entry.expires_at < datetime.utcnow():
logger.debug("Cache expired", cache_key=cache_key)
await self.delete(cache_entry.id)
return None
# Increment hit count
await self.update(cache_entry.id, {"hit_count": cache_entry.hit_count + 1})
logger.debug("Cache hit",
cache_key=cache_key,
hit_count=cache_entry.hit_count + 1)
return cache_entry
except Exception as e:
logger.error("Failed to get cached prediction",
tenant_id=tenant_id,
product_name=product_name,
error=str(e))
return None
async def invalidate_cache(
self,
tenant_id: str,
product_name: str = None,
location: str = None
) -> int:
"""Invalidate cache entries"""
try:
conditions = ["tenant_id = :tenant_id"]
params = {"tenant_id": tenant_id}
if product_name:
conditions.append("product_name = :product_name")
params["product_name"] = product_name
if location:
conditions.append("location = :location")
params["location"] = location
query_text = f"""
DELETE FROM prediction_cache
WHERE {' AND '.join(conditions)}
"""
result = await self.session.execute(text(query_text), params)
invalidated_count = result.rowcount
logger.info("Cache invalidated",
tenant_id=tenant_id,
product_name=product_name,
location=location,
invalidated_count=invalidated_count)
return invalidated_count
except Exception as e:
logger.error("Failed to invalidate cache",
tenant_id=tenant_id,
error=str(e))
raise DatabaseError(f"Cache invalidation failed: {str(e)}")
async def cleanup_expired_cache(self) -> int:
"""Clean up expired cache entries"""
try:
query_text = """
DELETE FROM prediction_cache
WHERE expires_at < :now
"""
result = await self.session.execute(text(query_text), {"now": datetime.utcnow()})
deleted_count = result.rowcount
logger.info("Cleaned up expired cache entries",
deleted_count=deleted_count)
return deleted_count
except Exception as e:
logger.error("Failed to cleanup expired cache",
error=str(e))
raise DatabaseError(f"Cache cleanup failed: {str(e)}")
async def get_cache_statistics(self, tenant_id: str = None) -> Dict[str, Any]:
"""Get cache performance statistics"""
try:
base_filter = "WHERE 1=1"
params = {}
if tenant_id:
base_filter = "WHERE tenant_id = :tenant_id"
params["tenant_id"] = tenant_id
# Get cache statistics
stats_query = text(f"""
SELECT
COUNT(*) as total_entries,
COUNT(CASE WHEN expires_at > :now THEN 1 END) as active_entries,
COUNT(CASE WHEN expires_at <= :now THEN 1 END) as expired_entries,
SUM(hit_count) as total_hits,
AVG(hit_count) as avg_hits_per_entry,
MAX(hit_count) as max_hits,
COUNT(DISTINCT product_name) as unique_products
FROM prediction_cache
{base_filter}
""")
params["now"] = datetime.utcnow()
result = await self.session.execute(stats_query, params)
row = result.fetchone()
if row:
return {
"total_entries": int(row.total_entries or 0),
"active_entries": int(row.active_entries or 0),
"expired_entries": int(row.expired_entries or 0),
"total_hits": int(row.total_hits or 0),
"avg_hits_per_entry": float(row.avg_hits_per_entry or 0),
"max_hits": int(row.max_hits or 0),
"unique_products": int(row.unique_products or 0),
"cache_hit_ratio": round((row.total_hits / max(row.total_entries, 1)), 2)
}
return {
"total_entries": 0,
"active_entries": 0,
"expired_entries": 0,
"total_hits": 0,
"avg_hits_per_entry": 0.0,
"max_hits": 0,
"unique_products": 0,
"cache_hit_ratio": 0.0
}
except Exception as e:
logger.error("Failed to get cache statistics",
tenant_id=tenant_id,
error=str(e))
return {
"total_entries": 0,
"active_entries": 0,
"expired_entries": 0,
"total_hits": 0,
"avg_hits_per_entry": 0.0,
"max_hits": 0,
"unique_products": 0,
"cache_hit_ratio": 0.0
}
async def get_most_accessed_predictions(
self,
tenant_id: str = None,
limit: int = 10
) -> List[Dict[str, Any]]:
"""Get most frequently accessed cached predictions"""
try:
base_filter = "WHERE hit_count > 0"
params = {"limit": limit}
if tenant_id:
base_filter = "WHERE tenant_id = :tenant_id AND hit_count > 0"
params["tenant_id"] = tenant_id
query_text = f"""
SELECT
product_name,
location,
hit_count,
predicted_demand,
created_at,
expires_at
FROM prediction_cache
{base_filter}
ORDER BY hit_count DESC
LIMIT :limit
"""
result = await self.session.execute(text(query_text), params)
popular_predictions = []
for row in result.fetchall():
popular_predictions.append({
"product_name": row.product_name,
"location": row.location,
"hit_count": int(row.hit_count),
"predicted_demand": float(row.predicted_demand),
"created_at": row.created_at.isoformat() if row.created_at else None,
"expires_at": row.expires_at.isoformat() if row.expires_at else None
})
return popular_predictions
except Exception as e:
logger.error("Failed to get most accessed predictions",
tenant_id=tenant_id,
error=str(e))
return []

27
services/forecasting/app/services/__init__.py
View File

@@ -0,0 +1,27 @@
"""
Forecasting Service Layer
Business logic services for demand forecasting and prediction
"""
from .forecasting_service import ForecastingService, EnhancedForecastingService
from .prediction_service import PredictionService
from .model_client import ModelClient
from .data_client import DataClient
from .messaging import (
publish_forecast_generated,
publish_batch_forecast_completed,
publish_forecast_alert,
ForecastingStatusPublisher
)
__all__ = [
"ForecastingService",
"EnhancedForecastingService",
"PredictionService",
"ModelClient",
"DataClient",
"publish_forecast_generated",
"publish_batch_forecast_completed",
"publish_forecast_alert",
"ForecastingStatusPublisher"
]

901
services/forecasting/app/services/forecasting_service.py
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File diff suppressed because it is too large Load Diff

65
services/forecasting/app/services/messaging.py
View File

@@ -149,4 +149,67 @@ async def publish_forecasts_deleted_event(tenant_id: str, deletion_stats: Dict[s
}
)
except Exception as e:
logger.error("Failed to publish forecasts deletion event", error=str(e))
logger.error("Failed to publish forecasts deletion event", error=str(e))
# Additional publishing functions for compatibility
async def publish_forecast_generated(data: dict) -> bool:
"""Publish forecast generated event"""
try:
if rabbitmq_client:
await rabbitmq_client.publish_event(
exchange="forecasting_events",
routing_key="forecast.generated",
message=data
)
return True
except Exception as e:
logger.error("Failed to publish forecast generated event", error=str(e))
return False
async def publish_batch_forecast_completed(data: dict) -> bool:
"""Publish batch forecast completed event"""
try:
if rabbitmq_client:
await rabbitmq_client.publish_event(
exchange="forecasting_events",
routing_key="forecast.batch.completed",
message=data
)
return True
except Exception as e:
logger.error("Failed to publish batch forecast event", error=str(e))
return False
async def publish_forecast_alert(data: dict) -> bool:
"""Publish forecast alert event"""
try:
if rabbitmq_client:
await rabbitmq_client.publish_event(
exchange="forecasting_events",
routing_key="forecast.alert",
message=data
)
return True
except Exception as e:
logger.error("Failed to publish forecast alert event", error=str(e))
return False
# Publisher class for compatibility
class ForecastingStatusPublisher:
"""Publisher for forecasting status events"""
async def publish_status(self, status: str, data: dict) -> bool:
"""Publish forecasting status"""
try:
if rabbitmq_client:
await rabbitmq_client.publish_event(
exchange="forecasting_events",
routing_key=f"forecast.status.{status}",
message=data
)
return True
except Exception as e:
logger.error(f"Failed to publish {status} status", error=str(e))
return False

39
services/forecasting/app/services/model_client.py
View File

@@ -9,17 +9,22 @@ from typing import Dict, Any, List, Optional
# Import shared clients - no more code duplication!
from shared.clients import get_service_clients, get_training_client, get_data_client
from shared.database.base import create_database_manager
from app.core.config import settings
logger = structlog.get_logger()
class ModelClient:
"""
Client for managing models in forecasting service
Client for managing models in forecasting service with dependency injection
Shows how to call multiple services cleanly
"""
def __init__(self):
def __init__(self, database_manager=None):
self.database_manager = database_manager or create_database_manager(
settings.DATABASE_URL, "forecasting-service"
)
# Option 1: Get all clients at once
self.clients = get_service_clients(settings, "forecasting")
@@ -114,6 +119,36 @@ class ModelClient:
logger.error(f"Error selecting best model: {e}", tenant_id=tenant_id)
return None
async def get_any_model_for_tenant(
self,
tenant_id: str
) -> Optional[Dict[str, Any]]:
"""
Get any available model for a tenant, used as fallback when specific product models aren't found
"""
try:
# First try to get any active models for this tenant
models = await self.get_available_models(tenant_id)
if models:
# Return the most recently trained model
sorted_models = sorted(models, key=lambda x: x.get('created_at', ''), reverse=True)
best_model = sorted_models[0]
logger.info("Found fallback model for tenant",
tenant_id=tenant_id,
model_id=best_model.get('id', 'unknown'),
product=best_model.get('product_name', 'unknown'))
return best_model
logger.warning("No fallback models available for tenant", tenant_id=tenant_id)
return None
except Exception as e:
logger.error("Error getting fallback model for tenant",
tenant_id=tenant_id,
error=str(e))
return None
async def validate_model_data_compatibility(
self,
tenant_id: str,

341
services/forecasting/app/services/prediction_service.py
View File

@@ -19,20 +19,50 @@ import joblib
from app.core.config import settings
from shared.monitoring.metrics import MetricsCollector
from shared.database.base import create_database_manager
logger = structlog.get_logger()
metrics = MetricsCollector("forecasting-service")
class PredictionService:
"""
Service for loading ML models and generating predictions
Service for loading ML models and generating predictions with dependency injection
Interfaces with trained Prophet models from the training service
"""
def __init__(self):
def __init__(self, database_manager=None):
self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "forecasting-service")
self.model_cache = {}
self.cache_ttl = 3600 # 1 hour cache
async def validate_prediction_request(self, request: Dict[str, Any]) -> Dict[str, Any]:
"""Validate prediction request"""
try:
required_fields = ["product_name", "model_id", "features"]
missing_fields = [field for field in required_fields if field not in request]
if missing_fields:
return {
"is_valid": False,
"errors": [f"Missing required fields: {missing_fields}"],
"validation_passed": False
}
return {
"is_valid": True,
"errors": [],
"validation_passed": True,
"validated_fields": list(request.keys())
}
except Exception as e:
logger.error("Validation error", error=str(e))
return {
"is_valid": False,
"errors": [str(e)],
"validation_passed": False
}
async def predict(self, model_id: str, model_path: str, features: Dict[str, Any],
confidence_level: float = 0.8) -> Dict[str, float]:
"""Generate prediction using trained model"""
@@ -74,10 +104,37 @@ class PredictionService:
# Record metrics
processing_time = (datetime.now() - start_time).total_seconds()
# Record metrics with proper type conversion
# Record metrics with proper registration and error handling
try:
metrics.register_histogram("prediction_processing_time_seconds", float(processing_time))
metrics.increment_counter("predictions_served_total")
# Register metrics if not already registered
if "prediction_processing_time" not in metrics._histograms:
metrics.register_histogram(
"prediction_processing_time",
"Time taken to process predictions",
labels=['service', 'model_type']
)
if "predictions_served_total" not in metrics._counters:
try:
metrics.register_counter(
"predictions_served_total",
"Total number of predictions served",
labels=['service', 'status']
)
except Exception as reg_error:
# Metric might already exist in global registry
logger.debug("Counter already exists in registry", error=str(reg_error))
# Now record the metrics
metrics.observe_histogram(
"prediction_processing_time",
processing_time,
labels={'service': 'forecasting-service', 'model_type': 'prophet'}
)
metrics.increment_counter(
"predictions_served_total",
labels={'service': 'forecasting-service', 'status': 'success'}
)
except Exception as metrics_error:
# Log metrics error but don't fail the prediction
logger.warning("Failed to record metrics", error=str(metrics_error))
@@ -93,7 +150,19 @@ class PredictionService:
logger.error("Error generating prediction",
error=str(e),
model_id=model_id)
metrics.increment_counter("prediction_errors_total")
try:
if "prediction_errors_total" not in metrics._counters:
metrics.register_counter(
"prediction_errors_total",
"Total number of prediction errors",
labels=['service', 'error_type']
)
metrics.increment_counter(
"prediction_errors_total",
labels={'service': 'forecasting-service', 'error_type': 'prediction_failed'}
)
except Exception:
pass # Don't fail on metrics errors
raise
async def _load_model(self, model_id: str, model_path: str):
@@ -268,139 +337,149 @@ class PredictionService:
df['is_autumn'] = int(df['season'].iloc[0] == 4)
df['is_winter'] = int(df['season'].iloc[0] == 1)
# Holiday features
df['is_holiday'] = int(features.get('is_holiday', False))
df['is_school_holiday'] = int(features.get('is_school_holiday', False))
# ✅ PERFORMANCE FIX: Build all features at once to avoid DataFrame fragmentation
# Month-based features (match training)
df['is_january'] = int(forecast_date.month == 1)
df['is_february'] = int(forecast_date.month == 2)
df['is_march'] = int(forecast_date.month == 3)
df['is_april'] = int(forecast_date.month == 4)
df['is_may'] = int(forecast_date.month == 5)
df['is_june'] = int(forecast_date.month == 6)
df['is_july'] = int(forecast_date.month == 7)
df['is_august'] = int(forecast_date.month == 8)
df['is_september'] = int(forecast_date.month == 9)
df['is_october'] = int(forecast_date.month == 10)
df['is_november'] = int(forecast_date.month == 11)
df['is_december'] = int(forecast_date.month == 12)
# Special day features
df['is_month_start'] = int(forecast_date.day <= 3)
df['is_month_end'] = int(forecast_date.day >= 28)
df['is_payday_period'] = int((forecast_date.day <= 5) or (forecast_date.day >= 25))
# ✅ FIX: Add ALL derived features that training service creates
# Weather-based derived features
df['temp_squared'] = df['temperature'].iloc[0] ** 2
df['is_cold_day'] = int(df['temperature'].iloc[0] < 10)
df['is_hot_day'] = int(df['temperature'].iloc[0] > 25)
df['is_pleasant_day'] = int(10 <= df['temperature'].iloc[0] <= 25)
# Humidity features
df['humidity_squared'] = df['humidity'].iloc[0] ** 2
df['is_high_humidity'] = int(df['humidity'].iloc[0] > 70)
df['is_low_humidity'] = int(df['humidity'].iloc[0] < 40)
# Pressure features
df['pressure_squared'] = df['pressure'].iloc[0] ** 2
df['is_high_pressure'] = int(df['pressure'].iloc[0] > 1020)
df['is_low_pressure'] = int(df['pressure'].iloc[0] < 1000)
# Wind features
df['wind_squared'] = df['wind_speed'].iloc[0] ** 2
df['is_windy'] = int(df['wind_speed'].iloc[0] > 15)
df['is_calm'] = int(df['wind_speed'].iloc[0] < 5)
# Precipitation features
df['precip_squared'] = df['precipitation'].iloc[0] ** 2
df['precip_log'] = float(np.log1p(df['precipitation'].iloc[0]))
df['is_rainy_day'] = int(df['precipitation'].iloc[0] > 0.1)
df['is_very_rainy_day'] = int(df['precipitation'].iloc[0] > 5.0)
df['is_heavy_rain'] = int(df['precipitation'].iloc[0] > 10)
df['rain_intensity'] = self._get_rain_intensity(df['precipitation'].iloc[0])
# ✅ FIX: Add ALL traffic-based derived features
if df['traffic_volume'].iloc[0] > 0:
traffic = df['traffic_volume'].iloc[0]
df['high_traffic'] = int(traffic > 150)
df['low_traffic'] = int(traffic < 50)
df['traffic_normalized'] = float((traffic - 100) / 50)
df['traffic_squared'] = traffic ** 2
df['traffic_log'] = float(np.log1p(traffic))
else:
df['high_traffic'] = 0
df['low_traffic'] = 0
df['traffic_normalized'] = 0.0
df['traffic_squared'] = 0.0
df['traffic_log'] = 0.0
# ✅ FIX: Add pedestrian-based features
pedestrians = df['pedestrian_count'].iloc[0]
df['high_pedestrian_count'] = int(pedestrians > 100)
df['low_pedestrian_count'] = int(pedestrians < 25)
df['pedestrian_normalized'] = float((pedestrians - 50) / 25)
df['pedestrian_squared'] = pedestrians ** 2
df['pedestrian_log'] = float(np.log1p(pedestrians))
# ✅ FIX: Add average_speed-based features
avg_speed = df['average_speed'].iloc[0]
df['high_speed'] = int(avg_speed > 40)
df['low_speed'] = int(avg_speed < 20)
df['speed_normalized'] = float((avg_speed - 30) / 10)
df['speed_squared'] = avg_speed ** 2
df['speed_log'] = float(np.log1p(avg_speed))
# ✅ FIX: Add congestion-based features
congestion = df['congestion_level'].iloc[0]
df['high_congestion'] = int(congestion > 3)
df['low_congestion'] = int(congestion < 2)
df['congestion_squared'] = congestion ** 2
# ✅ FIX: Add ALL interaction features that training creates
# Weekend interactions
is_weekend = df['is_weekend'].iloc[0]
# Extract values once to avoid repeated iloc calls
temperature = df['temperature'].iloc[0]
df['weekend_temp_interaction'] = is_weekend * temperature
df['weekend_pleasant_weather'] = is_weekend * df['is_pleasant_day'].iloc[0]
df['weekend_traffic_interaction'] = is_weekend * df['traffic_volume'].iloc[0]
# Holiday interactions
is_holiday = df['is_holiday'].iloc[0]
df['holiday_temp_interaction'] = is_holiday * temperature
df['holiday_traffic_interaction'] = is_holiday * df['traffic_volume'].iloc[0]
# Season interactions
humidity = df['humidity'].iloc[0]
pressure = df['pressure'].iloc[0]
wind_speed = df['wind_speed'].iloc[0]
precipitation = df['precipitation'].iloc[0]
traffic = df['traffic_volume'].iloc[0]
pedestrians = df['pedestrian_count'].iloc[0]
avg_speed = df['average_speed'].iloc[0]
congestion = df['congestion_level'].iloc[0]
season = df['season'].iloc[0]
df['season_temp_interaction'] = season * temperature
df['season_traffic_interaction'] = season * df['traffic_volume'].iloc[0]
is_weekend = df['is_weekend'].iloc[0]
# Rain-traffic interactions
is_rainy = df['is_rainy_day'].iloc[0]
df['rain_traffic_interaction'] = is_rainy * df['traffic_volume'].iloc[0]
df['rain_speed_interaction'] = is_rainy * df['average_speed'].iloc[0]
# Build all new features as a dictionary
new_features = {
# Holiday features
'is_holiday': int(features.get('is_holiday', False)),
'is_school_holiday': int(features.get('is_school_holiday', False)),
# Month-based features
'is_january': int(forecast_date.month == 1),
'is_february': int(forecast_date.month == 2),
'is_march': int(forecast_date.month == 3),
'is_april': int(forecast_date.month == 4),
'is_may': int(forecast_date.month == 5),
'is_june': int(forecast_date.month == 6),
'is_july': int(forecast_date.month == 7),
'is_august': int(forecast_date.month == 8),
'is_september': int(forecast_date.month == 9),
'is_october': int(forecast_date.month == 10),
'is_november': int(forecast_date.month == 11),
'is_december': int(forecast_date.month == 12),
# Special day features
'is_month_start': int(forecast_date.day <= 3),
'is_month_end': int(forecast_date.day >= 28),
'is_payday_period': int((forecast_date.day <= 5) or (forecast_date.day >= 25)),
# Weather-based derived features
'temp_squared': temperature ** 2,
'is_cold_day': int(temperature < 10),
'is_hot_day': int(temperature > 25),
'is_pleasant_day': int(10 <= temperature <= 25),
# Humidity features
'humidity_squared': humidity ** 2,
'is_high_humidity': int(humidity > 70),
'is_low_humidity': int(humidity < 40),
# Pressure features
'pressure_squared': pressure ** 2,
'is_high_pressure': int(pressure > 1020),
'is_low_pressure': int(pressure < 1000),
# Wind features
'wind_squared': wind_speed ** 2,
'is_windy': int(wind_speed > 15),
'is_calm': int(wind_speed < 5),
# Precipitation features
'precip_squared': precipitation ** 2,
'precip_log': float(np.log1p(precipitation)),
'is_rainy_day': int(precipitation > 0.1),
'is_very_rainy_day': int(precipitation > 5.0),
'is_heavy_rain': int(precipitation > 10),
'rain_intensity': self._get_rain_intensity(precipitation),
# Traffic-based features
'high_traffic': int(traffic > 150) if traffic > 0 else 0,
'low_traffic': int(traffic < 50) if traffic > 0 else 0,
'traffic_normalized': float((traffic - 100) / 50) if traffic > 0 else 0.0,
'traffic_squared': traffic ** 2,
'traffic_log': float(np.log1p(traffic)),
# Pedestrian features
'high_pedestrian_count': int(pedestrians > 100),
'low_pedestrian_count': int(pedestrians < 25),
'pedestrian_normalized': float((pedestrians - 50) / 25),
'pedestrian_squared': pedestrians ** 2,
'pedestrian_log': float(np.log1p(pedestrians)),
# Speed features
'high_speed': int(avg_speed > 40),
'low_speed': int(avg_speed < 20),
'speed_normalized': float((avg_speed - 30) / 10),
'speed_squared': avg_speed ** 2,
'speed_log': float(np.log1p(avg_speed)),
# Congestion features
'high_congestion': int(congestion > 3),
'low_congestion': int(congestion < 2),
'congestion_squared': congestion ** 2,
# Day features
'is_peak_bakery_day': int(day_of_week in [4, 5, 6]),
'is_high_demand_month': int(forecast_date.month in [6, 7, 8, 12]),
'is_warm_season': int(forecast_date.month in [4, 5, 6, 7, 8, 9])
}
# Day-weather interactions
df['day_temp_interaction'] = day_of_week * temperature
df['month_temp_interaction'] = forecast_date.month * temperature
# Calculate interaction features
is_holiday = new_features['is_holiday']
is_pleasant = new_features['is_pleasant_day']
is_rainy = new_features['is_rainy_day']
# Traffic-speed interactions
df['traffic_speed_interaction'] = df['traffic_volume'].iloc[0] * df['average_speed'].iloc[0]
df['pedestrian_speed_interaction'] = df['pedestrian_count'].iloc[0] * df['average_speed'].iloc[0]
interaction_features = {
# Weekend interactions
'weekend_temp_interaction': is_weekend * temperature,
'weekend_pleasant_weather': is_weekend * is_pleasant,
'weekend_traffic_interaction': is_weekend * traffic,
# Holiday interactions
'holiday_temp_interaction': is_holiday * temperature,
'holiday_traffic_interaction': is_holiday * traffic,
# Season interactions
'season_temp_interaction': season * temperature,
'season_traffic_interaction': season * traffic,
# Rain-traffic interactions
'rain_traffic_interaction': is_rainy * traffic,
'rain_speed_interaction': is_rainy * avg_speed,
# Day-weather interactions
'day_temp_interaction': day_of_week * temperature,
'month_temp_interaction': forecast_date.month * temperature,
# Traffic-speed interactions
'traffic_speed_interaction': traffic * avg_speed,
'pedestrian_speed_interaction': pedestrians * avg_speed,
# Congestion interactions
'congestion_temp_interaction': congestion * temperature,
'congestion_weekend_interaction': congestion * is_weekend
}
# Congestion-related interactions
df['congestion_temp_interaction'] = congestion * temperature
df['congestion_weekend_interaction'] = congestion * is_weekend
# Combine all features
all_new_features = {**new_features, **interaction_features}
# Add after the existing day-of-week features:
df['is_peak_bakery_day'] = int(day_of_week in [4, 5, 6]) # Friday, Saturday, Sunday
# Add after the month features:
df['is_high_demand_month'] = int(forecast_date.month in [6, 7, 8, 12]) # Summer and December
df['is_warm_season'] = int(forecast_date.month in [4, 5, 6, 7, 8, 9]) # Spring/summer months
# Add all features at once using pd.concat to avoid fragmentation
new_feature_df = pd.DataFrame([all_new_features])
df = pd.concat([df, new_feature_df], axis=1)
logger.debug("Complete Prophet features prepared",
feature_count=len(df.columns),

3
services/forecasting/requirements.txt
View File

@@ -17,6 +17,9 @@ python-multipart==0.0.6
# HTTP Client
httpx==0.25.2
# Date parsing
python-dateutil==2.8.2
# Machine Learning
prophet==1.1.4
scikit-learn==1.3.2