Improve AI logic
This commit is contained in:
Urtzi Alfaro
@@ -25,20 +25,52 @@ class BakeryPredictor:
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Advanced predictor for bakery demand forecasting with dependency injection
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Handles Prophet models and business-specific logic
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"""
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def __init__(self, database_manager=None):
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def __init__(self, database_manager=None, use_dynamic_rules=True):
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self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "forecasting-service")
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self.model_cache = {}
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self.business_rules = BakeryBusinessRules()
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self.use_dynamic_rules = use_dynamic_rules
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if use_dynamic_rules:
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from app.ml.dynamic_rules_engine import DynamicRulesEngine
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from shared.clients.ai_insights_client import AIInsightsClient
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self.rules_engine = DynamicRulesEngine()
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self.ai_insights_client = AIInsightsClient(
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base_url=settings.AI_INSIGHTS_SERVICE_URL or "http://ai-insights-service:8000"
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)
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else:
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self.business_rules = BakeryBusinessRules()
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class BakeryForecaster:
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"""
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Enhanced forecaster that integrates with repository pattern
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Uses enhanced features from training service for predictions
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"""
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def __init__(self, database_manager=None):
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def __init__(self, database_manager=None, use_enhanced_features=True):
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self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "forecasting-service")
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self.predictor = BakeryPredictor(database_manager)
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self.use_enhanced_features = use_enhanced_features
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if use_enhanced_features:
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# Import enhanced data processor from training service
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import sys
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import os
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# Add training service to path
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training_path = os.path.join(os.path.dirname(__file__), '../../../training')
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if training_path not in sys.path:
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sys.path.insert(0, training_path)
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try:
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from app.ml.data_processor import EnhancedBakeryDataProcessor
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self.data_processor = EnhancedBakeryDataProcessor(database_manager)
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logger.info("Enhanced features enabled for forecasting")
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except ImportError as e:
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logger.warning(f"Could not import EnhancedBakeryDataProcessor: {e}, falling back to basic features")
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self.use_enhanced_features = False
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self.data_processor = None
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else:
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self.data_processor = None
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async def generate_forecast_with_repository(self, tenant_id: str, inventory_product_id: str,
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forecast_date: date, model_id: str = None) -> Dict[str, Any]:
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@@ -110,45 +142,87 @@ class BakeryForecaster:
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logger.error("Error generating base prediction", error=str(e))
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raise
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def _prepare_prophet_dataframe(self, features: Dict[str, Any]) -> pd.DataFrame:
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"""Convert features to Prophet-compatible DataFrame"""
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async def _prepare_prophet_dataframe(self, features: Dict[str, Any],
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historical_data: pd.DataFrame = None) -> pd.DataFrame:
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"""
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Convert features to Prophet-compatible DataFrame.
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Uses enhanced features when available (60+ features vs basic 10).
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"""
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try:
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# Create base DataFrame
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df = pd.DataFrame({
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'ds': [pd.to_datetime(features['date'])]
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})
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# Add regressor features
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feature_mapping = {
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'temperature': 'temperature',
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'precipitation': 'precipitation',
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'humidity': 'humidity',
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'wind_speed': 'wind_speed',
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'traffic_volume': 'traffic_volume',
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'pedestrian_count': 'pedestrian_count'
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}
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for feature_key, df_column in feature_mapping.items():
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if feature_key in features and features[feature_key] is not None:
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df[df_column] = float(features[feature_key])
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else:
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df[df_column] = 0.0
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# Add categorical features
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df['day_of_week'] = int(features.get('day_of_week', 0))
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if self.use_enhanced_features and self.data_processor:
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# Use enhanced data processor from training service
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logger.info("Generating enhanced features for prediction")
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# Create future date range
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future_dates = pd.DatetimeIndex([pd.to_datetime(features['date'])])
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# Prepare weather forecast DataFrame
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weather_df = pd.DataFrame({
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'date': [pd.to_datetime(features['date'])],
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'temperature': [features.get('temperature', 15.0)],
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'precipitation': [features.get('precipitation', 0.0)],
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'humidity': [features.get('humidity', 60.0)],
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'wind_speed': [features.get('wind_speed', 5.0)],
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'pressure': [features.get('pressure', 1013.0)]
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})
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# Use data processor to create ALL enhanced features
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df = await self.data_processor.prepare_prediction_features(
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future_dates=future_dates,
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weather_forecast=weather_df,
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traffic_forecast=None, # Will add when traffic forecasting is implemented
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historical_data=historical_data # For lagged features
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)
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logger.info(f"Generated {len(df.columns)} enhanced features for prediction")
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return df
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else:
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# Fallback to basic features
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logger.info("Using basic features for prediction")
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# Create base DataFrame
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df = pd.DataFrame({
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'ds': [pd.to_datetime(features['date'])]
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})
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# Add regressor features
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feature_mapping = {
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'temperature': 'temperature',
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'precipitation': 'precipitation',
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'humidity': 'humidity',
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'wind_speed': 'wind_speed',
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'traffic_volume': 'traffic_volume',
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'pedestrian_count': 'pedestrian_count'
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}
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for feature_key, df_column in feature_mapping.items():
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if feature_key in features and features[feature_key] is not None:
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df[df_column] = float(features[feature_key])
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else:
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df[df_column] = 0.0
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# Add categorical features
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df['day_of_week'] = int(features.get('day_of_week', 0))
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df['is_weekend'] = int(features.get('is_weekend', False))
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df['is_holiday'] = int(features.get('is_holiday', False))
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# Business type
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business_type = features.get('business_type', 'individual')
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df['is_central_workshop'] = int(business_type == 'central_workshop')
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return df
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except Exception as e:
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logger.error(f"Error preparing Prophet dataframe: {e}, falling back to basic features")
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# Fallback to basic implementation on error
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df = pd.DataFrame({'ds': [pd.to_datetime(features['date'])]})
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df['temperature'] = features.get('temperature', 15.0)
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df['precipitation'] = features.get('precipitation', 0.0)
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df['is_weekend'] = int(features.get('is_weekend', False))
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df['is_holiday'] = int(features.get('is_holiday', False))
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# Business type
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business_type = features.get('business_type', 'individual')
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df['is_central_workshop'] = int(business_type == 'central_workshop')
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return df
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except Exception as e:
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logger.error("Error preparing Prophet dataframe", error=str(e))
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raise
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def _add_uncertainty_bands(self, prediction: Dict[str, float],
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features: Dict[str, Any]) -> Dict[str, float]:
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@@ -225,80 +299,256 @@ class BakeryForecaster:
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def _calculate_weekend_uncertainty(self, features: Dict[str, Any]) -> float:
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"""Calculate weekend-based uncertainty"""
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if features.get('is_weekend', False):
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return 0.1 # 10% additional uncertainty on weekends
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return 0.0
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async def _get_dynamic_rules(self, tenant_id: str, inventory_product_id: str, rule_type: str) -> Dict[str, float]:
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"""
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Fetch learned dynamic rules from AI Insights Service.
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Args:
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tenant_id: Tenant UUID
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inventory_product_id: Product UUID
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rule_type: Type of rules (weather, temporal, holiday, etc.)
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Returns:
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Dictionary of learned rules with factors
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"""
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try:
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from uuid import UUID
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# Fetch latest rules insight for this product
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insights = await self.ai_insights_client.get_insights(
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tenant_id=UUID(tenant_id),
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filters={
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'category': 'forecasting',
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'actionable_only': False,
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'page_size': 100
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}
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)
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if not insights or 'items' not in insights:
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return {}
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# Find the most recent rules insight for this product
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for insight in insights['items']:
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if insight.get('source_model') == 'dynamic_rules_engine':
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metrics = insight.get('metrics_json', {})
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if metrics.get('inventory_product_id') == inventory_product_id:
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rules_data = metrics.get('rules', {})
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return rules_data.get(rule_type, {})
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return {}
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except Exception as e:
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logger.warning(f"Failed to fetch dynamic rules: {e}")
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return {}
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class BakeryBusinessRules:
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"""
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Business rules for Spanish bakeries
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Applies domain-specific adjustments to predictions
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Supports both dynamic learned rules and hardcoded fallbacks
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"""
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def apply_rules(self, prediction: Dict[str, float], features: Dict[str, Any],
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business_type: str) -> Dict[str, float]:
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"""Apply all business rules to prediction"""
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def __init__(self, use_dynamic_rules=False, ai_insights_client=None):
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self.use_dynamic_rules = use_dynamic_rules
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self.ai_insights_client = ai_insights_client
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self.rules_cache = {}
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async def apply_rules(self, prediction: Dict[str, float], features: Dict[str, Any],
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business_type: str, tenant_id: str = None, inventory_product_id: str = None) -> Dict[str, float]:
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"""Apply all business rules to prediction (dynamic or hardcoded)"""
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adjusted_prediction = prediction.copy()
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# Apply weather rules
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adjusted_prediction = self._apply_weather_rules(adjusted_prediction, features)
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adjusted_prediction = await self._apply_weather_rules(
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adjusted_prediction, features, tenant_id, inventory_product_id
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)
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# Apply time-based rules
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adjusted_prediction = self._apply_time_rules(adjusted_prediction, features)
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adjusted_prediction = await self._apply_time_rules(
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adjusted_prediction, features, tenant_id, inventory_product_id
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)
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# Apply business type rules
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adjusted_prediction = self._apply_business_type_rules(adjusted_prediction, business_type)
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# Apply Spanish-specific rules
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adjusted_prediction = self._apply_spanish_rules(adjusted_prediction, features)
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return adjusted_prediction
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def _apply_weather_rules(self, prediction: Dict[str, float],
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features: Dict[str, Any]) -> Dict[str, float]:
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"""Apply weather-based business rules"""
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# Rain reduces foot traffic
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precipitation = features.get('precipitation', 0)
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if precipitation > 0:
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rain_factor = settings.RAIN_IMPACT_FACTOR
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prediction["yhat"] *= rain_factor
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prediction["yhat_lower"] *= rain_factor
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prediction["yhat_upper"] *= rain_factor
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# Extreme temperatures affect different products differently
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temperature = features.get('temperature')
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if temperature is not None:
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if temperature > settings.TEMPERATURE_THRESHOLD_HOT:
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# Hot weather reduces bread sales, increases cold drinks
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prediction["yhat"] *= 0.9
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elif temperature < settings.TEMPERATURE_THRESHOLD_COLD:
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# Cold weather increases hot beverage sales
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prediction["yhat"] *= 1.1
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async def _get_dynamic_rules(self, tenant_id: str, inventory_product_id: str, rule_type: str) -> Dict[str, float]:
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"""
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Fetch learned dynamic rules from AI Insights Service.
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Args:
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tenant_id: Tenant UUID
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inventory_product_id: Product UUID
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rule_type: Type of rules (weather, temporal, holiday, etc.)
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Returns:
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Dictionary of learned rules with factors
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"""
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# Check cache first
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cache_key = f"{tenant_id}:{inventory_product_id}:{rule_type}"
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if cache_key in self.rules_cache:
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return self.rules_cache[cache_key]
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try:
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from uuid import UUID
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if not self.ai_insights_client:
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return {}
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# Fetch latest rules insight for this product
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insights = await self.ai_insights_client.get_insights(
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tenant_id=UUID(tenant_id),
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filters={
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'category': 'forecasting',
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'actionable_only': False,
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'page_size': 100
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}
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)
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if not insights or 'items' not in insights:
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return {}
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# Find the most recent rules insight for this product
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for insight in insights['items']:
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if insight.get('source_model') == 'dynamic_rules_engine':
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metrics = insight.get('metrics_json', {})
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if metrics.get('inventory_product_id') == inventory_product_id:
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rules_data = metrics.get('rules', {})
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result = rules_data.get(rule_type, {})
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# Cache the result
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self.rules_cache[cache_key] = result
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return result
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return {}
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except Exception as e:
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logger.warning(f"Failed to fetch dynamic rules: {e}")
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return {}
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async def _apply_weather_rules(self, prediction: Dict[str, float],
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features: Dict[str, Any],
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tenant_id: str = None,
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inventory_product_id: str = None) -> Dict[str, float]:
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"""Apply weather-based business rules (dynamic or hardcoded fallback)"""
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if self.use_dynamic_rules and tenant_id and inventory_product_id:
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try:
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# Fetch dynamic weather rules
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rules = await self._get_dynamic_rules(tenant_id, inventory_product_id, 'weather')
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# Apply learned rain impact
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precipitation = features.get('precipitation', 0)
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if precipitation > 0:
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rain_factor = rules.get('rain_factor', settings.RAIN_IMPACT_FACTOR)
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prediction["yhat"] *= rain_factor
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prediction["yhat_lower"] *= rain_factor
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prediction["yhat_upper"] *= rain_factor
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# Apply learned temperature impact
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temperature = features.get('temperature')
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if temperature is not None:
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if temperature > settings.TEMPERATURE_THRESHOLD_HOT:
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hot_factor = rules.get('temperature_hot_factor', 0.9)
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prediction["yhat"] *= hot_factor
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elif temperature < settings.TEMPERATURE_THRESHOLD_COLD:
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cold_factor = rules.get('temperature_cold_factor', 1.1)
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prediction["yhat"] *= cold_factor
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except Exception as e:
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logger.warning(f"Failed to apply dynamic weather rules, using fallback: {e}")
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# Fallback to hardcoded
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precipitation = features.get('precipitation', 0)
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if precipitation > 0:
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prediction["yhat"] *= settings.RAIN_IMPACT_FACTOR
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prediction["yhat_lower"] *= settings.RAIN_IMPACT_FACTOR
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prediction["yhat_upper"] *= settings.RAIN_IMPACT_FACTOR
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temperature = features.get('temperature')
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if temperature is not None:
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if temperature > settings.TEMPERATURE_THRESHOLD_HOT:
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prediction["yhat"] *= 0.9
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elif temperature < settings.TEMPERATURE_THRESHOLD_COLD:
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prediction["yhat"] *= 1.1
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else:
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# Use hardcoded rules
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precipitation = features.get('precipitation', 0)
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if precipitation > 0:
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rain_factor = settings.RAIN_IMPACT_FACTOR
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prediction["yhat"] *= rain_factor
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prediction["yhat_lower"] *= rain_factor
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prediction["yhat_upper"] *= rain_factor
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temperature = features.get('temperature')
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if temperature is not None:
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if temperature > settings.TEMPERATURE_THRESHOLD_HOT:
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prediction["yhat"] *= 0.9
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elif temperature < settings.TEMPERATURE_THRESHOLD_COLD:
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prediction["yhat"] *= 1.1
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return prediction
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def _apply_time_rules(self, prediction: Dict[str, float],
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features: Dict[str, Any]) -> Dict[str, float]:
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"""Apply time-based business rules"""
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# Weekend adjustment
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if features.get('is_weekend', False):
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weekend_factor = settings.WEEKEND_ADJUSTMENT_FACTOR
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prediction["yhat"] *= weekend_factor
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prediction["yhat_lower"] *= weekend_factor
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prediction["yhat_upper"] *= weekend_factor
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# Holiday adjustment
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if features.get('is_holiday', False):
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holiday_factor = settings.HOLIDAY_ADJUSTMENT_FACTOR
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prediction["yhat"] *= holiday_factor
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prediction["yhat_lower"] *= holiday_factor
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prediction["yhat_upper"] *= holiday_factor
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async def _apply_time_rules(self, prediction: Dict[str, float],
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features: Dict[str, Any],
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tenant_id: str = None,
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inventory_product_id: str = None) -> Dict[str, float]:
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"""Apply time-based business rules (dynamic or hardcoded fallback)"""
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if self.use_dynamic_rules and tenant_id and inventory_product_id:
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try:
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# Fetch dynamic temporal rules
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rules = await self._get_dynamic_rules(tenant_id, inventory_product_id, 'temporal')
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# Apply learned weekend adjustment
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if features.get('is_weekend', False):
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weekend_factor = rules.get('weekend_factor', settings.WEEKEND_ADJUSTMENT_FACTOR)
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prediction["yhat"] *= weekend_factor
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prediction["yhat_lower"] *= weekend_factor
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prediction["yhat_upper"] *= weekend_factor
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# Apply learned holiday adjustment
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if features.get('is_holiday', False):
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holiday_factor = rules.get('holiday_factor', settings.HOLIDAY_ADJUSTMENT_FACTOR)
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prediction["yhat"] *= holiday_factor
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prediction["yhat_lower"] *= holiday_factor
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prediction["yhat_upper"] *= holiday_factor
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except Exception as e:
|
||||
logger.warning(f"Failed to apply dynamic time rules, using fallback: {e}")
|
||||
# Fallback to hardcoded
|
||||
if features.get('is_weekend', False):
|
||||
prediction["yhat"] *= settings.WEEKEND_ADJUSTMENT_FACTOR
|
||||
prediction["yhat_lower"] *= settings.WEEKEND_ADJUSTMENT_FACTOR
|
||||
prediction["yhat_upper"] *= settings.WEEKEND_ADJUSTMENT_FACTOR
|
||||
|
||||
if features.get('is_holiday', False):
|
||||
prediction["yhat"] *= settings.HOLIDAY_ADJUSTMENT_FACTOR
|
||||
prediction["yhat_lower"] *= settings.HOLIDAY_ADJUSTMENT_FACTOR
|
||||
prediction["yhat_upper"] *= settings.HOLIDAY_ADJUSTMENT_FACTOR
|
||||
else:
|
||||
# Use hardcoded rules
|
||||
if features.get('is_weekend', False):
|
||||
weekend_factor = settings.WEEKEND_ADJUSTMENT_FACTOR
|
||||
prediction["yhat"] *= weekend_factor
|
||||
prediction["yhat_lower"] *= weekend_factor
|
||||
prediction["yhat_upper"] *= weekend_factor
|
||||
|
||||
if features.get('is_holiday', False):
|
||||
holiday_factor = settings.HOLIDAY_ADJUSTMENT_FACTOR
|
||||
prediction["yhat"] *= holiday_factor
|
||||
prediction["yhat_lower"] *= holiday_factor
|
||||
prediction["yhat_upper"] *= holiday_factor
|
||||
|
||||
return prediction
|
||||
|
||||
def _apply_business_type_rules(self, prediction: Dict[str, float],
|
||||
|
||||
Reference in New Issue
Block a user