254 lines
8.3 KiB
Python
254 lines
8.3 KiB
Python
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"""
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Event Feature Generator
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Converts calendar events into features for demand forecasting
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"""
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import pandas as pd
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import numpy as np
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from typing import List, Dict, Any, Optional
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from datetime import date, timedelta
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import structlog
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logger = structlog.get_logger()
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class EventFeatureGenerator:
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"""
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Generate event-related features for demand forecasting.
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Features include:
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- Binary flags for event presence
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- Event impact multipliers
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- Event type indicators
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- Days until/since major events
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"""
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# Event type impact weights (default multipliers)
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EVENT_IMPACT_WEIGHTS = {
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'promotion': 1.3,
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'festival': 1.8,
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'holiday': 0.7, # Bakeries often close or have reduced demand
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'weather_event': 0.8, # Bad weather reduces foot traffic
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'school_break': 1.2,
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'sport_event': 1.4,
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'market': 1.5,
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'concert': 1.3,
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'local_event': 1.2
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}
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def __init__(self):
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pass
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def generate_event_features(
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self,
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dates: pd.DatetimeIndex,
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events: List[Dict[str, Any]]
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) -> pd.DataFrame:
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"""
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Generate event features for given dates.
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Args:
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dates: Dates to generate features for
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events: List of event dictionaries with keys:
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- event_date: date
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- event_type: str
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- impact_multiplier: float (optional)
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- event_name: str
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Returns:
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DataFrame with event features
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"""
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df = pd.DataFrame({'date': dates})
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# Initialize feature columns
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df['has_event'] = 0
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df['event_impact'] = 1.0 # Neutral impact
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df['is_promotion'] = 0
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df['is_festival'] = 0
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df['is_local_event'] = 0
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df['days_to_next_event'] = 365
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df['days_since_last_event'] = 365
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if not events:
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logger.debug("No events provided, returning default features")
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return df
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# Convert events to DataFrame for easier processing
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events_df = pd.DataFrame(events)
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events_df['event_date'] = pd.to_datetime(events_df['event_date'])
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for idx, row in df.iterrows():
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current_date = pd.to_datetime(row['date'])
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# Check if there's an event on this date
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day_events = events_df[events_df['event_date'] == current_date]
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if not day_events.empty:
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df.at[idx, 'has_event'] = 1
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# Use custom impact multiplier if provided, else use default
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if 'impact_multiplier' in day_events.columns and not day_events['impact_multiplier'].isna().all():
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impact = day_events['impact_multiplier'].max()
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else:
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# Use default impact based on event type
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event_types = day_events['event_type'].tolist()
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impacts = [self.EVENT_IMPACT_WEIGHTS.get(et, 1.0) for et in event_types]
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impact = max(impacts)
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df.at[idx, 'event_impact'] = impact
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# Set event type flags
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event_types = day_events['event_type'].tolist()
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if 'promotion' in event_types:
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df.at[idx, 'is_promotion'] = 1
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if 'festival' in event_types:
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df.at[idx, 'is_festival'] = 1
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if 'local_event' in event_types or 'market' in event_types:
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df.at[idx, 'is_local_event'] = 1
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# Calculate days to/from nearest event
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future_events = events_df[events_df['event_date'] > current_date]
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if not future_events.empty:
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next_event_date = future_events['event_date'].min()
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df.at[idx, 'days_to_next_event'] = (next_event_date - current_date).days
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past_events = events_df[events_df['event_date'] < current_date]
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if not past_events.empty:
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last_event_date = past_events['event_date'].max()
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df.at[idx, 'days_since_last_event'] = (current_date - last_event_date).days
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# Cap days values at 365
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df['days_to_next_event'] = df['days_to_next_event'].clip(upper=365)
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df['days_since_last_event'] = df['days_since_last_event'].clip(upper=365)
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logger.debug("Generated event features",
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total_days=len(df),
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days_with_events=df['has_event'].sum())
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return df
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def add_event_features_to_forecast_data(
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self,
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forecast_data: pd.DataFrame,
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event_features: pd.DataFrame
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) -> pd.DataFrame:
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"""
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Add event features to forecast input data.
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Args:
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forecast_data: Existing forecast data with 'date' column
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event_features: Event features from generate_event_features()
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Returns:
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Enhanced forecast data with event features
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"""
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forecast_data = forecast_data.copy()
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forecast_data['date'] = pd.to_datetime(forecast_data['date'])
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event_features['date'] = pd.to_datetime(event_features['date'])
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# Merge event features
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enhanced_data = forecast_data.merge(
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event_features[[
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'date', 'has_event', 'event_impact', 'is_promotion',
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'is_festival', 'is_local_event', 'days_to_next_event',
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'days_since_last_event'
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]],
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on='date',
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how='left'
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)
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# Fill missing with defaults
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enhanced_data['has_event'].fillna(0, inplace=True)
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enhanced_data['event_impact'].fillna(1.0, inplace=True)
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enhanced_data['is_promotion'].fillna(0, inplace=True)
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enhanced_data['is_festival'].fillna(0, inplace=True)
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enhanced_data['is_local_event'].fillna(0, inplace=True)
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enhanced_data['days_to_next_event'].fillna(365, inplace=True)
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enhanced_data['days_since_last_event'].fillna(365, inplace=True)
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return enhanced_data
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def get_event_summary(self, events: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Get summary statistics about events.
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Args:
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events: List of event dictionaries
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Returns:
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Summary dict with counts by type, avg impact, etc.
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"""
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if not events:
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return {
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'total_events': 0,
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'events_by_type': {},
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'avg_impact': 1.0
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}
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events_df = pd.DataFrame(events)
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summary = {
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'total_events': len(events),
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'events_by_type': events_df['event_type'].value_counts().to_dict(),
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'date_range': {
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'start': events_df['event_date'].min().isoformat() if not events_df.empty else None,
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'end': events_df['event_date'].max().isoformat() if not events_df.empty else None
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}
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}
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if 'impact_multiplier' in events_df.columns:
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summary['avg_impact'] = float(events_df['impact_multiplier'].mean())
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return summary
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def create_event_calendar_features(
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dates: pd.DatetimeIndex,
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tenant_id: str,
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event_repository = None
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) -> pd.DataFrame:
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"""
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Convenience function to fetch events from database and generate features.
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Args:
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dates: Dates to generate features for
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tenant_id: Tenant UUID
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event_repository: EventRepository instance (optional)
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Returns:
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DataFrame with event features
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"""
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if event_repository is None:
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logger.warning("No event repository provided, using empty events")
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events = []
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else:
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# Fetch events from database
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from datetime import date
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start_date = dates.min().date()
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end_date = dates.max().date()
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try:
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import asyncio
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from uuid import UUID
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loop = asyncio.get_event_loop()
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events_objects = loop.run_until_complete(
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event_repository.get_events_by_date_range(
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tenant_id=UUID(tenant_id),
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start_date=start_date,
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end_date=end_date,
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confirmed_only=False
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)
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)
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# Convert to dict format
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events = [event.to_dict() for event in events_objects]
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except Exception as e:
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logger.error(f"Failed to fetch events from database: {e}")
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events = []
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# Generate features
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generator = EventFeatureGenerator()
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return generator.generate_event_features(dates, events)
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