589 lines
23 KiB
Python
589 lines
23 KiB
Python
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"""
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Shared Feature Calculator for Training and Prediction Services
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This module provides unified feature calculation logic to ensure consistency
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between model training and inference (prediction), preventing train/serve skew.
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Key principles:
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- Same lag calculation logic in training and prediction
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- Same rolling window statistics in training and prediction
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- Same trend feature calculations in training and prediction
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- Graceful handling of sparse/missing data with consistent fallbacks
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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 Dict, List, Optional, Union, Tuple
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from datetime import datetime
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import structlog
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logger = structlog.get_logger()
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class HistoricalFeatureCalculator:
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"""
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Unified historical feature calculator for both training and prediction.
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This class ensures that features are calculated identically whether
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during model training or during inference, preventing train/serve skew.
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"""
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def __init__(self):
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"""Initialize the feature calculator."""
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self.feature_columns = []
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def calculate_lag_features(
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self,
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sales_data: Union[pd.Series, pd.DataFrame],
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lag_days: List[int] = None,
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mode: str = 'training'
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) -> Union[pd.DataFrame, Dict[str, float]]:
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"""
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Calculate lagged sales features consistently for training and prediction.
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Args:
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sales_data: Sales data as Series (prediction) or DataFrame (training) with 'quantity' column
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lag_days: List of lag periods (default: [1, 7, 14])
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mode: 'training' returns DataFrame with lag columns, 'prediction' returns dict of features
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Returns:
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DataFrame with lag columns (training mode) or dict of lag features (prediction mode)
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"""
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if lag_days is None:
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lag_days = [1, 7, 14]
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if mode == 'training':
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return self._calculate_lag_features_training(sales_data, lag_days)
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else:
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return self._calculate_lag_features_prediction(sales_data, lag_days)
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def _calculate_lag_features_training(
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self,
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df: pd.DataFrame,
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lag_days: List[int]
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) -> pd.DataFrame:
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"""
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Calculate lag features for training (operates on DataFrame).
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Args:
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df: DataFrame with 'quantity' column
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lag_days: List of lag periods
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Returns:
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DataFrame with added lag columns
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"""
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df = df.copy()
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# Calculate overall statistics for fallback (consistent with prediction)
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overall_mean = float(df['quantity'].mean()) if len(df) > 0 else 0.0
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overall_std = float(df['quantity'].std()) if len(df) > 1 else 0.0
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for lag in lag_days:
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col_name = f'lag_{lag}_day'
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# Use pandas shift
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df[col_name] = df['quantity'].shift(lag)
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# Fill NaN values using same logic as prediction mode
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# For missing lags, use cascading fallback: previous lag -> last value -> mean
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if lag == 1:
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# For lag_1, fill with last available or mean
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df[col_name] = df[col_name].fillna(df['quantity'].iloc[0] if len(df) > 0 else overall_mean)
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elif lag == 7:
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# For lag_7, fill with lag_1 if available, else last value, else mean
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mask = df[col_name].isna()
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if 'lag_1_day' in df.columns:
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df.loc[mask, col_name] = df.loc[mask, 'lag_1_day']
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else:
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df.loc[mask, col_name] = df['quantity'].iloc[0] if len(df) > 0 else overall_mean
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elif lag == 14:
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# For lag_14, fill with lag_7 if available, else lag_1, else last value, else mean
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mask = df[col_name].isna()
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if 'lag_7_day' in df.columns:
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df.loc[mask, col_name] = df.loc[mask, 'lag_7_day']
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elif 'lag_1_day' in df.columns:
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df.loc[mask, col_name] = df.loc[mask, 'lag_1_day']
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else:
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df.loc[mask, col_name] = df['quantity'].iloc[0] if len(df) > 0 else overall_mean
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# Fill any remaining NaN with mean
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df[col_name] = df[col_name].fillna(overall_mean)
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self.feature_columns.append(col_name)
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logger.debug(f"Added {len(lag_days)} lagged features (training mode)", lags=lag_days)
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return df
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def _calculate_lag_features_prediction(
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self,
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historical_sales: pd.Series,
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lag_days: List[int]
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) -> Dict[str, float]:
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"""
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Calculate lag features for prediction (operates on Series, returns dict).
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Args:
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historical_sales: Series of sales quantities indexed by date
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lag_days: List of lag periods
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Returns:
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Dictionary of lag features
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"""
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features = {}
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if len(historical_sales) == 0:
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# Return default values if no data
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for lag in lag_days:
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features[f'lag_{lag}_day'] = 0.0
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return features
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# Calculate overall statistics for fallback
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overall_mean = float(historical_sales.mean())
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overall_std = float(historical_sales.std()) if len(historical_sales) > 1 else 0.0
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# Calculate lag_1_day
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if 1 in lag_days:
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if len(historical_sales) >= 1:
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features['lag_1_day'] = float(historical_sales.iloc[-1])
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else:
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features['lag_1_day'] = overall_mean
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# Calculate lag_7_day
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if 7 in lag_days:
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if len(historical_sales) >= 7:
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features['lag_7_day'] = float(historical_sales.iloc[-7])
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else:
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# Fallback to last value if insufficient data
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features['lag_7_day'] = float(historical_sales.iloc[-1]) if len(historical_sales) > 0 else overall_mean
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# Calculate lag_14_day
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if 14 in lag_days:
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if len(historical_sales) >= 14:
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features['lag_14_day'] = float(historical_sales.iloc[-14])
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else:
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# Cascading fallback: lag_7 -> lag_1 -> last value -> mean
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if len(historical_sales) >= 7:
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features['lag_14_day'] = float(historical_sales.iloc[-7])
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else:
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features['lag_14_day'] = float(historical_sales.iloc[-1]) if len(historical_sales) > 0 else overall_mean
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logger.debug("Calculated lag features (prediction mode)", features=features)
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return features
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def calculate_rolling_features(
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self,
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sales_data: Union[pd.Series, pd.DataFrame],
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windows: List[int] = None,
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statistics: List[str] = None,
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mode: str = 'training'
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) -> Union[pd.DataFrame, Dict[str, float]]:
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"""
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Calculate rolling window statistics consistently for training and prediction.
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Args:
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sales_data: Sales data as Series (prediction) or DataFrame (training) with 'quantity' column
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windows: List of window sizes in days (default: [7, 14, 30])
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statistics: List of statistics to calculate (default: ['mean', 'std', 'max', 'min'])
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mode: 'training' returns DataFrame, 'prediction' returns dict
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Returns:
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DataFrame with rolling columns (training mode) or dict of rolling features (prediction mode)
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"""
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if windows is None:
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windows = [7, 14, 30]
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if statistics is None:
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statistics = ['mean', 'std', 'max', 'min']
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if mode == 'training':
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return self._calculate_rolling_features_training(sales_data, windows, statistics)
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else:
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return self._calculate_rolling_features_prediction(sales_data, windows, statistics)
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def _calculate_rolling_features_training(
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self,
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df: pd.DataFrame,
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windows: List[int],
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statistics: List[str]
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) -> pd.DataFrame:
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"""
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Calculate rolling features for training (operates on DataFrame).
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Args:
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df: DataFrame with 'quantity' column
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windows: List of window sizes
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statistics: List of statistics to calculate
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Returns:
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DataFrame with added rolling columns
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"""
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df = df.copy()
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# Calculate overall statistics for fallback
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overall_mean = float(df['quantity'].mean()) if len(df) > 0 else 0.0
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overall_std = float(df['quantity'].std()) if len(df) > 1 else 0.0
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overall_max = float(df['quantity'].max()) if len(df) > 0 else 0.0
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overall_min = float(df['quantity'].min()) if len(df) > 0 else 0.0
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fallback_values = {
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'mean': overall_mean,
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'std': overall_std,
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'max': overall_max,
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'min': overall_min
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}
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for window in windows:
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for stat in statistics:
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col_name = f'rolling_{stat}_{window}d'
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# Calculate rolling statistic with full window required (consistent with prediction)
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# Use min_periods=window to match prediction behavior
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if stat == 'mean':
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df[col_name] = df['quantity'].rolling(window=window, min_periods=window).mean()
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elif stat == 'std':
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df[col_name] = df['quantity'].rolling(window=window, min_periods=window).std()
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elif stat == 'max':
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df[col_name] = df['quantity'].rolling(window=window, min_periods=window).max()
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elif stat == 'min':
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df[col_name] = df['quantity'].rolling(window=window, min_periods=window).min()
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# Fill NaN values using cascading fallback (consistent with prediction)
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# Use smaller window values if available, otherwise use overall statistics
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mask = df[col_name].isna()
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if window == 14 and f'rolling_{stat}_7d' in df.columns:
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# Use 7-day window for 14-day NaN
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df.loc[mask, col_name] = df.loc[mask, f'rolling_{stat}_7d']
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elif window == 30 and f'rolling_{stat}_14d' in df.columns:
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# Use 14-day window for 30-day NaN
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df.loc[mask, col_name] = df.loc[mask, f'rolling_{stat}_14d']
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elif window == 30 and f'rolling_{stat}_7d' in df.columns:
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# Use 7-day window for 30-day NaN if 14-day not available
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df.loc[mask, col_name] = df.loc[mask, f'rolling_{stat}_7d']
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# Fill any remaining NaN with overall statistics
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df[col_name] = df[col_name].fillna(fallback_values[stat])
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self.feature_columns.append(col_name)
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logger.debug(f"Added rolling features (training mode)", windows=windows, statistics=statistics)
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return df
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def _calculate_rolling_features_prediction(
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self,
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historical_sales: pd.Series,
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windows: List[int],
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statistics: List[str]
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) -> Dict[str, float]:
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"""
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Calculate rolling features for prediction (operates on Series, returns dict).
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Args:
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historical_sales: Series of sales quantities indexed by date
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windows: List of window sizes
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statistics: List of statistics to calculate
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Returns:
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Dictionary of rolling features
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"""
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features = {}
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if len(historical_sales) == 0:
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# Return default values if no data
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for window in windows:
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for stat in statistics:
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features[f'rolling_{stat}_{window}d'] = 0.0
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return features
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# Calculate overall statistics for fallback
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overall_mean = float(historical_sales.mean())
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overall_std = float(historical_sales.std()) if len(historical_sales) > 1 else 0.0
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overall_max = float(historical_sales.max())
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overall_min = float(historical_sales.min())
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fallback_values = {
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'mean': overall_mean,
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'std': overall_std,
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'max': overall_max,
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'min': overall_min
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}
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# Calculate for each window
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for window in windows:
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if len(historical_sales) >= window:
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# Have enough data for full window
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window_data = historical_sales.iloc[-window:]
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for stat in statistics:
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col_name = f'rolling_{stat}_{window}d'
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if stat == 'mean':
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features[col_name] = float(window_data.mean())
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elif stat == 'std':
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features[col_name] = float(window_data.std()) if len(window_data) > 1 else 0.0
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elif stat == 'max':
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features[col_name] = float(window_data.max())
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elif stat == 'min':
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features[col_name] = float(window_data.min())
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else:
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# Insufficient data - use cascading fallback
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for stat in statistics:
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col_name = f'rolling_{stat}_{window}d'
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# Try to use smaller window if available
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if window == 14 and f'rolling_{stat}_7d' in features:
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features[col_name] = features[f'rolling_{stat}_7d']
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elif window == 30 and f'rolling_{stat}_14d' in features:
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features[col_name] = features[f'rolling_{stat}_14d']
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elif window == 30 and f'rolling_{stat}_7d' in features:
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features[col_name] = features[f'rolling_{stat}_7d']
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else:
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# Use overall statistics
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features[col_name] = fallback_values[stat]
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logger.debug("Calculated rolling features (prediction mode)", num_features=len(features))
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return features
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def calculate_trend_features(
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self,
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sales_data: Union[pd.Series, pd.DataFrame],
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reference_date: Optional[datetime] = None,
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lag_features: Optional[Dict[str, float]] = None,
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rolling_features: Optional[Dict[str, float]] = None,
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mode: str = 'training'
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) -> Union[pd.DataFrame, Dict[str, float]]:
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"""
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Calculate trend-based features consistently for training and prediction.
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Args:
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sales_data: Sales data as Series (prediction) or DataFrame (training)
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reference_date: Reference date for calculations (prediction mode)
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lag_features: Pre-calculated lag features (prediction mode)
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rolling_features: Pre-calculated rolling features (prediction mode)
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mode: 'training' returns DataFrame, 'prediction' returns dict
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Returns:
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||
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|
DataFrame with trend columns (training mode) or dict of trend features (prediction mode)
|
||
|
|
"""
|
||
|
|
if mode == 'training':
|
||
|
|
return self._calculate_trend_features_training(sales_data)
|
||
|
|
else:
|
||
|
|
return self._calculate_trend_features_prediction(
|
||
|
|
sales_data,
|
||
|
|
reference_date,
|
||
|
|
lag_features,
|
||
|
|
rolling_features
|
||
|
|
)
|
||
|
|
|
||
|
|
def _calculate_trend_features_training(
|
||
|
|
self,
|
||
|
|
df: pd.DataFrame,
|
||
|
|
date_column: str = 'date'
|
||
|
|
) -> pd.DataFrame:
|
||
|
|
"""
|
||
|
|
Calculate trend features for training (operates on DataFrame).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
df: DataFrame with date and lag/rolling features
|
||
|
|
date_column: Name of date column
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
DataFrame with added trend columns
|
||
|
|
"""
|
||
|
|
df = df.copy()
|
||
|
|
|
||
|
|
# Days since start
|
||
|
|
df['days_since_start'] = (df[date_column] - df[date_column].min()).dt.days
|
||
|
|
|
||
|
|
# Momentum (difference between lag_1 and lag_7)
|
||
|
|
if 'lag_1_day' in df.columns and 'lag_7_day' in df.columns:
|
||
|
|
df['momentum_1_7'] = df['lag_1_day'] - df['lag_7_day']
|
||
|
|
self.feature_columns.append('momentum_1_7')
|
||
|
|
else:
|
||
|
|
df['momentum_1_7'] = 0.0
|
||
|
|
self.feature_columns.append('momentum_1_7')
|
||
|
|
|
||
|
|
# Trend (difference between 7-day and 30-day rolling means)
|
||
|
|
if 'rolling_mean_7d' in df.columns and 'rolling_mean_30d' in df.columns:
|
||
|
|
df['trend_7_30'] = df['rolling_mean_7d'] - df['rolling_mean_30d']
|
||
|
|
self.feature_columns.append('trend_7_30')
|
||
|
|
else:
|
||
|
|
df['trend_7_30'] = 0.0
|
||
|
|
self.feature_columns.append('trend_7_30')
|
||
|
|
|
||
|
|
# Velocity (rate of change over week)
|
||
|
|
if 'lag_1_day' in df.columns and 'lag_7_day' in df.columns:
|
||
|
|
df['velocity_week'] = (df['lag_1_day'] - df['lag_7_day']) / 7.0
|
||
|
|
self.feature_columns.append('velocity_week')
|
||
|
|
else:
|
||
|
|
df['velocity_week'] = 0.0
|
||
|
|
self.feature_columns.append('velocity_week')
|
||
|
|
|
||
|
|
self.feature_columns.append('days_since_start')
|
||
|
|
|
||
|
|
logger.debug("Added trend features (training mode)")
|
||
|
|
return df
|
||
|
|
|
||
|
|
def _calculate_trend_features_prediction(
|
||
|
|
self,
|
||
|
|
historical_sales: pd.Series,
|
||
|
|
reference_date: datetime,
|
||
|
|
lag_features: Dict[str, float],
|
||
|
|
rolling_features: Dict[str, float]
|
||
|
|
) -> Dict[str, float]:
|
||
|
|
"""
|
||
|
|
Calculate trend features for prediction (operates on Series, returns dict).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
historical_sales: Series of sales quantities indexed by date
|
||
|
|
reference_date: The date we're forecasting for
|
||
|
|
lag_features: Pre-calculated lag features
|
||
|
|
rolling_features: Pre-calculated rolling features
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Dictionary of trend features
|
||
|
|
"""
|
||
|
|
features = {}
|
||
|
|
|
||
|
|
if len(historical_sales) == 0:
|
||
|
|
return {
|
||
|
|
'days_since_start': 0,
|
||
|
|
'momentum_1_7': 0.0,
|
||
|
|
'trend_7_30': 0.0,
|
||
|
|
'velocity_week': 0.0
|
||
|
|
}
|
||
|
|
|
||
|
|
# Days since first sale
|
||
|
|
features['days_since_start'] = (reference_date - historical_sales.index[0]).days
|
||
|
|
|
||
|
|
# Momentum (difference between lag_1 and lag_7)
|
||
|
|
if 'lag_1_day' in lag_features and 'lag_7_day' in lag_features:
|
||
|
|
if len(historical_sales) >= 7:
|
||
|
|
features['momentum_1_7'] = lag_features['lag_1_day'] - lag_features['lag_7_day']
|
||
|
|
else:
|
||
|
|
features['momentum_1_7'] = 0.0 # Insufficient data
|
||
|
|
else:
|
||
|
|
features['momentum_1_7'] = 0.0
|
||
|
|
|
||
|
|
# Trend (difference between 7-day and 30-day rolling means)
|
||
|
|
if 'rolling_mean_7d' in rolling_features and 'rolling_mean_30d' in rolling_features:
|
||
|
|
if len(historical_sales) >= 30:
|
||
|
|
features['trend_7_30'] = rolling_features['rolling_mean_7d'] - rolling_features['rolling_mean_30d']
|
||
|
|
else:
|
||
|
|
features['trend_7_30'] = 0.0 # Insufficient data
|
||
|
|
else:
|
||
|
|
features['trend_7_30'] = 0.0
|
||
|
|
|
||
|
|
# Velocity (rate of change over week)
|
||
|
|
if 'lag_1_day' in lag_features and 'lag_7_day' in lag_features:
|
||
|
|
if len(historical_sales) >= 7:
|
||
|
|
recent_value = lag_features['lag_1_day']
|
||
|
|
past_value = lag_features['lag_7_day']
|
||
|
|
features['velocity_week'] = float((recent_value - past_value) / 7.0)
|
||
|
|
else:
|
||
|
|
features['velocity_week'] = 0.0 # Insufficient data
|
||
|
|
else:
|
||
|
|
features['velocity_week'] = 0.0
|
||
|
|
|
||
|
|
logger.debug("Calculated trend features (prediction mode)", features=features)
|
||
|
|
return features
|
||
|
|
|
||
|
|
def calculate_data_freshness_metrics(
|
||
|
|
self,
|
||
|
|
historical_sales: pd.Series,
|
||
|
|
forecast_date: datetime
|
||
|
|
) -> Dict[str, Union[int, float]]:
|
||
|
|
"""
|
||
|
|
Calculate data freshness and availability metrics.
|
||
|
|
|
||
|
|
This is used by prediction service to assess data quality and adjust confidence.
|
||
|
|
Not used in training mode.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
historical_sales: Series of sales quantities indexed by date
|
||
|
|
forecast_date: The date we're forecasting for
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Dictionary with freshness metrics
|
||
|
|
"""
|
||
|
|
if len(historical_sales) == 0:
|
||
|
|
return {
|
||
|
|
'days_since_last_sale': 999, # Very large number indicating no data
|
||
|
|
'historical_data_availability_score': 0.0
|
||
|
|
}
|
||
|
|
|
||
|
|
last_available_date = historical_sales.index.max()
|
||
|
|
days_since_last_sale = (forecast_date - last_available_date).days
|
||
|
|
|
||
|
|
# Calculate data availability score (0-1 scale, 1 being recent data)
|
||
|
|
max_considered_days = 180 # Consider data older than 6 months as very stale
|
||
|
|
availability_score = max(0.0, 1.0 - (days_since_last_sale / max_considered_days))
|
||
|
|
|
||
|
|
return {
|
||
|
|
'days_since_last_sale': days_since_last_sale,
|
||
|
|
'historical_data_availability_score': availability_score
|
||
|
|
}
|
||
|
|
|
||
|
|
def calculate_all_features(
|
||
|
|
self,
|
||
|
|
sales_data: Union[pd.Series, pd.DataFrame],
|
||
|
|
reference_date: Optional[datetime] = None,
|
||
|
|
mode: str = 'training',
|
||
|
|
date_column: str = 'date'
|
||
|
|
) -> Union[pd.DataFrame, Dict[str, float]]:
|
||
|
|
"""
|
||
|
|
Calculate all historical features in one call.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sales_data: Sales data as Series (prediction) or DataFrame (training)
|
||
|
|
reference_date: Reference date for predictions (prediction mode only)
|
||
|
|
mode: 'training' or 'prediction'
|
||
|
|
date_column: Name of date column (training mode only)
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
DataFrame with all features (training) or dict of all features (prediction)
|
||
|
|
"""
|
||
|
|
if mode == 'training':
|
||
|
|
df = sales_data.copy()
|
||
|
|
|
||
|
|
# Calculate lag features
|
||
|
|
df = self.calculate_lag_features(df, mode='training')
|
||
|
|
|
||
|
|
# Calculate rolling features
|
||
|
|
df = self.calculate_rolling_features(df, mode='training')
|
||
|
|
|
||
|
|
# Calculate trend features
|
||
|
|
df = self.calculate_trend_features(df, mode='training')
|
||
|
|
|
||
|
|
logger.info(f"Calculated all features (training mode)", feature_count=len(self.feature_columns))
|
||
|
|
return df
|
||
|
|
|
||
|
|
else: # prediction mode
|
||
|
|
if reference_date is None:
|
||
|
|
raise ValueError("reference_date is required for prediction mode")
|
||
|
|
|
||
|
|
features = {}
|
||
|
|
|
||
|
|
# Calculate lag features
|
||
|
|
lag_features = self.calculate_lag_features(sales_data, mode='prediction')
|
||
|
|
features.update(lag_features)
|
||
|
|
|
||
|
|
# Calculate rolling features
|
||
|
|
rolling_features = self.calculate_rolling_features(sales_data, mode='prediction')
|
||
|
|
features.update(rolling_features)
|
||
|
|
|
||
|
|
# Calculate trend features
|
||
|
|
trend_features = self.calculate_trend_features(
|
||
|
|
sales_data,
|
||
|
|
reference_date=reference_date,
|
||
|
|
lag_features=lag_features,
|
||
|
|
rolling_features=rolling_features,
|
||
|
|
mode='prediction'
|
||
|
|
)
|
||
|
|
features.update(trend_features)
|
||
|
|
|
||
|
|
# Calculate data freshness metrics
|
||
|
|
freshness_metrics = self.calculate_data_freshness_metrics(sales_data, reference_date)
|
||
|
|
features.update(freshness_metrics)
|
||
|
|
|
||
|
|
logger.info(f"Calculated all features (prediction mode)", feature_count=len(features))
|
||
|
|
return features
|