Add all the code for training service
This commit is contained in:
Urtzi Alfaro
493
services/training/app/ml/data_processor.py
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493
services/training/app/ml/data_processor.py
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# services/training/app/ml/data_processor.py
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"""
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Data Processor for Training Service
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Handles data preparation and feature engineering for ML training
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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, Any, Optional, Tuple
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from datetime import datetime, timedelta
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import logging
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from sklearn.preprocessing import StandardScaler
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from sklearn.impute import SimpleImputer
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logger = logging.getLogger(__name__)
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class BakeryDataProcessor:
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"""
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Enhanced data processor for bakery forecasting training service.
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Handles data cleaning, feature engineering, and preparation for ML models.
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"""
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def __init__(self):
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self.scalers = {} # Store scalers for each feature
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self.imputers = {} # Store imputers for missing value handling
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async def prepare_training_data(self,
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sales_data: pd.DataFrame,
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weather_data: pd.DataFrame,
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traffic_data: pd.DataFrame,
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product_name: str) -> pd.DataFrame:
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"""
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Prepare comprehensive training data for a specific product.
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Args:
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sales_data: Historical sales data for the product
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weather_data: Weather data
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traffic_data: Traffic data
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product_name: Product name for logging
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Returns:
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DataFrame ready for Prophet training with 'ds' and 'y' columns plus features
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"""
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try:
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logger.info(f"Preparing training data for product: {product_name}")
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# Convert and validate sales data
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sales_clean = await self._process_sales_data(sales_data, product_name)
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# Aggregate to daily level
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daily_sales = await self._aggregate_daily_sales(sales_clean)
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# Add temporal features
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daily_sales = self._add_temporal_features(daily_sales)
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# Merge external data sources
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daily_sales = self._merge_weather_features(daily_sales, weather_data)
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daily_sales = self._merge_traffic_features(daily_sales, traffic_data)
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# Engineer additional features
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daily_sales = self._engineer_features(daily_sales)
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# Handle missing values
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daily_sales = self._handle_missing_values(daily_sales)
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# Prepare for Prophet (rename columns and validate)
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prophet_data = self._prepare_prophet_format(daily_sales)
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logger.info(f"Prepared {len(prophet_data)} data points for {product_name}")
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return prophet_data
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except Exception as e:
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logger.error(f"Error preparing training data for {product_name}: {str(e)}")
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raise
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async def prepare_prediction_features(self,
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future_dates: pd.DatetimeIndex,
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weather_forecast: pd.DataFrame = None,
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traffic_forecast: pd.DataFrame = None) -> pd.DataFrame:
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"""
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Create features for future predictions.
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Args:
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future_dates: Future dates to predict
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weather_forecast: Weather forecast data
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traffic_forecast: Traffic forecast data
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Returns:
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DataFrame with features for prediction
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"""
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try:
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# Create base future dataframe
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future_df = pd.DataFrame({'ds': future_dates})
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# Add temporal features
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future_df = self._add_temporal_features(
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future_df.rename(columns={'ds': 'date'})
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).rename(columns={'date': 'ds'})
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# Add weather features
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if weather_forecast is not None and not weather_forecast.empty:
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weather_features = weather_forecast.copy()
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if 'date' in weather_features.columns:
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weather_features = weather_features.rename(columns={'date': 'ds'})
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future_df = future_df.merge(weather_features, on='ds', how='left')
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# Add traffic features
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if traffic_forecast is not None and not traffic_forecast.empty:
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traffic_features = traffic_forecast.copy()
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if 'date' in traffic_features.columns:
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traffic_features = traffic_features.rename(columns={'date': 'ds'})
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future_df = future_df.merge(traffic_features, on='ds', how='left')
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# Engineer additional features
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future_df = self._engineer_features(future_df.rename(columns={'ds': 'date'}))
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future_df = future_df.rename(columns={'date': 'ds'})
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# Handle missing values in future data
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numeric_columns = future_df.select_dtypes(include=[np.number]).columns
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for col in numeric_columns:
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if future_df[col].isna().any():
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# Use reasonable defaults for Madrid
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if col == 'temperature':
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future_df[col] = future_df[col].fillna(15.0) # Default Madrid temp
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elif col == 'precipitation':
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future_df[col] = future_df[col].fillna(0.0) # Default no rain
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elif col == 'humidity':
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future_df[col] = future_df[col].fillna(60.0) # Default humidity
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elif col == 'traffic_volume':
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future_df[col] = future_df[col].fillna(100.0) # Default traffic
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else:
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future_df[col] = future_df[col].fillna(future_df[col].median())
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return future_df
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except Exception as e:
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logger.error(f"Error creating prediction features: {e}")
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# Return minimal features if error
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return pd.DataFrame({'ds': future_dates})
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async def _process_sales_data(self, sales_data: pd.DataFrame, product_name: str) -> pd.DataFrame:
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"""Process and clean sales data"""
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sales_clean = sales_data.copy()
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# Ensure date column exists and is datetime
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if 'date' not in sales_clean.columns:
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raise ValueError("Sales data must have a 'date' column")
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sales_clean['date'] = pd.to_datetime(sales_clean['date'])
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# Ensure quantity column exists and is numeric
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if 'quantity' not in sales_clean.columns:
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raise ValueError("Sales data must have a 'quantity' column")
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sales_clean['quantity'] = pd.to_numeric(sales_clean['quantity'], errors='coerce')
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# Remove rows with invalid quantities
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sales_clean = sales_clean.dropna(subset=['quantity'])
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sales_clean = sales_clean[sales_clean['quantity'] >= 0] # No negative sales
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# Filter for the specific product if product_name column exists
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if 'product_name' in sales_clean.columns:
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sales_clean = sales_clean[sales_clean['product_name'] == product_name]
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return sales_clean
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async def _aggregate_daily_sales(self, sales_data: pd.DataFrame) -> pd.DataFrame:
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"""Aggregate sales to daily level"""
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daily_sales = sales_data.groupby('date').agg({
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'quantity': 'sum'
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}).reset_index()
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# Ensure we have data for all dates in the range
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date_range = pd.date_range(
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start=daily_sales['date'].min(),
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end=daily_sales['date'].max(),
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freq='D'
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)
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full_date_df = pd.DataFrame({'date': date_range})
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daily_sales = full_date_df.merge(daily_sales, on='date', how='left')
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daily_sales['quantity'] = daily_sales['quantity'].fillna(0) # Fill missing days with 0 sales
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return daily_sales
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def _add_temporal_features(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Add temporal features like day of week, month, etc."""
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df = df.copy()
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# Ensure we have a date column
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if 'date' not in df.columns:
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raise ValueError("DataFrame must have a 'date' column")
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df['date'] = pd.to_datetime(df['date'])
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# Day of week (0=Monday, 6=Sunday)
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df['day_of_week'] = df['date'].dt.dayofweek
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df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)
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# Month and season
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df['month'] = df['date'].dt.month
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df['season'] = df['month'].apply(self._get_season)
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# Week of year
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df['week_of_year'] = df['date'].dt.isocalendar().week
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# Quarter
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df['quarter'] = df['date'].dt.quarter
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# Holiday indicators (basic Spanish holidays)
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df['is_holiday'] = df['date'].apply(self._is_spanish_holiday).astype(int)
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# School calendar effects (approximate)
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df['is_school_holiday'] = df['date'].apply(self._is_school_holiday).astype(int)
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return df
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def _merge_weather_features(self,
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daily_sales: pd.DataFrame,
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weather_data: pd.DataFrame) -> pd.DataFrame:
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"""Merge weather features with sales data"""
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if weather_data.empty:
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# Add default weather columns with neutral values
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daily_sales['temperature'] = 15.0 # Mild temperature
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daily_sales['precipitation'] = 0.0 # No rain
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daily_sales['humidity'] = 60.0 # Moderate humidity
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daily_sales['wind_speed'] = 5.0 # Light wind
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return daily_sales
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try:
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weather_clean = weather_data.copy()
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# Ensure weather data has date column
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if 'date' not in weather_clean.columns and 'ds' in weather_clean.columns:
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weather_clean = weather_clean.rename(columns={'ds': 'date'})
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weather_clean['date'] = pd.to_datetime(weather_clean['date'])
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# Select relevant weather features
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weather_features = ['date']
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# Add available weather columns with default names
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weather_mapping = {
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'temperature': ['temperature', 'temp', 'temperatura'],
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'precipitation': ['precipitation', 'rain', 'lluvia', 'precipitacion'],
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'humidity': ['humidity', 'humedad'],
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'wind_speed': ['wind_speed', 'viento', 'wind']
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}
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for standard_name, possible_names in weather_mapping.items():
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for possible_name in possible_names:
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if possible_name in weather_clean.columns:
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weather_clean[standard_name] = weather_clean[possible_name]
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weather_features.append(standard_name)
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break
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# Keep only the features we found
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weather_clean = weather_clean[weather_features].copy()
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# Merge with sales data
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merged = daily_sales.merge(weather_clean, on='date', how='left')
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# Fill missing weather values with reasonable defaults
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if 'temperature' in merged.columns:
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merged['temperature'] = merged['temperature'].fillna(15.0)
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if 'precipitation' in merged.columns:
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merged['precipitation'] = merged['precipitation'].fillna(0.0)
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if 'humidity' in merged.columns:
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merged['humidity'] = merged['humidity'].fillna(60.0)
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if 'wind_speed' in merged.columns:
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merged['wind_speed'] = merged['wind_speed'].fillna(5.0)
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return merged
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except Exception as e:
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logger.warning(f"Error merging weather data: {e}")
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# Add default weather columns if merge fails
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daily_sales['temperature'] = 15.0
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daily_sales['precipitation'] = 0.0
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daily_sales['humidity'] = 60.0
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daily_sales['wind_speed'] = 5.0
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return daily_sales
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def _merge_traffic_features(self,
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daily_sales: pd.DataFrame,
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traffic_data: pd.DataFrame) -> pd.DataFrame:
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"""Merge traffic features with sales data"""
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if traffic_data.empty:
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# Add default traffic column
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daily_sales['traffic_volume'] = 100.0 # Neutral traffic level
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return daily_sales
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try:
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traffic_clean = traffic_data.copy()
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# Ensure traffic data has date column
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if 'date' not in traffic_clean.columns and 'ds' in traffic_clean.columns:
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traffic_clean = traffic_clean.rename(columns={'ds': 'date'})
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traffic_clean['date'] = pd.to_datetime(traffic_clean['date'])
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# Select relevant traffic features
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traffic_features = ['date']
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# Map traffic column names
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traffic_mapping = {
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'traffic_volume': ['traffic_volume', 'traffic_intensity', 'trafico', 'intensidad'],
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'pedestrian_count': ['pedestrian_count', 'peatones'],
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'occupancy_rate': ['occupancy_rate', 'ocupacion']
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}
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for standard_name, possible_names in traffic_mapping.items():
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for possible_name in possible_names:
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if possible_name in traffic_clean.columns:
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traffic_clean[standard_name] = traffic_clean[possible_name]
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traffic_features.append(standard_name)
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break
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# Keep only the features we found
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traffic_clean = traffic_clean[traffic_features].copy()
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# Merge with sales data
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merged = daily_sales.merge(traffic_clean, on='date', how='left')
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# Fill missing traffic values
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if 'traffic_volume' in merged.columns:
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merged['traffic_volume'] = merged['traffic_volume'].fillna(100.0)
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if 'pedestrian_count' in merged.columns:
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merged['pedestrian_count'] = merged['pedestrian_count'].fillna(50.0)
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if 'occupancy_rate' in merged.columns:
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merged['occupancy_rate'] = merged['occupancy_rate'].fillna(0.5)
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return merged
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except Exception as e:
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logger.warning(f"Error merging traffic data: {e}")
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# Add default traffic column if merge fails
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daily_sales['traffic_volume'] = 100.0
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return daily_sales
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def _engineer_features(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Engineer additional features from existing data"""
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df = df.copy()
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# Weather-based features
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if 'temperature' in df.columns:
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df['temp_squared'] = df['temperature'] ** 2
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df['is_hot_day'] = (df['temperature'] > 25).astype(int)
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df['is_cold_day'] = (df['temperature'] < 10).astype(int)
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if 'precipitation' in df.columns:
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df['is_rainy_day'] = (df['precipitation'] > 0).astype(int)
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df['heavy_rain'] = (df['precipitation'] > 10).astype(int)
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# Traffic-based features
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if 'traffic_volume' in df.columns:
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df['high_traffic'] = (df['traffic_volume'] > df['traffic_volume'].quantile(0.75)).astype(int)
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df['low_traffic'] = (df['traffic_volume'] < df['traffic_volume'].quantile(0.25)).astype(int)
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# Interaction features
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if 'is_weekend' in df.columns and 'temperature' in df.columns:
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df['weekend_temp_interaction'] = df['is_weekend'] * df['temperature']
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if 'is_rainy_day' in df.columns and 'traffic_volume' in df.columns:
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df['rain_traffic_interaction'] = df['is_rainy_day'] * df['traffic_volume']
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return df
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def _handle_missing_values(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Handle missing values in the dataset"""
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df = df.copy()
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# For numeric columns, use median imputation
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numeric_columns = df.select_dtypes(include=[np.number]).columns
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for col in numeric_columns:
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if col != 'quantity' and df[col].isna().any():
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median_value = df[col].median()
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df[col] = df[col].fillna(median_value)
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return df
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def _prepare_prophet_format(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Prepare data in Prophet format with 'ds' and 'y' columns"""
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prophet_df = df.copy()
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# Rename columns for Prophet
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if 'date' in prophet_df.columns:
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prophet_df = prophet_df.rename(columns={'date': 'ds'})
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if 'quantity' in prophet_df.columns:
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prophet_df = prophet_df.rename(columns={'quantity': 'y'})
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# Ensure ds is datetime
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if 'ds' in prophet_df.columns:
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prophet_df['ds'] = pd.to_datetime(prophet_df['ds'])
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# Validate required columns
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if 'ds' not in prophet_df.columns or 'y' not in prophet_df.columns:
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raise ValueError("Prophet data must have 'ds' and 'y' columns")
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# Remove any rows with missing target values
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prophet_df = prophet_df.dropna(subset=['y'])
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# Sort by date
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prophet_df = prophet_df.sort_values('ds').reset_index(drop=True)
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return prophet_df
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def _get_season(self, month: int) -> int:
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"""Get season from month (1-4 for Winter, Spring, Summer, Autumn)"""
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if month in [12, 1, 2]:
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return 1 # Winter
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elif month in [3, 4, 5]:
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return 2 # Spring
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elif month in [6, 7, 8]:
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return 3 # Summer
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else:
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return 4 # Autumn
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def _is_spanish_holiday(self, date: datetime) -> bool:
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"""Check if a date is a major Spanish holiday"""
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month_day = (date.month, date.day)
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# Major Spanish holidays that affect bakery sales
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spanish_holidays = [
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(1, 1), # New Year
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(1, 6), # Epiphany
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(5, 1), # Labour Day
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(8, 15), # Assumption
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(10, 12), # National Day
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(11, 1), # All Saints
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(12, 6), # Constitution
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(12, 8), # Immaculate Conception
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(12, 25), # Christmas
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(5, 15), # San Isidro (Madrid)
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(5, 2), # Madrid Community Day
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]
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return month_day in spanish_holidays
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def _is_school_holiday(self, date: datetime) -> bool:
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"""Check if a date is during school holidays (approximate)"""
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month = date.month
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# Approximate Spanish school holiday periods
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# Summer holidays (July-August)
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if month in [7, 8]:
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return True
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# Christmas holidays (mid December to early January)
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if month == 12 and date.day >= 20:
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return True
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if month == 1 and date.day <= 10:
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return True
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# Easter holidays (approximate - first two weeks of April)
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if month == 4 and date.day <= 14:
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return True
|
||||
|
||||
return False
|
||||
|
||||
def calculate_feature_importance(self,
|
||||
model_data: pd.DataFrame,
|
||||
target_column: str = 'y') -> Dict[str, float]:
|
||||
"""
|
||||
Calculate feature importance for the model.
|
||||
"""
|
||||
try:
|
||||
# Simple correlation-based importance
|
||||
numeric_features = model_data.select_dtypes(include=[np.number]).columns
|
||||
numeric_features = [col for col in numeric_features if col != target_column]
|
||||
|
||||
importance_scores = {}
|
||||
|
||||
for feature in numeric_features:
|
||||
if feature in model_data.columns:
|
||||
correlation = model_data[feature].corr(model_data[target_column])
|
||||
importance_scores[feature] = abs(correlation) if not pd.isna(correlation) else 0.0
|
||||
|
||||
# Sort by importance
|
||||
importance_scores = dict(sorted(importance_scores.items(),
|
||||
key=lambda x: x[1], reverse=True))
|
||||
|
||||
return importance_scores
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating feature importance: {e}")
|
||||
return {}
|
||||
Reference in New Issue
Block a user