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| import pandas as pd | |
| import numpy as np | |
| from pathlib import Path | |
| import sys | |
| sys.path.append(str(Path(__file__).resolve().parents[1])) | |
| from utils.logger import * | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| def engineer_news_features(df): | |
| KEYWORDS = { | |
| "strike": ["strike", "walkout", "protest"], | |
| "disaster": ["earthquake", "flood", "hurricane", "typhoon", "fire", "storm"], | |
| "accident": ["collision", "accident", "spill", "blockage"], | |
| } | |
| for key, words in KEYWORDS.items(): | |
| df[f"is_{key}"] = ( | |
| df['title'].str.contains('|'.join(words), case=False, na=False) | | |
| df['description'].str.contains('|'.join(words), case=False, na=False) | |
| ) | |
| if "publishedAt" in df.columns: | |
| df["event_weekday"] = pd.to_datetime(df["publishedAt"], errors='coerce').dt.weekday | |
| df["event_hour"] = pd.to_datetime(df["publishedAt"], errors='coerce').dt.hour | |
| logger.info(f"Engineered news event features: {df.shape}") | |
| return df | |
| def engineer_weather_features(df): | |
| severe_words = ["Storm", "Thunderstorm", "Rain", "Snow", "Hurricane", "Extreme"] | |
| df["severe_weather"] = df["weather_main"].str.contains('|'.join(severe_words), case=False, na=False) | |
| if "weather_main" in df.columns: | |
| df = pd.get_dummies(df, columns=["weather_main"], prefix="weather") | |
| if "timestamp" in df.columns: | |
| df["month"] = pd.to_datetime(df["timestamp"], errors='coerce').dt.month | |
| df["season"] = pd.to_datetime(df["timestamp"], errors='coerce').dt.month % 12 // 3 + 1 | |
| logger.info(f"Engineered weather features: {df.shape}") | |
| return df | |
| def engineer_supply_chain_features(df): | |
| if "order date (DateOrders)" in df.columns and "shipping date (DateOrders)" in df.columns: | |
| df["lead_time_days"] = ( | |
| pd.to_datetime(df["shipping date (DateOrders)"], errors='coerce') - | |
| pd.to_datetime(df["order date (DateOrders)"], errors='coerce') | |
| ).dt.days | |
| for col in ["Order Status", "Product Status", "Shipping Mode", "Order Region", "Order Country"]: | |
| if col in df.columns: | |
| df = pd.get_dummies(df, columns=[col], prefix=col.replace(' ', '_')) | |
| if "Late_delivery_risk" in df.columns: | |
| df["is_late"] = df["Late_delivery_risk"] > 0 | |
| logger.info(f"Engineered supply chain features: {df.shape}") | |
| return df | |
| if __name__ == "__main__": | |
| processed_dir = Path(__file__).resolve().parents[2] / "artifacts" / "data" / "processed" | |
| try: | |
| news_df = pd.read_csv(processed_dir / "news_events_clean.csv") | |
| news_feats = engineer_news_features(news_df) | |
| news_feats.to_csv(processed_dir / "news_events_features.csv", index=False) | |
| logger.info("Saved engineered news features.") | |
| except Exception as e: | |
| logger.error(f"Error engineering news features: {e}") | |
| try: | |
| weather_df = pd.read_csv(processed_dir / "weather_alerts_clean.csv") | |
| weather_feats = engineer_weather_features(weather_df) | |
| weather_feats.to_csv(processed_dir / "weather_alerts_features.csv", index=False) | |
| logger.info("Saved engineered weather features.") | |
| except Exception as e: | |
| logger.error(f"Error engineering weather features: {e}") | |
| try: | |
| sc_df = pd.read_csv(processed_dir / "supply_chain_disruptions_clean.csv", encoding="utf-8") | |
| sc_feats = engineer_supply_chain_features(sc_df) | |
| sc_feats.to_csv(processed_dir / "supply_chain_disruptions_features.csv", index=False) | |
| logger.info("Saved engineered supply chain features.") | |
| except Exception as e: | |
| logger.error(f"Error engineering supply chain features: {e}") | |