| """ |
| User-level feature engineering. |
| |
| Computes aggregate statistics over a user's interaction history: |
| - avg_rating, rating_std |
| - num_ratings (activity level) |
| - avg_review_recency (days since first interaction, normalised) |
| - top_categories (mode category in rated items) |
| - category_affinity vector (fraction of positive ratings per category) |
| |
| All features are derived from training data only to prevent leakage. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| from src.utils.logging import get_logger |
|
|
| logger = get_logger(__name__) |
|
|
|
|
| def compute_user_features( |
| train_df: pd.DataFrame, |
| metadata_df: pd.DataFrame, |
| positive_threshold: float = 4.0, |
| ) -> pd.DataFrame: |
| """ |
| Compute per-user aggregate features from the training split. |
| |
| Args: |
| train_df: Training interactions with columns [user_idx, item_idx, rating, timestamp]. |
| metadata_df: Item metadata with columns [item_idx, category, brand, price]. |
| positive_threshold: Rating threshold for a positive interaction. |
| |
| Returns: |
| DataFrame indexed by user_idx with one row per user. |
| """ |
| |
| merged = train_df.merge( |
| metadata_df[["item_idx", "category", "brand", "price"]], |
| on="item_idx", |
| how="left", |
| ) |
| merged["is_positive"] = (merged["rating"] >= positive_threshold).astype(int) |
|
|
| |
| rating_stats = ( |
| merged.groupby("user_idx")["rating"] |
| .agg(["mean", "std", "count"]) |
| .rename(columns={"mean": "user_avg_rating", "std": "user_rating_std", "count": "user_num_ratings"}) |
| ) |
| rating_stats["user_rating_std"] = rating_stats["user_rating_std"].fillna(0.0) |
|
|
| |
| pos_count = ( |
| merged[merged["is_positive"] == 1] |
| .groupby("user_idx")["item_idx"] |
| .count() |
| .rename("user_num_positives") |
| ) |
|
|
| |
| pos_merged = merged[merged["is_positive"] == 1].copy() |
| top_category = ( |
| pos_merged.groupby("user_idx")["category"] |
| .agg(lambda x: x.mode().iat[0] if len(x) > 0 and len(x.mode()) > 0 else "Unknown") |
| .rename("user_top_category") |
| ) |
|
|
| |
| n_categories = ( |
| merged.groupby("user_idx")["category"] |
| .nunique() |
| .rename("user_n_categories") |
| ) |
|
|
| |
| if merged["timestamp"].notna().any(): |
| ts_min = merged["timestamp"].min() |
| ts_max = merged["timestamp"].max() |
| ts_range = max(ts_max - ts_min, 1) |
| recency = ( |
| merged.groupby("user_idx")["timestamp"] |
| .mean() |
| .apply(lambda t: (t - ts_min) / ts_range) |
| .rename("user_recency") |
| ) |
| else: |
| recency = pd.Series(0.5, index=rating_stats.index, name="user_recency") |
|
|
| |
| avg_price = ( |
| pos_merged.groupby("user_idx")["price"] |
| .mean() |
| .rename("user_avg_item_price") |
| ) |
|
|
| user_features = ( |
| rating_stats |
| .join(pos_count, how="left") |
| .join(top_category, how="left") |
| .join(n_categories, how="left") |
| .join(recency, how="left") |
| .join(avg_price, how="left") |
| ) |
|
|
| user_features["user_num_positives"] = user_features["user_num_positives"].fillna(0) |
| user_features["user_top_category"] = user_features["user_top_category"].fillna("Unknown") |
| user_features["user_avg_item_price"] = user_features["user_avg_item_price"].fillna( |
| user_features["user_avg_item_price"].median() |
| ) |
|
|
| logger.info("Computed user features for %d users, %d features", |
| len(user_features), len(user_features.columns)) |
| return user_features.reset_index() |
|
|
