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80843b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import pandas as pd
import numpy as np
import pickle
import logging
from pathlib import Path
from scipy.sparse import csr_matrix, save_npz
logging.basicConfig(level=logging.INFO, format='%(asctime)s — %(message)s')
log = logging.getLogger(__name__)
def load_raw_data(raw_dir: Path) -> pd.DataFrame:
path = raw_dir / "all_beauty_reviews.parquet"
if not path.exists():
raise FileNotFoundError(f"Raw data not found at {path}")
log.info(f"Loading raw data from {path}")
df = pd.read_parquet(path)
log.info(f"Loaded {len(df):,} rows")
return df
def clean_and_select(df: pd.DataFrame) -> pd.DataFrame:
df = df.rename(columns={'parent_asin': 'item_id'})
core = ['user_id', 'item_id', 'rating', 'timestamp']
df = df[core].copy()
df = df.dropna(subset=core)
df['rating'] = pd.to_numeric(df['rating'], errors='coerce')
df['timestamp'] = pd.to_numeric(df['timestamp'], errors='coerce')
df = df.dropna(subset=['rating', 'timestamp'])
if df['timestamp'].median() > 1e12:
df['timestamp'] = (df['timestamp'] / 1000).astype(int)
df['rating'] = df['rating'].astype(float)
df['timestamp'] = df['timestamp'].astype(int)
log.info(f"Clean dataset: {len(df):,} rows")
return df.reset_index(drop=True)
def filter_kcore(df: pd.DataFrame, min_interactions: int = 5) -> pd.DataFrame:
iteration = 0
while True:
iteration += 1
before = len(df)
user_counts = df['user_id'].value_counts()
df = df[df['user_id'].isin(
user_counts[user_counts >= min_interactions].index
)]
item_counts = df['item_id'].value_counts()
df = df[df['item_id'].isin(
item_counts[item_counts >= min_interactions].index
)]
after = len(df)
log.info(f"k-core iter {iteration}: {before:,} → {after:,} rows")
if before == after:
break
log.info(f"Final: {df['user_id'].nunique():,} users, "
f"{df['item_id'].nunique():,} items, "
f"{len(df):,} interactions")
return df.reset_index(drop=True)
def encode_ids(df: pd.DataFrame):
user2idx = {u: i for i, u in enumerate(sorted(df['user_id'].unique()))}
item2idx = {it: i for i, it in enumerate(sorted(df['item_id'].unique()))}
idx2user = {v: k for k, v in user2idx.items()}
idx2item = {v: k for k, v in item2idx.items()}
df = df.copy()
df['user_idx'] = df['user_id'].map(user2idx)
df['item_idx'] = df['item_id'].map(item2idx)
log.info(f"Encoded {len(user2idx):,} users and {len(item2idx):,} items")
return df, user2idx, item2idx, idx2user, idx2item
def temporal_split(df: pd.DataFrame):
df = df.sort_values(['user_id', 'timestamp'])
test_idx = df.groupby('user_id')['timestamp'].idxmax()
test_df = df.loc[test_idx].copy()
train_df = df.drop(index=test_idx).copy()
log.info(f"Train: {len(train_df):,} | Test: {len(test_df):,}")
return train_df.reset_index(drop=True), test_df.reset_index(drop=True)
def build_sparse_matrix(df: pd.DataFrame) -> csr_matrix:
n_users = df['user_idx'].max() + 1
n_items = df['item_idx'].max() + 1
matrix = csr_matrix(
(
df['rating'].values.astype(float),
(df['user_idx'].values, df['item_idx'].values)
),
shape=(n_users, n_items)
)
density = matrix.nnz / (n_users * n_items)
log.info(f"Sparse matrix: {matrix.shape}, density: {density:.4%}")
return matrix
def run_pipeline(raw_dir: Path, processed_dir: Path, min_interactions: int = 5) -> dict:
processed_dir.mkdir(parents=True, exist_ok=True)
df_raw = load_raw_data(raw_dir)
df_clean = clean_and_select(df_raw)
df_filtered = filter_kcore(df_clean, min_interactions)
df_encoded, user2idx, item2idx, idx2user, idx2item = encode_ids(df_filtered)
train_df, test_df = temporal_split(df_encoded)
matrix = build_sparse_matrix(train_df)
train_df.to_parquet(processed_dir / 'train.parquet', index=False)
test_df.to_parquet(processed_dir / 'test.parquet', index=False)
save_npz(str(processed_dir / 'interaction_matrix.npz'), matrix)
with open(processed_dir / 'mappings.pkl', 'wb') as f:
pickle.dump({
'user2idx': user2idx, 'item2idx': item2idx,
'idx2user': idx2user, 'idx2item': idx2item,
}, f)
log.info("All artifacts saved.")
return {
'df_raw': df_raw,
'train_df': train_df,
'test_df': test_df,
'matrix': matrix,
'user2idx': user2idx,
'item2idx': item2idx,
'idx2user': idx2user,
'idx2item': idx2item,
} |