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cdb73a8 | 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 | import logging
import os
import pickle
import sys
import numpy as np
import pandas as pd
# Add project root
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
import src.book_recommender.core.config as config
from src.book_recommender.ml.clustering import cluster_books, get_cluster_names
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
def precompute_clusters():
logger.info("--- Starting Cluster Pre-computation ---")
# 1. Load Data
if not os.path.exists(config.PROCESSED_DATA_PATH) or not os.path.exists(config.EMBEDDINGS_PATH):
logger.error("Data files missing. Run data processor and embedder first.")
return
logger.info(f"Loading book data from {config.PROCESSED_DATA_PATH}...")
book_data_df = pd.read_parquet(config.PROCESSED_DATA_PATH)
logger.info(f"Loading embeddings from {config.EMBEDDINGS_PATH}...")
embeddings_arr = np.load(config.EMBEDDINGS_PATH)
# 2. Cluster
n_clusters = config.NUM_CLUSTERS
logger.info(f"Clustering {len(book_data_df)} books into {n_clusters} clusters...")
clusters_arr, _ = cluster_books(embeddings_arr, n_clusters=n_clusters)
# 3. Name Clusters
book_data_df["cluster_id"] = clusters_arr
names = get_cluster_names(book_data_df, clusters_arr)
# 4. Save Cache
cache_path = config.PROCESSED_DATA_DIR / "cluster_cache.pkl"
logger.info(f"Saving cache to {cache_path}...")
try:
with open(cache_path, "wb") as f:
# Must match the tuple structure expected by api/dependencies.py
# (clusters_arr, names, book_data_df)
pickle.dump((clusters_arr, names, book_data_df), f)
logger.info("Successfully pre-computed and cached clusters.")
except Exception as e:
logger.error(f"Failed to save cache: {e}")
if __name__ == "__main__":
precompute_clusters()
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