File size: 7,728 Bytes
3751f09 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | from __future__ import annotations
import json
import logging
import os
import pickle
import sys
import time
_SCRIPTS_DIR = os.path.dirname(os.path.abspath(__file__))
_PROJECT_ROOT = os.path.dirname(_SCRIPTS_DIR)
_SRC_DIR = os.path.join(_PROJECT_ROOT, "src")
for _p in [_SRC_DIR, _PROJECT_ROOT]:
if _p not in sys.path:
sys.path.insert(0, _p)
import numpy as np
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)s %(message)s",
datefmt="%H:%M:%S",
)
logger = logging.getLogger(__name__)
BASE_DIR = _PROJECT_ROOT
PRECOMPUTED_DIR = os.path.join(BASE_DIR, "precomputed")
CANDIDATES_PATH = os.path.join(BASE_DIR, "candidates.jsonl")
def build_numpy_bm25_artifacts(bm25, precomputed_dir: str) -> None:
"""
Build scipy sparse BM25 score matrix from an existing BM25Okapi object.
Saves:
vocab.pkl - {term: row_index} mapping (tiny, fast to load)
bm25_matrix.npz - scipy sparse CSR (vocab_size × n_docs), float32
Each entry [term_idx, doc_idx] = precomputed
idf(term) × bm25_tf_adjusted(term, doc)
Scoring at runtime:
q_vec (1 × vocab_size) @ bm25_matrix (vocab_size × n_docs)
→ (1 × n_docs) dense result in a single scipy sparse op (<10 ms).
"""
try:
from scipy.sparse import coo_matrix, save_npz
except ImportError:
import subprocess
subprocess.check_call([sys.executable, "-m", "pip", "install", "scipy"])
from scipy.sparse import coo_matrix, save_npz
logger.info("Building NumPy sparse BM25 matrix …")
t0 = time.perf_counter()
k1: float = getattr(bm25, "k1", 1.5)
b: float = getattr(bm25, "b", 0.75)
avgdl: float = float(bm25.avgdl)
doc_len_arr = np.array(bm25.doc_len, dtype=np.float32)
n_docs: int = int(bm25.corpus_size)
# term -> row index
vocab: dict = {term: idx for idx, term in enumerate(bm25.idf.keys())}
idf_array = np.array([bm25.idf[term] for term in vocab], dtype=np.float32)
n_vocab: int = len(vocab)
logger.info(" vocab_size=%d n_docs=%d", n_vocab, n_docs)
rows_list: list = []
cols_list: list = []
data_list: list = []
checkpoint = max(1, n_docs // 10)
for doc_idx, doc_freq_dict in enumerate(bm25.doc_freqs):
dl = float(doc_len_arr[doc_idx])
denom_k = k1 * (1.0 - b + b * dl / avgdl)
for term, tf in doc_freq_dict.items():
term_idx = vocab.get(term)
if term_idx is None:
continue
tf_f = float(tf)
tf_adj = (tf_f * (k1 + 1.0)) / (tf_f + denom_k)
rows_list.append(term_idx)
cols_list.append(doc_idx)
data_list.append(float(idf_array[term_idx]) * tf_adj)
if doc_idx % checkpoint == 0 and doc_idx > 0:
logger.info(" … %d / %d docs processed", doc_idx, n_docs)
nnz = len(data_list)
logger.info(" COO built: nnz=%d (%.1f s)", nnz, time.perf_counter() - t0)
bm25_matrix = coo_matrix(
(
np.array(data_list, dtype=np.float32),
(np.array(rows_list, dtype=np.int32),
np.array(cols_list, dtype=np.int32)),
),
shape=(n_vocab, n_docs),
).tocsr()
elapsed = time.perf_counter() - t0
logger.info(" CSR matrix: shape=%s nnz=%d (%.1f s total)",
bm25_matrix.shape, bm25_matrix.nnz, elapsed)
vocab_path = os.path.join(precomputed_dir, "vocab.pkl")
matrix_path = os.path.join(precomputed_dir, "bm25_matrix.npz")
with open(vocab_path, "wb") as f:
pickle.dump(vocab, f, protocol=pickle.HIGHEST_PROTOCOL)
save_npz(matrix_path, bm25_matrix)
logger.info(" Saved vocab.pkl (%d terms)", n_vocab)
logger.info(" Saved bm25_matrix.npz (%.1f MB)",
os.path.getsize(matrix_path) / 1e6)
def build_candidate_offset_index(candidates_path: str, precomputed_dir: str) -> None:
"""
Scan candidates.jsonl once in binary mode and record the byte offset of
each candidate_id.
Saves candidate_offsets.pkl: {candidate_id: byte_offset}
At runtime Stage 2 uses f.seek(offset) + f.readline() for each of the
~8500 stage-1 candidates instead of streaming all 487 MB. Reduces
Stage 2 from ~4 s to ~0.1–0.3 s.
"""
logger.info("Building candidate byte-offset index …")
t0 = time.perf_counter()
offsets: dict = {}
size_bytes = os.path.getsize(candidates_path)
with open(candidates_path, "rb") as f:
while True:
offset = f.tell()
raw_line = f.readline()
if not raw_line:
break
stripped = raw_line.strip()
if not stripped:
continue
try:
cid = json.loads(stripped).get("candidate_id")
if cid:
offsets[cid] = offset
except json.JSONDecodeError:
pass
if len(offsets) % 10_000 == 0 and len(offsets) > 0:
pct = f.tell() / size_bytes * 100
logger.info(" … %d candidates indexed (%.0f%% of file)", len(offsets), pct)
elapsed = time.perf_counter() - t0
logger.info(" Offset index: %d candidates in %.1f s", len(offsets), elapsed)
out_path = os.path.join(precomputed_dir, "candidate_offsets.pkl")
with open(out_path, "wb") as f:
pickle.dump(offsets, f, protocol=pickle.HIGHEST_PROTOCOL)
logger.info(" Saved candidate_offsets.pkl (%.1f MB)",
os.path.getsize(out_path) / 1e6)
def export_lgbm_native(precomputed_dir: str) -> None:
"""
Re-save lgbm_model.pkl in LightGBM's native text format.
lgb.Booster(model_file=...) loads ~10-20x faster than pickle.
"""
import lightgbm as lgb
pkl_path = os.path.join(precomputed_dir, "lgbm_model.pkl")
txt_path = os.path.join(precomputed_dir, "lgbm_model.txt")
logger.info("Exporting LightGBM model to native text format …")
t0 = time.perf_counter()
with open(pkl_path, "rb") as f:
model = pickle.load(f)
model.save_model(txt_path)
logger.info(" Saved lgbm_model.txt (%.1f MB) in %.2f s",
os.path.getsize(txt_path) / 1e6,
time.perf_counter() - t0)
def main() -> None:
logger.info("=" * 60)
logger.info("REBUILD FAST ARTIFACTS")
logger.info("=" * 60)
t_total = time.perf_counter()
bm25_pkl = os.path.join(PRECOMPUTED_DIR, "bm25_index.pkl")
logger.info("Loading bm25_index.pkl (%.1f MB) …",
os.path.getsize(bm25_pkl) / 1e6)
t0 = time.perf_counter()
with open(bm25_pkl, "rb") as f:
bm25 = pickle.load(f)
logger.info(" Loaded in %.2f s", time.perf_counter() - t0)
build_numpy_bm25_artifacts(bm25, PRECOMPUTED_DIR)
build_candidate_offset_index(CANDIDATES_PATH, PRECOMPUTED_DIR)
export_lgbm_native(PRECOMPUTED_DIR)
logger.info("=" * 60)
logger.info("ALL ARTIFACTS BUILT in %.1f s", time.perf_counter() - t_total)
logger.info("New files in precomputed/:")
for fname in ["vocab.pkl", "bm25_matrix.npz", "candidate_offsets.pkl", "lgbm_model.txt"]:
fpath = os.path.join(PRECOMPUTED_DIR, fname)
if os.path.isfile(fpath):
logger.info(" %-30s %.1f MB", fname, os.path.getsize(fpath) / 1e6)
logger.info("rank.py will auto-detect these and use the fast paths.")
logger.info("=" * 60)
if __name__ == "__main__":
main()
|