File size: 3,342 Bytes
cd3d2c2 | 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 | import os
import pickle
from concurrent.futures import ProcessPoolExecutor, as_completed
from tqdm import tqdm
from src.score_career import compute_A, compute_keyword_max
from src.score_skills import compute_B
from src.score_embed import load_artifacts, get_C_map
def candidate_worker(candidate: dict, jd: dict, tfidf, keyword_max: float) -> tuple[dict, dict, dict]:
"""Module-level worker function to score a single candidate (picklable for ProcessPoolExecutor)."""
A_res = compute_A(candidate, jd, tfidf, keyword_max)
B_res = compute_B(candidate, jd)
return candidate, A_res, B_res
def compute_raw_scores(survivors: list[dict], jd: dict) -> list[dict]:
"""Hub module that coordinates Stage 2A, 2B, and 2C to compute raw scores in parallel."""
# Resolve paths
script_dir = os.path.dirname(os.path.abspath(__file__))
vettly_dir = os.path.dirname(script_dir)
precomputed_dir = os.path.join(vettly_dir, "data", "precomputed")
tfidf_pkl_path = os.path.join(precomputed_dir, "tfidf.pkl")
print("Loading TF-IDF vectorizer...")
with open(tfidf_pkl_path, "rb") as f:
tfidf = pickle.load(f)
print("Loading embedding artifacts...")
jd_vec, cand_vecs, cand_ids = load_artifacts(precomputed_dir)
print("Computing embedding similarities (Stage 2C)...")
C_map = get_C_map(jd_vec, cand_vecs, cand_ids)
print("Computing pool-wide keyword density maximum...")
keyword_max = compute_keyword_max(survivors, jd, tfidf)
results = []
# Process in parallel
print(f"Parallelizing scoring for {len(survivors)} candidates across CPU cores...")
max_workers = os.cpu_count() or 4
with ProcessPoolExecutor(max_workers=max_workers) as executor:
futures = {
executor.submit(candidate_worker, cand, jd, tfidf, keyword_max): cand
for cand in survivors
}
for future in tqdm(as_completed(futures), total=len(futures), desc="Scoring candidates"):
try:
candidate, A_res, B_res = future.result()
cand_id = str(candidate.get("id") or candidate.get("candidate_id") or "")
# Get Stage 2C embedding score
C = C_map.get(cand_id, 0.0)
# Assemble raw score: 0.40*A + 0.35*B + 0.25*C
A = A_res["A"]
B = B_res["B"]
raw_score = round(0.40 * A + 0.35 * B + 0.25 * C, 4)
results.append({
"candidate_id": cand_id,
"candidate": candidate,
"A": A,
"title_sim": A_res["title_sim"],
"industry_match": A_res["industry_match"],
"prod_keyword_density": A_res["prod_keyword_density"],
"yoe_score": A_res["yoe_score"],
"B": B,
"must_have_coverage": B_res["must_have_coverage"],
"nice_coverage": B_res["nice_coverage"],
"cert_bonus": B_res["cert_bonus"],
"C": C,
"raw_score": raw_score
})
except Exception as e:
print(f"Error scoring candidate: {e}")
return results
|