File size: 27,733 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 | from __future__ import annotations
import argparse
import json
import logging
import math
import os
import pickle
import sys
import time
from datetime import datetime
from typing import Dict, List, Optional, Tuple
_SRC_DIR = os.path.dirname(os.path.abspath(__file__))
_PROJECT_ROOT = os.path.dirname(_SRC_DIR)
_SCRIPTS_DIR = os.path.join(_PROJECT_ROOT, "scripts")
for _p in [_SRC_DIR, _SCRIPTS_DIR, _PROJECT_ROOT]:
if _p not in sys.path:
sys.path.insert(0, _p)
import numpy as np
import pandas as pd
from rank_bm25 import BM25Okapi
def setup_logging(base_dir: str) -> logging.Logger:
"""Set up file + console logging."""
logs_dir = os.path.join(base_dir, "logs")
os.makedirs(logs_dir, exist_ok=True)
timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
log_file = os.path.join(logs_dir, f"rank_{timestamp}.log")
logger = logging.getLogger("rank")
logger.setLevel(logging.DEBUG)
fh = logging.FileHandler(log_file, encoding="utf-8")
fh.setLevel(logging.DEBUG)
fh.setFormatter(logging.Formatter(
"%(asctime)s %(levelname)s [%(name)s] %(message)s",
datefmt="%H:%M:%S"
))
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(logging.INFO)
ch.setFormatter(logging.Formatter(
"%(asctime)s %(levelname)s %(message)s",
datefmt="%H:%M:%S"
))
logger.addHandler(fh)
logger.addHandler(ch)
logger.info("Log file: %s", log_file)
return logger
def load_artifacts(precomputed_dir: str, logger: logging.Logger):
"""Load BM25 scorer, candidate IDs, and LightGBM model.
Tries fast NumPy / native-format artifacts first; falls back to pickle
if the fast artifacts haven't been built yet (backward-compatible).
"""
from retrieval import load_numpy_bm25_artifacts
bm25 = load_numpy_bm25_artifacts(precomputed_dir)
if bm25 is not None:
logger.info("Stage 0: NumpyBM25 loaded (fast path)")
else:
bm25_path = os.path.join(precomputed_dir, "bm25_index.pkl")
if not os.path.isfile(bm25_path):
logger.error("Missing artifact: %s — run precompute.py first", bm25_path)
sys.exit(1)
with open(bm25_path, "rb") as f:
bm25 = pickle.load(f)
logger.info("Stage 0: BM25Okapi loaded (legacy pickle path)")
ids_path = os.path.join(precomputed_dir, "candidate_ids.pkl")
if not os.path.isfile(ids_path):
logger.error("Missing artifact: %s — run precompute.py first", ids_path)
sys.exit(1)
with open(ids_path, "rb") as f:
candidate_ids = pickle.load(f)
lgbm_txt = os.path.join(precomputed_dir, "lgbm_model.txt")
lgbm_pkl = os.path.join(precomputed_dir, "lgbm_model.pkl")
model = None
if os.path.isfile(lgbm_txt):
try:
import lightgbm as lgb
t0 = time.time()
model = lgb.Booster(model_file=lgbm_txt)
logger.info("Stage 0: LightGBM loaded from native text (%.2f s)", time.time() - t0)
except Exception as exc:
logger.warning("lgbm native load failed (%s), falling back to pickle", exc)
if model is None:
if not os.path.isfile(lgbm_pkl):
logger.error("Missing artifact: %s — run precompute.py first", lgbm_pkl)
sys.exit(1)
with open(lgbm_pkl, "rb") as f:
model = pickle.load(f)
logger.info("Stage 0: LightGBM loaded from pickle (legacy path)")
static_path = os.path.join(precomputed_dir, "static_features.pkl")
static_features = None
if os.path.isfile(static_path):
try:
t0 = time.time()
with open(static_path, "rb") as f:
static_features = pickle.load(f)
logger.info("Stage 0: Loaded static features (%d candidates) in %.2fs", len(static_features), time.time() - t0)
except Exception as exc:
logger.warning("static_features.pkl load failed (%s), falling back to live calculation", exc)
else:
logger.warning("static_features.pkl not found — falling back to live calculation")
logger.info(
"Artifacts loaded: BM25 scorer (%s, %d candidates), LightGBM model",
type(bm25).__name__,
len(candidate_ids),
)
return bm25, candidate_ids, model, static_features
def load_stage1_candidates(
candidates_path: str,
stage1_ids: List[str],
logger: logging.Logger,
) -> Tuple[List[dict], int]:
"""
Stream-read candidates.jsonl and return only Stage 1 candidates.
Defensive against malformed records, missing fields, null values.
Returns:
(candidate_list, malformed_count)
"""
stage1_set = set(stage1_ids)
found: Dict[str, dict] = {}
malformed_count = 0
with open(candidates_path, "r", encoding="utf-8") as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if not line:
continue
try:
c = json.loads(line)
except json.JSONDecodeError as e:
malformed_count += 1
logger.warning("Malformed JSON at line %d: %s", line_num, e)
continue
cid = c.get("candidate_id")
if cid and cid in stage1_set:
found[cid] = c
if len(found) == len(stage1_set):
break
if malformed_count > 0:
logger.warning("Skipped %d malformed JSONL lines during loading", malformed_count)
missing = stage1_set - set(found.keys())
if missing:
logger.warning(
"%d stage1 candidates not found in JSONL: %s...",
len(missing), list(missing)[:5]
)
ordered = [found[cid] for cid in stage1_ids if cid in found]
logger.info(
"Loaded %d stage1 candidates (%d missing, %d malformed)",
len(ordered), len(missing), malformed_count
)
return ordered, malformed_count
def load_stage1_candidates_fast(
candidates_path: str,
stage1_ids: List[str],
offsets: Dict[str, int],
logger: logging.Logger,
) -> Tuple[List[dict], int]:
"""
Load Stage 1 candidate records using a precomputed byte-offset index.
Instead of streaming all 487 MB of candidates.jsonl, performs one
f.seek() + f.readline() per candidate. For ~8500 candidates this
reads ~43 MB total instead of 487 MB, reducing Stage 2 from ~4 s
to ~0.1–0.3 s.
Returns:
(candidate_list, malformed_count)
"""
ordered: List[dict] = []
malformed_count = 0
missing: List[str] = []
with open(candidates_path, "rb") as f:
for cid in stage1_ids:
offset = offsets.get(cid)
if offset is None:
missing.append(cid)
continue
f.seek(offset)
raw = f.readline()
try:
c = json.loads(raw.decode("utf-8", errors="ignore").strip())
ordered.append(c)
except json.JSONDecodeError as exc:
logger.warning("Malformed record at offset %d for %s: %s", offset, cid, exc)
malformed_count += 1
if missing:
logger.warning(
"%d stage1 candidates not in offset index: %s ...",
len(missing), missing[:5],
)
logger.info(
"Loaded %d stage1 candidates via offset index (%d missing, %d malformed)",
len(ordered), len(missing), malformed_count,
)
return ordered, malformed_count
def extract_features_for_ranking(
candidates: List[dict],
jd_config,
bm25_scores: Dict[str, float],
stage1_bm25_median: float,
logger: logging.Logger,
static_features: Optional[Dict[str, Dict[str, float]]] = None,
) -> Tuple[np.ndarray, List[str], Dict[str, float]]:
"""
Extract the 22-feature matrix for all Stage 1 candidates.
Returns:
(X: np.ndarray[N, 22], ordered_ids: List[str], consistency_map: Dict[str, float])
"""
from features import build_feature_vector, FEATURE_COLUMNS
feature_rows = []
ordered_ids = []
consistency_map = {}
failed_count = 0
for candidate in candidates:
cid = candidate.get("candidate_id", "UNKNOWN")
bm25_score = bm25_scores.get(cid, 0.0)
try:
fv = build_feature_vector(
candidate, jd_config,
bm25_score=bm25_score,
stage1_bm25_median=stage1_bm25_median,
precomputed_static=static_features.get(cid) if static_features else None
)
row = [fv[col] for col in FEATURE_COLUMNS]
consistency_map[cid] = float(fv.get("consistency_score", 1.0))
except Exception as e:
logger.warning("Feature extraction failed for %s: %s", cid, e)
row = [0.0] * len(FEATURE_COLUMNS)
consistency_map[cid] = 1.0
failed_count += 1
feature_rows.append(row)
ordered_ids.append(cid)
if failed_count > 0:
logger.warning("Feature extraction failed for %d candidates (zeroed out)", failed_count)
X = np.array(feature_rows, dtype=np.float32)
logger.info("Feature matrix: shape=%s", X.shape)
return X, ordered_ids, consistency_map
def run_lightgbm_inference(
model,
X: np.ndarray,
ordered_ids: List[str],
logger: logging.Logger,
) -> Dict[str, float]:
"""
Run LightGBM predict on the feature matrix.
Returns:
{candidate_id: lgbm_score}
"""
t0 = time.time()
raw_scores = model.predict(X)
elapsed = time.time() - t0
logger.info(
"LightGBM inference: %d candidates in %.2fs", len(ordered_ids), elapsed
)
return {cid: float(score) for cid, score in zip(ordered_ids, raw_scores)}
def _normalize_scores(top_100_raw: List[Tuple[str, float]], logger: logging.Logger) -> List[Tuple[str, float, int]]:
"""
Apply min-max normalization to raw scores and assign ranks 1..N.
Returns: List of (candidate_id, score, rank) sorted by rank.
"""
if not top_100_raw:
return []
top_scores = [s for _, s in top_100_raw]
score_min = top_scores[-1]
score_max = top_scores[0]
score_range = score_max - score_min
result = []
prev_normalized = None
for rank, (cid, raw_score) in enumerate(top_100_raw, 1):
if score_range > 0:
normalized = 0.01 + 0.99 * (raw_score - score_min) / score_range
else:
normalized = 1.0 - (rank - 1) / 99.0
if prev_normalized is not None and normalized > prev_normalized + 1e-9:
logger.error("MONOTONICITY VIOLATION at rank %d", rank)
prev_normalized = normalized
result.append((cid, normalized, rank))
logger.info("Top 100 selected: score range [%.6f, %.6f]",
result[-1][1], result[0][1])
return result
def sort_and_enforce_monotonicity(
lgbm_scores: Dict[str, float],
logger: logging.Logger,
) -> List[Tuple[str, float, int]]:
"""
Sort candidates by score descending. Break ties by ascending candidate_id.
Assign ranks 1..N.
Returns:
List of (candidate_id, score, rank) sorted by rank.
"""
sorted_candidates = sorted(
lgbm_scores.items(),
key=lambda x: (-x[1], x[0]),
)
top_100_raw = sorted_candidates[:100]
return _normalize_scores(top_100_raw, logger)
def assert_monotonicity(ranked: List[Tuple[str, float, int]]) -> None:
"""
Explicit runtime assertion: scores must be monotonically non-increasing by rank.
This runs BEFORE writing the CSV — not just by sorting and hoping.
Raises AssertionError if violated.
"""
for i in range(1, len(ranked)):
prev_score = ranked[i-1][1]
curr_score = ranked[i][1]
assert curr_score <= prev_score + 1e-9, (
f"Monotonicity violation: rank {i} score {prev_score:.8f} "
f"< rank {i+1} score {curr_score:.8f}"
)
def run_honeypot_audit(
top_100_candidates: List[dict],
feature_vectors: Dict[str, dict],
logger: logging.Logger,
) -> None:
"""
Section 8.1: Pre-Submission Honeypot Audit.
assert count(consistency_score < 0.25 in top_100) < 10.
If this assertion fails, rank.py exits non-zero.
"""
low_consistency_count = sum(
1 for c in top_100_candidates
if feature_vectors.get(c.get("candidate_id", ""), {}).get("consistency_score", 1.0) < 0.25
)
logger.info(
"Honeypot audit: %d of 100 candidates have consistency_score < 0.25",
low_consistency_count
)
if low_consistency_count >= 10:
logger.error(
"HONEYPOT AUDIT FAILED: %d candidates with consistency_score < 0.25 "
"(threshold: < 10). Pipeline is broken — honeypots bypassed filters.",
low_consistency_count
)
sys.exit(2)
logger.info("Honeypot audit PASSED.")
def run_diversity_audit(
top_100_candidates: List[dict],
feature_vectors: Dict[str, dict],
logger: logging.Logger,
) -> None:
"""
Section 8.2: Top 100 Diversity & Homogeneity Audit.
Uses validate_pipeline.check_top100_diversity.
If the check fails, rank.py exits non-zero with a clear error.
This is a BLOCKING check — not just a warning.
"""
from validate_pipeline import check_top100_diversity, print_diversity_report
report = check_top100_diversity(
top_100_candidates,
feature_vectors,
max_signature_share=0.25,
max_single_company_share=0.30,
)
print_diversity_report(report)
logger.info(
"Diversity audit: %d distinct archetypes, max_company=%.1f%%, max_sig=%.1f%%",
report["n_distinct_signatures"],
report["most_common_company_share"] * 100,
report["most_common_signature_share"] * 100,
)
if not report["pass"]:
if report["flagged_companies"]:
logger.error(
"DIVERSITY AUDIT FAILED: company concentration too high: %s",
report["flagged_companies"]
)
if report["flagged_signatures"]:
logger.error(
"DIVERSITY AUDIT FAILED: archetype signature concentration too high: %s",
report["flagged_signatures"]
)
sys.exit(3)
logger.info("Diversity audit PASSED.")
def write_reasoning_trace(
top_30_traces: List[dict],
base_dir: str,
logger: logging.Logger,
) -> None:
"""Write reasoning_trace.jsonl for top 30 candidates."""
trace_path = os.path.join(base_dir, "reasoning_trace.jsonl")
with open(trace_path, "w", encoding="utf-8") as f:
for trace in top_30_traces:
f.write(json.dumps(trace, ensure_ascii=False) + "\n")
logger.info("Reasoning trace written: %s (%d entries)", trace_path, len(top_30_traces))
def pipeline_fn(
candidates: List[dict],
jd_config,
disable_consistency: bool = False,
disable_param_a: bool = False,
disable_features: bool = False,
) -> List[str]:
"""
Pipeline function compatible with validate_pipeline.run_ablation.
Accepts a list of candidate dicts + jd_config, returns ranked candidate_ids.
This runs the full in-memory pipeline (for small candidate sets).
"""
from features import build_feature_vector, FEATURE_COLUMNS, consistency_score
from precompute import tokenize_candidate
corpus = [tokenize_candidate(c) for c in candidates]
bm25 = BM25Okapi(corpus)
cids = [c.get("candidate_id", f"IDX_{i}") for i, c in enumerate(candidates)]
from retrieval import tokenize_query
query_tokens = tokenize_query(jd_config.get_all_query_terms() + jd_config.production_keywords)
raw_scores = bm25.get_scores(query_tokens)
bm25_scores = {cids[i]: float(raw_scores[i]) for i in range(len(cids))}
median_bm25 = float(np.median(list(bm25_scores.values())))
feature_rows = []
for c in candidates:
cid = c.get("candidate_id", "")
bs = bm25_scores.get(cid, 0.0)
if disable_features:
row = [bs] + [0.0] * 21
else:
try:
fv = build_feature_vector(c, jd_config, bs, median_bm25)
if disable_consistency:
fv["consistency_score"] = 1.0
if disable_param_a:
fv["Param_A_Systems_Depth"] = 0.0
row = [fv[col] for col in FEATURE_COLUMNS]
except Exception:
row = [bs] + [0.0] * 21
feature_rows.append(row)
try:
import pickle
base = _PROJECT_ROOT
with open(os.path.join(base, "precomputed", "lgbm_model.pkl"), "rb") as f:
model = pickle.load(f)
X = np.array(feature_rows, dtype=np.float32)
scores = model.predict(X)
except Exception:
scores = np.array([bm25_scores.get(cid, 0.0) for cid in cids])
ranked = sorted(
zip(cids, scores.tolist()),
key=lambda x: (-x[1], x[0])
)
return [cid for cid, _ in ranked]
def main() -> None:
parser = argparse.ArgumentParser(
description="Redrob Candidate Ranking Pipeline",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--candidates",
required=True,
help="Path to candidates.jsonl",
)
parser.add_argument(
"--out",
default="./CTRL_COFFEE_REPEAT.csv",
help="Path for output CTRL_COFFEE_REPEAT.csv",
)
parser.add_argument(
"--base-dir",
default=None,
help="Base directory (defaults to directory containing rank.py)",
)
args = parser.parse_args()
script_dir = _PROJECT_ROOT
base_dir = os.path.abspath(args.base_dir) if args.base_dir else script_dir
candidates_path = os.path.abspath(args.candidates)
out_path = os.path.abspath(args.out)
precomputed_dir = os.path.join(base_dir, "precomputed")
logger = setup_logging(base_dir)
wall_start = time.time()
logger.info("=" * 60)
logger.info("REDROB RANKING PIPELINE")
logger.info("Candidates: %s", candidates_path)
logger.info("Output: %s", out_path)
logger.info("Base dir: %s", base_dir)
logger.info("=" * 60)
t0 = time.time()
bm25, candidate_ids, model, static_features = load_artifacts(precomputed_dir, logger)
logger.info("Stage 0 (load artifacts): %.2fs", time.time() - t0)
t1 = time.time()
from jd_parser import parse_jd
from retrieval import run_dual_pass_retrieval
jd_config = parse_jd(os.path.join(base_dir, "data", "skill_aliases.json"))
stage1_ids, bm25_scores = run_dual_pass_retrieval(bm25, candidate_ids, jd_config)
stage1_bm25_scores_list = list(bm25_scores.values())
stage1_bm25_median = float(np.median(stage1_bm25_scores_list))
logger.info(
"Stage 1 (retrieval): %d candidates retrieved, median BM25=%.4f in %.2fs",
len(stage1_ids), stage1_bm25_median, time.time() - t1
)
t2 = time.time()
offsets_path = os.path.join(precomputed_dir, "candidate_offsets.pkl")
if os.path.isfile(offsets_path):
with open(offsets_path, "rb") as f:
candidate_offsets = pickle.load(f)
stage1_candidates, malformed_count = load_stage1_candidates_fast(
candidates_path, stage1_ids, candidate_offsets, logger
)
else:
logger.info("Stage 2: offset index not found — streaming full JSONL (slow)")
stage1_candidates, malformed_count = load_stage1_candidates(
candidates_path, stage1_ids, logger
)
logger.info(
"Stage 2 (load records): %d candidates loaded (%d malformed) in %.2f s",
len(stage1_candidates), malformed_count, time.time() - t2
)
t2b = time.time()
X, ordered_ids, consistency_map = extract_features_for_ranking(
stage1_candidates, jd_config, bm25_scores, stage1_bm25_median, logger,
static_features=static_features
)
logger.info("Stage 2b (features): %.2fs", time.time() - t2b)
t4 = time.time()
lgbm_scores = run_lightgbm_inference(model, X, ordered_ids, logger)
for cid in lgbm_scores:
lgbm_scores[cid] *= consistency_map.get(cid, 1.0)
logger.info("Stage 4 (LightGBM + multiplier): %.2fs", time.time() - t4)
t5 = time.time()
ranked_top100 = sort_and_enforce_monotonicity(lgbm_scores, logger)
assert_monotonicity(ranked_top100)
logger.info("Monotonicity assertion PASSED.")
assert len(ranked_top100) == 100, (
f"Expected exactly 100 candidates, got {len(ranked_top100)}"
)
logger.info("Count assertion PASSED: exactly 100 candidates.")
top100_ids = [cid for cid, _, _ in ranked_top100]
from features import build_feature_vector, FEATURE_COLUMNS
candidate_lookup: Dict[str, dict] = {
c.get("candidate_id"): c for c in stage1_candidates
}
feature_vectors: Dict[str, dict] = {}
for cid in top100_ids:
c = candidate_lookup.get(cid)
if c is None:
feature_vectors[cid] = {col: 0.0 for col in FEATURE_COLUMNS}
continue
bs = bm25_scores.get(cid, 0.0)
try:
feature_vectors[cid] = build_feature_vector(
c, jd_config, bs, stage1_bm25_median,
precomputed_static=static_features.get(cid) if static_features else None
)
except Exception:
feature_vectors[cid] = {col: 0.0 for col in FEATURE_COLUMNS}
top100_candidates = [candidate_lookup[cid] for cid in top100_ids if cid in candidate_lookup]
run_honeypot_audit(top100_candidates, feature_vectors, logger)
run_diversity_audit(top100_candidates, feature_vectors, logger)
t5r = time.time()
from reasoning import ReasoningCompiler
all_lgbm_scores = [lgbm_scores[cid] for cid in top100_ids if cid in lgbm_scores]
compiler = ReasoningCompiler(jd_config, all_scores=all_lgbm_scores)
reasoning_texts: Dict[str, str] = {}
reasoning_traces: List[dict] = []
for cid, norm_score, rank in ranked_top100:
c = candidate_lookup.get(cid, {"candidate_id": cid})
fv = feature_vectors.get(cid, {col: 0.0 for col in FEATURE_COLUMNS})
raw_lgbm = lgbm_scores.get(cid, 0.0)
if rank <= 30:
trace = compiler.compile_trace(c, fv, raw_lgbm, rank)
reasoning_traces.append(trace)
reasoning_texts[cid] = trace["reasoning"]
else:
reasoning_texts[cid] = compiler.compile(c, fv, raw_lgbm, rank)
logger.info("Stage 5 (reasoning): %.2fs", time.time() - t5r)
write_reasoning_trace(reasoning_traces, base_dir, logger)
rows = []
for cid, norm_score, rank in ranked_top100:
rows.append({
"candidate_id": cid,
"rank": rank,
"score": round(norm_score, 6),
"reasoning": reasoning_texts.get(cid, ""),
})
df = pd.DataFrame(rows, columns=["candidate_id", "rank", "score", "reasoning"])
assert len(df) == 100, f"DataFrame has {len(df)} rows, expected 100"
assert list(df.columns) == ["candidate_id", "rank", "score", "reasoning"], \
f"Unexpected columns: {list(df.columns)}"
scores_arr = df["score"].values
for i in range(1, len(scores_arr)):
assert scores_arr[i] <= scores_arr[i-1] + 1e-9, (
f"DataFrame monotonicity violation at row {i}: "
f"{scores_arr[i-1]:.8f} -> {scores_arr[i]:.8f}"
)
logger.info("Final DataFrame monotonicity assertion PASSED.")
df.to_csv(out_path, index=False, encoding="utf-8")
logger.info("Submission CSV written: %s", out_path)
wall_elapsed = time.time() - wall_start
logger.info("=" * 60)
logger.info("PIPELINE COMPLETE")
logger.info("Wall-clock time: %.2fs (limit: 300s)", wall_elapsed)
logger.info("Output: %s", out_path)
logger.info("Candidates ranked: 100")
logger.info("=" * 60)
if wall_elapsed > 300:
logger.error(
"TIMING VIOLATION: Pipeline took %.1fs > 300s limit", wall_elapsed
)
sys.exit(4)
print("\n--- submission.csv (first 5 rows) ---")
print(df.head(5).to_string(index=False))
print(f"\nTotal rows: {len(df)}")
print(f"Score range: [{df['score'].min():.6f}, {df['score'].max():.6f}]")
print(f"Wall-clock: {wall_elapsed:.1f}s")
if __name__ == "__main__":
main()
|