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9df97a2 | 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 | #!/usr/bin/env python3
"""Quick TF-IDF -> XGBoost training script for small validation runs.
Reads a JSONL of extraction records written by `run_extraction.py` (field `file`).
Builds synthetic positive/negative pairs and trains a lightweight classifier.
Usage:
PYTHONPATH=backend python backend/scripts/quick_train_tfidf_xgb.py --input data/extracted_test.jsonl --out models/test_match_model.joblib --limit 20
"""
from __future__ import annotations
import argparse
import json
import random
from pathlib import Path
import time
import joblib
import numpy as np
try:
from app.services.cv_extractor import CVExtractionService
except Exception:
CVExtractionService = None
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.ensemble import GradientBoostingClassifier
try:
from xgboost import XGBClassifier
XGB_AVAILABLE = True
except Exception:
XGB_AVAILABLE = False
def read_files_from_extracted(jsonl_path: Path, limit: int | None = None) -> list[Path]:
files: list[Path] = []
with jsonl_path.open("r", encoding="utf-8") as fh:
for i, line in enumerate(fh):
if limit and i >= limit:
break
try:
rec = json.loads(line)
files.append(Path(rec.get("file")))
except Exception:
continue
return files
def extract_texts(file_paths: list[Path]) -> list[str]:
texts: list[str] = []
service = None
if CVExtractionService is not None:
service = CVExtractionService()
for p in file_paths:
try:
if p.suffix.lower() == ".txt":
texts.append(p.read_text(encoding="utf-8", errors="ignore"))
else:
if service is not None:
res = service.extract_from_pdf(str(p))
texts.append(res.raw_text or "")
else:
# fallback: try reading as text
texts.append(p.read_text(encoding="utf-8", errors="ignore"))
except Exception:
texts.append("")
return texts
def build_pairs(texts: list[str], negative_ratio: float = 1.0):
pairs = []
labels = []
n = len(texts)
for i in range(n):
pairs.append((texts[i], texts[i]))
labels.append(1)
# negatives: random pairings
negatives = int(n * negative_ratio)
for _ in range(negatives):
a, b = random.sample(range(n), 2)
pairs.append((texts[a], texts[b]))
labels.append(0)
return pairs, labels
def pair_features(pairs, vectorizer, svd=None):
# Flatten texts to fit vectorizer
flat = [t for pair in pairs for t in pair]
X_flat = vectorizer.transform(flat)
if svd is not None:
X_flat = svd.transform(X_flat)
# reconstruct pairs
Xp = []
for i in range(0, len(flat), 2):
v1 = X_flat[i]
v2 = X_flat[i + 1]
diff = np.abs(v1 - v2)
cos = cosine_similarity(v1.reshape(1, -1), v2.reshape(1, -1))[0][0]
feat = np.hstack([diff, [cos]])
Xp.append(feat)
return np.vstack(Xp)
def main(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, help="JSONL produced by run_extraction.py")
parser.add_argument("--out", required=True, help="Output joblib model path")
parser.add_argument("--limit", type=int, default=50, help="Max files to read")
args = parser.parse_args(argv)
jsonl = Path(args.input)
files = read_files_from_extracted(jsonl, limit=args.limit)
if not files:
print("No files found in extracted JSONL")
return 2
print(f"Found {len(files)} files, extracting texts...")
texts = extract_texts(files)
# minimal preprocessing: filter empty
texts = [t if t else "" for t in texts]
pairs, labels = build_pairs(texts, negative_ratio=1.0)
# Fit vectorizer on single texts
corpus = texts
vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1,2))
vectorizer.fit(corpus)
# Transform full corpus for SVD fit
X_corpus = vectorizer.transform(corpus)
svd = TruncatedSVD(n_components=min(50, X_corpus.shape[1]-1)) if X_corpus.shape[1] > 2 else None
if svd is not None:
svd.fit(X_corpus)
print("Building pair features...")
X = pair_features(pairs, vectorizer, svd)
y = np.array(labels)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
if XGB_AVAILABLE:
model = XGBClassifier(use_label_encoder=False, eval_metric="logloss", n_estimators=50, verbosity=0)
else:
model = GradientBoostingClassifier(n_estimators=50)
print("Training model...")
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
print(f"Validation accuracy: {score:.3f}")
out_path = Path(args.out)
out_path.parent.mkdir(parents=True, exist_ok=True)
joblib.dump({"model": model, "vectorizer": vectorizer, "svd": svd}, out_path)
print(f"Saved model to {out_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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