File size: 6,177 Bytes
fc329a3 | 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 | """Build a fully open AffectiveText prediction cache with out-of-fold regressors."""
from __future__ import annotations
import argparse
import json
import logging
from pathlib import Path
import numpy as np
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import KFold
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import Normalizer
import sys
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from src.data import EMOTION_NAMES, load_affective_text
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)
def macro_pearson(a: np.ndarray, b: np.ndarray) -> float:
vals = []
for j in range(a.shape[1]):
aj = a[:, j]
bj = b[:, j]
if np.std(aj) <= 1e-12 or np.std(bj) <= 1e-12:
continue
vals.append(float(np.corrcoef(aj, bj)[0, 1]))
return float(np.mean(vals)) if vals else float("nan")
def fit_predict_fold(
train_texts: list[str],
test_texts: list[str],
train_targets: np.ndarray,
n_components: int,
n_neighbors: int,
) -> np.ndarray:
vectorizer = TfidfVectorizer(
lowercase=True,
strip_accents="unicode",
sublinear_tf=True,
ngram_range=(1, 2),
min_df=1,
max_df=0.95,
stop_words="english",
)
x_train = vectorizer.fit_transform(train_texts)
x_test = vectorizer.transform(test_texts)
max_rank = min(x_train.shape[0] - 1, x_train.shape[1] - 1)
if max_rank >= 2:
rank = min(n_components, max_rank)
svd = TruncatedSVD(n_components=rank, random_state=0)
normalizer = Normalizer(copy=False)
x_train = normalizer.fit_transform(svd.fit_transform(x_train))
x_test = normalizer.transform(svd.transform(x_test))
else:
x_train = x_train.toarray()
x_test = x_test.toarray()
knn = KNeighborsRegressor(
n_neighbors=min(n_neighbors, len(train_texts)),
weights="distance",
metric="minkowski",
p=2,
)
knn.fit(x_train, train_targets)
return np.asarray(knn.predict(x_test), dtype=float)
def build_open_predictions(
headlines: list[str],
raw_scores: np.ndarray,
n_splits: int,
n_components: int,
n_neighbors: int,
seed: int,
) -> tuple[np.ndarray, np.ndarray]:
n = len(headlines)
preds = np.zeros_like(raw_scores, dtype=float)
folds = np.full(n, -1, dtype=int)
splitter = KFold(n_splits=n_splits, shuffle=True, random_state=seed)
global_mean = raw_scores.mean(axis=0)
for fold_id, (train_idx, test_idx) in enumerate(splitter.split(headlines)):
train_texts = [headlines[i] for i in train_idx]
test_texts = [headlines[i] for i in test_idx]
train_targets = raw_scores[train_idx]
fold_preds = fit_predict_fold(
train_texts=train_texts,
test_texts=test_texts,
train_targets=train_targets,
n_components=n_components,
n_neighbors=n_neighbors,
)
fold_preds = np.clip(fold_preds, 0.0, None)
zero_rows = fold_preds.sum(axis=1) <= 1e-12
if np.any(zero_rows):
fold_preds[zero_rows] = global_mean
preds[test_idx] = fold_preds
folds[test_idx] = fold_id
log.info("Finished fold %d/%d", fold_id + 1, n_splits)
return preds, folds
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", default="data/raw/AffectiveText.Semeval.2007")
parser.add_argument("--output", default="data/processed/affective_text_open_oof_predictions.jsonl")
parser.add_argument("--n-splits", type=int, default=5)
parser.add_argument("--n-components", type=int, default=128)
parser.add_argument("--n-neighbors", type=int, default=25)
parser.add_argument("--seed", type=int, default=2026)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--overwrite", action="store_true")
args = parser.parse_args()
output_path = Path(args.output)
if output_path.exists() and not args.overwrite:
raise FileExistsError(f"Output already exists: {output_path}")
data = load_affective_text(args.data_dir)
ids = data["ids"]
headlines = data["headlines"]
raw_scores = np.asarray(data["raw_scores"], dtype=float)
if args.limit is not None:
ids = ids[:args.limit]
headlines = headlines[:args.limit]
raw_scores = raw_scores[:args.limit]
pred_scores, folds = build_open_predictions(
headlines=headlines,
raw_scores=raw_scores,
n_splits=args.n_splits,
n_components=args.n_components,
n_neighbors=args.n_neighbors,
seed=args.seed,
)
macro_r = macro_pearson(raw_scores, pred_scores)
flat_r = float(np.corrcoef(raw_scores.reshape(-1), pred_scores.reshape(-1))[0, 1])
log.info(
"Open fallback predictor quality: macro Pearson=%.3f, flattened Pearson=%.3f",
macro_r,
flat_r,
)
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, "w", encoding="utf-8") as f:
for idx, headline, scores, fold_id in zip(ids, headlines, pred_scores, folds):
row = {
"id": idx,
"headline": headline,
"emotions": EMOTION_NAMES,
"scores": [float(x) for x in scores],
"provider": "open_fallback",
"model": "tfidf_svd_knn_oof",
"fold": int(fold_id),
"builder": {
"n_splits": int(args.n_splits),
"n_components": int(args.n_components),
"n_neighbors": int(args.n_neighbors),
"seed": int(args.seed),
},
"notes": "Deterministic out-of-fold TF-IDF+SVD+kNN regression fallback.",
}
f.write(json.dumps(row, ensure_ascii=True) + "\n")
log.info("Finished. Predictions cached at %s", output_path)
if __name__ == "__main__":
main()
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