Datasets:
File size: 3,526 Bytes
92c67c9 | 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 | """Character TF-IDF + logistic regression smoke baseline (train-only fit).
Run from the dataset root: python scripts/v1_1/baseline.py
Writes reports/baseline-metrics.json. No checkpoint is saved.
"""
from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, f1_score
ROOT = Path(__file__).resolve().parents[2]
ORIGIN_V11 = "synthetic_programmatic_template"
def _load(config: str, split: str) -> pd.DataFrame:
return pd.read_json(ROOT / f"data/{config}/{split}.jsonl", lines=True, dtype={"label": str})
def _score(y_true, y_pred) -> dict:
return {"rows": int(len(y_true)), "accuracy": round(float(accuracy_score(y_true, y_pred)), 4),
"macro_f1": round(float(f1_score(y_true, y_pred, average="macro", zero_division=0)), 4)}
def main() -> None:
out = {"purpose": "Synthetic classifier smoke benchmark; not real-document acceptance.",
"estimator": "character TF-IDF ngrams 3..5 (sublinear tf) and balanced logistic regression C=2.0",
"sklearn_version": sklearn.__version__, "train_only_fit": True, "model_checkpoint_saved": False, "tasks": {}}
masked = pd.read_parquet(ROOT / "robustness_views/test_header_masked_v1_1.parquet")
for task in ("document_type", "document_family"):
train, val, test = (_load(task, s) for s in ("train", "validation", "test"))
vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(3, 5), sublinear_tf=True, min_df=2, max_features=300000)
x = vec.fit_transform(train["text"])
clf = LogisticRegression(C=2.0, class_weight="balanced", max_iter=3000)
clf.fit(x, train["label"])
res = {}
for name, frame in (("validation", val), ("test", test)):
pred = clf.predict(vec.transform(frame["text"]))
res[name] = _score(frame["label"], pred)
for origin, tag in (("synthetic_AI_labeled", "v1_0_rows"), (ORIGIN_V11, "v1_1_rows")):
m = (frame["origin"] == origin).to_numpy()
res[name][tag] = _score(frame["label"][m], pred[m])
if name == "test":
rep = classification_report(frame["label"], pred, output_dict=True, zero_division=0)
res[name]["per_label"] = {k: {kk: round(vv, 4) for kk, vv in v.items()} for k, v in rep.items()
if isinstance(v, dict) and k not in ("macro avg", "weighted avg")}
errors = frame.assign(pred=pred).loc[lambda f: f["label"] != f["pred"]]
res[name]["confusions"] = errors.groupby(["label", "pred"]).size().sort_values(ascending=False).head(25) \
.reset_index().rename(columns={0: "count"}).to_dict("records")
target = masked["label"] if task == "document_type" else masked["family"]
res["test_header_masked_v1_1"] = _score(target, clf.predict(vec.transform(masked["text"])))
out["tasks"][task] = res
(ROOT / "reports/baseline-metrics.json").write_text(json.dumps(out, indent=2) + "\n")
print(json.dumps({t: {k: {kk: vv for kk, vv in v.items() if kk not in ("per_label", "confusions")}
for k, v in r.items()} for t, r in out["tasks"].items()}, indent=1))
print(json.dumps(out["tasks"]["document_type"]["test"]["confusions"][:15]))
if __name__ == "__main__":
main()
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