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from __future__ import annotations
import hashlib
import json
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.metrics import roc_curve
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import SGDClassifier
ROOT = Path(__file__).resolve().parents[1]
CORPUS = ROOT / "data/processed/corpus.parquet"
TRAIN = ROOT / "data/splits/chronological/train.json"
DATE_CACHE = ROOT / "data/processed/secvuleval_commit_dates.jsonl"
OUT_JSON = ROOT / "results/phase7/char_forward_train_subgroups.json"
OUT_MD = ROOT / "results/phase7/char_forward_train_subgroups.md"
SEEDS = tuple(range(10))
FOLDS = (2019, 2020, 2021)
def wilson(k: int, n: int) -> list[float] | None:
if not n:
return None
z, p = 1.96, k / n
d = 1 + z*z/n
c = p + z*z/(2*n)
h = z*np.sqrt(p*(1-p)/n + z*z/(4*n*n))
return [float((c-h)/d), float((c+h)/d)]
def metric(mask: np.ndarray, y: np.ndarray, pred: np.ndarray) -> dict:
safe = mask & (y == 0)
vuln = mask & (y == 1)
good = pred == y
n, k = int(mask.sum()), int(np.sum(mask & good))
ks, kv = int(np.sum(safe & good)), int(np.sum(vuln & good))
return {
"n_rows": n,
"n_pairs": n // 2,
"accuracy": k/n if n else None,
"wilson_95": wilson(k, n),
"safe_n": int(safe.sum()),
"safe_accuracy": ks/int(safe.sum()) if safe.any() else None,
"safe_wilson_95": wilson(ks, int(safe.sum())),
"vulnerable_n": int(vuln.sum()),
"vulnerable_accuracy": kv/int(vuln.sum()) if vuln.any() else None,
"vulnerable_wilson_95": wilson(kv, int(vuln.sum())),
}
def fit_score(fit: pd.DataFrame, held: pd.DataFrame,
sample_weight: np.ndarray | None = None) -> np.ndarray:
vectorizer = TfidfVectorizer(analyzer="char", ngram_range=(3, 5), min_df=2,
max_features=300_000, sublinear_tf=True,
dtype=np.float32)
xfit = vectorizer.fit_transform(fit.text.tolist())
xheld = vectorizer.transform(held.text.tolist())
yfit = fit.target.to_numpy(dtype=np.int8)
all_scores = []
for seed in SEEDS:
model = SGDClassifier(loss="log_loss", alpha=1e-5, max_iter=200,
tol=1e-3, random_state=seed, average=True)
model.fit(xfit, yfit, sample_weight=sample_weight)
all_scores.append(model.decision_function(xheld))
return np.mean(np.stack(all_scores), axis=0)
def length_label_weights(fit: pd.DataFrame) -> np.ndarray:
"""Equalize the six observed label × fixed-length cells using TRAIN only."""
labels = fit.target.to_numpy(dtype=np.int8)
lines = fit.vuln_lines.to_numpy()
bins = np.where(lines <= 30, 0, np.where(lines <= 100, 1, 2))
cell = labels * 3 + bins
counts = np.bincount(cell, minlength=6)
present = counts > 0
n_present = int(present.sum())
weights = np.asarray([len(fit) / (n_present * counts[c]) for c in cell],
dtype=np.float64)
if not np.isclose(weights.mean(), 1.0):
raise AssertionError("sample weights must have mean one")
return weights
def select_threshold(scores: np.ndarray, labels: np.ndarray) -> float:
"""Select a balanced-accuracy threshold on calibration-only predictions."""
fpr, tpr, thresholds = roc_curve(labels, scores, pos_label=1)
finite = np.isfinite(thresholds)
thresholds, fpr, tpr = thresholds[finite], fpr[finite], tpr[finite]
prevalence = float(np.mean(labels == 1))
ba = (tpr + 1 - fpr) / 2
accuracy = prevalence * tpr + (1 - prevalence) * (1 - fpr)
order = np.lexsort((-
np.abs(thresholds), accuracy, ba))
return float(thresholds[order[-1]])
def nested_length_thresholds(oof: pd.DataFrame) -> list[dict]:
"""Calibrate per fixed length band on earlier OOF years, score next year."""
reports = []
rng = np.random.default_rng(42)
bands = (("short_le_30", 1, 30), ("medium_31_100", 31, 100),
("long_gt_100", 101, None))
for year in FOLDS:
calibration = oof[oof.year < year]
held = oof[oof.year == year]
if calibration.empty:
continue
cy = calibration.target.to_numpy(dtype=np.int8)
cs = calibration.score.to_numpy()
global_threshold = select_threshold(cs, cy)
thresholds = {}
for name, low, high in bands:
cmask = calibration.vuln_lines.to_numpy() >= low
if high is not None:
cmask &= calibration.vuln_lines.to_numpy() <= high
counts = [int(np.sum(cmask & (cy == label))) for label in (0, 1)]
if min(counts) >= 50:
threshold = select_threshold(cs[cmask], cy[cmask])
fallback = False
else:
threshold = global_threshold
fallback = True
thresholds[name] = {"threshold": threshold,
"calibration_safe_n": counts[0],
"calibration_vulnerable_n": counts[1],
"pooled_threshold_fallback": fallback}
ey = held.target.to_numpy(dtype=np.int8)
es = held.score.to_numpy()
lens = held.vuln_lines.to_numpy()
pred = np.empty(len(held), dtype=np.int8)
for name, low, high in bands:
mask = lens >= low
if high is not None:
mask &= lens <= high
pred[mask] = (es[mask] >= thresholds[name]["threshold"]).astype(np.int8)
fixed = (es >= 0).astype(np.int8)
pair_effects = []
rows = pd.DataFrame({"pair_id": held.pair_id.to_numpy(), "y": ey,
"fixed": fixed, "calibrated": pred})
for _, group in rows.groupby("pair_id", sort=True):
if len(group) != 2 or set(group.y.astype(int)) != {0, 1}:
raise ValueError("pair integrity failed in threshold comparison")
pair_effects.append(float(np.mean(group.calibrated.to_numpy() == group.y.to_numpy()) -
np.mean(group.fixed.to_numpy() == group.y.to_numpy())))
effects = np.asarray(pair_effects)
draws = effects[rng.integers(0, len(effects), size=(10_000, len(effects)))].mean(axis=1)
reports.append({
"held_year": int(year),
"calibration_years": sorted(int(y) for y in calibration.year.unique()),
"calibration_pairs": int(calibration.pair_id.nunique()),
"thresholds_by_length": thresholds,
"fixed_zero_threshold": metric(np.ones(len(held), dtype=bool), ey, fixed),
"length_calibrated": metric(np.ones(len(held), dtype=bool), ey, pred),
"balanced_accuracy_delta_pp": float(100 * effects.mean()),
"pair_bootstrap_95_ci_delta_pp": [
float(100 * x) for x in np.quantile(draws, [0.025, 0.975])],
})
return reports
def main() -> None:
frame = pd.read_parquet(CORPUS)
date_map = {}
for line in DATE_CACHE.read_text().splitlines():
obj = json.loads(line)
if obj.get("date"):
date_map[str(obj["commit_id"])] = str(obj["date"])
missing = frame.commit_date.isna()
frame.loc[missing, "commit_date"] = frame.loc[missing, "commit_id"].astype(str).map(date_map)
frame["year"] = pd.to_numeric(frame.commit_date.astype(str).str[:4], errors="coerce")
train_ids = set(map(str, json.loads(TRAIN.read_text())))
sub = frame[frame.pair_id.astype(str).isin(train_ids)]
records = []
for pid, grp in sub.groupby("pair_id", sort=True):
if len(grp) != 2 or set(grp.target.astype(int)) != {0, 1}:
raise ValueError(f"pair integrity failure: {pid}")
if grp.func_vuln.nunique() != 1 or grp.func_patch.nunique() != 1:
raise ValueError(f"paired code mismatch: {pid}")
if grp.cwe.nunique(dropna=False) != 1 or grp.year.nunique(dropna=False) != 1:
raise ValueError(f"paired metadata mismatch: {pid}")
vrow = grp[grp.target.astype(int) == 1].iloc[0]
for target in (0, 1):
row = grp[grp.target.astype(int) == target].iloc[0]
records.append({
"pair_id": str(pid), "target": target, "year": row.year,
"text": str(row.func_vuln if target else row.func_patch),
"cwe": str(vrow.cwe) if pd.notna(vrow.cwe) else "UNKNOWN",
"vuln_lines": max(1, len(str(vrow.func_vuln).splitlines())),
})
train = pd.DataFrame(records)
oof_parts = []
weighted_parts = []
fold_info = []
for year in FOLDS:
fit = train[train.year < year].reset_index(drop=True)
held = train[train.year == year].reset_index(drop=True)
if fit.empty or held.empty or held.target.nunique() != 2:
raise ValueError(f"invalid forward fold {year}")
scores = fit_score(fit, held)
weighted_scores = fit_score(fit, held, length_label_weights(fit))
held = held.copy()
held["score"] = scores
held["prediction"] = (scores >= 0).astype(np.int8)
oof_parts.append(held)
weighted_held = held.drop(columns=["score", "prediction"]).copy()
weighted_held["score"] = weighted_scores
weighted_held["prediction"] = (weighted_scores >= 0).astype(np.int8)
weighted_parts.append(weighted_held)
fold_info.append({"held_year": year,
"fit_pairs": int(fit.pair_id.nunique()),
"held_pairs": int(held.pair_id.nunique()),
"metrics": metric(np.ones(len(held), dtype=bool),
held.target.to_numpy(),
held.prediction.to_numpy())})
print(json.dumps({"held_year": year,
"fit_pairs": int(fit.pair_id.nunique()),
"held_pairs": int(held.pair_id.nunique())}), flush=True)
oof = pd.concat(oof_parts, ignore_index=True)
weighted_oof = pd.concat(weighted_parts, ignore_index=True)
y = oof.target.to_numpy(dtype=np.int8)
pred = oof.prediction.to_numpy(dtype=np.int8)
all_mask = np.ones(len(oof), dtype=bool)
nested_calibration = nested_length_thresholds(oof)
# Paired out-of-fold comparison for TRAIN-only length × label weighting.
wy = weighted_oof.target.to_numpy(dtype=np.int8)
wp = weighted_oof.prediction.to_numpy(dtype=np.int8)
base_rows = oof[["pair_id", "year", "target", "vuln_lines", "prediction"]].copy()
base_rows = base_rows.rename(columns={"prediction": "base_prediction"})
weighted_rows = weighted_oof[["pair_id", "target", "prediction"]].rename(
columns={"prediction": "weighted_prediction"})
paired_compare = base_rows.merge(weighted_rows, on=["pair_id", "target"],
validate="one_to_one")
if len(paired_compare) != len(oof):
raise AssertionError("weighted and baseline OOF row alignment failed")
pair_effects = []
for _, group in paired_compare.groupby("pair_id", sort=True):
if len(group) != 2 or set(group.target.astype(int)) != {0, 1}:
raise ValueError("pair integrity failure in weighted comparison")
base_ok = group.base_prediction.to_numpy() == group.target.to_numpy()
weighted_ok = group.weighted_prediction.to_numpy() == group.target.to_numpy()
pair_effects.append(float(weighted_ok.mean() - base_ok.mean()))
pair_effects = np.asarray(pair_effects)
rng = np.random.default_rng(77)
pair_draws = pair_effects[rng.integers(0, len(pair_effects),
size=(10_000, len(pair_effects)))].mean(axis=1)
weighted_comparison = {
"weighting": "equal total sample weight for each observed target × fixed length band cell",
"baseline": metric(np.ones(len(oof), dtype=bool), y, pred),
"length_label_weighted": metric(np.ones(len(weighted_oof), dtype=bool), wy, wp),
"paired_accuracy_delta_pp": float(100 * pair_effects.mean()),
"pair_bootstrap_95_ci_delta_pp": [
float(100 * v) for v in np.quantile(pair_draws, [0.025, 0.975])],
"by_held_year": {},
"by_fixed_length_band": {},
}
for year in FOLDS:
bm = oof.year.to_numpy() == year
wm = weighted_oof.year.to_numpy() == year
weighted_comparison["by_held_year"][str(year)] = {
"baseline": metric(bm, y, pred),
"length_label_weighted": metric(wm, wy, wp),
}
for name, low, high in (("short_le_30", 1, 30),
("medium_31_100", 31, 100),
("long_gt_100", 101, None)):
lm = oof.vuln_lines.to_numpy() >= low
if high is not None:
lm &= oof.vuln_lines.to_numpy() <= high
wlm = weighted_oof.vuln_lines.to_numpy() >= low
if high is not None:
wlm &= weighted_oof.vuln_lines.to_numpy() <= high
weighted_comparison["by_fixed_length_band"][name] = {
"baseline": metric(lm, y, pred),
"length_label_weighted": metric(wlm, wy, wp),
}
cwe_results = []
for cwe, inds in oof.groupby("cwe", sort=True).groups.items():
mask = np.zeros(len(oof), dtype=bool)
mask[np.asarray(list(inds), dtype=int)] = True
by_year = {}
for year in FOLDS:
ym = mask & (oof.year.to_numpy() == year)
by_year[str(year)] = metric(ym, y, pred)
cwe_results.append({"cwe": cwe, "pooled": metric(mask, y, pred),
"by_held_year": by_year})
length_results = []
bands = [("short_le_30", 1, 30), ("medium_31_100", 31, 100),
("long_gt_100", 101, None)]
for name, low, high in bands:
lens = oof.vuln_lines.to_numpy()
mask = (lens >= low) & ((lens <= high) if high is not None else True)
by_year = {str(year): metric(mask & (oof.year.to_numpy() == year), y, pred)
for year in FOLDS}
length_results.append({"band": name, "min_lines": low,
"max_lines": high, "pooled": metric(mask, y, pred),
"by_held_year": by_year})
result = {
"method": "fixed ten-seed char TF-IDF/SGD, forward TRAIN OOF, zero threshold",
"folds": fold_info,
"pooled_oof": metric(all_mask, y, pred),
"cwe_slices": cwe_results,
"vulnerable_function_length_bands": length_results,
"nested_forward_length_calibration": nested_calibration,
"length_label_weighting_comparison": weighted_comparison,
"coverage": {
"train_pairs": len(train_ids),
"oof_pairs": int(oof.pair_id.nunique()),
"oof_rows": len(oof),
"undated_or_other_year_pairs_not_in_folds": len(train_ids - set(oof.pair_id)),
},
"protocol": {
"held_years": list(FOLDS), "seeds": list(SEEDS),
"threshold": 0.0,
"subgroup_analysis_is_descriptive": True,
"length_bands_prespecified_lines": [30, 100],
"valid_used_for_selection": False, "test_read": False,
},
"provenance": {
"corpus_sha256": hashlib.sha256(CORPUS.read_bytes()).hexdigest(),
"train_split_sha256": hashlib.sha256(TRAIN.read_bytes()).hexdigest(),
"date_cache_sha256": hashlib.sha256(DATE_CACHE.read_bytes()).hexdigest(),
},
"limitations": [
"Only TRAIN pairs dated 2019–2021 with earlier dated TRAIN history are scored.",
"CWE labels may be incomplete/noisy; small subgroup estimates are unstable.",
"Forward folds share overlapping fit histories and are not independent replications.",
"Per-row Wilson intervals do not account for repository clustering.",
],
}
OUT_JSON.parent.mkdir(parents=True, exist_ok=True)
OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n")
lines = ["# Forward TRAIN subgroup replication", "",
"Fixed model and zero threshold; all rows are forward out-of-fold TRAIN predictions. "
"No VALID selection or TEST access.", "",
f"Pooled: {result['pooled_oof']['accuracy']:.1%} accuracy "
f"(Wilson 95% {result['pooled_oof']['wilson_95'][0]:.1%}–"
f"{result['pooled_oof']['wilson_95'][1]:.1%}); SAFE "
f"{result['pooled_oof']['safe_accuracy']:.1%}, VULNERABLE "
f"{result['pooled_oof']['vulnerable_accuracy']:.1%}; "
f"{result['coverage']['oof_pairs']} pairs.", "",
"## Prespecified function-length bands", "",
"| Band | pairs | SAFE accuracy | VULNERABLE accuracy | overall |",
"|---|---:|---:|---:|---:|"]
for item in length_results:
m = item["pooled"]
lines.append(f"| {item['band']} | {m['n_pairs']} | {m['safe_accuracy']:.1%} | "
f"{m['vulnerable_accuracy']:.1%} | {m['accuracy']:.1%} |")
lines += ["", "## CWE slices with at least 30 pooled OOF pairs", "",
"| CWE | pairs | SAFE accuracy | VULNERABLE accuracy | held-year vulnerable recalls |",
"|---|---:|---:|---:|---|"]
for item in cwe_results:
m = item["pooled"]
if m["n_pairs"] < 30:
continue
yearly = ", ".join(f"{yr}: {v['vulnerable_accuracy']:.1%}"
for yr, v in item["by_held_year"].items()
if v["vulnerable_n"])
lines.append(f"| {item['cwe']} | {m['n_pairs']} | {m['safe_accuracy']:.1%} | "
f"{m['vulnerable_accuracy']:.1%} | {yearly} |")
lines += ["", "## Nested prior-year length calibration", "",
"For each held year, thresholds use only earlier-year OOF scores; year 2019 has no prior OOF calibration and is omitted.", "",
"| Held year | fixed balanced acc. | calibrated balanced acc. | delta pp (pair-bootstrap 95% CI) |",
"|---:|---:|---:|---:|"]
for item in nested_calibration:
fixed, calibrated = item["fixed_zero_threshold"], item["length_calibrated"]
fixed_ba = (fixed["safe_accuracy"] + fixed["vulnerable_accuracy"]) / 2
cal_ba = (calibrated["safe_accuracy"] + calibrated["vulnerable_accuracy"]) / 2
ci = item["pair_bootstrap_95_ci_delta_pp"]
lines.append(f"| {item['held_year']} | {fixed_ba:.2%} | {cal_ba:.2%} | "
f"{item['balanced_accuracy_delta_pp']:+.2f} ({ci[0]:+.2f}, {ci[1]:+.2f}) |")
lines += ["", "## TRAIN-only length × label sample weighting", "",
f"Pooled paired accuracy delta: {weighted_comparison['paired_accuracy_delta_pp']:+.2f} pp "
f"(pair-bootstrap 95% CI {weighted_comparison['pair_bootstrap_95_ci_delta_pp'][0]:+.2f}, "
f"{weighted_comparison['pair_bootstrap_95_ci_delta_pp'][1]:+.2f}).", "",
"| Held year | baseline accuracy | weighted accuracy |",
"|---:|---:|---:|"]
for year, values in weighted_comparison["by_held_year"].items():
lines.append(f"| {year} | {values['baseline']['accuracy']:.2%} | "
f"{values['length_label_weighted']['accuracy']:.2%} |")
lines += ["", "This is one forward TRAIN diagnostic; length × label weights use only the corresponding fit fold. "
"CWE and length slices are descriptive; no VALID selection or TEST access.", ""]
OUT_MD.write_text("\n".join(lines))
print(json.dumps({"pooled_oof": result["pooled_oof"],
"coverage": result["coverage"],
"artifacts": [str(OUT_JSON), str(OUT_MD)]}, indent=2), flush=True)
if __name__ == "__main__":
main()
|