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2360cda | 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 | """Build the clean real-seed pool (~9k): drop noisy/conflicting rows, then prune hub intents of the confusion graph."""
import sys
from collections import Counter
import numpy as np, pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import NearestNeighbors
TARGET = int(sys.argv[1]) if len(sys.argv) > 1 else 9000
def main():
d = pd.read_parquet("data/seeds_audited.parquet")
d["votes"] = d.suspect.astype(int) + d.lr_bad.astype(int) + d.knn_bad.astype(int)
p = np.load("data/seed_cvprob_bge.npy")
d["cv_pred"] = p.argmax(1)
# row-level: any detector flag, or in a near-duplicate pair with a different label
X = TfidfVectorizer(analyzer="char_wb", ngram_range=(3, 5), sublinear_tf=True).fit_transform(d.text.str.lower())
dist, idx = NearestNeighbors(n_neighbors=4, metric="cosine").fit(X).kneighbors(X)
conflict = np.zeros(len(d), bool)
for i in range(len(d)):
for dj, j in zip(dist[i, 1:], idx[i, 1:]):
if 1 - dj >= 0.85 and d.label.values[i] != d.label.values[j]: conflict[i] = conflict[j] = True
d["conflict_nd"] = conflict
keep = d[(d.votes == 0) & ~d.conflict_nd].copy()
print(f"row-level: {len(d)} -> {len(keep)} (flagged {int((d.votes>0).sum())}, conflicting near-dup {int(conflict.sum())})")
# intent-level: confusion graph from CV predictions on the rows we kept; drop hub intents greedily
kk = keep[keep.cv_pred != keep.label]
mass = Counter()
edges = Counter()
for a, b in zip(kk.intent, kk.cv_pred.map(dict(zip(d.label, d.intent)))):
edges[a] += 1; edges[b] += 1; mass[tuple(sorted((a, b)))] += 1
dropped = []
while len(keep) > TARGET:
deg = Counter()
for (a, b), n in mass.items(): deg[a] += n; deg[b] += n
if not deg: break
worst = deg.most_common(1)[0][0]
dropped.append((worst, int(deg[worst]), int((keep.intent == worst).sum())))
keep = keep[keep.intent != worst]
mass = Counter({k: v for k, v in mass.items() if worst not in k})
print(f"intent-level: dropped {len(dropped)} intents -> {len(keep)} rows, {keep.intent.nunique()} intents")
for n, m, r in dropped: print(f" drop {n:45s} confusion={m:3d} rows={r}")
keep.drop(columns=["cv_pred"]).to_parquet("data/clean_pool.parquet")
print("per-intent rows: min", keep.intent.value_counts().min(), "max", keep.intent.value_counts().max())
if __name__ == "__main__":
main()
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