| """v2 candidates: clean v1 rows (25% perturbed) + messy LLM rows, then PII injected up to a target rate. Labels are never touched.""" |
| import argparse, json, random, re |
| import pandas as pd |
|
|
| SLANG = [("please", "pls"), ("thanks", "thx"), ("because", "bc"), ("going to", "gonna"), ("want to", "wanna"), ("do not", "dont"), |
| ("cannot", "cant"), ("i am", "im"), ("you are", "ur"), ("what is", "whats"), ("i have", "ive"), ("my account", "my acct")] |
| PREFIX = ["Hi, I'm {NAME}. ", "Hello, this is {NAME}. ", "{NAME} here. ", "hi its {NAME}, ", "Hi, {NAME} speaking. "] |
| SUFFIX = [" My ref is {REF}.", " ref {REF}", " (account holder {NAME})", " I'm in {CITY} at the moment.", " This was on {DATE}.", " Ref: {REF}"] |
| TYPES = {"NAME": "name", "AMOUNT": "amount", "LAST4": "card_digits", "MERCHANT": "merchant", "CITY": "location", "DATE": "date", "REF": "reference"} |
|
|
| def perturb(text, rng): |
| """Light input noise: slang, typos, dropped word/punctuation, casing. Placeholders are never touched.""" |
| for f, s in SLANG: |
| if rng.random() < 0.35: text = re.sub(rf"\b{f}\b", s, text, flags=re.I) |
| w = text.split() |
| for _ in range(rng.choice([1, 1, 2, 3])): |
| i = rng.randrange(len(w)) |
| t = w[i] |
| if "{" in t or len(t) < 4: continue |
| j = rng.randrange(1, len(t) - 1) |
| op = rng.choice(["swap", "drop", "dup"]) |
| w[i] = t[:j] + t[j + 1] + t[j] + t[j + 2:] if op == "swap" else t[:j] + t[j + 1:] if op == "drop" else t[:j] + t[j] + t[j:] |
| k = rng.randrange(1, len(w) - 1) if len(w) > 5 else 0 |
| if k and rng.random() < 0.3 and "{" not in w[k]: w.pop(k) |
| text = " ".join(w) |
| if rng.random() < 0.4: text = text.rstrip("?.!") |
| if rng.random() < 0.35 and "{" not in text: text = text.lower() |
| return text |
|
|
| def inject(text, rng): |
| if rng.random() < 0.5: return rng.choice(PREFIX) + (text if rng.random() < 0.5 else text[0].lower() + text[1:]) |
| return text.rstrip() + rng.choice(SUFFIX) |
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--perturb-frac", type=float, default=0.25) |
| ap.add_argument("--pii-target", type=float, default=0.32) |
| ap.add_argument("--seed", type=int, default=0) |
| ap.add_argument("--out", default="data/cand_v2.jsonl") |
| a = ap.parse_args() |
| rng = random.Random(a.seed) |
|
|
| clean = pd.read_json("data/synth_verified.jsonl", lines=True) |
| clean = clean[clean.verified][["intent", "text"]].assign(noise_type="clean") |
| noisy = pd.read_json("data/synth_raw_v2.jsonl", lines=True).assign(noise_type="llm_noisy") |
| pert = clean.sample(frac=a.perturb_frac, random_state=a.seed) |
| clean = clean.drop(pert.index) |
| pert = pert.assign(text=[perturb(t, rng) for t in pert.text], noise_type="perturbed") |
| df = pd.concat([clean, pert, noisy], ignore_index=True) |
|
|
| has = df.text.str.contains(r"\{\w+\}") |
| need = int(a.pii_target * len(df)) - int(has.sum()) |
| cand = df.index[~has].tolist() |
| rng.shuffle(cand) |
| for i in cand[:max(0, need)]: df.at[i, "text"] = inject(df.at[i, "text"], rng) |
| after = df.text.str.contains(r"\{\w+\}").mean() |
| print(f"rows={len(df)} noise_type={df.noise_type.value_counts().to_dict()} pii_rate {has.mean():.1%} -> {after:.1%}") |
| df.to_json(a.out, orient="records", lines=True) |
|
|
| if __name__ == "__main__": |
| main() |
|
|
