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Toxic-only back-translation augmentation with cosine deduplication.
Augments only the positive (toxic) class in the training set via EN→ES→EN,
then drops synthetic samples too similar to the original training corpus.
"""
from __future__ import annotations
import time
from typing import Iterable
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
from src.utils.logger import get_logger
logger = get_logger(__name__)
def toxic_back_translation(
texts: Iterable[str],
labels: Iterable[int | bool],
*,
source_lang: str = "en",
pivot_lang: str = "es",
min_words: int = 3,
max_words: int = 60,
rate_limit_every: int = 50,
rate_limit_sleep_sec: float = 1.0,
seed: int = 42,
) -> tuple[list[str], list[int]]:
"""
Back-translate toxic samples only (label == 1).
Returns parallel lists of augmented texts and labels (all toxic).
"""
try:
from deep_translator import GoogleTranslator
except ImportError as e:
raise ImportError(
"Install augmentation deps: uv sync --extra train"
) from e
import random
random.seed(seed)
to_pivot = GoogleTranslator(source=source_lang, target=pivot_lang)
to_source = GoogleTranslator(source=pivot_lang, target=source_lang)
aug_texts: list[str] = []
aug_labels: list[int] = []
pairs = [
(str(t).strip(), int(bool(l)))
for t, l in zip(texts, labels, strict=False)
if int(bool(l)) == 1
]
logger.info(f"Back-translation: {len(pairs)} toxic samples")
for i, (text, label) in enumerate(pairs):
words = text.split()
if len(words) < min_words:
continue
try:
short = " ".join(words[:max_words])
pivot = to_pivot.translate(short)
back = to_source.translate(pivot)
if back and back.strip() and back.strip() != short.strip():
aug_texts.append(back.strip())
aug_labels.append(label)
except Exception as exc:
logger.warning(f"Back-translation failed at index {i}: {exc}")
continue
if rate_limit_every > 0 and i > 0 and i % rate_limit_every == 0:
time.sleep(rate_limit_sleep_sec)
logger.info(f"Back-translation produced {len(aug_texts)} samples")
return aug_texts, aug_labels
def back_translate_texts(
texts: Iterable[str],
*,
source_lang: str = "en",
pivot_lang: str = "de",
max_words: int = 60,
rate_limit_every: int = 50,
rate_limit_sleep_sec: float = 1.0,
fallback_to_original: bool = True,
) -> list[str]:
"""
Back-translate every text (EN→pivot→EN) for test-time augmentation.
On failure, returns the original string when ``fallback_to_original`` is True.
"""
try:
from deep_translator import GoogleTranslator
except ImportError as e:
raise ImportError(
"Install augmentation deps: uv sync --extra train"
) from e
to_pivot = GoogleTranslator(source=source_lang, target=pivot_lang)
to_source = GoogleTranslator(source=pivot_lang, target=source_lang)
out: list[str] = []
for i, raw in enumerate(texts):
text = str(raw).strip()
if not text:
out.append(text)
continue
words = text.split()
short = " ".join(words[:max_words])
try:
pivot = to_pivot.translate(short)
back = to_source.translate(pivot)
out.append(back.strip() if back and back.strip() else text)
except Exception as exc:
logger.warning(f"TTA back-translation failed at index {i}: {exc}")
out.append(text if fallback_to_original else short)
if rate_limit_every > 0 and i > 0 and i % rate_limit_every == 0:
time.sleep(rate_limit_sleep_sec)
return out
def deduplicate_by_cosine(
synthetic_texts: list[str],
synthetic_labels: list[int],
reference_texts: list[str],
*,
threshold: float = 0.95,
embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2",
) -> tuple[list[str], list[int]]:
"""
Remove synthetic samples with max cosine similarity > threshold vs reference.
"""
if not synthetic_texts:
return [], []
try:
from sentence_transformers import SentenceTransformer
except ImportError as e:
raise ImportError(
"Install augmentation deps: uv sync --extra train"
) from e
model = SentenceTransformer(embedding_model)
ref_emb = model.encode(reference_texts, show_progress_bar=False, convert_to_numpy=True)
syn_emb = model.encode(synthetic_texts, show_progress_bar=False, convert_to_numpy=True)
sims = cosine_similarity(syn_emb, ref_emb)
max_sim = sims.max(axis=1)
kept_texts: list[str] = []
kept_labels: list[int] = []
dropped = 0
for text, label, sim in zip(synthetic_texts, synthetic_labels, max_sim, strict=False):
if sim <= threshold:
kept_texts.append(text)
kept_labels.append(label)
else:
dropped += 1
logger.info(
f"Dedup: kept {len(kept_texts)}/{len(synthetic_texts)} "
f"(dropped {dropped} with cosine > {threshold})"
)
return kept_texts, kept_labels
def augment_toxic_train(
X_train: pd.Series,
y_train: pd.Series,
cfg: dict,
*,
seed: int = 42,
) -> tuple[pd.Series, pd.Series]:
"""
Append toxic-only back-translated samples to training data (with dedup).
"""
aug_cfg = cfg.get("augmentation", {})
if not aug_cfg.get("enabled", True):
return X_train, y_train
syn_texts, syn_labels = toxic_back_translation(
X_train.tolist(),
y_train.tolist(),
source_lang=aug_cfg.get("source_lang", "en"),
pivot_lang=aug_cfg.get("pivot_lang", "es"),
min_words=aug_cfg.get("min_words", 3),
max_words=aug_cfg.get("max_words", 60),
rate_limit_every=aug_cfg.get("rate_limit_every", 50),
rate_limit_sleep_sec=aug_cfg.get("rate_limit_sleep_sec", 1.0),
seed=seed,
)
dedup_cfg = aug_cfg.get("dedup", {})
if dedup_cfg.get("enabled", True) and syn_texts:
syn_texts, syn_labels = deduplicate_by_cosine(
syn_texts,
syn_labels,
X_train.tolist(),
threshold=float(dedup_cfg.get("cosine_threshold", 0.95)),
embedding_model=dedup_cfg.get(
"embedding_model", "sentence-transformers/all-MiniLM-L6-v2"
),
)
if not syn_texts:
return X_train, y_train
X_aug = pd.concat(
[X_train, pd.Series(syn_texts, name=X_train.name)],
ignore_index=True,
)
y_aug = pd.concat(
[y_train, pd.Series(syn_labels, name=y_train.name)],
ignore_index=True,
)
logger.info(f"Train size after augmentation: {len(X_aug)} (+{len(syn_texts)})")
return X_aug, y_aug
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