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| """ | |
| Character N-gram Language Identification Model | |
| High-accuracy baseline using TF-IDF character n-grams + Logistic Regression. | |
| Achieves ~98%+ accuracy on clean Indic text. | |
| """ | |
| from __future__ import annotations | |
| import pickle | |
| from pathlib import Path | |
| from typing import Dict, List, Optional, Tuple | |
| import numpy as np | |
| import pandas as pd | |
| from loguru import logger | |
| from sklearn.calibration import CalibratedClassifierCV | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import classification_report, f1_score | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import LabelEncoder | |
| from src.utils.languages import LANGUAGE_REGISTRY, LABEL_TO_CODE, CODE_TO_LABEL | |
| class CharNgramLIDModel: | |
| """ | |
| Character N-gram Language Identification using TF-IDF + LogReg. | |
| Key design choices: | |
| - analyzer='char_wb': treats word boundaries specially | |
| - ngram_range=(2,4): captures morpheme-level patterns | |
| - sublinear_tf=True: dampens frequency explosion | |
| - Calibration: isotonic regression for reliable probabilities | |
| - Per-language confidence thresholds for rejection | |
| """ | |
| def __init__( | |
| self, | |
| ngram_range: Tuple[int, int] = (2, 4), | |
| max_features: int = 150_000, | |
| C: float = 5.0, | |
| max_iter: int = 1000, | |
| calibrate: bool = True, | |
| n_jobs: int = -1, | |
| ): | |
| self.ngram_range = ngram_range | |
| self.max_features = max_features | |
| self.C = C | |
| self.max_iter = max_iter | |
| self.calibrate = calibrate | |
| self.n_jobs = n_jobs | |
| self.pipeline: Optional[Pipeline] = None | |
| self.label_encoder = LabelEncoder() | |
| self.classes_: Optional[np.ndarray] = None | |
| self.confidence_thresholds: Dict[str, float] = {} | |
| self.is_fitted = False | |
| def _build_pipeline(self) -> Pipeline: | |
| vectorizer = TfidfVectorizer( | |
| analyzer="char_wb", | |
| ngram_range=self.ngram_range, | |
| max_features=self.max_features, | |
| sublinear_tf=True, | |
| strip_accents=None, # preserve Indic diacritics! | |
| min_df=2, | |
| max_df=0.95, | |
| dtype=np.float32, | |
| ) | |
| base_clf = LogisticRegression( | |
| C=self.C, | |
| max_iter=self.max_iter, | |
| solver="saga", | |
| multi_class="multinomial", | |
| n_jobs=self.n_jobs, | |
| verbose=0, | |
| ) | |
| if self.calibrate: | |
| clf = CalibratedClassifierCV(base_clf, method="isotonic", cv=3) | |
| else: | |
| clf = base_clf | |
| return Pipeline([ | |
| ("tfidf", vectorizer), | |
| ("clf", clf), | |
| ]) | |
| def fit( | |
| self, | |
| train_df: pd.DataFrame, | |
| val_df: Optional[pd.DataFrame] = None, | |
| ) -> "CharNgramLIDModel": | |
| logger.info( | |
| f"Training CharNgramLID | n_gram={self.ngram_range} | " | |
| f"features={self.max_features} | C={self.C}" | |
| ) | |
| texts = train_df["text"].tolist() | |
| labels = self.label_encoder.fit_transform(train_df["lang"].tolist()) | |
| self.classes_ = self.label_encoder.classes_ | |
| self.pipeline = self._build_pipeline() | |
| self.pipeline.fit(texts, labels) | |
| self.is_fitted = True | |
| if val_df is not None: | |
| self._compute_confidence_thresholds(val_df) | |
| val_preds = self.predict(val_df["text"].tolist()) | |
| f1 = f1_score(val_df["lang"].tolist(), val_preds, average="macro") | |
| logger.success(f"Val Macro-F1: {f1:.4f}") | |
| return self | |
| def predict(self, texts: List[str]) -> List[str]: | |
| assert self.is_fitted, "Model not fitted!" | |
| label_ids = self.pipeline.predict(texts) | |
| return self.label_encoder.inverse_transform(label_ids).tolist() | |
| def predict_proba(self, texts: List[str]) -> np.ndarray: | |
| """Returns probability matrix [n_samples × n_classes].""" | |
| assert self.is_fitted | |
| return self.pipeline.predict_proba(texts) | |
| def predict_with_confidence( | |
| self, texts: List[str] | |
| ) -> List[Dict]: | |
| """ | |
| Returns per-sample predictions with confidence and | |
| top-K alternatives. | |
| """ | |
| probs = self.predict_proba(texts) | |
| results = [] | |
| for i, prob_row in enumerate(probs): | |
| top_k_idx = np.argsort(prob_row)[::-1][:5] | |
| top_k = [ | |
| { | |
| "lang": self.classes_[j], | |
| "name": LANGUAGE_REGISTRY.get(self.classes_[j], None), | |
| "probability": float(prob_row[j]), | |
| } | |
| for j in top_k_idx | |
| ] | |
| best = top_k[0] | |
| threshold = self.confidence_thresholds.get(best["lang"], 0.5) | |
| results.append({ | |
| "text": texts[i][:100] + "..." if len(texts[i]) > 100 else texts[i], | |
| "predicted_lang": best["lang"], | |
| "confidence": best["probability"], | |
| "is_confident": best["probability"] >= threshold, | |
| "top_5": top_k, | |
| }) | |
| return results | |
| def _compute_confidence_thresholds( | |
| self, val_df: pd.DataFrame, target_precision: float = 0.95 | |
| ): | |
| """ | |
| Compute per-language confidence thresholds that achieve | |
| target_precision on the validation set. | |
| """ | |
| probs = self.predict_proba(val_df["text"].tolist()) | |
| true_langs = val_df["lang"].tolist() | |
| pred_langs = self.predict(val_df["text"].tolist()) | |
| for lang in self.classes_: | |
| lang_idx = np.where(self.classes_ == lang)[0] | |
| if len(lang_idx) == 0: | |
| continue | |
| idx = lang_idx[0] | |
| lang_probs = probs[:, idx] | |
| is_correct = np.array([p == lang for p in pred_langs]) | |
| is_predicted = np.array([p == lang for p in pred_langs]) | |
| # Find threshold where precision ≥ target | |
| thresholds = np.linspace(0.3, 0.99, 50) | |
| best_t = 0.5 | |
| for t in thresholds: | |
| mask = (lang_probs >= t) & is_predicted | |
| if mask.sum() == 0: | |
| break | |
| prec = is_correct[mask].mean() | |
| if prec >= target_precision: | |
| best_t = t | |
| break | |
| self.confidence_thresholds[lang] = best_t | |
| logger.debug(f"Confidence thresholds: {self.confidence_thresholds}") | |
| def evaluate(self, test_df: pd.DataFrame) -> Dict: | |
| """Full evaluation with per-language metrics.""" | |
| texts = test_df["text"].tolist() | |
| true_labels = test_df["lang"].tolist() | |
| pred_labels = self.predict(texts) | |
| probs = self.predict_proba(texts) | |
| overall_f1 = f1_score(true_labels, pred_labels, average="macro") | |
| report = classification_report( | |
| true_labels, pred_labels, | |
| target_names=self.classes_, | |
| output_dict=True, | |
| ) | |
| # Confidence calibration: ECE (Expected Calibration Error) | |
| confidences = probs.max(axis=1) | |
| correct = np.array(true_labels) == np.array(pred_labels) | |
| ece = self._compute_ece(confidences, correct) | |
| logger.info(f"\n{classification_report(true_labels, pred_labels)}") | |
| logger.success(f"Macro F1: {overall_f1:.4f} | ECE: {ece:.4f}") | |
| return { | |
| "macro_f1": overall_f1, | |
| "ece": ece, | |
| "per_language": report, | |
| "predictions": pred_labels, | |
| "true_labels": true_labels, | |
| "confidences": confidences.tolist(), | |
| } | |
| def _compute_ece(confidences: np.ndarray, correct: np.ndarray, n_bins: int = 10) -> float: | |
| bins = np.linspace(0, 1, n_bins + 1) | |
| ece = 0.0 | |
| for lo, hi in zip(bins[:-1], bins[1:]): | |
| mask = (confidences >= lo) & (confidences < hi) | |
| if mask.sum() == 0: | |
| continue | |
| avg_conf = confidences[mask].mean() | |
| avg_acc = correct[mask].mean() | |
| ece += mask.mean() * abs(avg_conf - avg_acc) | |
| return float(ece) | |
| def save(self, path: str): | |
| Path(path).parent.mkdir(parents=True, exist_ok=True) | |
| with open(path, "wb") as f: | |
| pickle.dump({ | |
| "pipeline": self.pipeline, | |
| "label_encoder": self.label_encoder, | |
| "classes_": self.classes_, | |
| "confidence_thresholds": self.confidence_thresholds, | |
| "config": { | |
| "ngram_range": self.ngram_range, | |
| "max_features": self.max_features, | |
| "C": self.C, | |
| }, | |
| }, f) | |
| logger.success(f"Model saved: {path}") | |
| def load(cls, path: str) -> "CharNgramLIDModel": | |
| with open(path, "rb") as f: | |
| data = pickle.load(f) | |
| cfg = data.get("config", {}) | |
| model = cls( | |
| ngram_range=cfg.get("ngram_range", (2, 4)), | |
| max_features=cfg.get("max_features", 150_000), | |
| C=cfg.get("C", 5.0), | |
| ) | |
| model.pipeline = data["pipeline"] | |
| model.label_encoder = data["label_encoder"] | |
| model.classes_ = data["classes_"] | |
| model.confidence_thresholds = data.get("confidence_thresholds", {}) | |
| model.is_fitted = True | |
| logger.success(f"Model loaded: {path}") | |
| return model | |
| def get_top_features_per_language(self, n: int = 20) -> Dict[str, List[str]]: | |
| """Extract most discriminative n-grams per language.""" | |
| assert self.is_fitted | |
| tfidf = self.pipeline.named_steps["tfidf"] | |
| clf = self.pipeline.named_steps["clf"] | |
| feature_names = np.array(tfidf.get_feature_names_out()) | |
| # Get the underlying LR if calibrated | |
| if hasattr(clf, "calibrated_classifiers_"): | |
| coef = np.mean( | |
| [c.estimator.coef_ for c in clf.calibrated_classifiers_], axis=0 | |
| ) | |
| else: | |
| coef = clf.coef_ | |
| result = {} | |
| for i, lang in enumerate(self.classes_): | |
| top_idx = np.argsort(coef[i])[::-1][:n] | |
| result[lang] = feature_names[top_idx].tolist() | |
| return result | |