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eb72d30 | 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 | import numpy as np
class SOTAStackingDetector:
"""State-of-the-Art Stacked Ensemble Classifier for AI text detection.
Combines stylistic baselines, tree-based stylometrics, and vocabulary-based n-grams
using a Meta-Classifier to achieve robust generalization.
"""
def __init__(self, lr_sty, xgb_sty, lr_ng, meta_model, num_sty_features):
self.lr_sty = lr_sty
self.xgb_sty = xgb_sty
self.lr_ng = lr_ng
self.meta_model = meta_model
self.num_sty_features = num_sty_features
# Compatibility fields for SHAP / Explainability coefficients
self.coef_ = lr_sty.coef_
self.intercept_ = lr_sty.intercept_
def _extract_base_features(self, X_hybrid_scaled):
X_sty_scaled = X_hybrid_scaled[:, :self.num_sty_features]
X_ng_scaled = X_hybrid_scaled[:, self.num_sty_features:]
return X_sty_scaled, X_ng_scaled
def predict_proba(self, X_hybrid_scaled):
X_sty_scaled, X_ng_scaled = self._extract_base_features(X_hybrid_scaled)
# Get base model probabilities
p_lr_sty = self.lr_sty.predict_proba(X_sty_scaled)[:, 1]
p_xgb_sty = self.xgb_sty.predict_proba(X_sty_scaled)[:, 1]
p_lr_ng = self.lr_ng.predict_proba(X_ng_scaled)[:, 1]
# Construct meta features
X_meta = np.column_stack([p_lr_sty, p_xgb_sty, p_lr_ng])
return self.meta_model.predict_proba(X_meta)
def predict(self, X_hybrid_scaled):
prob = self.predict_proba(X_hybrid_scaled)[:, 1]
return (prob >= 0.5).astype(int)
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