import os import sys import yaml import argparse import pandas as pd import numpy as np import joblib import shap from sklearn.metrics import f1_score, accuracy_score sys.path.append(os.path.dirname(os.path.abspath(__file__))) from models_v2 import SOTAHybridDetector STYLOMETRIC_COLS_V2 = [ 'num_chars', 'num_words', 'num_sentences', 'avg_sentence_len', 'std_sentence_len', 'slv_normalized', 'avg_word_len', 'ratio_long_words', 'vocabulary_diversity', 'hapax_ratio', 'yules_k', 'maas_index', 'information_entropy', 'brunet_w', 'ratio_punctuation', 'freq_uppercase', 'freq_digits', 'connector_ratio', 'connector_diversity', 'repetition_ratio', 'stopword_ratio', 'mean_polarity_diff', 'syntactic_complexity_score', 'ratio_interrogative', 'ratio_exclamative', 'ratio_declarative', 'imparfait_ratio', 'futur_ratio', 'conditional_ratio', 'passive_voice_ratio' ] def load_config(config_path): with open(config_path, "r", encoding="utf-8") as f: return yaml.safe_load(f) def main(): parser = argparse.ArgumentParser(description="Inférence SOTA v2 sur les débats récents avec explications SHAP.") parser.add_argument("--config", default="configs/config.yaml", help="Chemin vers le fichier de config") args = parser.parse_args() config = load_config(args.config) processed_dir = config["paths"]["processed_dir"] models_dir = config["paths"]["models_dir"] output_dir = config["paths"]["output_dir"] os.makedirs(output_dir, exist_ok=True) # 1. Load Model Package model_path = os.path.join(models_dir, "best_detector_v2.pkl") if not os.path.exists(model_path): print(f"ERREUR: Modèle v2 introuvable à {model_path}. Lancez train_sota_v2.py d'abord.") sys.exit(1) pkg = joblib.load(model_path) detector = pkg["model"] xgb_raw = pkg["xgb_raw"] scalers = pkg["scalers"] friendly_sty = pkg["friendly_names_sty"] print(f"Modèle chargé: {pkg['model_name']}") # 2. Load Recent Features recent_sty_path = os.path.join(processed_dir, "recent_features_v2.csv") recent_emb_path = os.path.join(processed_dir, "recent_embeddings_camembert.csv") if not os.path.exists(recent_sty_path) or not os.path.exists(recent_emb_path): print("ERREUR: Features récentes introuvables. Lancez build_features_v2.py et camembert_encoder.py.") sys.exit(1) df_sty = pd.read_csv(recent_sty_path) df_emb = pd.read_csv(recent_emb_path) print(f"Débats récents chargés: {len(df_sty)} textes") # 3. Prepare Features X_sty = df_sty[STYLOMETRIC_COLS_V2].values emb_cols = pkg["emb_cols"] X_emb = df_emb[emb_cols].values # Scale and combine X_sty_scaled = scalers["sty"].transform(X_sty) X_emb_scaled = scalers["emb"].transform(X_emb) X_combined = np.hstack([X_sty_scaled, X_emb_scaled]) # 4. Predict print("Inférence en cours...") prob_ai = xgb_raw.predict_proba(X_combined)[:, 1] predictions = (prob_ai >= 0.5).astype(int) confidence = 2.0 * np.abs(prob_ai - 0.5) df_sty["prob_ai"] = prob_ai df_sty["prob_human"] = 1.0 - prob_ai df_sty["prediction"] = predictions df_sty["confidence_score"] = confidence # 5. SHAP Explanations (on a subsample for speed) print("Calcul des explications SHAP locales...") explainer = shap.TreeExplainer(xgb_raw) # For each prediction, get the top contributing stylometric feature all_feature_names = STYLOMETRIC_COLS_V2 + emb_cols n_sty = len(STYLOMETRIC_COLS_V2) top_ai_features = [] top_human_features = [] shap_explanations = [] # Process in chunks for memory chunk_size = 500 for start in range(0, len(X_combined), chunk_size): end = min(start + chunk_size, len(X_combined)) chunk_shap = explainer.shap_values(X_combined[start:end]) for i in range(chunk_shap.shape[0]): sv = chunk_shap[i] # Focus on stylometric features only for interpretability sv_sty = sv[:n_sty] top_ai_idx = np.argmax(sv_sty) top_human_idx = np.argmin(sv_sty) top_ai_features.append(friendly_sty[top_ai_idx]) top_human_features.append(friendly_sty[top_human_idx]) # Build explanation string sorted_idx = np.argsort(np.abs(sv_sty))[::-1][:3] parts = [] for idx in sorted_idx: direction = "→IA" if sv_sty[idx] > 0 else "→Humain" parts.append(f"{friendly_sty[idx]} ({sv_sty[idx]:+.3f} {direction})") shap_explanations.append(" | ".join(parts)) df_sty["explanation_top_ai_feature"] = top_ai_features df_sty["explanation_top_human_feature"] = top_human_features df_sty["shap_explanation"] = shap_explanations # 6. Save predictions cols_to_save = [ "date", "speaker", "party", "chamber", "document_type", "legislature", "prob_ai", "prob_human", "confidence_score", "prediction", "explanation_top_ai_feature", "explanation_top_human_feature", "shap_explanation" ] if "actual_label" in df_sty.columns: cols_to_save.append("actual_label") if "ai_model" in df_sty.columns: cols_to_save.append("ai_model") df_out = df_sty[cols_to_save + ["text"]] preds_path = os.path.join(output_dir, "recent_debates_predictions_v2.csv") df_out.to_csv(preds_path, index=False) print(f"Prédictions sauvegardées dans {preds_path}") # 7. Aggregate Stats df_sty["date_dt"] = pd.to_datetime(df_sty["date"]) df_sty["year"] = df_sty["date_dt"].dt.year df_sty["week"] = df_sty["date_dt"] - pd.to_timedelta(df_sty["date_dt"].dt.weekday, unit='D') # Weekly week_stats = df_sty.groupby("week")["prob_ai"].agg(["count", "mean", "std"]).reset_index() week_stats.columns = ["week", "speech_count", "mean_ai_suspicion", "std_ai_suspicion"] week_stats = week_stats.sort_values(by="week") week_stats_save = week_stats.copy() week_stats_save["week"] = week_stats_save["week"].dt.strftime("%Y-%m-%d") week_stats_save.to_csv(os.path.join(output_dir, "stats_by_week_v2.csv"), index=False) print(f"Stats hebdomadaires: {len(week_stats)} semaines") # By Deputy deputy_stats = df_sty.groupby("speaker")["prob_ai"].agg(["count", "mean", "std"]).reset_index() deputy_stats.columns = ["speaker", "speech_count", "mean_ai_suspicion", "std_ai_suspicion"] deputy_stats = deputy_stats.sort_values(by="mean_ai_suspicion", ascending=False) deputy_stats.to_csv(os.path.join(output_dir, "stats_by_deputy_v2.csv"), index=False) # By Party party_stats = df_sty.groupby("party")["prob_ai"].agg(["count", "mean", "std"]).reset_index() party_stats.columns = ["party", "speech_count", "mean_ai_suspicion", "std_ai_suspicion"] party_stats = party_stats.sort_values(by="mean_ai_suspicion", ascending=False) party_stats.to_csv(os.path.join(output_dir, "stats_by_party_v2.csv"), index=False) # By Doc Type doc_stats = df_sty.groupby("document_type")["prob_ai"].agg(["count", "mean", "std"]).reset_index() doc_stats.columns = ["document_type", "speech_count", "mean_ai_suspicion", "std_ai_suspicion"] doc_stats.to_csv(os.path.join(output_dir, "stats_by_doc_type_v2.csv"), index=False) # Accuracy if labels exist if "actual_label" in df_sty.columns: actuals = df_sty["actual_label"].values acc = accuracy_score(actuals, predictions) f1 = f1_score(actuals, predictions, zero_division=0) print(f"\nÉvaluation vs ground-truth: Accuracy={acc:.4f} | F1={f1:.4f}") print(f"\n✅ Inférence SOTA v2 terminée avec succès.") if __name__ == "__main__": main()