Add fusion model and research report

fusion_model.py

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+"""
+Late Fusion: App序列向量 + 征信模型预测 → 最终风控决策
+=========================================================
+两个模型各自独立建模后，在决策层融合
+
+方法: Late Fusion (拼接各自输出 → 简单分类器)
+原因: App 序列和征信数据本质不同，early fusion 会相互干扰
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import roc_auc_score
+from scipy.stats import ks_2samp
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def late_fusion(
+    app_embeddings: np.ndarray,       # (n_users, 256) CoLES 用户向量
+    app_risk_prob: np.ndarray,        # (n_users,) App 模型预测概率
+    credit_risk_prob: np.ndarray,     # (n_users,) 征信模型预测概率
+    credit_features: np.ndarray,      # (n_users, n_features) 征信原始特征 (可选)
+    y_true: np.ndarray,              # (n_users,) 真实标签
+    method: str = 'stacking'         # 'simple_avg', 'weighted_avg', 'stacking', 'lgbm'
+):
+    """
+    融合策略:
+    
+    1. simple_avg: 简单平均两个模型的概率
+    2. weighted_avg: 加权平均 (权重由验证集确定)
+    3. stacking: 用两个模型的输出 + App embedding 作为特征，训练 LR
+    4. lgbm: 用 LightGBM 做 stacking (最强)
+    """
+    
+    if method == 'simple_avg':
+        fusion_pred = 0.5 * app_risk_prob + 0.5 * credit_risk_prob
+    
+    elif method == 'weighted_avg':
+        best_auc = 0
+        best_w = 0.5
+        for w in np.arange(0.1, 1.0, 0.05):
+            pred = w * app_risk_prob + (1 - w) * credit_risk_prob
+            auc = roc_auc_score(y_true, pred)
+            if auc > best_auc:
+                best_auc = auc
+                best_w = w
+        fusion_pred = best_w * app_risk_prob + (1 - best_w) * credit_risk_prob
+        logger.info(f"Optimal weight: App={best_w:.2f}, Credit={1-best_w:.2f}")
+    
+    elif method == 'stacking':
+        X_stack = np.column_stack([
+            app_risk_prob.reshape(-1, 1),
+            credit_risk_prob.reshape(-1, 1),
+            app_embeddings,
+        ])
+        
+        n = len(y_true)
+        split = int(n * 0.8)
+        
+        lr = LogisticRegression(C=1.0, max_iter=1000, class_weight='balanced')
+        lr.fit(X_stack[:split], y_true[:split])
+        fusion_pred = lr.predict_proba(X_stack[split:])[:, 1]
+        
+        y_eval = y_true[split:]
+        auc = roc_auc_score(y_eval, fusion_pred)
+        ks = ks_2samp(fusion_pred[y_eval==1], fusion_pred[y_eval==0]).statistic
+        logger.info(f"Stacking (LR): AUC={auc:.4f}, KS={ks:.4f}")
+        return fusion_pred, auc, ks
+    
+    elif method == 'lgbm':
+        import lightgbm as lgb
+        
+        X_stack = np.column_stack([
+            app_risk_prob.reshape(-1, 1),
+            credit_risk_prob.reshape(-1, 1),
+            app_embeddings,
+        ])
+        
+        n = len(y_true)
+        split = int(n * 0.8)
+        
+        train_data = lgb.Dataset(X_stack[:split], label=y_true[:split])
+        val_data = lgb.Dataset(X_stack[split:], label=y_true[split:])
+        
+        params = {
+            'objective': 'binary', 'metric': 'auc',
+            'learning_rate': 0.05, 'num_leaves': 31,
+            'verbose': -1, 'n_jobs': -1,
+        }
+        
+        model = lgb.train(params, train_data, num_boost_round=200,
+                         valid_sets=[val_data],
+                         callbacks=[lgb.early_stopping(30)])
+        
+        fusion_pred = model.predict(X_stack[split:])
+        y_eval = y_true[split:]
+        auc = roc_auc_score(y_eval, fusion_pred)
+        ks = ks_2samp(fusion_pred[y_eval==1], fusion_pred[y_eval==0]).statistic
+        logger.info(f"Stacking (LightGBM): AUC={auc:.4f}, KS={ks:.4f}")
+        return fusion_pred, auc, ks
+    
+    # 评估
+    auc = roc_auc_score(y_true, fusion_pred)
+    ks = ks_2samp(fusion_pred[y_true==1], fusion_pred[y_true==0]).statistic
+    logger.info(f"Fusion ({method}): AUC={auc:.4f}, KS={ks:.4f}")
+    
+    return fusion_pred, auc, ks
+
+
+# ============================================================
+# 使用示例
+# ============================================================
+"""
+完整工作流:
+
+# 1. App 序列模型
+from app_sequence_model import pretrain_coles, preprocess_app_sequence
+pretrained = pretrain_coles(user_sequences)
+app_embeddings = extract_user_embeddings(pretrained, user_sequences)  # (N, 256)
+app_risk_prob = app_classifier.predict_proba(app_embeddings)
+
+# 2. 征信模型
+from credit_bureau_model import train_tabm, train_lightgbm
+tabm_pred = tabm_model.predict(credit_features)
+lgb_pred = lgb_model.predict(credit_features)
+credit_risk_prob = 0.5 * tabm_pred + 0.5 * lgb_pred
+
+# 3. 融合
+fusion_pred, auc, ks = late_fusion(
+    app_embeddings=app_embeddings,
+    app_risk_prob=app_risk_prob,
+    credit_risk_prob=credit_risk_prob,
+    credit_features=None,
+    y_true=labels,
+    method='lgbm'  # 推荐
+)
+
+# 4. 阈值决策
+threshold = 0.15  # 由 KS 校准确定
+decision = (fusion_pred >= threshold).astype(int)
+# 0 = 通过, 1 = 拒绝
+"""