Create ensemble_models.py

Author: Prophet2023

ensemble_learning/ensemble_models.py

@@ -0,0 +1,53 @@
+import pandas as pd
+import numpy as np
+import sklearn
+import os
+import pickle
+import copy
+from sklearn.metrics import roc_curve, auc
+from sklearn.metrics import accuracy_score, classification_report  # 用于评估准确率和分类报告
+import joblib
+
+def ensemable_classcification(X, y = None, rt_type='Prob'):
+    # 加载 LabelEncoder
+    label_encoder_path = "label_encoder.pkl"
+    label_encoder = joblib.load(label_encoder_path)
+    print(f"LabelEncoder已加载，类别标签为: {label_encoder.classes_}")
+
+    # 加载模型
+    svm_model_path = "svm_model.pkl"
+    svm_model = joblib.load(svm_model_path)
+    print(f"SVM模型已加载")
+
+    log_reg_model_path = "log_reg_model.pkl"
+    log_reg_model = joblib.load(log_reg_model_path)
+    print(f"Logistic回归模型已加载")
+
+    xgb_model_path = "xgb_model.pkl"
+    xgb_model = joblib.load(xgb_model_path)
+    print(f"XGBoost模型已加载")
+
+    if y:
+        print(classification_report(y, svm_model.predict(X), target_names=label_encoder.classes_))
+        print(classification_report(y, log_reg_model.predict(X), target_names=label_encoder.classes_))
+        print(classification_report(y, xgb_model.predict(X), target_names=label_encoder.classes_))
+
+    if rt_type == 'Class':
+        svm_result = svm_model.predict(X)
+        log_reg_result = log_reg_model.predict(X)
+        xgb_result = xgb_model.predict(X)
+        return svm_result, log_reg_result, xgb_result
+    elif rt_type == 'Prob':
+        svm_result = svm_model.predict_proba(X)
+        log_reg_result = log_reg_model.predict_proba(X)
+        xgb_result = xgb_model.predict_proba(X)
+        return svm_result, log_reg_result, xgb_result
+    elif rt_type == 'mean_Prob':
+        svm_result = svm_model.predict_proba(X)
+        log_reg_result = log_reg_model.predict_proba(X)
+        xgb_result = xgb_model.predict_proba(X)
+        mean_prob = {label_encoder.classes_[i]: np.mean([svm_result[:, i], log_reg_result[:, i], xgb_result[:, i]], axis=0)
+                     for i in range(len(label_encoder.classes_))}
+        return mean_prob
+    else:
+        raise TypeError('Please provide a legal return param.')