保存预测结果到本地

yugou_best.py

@@ -111,7 +111,6 @@
 
 X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.55, random_state=10)
 
-
 # 初始化梯度提升树分类器
 gradient_boosting = GradientBoostingClassifier(n_estimators=100, random_state=42)  # 这里 n_estimators 表示基学习器的数量
 
@@ -132,9 +131,9 @@
 
 # 定义参数网格
 param_grid = {
-    'n_estimators': [15,25,50],  # 调整基学习器的数量
-    'learning_rate': [0.003,0.005,0.01],  # 学习率
-    'max_depth': [1,3, 5]  # 调整树的深度
+    'n_estimators': [15, 25, 50],  # 调整基学习器的数量
+    'learning_rate': [0.003, 0.005, 0.01],  # 学习率
+    'max_depth': [1, 3, 5]  # 调整树的深度
     # 其他需要调整的参数
 }
 
@@ -145,7 +144,6 @@
 # 输出最佳参数组合和对应的准确率
 print("Best Parameters:", grid_search.best_params_)
 
-
 # 使用最佳参数组合重新训练模型
 best_gradient_boosting = grid_search.best_estimator_
 best_gradient_boosting.fit(X_train_selected, y_train)
@@ -155,4 +153,19 @@
 
 # 计算准确率
 accuracy = accuracy_score(y_test, predictions)
-print("Gradient Boosting Accuracy after Parameter Tuning:", accuracy)
+print("Gradient Boosting Accuracy after Parameter Tuning:", accuracy)
+
+# choose = ["user_id", "merchant_id", "mlp_prob"]
+# res = df_test[choose]
+# res.rename(columns={"mlp_prob": "prob"}, inplace=True)
+# print(res.head(10))
+# res.to_csv(path_or_buf=r"data/prediction.csv", index=False)
+
+pX = df_test.drop(['user_id', 'merchant_id'], axis=1)
+pX_selected = select_features.transform(pX)
+pPredictions = best_gradient_boosting.predict_proba(pX_selected)
+df_test['prob'] = pPredictions
+choose = ["user_id", "merchant_id", "label"]
+res = df_test[choose]
+print(res.head(10))
+res.to_csv(path_or_buf=r"data/prediction.csv", index=False)