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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb  3 09:37:21 2023
+
+@author: ming
+"""
+
+import pandas as pd
+from fancyimpute import KNN
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neural_network import MLPClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report,confusion_matrix
+from sklearn import preprocessing
+
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import GridSearchCV
+
+
+#load data
+X=pd.read_csv(r"/Users/ming/Downloads/Xset1.csv")
+Y=pd.read_csv(r"/Users/ming/Downloads/Y.csv")
+
+#del columns
+X.dropna(thresh=100)
+
+#impute
+Xc=X.columns
+
+X = pd.DataFrame(KNN(k=6).fit_transform(X)) 
+X.columns=Xc
+
+#standardize
+standardized_X = preprocessing.scale(X)
+
+#oversampling
+
+#imblearn进行随机过采样
+from imblearn.over_sampling import RandomOverSampler
+ros = RandomOverSampler(random_state=0)
+X_resampled, y_resampled = ros.fit_resample(standardized_X, Y)
+
+
+models = {
+    "knn": KNeighborsClassifier(n_neighbors=1),
+    "naive_bayes": GaussianNB(),
+    "logit": LogisticRegression(solver="lbfgs", multi_class="auto"),
+    "svm": SVC(kernel="rbf", gamma="auto"),
+    "decision_tree": DecisionTreeClassifier(criterion='gini',splitter='random',max_depth=15,min_samples_leaf=10,min_samples_split=10), 
+    "random_forest": RandomForestClassifier(n_estimators=60,max_depth=6,max_samples=70,max_features=5),
+    "mlp": MLPClassifier()
+}
+
+trainX, testX, trainY, testY = train_test_split(X_resampled, y_resampled, random_state=3, test_size=0.2)
+
+print("use '{}' bulid model...".format("random_forest"))
+model = models["random_forest"]
+model.fit(trainX, trainY)
+
+'''
+tree_param_grid = {'n_estimators': range(50,100,10),
+                  'min_samples_split':range(30,111,20),
+                  'max_features':range(5,76,10),
+                  'max_depth':range(3,22,3)
+                  }
+ 
+tree_best = GridSearchCV(model,param_grid = tree_param_grid,cv = 3,scoring="roc_auc",n_jobs= -1, verbose = 100) 
+#Gridsearch turning parameters  
+tree_best.fit(trainX, trainY)
+
+print(tree_best.best_estimator_)
+print(tree_best.best_params_,"  ","得分：",tree_best.best_score_)
+'''
+
+features_importance = pd.concat([pd.DataFrame(Xc).T,pd.DataFrame(model.feature_importances_).T],axis=0).T
+features_importance.columns=["feature","importance"]
+
+## prediction
+print("elvaluation...")
+predictions = model.predict(testX)
+print(classification_report(testY, predictions))
+auc_score = roc_auc_score(testY, predictions)
+print('CONFUSION MATRIX')
+print(confusion_matrix(testY, predictions))
+print("AUC")
+print(auc_score)
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