用树模型选择特征变量，可见哪些变量

#-*- coding: utf-8 -*-
#提取日历史数据
import pandas as pd
from finta import TA  #金融指标库，我这里使用它帮我计算均线值
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns   # 这个库可以用来画 heatmap 图
import pymysql
import time
import datetime
import sklearn
from sqlalchemy import create_engine
dbconn = pymysql.connect(
        host="127.0.0.1",
        database="A_HISDATA",
        user="root",
        password="111111",
        port=3306,
        charset='utf8')
connt = create_engine('mysql+mysqldb://root:111111@localhost:3306/A_HISDATA?charset=utf8')


#  数据，来自数据库,指定日期第一段
sqlcmd1 = "select * from a_hisdata.telco"
df = pd.read_sql(sqlcmd1, dbconn)

print(df.columns)

#开始模型数据准备
X = df.drop(['index','churn'],axis=1)
y = df['churn']

from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
print(X.shape)

clf = ExtraTreesClassifier()
clf = clf.fit(X, y)
print(clf.feature_importances_)
print(clf.n_features_)


#model = SelectFromModel(clf, prefit=True)
#X_new = model.transform(X)
#print(X_new.shape)

t = pd.DataFrame(X.columns)
t.columns = ['var']
t['importance'] = clf.feature_importances_
t = t.sort_values(by=['importance'],ascending=False)
print(t.columns)
var_ok = t[t['importance']>0.05]['var'].values#按需选择变量
print(var_ok)

X_ok = X[var_ok]
print(X_ok)