获取AdaBoost模型的重要特征

获取AdaBoost模型的重要特征

模型训练之后，想要得到比较重要的特征，可以通过python的sklearn包来实现。

python实现代码如下所示：

 AdaBoostClassifier.py

# -*- coding: utf-8 -*-
import IOUtil as iou
import numpy as np
import pandas as pd
from sklearn import svm
from sklearn import metrics
import csv

def report():
    
    report = metrics.classification_report(y_test, y_predict)
    m = metrics.confusion_matrix(y_test, y_predict)
    print (report)
    print (m)
      

def result():

    pred= model.predict_proba(X_test)
    result= pd. DataFrame(pred)
    result.to_csv('result_AdaBoostClassifier.csv')
    
#### 训练
print ("AdaBoostClassifier Start")

X = iou.readArray("X_train.csv")
y = iou.readArray("y_train.csv")
y = y.ravel()
print ("AdaBoostClassifier Data OK")

from sklearn.ensemble import AdaBoostClassifier
model = AdaBoostClassifier()

model.fit(X, y)

n=model.feature_importances_#模型的重要特征
print(n)


###### 预测

X_test = iou.readArray("X_test.csv")
y_test = iou.readArray("y_test.csv")

print ("AdaBoostClassifier predict...")
y_predict = model.predict(X_test)


result()
report()


print ("AdaBoostClassifier Done!!!!!!")

 读取文件的代码如下所示：

IOUtil.py 

import csv
import numpy as np
from sklearn.externals import joblib


def readArray( file ):
    Data = []
    reader = csv.reader(open(file),delimiter=',', quotechar='\'')

    for row in reader:
        row = [float(x) for x in row]
        Data.append(row)

    Data = np.array(Data)

    return Data