使用SGD分类器和GridsearchCV查找主要功能

时间:2018-10-04 10:36:48

标签: python machine-learning scikit-learn grid-search

# Implementing Linear_SGD classifier
clf = linear_model.SGDClassifier(max_iter=1000)
Cs = [0.0001,0.001, 0.01, 0.1, 1, 10]
tuned_parameters = [{'alpha': Cs}]
model = GridSearchCV(clf, tuned_parameters, scoring = 'accuracy', cv=2)
model.fit(x_train, Y_train)

如何从下面的代码中查找最重要的重要功能,因为它显示错误feature_count _。

这里我的矢量化器是BOW,分类器是SGD分类器,具有铰链损耗

def important_features(vectorizer,classifier,n=20):
    class_labels = classifier.classes_
    feature_names =vectorizer.get_feature_names()
    topn_class1 = sorted(zip(classifier.feature_count_[0], 
    feature_names),reverse=True)[:n]
    topn_class2 = sorted(zip(classifier.feature_count_[1], 
    feature_names),reverse=True)[:n]
    print("Important words in negative reviews")

我尝试使用上面的代码,但是显示为

AttributeError                            Traceback (most recent call last)
<ipython-input-77-093048fb461e> in <module>()
----> 1 important_features(Timesort_X_vec,model)

<ipython-input-75-10b9d6ee3f81> in important_features(vectorizer, 
classifier, n)
  2     class_labels = classifier.classes_
  3     feature_names =vectorizer.get_feature_names()
   ----> 4     topn_class1 = sorted(zip(classifier.feature_count_[0], 
feature_names),reverse=True)[:n]
  5     topn_class2 = sorted(zip(classifier.feature_count_[1], 
feature_names),reverse=True)[:n]
  6     print("Important words in negative reviews")

 AttributeError: 'GridSearchCV' object has no attribute 'feature_count_'.

由于我是编程的新手,请帮助我解决您的问题。谢谢

1 个答案:

答案 0 :(得分:0)

发生错误的原因是您使用的SGDClassifier没有feature_count_属性(请检查docs中的可用属性):

from sklearn.linear_model import SGDClassifier  

X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=5)
clf.fit(X, y) 

clf.feature_count_
[...]
AttributeError: 'SGDClassifier' object has no attribute 'feature_count_'

最初,我认为问题在于您使用的是GridSearchCV对象,但事实并非如此,因为函数中的行class_labels = classifier.classes_不会引发任何错误;尽管从文档来看,SGDClassifier似乎甚至没有classes_属性,但实际上它确实具有:

clf.classes_
# array([0, 1])

我知道scikit-learn中唯一包含feature_count_属性的分类器是BernoulliNBMultinomialNBComplementNB,它们都是Naive Bayes家族的,尽管我不太确定它是否可以使用,因为您打算在这里使用它...