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如何为不同类别的scikit-learn分类器获取最丰富的信息？

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8

NLTK包提供了一个方法 show_most_informative_features() 来为这两个类找到最重要的特性，输出如下：

contains(outstanding) = True              pos : neg    =     11.1 : 1.0
        contains(seagal) = True              neg : pos    =      7.7 : 1.0
   contains(wonderfully) = True              pos : neg    =      6.8 : 1.0
         contains(damon) = True              pos : neg    =      5.9 : 1.0
        contains(wasted) = True              neg : pos    =      5.8 : 1.0

正如在这个问题How to get most informative features for scikit-learn classifiers?中所回答的，这也适用于scikit-learn . 但是，对于二元分类器，该问题的答案仅输出最佳特征本身 .

所以我的问题是，我如何识别该特征的相关类，如上面的例子（在pos类中最出色的信息，而在负类中，seagal是最有用的信息）？

编辑：实际上我想要的是每个 class 最具信息性的单词列表 . 我怎样才能做到这一点？谢谢！

python machine-learning scikit-learn nltk

3 回答

  • 9

    基本上你需要：

    def most_informative_feature_for_class(vectorizer, classifier, classlabel, n=10):
    labelid = list(classifier.classes_).index(classlabel)
    feature_names = vectorizer.get_feature_names()
    topn = sorted(zip(classifier.coef_[labelid], feature_names))[-n:]

    for coef, feat in topn:
        print classlabel, feat, coef

    • classifier.classes_ 访问分类器中的类标签的索引

    • vectorizer.get_feature_names() 不言自明

    • sorted(zip(classifier.coef_[labelid], feature_names))[-n:] 检索给定类标签的分类器系数，然后按升序对其进行排序 .

    我将使用https://github.com/alvations/bayesline中的一个简单示例

    输入文件 train.txt ：

    $ echo """Pošto je EULEX obećao da će obaviti istragu o prošlosedmičnom izbijanju nasilja na sjeveru Kosova, taj incident predstavlja još jedan ispit kapaciteta misije da doprinese jačanju vladavine prava.
> De todas as provações que teve de suplantar ao longo da vida, qual foi a mais difícil? O início. Qualquer começo apresenta dificuldades que parecem intransponíveis. Mas tive sempre a minha mãe do meu lado. Foi ela quem me ajudou a encontrar forças para enfrentar as situações mais decepcionantes, negativas, as que me punham mesmo furiosa.
> Al parecer, Andrea Guasch pone que una relación a distancia es muy difícil de llevar como excusa. Algo con lo que, por lo visto, Alex Lequio no está nada de acuerdo. ¿O es que más bien ya ha conseguido la fama que andaba buscando?
> Vo väčšine golfových rezortov ide o veľký komplex niekoľkých ihrísk blízko pri sebe spojených s hotelmi a ďalšími možnosťami trávenia voľného času – nie vždy sú manželky či deti nadšenými golfistami, a tak potrebujú iný druh vyžitia. Zaujímavé kombinácie ponúkajú aj rakúske, švajčiarske či talianske Alpy, kde sa dá v zime lyžovať a v lete hrať golf pod vysokými alpskými končiarmi.""" > test.in

    码：

    import codecs, re, time
from itertools import chain

import numpy as np

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

trainfile = 'train.txt'

# Vectorizing data.
train = []
word_vectorizer = CountVectorizer(analyzer='word')
trainset = word_vectorizer.fit_transform(codecs.open(trainfile,'r','utf8'))
tags = ['bs','pt','es','sr']

# Training NB
mnb = MultinomialNB()
mnb.fit(trainset, tags)

def most_informative_feature_for_class(vectorizer, classifier, classlabel, n=10):
    labelid = list(classifier.classes_).index(classlabel)
    feature_names = vectorizer.get_feature_names()
    topn = sorted(zip(classifier.coef_[labelid], feature_names))[-n:]

    for coef, feat in topn:
        print classlabel, feat, coef



most_informative_feature_for_class(word_vectorizer, mnb, 'bs')
print 
most_informative_feature_for_class(word_vectorizer, mnb, 'pt')

    [OUT]：

    bs obećao -4.50534985071
bs pošto -4.50534985071
bs prava -4.50534985071
bs predstavlja -4.50534985071
bs prošlosedmičnom -4.50534985071
bs sjeveru -4.50534985071
bs taj -4.50534985071
bs vladavine -4.50534985071
bs će -4.50534985071
bs da -4.0998847426

pt teve -4.63472898823
pt tive -4.63472898823
pt todas -4.63472898823
pt vida -4.63472898823
pt de -4.22926388012
pt foi -4.22926388012
pt mais -4.22926388012
pt me -4.22926388012
pt as -3.94158180767
pt que -3.94158180767
    回复于
  • 8

    您可以在左侧和右侧使用两个类来获得相同的内容：

    precision    recall  f1-score   support

 Irrelevant       0.77      0.98      0.86       129
   Relevant       0.78      0.15      0.25        46

avg / total       0.77      0.77      0.70       175

    -1.3914 davis                   1.4809  austin
    -1.1023 suicide                 1.0695  march
    -1.0609 arrested                1.0379  call
    -1.0145 miller                  1.0152  tsa
    -0.8902 packers                 0.9848  passengers
    -0.8370 train                   0.9547  pensacola
    -0.7557 trevor                  0.7432  bag
    -0.7457 near                    0.7056  conditt
    -0.7359 military                0.7002  midamerica
    -0.7302 berlin                  0.6987  mark
    -0.6880 april                   0.6799  grenade
    -0.6581 plane                   0.6357  suspicious
    -0.6351 disposal                0.6348  death
    -0.5804 wwii                    0.6053  flight
    -0.5723 terminal                0.5745  marabi


def Show_most_informative_features(vectorizer, clf, n=20):
    feature_names = vectorizer.get_feature_names()
    coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))
    top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n + 1):-1])
    for (coef_1, fn_1), (coef_2, fn_2) in top:
      print ("\t%.4f\t%-15s\t\t%.4f\t%-15s" % (coef_1, fn_1, coef_2, fn_2))
    回复于
  • 0

    在二进制分类的情况下，似乎系数数组已经变平 .

    让我们尝试仅使用两个标签重新标记我们的数据：

    import codecs, re, time
from itertools import chain

import numpy as np

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

trainfile = 'train.txt'

# Vectorizing data.
train = []
word_vectorizer = CountVectorizer(analyzer='word')
trainset = word_vectorizer.fit_transform(codecs.open(trainfile,'r','utf8'))
tags = ['bs','pt','bs','pt']

# Training NB
mnb = MultinomialNB()
mnb.fit(trainset, tags)

print mnb.classes_
print mnb.coef_[0]
print mnb.coef_[1]

    [OUT]：

    ['bs' 'pt']
[-5.55682806 -4.86368088 -4.86368088 -5.55682806 -5.55682806 -5.55682806
 -4.86368088 -4.86368088 -5.55682806 -5.55682806 -4.86368088 -4.86368088
 -4.1705337  -5.55682806 -4.86368088 -5.55682806 -4.86368088 -5.55682806
 -5.55682806 -5.55682806 -4.86368088 -4.45821577 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -5.55682806
 -5.55682806 -5.55682806 -5.55682806 -4.45821577 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088
 -4.86368088 -5.55682806 -5.55682806 -5.55682806 -5.55682806 -5.55682806
 -5.55682806 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -4.86368088
 -4.86368088 -5.55682806 -5.55682806 -4.86368088 -5.55682806 -4.86368088
 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -4.45821577 -4.86368088
 -4.86368088 -4.45821577 -4.86368088 -4.86368088 -4.86368088 -5.55682806
 -4.86368088 -5.55682806 -5.55682806 -4.86368088 -5.55682806 -5.55682806
 -4.86368088 -5.55682806 -4.86368088 -4.86368088 -4.86368088 -5.55682806
 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -5.55682806 -4.86368088
 -5.55682806 -4.86368088 -5.55682806 -4.86368088 -5.55682806 -5.55682806
 -5.55682806 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088
 -4.86368088 -4.1705337  -4.86368088 -4.86368088 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088
 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088 -5.55682806
 -4.86368088 -4.45821577 -4.86368088 -4.86368088]
Traceback (most recent call last):
  File "test.py", line 24, in <module>
    print mnb.coef_[1]
IndexError: index 1 is out of bounds for axis 0 with size 1

    那么让我们做一些诊断：

    print mnb.feature_count_
print mnb.coef_[0]

    [OUT]：

    [[ 1.  0.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  0.  1.  0.  1.  0.  1.
   1.  1.  2.  2.  0.  0.  0.  1.  1.  0.  1.  0.  0.  0.  0.  0.  2.  1.
   1.  1.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.
   0.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  1.  1.  0.  1.  0.
   1.  2.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  1.  0.  1.  1.
   0.  1.  0.  0.  0.  1.  1.  1.  0.  0.  1.  0.  1.  0.  1.  0.  1.  1.
   1.  0.  0.  1.  0.  0.  0.  4.  0.  0.  1.  0.  0.  0.  0.  0.  1.  0.
   0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  0.
   0.  0.  1.  0.  0.  1.  0.  0.  0.  0.]
 [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  0.  1.  1.  3.  0.  1.  0.  1.  0.
   0.  0.  1.  2.  1.  1.  1.  1.  0.  1.  0.  1.  1.  1.  1.  1.  0.  0.
   0.  0.  0.  2.  1.  1.  1.  1.  1.  0.  0.  1.  1.  1.  1.  0.  1.  1.
   1.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  1.  0.  1.
   0.  0.  1.  1.  2.  1.  1.  2.  1.  1.  1.  0.  1.  0.  0.  1.  0.  0.
   1.  0.  1.  1.  1.  0.  0.  0.  1.  1.  0.  1.  0.  1.  0.  1.  0.  0.
   0.  1.  1.  0.  1.  1.  1.  3.  1.  1.  0.  1.  1.  1.  1.  1.  0.  1.
   1.  1.  0.  1.  1.  1.  1.  1.  1.  0.  1.  1.  0.  0.  1.  1.  1.  1.
   1.  1.  0.  1.  1.  0.  1.  2.  1.  1.]]
[-5.55682806 -4.86368088 -4.86368088 -5.55682806 -5.55682806 -5.55682806
 -4.86368088 -4.86368088 -5.55682806 -5.55682806 -4.86368088 -4.86368088
 -4.1705337  -5.55682806 -4.86368088 -5.55682806 -4.86368088 -5.55682806
 -5.55682806 -5.55682806 -4.86368088 -4.45821577 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -5.55682806
 -5.55682806 -5.55682806 -5.55682806 -4.45821577 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088
 -4.86368088 -5.55682806 -5.55682806 -5.55682806 -5.55682806 -5.55682806
 -5.55682806 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -4.86368088
 -4.86368088 -5.55682806 -5.55682806 -4.86368088 -5.55682806 -4.86368088
 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -4.45821577 -4.86368088
 -4.86368088 -4.45821577 -4.86368088 -4.86368088 -4.86368088 -5.55682806
 -4.86368088 -5.55682806 -5.55682806 -4.86368088 -5.55682806 -5.55682806
 -4.86368088 -5.55682806 -4.86368088 -4.86368088 -4.86368088 -5.55682806
 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -5.55682806 -4.86368088
 -5.55682806 -4.86368088 -5.55682806 -4.86368088 -5.55682806 -5.55682806
 -5.55682806 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088
 -4.86368088 -4.1705337  -4.86368088 -4.86368088 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -4.86368088
 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088
 -5.55682806 -5.55682806 -4.86368088 -4.86368088 -4.86368088 -4.86368088
 -4.86368088 -4.86368088 -5.55682806 -4.86368088 -4.86368088 -5.55682806
 -4.86368088 -4.45821577 -4.86368088 -4.86368088]

    看起来像功能被计算，然后当矢量化它被展平以节省内存，所以让我们尝试：

    index = 0
coef_features_c1_c2 = []

for feat, c1, c2 in zip(word_vectorizer.get_feature_names(), mnb.feature_count_[0], mnb.feature_count_[1]):
    coef_features_c1_c2.append(tuple([mnb.coef_[0][index], feat, c1, c2]))
    index+=1

for i in sorted(coef_features_c1_c2):
    print i

    [OUT]：

    (-5.5568280616995374, u'acuerdo', 1.0, 0.0)
(-5.5568280616995374, u'al', 1.0, 0.0)
(-5.5568280616995374, u'alex', 1.0, 0.0)
(-5.5568280616995374, u'algo', 1.0, 0.0)
(-5.5568280616995374, u'andaba', 1.0, 0.0)
(-5.5568280616995374, u'andrea', 1.0, 0.0)
(-5.5568280616995374, u'bien', 1.0, 0.0)
(-5.5568280616995374, u'buscando', 1.0, 0.0)
(-5.5568280616995374, u'como', 1.0, 0.0)
(-5.5568280616995374, u'con', 1.0, 0.0)
(-5.5568280616995374, u'conseguido', 1.0, 0.0)
(-5.5568280616995374, u'distancia', 1.0, 0.0)
(-5.5568280616995374, u'doprinese', 1.0, 0.0)
(-5.5568280616995374, u'es', 2.0, 0.0)
(-5.5568280616995374, u'est\xe1', 1.0, 0.0)
(-5.5568280616995374, u'eulex', 1.0, 0.0)
(-5.5568280616995374, u'excusa', 1.0, 0.0)
(-5.5568280616995374, u'fama', 1.0, 0.0)
(-5.5568280616995374, u'guasch', 1.0, 0.0)
(-5.5568280616995374, u'ha', 1.0, 0.0)
(-5.5568280616995374, u'incident', 1.0, 0.0)
(-5.5568280616995374, u'ispit', 1.0, 0.0)
(-5.5568280616995374, u'istragu', 1.0, 0.0)
(-5.5568280616995374, u'izbijanju', 1.0, 0.0)
(-5.5568280616995374, u'ja\u010danju', 1.0, 0.0)
(-5.5568280616995374, u'je', 1.0, 0.0)
(-5.5568280616995374, u'jedan', 1.0, 0.0)
(-5.5568280616995374, u'jo\u0161', 1.0, 0.0)
(-5.5568280616995374, u'kapaciteta', 1.0, 0.0)
(-5.5568280616995374, u'kosova', 1.0, 0.0)
(-5.5568280616995374, u'la', 1.0, 0.0)
(-5.5568280616995374, u'lequio', 1.0, 0.0)
(-5.5568280616995374, u'llevar', 1.0, 0.0)
(-5.5568280616995374, u'lo', 2.0, 0.0)
(-5.5568280616995374, u'misije', 1.0, 0.0)
(-5.5568280616995374, u'muy', 1.0, 0.0)
(-5.5568280616995374, u'm\xe1s', 1.0, 0.0)
(-5.5568280616995374, u'na', 1.0, 0.0)
(-5.5568280616995374, u'nada', 1.0, 0.0)
(-5.5568280616995374, u'nasilja', 1.0, 0.0)
(-5.5568280616995374, u'no', 1.0, 0.0)
(-5.5568280616995374, u'obaviti', 1.0, 0.0)
(-5.5568280616995374, u'obe\u0107ao', 1.0, 0.0)
(-5.5568280616995374, u'parecer', 1.0, 0.0)
(-5.5568280616995374, u'pone', 1.0, 0.0)
(-5.5568280616995374, u'por', 1.0, 0.0)
(-5.5568280616995374, u'po\u0161to', 1.0, 0.0)
(-5.5568280616995374, u'prava', 1.0, 0.0)
(-5.5568280616995374, u'predstavlja', 1.0, 0.0)
(-5.5568280616995374, u'pro\u0161losedmi\u010dnom', 1.0, 0.0)
(-5.5568280616995374, u'relaci\xf3n', 1.0, 0.0)
(-5.5568280616995374, u'sjeveru', 1.0, 0.0)
(-5.5568280616995374, u'taj', 1.0, 0.0)
(-5.5568280616995374, u'una', 1.0, 0.0)
(-5.5568280616995374, u'visto', 1.0, 0.0)
(-5.5568280616995374, u'vladavine', 1.0, 0.0)
(-5.5568280616995374, u'ya', 1.0, 0.0)
(-5.5568280616995374, u'\u0107e', 1.0, 0.0)
(-4.863680881139592, u'aj', 0.0, 1.0)
(-4.863680881139592, u'ajudou', 0.0, 1.0)
(-4.863680881139592, u'alpsk\xfdmi', 0.0, 1.0)
(-4.863680881139592, u'alpy', 0.0, 1.0)
(-4.863680881139592, u'ao', 0.0, 1.0)
(-4.863680881139592, u'apresenta', 0.0, 1.0)
(-4.863680881139592, u'bl\xedzko', 0.0, 1.0)
(-4.863680881139592, u'come\xe7o', 0.0, 1.0)
(-4.863680881139592, u'da', 2.0, 1.0)
(-4.863680881139592, u'decepcionantes', 0.0, 1.0)
(-4.863680881139592, u'deti', 0.0, 1.0)
(-4.863680881139592, u'dificuldades', 0.0, 1.0)
(-4.863680881139592, u'dif\xedcil', 1.0, 1.0)
(-4.863680881139592, u'do', 0.0, 1.0)
(-4.863680881139592, u'druh', 0.0, 1.0)
(-4.863680881139592, u'd\xe1', 0.0, 1.0)
(-4.863680881139592, u'ela', 0.0, 1.0)
(-4.863680881139592, u'encontrar', 0.0, 1.0)
(-4.863680881139592, u'enfrentar', 0.0, 1.0)
(-4.863680881139592, u'for\xe7as', 0.0, 1.0)
(-4.863680881139592, u'furiosa', 0.0, 1.0)
(-4.863680881139592, u'golf', 0.0, 1.0)
(-4.863680881139592, u'golfistami', 0.0, 1.0)
(-4.863680881139592, u'golfov\xfdch', 0.0, 1.0)
(-4.863680881139592, u'hotelmi', 0.0, 1.0)
(-4.863680881139592, u'hra\u0165', 0.0, 1.0)
(-4.863680881139592, u'ide', 0.0, 1.0)
(-4.863680881139592, u'ihr\xedsk', 0.0, 1.0)
(-4.863680881139592, u'intranspon\xedveis', 0.0, 1.0)
(-4.863680881139592, u'in\xedcio', 0.0, 1.0)
(-4.863680881139592, u'in\xfd', 0.0, 1.0)
(-4.863680881139592, u'kde', 0.0, 1.0)
(-4.863680881139592, u'kombin\xe1cie', 0.0, 1.0)
(-4.863680881139592, u'komplex', 0.0, 1.0)
(-4.863680881139592, u'kon\u010diarmi', 0.0, 1.0)
(-4.863680881139592, u'lado', 0.0, 1.0)
(-4.863680881139592, u'lete', 0.0, 1.0)
(-4.863680881139592, u'longo', 0.0, 1.0)
(-4.863680881139592, u'ly\u017eova\u0165', 0.0, 1.0)
(-4.863680881139592, u'man\u017eelky', 0.0, 1.0)
(-4.863680881139592, u'mas', 0.0, 1.0)
(-4.863680881139592, u'mesmo', 0.0, 1.0)
(-4.863680881139592, u'meu', 0.0, 1.0)
(-4.863680881139592, u'minha', 0.0, 1.0)
(-4.863680881139592, u'mo\u017enos\u0165ami', 0.0, 1.0)
(-4.863680881139592, u'm\xe3e', 0.0, 1.0)
(-4.863680881139592, u'nad\u0161en\xfdmi', 0.0, 1.0)
(-4.863680881139592, u'negativas', 0.0, 1.0)
(-4.863680881139592, u'nie', 0.0, 1.0)
(-4.863680881139592, u'nieko\u013ek\xfdch', 0.0, 1.0)
(-4.863680881139592, u'para', 0.0, 1.0)
(-4.863680881139592, u'parecem', 0.0, 1.0)
(-4.863680881139592, u'pod', 0.0, 1.0)
(-4.863680881139592, u'pon\xfakaj\xfa', 0.0, 1.0)
(-4.863680881139592, u'potrebuj\xfa', 0.0, 1.0)
(-4.863680881139592, u'pri', 0.0, 1.0)
(-4.863680881139592, u'prova\xe7\xf5es', 0.0, 1.0)
(-4.863680881139592, u'punham', 0.0, 1.0)
(-4.863680881139592, u'qual', 0.0, 1.0)
(-4.863680881139592, u'qualquer', 0.0, 1.0)
(-4.863680881139592, u'quem', 0.0, 1.0)
(-4.863680881139592, u'rak\xfaske', 0.0, 1.0)
(-4.863680881139592, u'rezortov', 0.0, 1.0)
(-4.863680881139592, u'sa', 0.0, 1.0)
(-4.863680881139592, u'sebe', 0.0, 1.0)
(-4.863680881139592, u'sempre', 0.0, 1.0)
(-4.863680881139592, u'situa\xe7\xf5es', 0.0, 1.0)
(-4.863680881139592, u'spojen\xfdch', 0.0, 1.0)
(-4.863680881139592, u'suplantar', 0.0, 1.0)
(-4.863680881139592, u's\xfa', 0.0, 1.0)
(-4.863680881139592, u'tak', 0.0, 1.0)
(-4.863680881139592, u'talianske', 0.0, 1.0)
(-4.863680881139592, u'teve', 0.0, 1.0)
(-4.863680881139592, u'tive', 0.0, 1.0)
(-4.863680881139592, u'todas', 0.0, 1.0)
(-4.863680881139592, u'tr\xe1venia', 0.0, 1.0)
(-4.863680881139592, u've\u013ek\xfd', 0.0, 1.0)
(-4.863680881139592, u'vida', 0.0, 1.0)
(-4.863680881139592, u'vo', 0.0, 1.0)
(-4.863680881139592, u'vo\u013en\xe9ho', 0.0, 1.0)
(-4.863680881139592, u'vysok\xfdmi', 0.0, 1.0)
(-4.863680881139592, u'vy\u017eitia', 0.0, 1.0)
(-4.863680881139592, u'v\xe4\u010d\u0161ine', 0.0, 1.0)
(-4.863680881139592, u'v\u017edy', 0.0, 1.0)
(-4.863680881139592, u'zauj\xedmav\xe9', 0.0, 1.0)
(-4.863680881139592, u'zime', 0.0, 1.0)
(-4.863680881139592, u'\u010dasu', 0.0, 1.0)
(-4.863680881139592, u'\u010fal\u0161\xedmi', 0.0, 1.0)
(-4.863680881139592, u'\u0161vaj\u010diarske', 0.0, 1.0)
(-4.4582157730314274, u'de', 2.0, 2.0)
(-4.4582157730314274, u'foi', 0.0, 2.0)
(-4.4582157730314274, u'mais', 0.0, 2.0)
(-4.4582157730314274, u'me', 0.0, 2.0)
(-4.4582157730314274, u'\u010di', 0.0, 2.0)
(-4.1705337005796466, u'as', 0.0, 3.0)
(-4.1705337005796466, u'que', 4.0, 3.0)

    现在我们看到一些模式......似乎较高的系数有利于一个类而另一个有利于另一个，所以你可以简单地这样做：

    import codecs, re, time
from itertools import chain

import numpy as np

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

trainfile = 'train.txt'

# Vectorizing data.
train = []
word_vectorizer = CountVectorizer(analyzer='word')
trainset = word_vectorizer.fit_transform(codecs.open(trainfile,'r','utf8'))
tags = ['bs','pt','bs','pt']

# Training NB
mnb = MultinomialNB()
mnb.fit(trainset, tags)

def most_informative_feature_for_binary_classification(vectorizer, classifier, n=10):
    class_labels = classifier.classes_
    feature_names = vectorizer.get_feature_names()
    topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]
    topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]

    for coef, feat in topn_class1:
        print class_labels[0], coef, feat

    print

    for coef, feat in reversed(topn_class2):
        print class_labels[1], coef, feat


most_informative_feature_for_binary_classification(word_vectorizer, mnb)

    [OUT]：

    bs -5.5568280617 acuerdo
bs -5.5568280617 al
bs -5.5568280617 alex
bs -5.5568280617 algo
bs -5.5568280617 andaba
bs -5.5568280617 andrea
bs -5.5568280617 bien
bs -5.5568280617 buscando
bs -5.5568280617 como
bs -5.5568280617 con

pt -4.17053370058 que
pt -4.17053370058 as
pt -4.45821577303 či
pt -4.45821577303 me
pt -4.45821577303 mais
pt -4.45821577303 foi
pt -4.45821577303 de
pt -4.86368088114 švajčiarske
pt -4.86368088114 ďalšími
pt -4.86368088114 času

    实际上如果How to get most informative features for scikit-learn classifiers?系数How to get most informative features for scikit-learn classifiers?

    回复于

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