我有这个代码。我有两个特点。如何一起训练这两个特征?
from textblob import TextBlob, Word, Blobber
from textblob.classifiers import NaiveBayesClassifier
from textblob.taggers import NLTKTagger
import re
import nltk
def get_word_before_you_feature(mystring):
keyword = 'you'
before_keyword, keyword, after_keyword = mystring.partition(keyword)
before_keyword = before_keyword.rsplit(None, 1)[-1]
return {'word_after_you': before_keyword}
def get_word_after_you_feature(mystring):
keyword = 'you'
before_keyword, keyword, after_keyword = mystring.partition(keyword)
after_keyword = after_keyword.split(None, 1)[0]
return {'word_after_you': after_keyword}
classifier = nltk.NaiveBayesClassifier.train(train)
lang_detector = NaiveBayesClassifier(train, feature_extractor=get_word_after_you_feature)
lang_detector = NaiveBayesClassifier(train, feature_extractor=get_word_before_you_feature)
print(lang_detector.accuracy(test))
print(lang_detector.show_informative_features(5))
这是我得到的输出。
word_before_you = 'do' 裁判:generi = 2.2:1.0
word_before_you = 'when'generi:裁判 = 1.1:1.0
它似乎只获得了最后一个功能。如何让分类器训练两个特征而不是一个。