我正在自学(可能是我的第一个错误)课程和方法，把情绪分析脚本转换为使用它们。

我想我已经准备好了所有的方法，但是我一直

global name 'get_bigram_word_feats' is not defined

我敢肯定我也会因为get_word_feats而得到一个错误，如果它达到了这个程度。

这一次我头撞得很厉害。我试着删除staticmethod并添加self。我做错什么了？

这是我的代码：def word_feats(words):

return dict([(word, True) for word in words])

class SentClassifier:

def __init__(self, name, location):

self.name = name

self.location = location

self.fullpath = location + "/" + name

def doesexist(self):

return os.path.isfile(self.fullpath)

def save_classifier(self):

rf = open(self.fullpath, 'wb')

pickle.dump(self.fullpath, rf)

rf.close()

def load_classifier(self):

sf = open(self.fullpath, 'rb')

sclassifier = pickle.load(sf)

sf.close()

return sclassifier

class Training:

def __init__(self, neg, pos):

self.neg = neg

self.pos = pos

self.negids = open(self.neg, 'rb').read().splitlines(True)

self.posids = open(self.pos, 'rb').read().splitlines(True)

self.exclude = set(string.punctuation)

self.exclude = self.exclude, '...'

self.swords = stopwords.words('english')

def tokens(self, words):

words = [w for w in nltk.word_tokenize(words) if w not in self.exclude and len(w) > 1

and w not in self.swords and wordnet.synsets(w)]

return words

def idlist(self, words):

thisidlist = [self.tokens(tf) for tf in words]

return thisidlist

@staticmethod

def get_word_feats(words):

return dict([(word, True) for word in words])

@staticmethod

def get_bigram_word_feats(twords, score_fn=BigramAssocMeasures.chi_sq, tn=200):

words = [w for w in twords]

bigram_finder = BigramCollocationFinder.from_words(words)

bigrams = bigram_finder.nbest(score_fn, tn)

return dict([(ngram, True) for ngram in itertools.chain(words, bigrams)])

@staticmethod

def label_feats(thelist, label):

return [(get_word_feats(lf), label) for lf in thelist]

@staticmethod

def label_grams(thelist, label):

return [(get_bigram_word_feats(gf), label) for gf in thelist()]

@staticmethod

def combinegrams(grams, feats):

for g in grams():

feats.append(g)

return feats

def negidlist(self):

return self.idlist(self.negids)

def posidlist(self):

return self.idlist(self.posids)

def posgrams(self):

return self.label_grams(self.posidlist, 'pos')

def neggrams(self):

return self.label_grams(self.negidlist, 'neg')

def negwords(self):

return self.label_feats(self.negidlist, 'neg')

def poswords(self):

return self.label_feats(self.posidlist, 'pos')

def negfeats(self):

return self.combinegrams(self.neggrams, self.negwords)

def posfeats(self):

return self.combinegrams(self.posgrams, self.poswords)

starttime = time.time()

myclassifier = SentClassifier("sentanalyzer.pickle", "classifiers")

if myclassifier.doesexist() is False:

print "training new classifier"

trainset = Training('data/neg.txt', 'data/pos.txt')

negfeats = trainset.negfeats()

posfeats = trainset.posfeats()

negcutoff = len(negfeats) * 8 / 10

poscutoff = len(posfeats) * 8 / 10

trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]

testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]

print 'train on %d instances, test on %d instances' % (len(trainfeats), len(testfeats))

classifier = NaiveBayesClassifier.train(trainfeats)

print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)

myclassifier.save_classifier()

else:

print "using existing classifier"

classifier = myclassifier.load_classifier()

classifier.show_most_informative_features(20)

mystr = "16 steps to an irresistible sales pitch, via @vladblagi: slidesha.re/1bVV7OS"

myfeat = word_feats(nltk.word_tokenize(mystr))

print classifier.classify(myfeat)

probd = classifier.prob_classify(myfeat)

print probd.prob('neg')

print probd.prob('pos')

donetime = time.time() - starttime

print donetime