Question

我通过转换情绪分析脚本来教我自己（可能是我的第一个错误）类和方法。

我思考我已经掌握了所有方法，但我一直在

global name 'get_bigram_word_feats' is not defined

我确定get_word_feats我也会收到错误，如果它已经那么远了。

我正在撞击这个伟大的时间。我尝试删除staticmethod并添加自己。我做错了什么？

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

Answer 1

您需要的所有信息都在异常消息中：

全局名称'get_bigram_word_feats'未定义

（我的重点）

Python不理解您要从类中访问该方法，因为您没有将类名指定为方法调用的一部分。因此，它正在全局命名空间中查找该函数，但未能找到它。

如果你回忆一下调用实例方法，你需要在方法前加上self.，以使Python解释器看起来正确，这也适用于静态方法，尽管你没有指定{{1}而是指定类名。

因此，为了解决这个问题，请使用类名称对方法进行前缀：

self.

Answer 2

未定义全局名称“get_bigram_word_feats”

您的通话应如下所示（请注意此处使用的班级名称）：

@staticmethod
def label_grams(thelist, label):
    return [(Training.get_bigram_word_feats(gf), label) for gf in thelist()]

通常，对于静态方法，请使用类名。

我尝试删除staticmethod并添加self。我做错了什么？

在这种情况下，您使用self.funcName(..)。如下所示：

def label_grams(self, thelist, label):
    return [(self.get_bigram_word_feats(gf), label) for gf in thelist()]

Answer 3

好消息：修复很简单。这样称呼：Training.get_bigram_word_feats(...)。

如，

@staticmethod
def label_grams(thelist, label):
    return [(Training.get_bigram_word_feats(gf), label) for gf in thelist()]

未找到Python方法，但在类中定义

3 个答案: