python-3.x - 来自MultinomialNB的TypeError:float()参数必须为字符串或数字

我正在尝试比较多项式，二项式和伯努利分类器的性能，但出现错误：


  TypeError：float（）参数必须是字符串或数字，而不是“ set”


下面的代码是MultinomialNB。

documents = [(list(movie_reviews.words(fileid)), category)
             for category in movie_reviews.categories()
             for fileid in movie_reviews.fileids(category)]

random.shuffle(documents)

#print(documents[1])

all_words = []

for w in movie_reviews.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3000]

def look_for_features(document):
    words = set(document)
    features = {}
    for x in word_features:
        features[x] = {x in words}
    return features

#feature set will be finding features and category
featuresets = [(look_for_features(rev), category) for (rev, category) in documents]

training_set = featuresets[:1400]
testing_set = featuresets[1400:]

#Multinomial
MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print ("Accuracy: ", (nltk.classify.accuracy(MNB_classifier,testing_set))*100)

错误似乎在MNB_classifier.train(training_set)中。
此代码中的错误类似于错误here。

最佳答案

更改...

features[x] = {x in words}

至...

features[x] = x in words

第一行创建对featuresets或(word, {True})的列表(word, {False})，即第二个元素是set。 SklearnClassifier不应将此作为标签。



该代码非常类似于"Creating a module for Sentiment Analysis with NLTK"中的代码。作者在那里使用了元组(x in words)，但这与x in words没什么不同。

关于python-3.x - 来自MultinomialNB的TypeError:float()参数必须为字符串或数字，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49415195/