MultinomialNB 的 TypeError：float() 参数必须是字符串或数字

Question

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() argument must be a string or a number, not 'set'

下面的代码直到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)。此代码中的错误类似于错误 here.

Answer 1

改变...

features[x] = {x in words}

到...

features[x] = x in words

第一行创建一个 featuresets 对 (word, {True}) 或 (word, {False}) 的列表，即第二个元素是 set。 SklearnClassifier 不希望将此作为标签。

该代码与 "Creating a module for Sentiment Analysis with NLTK" 中的代码非常相似。作者在那里使用了一个元组 (x in words)，但它与 x in words.

没有什么不同

MultinomialNB 的 TypeError：float() 参数必须是字符串或数字

TypeError from MultinomialNB: float() argument must be a string or a number

machine-learning

python-3.x

scikit-learn

text-classification

naivebayes