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)
错误似乎出现在 MNB_classifier.train(training_set)
中。
此代码中的错误类似于错误 here .
最佳答案
改变...
features[x] = {x in words}
到...
features[x] = x in words
第一行创建一个由 (word, {True})
或 (word, {False})
对组成的 featuresets
列表,即第二个元素是一个集合
。 SklearnClassifier
不希望将其作为标签。
该代码看起来非常类似于 "Creating a module for Sentiment Analysis with NLTK" 中的代码。作者在那里使用了一个元组 (x in Words)
,但它与 x in Words
没有什么不同。
关于python-3.x - 来自 MultinomialNB : float() argument must be a string or a number 的类型错误,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/49415195/