本文整理匯總了Python中sklearn.feature_extraction.text.TfidfVectorizer._validate_vocabulary方法的典型用法代碼示例。如果您正苦於以下問題：Python TfidfVectorizer._validate_vocabulary方法的具體用法？Python TfidfVectorizer._validate_vocabulary怎麽用？Python TfidfVectorizer._validate_vocabulary使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.feature_extraction.text.TfidfVectorizer的用法示例。
在下文中一共展示了TfidfVectorizer._validate_vocabulary方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: vectorize_words
# 需要導入模塊: from sklearn.feature_extraction.text import TfidfVectorizer [as 別名]
# 或者: from sklearn.feature_extraction.text.TfidfVectorizer import _validate_vocabulary [as 別名]
def vectorize_words(self, clean_profiles, max_features = 500) :
# Vectorize the words in the cleaned profiles using
# term frequency/inverse document frequency (TF-IDF)
print "Creating the bag of words...\n"
# Initialize the "CountVectorizer" object, which is scikit-learn's
# bag of words tool.
vectorizer = TfidfVectorizer(min_df=1, max_features = max_features)
vectorizer._validate_vocabulary()
# fit_transform() does two functions: First, it fits the model
# and learns the vocabulary; second, it transforms our training data
# into feature vectors. The input to fit_transform should be a list of
# strings.
data_features = vectorizer.fit_transform(clean_profiles)
# Numpy arrays are easy to work with, so convert the result to an
# array
data_features = data_features.toarray()
print data_features.shape
vocab = vectorizer.get_feature_names()
# Sum up the counts of each vocabulary word
dist = np.sum(data_features, axis=0)
return vectorizer, data_features, vocab, dist