from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
vectorizer = vectorizer.fit(word_data)
freq_term_mat = vectorizer.transform(word_data)
from sklearn.feature_extraction.text import TfidfTransformer
tfidf = TfidfTransformer(norm="l2")
tfidf = tfidf.fit(freq_term_mat)
Ttf_idf_matrix = tfidf.transform(freq_term_mat)
voc_words = Ttf_idf_matrix.getfeature_names()
print "The num of words = ",len(voc_words)
当我运行包含这段代码的程序时，出现以下错误:
Traceback (most recent call last): File "vectorize_text.py", line 87, in
voc_words = Ttf_idf_matrix.getfeature_names()
File "/home/farheen/anaconda/lib/python2.7/site- >packages/scipy/sparse/base.py", line 499, in getattr
raise AttributeError(attr + " not found")
AttributeError: get_feature_names not found
请给我一个解决方案。
最佳答案
我发现您的代码有两个问题。首先，您将 get_feature_names() 应用于矩阵输出，而不是矢量化器。您需要将其应用于矢量化器。其次，您不必要地将其分解为太多步骤。您可以使用 TfidfVectorizer.fit_transform() 在更小的空间内完成您想做的事情。试试这个:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
transformed = vectorizer.fit_transform(word_data)
print "Num words:", len(vectorizer.get_feature_names())
关于python - 属性错误 : getfeature_names not found ; using scikit-learn，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/31633128/