小编Ima*_*e.r的帖子

我想要为包含我的OneClassSVM分类器的训练语料库的txt文件进行矢量化.为此,我正在使用scikit-learn库中的CountVectorizer.这是我的代码:

def file_to_corpse(file_name, stop_words):
    array_file = []
    with open(file_name) as fd:
        corp = fd.readlines()
    array_file = np.array(corp)
    stwf = stopwords.words('french')
    for w in stop_words:
        stwf.append(w)
    vectorizer = CountVectorizer(decode_error = 'replace', stop_words=stwf, min_df=1)
    X = vectorizer.fit_transform(array_file)
    return X

当我在我的文件上运行我的函数(大约206346行)时,我得到以下错误,我似乎无法理解它:

Traceback (most recent call last):
  File "svm.py", line 93, in <module>
    clf_svm.fit(training_data)
  File "/home/imane/anaconda/lib/python2.7/site-packages/sklearn/svm/classes.py", line 1028, in fit
    super(OneClassSVM, self).fit(X, np.ones(_num_samples(X)), sample_weight=sample_weight,
  File "/home/imane/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py", line 122, in _num_samples
    " a valid collection." % x)
TypeError: Singleton array array(<536172x13800 sparse matrix of type …