TfidfVectorizer dtype 不匹配 [英] TfidfVectorizer dtype mismatch

问题描述

我正在尝试在语料库上使用 TfidfVectorizer，但每次我都遇到此错误

I'm trying to use the TfidfVectorizer on a corpus but every time I end up with this error

File "sparsefuncs.pyx", line 117, in sklearn.utils.sparsefuncs.inplace_csr_row_normalize_l2 (sklearn\utils\sparsefuncs.c:2328)
ValueError: Buffer dtype mismatch, expected 'int' but got 'long long'

这是我的代码

corpus = []
testCorpus = []
trainType = []
testType = []

with open("stone_sku.csv") as f:
    cr = csv.DictReader(f)
    for row in cr:
        corpus.append(row['sku'])
        trainType.append(row['sku'])

with open("stone_sku.csv") as f:
    crTest = csv.DictReader(f)
    for row in crTest:
        testCorpus.append(row['sku'])
        testType.append(row['sku'])

cv = TfidfVectorizer(min_df=1, analyzer='char', ngram_range=(2,3))

trainCounts = cv.fit_transform(corpus)

它与 CountVectorizer 一起工作正常，如果我尝试使用 TfidfTransformer 转换数据会发生同样的错误

It works fine with CountVectorizer and the same error occurs if I try to transform the data using TfidfTransformer

推荐答案

您运行的是 64 位 Windows 吗?这可能是由最近在 master 分支中修复的已知问题引起的.

Are you running 64 bit Windows? This might be caused by a known issue that has been recently fixed in the master branch.

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