python - TfidfVectorizer 数据类型不匹配

Question

我尝试在语料库上使用 TfidfVectorizer，但每次都会出现此错误

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 配合得很好，如果我尝试使用 TfidfTransformer 转换数据，也会发生同样的错误

Answer 1

您运行的是 64 位 Windows 吗？这可能是由 master 分支最近修复的已知问题引起的。