machine-learning - 如何将 Kfold 与 TfidfVectorizer 一起应用？

Question

我在使用 Tfidf 应用 K 折交叉验证时遇到问题。它给了我这个错误

ValueError: setting an array element with a sequence.

我见过其他有同样问题的问题，但他们使用的是train_test_split()，这与K-fold有点不同

for train_fold, valid_fold in kf.split(reviews_p1):
    vec = TfidfVectorizer(ngram_range=(1,1))
    reviews_p1 = vec.fit_transform(reviews_p1)

    train_x = [reviews_p1[i] for i in train_fold]        # Extract train data with train indices
    train_y = [labels_p1[i] for i in train_fold]        # Extract train data with train indices

    valid_x = [reviews_p1[i] for i in valid_fold]        # Extract valid data with cv indices
    valid_y = [labels_p1[i] for i in valid_fold]        # Extract valid data with cv indices

    svc = LinearSVC()
    model = svc.fit(X = train_x, y = train_y) # We fit the model with the fold train data
    y_pred = model.predict(valid_x)

实际上，我发现了问题所在，但我找不到解决方法，基本上，当我们使用 cv/train 索引提取训练数据时，我们会得到一个稀疏矩阵列表

[<1x21185 sparse matrix of type '<class 'numpy.float64'>'
    with 54 stored elements in Compressed Sparse Row format>,
 <1x21185 sparse matrix of type '<class 'numpy.float64'>'
    with 47 stored elements in Compressed Sparse Row format>,
 <1x21185 sparse matrix of type '<class 'numpy.float64'>'
    with 18 stored elements in Compressed Sparse Row format>, ....]

我尝试在分割后对数据应用 Tfidf，但它不起作用，因为特征数量不一样。

那么有没有办法在不创建稀疏矩阵列表的情况下将数据分割为 K 倍？

Answer 1

在类似问题的回答中Do I use the same Tfidf vocabulary in k-fold cross_validation他们建议

for train_index, test_index in kf.split(data_x, data_y):
   x_train, x_test = data_x[train_index], data_x[test_index]
   y_train, y_test = data_y[train_index], data_y[test_index]

   tfidf = TfidfVectorizer()
   x_train = tfidf.fit_transform(x_train)
   x_test = tfidf.transform(x_test)

   clf = SVC()
   clf.fit(x_train, y_train)
   y_pred = clf.predict(x_test)
   score = accuracy_score(y_test, y_pred)
   print(score)