machine-learning - 如何在TfidfVectorizer中应用Kfold？

我在使用Tfidf进行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.

我看过其他有相同问题的问题，但他们使用的是train_test_split（）与K折有点不同

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>, ....]

拆分后，我尝试在数据上应用Tfidf，但由于功能数量不同，因此无法正常工作。

那么有没有办法在不创建稀疏矩阵列表的情况下将数据拆分为K折呢？

最佳答案

在回答类似问题Do I use the same Tfidf vocabulary in k-fold cross_validation时，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)

关于machine-learning - 如何在TfidfVectorizer中应用Kfold？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/59284471/