Python Sklearn逻辑回归形状错误，但x、y形状一致

我得到一个ValueError：运行以下命令时发现输入变量的样本数不一致：[20000，1]，即使x和y的行值正确。我加载RCV1数据集，获取具有前x个文档的类别索引，为每个类别创建具有相同数量随机选择的正数和负数的元组列表，然后最后尝试对其中一个类别运行逻辑回归

import sklearn.datasets
from sklearn import model_selection, preprocessing
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot as plt
from scipy import sparse

rcv1 = sklearn.datasets.fetch_rcv1()

def get_top_cat_indices(target_matrix, num_cats):
    cat_counts = target_matrix.sum(axis=0)
    #cat_counts = cat_counts.reshape((1,103)).tolist()[0]
    cat_counts = cat_counts.reshape((103,))

    #b = sorted(cat_counts, reverse=True)
    ind_temp = np.argsort(cat_counts)[::-1].tolist()[0]

    ind = [ind_temp[i] for i in range(5)]
    return ind

def prepare_data(x, y, top_cat_indices, sample_size):
    res_lst = []

    for i in top_cat_indices:

        # get column of indices with relevant cat
        temp = y.tocsc()[:, i]

        # all docs with labeled category
        cat_present = x.tocsr()[np.where(temp.sum(axis=1)>0)[0],:]
        # all docs other than labelled category
        cat_notpresent = x.tocsr()[np.where(temp.sum(axis=1)==0)[0],:]
        # get indices equal to 1/2 of sample size
        idx_cat = np.random.randint(cat_present.shape[0], size=int(sample_size/2))
        idx_nocat = np.random.randint(cat_notpresent.shape[0], size=int(sample_size/2))
        # concatenate the ids

        sampled_x_pos = cat_present.tocsr()[idx_cat,:]
        sampled_x_neg = cat_notpresent.tocsr()[idx_nocat,:]
        sampled_x = sparse.vstack((sampled_x_pos, sampled_x_neg))

        sampled_y_pos = temp.tocsr()[idx_cat,:]
        sampled_y_neg = temp.tocsr()[idx_nocat,:]
        sampled_y = sparse.vstack((sampled_y_pos, sampled_y_neg))

        res_lst.append((sampled_x, sampled_y))

    return res_lst

ind = get_top_cat_indices(rcv1.target, 5)
test_res = prepare_data(train_x, train_y, ind, 20000)

x, y = test_res[0]
print(x.shape)
print(y.shape)
LogisticRegression().fit(x, y)

---

这可能是稀疏矩阵的问题，还是维数的问题（有20K个样本和47K个特征）

当我运行代码时，我发现以下错误：

AttributeError:“bool”对象没有属性“any”

这是因为

LogisticRegression

的

y

需要numpy数组。因此，我将最后一行更改为：

LogisticRegression().fit(x, y.A.flatten())

然后我得到以下错误：

ValueError:此解算器需要数据中至少2个类的样本，但数据仅包含一个类：0

这是因为采样代码有一个bug。在使用采样索引之前，需要使用具有该类别的行对y数组进行子集划分。见下面的代码：

def prepare_data(x, y, top_cat_indices, sample_size):
    res_lst = []

    for i in top_cat_indices:

        # get column of indices with relevant cat
        temp = y.tocsc()[:, i]

        # all docs with labeled category
        c1 = np.where(temp.sum(axis=1)>0)[0]
        c2 = np.where(temp.sum(axis=1)==0)[0]
        cat_present = x.tocsr()[c1,:]
        # all docs other than labelled category
        cat_notpresent = x.tocsr()[c2,:]
        # get indices equal to 1/2 of sample size
        idx_cat = np.random.randint(cat_present.shape[0], size=int(sample_size/2))
        idx_nocat = np.random.randint(cat_notpresent.shape[0], size=int(sample_size/2))
        # concatenate the ids

        sampled_x_pos = cat_present.tocsr()[idx_cat,:]
        sampled_x_neg = cat_notpresent.tocsr()[idx_nocat,:]
        sampled_x = sparse.vstack((sampled_x_pos, sampled_x_neg))

        sampled_y_pos = temp.tocsr()[c1][idx_cat,:]
        print(sampled_y_pos.nnz)
        sampled_y_neg = temp.tocsr()[c2][idx_nocat,:]
        print(sampled_y_neg.nnz)

        sampled_y = sparse.vstack((sampled_y_pos, sampled_y_neg))

        res_lst.append((sampled_x, sampled_y))

    return res_lst

---

现在，一切都像一个符咒一样运行

嗨，在这个例子中，train_x和train_y是什么？它们是

train\u x，train\u y=rcv1.data，rcv1.target

看起来很棒。当我们拟合逻辑回归时，我们真的需要展平法吗？好像没什么意思，我没弄到