python - 带有索引的 Scikit-learn train_test_split

Question

使用train_test_split()时如何获取数据的原始索引？

我拥有的是以下

from sklearn.cross_validation import train_test_split
import numpy as np
data = np.reshape(np.randn(20),(10,2)) # 10 training examples
labels = np.random.randint(2, size=10) # 10 labels
x1, x2, y1, y2 = train_test_split(data, labels, size=0.2)

但这并没有给出原始数据的索引。一种解决方法是将索引添加到数据(例如 data = [(i, d) for i, d in enumerate(data)])，然后将它们传递到 train_test_split然后再次展开。有没有更清洁的解决方案？

Answer 1

您可以像 Julien 所说的那样使用 pandas 数据帧或系列，但如果您想将自己限制为 numpy，您可以传递一个额外的索引数组:

from sklearn.model_selection import train_test_split
import numpy as np
n_samples, n_features, n_classes = 10, 2, 2
data = np.random.randn(n_samples, n_features)  # 10 training examples
labels = np.random.randint(n_classes, size=n_samples)  # 10 labels
indices = np.arange(n_samples)
(
    data_train,
    data_test,
    labels_train,
    labels_test,
    indices_train,
    indices_test,
) = train_test_split(data, labels, indices, test_size=0.2)