Scikit-learn train_test_split with indices

Question

How do I get the original indices of the data when using train_test_split()?

What I have is the following

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)

But this does not give the indices of the original data. One workaround is to add the indices to data (e.g. data = [(i, d) for i, d in enumerate(data)]) and then pass them inside train_test_split and then expand again. Are there any cleaner solutions?

Answer 1

You can use pandas dataframes or series as Julien said but if you want to restrict your-self to numpy you can pass an additional array of indices:

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)
x1, x2, y1, y2, idx1, idx2 = train_test_split(
    data, labels, indices, test_size=0.2)