使用 train_test_split 的一个命令创建数据集的多重拆分

Question

• 我的数据集有 42000 行
• 我需要将数据集分成training, cross-validation and test 集，分割为60%, 20% and 20%。这是 Andrew Ng 教授在他的 ml-class 讲座中的建议。
• 我意识到 scikit-learn 有一个方法 train_test_split 可以做到这一点。但我不能让它工作，所以我在一个班轮命令中得到了0.6, 0.2, 0.2 的拆分

我做的是

# split data into training, cv and test sets
from sklearn import cross_validation
train, intermediate_set = cross_validation.train_test_split(input_set, train_size=0.6, test_size=0.4)
cv, test = cross_validation.train_test_split(intermediate_set, train_size=0.5, test_size=0.5)


# preparing the training dataset
print 'training shape(Tuple of array dimensions) = ', train.shape
print 'training dimension(Number of array dimensions) = ', train.ndim
print 'cv shape(Tuple of array dimensions) = ', cv.shape
print 'cv dimension(Number of array dimensions) = ', cv.ndim
print 'test shape(Tuple of array dimensions) = ', test.shape
print 'test dimension(Number of array dimensions) = ', test.ndim

并得到我的结果

training shape(Tuple of array dimensions) =  (25200, 785)
training dimension(Number of array dimensions) =  2
cv shape(Tuple of array dimensions) =  (8400, 785)
cv dimension(Number of array dimensions) =  2
test shape(Tuple of array dimensions) =  (8400, 785)
test dimension(Number of array dimensions) =  2
features shape =  (25200, 784)
labels shape =  (25200,)

如何在一个命令中完成这项工作？

Answer 1

阅读train_test_split 及其配套类ShuffleSplit 的源代码，并根据您的用例进行调整。不是很大的功能，应该不会很复杂。