python之使用 train_test_split 的一个命令创建数据集的多分割

• 我的数据集有 42000 行
• 我需要将数据集分为训练、交叉验证和测试 集，分割比例为60%、20% 和20%。这是根据 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去做这个。但是我无法让它工作，所以我在一个线性命令中将拆分为 0.6, 0.2, 0.2

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 

然后得到结果

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？

请您参考如下方法:

阅读train_test_split的源代码及其同伴类 ShuffleSplit并使其适应您的用例。这不是一个很大的功能，应该不会很复杂。