dask_ml.model_selection
.ShuffleSplit¶

class
dask_ml.model_selection.
ShuffleSplit
(n_splits=10, test_size=0.1, train_size=None, blockwise=True, random_state=None)¶ Random permutation crossvalidator.
Yields indices to split data into training and test sets.
Warning
By default, this performs a blockwiseshuffle. That is, each block is shuffled internally, but data are not shuffled between blocks. If your data is ordered, then set
blockwise=False
.Note: contrary to other crossvalidation strategies, random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.
Parameters:  n_splits : int, default 10
Number of reshuffling & splitting iterations.
 test_size : float, int, None, default=0.1
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size.
 train_size : float, int, or None, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.
 blockwise : bool, default True
Whether to shuffle data only within blocks (True), or allow data to be shuffled between blocks (False). Shuffling between blocks can be much more expensive, especially in distributed environments.
 random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Methods
get_n_splits
(self[, X, y, groups])Returns the number of splitting iterations in the crossvalidator split
(self, X[, y, groups])Generate indices to split data into training and test set. 
__init__
(self, n_splits=10, test_size=0.1, train_size=None, blockwise=True, random_state=None)¶ Initialize self. See help(type(self)) for accurate signature.

get_n_splits
(self, X=None, y=None, groups=None)¶ Returns the number of splitting iterations in the crossvalidator

split
(self, X, y=None, groups=None)¶ Generate indices to split data into training and test set.
Parameters:  X : arraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, of length n_samples
The target variable for supervised learning problems.
 groups : arraylike, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
Yields:  train : ndarray
The training set indices for that split.
 test : ndarray
The testing set indices for that split.