mars.learn.model_selection.train_test_split¶

mars.learn.model_selection.
train_test_split
(*arrays, **options)[source]¶ Split arrays or matrices into random train and test subsets
 Parameters
*arrays (
sequence
ofindexables with same length / shape[0]
) – Allowed inputs are lists, numpy arrays, scipysparse matrices or pandas dataframes.test_size (
float
,int
orNone
,optional (default=None)
) – 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. Iftrain_size
is also None, it will be set to 0.25.train_size (
float
,int
, orNone
,(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.random_state (
int
,RandomState instance
orNone
,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.shuffle (
boolean
,optional (default=True)
) – Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.stratify (
arraylike
orNone (default=None)
) – If not None, data is split in a stratified fashion, using this as the class labels.
 Returns
splitting – List containing traintest split of inputs.
 Return type
list
,length=2 * len(arrays)
Examples
>>> import mars.tensor as mt >>> from mars.learn.model_selection import train_test_split >>> X, y = mt.arange(10).reshape((5, 2)), range(5) >>> X.execute() array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) >>> list(y) [0, 1, 2, 3, 4]
>>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.33, random_state=42) ... >>> X_train.execute() array([[8, 9], [0, 1], [4, 5]]) >>> y_train.execute() array([4, 0, 2]) >>> X_test.execute() array([[2, 3], [6, 7]]) >>> y_test.execute() array([1, 3])
>>> train_test_split(y, shuffle=False) [array([0, 1, 2]), array([3, 4])]