ray.data.Dataset.train_test_split
ray.data.Dataset.train_test_split#
- Dataset.train_test_split(test_size: Union[int, float], *, shuffle: bool = False, seed: Optional[int] = None) Tuple[ray.data.dataset.MaterializedDataset, ray.data.dataset.MaterializedDataset] [source]#
Materialize and split the dataset into train and test subsets.
Note
This operation will trigger execution of the lazy transformations performed on this dataset.
Examples
>>> import ray >>> ds = ray.data.range(8) >>> train, test = ds.train_test_split(test_size=0.25) >>> train.take_batch() {'id': array([0, 1, 2, 3, 4, 5])} >>> test.take_batch() {'id': array([6, 7])}
- Parameters
test_size – 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. The train split always complements the test split.
shuffle – Whether or not to globally shuffle the dataset before splitting. Defaults to
False
. This may be a very expensive operation with a large dataset.seed – Fix the random seed to use for shuffle, otherwise one is chosen based on system randomness. Ignored if
shuffle=False
.
- Returns
Train and test subsets as two
MaterializedDatasets
.
See also