TemporalTrainTestSplitter

Temporal train-test splitter, based on sample sizes of train or test set.

Quickstart

from sktime.split.temporal_train_test_split import TemporalTrainTestSplitter

estimator = TemporalTrainTestSplitter(train_size=None, test_size=None, anchor='start')

Parameters(3)

test_sizefloat, int or None, optional (default=None)

If float, must be between 0.0 and 1.0, and is interpreted as the proportion of the dataset to include in the test split. Proportions are rounded to the next higher integer count of samples (ceil). If int, is interpreted as total number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.

train_sizefloat, int, or None, (default=None)

If float, must be between 0.0 and 1.0, and is interpreted as the proportion of the dataset to include in the train split. Proportions are rounded to the next lower integer count of samples (floor). If int, is interpreted as total number of train samples. If None, the value is set to the complement of the test size.

anchorstr, “start” (default) or “end”

determines behaviour if train and test sizes do not sum up to all data if “start”, cuts train and test set from start of available series if “end”, cuts train and test set from end of available series

Examples

>>> import numpy as np
>>> from sktime.split import TemporalTrainTestSplitter
>>> ts = np. arange (10)
>>> splitter = TemporalTrainTestSplitter (test_size = 0.3)
>>> list (splitter. split (ts)) [(array([0, 1, 2, 3, 4, 5, 6]), array([7, 8, 9]))]