GapLeavePOut

class tscv.GapLeavePOut(p, gap_before=0, gap_after=0)[source]

Leave-P-Out cross-validator with Gaps

Provides train/test indices to split data in train/test sets. This results in testing on only contiguous samples of size p, while the remaining samples (with the gaps removed) form the training set in each iteration.

Parameters

pint: Size of the test sets.
gap_beforeint, default=0: Gap before the test sets.
gap_afterint, default=0: Gap after the test sets.

Examples

>>> import numpy as np
>>> from tscv import GapLeavePOut
>>> glpo = GapLeavePOut(2, 1, 1)
>>> glpo.get_n_splits([0, 1, 2, 3, 4])
4
>>> print(glpo)
GapLeavePOut(gap_after=1, gap_before=1, p=2)
>>> for train_index, test_index in glpo.split([0, 1, 2, 3, 4]):
...    print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [3 4] TEST: [0 1]
TRAIN: [4] TEST: [1 2]
TRAIN: [0] TEST: [2 3]
TRAIN: [0 1] TEST: [3 4]

get_n_splits(X, y=None, groups=None)[source]

Returns the number of splitting iterations in the cross-validator

Parameters

Xarray-like, shape (n_samples, n_features): Training data, where n_samples is the number of samples and n_features is the number of features.
yobject: Always ignored, exists for compatibility.
groupsobject: Always ignored, exists for compatibility.

split(X, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters

Xarray-like, shape (n_samples, n_features): Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like, of length n_samples: The target variable for supervised learning problems.
groupsarray-like, with shape (n_samples,), optional: Group labels for the samples used while splitting the dataset into train/test set.

Yields

trainndarray: The training set indices for that split.
testndarray: The testing set indices for that split.