kwcoco.util.util_sklearn module

Extensions to sklearn constructs

class kwcoco.util.util_sklearn.StratifiedGroupKFold(n_splits=3, shuffle=False, random_state=None)

Bases: _BaseKFold

Stratified K-Folds cross-validator with Grouping

Provides train/test indices to split data in train/test sets.

This cross-validation object is a variation of GroupKFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

This is an old interface and should likely be refactored and modernized.

Parameters:

n_splits (int, default=3) – Number of folds. Must be at least 2.

_make_test_folds(X, y=None, groups=None)

Parameters:

• X (ndarray) – data

• y (ndarray) – labels

• groups (ndarray) – groupids for items. Items with the same groupid must be placed in the same group.

Returns:

test_folds

Return type:

list

Example

>>> from kwcoco.util.util_sklearn import *  # NOQA
>>> import kwarray
>>> rng = kwarray.ensure_rng(0)
>>> groups = [1, 1, 3, 4, 2, 2, 7, 8, 8]
>>> y      = [1, 1, 1, 1, 2, 2, 2, 3, 3]
>>> X = np.empty((len(y), 0))
>>> self = StratifiedGroupKFold(random_state=rng, shuffle=True)
>>> skf_list = list(self.split(X=X, y=y, groups=groups))
>>> import ubelt as ub
>>> print(ub.urepr(skf_list, nl=1, with_dtype=False))
[
    (np.array([2, 3, 4, 5, 6]), np.array([0, 1, 7, 8])),
    (np.array([0, 1, 2, 7, 8]), np.array([3, 4, 5, 6])),
    (np.array([0, 1, 3, 4, 5, 6, 7, 8]), np.array([2])),
]

_iter_test_masks(X, y=None, groups=None)

split(X, y, groups=None)

Generate indices to split data into training and test set.

_abc_impl = <_abc._abc_data object>