kwcoco.util.util_sklearn

Extensions to sklearn constructs

Module Contents

Classes

StratifiedGroupKFold

Stratified K-Folds cross-validator with Grouping

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

Bases: sklearn.model_selection._split._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.

Read more in the User Guide.

Parameters

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

_make_test_folds(self, 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

>>> 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.repr2(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(self, X, y=None, groups=None)

Generates boolean masks corresponding to test sets.

By default, delegates to _iter_test_indices(X, y, groups)

split(self, X, y, groups=None)

Generate indices to split data into training and test set.