kwcoco.util

mkinit ~/code/kwcoco/kwcoco/util/__init__.py -w

Submodules

Package Contents

Classes

Executor

Wrapper around a specific executor.

SerialExecutor

Implements the concurrent.futures API around a single-threaded backend

StratifiedGroupKFold

Stratified K-Folds cross-validator with Grouping
class kwcoco.util.Executor(mode='thread', max_workers=0)[source]

Bases: object

Wrapper around a specific executor.

Abstracts Serial, Thread, and Process Executor via arguments.

Parameters

  • mode (str, default=’thread’) – either thread, serial, or process

  • max_workers (int, default=0) – number of workers. If 0, serial is forced.

__enter__(self)
__exit__(self, ex_type, ex_value, tb)
submit(self, func, *args, **kw)
shutdown(self)
class kwcoco.util.SerialExecutor[source]

Bases: object

Implements the concurrent.futures API around a single-threaded backend

Example

>>> with SerialExecutor() as executor:
>>>     futures = []
>>>     for i in range(100):
>>>         f = executor.submit(lambda x: x + 1, i)
>>>         futures.append(f)
>>>     for f in concurrent.futures.as_completed(futures):
>>>         assert f.result() > 0
>>>     for i, f in enumerate(futures):
>>>         assert i + 1 == f.result()
__enter__(self)
__exit__(self, ex_type, ex_value, tb)
submit(self, func, *args, **kw)
shutdown(self)
class kwcoco.util.StratifiedGroupKFold(n_splits=3, shuffle=False, random_state=None)[source]

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.