`sklearn`.config_context¶

sklearn.config_context(**new_config)[source]¶

Context manager for global scikit-learn configuration

Parameters

assume_finitebool, optional: If True, validation for finiteness will be skipped, saving time, but leading to potential crashes. If False, validation for finiteness will be performed, avoiding error. Global default: False.
working_memoryint, optional: If set, scikit-learn will attempt to limit the size of temporary arrays to this number of MiB (per job when parallelised), often saving both computation time and memory on expensive operations that can be performed in chunks. Global default: 1024.
print_changed_onlybool, optional: If True, only the parameters that were set to non-default values will be printed when printing an estimator. For example, print(SVC()) while True will only print ‘SVC()’ while the default behaviour would be to print ‘SVC(C=1.0, cache_size=200, …)’ with all the non-changed parameters.

See also

set_config: Set global scikit-learn configuration
get_config: Retrieve current values of the global configuration

Notes

All settings, not just those presently modified, will be returned to their previous values when the context manager is exited. This is not thread-safe.

Examples

>>> import sklearn
>>> from sklearn.utils.validation import assert_all_finite
>>> with sklearn.config_context(assume_finite=True):
...     assert_all_finite([float('nan')])
>>> with sklearn.config_context(assume_finite=True):
...     with sklearn.config_context(assume_finite=False):
...         assert_all_finite([float('nan')])
Traceback (most recent call last):
...
ValueError: Input contains NaN, ...

sklearn.config_context¶

`sklearn`.config_context¶