Function config_context

sklearn/_config.py:249–435

Context manager to temporarily change the global scikit-learn configuration. This context manager can be used to apply scikit-learn configuration changes within the scope of the with statement. Once the context exits, the global configuration is restored again. The default global c

(
    *,
    assume_finite=None,
    working_memory=None,
    print_changed_only=None,
    display=None,
    pairwise_dist_chunk_size=None,
    enable_cython_pairwise_dist=None,
    array_api_dispatch=None,
    transform_output=None,
    enable_metadata_routing=None,
    skip_parameter_validation=None,
    sparse_interface=None,
)

@contextmanager
def config_context(
    *,
    assume_finite=None,
    working_memory=None,
    print_changed_only=None,
    display=None,
    pairwise_dist_chunk_size=None,
    enable_cython_pairwise_dist=None,
    array_api_dispatch=None,
    transform_output=None,
    enable_metadata_routing=None,
    skip_parameter_validation=None,
    sparse_interface=None,
):
    """Context manager to temporarily change the global scikit-learn configuration.

    This context manager can be used to apply scikit-learn configuration changes within
    the scope of the with statement. Once the context exits, the global configuration is
    restored again.

    The default global configurations (which take effect when scikit-learn is imported)
    are defined below in the parameter list.

    Parameters
    ----------
    assume_finite : bool, default=None
        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. If None, the existing configuration won't change.
        Global default: False.

    working_memory : int, default=None
        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. If None, the existing configuration won't change.
        Global default: 1024.

    print_changed_only : bool, default=None
        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()', but would print
        'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters
        when False. If None, the existing configuration won't change.
        Global default: True.

        .. versionchanged:: 0.23
           Global default configuration changed from False to True.

    display : {'text', 'diagram'}, default=None
        If 'diagram', estimators will be displayed as a diagram in a Jupyter
        lab or notebook context. If 'text', estimators will be displayed as
        text. If None, the existing configuration won't change.
        Global default: 'diagram'.

        .. versionadded:: 0.23