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# `sklearn`.config_context

sklearn.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)

  Context manager for global scikit-learn configuration.

  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 value won’t change. The default value is 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 value won’t change. The default value is 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 value won’t change. The default value is True.

      Changed in version 0.23: Default 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 value won’t change. The default value is ‘diagram’.

      New in version 0.23.

    pairwise_dist_chunk_size int, default=None

      The number of row vectors per chunk for the accelerated pairwise- distances reduction backend. Default is 256 (suitable for most of modern laptops’ caches and architectures).

      Intended for easier benchmarking and testing of scikit-learn internals. End users are not expected to benefit from customizing this configuration setting.

      New in version 1.1.

    enable_cython_pairwise_dist bool, default=None

      Use the accelerated pairwise-distances reduction backend when possible. Global default: True.

      Intended for easier benchmarking and testing of scikit-learn internals. End users are not expected to benefit from customizing this configuration setting.

      New in version 1.1.

    array_api_dispatch bool, default=None

      Use Array API dispatching when inputs follow the Array API standard. Default is False.

      See the User Guide for more details.

      New in version 1.2.

    transform_output str, default=None

      Configure output of `transform` and `fit_transform`.

      See Introducing the set_output API for an example on how to use the API.

      - `"default"`: Default output format of a transformer

      - `"pandas"`: DataFrame output

      - `None`: Transform configuration is unchanged

      New in version 1.2.

    enable_metadata_routing bool, default=None

      Enable metadata routing. By default this feature is disabled.

      Refer to metadata routing user guide for more details.

      - `True`: Metadata routing is enabled

      - `False`: Metadata routing is disabled, use the old syntax.

      - `None`: Configuration is unchanged

      New in version 1.3.

    skip_parameter_validation bool, default=None

      If `True`, disable the validation of the hyper-parameters’ types and values in the fit method of estimators and for arguments passed to public helper functions. It can save time in some situations but can lead to low level crashes and exceptions with confusing error messages.

      Note that for data parameters, such as `X` and `y`, only type validation is skipped but validation with `check_array` will continue to run.

      New in version 1.3.

  Yields:
    None.

  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.

  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...

## Examples using `sklearn.config_context`

Introducing the set_output API

Introducing the set_output API