set_config#

scikitplot.set_config(assume_finite=None, working_memory=None, display=None, array_api_dispatch=None, transform_output=None, skip_parameter_validation=None)[source]#

Set global scikit-plots configuration.

Added in version 0.19.

Parameters:
assume_finitebool, 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. Global default: False.

Added in version 0.4.

working_memoryint, default=None

If set, scikit-plots 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.

Added in version 0.4.

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. Default is ‘diagram’.

Added in version 0.4.

array_api_dispatchbool, default=None

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

See the User Guide for more details.

Added in version 0.4.

transform_outputstr, 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

  • "polars": Polars output

  • None: Transform configuration is unchanged

Added in version 0.4.

skip_parameter_validationbool, 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.

Added in version 0.4.

See also

config_context

Context manager for global scikit-plots configuration.

get_config

Retrieve current values of the global configuration.

Examples

>>> from scikitplot import set_config
>>> set_config(display='diagram')
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