sklearn.set_config

sklearn.set_config(assume_finite=None, working_memory=None, print_changed_only=None, display=None, pairwise_dist_chunk_size=None, enable_cython_pairwise_dist=None)[source]

Set global scikit-learn configuration

New 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.

New in version 0.19.

working_memoryint, 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. Global default: 1024.

New in version 0.20.

print_changed_onlybool, 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()’ while the default behaviour would be to print ‘SVC(C=1.0, cache_size=200, …)’ with all the non-changed parameters.

New in version 0.21.

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’.

New in version 0.23.

pairwise_dist_chunk_sizeint, default=None

The number of row vectors per chunk for PairwiseDistancesReduction. 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_distbool, default=None

Use PairwiseDistancesReduction when possible. Default is 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.

See also

config_context

Context manager for global scikit-learn configuration.

get_config

Retrieve current values of the global configuration.

Examples using sklearn.set_config

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23
Compact estimator representations

Compact estimator representations

Compact estimator representations
Displaying Pipelines

Displaying Pipelines

Displaying Pipelines