-iz: tSrSSKrSSKrSSKJr \"\R RSS55\"\R RSS55SS \"\R RS S 55SSS SSS . r \R"5r Sr Sr SSjr\SSSSSSSSSSS . Sj5rg)z7Global configuration state and functions for managementN)contextmanagerSKLEARN_ASSUME_FINITEFSKLEARN_WORKING_MEMORYiTdiagram SKLEARN_PAIRWISE_DIST_CHUNK_SIZEdefault assume_finiteworking_memoryprint_changed_onlydisplaypairwise_dist_chunk_sizeenable_cython_pairwise_distarray_api_dispatchtransform_outputenable_metadata_routingskip_parameter_validationc[[S5(d[R5[l[R$)zxGet a threadlocal **mutable** configuration. If the configuration does not exist, copy the default global configuration. global_config)hasattr _threadlocal_global_configcopyrB/var/www/html/venv/lib/python3.13/site-packages/sklearn/_config.py_get_threadlocal_configrs. < 1 1%3%8%8%: "  % %%rc2[5R5$)a1Retrieve the current scikit-learn configuration. This reflects the effective global configurations as established by default upon library import, or modified via :func:`set_config` or :func:`config_context`. Returns ------- config : dict Keys are parameter names that can be passed to :func:`set_config`. See Also -------- config_context : Context manager for global scikit-learn configuration. set_config : Set global scikit-learn configuration. Examples -------- >>> import sklearn >>> config = sklearn.get_config() >>> config.keys() dict_keys([...]) )rrrrr get_configr #s2 # $ ) ) ++rc [5n UbX S'UbXS'UbX*S'UbX:S'UbXJS'UbXZS'UbSS KJn U "U5 XjS 'UbXzS 'UbXS 'U bXS 'gg)a/Set global scikit-learn configuration. These settings control the behaviour of scikit-learn functions during a library usage session. Global configuration defaults (as described in the parameter list below) take effect when scikit-learn is imported. This function can be used to modify the global scikit-learn configuration at runtime. Passing `None` as an argument (the default) leaves the corresponding setting unchanged. This allows users to selectively update the global configuration values without affecting the others. .. versionadded:: 0.19 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. Global default: False. .. versionadded:: 0.19 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. Global default: 1024. .. versionadded:: 0.20 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()' while the default behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters. Global default: True. .. versionadded:: 0.21 .. 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. Global default: 'diagram'. .. versionadded:: 0.23 pairwise_dist_chunk_size : int, default=None The number of row vectors per chunk for the accelerated pairwise- distances reduction backend. Global default: 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. .. versionadded:: 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. .. versionadded:: 1.1 array_api_dispatch : bool, default=None Use Array API dispatching when inputs follow the Array API standard. Global default: False. See the :ref:`User Guide ` for more details. .. versionadded:: 1.2 transform_output : str, default=None Configure output of `transform` and `fit_transform`. See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` 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 Global default: "default". .. versionadded:: 1.2 .. versionadded:: 1.4 `"polars"` option was added. enable_metadata_routing : bool, default=None Enable metadata routing. By default this feature is disabled. Refer to :ref:`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 Global default: False. .. versionadded:: 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. Global default: False. Note that for data parameters, such as `X` and `y`, only type validation is skipped but validation with `check_array` will continue to run. .. versionadded:: 1.3 See Also -------- config_context : Context manager for global scikit-learn configuration. get_config : Retrieve current values of the global configuration. Examples -------- >>> from sklearn import set_config >>> set_config(display='diagram') # doctest: +SKIP Nr r r rrr)_check_array_api_dispatchrrrr)rutils._array_apir#) r r r rrrrrrr local_configr#s r set_configr&?s^+,L (5_%!)7%&%-?)*")Y+3K/0".6Q23%?!"45-?)*#+;'(*2I./ ,4M01-rc ## [5n [UUUUUUUUUU S9 Sv [S0U D6 g![S0U D6 f=f7f)aContext 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 pairwise_dist_chunk_size : int, default=None The number of row vectors per chunk for the accelerated pairwise- distances reduction backend. Global default: 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. .. versionadded:: 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. .. versionadded:: 1.1 array_api_dispatch : bool, default=None Use Array API dispatching when inputs follow the Array API standard. Global default: False. See the :ref:`User Guide ` for more details. .. versionadded:: 1.2 transform_output : str, default=None Configure output of `transform` and `fit_transform`. See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` 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 Global default: "default". .. versionadded:: 1.2 .. versionadded:: 1.4 `"polars"` option was added. enable_metadata_routing : bool, default=None Enable metadata routing. By default this feature is disabled. Refer to :ref:`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 Global default: False. .. versionadded:: 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. Global default: False. Note that for data parameters, such as `X` and `y`, only type validation is skipped but validation with `check_array` will continue to run. .. versionadded:: 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... r Nr)r r&) r r r rrrrrrr old_configs rconfig_contextr)sSzJ#%-!9$?-) 7"; !  Z  Z sA0 A =A) NNNNNNNNNN)__doc__os threading contextlibrboolenvirongetintrlocalrrr r&r)rrrr3s=  7"**..)@%HI"**..)A4HI # 93?!$(!$!&  &,: ! $ "gNT ! $ "m!m!r