B 0dg@sdZddlZddlmZddlZeejddeejdddd d Z e Z d d Z d dZ dddZeddddd ddZdS)z8Global configuration state and functions for management N)contextmanagerZSKLEARN_ASSUME_FINITEFZSKLEARN_WORKING_MEMORYiTtext) assume_finiteworking_memoryprint_changed_onlydisplaycCsttdstt_tjS)z|Get a threadlocal **mutable** configuration. If the configuration does not exist, copy the default global configuration. global_config)hasattr _threadlocal_global_configcopyrr r A/var/www/html/venv/lib/python3.7/site-packages/sklearn/_config.py_get_threadlocal_configs  rcCs tS)a^Retrieve current values for configuration set by :func:`set_config`. 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. )rr r r r r get_configsrcCsJt}|dk r||d<|dk r&||d<|dk r6||d<|dk rF||d<dS)a>Set global scikit-learn configuration .. 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. .. versionadded:: 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 'text'. .. versionadded:: 0.23 See Also -------- config_context : Context manager for global scikit-learn configuration. get_config : Retrieve current values of the global configuration. Nrrrr)r)rrrrZ local_configr r r set_config*s.rc cs2t}t||||dz dVWdtf|XdS)a 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. .. versionchanged:: 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 'text'. .. versionadded:: 0.23 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... )rrrrN)rr)rrrrZ old_configr r rconfig_contextdsD r)NNNN)__doc__os contextlibr threadingboolenvirongetintr localr rrrrr r r rs  9