151 lines
5.1 KiB
Python
151 lines
5.1 KiB
Python
"""Global configuration state and functions for management
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"""
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import os
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from contextlib import contextmanager as contextmanager
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_global_config = {
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'assume_finite': bool(os.environ.get('SKLEARN_ASSUME_FINITE', False)),
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'working_memory': int(os.environ.get('SKLEARN_WORKING_MEMORY', 1024)),
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'print_changed_only': True,
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'display': 'text',
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}
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def get_config():
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"""Retrieve current values for configuration set by :func:`set_config`
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Returns
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-------
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config : dict
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Keys are parameter names that can be passed to :func:`set_config`.
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See Also
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--------
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config_context : Context manager for global scikit-learn configuration.
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set_config : Set global scikit-learn configuration.
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"""
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return _global_config.copy()
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def set_config(assume_finite=None, working_memory=None,
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print_changed_only=None, display=None):
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"""Set global scikit-learn configuration
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.. versionadded:: 0.19
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Parameters
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----------
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assume_finite : bool, default=None
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If True, validation for finiteness will be skipped,
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saving time, but leading to potential crashes. If
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False, validation for finiteness will be performed,
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avoiding error. Global default: False.
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.. versionadded:: 0.19
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working_memory : int, default=None
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If set, scikit-learn will attempt to limit the size of temporary arrays
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to this number of MiB (per job when parallelised), often saving both
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computation time and memory on expensive operations that can be
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performed in chunks. Global default: 1024.
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.. versionadded:: 0.20
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print_changed_only : bool, default=None
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If True, only the parameters that were set to non-default
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values will be printed when printing an estimator. For example,
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``print(SVC())`` while True will only print 'SVC()' while the default
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behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with
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all the non-changed parameters.
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.. versionadded:: 0.21
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display : {'text', 'diagram'}, default=None
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If 'diagram', estimators will be displayed as a diagram in a Jupyter
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lab or notebook context. If 'text', estimators will be displayed as
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text. Default is 'text'.
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.. versionadded:: 0.23
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See Also
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--------
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config_context : Context manager for global scikit-learn configuration.
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get_config : Retrieve current values of the global configuration.
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"""
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if assume_finite is not None:
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_global_config['assume_finite'] = assume_finite
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if working_memory is not None:
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_global_config['working_memory'] = working_memory
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if print_changed_only is not None:
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_global_config['print_changed_only'] = print_changed_only
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if display is not None:
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_global_config['display'] = display
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@contextmanager
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def config_context(**new_config):
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"""Context manager for global scikit-learn configuration
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Parameters
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----------
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assume_finite : bool, default=False
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If True, validation for finiteness will be skipped,
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saving time, but leading to potential crashes. If
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False, validation for finiteness will be performed,
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avoiding error. Global default: False.
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working_memory : int, default=1024
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If set, scikit-learn will attempt to limit the size of temporary arrays
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to this number of MiB (per job when parallelised), often saving both
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computation time and memory on expensive operations that can be
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performed in chunks. Global default: 1024.
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print_changed_only : bool, default=True
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If True, only the parameters that were set to non-default
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values will be printed when printing an estimator. For example,
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``print(SVC())`` while True will only print 'SVC()', but would print
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'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters
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when False. Default is True.
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.. versionchanged:: 0.23
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Default changed from False to True.
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display : {'text', 'diagram'}, default='text'
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If 'diagram', estimators will be displayed as a diagram in a Jupyter
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lab or notebook context. If 'text', estimators will be displayed as
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text. Default is 'text'.
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.. versionadded:: 0.23
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Notes
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-----
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All settings, not just those presently modified, will be returned to
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their previous values when the context manager is exited. This is not
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thread-safe.
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Examples
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--------
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>>> import sklearn
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>>> from sklearn.utils.validation import assert_all_finite
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>>> with sklearn.config_context(assume_finite=True):
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... assert_all_finite([float('nan')])
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>>> with sklearn.config_context(assume_finite=True):
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... with sklearn.config_context(assume_finite=False):
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... assert_all_finite([float('nan')])
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Traceback (most recent call last):
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...
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ValueError: Input contains NaN, ...
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See Also
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--------
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set_config : Set global scikit-learn configuration.
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get_config : Retrieve current values of the global configuration.
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"""
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old_config = get_config().copy()
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set_config(**new_config)
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try:
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yield
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finally:
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set_config(**old_config)
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