forked from scikit-learn/scikit-learn
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_config.py
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_config.py
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"""Global configuration state and functions for management
"""
import os
from contextlib import contextmanager as contextmanager
_global_config = {
'assume_finite': bool(os.environ.get('SKLEARN_ASSUME_FINITE', False)),
'working_memory': int(os.environ.get('SKLEARN_WORKING_MEMORY', 1024)),
'print_changed_only': True,
'display': 'text',
}
def get_config():
"""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
"""
return _global_config.copy()
def set_config(assume_finite=None, working_memory=None,
print_changed_only=None, display=None):
"""Set global scikit-learn configuration
.. versionadded:: 0.19
Parameters
----------
assume_finite : bool, optional
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, optional
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, optional
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'}, optional
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
"""
if assume_finite is not None:
_global_config['assume_finite'] = assume_finite
if working_memory is not None:
_global_config['working_memory'] = working_memory
if print_changed_only is not None:
_global_config['print_changed_only'] = print_changed_only
if display is not None:
_global_config['display'] = display
@contextmanager
def config_context(**new_config):
"""Context manager for global scikit-learn configuration
Parameters
----------
assume_finite : bool, optional
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.
working_memory : int, optional
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.
print_changed_only : bool, optional
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. Default is True.
.. versionchanged:: 0.23
Default changed from False to True.
display : {'text', 'diagram'}, optional
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
Notes
-----
All settings, not just those presently modified, will be returned to
their previous values when the context manager is exited. This is not
thread-safe.
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, ...
See Also
--------
set_config: Set global scikit-learn configuration
get_config: Retrieve current values of the global configuration
"""
old_config = get_config().copy()
set_config(**new_config)
try:
yield
finally:
set_config(**old_config)