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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))
}
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`.
"""
return _global_config.copy()
def set_config(assume_finite=None, working_memory=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
"""
if assume_finite is not None:
_global_config['assume_finite'] = assume_finite
if working_memory is not None:
_global_config['working_memory'] = working_memory
@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.
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')])
... # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Input contains NaN, ...
"""
old_config = get_config().copy()
set_config(**new_config)
try:
yield
finally:
set_config(**old_config)
|
