374 lines
13 KiB
Python
374 lines
13 KiB
Python
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"""Global configuration state and functions for management"""
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import os
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import threading
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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": "diagram",
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"pairwise_dist_chunk_size": int(
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os.environ.get("SKLEARN_PAIRWISE_DIST_CHUNK_SIZE", 256)
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),
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"enable_cython_pairwise_dist": True,
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"array_api_dispatch": False,
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"transform_output": "default",
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"enable_metadata_routing": False,
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"skip_parameter_validation": False,
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}
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_threadlocal = threading.local()
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def _get_threadlocal_config():
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"""Get a threadlocal **mutable** configuration. If the configuration
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does not exist, copy the default global configuration."""
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if not hasattr(_threadlocal, "global_config"):
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_threadlocal.global_config = _global_config.copy()
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return _threadlocal.global_config
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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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Examples
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--------
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>>> import sklearn
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>>> config = sklearn.get_config()
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>>> config.keys()
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dict_keys([...])
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"""
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# Return a copy of the threadlocal configuration so that users will
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# not be able to modify the configuration with the returned dict.
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return _get_threadlocal_config().copy()
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def set_config(
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assume_finite=None,
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working_memory=None,
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print_changed_only=None,
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display=None,
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pairwise_dist_chunk_size=None,
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enable_cython_pairwise_dist=None,
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array_api_dispatch=None,
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transform_output=None,
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enable_metadata_routing=None,
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skip_parameter_validation=None,
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):
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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 'diagram'.
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.. versionadded:: 0.23
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pairwise_dist_chunk_size : int, default=None
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The number of row vectors per chunk for the accelerated pairwise-
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distances reduction backend. Default is 256 (suitable for most of
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modern laptops' caches and architectures).
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Intended for easier benchmarking and testing of scikit-learn internals.
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End users are not expected to benefit from customizing this configuration
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setting.
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.. versionadded:: 1.1
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enable_cython_pairwise_dist : bool, default=None
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Use the accelerated pairwise-distances reduction backend when
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possible. Global default: True.
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Intended for easier benchmarking and testing of scikit-learn internals.
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End users are not expected to benefit from customizing this configuration
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setting.
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.. versionadded:: 1.1
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array_api_dispatch : bool, default=None
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Use Array API dispatching when inputs follow the Array API standard.
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Default is False.
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See the :ref:`User Guide <array_api>` for more details.
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.. versionadded:: 1.2
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transform_output : str, default=None
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Configure output of `transform` and `fit_transform`.
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See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
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for an example on how to use the API.
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- `"default"`: Default output format of a transformer
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- `"pandas"`: DataFrame output
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- `"polars"`: Polars output
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- `None`: Transform configuration is unchanged
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.. versionadded:: 1.2
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.. versionadded:: 1.4
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`"polars"` option was added.
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enable_metadata_routing : bool, default=None
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Enable metadata routing. By default this feature is disabled.
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Refer to :ref:`metadata routing user guide <metadata_routing>` for more
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details.
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- `True`: Metadata routing is enabled
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- `False`: Metadata routing is disabled, use the old syntax.
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- `None`: Configuration is unchanged
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.. versionadded:: 1.3
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skip_parameter_validation : bool, default=None
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If `True`, disable the validation of the hyper-parameters' types and values in
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the fit method of estimators and for arguments passed to public helper
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functions. It can save time in some situations but can lead to low level
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crashes and exceptions with confusing error messages.
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Note that for data parameters, such as `X` and `y`, only type validation is
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skipped but validation with `check_array` will continue to run.
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.. versionadded:: 1.3
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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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Examples
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--------
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>>> from sklearn import set_config
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>>> set_config(display='diagram') # doctest: +SKIP
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"""
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local_config = _get_threadlocal_config()
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if assume_finite is not None:
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local_config["assume_finite"] = assume_finite
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if working_memory is not None:
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local_config["working_memory"] = working_memory
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if print_changed_only is not None:
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local_config["print_changed_only"] = print_changed_only
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if display is not None:
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local_config["display"] = display
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if pairwise_dist_chunk_size is not None:
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local_config["pairwise_dist_chunk_size"] = pairwise_dist_chunk_size
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if enable_cython_pairwise_dist is not None:
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local_config["enable_cython_pairwise_dist"] = enable_cython_pairwise_dist
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if array_api_dispatch is not None:
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from .utils._array_api import _check_array_api_dispatch
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_check_array_api_dispatch(array_api_dispatch)
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local_config["array_api_dispatch"] = array_api_dispatch
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if transform_output is not None:
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local_config["transform_output"] = transform_output
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if enable_metadata_routing is not None:
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local_config["enable_metadata_routing"] = enable_metadata_routing
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if skip_parameter_validation is not None:
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local_config["skip_parameter_validation"] = skip_parameter_validation
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@contextmanager
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def config_context(
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*,
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assume_finite=None,
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working_memory=None,
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print_changed_only=None,
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display=None,
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pairwise_dist_chunk_size=None,
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enable_cython_pairwise_dist=None,
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array_api_dispatch=None,
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transform_output=None,
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enable_metadata_routing=None,
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skip_parameter_validation=None,
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):
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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=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. If None, the existing value won't change.
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The default value is False.
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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. If None, the existing value won't change.
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The default value is 1024.
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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()', 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. If None, the existing value won't change.
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The default value 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=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. If None, the existing value won't change.
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The default value is 'diagram'.
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.. versionadded:: 0.23
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pairwise_dist_chunk_size : int, default=None
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The number of row vectors per chunk for the accelerated pairwise-
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distances reduction backend. Default is 256 (suitable for most of
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modern laptops' caches and architectures).
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Intended for easier benchmarking and testing of scikit-learn internals.
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End users are not expected to benefit from customizing this configuration
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setting.
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.. versionadded:: 1.1
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enable_cython_pairwise_dist : bool, default=None
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Use the accelerated pairwise-distances reduction backend when
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possible. Global default: True.
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Intended for easier benchmarking and testing of scikit-learn internals.
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End users are not expected to benefit from customizing this configuration
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setting.
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.. versionadded:: 1.1
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array_api_dispatch : bool, default=None
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Use Array API dispatching when inputs follow the Array API standard.
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Default is False.
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See the :ref:`User Guide <array_api>` for more details.
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.. versionadded:: 1.2
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transform_output : str, default=None
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Configure output of `transform` and `fit_transform`.
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See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
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for an example on how to use the API.
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- `"default"`: Default output format of a transformer
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- `"pandas"`: DataFrame output
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- `"polars"`: Polars output
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- `None`: Transform configuration is unchanged
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.. versionadded:: 1.2
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.. versionadded:: 1.4
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`"polars"` option was added.
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enable_metadata_routing : bool, default=None
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Enable metadata routing. By default this feature is disabled.
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Refer to :ref:`metadata routing user guide <metadata_routing>` for more
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details.
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- `True`: Metadata routing is enabled
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- `False`: Metadata routing is disabled, use the old syntax.
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- `None`: Configuration is unchanged
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.. versionadded:: 1.3
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skip_parameter_validation : bool, default=None
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If `True`, disable the validation of the hyper-parameters' types and values in
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the fit method of estimators and for arguments passed to public helper
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functions. It can save time in some situations but can lead to low level
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crashes and exceptions with confusing error messages.
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Note that for data parameters, such as `X` and `y`, only type validation is
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skipped but validation with `check_array` will continue to run.
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.. versionadded:: 1.3
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Yields
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------
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None.
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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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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.
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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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"""
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old_config = get_config()
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set_config(
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assume_finite=assume_finite,
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working_memory=working_memory,
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print_changed_only=print_changed_only,
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display=display,
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pairwise_dist_chunk_size=pairwise_dist_chunk_size,
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enable_cython_pairwise_dist=enable_cython_pairwise_dist,
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array_api_dispatch=array_api_dispatch,
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transform_output=transform_output,
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enable_metadata_routing=enable_metadata_routing,
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skip_parameter_validation=skip_parameter_validation,
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)
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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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