8000 [MRG+1] Option to suppress validation for finiteness (#7548) · Sundrique/scikit-learn@95461f1 · GitHub
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[MRG+1] Option to suppress validation for finiteness (scikit-learn#7548)
* ENH add suppress validation option * TST skip problematic doctest * Rename SUPPRESS_VALIDATION to PRESUME_FINITE * Change PRESUME_ to ASSUME_ for convention's sake * DOC add note regarding assert_all_finite * ENH add set_config context manager for ASSUME_FINITE * Make ASSUME_FINITE private and provide get_config * Fix ImportError due to incomplete change in last commit * TST/DOC tests and more cautious documentation for set_config * DOC what's new entry for validation suppression * context manager is now config_context; set_config affects global config * Rename missed set_config to config_context * Fix mis-named test * Mention set_config in narrative docs * More explicit about limmited restoration of context * Handle case where error raised in config_context * Reset all settings after exiting context manager
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‎doc/modules/classes.rst

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@@ -40,6 +40,9 @@ Functions
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:template: function.rst
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base.clone
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config_context
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set_config
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get_config
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.. _cluster_ref:

‎doc/modules/computational_performance.rst

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:ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py`. This should give
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you an estimate of the order of magnitude of the prediction latency.
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.. topic:: Configuring Scikit-learn for reduced validation overhead
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Scikit-learn does some validation on data that increases the overhead per
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call to ``predict`` and similar functions. In particular, checking that
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features are finite (not NaN or infinite) involves a full pass over the
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data. If you ensure that your data is acceptable, you may suppress
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checking for finiteness by setting the environment variable
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``SKLEARN_ASSUME_FINITE`` to a non-empty string before importing
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scikit-learn, or configure it in Python with :func:`sklearn.set_config`.
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For more control than these global settings, a :func:`config_context`
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allows you to set this configuration within a specified context::
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>>> import sklearn
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>>> with sklearn.config_context(assume_finite=True):
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... pass # do learning/prediction here with reduced validation
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Note that this will affect all uses of
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:func:`sklearn.utils.assert_all_finite` within the context.
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Influence of the Number of Features
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-----------------------------------
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‎doc/whats_new.rst

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New features
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............
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- Validation that input data contains no NaN or inf can now be suppressed
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using :func:`config_context`, at your own risk. This will save on runtime,
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and may be particularly useful for prediction time. :issue:`7548` by
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`Joel Nothman`_.
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- Added the :class:`neighbors.LocalOutlierFactor` class for anomaly
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detection based on nearest neighbors.
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:issue:`5279` by `Nicolas Goix`_ and `Alexandre Gramfort`_.

‎sklearn/__init__.py

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import sys
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import re
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import warnings
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import os
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from contextlib import contextmanager as _contextmanager
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_ASSUME_FINITE = bool(os.environ.get('SKLEARN_ASSUME_FINITE', False))
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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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"""
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return {'assume_finite': _ASSUME_FINITE}
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def set_config(assume_finite=None):
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"""Set global scikit-learn configuration
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Parameters
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----------
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assume_finite : bool, optional
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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.
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"""
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global _ASSUME_FINITE
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if assume_finite is not None:
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_ASSUME_FINITE = assume_finite
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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, optional
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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.
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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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... # doctest: +ELLIPSIS
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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().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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# Make sure that DeprecationWarning within this package always gets printed

‎sklearn/tests/test_config.py

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from sklearn import get_config, set_config, config_context
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from sklearn.utils.testing import assert_equal, assert_raises
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def test_config_context():
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assert_equal(get_config(), {'assume_finite': False})
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# Not using as a context manager affects nothing
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config_context(assume_finite=True)
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assert_equal(get_config(), {'assume_finite': False})
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with config_context(assume_finite=True):
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assert_equal(get_config(), {'assume_finite': True})
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assert_equal(get_config(), {'assume_finite': False})
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with config_context(assume_finite=True):
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with config_context(assume_finite=None):
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assert_equal(get_config(), {'assume_finite': True})
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assert_equal(get_config(), {'assume_finite': True})
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with config_context(assume_finite=False):
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assert_equal(get_config(), {'assume_finite': False})
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with config_context(assume_finite=None):
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assert_equal(get_config(), {'assume_finite': False})
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# global setting will not be retained outside of context that
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# did not modify this setting
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set_config(assume_finite=True)
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assert_equal(get_config(), {'assume_finite': True})
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assert_equal(get_config(), {'assume_finite': False})
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assert_equal(get_config(), {'assume_finite': True})
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assert_equal(get_config(), {'assume_finite': False})
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# No positional arguments
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assert_raises(TypeError, config_context, True)
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# No unknown arguments
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assert_raises(TypeError, config_context(do_something_else=True).__enter__)
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def test_config_context_exception():
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assert_equal(get_config(), {'assume_finite': False})
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try:
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with config_context(assume_finite=True):
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assert_equal(get_config(), {'assume_finite': True})
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raise ValueError()
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except ValueError:
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pass
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assert_equal(get_config(), {'assume_finite': False})
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def test_set_config():
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assert_equal(get_config(), {'assume_finite': False})
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set_config(assume_finite=None)
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assert_equal(get_config(), {'assume_finite': False})
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set_config(assume_finite=True)
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assert_equal(get_config(), {'assume_finite': True})
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set_config(assume_finite=None)
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assert_equal(get_config(), {'assume_finite': True})
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set_config(assume_finite=False)
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assert_equal(get_config(), {'assume_finite': False})
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# No unknown arguments
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assert_raises(TypeError, set_config, do_something_else=True)

‎sklearn/utils/tests/test_validation.py

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has_fit_parameter,
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check_is_fitted,
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check_consistent_length,
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assert_all_finite,
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)
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import sklearn
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from sklearn.exceptions import NotFittedError
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from sklearn.exceptions import DataConversionWarning
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from sklearn.utils.testing import assert_raise_message
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def test_as_float_array():
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# Test function for as_float_array
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X = np.ones((3, 10), dtype=np.int32)
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check_consistent_length(X_df)
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except ImportError:
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raise SkipTest("Pandas not found")
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def test_suppress_validation():
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X = np.array([0, np.inf])
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assert_raises(ValueError, assert_all_finite, X)
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sklearn.set_config(assume_finite=True)
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assert_all_finite(X)
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sklearn.set_config(assume_finite=False)
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assert_raises(ValueError, assert_all_finite, X)

‎sklearn/utils/validation.py

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from ..externals import six
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from ..utils.fixes import signature
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from .. import get_config as _get_config
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from ..exceptions import NonBLASDotWarning
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from ..exceptions import NotFittedError
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from ..exceptions import DataConversionWarning
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def _assert_all_finite(X):
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"""Like assert_all_finite, but only for ndarray."""
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if _get_config()['assume_finite']:
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return
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X = np.asanyarray(X)
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# First try an O(n) time, O(1) space solution for the common case that
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# everything is finite; fall back to O(n) space np.isfinite to prevent

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