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Aluode/PerceptionLabPortable

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rootpython_embedLibsite-packagessklearnutils__pycache___testing.cpython-310.pyc
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RegressorTags�Tags�8TargetTags�TransformerTags)�_check_array_api_dispatch)�	_IS_32BIT�VisibleDeprecationWarning�#_in_unstable_openblas_configuration)�check_classification_targets)�check_array�check_is_fitted�	check_X_y)�SkipTestrrrrr�'assert_run_python_script_without_outputcCsJt|t�rt|t�r|j}tdj|d���t|�r t|d�|�St|d�S)a Context manager and decorator to ignore warnings.9 10    Note: Using this (in both variants) will clear all warnings11    from all python modules loaded. In case you need to test12    cross-module-warning-logging, this is not your tool of choice.13 14    Parameters15    ----------16    obj : callable, default=None17        callable where you want to ignore the warnings.18    category : warning class, default=Warning19        The category to filter. If Warning, all categories will be muted.20 21    Examples22    --------23    >>> import warnings24    >>> from sklearn.utils._testing import ignore_warnings25    >>> with ignore_warnings():26    ...     warnings.warn('buhuhuhu')27 28    >>> def nasty_warn():29    ...     warnings.warn('buhuhuhu')30    ...     print(42)31 32    >>> ignore_warnings(nasty_warn)()33    4234    z�'obj' should be a callable where you want to ignore warnings. You passed a warning class instead: 'obj={warning_name}'. If you want to pass a warning class to ignore_warnings, you should use 'category={warning_name}')�warning_name��category)	�35isinstance�type�36issubclass�Warning�__name__�37ValueError�format�callable�_IgnoreWarnings)�objr&r$�r1��E:\DocsHouse\542 percep lab latest\PerceptionLab\PerceptionLab_Portable\python_embed\Lib\site-packages\sklearn/utils/_testing.py�ignore_warningsJs��38r3c@s8eZdZdZdd�Zdd�Zdd�Zdd	�Zd39d�ZdS)
r/atImproved and simplified Python warnings context manager and decorator.40 41    This class allows the user to ignore the warnings raised by a function.42    Copied from Python 2.7.5 and modified as required.43 44    Parameters45    ----------46    category : tuple of warning class, default=Warning47        The category to filter. 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It should be adjusted manually70    to an assertion-specific value in case there are null values expected71    in `desired`.72 73    The aggregate tolerance is `atol + rtol * abs(desired)`.74 75    Parameters76    ----------77    actual : array_like78        Array obtained.79    desired : array_like80        Array desired.81    rtol : float, optional, default=None82        Relative tolerance.83        If None, it is set based on the provided arrays' dtypes.84    atol : float, optional, default=0.85        Absolute tolerance.86    equal_nan : bool, optional, default=True87        If True, NaNs will compare equal.88    err_msg : str, optional, default=''89        The error message to be printed in case of failure.90    verbose : bool, optional, default=True91        If True, the conflicting values are appended to the error message.92 93    Raises94    ------95    AssertionError96        If actual and desired are not equal up to specified precision.97 98    See Also99    --------100    numpy.testing.assert_allclose101 102    Examples103    --------104    >>> import numpy as np105    >>> from sklearn.utils._testing import assert_allclose106    >>> x = [1e-5, 1e-3, 1e-1]107    >>> y = np.arccos(np.cos(x))108    >>> assert_allclose(x, y, rtol=1e-5, atol=0)109    >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32)110    >>> assert_allclose(a, 1e-5)111    NcSsg|]}|tjkrdnd�qS)g-C��6?�H�����z>)�npZfloat32)�.0�dtyper1r1r2�112<listcomp>�sz#assert_allclose.<locals>.<listcomp>)�rtol�atol�	equal_nan�err_msg�verbose)rZZ113asanyarrayr\�max�np_assert_allclose)	�actualZdesiredr^r_r`rarbZdtypesZrtolsr1r1r2r�s5114�rrY��&�.>cCs�tj�|�r;tj�|�r;|��}|��}|��|��t|j|j|d�t|j|j|d�t|j	|j	|||d�dStj�|�sRtj�|�sRt|||||d�dSt115d��)a�Assert allclose for sparse and dense data.116 117    Both x and y need to be either sparse or dense, they118    can't be mixed.119 120    Parameters121    ----------122    x : {array-like, sparse matrix}123        First array to compare.124 125    y : {array-like, sparse matrix}126        Second array to compare.127 128    rtol : float, default=1e-07129        relative tolerance; see numpy.allclose.130 131    atol : float, default=1e-9132        absolute tolerance; see numpy.allclose. 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7}t	|��q|r�t�d|�d|�d��dSdS)a�Helper to check docstring consistency of all `items_docs`.270 271    If item is not present in all objects, checking is skipped and warning raised.272    If `regex` provided, match descriptions to all descriptions.273 274    Parameters275    ----------276    items_doc : dict of dict of str277        Dictionary where the key is the string type or description, value is278        a dictionary where the key is "type description" or "description"279        and the value is a list of object names with the same string type or280        description.281 282    type_or_desc : {"type description", "description"}283        Whether to check type description or description between objects.284 285    section : {"Parameters", "Attributes", "Returns"}286        Name of the section type.287 288    n_objects : int289        Total number of objects.290 291    descr_regex_pattern : str, default=""292        Regex pattern to match for description of all objects.293        Ignored when `type_or_desc="type description".294 295    ignore_types : tuple of str, default=()296        Tuple of parameter/attribute/return names for which type description297        matching is ignored. Ignored when `type_or_desc="description".298    cSsg|]}t|��qSr1)r�)r[Zobjsr1r1r2r]�sz,_check_consistency_items.<locals>.<listcomp>�descriptionzThe description of Nr�z 'z' in z' does not match 'descr_regex_pattern': r��type specification�z and css�|]}t|�VqdSr�)r�)r[r�r1r1r2r��s�299�z+_check_consistency_items.<locals>.<genexpr>zThe z of z' is inconsistent between r�zChecking was skipped for z: z' as they were not found in all objects.)r��sumr�rG�re�searchr��textwrap�fillrur��keysr�rHr4r�)Z300items_docsZtype_or_desc�section�	n_objects�descr_regex_pattern�ignore_typesZskippedr�r�Znot_matchedr�r��msgr�Z301obj_groupsr1r1r2�_check_consistency_items�sL&302����303������rc		s�ddlm}	tdgd����fdd�}304i|305||dd��|306||d	d307��|308||dd��}t�}|D]%}
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�D];\}}||D]2\}}}t||�r�d�|�����}d�t�dd�|D���}|||�|�|||�|�qyqqt|d|||d�t|d|||d�q]dS)a�Check consistency between docstring parameters/attributes/returns of objects.310 311    Checks if parameters/attributes/returns have the same type specification and312    description (ignoring whitespace) across `objects`. Intended to be used for313    related classes/functions/data descriptors.314 315    Entries that do not appear across all `objects` are ignored.316 317    Parameters318    ----------319    objects : list of {classes, functions, data descriptors}320        Objects to check.321        Objects may be classes, functions or data descriptors with docstrings that322        can be parsed by numpydoc.323 324    include_params : list of str or bool, default=False325        List of parameters to be included. If True, all parameters are included,326        if False, checking is skipped for parameters.327        Can only be set if `exclude_params` is None.328 329    exclude_params : list of str or None, default=None330        List of parameters to be excluded. If None, no parameters are excluded.331        Can only be set if `include_params` is True.332 333    include_attrs : list of str or bool, default=False334        List of attributes to be included. If True, all attributes are included,335        if False, checking is skipped for attributes.336        Can only be set if `exclude_attrs` is None.337 338    exclude_attrs : list of str or None, default=None339        List of attributes to be excluded. If None, no attributes are excluded.340        Can only be set if `include_attrs` is True.341 342    include_returns : list of str or bool, default=False343        List of returns to be included. If True, all returns are included,344        if False, checking is skipped for returns.345        Can only be set if `exclude_returns` is None.346 347    exclude_returns : list of str or None, default=None348        List of returns to be excluded. If None, no returns are excluded.349        Can only be set if `include_returns` is True.350 351    descr_regex_pattern : str, default=None352        Regular expression to match to all descriptions of included353        parameters/attributes/returns. If None, will revert to default behavior354        of comparing descriptions between objects.355 356    ignore_types : tuple of str, default=tuple()357        Tuple of parameter/attribute/return names to exclude from type description358        matching between objects.359 360    Examples361    --------362    >>> from sklearn.metrics import (accuracy_score, classification_report,363    ... mean_absolute_error, mean_squared_error, median_absolute_error)364    >>> from sklearn.utils._testing import assert_docstring_consistency365    ... # doctest: +SKIP366    >>> assert_docstring_consistency([mean_absolute_error, mean_squared_error],367    ... include_params=['y_true', 'y_pred', 'sample_weight'])  # doctest: +SKIP368    >>> assert_docstring_consistency([median_absolute_error, mean_squared_error],369    ... include_params=True)  # doctest: +SKIP370    >>> assert_docstring_consistency([accuracy_score, classification_report],371    ... include_params=["y_true"],372    ... descr_regex_pattern=r"Ground truth \(correct\) (labels|target values)")373    ... # doctest: +SKIP374    r)�NumpyDocStringrA)r�r��arg_namecs>|r|durtd|�d|�d���|duriS|�|||�iS)NTz
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