8000 DOC Fix doc of defaults in sklearn.utils.validation.py (#18048) · scikit-learn/scikit-learn@6a29e2c · GitHub
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DOC Fix doc of defaults in sklearn.utils.validation.py (#18048)
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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‎sklearn/utils/validation.py

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Original file line numberDiff line numberDiff line change
@@ -33,15 +33,15 @@
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def _deprecate_positional_args(f):
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"""Decorator for methods that issues warnings for positional arguments
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"""Decorator for methods that issues warnings for positional arguments.
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Using the keyword-only argument syntax in pep 3102, arguments after the
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* will issue a warning when passed as a positional argument.
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Parameters
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----------
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f : function
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function to check arguments on
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f : callable
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Function to check arguments on.
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"""
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sig = signature(f)
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kwonly_args = []
@@ -109,9 +109,9 @@ def assert_all_finite(X, *, allow_nan=False):
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Parameters
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----------
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X : array or sparse matrix
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X : {ndarray, sparse matrix}
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allow_nan : bool
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allow_nan : bool, default=False
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"""
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_assert_all_finite(X.data if sp.issparse(X) else X, allow_nan)
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@@ -132,7 +132,7 @@ def as_float_array(X, *, copy=True, force_all_finite=True):
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If True, a copy of X will be created. If False, a copy may still be
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returned if X's dtype is not a floating point type.
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force_all_finite : boolean or 'allow-nan', default=True
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force_all_finite : bool or 'allow-nan', default=True
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Whether to raise an error on np.inf, np.nan, pd.NA in X. The
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possibilities are:
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@@ -149,8 +149,8 @@ def as_float_array(X, *, copy=True, force_all_finite=True):
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Returns
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-------
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XT : {array, sparse matrix}
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An array of type float
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XT : {ndarray, sparse matrix}
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An array of type float.
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"""
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if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
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and not sp.issparse(X)):
@@ -170,7 +170,7 @@ def as_float_array(X, *, copy=True, force_all_finite=True):
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def _is_arraylike(x):
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"""Returns whether the input is array-like"""
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"""Returns whether the input is array-like."""
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return (hasattr(x, '__len__') or
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hasattr(x, 'shape') or
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hasattr(x, '__array__'))
@@ -263,7 +263,7 @@ def _make_indexable(iterable):
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Parameters
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----------
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iterable : {list, dataframe, array, sparse} or None
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iterable : {list, dataframe, ndarray, sparse matrix} or None
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Object to be converted to an indexable iterable.
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"""
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if sp.issparse(iterable):
@@ -284,7 +284,7 @@ def indexable(*iterables):
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Parameters
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----------
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*iterables : lists, dataframes, arrays, sparse matrices
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*iterables : {lists, dataframes, ndarrays, sparse matrices}
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List of objects to ensure sliceability.
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"""
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result = [_make_indexable(X) for X in iterables]
@@ -300,24 +300,24 @@ def _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy,
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Parameters
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----------
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spmatrix : scipy sparse matrix
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spmatrix : sparse matrix
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Input to validate and convert.
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accept_sparse : string, boolean or list/tuple of strings
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accept_sparse : str, bool or list/tuple of str
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String[s] representing allowed sparse matrix formats ('csc',
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'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). If the input is sparse but
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not in the allowed format, it will be converted to the first listed
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format. True allows the input to be any format. False means
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that a sparse matrix input will raise an error.
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dtype : string, type or None
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dtype : str, type or None
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Data type of result. If None, the dtype of the input is preserved.
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copy : boolean
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copy : bool
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Whether a forced copy will be triggered. If copy=False, a copy might
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be triggered by a conversion.
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force_all_finite : boolean or 'allow-nan', default=True
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force_all_finite : bool or 'allow-nan'
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Whether to raise an error on np.inf, np.nan, pd.NA in X. The
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possibilities are:
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@@ -334,7 +334,7 @@ def _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy,
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Returns
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-------
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spmatrix_converted : scipy sparse matrix.
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spmatrix_converted : sparse matrix.
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Matrix that is ensured to have an allowed type.
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"""
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if dtype is None:
@@ -410,7 +410,7 @@ def check_array(array, accept_sparse=False, *, accept_large_sparse=True,
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array : object
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Input object to check / convert.
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accept_sparse : string, boolean or list/tuple of strings, default=False
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accept_sparse : str, bool or list/tuple of str, default=False
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String[s] representing allowed sparse matrix formats, such as 'csc',
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'csr', etc. If the input is sparse but not in the allowed format,
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it will be converted to the first listed format. True allows the input
@@ -424,24 +424,24 @@ def check_array(array, accept_sparse=False, *, accept_large_sparse=True,
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.. versionadded:: 0.20
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dtype : string, type, list of types or None, default="numeric"
427+
dtype : 'numeric', type, list of type or None, default='numeric'
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Data type of result. If None, the dtype of the input is preserved.
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If "numeric", dtype is preserved unless array.dtype is object.
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If dtype is a list of types, conversion on the first type is only
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performed if the dtype of the input is not in the list.
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order : 'F', 'C' or None, default=None
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order : {'F', 'C'} or None, default=None
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Whether an array will be forced to be fortran or c-style.
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When order is None (default), then if copy=False, nothing is ensured
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about the memory layout of the output array; otherwise (copy=True)
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the memory layout of the returned array is kept as close as possible
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to the original array.
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copy : boolean, default=False
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copy : bool, default=False
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Whether a forced copy will be triggered. If copy=False, a copy might
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be triggered by a conversion.
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force_all_finite : boolean or 'allow-nan', default=True
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force_all_finite : bool or 'allow-nan', default=True
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Whether to raise an error on np.inf, np.nan, pd.NA in array. The
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possibilities are:
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@@ -456,10 +456,10 @@ def check_array(array, accept_sparse=False, *, accept_large_sparse=True,
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.. versionchanged:: 0.23
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Accepts `pd.NA` and converts it into `np.nan`
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ensure_2d : boolean, default=True
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ensure_2d : bool, default=True
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Whether to raise a value error if array is not 2D.
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allow_nd : boolean, default=False
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allow_nd : bool, default=False
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Whether to allow array.ndim > 2.
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ensure_min_samples : int, default=1
@@ -701,13 +701,13 @@ def check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True,
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Parameters
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----------
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X : nd-array, list or sparse matrix
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X : {ndarray, list, sparse matrix}
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Input data.
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y : nd-array, list or sparse matrix
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y : {ndarray, list, sparse matrix}
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Labels.
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accept_sparse : string, boolean or list of string, default=False
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accept_sparse : str, bool or list of str, default=False
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String[s] representing allowed sparse matrix formats, such as 'csc',
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'csr', etc. If the input is sparse but not in the allowed format,
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it will be converted to the first listed format. True allows the input
@@ -721,20 +721,20 @@ def check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True,
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.. versionadded:: 0.20
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dtype : string, type, list of types or None, default="numeric"
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dtype : 'numeric', type, list of type or None, default='numeric'
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Data type of result. If None, the dtype of the input is preserved.
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If "numeric", dtype is preserved unless array.dtype is object.
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If dtype is a list of types, conversion on the first type is only
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performed if the dtype of the input is not in the list.
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order : 'F', 'C' or None, default=None
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order : {'F', 'C'}, default=None
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Whether an array will be forced to be fortran or c-style.
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copy : boolean, default=False
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copy : bool, default=False
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Whether a forced copy will be triggered. If copy=False, a copy might
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be triggered by a conversion.
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force_all_finite : boolean or 'allow-nan', default=True
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force_all_finite : bool or 'allow-nan', default=True
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Whether to raise an error on np.inf, np.nan, pd.NA in X. This parameter
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does not influence whether y can have np.inf, np.nan, pd.NA values.
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The possibilities are:
@@ -750,13 +750,13 @@ def check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True,
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.. versionchanged:: 0.23
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Accepts `pd.NA` and converts it into `np.nan`
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ensure_2d : boolean, default=True
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ensure_2d : bool, default=True
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Whether to raise a value error if X is not 2D.
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allow_nd : boolean, default=False
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allow_nd : bool, default=False
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Whether to allow X.ndim > 2.
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multi_output : boolean, default=False
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multi_output : bool, default=False
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Whether to allow 2D y (array or sparse matrix). If false, y will be
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validated as a vector. y cannot have np.nan or np.inf values if
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multi_output=True.
@@ -772,7 +772,7 @@ def check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True,
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is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
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this check.
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y_numeric : boolean, default=False
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y_numeric : bool, default=False
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Whether to ensure that y has a numeric type. If dtype of y is object,
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it is converted to float64. Should only be used for regression
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algorithms.
@@ -815,18 +815,18 @@ def check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True,
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@_deprecate_positional_args
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def column_or_1d(y, *, warn=False):
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""" Ravel column or 1d numpy array, else raises an error
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""" Ravel column or 1d numpy array, else raises an error.
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Parameters
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----------
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y : array-like
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warn : boolean, default False
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warn : bool, default=False
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To control display of warnings.
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Returns
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-------
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y : array
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y : ndarray
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"""
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y = np.asarray(y)
@@ -851,7 +851,7 @@ def check_random_state(seed):
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Parameters
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----------
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seed : None | int | instance of RandomState
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seed : None, int or instance of RandomState
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If seed is None, return the RandomState singleton used by np.random.
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If seed is an int, return a new RandomState instance seeded with seed.
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If seed is already a RandomState instance, return it.
@@ -905,19 +905,22 @@ def check_symmetric(array, *, tol=1E-10, raise_warning=True,
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Parameters
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----------
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array : nd-array or sparse matrix
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array : {ndarray, sparse matrix}
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Input object to check / convert. Must be two-dimensional and square,
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otherwise a ValueError will be raised.
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tol : float
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tol : float, default=1e-10
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Absolute tolerance for equivalence of arrays. Default = 1E-10.
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raise_warning : boolean, default=True
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raise_warning : bool, default=True
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If True then raise a warning if conversion is required.
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raise_exception : boolean, default=False
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raise_exception : bool, default=False
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If True then raise an exception if array is not symmetric.
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Returns
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-------
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array_sym : ndarray or sparse matrix
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array_sym : {ndarray, sparse matrix}
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Symmetrized version of the input array, i.e. the average of array
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and array.transpose(). If sparse, then duplicate entries are first
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summed and zeros are eliminated.
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Parameters
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----------
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estimator : estimator instance.
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estimator : estimator instance
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estimator instance for which the check is performed.
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attributes : str, list or tuple of str, default=None
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attribute that ends with a underscore and does not start with double
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underscore.
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msg : string
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msg : str, default=None
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The default error message is, "This %(name)s instance is not fitted
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yet. Call 'fit' with appropriate arguments before using this
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estimator."
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Parameters
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----------
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X : array-like or sparse matrix
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X : {array-like, sparse matrix}
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Input data.
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whom : string
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whom : str
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Who passed X to this funct 10000 ion.
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"""
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# avoid X.min() on sparse matrix since it also sorts the indices
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sample_weight : {ndarray, Number or None}, shape (n_samples,)
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Input sample weights.
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X : nd-array, list or sparse matrix
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X : {ndarray, list, sparse matrix}
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Input data.
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dtype: dtype
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Returns
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-------
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sample_weight : ndarray, shape (n_samples,)
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sample_weight : ndarray of shape (n_samples,)
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Validated sample weight. It is guaranteed to be "C" contiguous.
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"""
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n_samples = _num_samples(X)
@@ -1311,14 +1314,14 @@ def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):
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Parameters
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----------
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x : array-like or sparse matrix
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x : {array-like, sparse matrix}
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First array to compare.
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y : array-like or sparse matrix
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y : {array-like, sparse matrix}
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Second array to compare.
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rtol : float, default=1e-7
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relative tolerance; see numpy.allclose
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Relative tolerance; see numpy.allclose.
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atol : float, default=1e-9
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absolute tolerance; see numpy.allclose. Note that the default here is

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