10000 DOC Minor fixes for parameter documentation in ridge by alexitkes · Pull Request #14453 · scikit-learn/scikit-learn · GitHub
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DOC Minor fixes for parameter documentation in ridge #14453

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merged 15 commits into from
Oct 8, 2019
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DOC Minor fixes for parameter documentation in ridge #14453

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3e55d6e
Document default values for linear module parameters.
alexitkes Jul 23, 2019
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Update ridge.py
alexitkes Jul 23, 2019
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alexitkes Jul 24, 2019
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alexitkes Jul 24, 2019
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alexitkes Jul 24, 2019
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alexitkes Jul 26, 2019
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alexitkes Jul 26, 2019
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Update sklearn/linear_model/ridge.py
alexitkes Oct 8, 2019
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Update sklearn/linear_model/ridge.py
alexitkes Oct 8, 2019
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Update sklearn/linear_model/ridge.py
alexitkes Oct 8, 2019
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Update sklearn/linear_model/ridge.py
alexitkes Oct 8, 2019
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Update sklearn/linear_model/ridge.py
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54 changes: 27 additions & 27 deletions sklearn/linear_model/ridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -262,7 +262,7 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
assumed to be specific to the targets. Hence they must correspond in
number.

sample_weight : float or numpy array of shape [n_samples]
sample_weight : float or numpy array of shape (n_samples,), default=None
Individual weights for each sample. If sample_weight is not None and
solver='auto', the solver will be set to 'cholesky'.

Expand Down Expand Up @@ -314,10 +314,10 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
by scipy.sparse.linalg. For 'sag' and saga solver, the default value is
1000.

tol : float
tol : float, default=1e-3
Precision of the solution.

verbose : int
verbose : int, default=0
Verbosity level. Setting verbose > 0 will display additional
information depending on the solver used.

Expand All @@ -328,21 +328,21 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
generator; If None, the random number generator is the RandomState
instance used by `np.random`. Used when ``solver`` == 'sag'.

return_n_iter : boolean, default False
return_n_iter : bool, default=False
If True, the method also returns `n_iter`, the actual number of
iteration performed by the solver.

.. versionadded:: 0.17

return_intercept : boolean, default False
return_intercept : bool, default=False
If True and if X is sparse, the method also returns the intercept,
and the solver is automatically changed to 'sag'. This is only a
temporary fix for fitting the intercept with sparse data. For dense
data, use sklearn.linear_model._preprocess_data before your regression.

.. versionadded:: 0.17

check_input : boolean, default True
check_input : bool, default=True
If False, the input arrays X and y will not be checked.

.. versionadded:: 0.21
Expand Down Expand Up @@ -619,7 +619,7 @@ class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):

Parameters
----------
alpha : {float, array-like}, shape (n_targets)
alpha : {float, array-like of shape (n_targets,)}, default=1.0
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
Expand All @@ -628,28 +628,28 @@ class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
assumed to be specific to the targets. Hence they must correspond in
number.

fit_intercept : bool, default True
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be centered).

normalize : boolean, optional, default False
normalize : bool, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.

copy_X : boolean, optional, default True
copy_X : bool, default=True
If True, X will be copied; else, it may be overwritten.

max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
For 'sparse_cg' and 'lsqr' solvers, the default value is determined
by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.

tol : float
tol : float, default=1e-3
Precision of the solution.

solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'}
Expand Down Expand Up @@ -771,34 +771,34 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):

Parameters
----------
alpha : float
alpha : float, default=1.0
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
Alpha corresponds to ``C^-1`` in other linear models such as
LogisticRegression or LinearSVC.

fit_intercept : boolean
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set to false, no
intercept will be used in calculations (e.g. data is expected to be
already centered).

normalize : boolean, optional, default False
normalize : bool, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.

copy_X : boolean, optional, default True
copy_X : bool, default=True
If True, X will be copied; else, it may be overwritten.

max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.

tol : float
tol : float, default=1e-3
Precision of the solution.

class_weight : dict or 'balanced', optional
Expand Down Expand Up @@ -843,7 +843,7 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
.. versionadded:: 0.19
SAGA solver.

random_state : int, RandomState instance or None, optional, default None
random_state : int, RandomState instance or None, default=None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
Expand Down Expand Up @@ -909,7 +909,7 @@ def fit(self, X, y, sample_weight=None):
y : array-like of shape (n_samples,)
Target values

sample_weight : float or numpy array of shape (n_samples,)
sample_weight : {float, array-like of shape (n_samples,)}, default=None
Sample weight.

.. versionadded:: 0.17
Expand Down Expand Up @@ -1590,7 +1590,7 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):

Parameters
----------
alphas : numpy array of shape [n_alphas]
alphas : numpy array of shape (n_alphas,), default=(0.1, 1.0, 10.0)
Array of alpha values to try.
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
Expand All @@ -1599,20 +1599,20 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
LogisticRegression or LinearSVC.
If using generalized cross-validation, alphas must be positive.

fit_intercept : bool, default True
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be centered).

normalize : boolean, optional, default False
normalize : bool, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.

scoring : string, callable or None, optional, default: None
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
Expand Down Expand Up @@ -1704,28 +1704,28 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):

Parameters
----------
alphas : numpy array of shape [n_alphas]
alphas : numpy array of shape (n_alphas,), default=(0.1, 1.0, 10.0)
Array of alpha values to try.
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
Alpha corresponds to ``C^-1`` in other linear models such as
LogisticRegression or LinearSVC.

fit_intercept : boolean
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be centered).

normalize : boolean, optional, default False
normalize : bool, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.

scoring : string, callable or None, optional, default: None
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
Expand Down Expand Up @@ -1823,7 +1823,7 @@ def fit(self, X, y, sample_weight=None):
y : array-like, shape (n_samples,)
Target values. Will be cast to X's dtype if necessary

sample_weight : float or numpy array of shape (n_samples,)
sample_weight : {float, array-like of shape (n_samples,)}, default=None
Sample weight.

Returns
Expand Down
0