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DOC add interval range for parameter of SGDRegressor #28373

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Merged
merged 17 commits into from
Feb 9, 2024
Merged

DOC add interval range for parameter of SGDRegressor #28373

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7793726
Fix `SGDRegressor` documentation
bmgdc Feb 6, 2024
3835468
Include contraints in other parameters in the documentation
bmgdc Feb 6, 2024
8262eba
Remove awkward default value references
bmgdc Feb 6, 2024
96d59fb
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 6, 2024
5cd7a62
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 7, 2024
cd02686
Implement https://github.com/scikit-learn/scikit-learn/pull/28373#dis…
bmgdc Feb 8, 2024
b1253fb
Address https://github.com/scikit-learn/scikit-learn/pull/28373#pullr…
bmgdc Feb 8, 2024
6aa087d
Address https://github.com/scikit-learn/scikit-learn/pull/28373#discu…
bmgdc Feb 8, 2024
9d14b4c
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 8, 2024
97d5b4d
Document default values in `SGDOneClassSVM`
bmgdc Feb 8, 2024
d181bda
Fix/document `average` range values across the board
bmgdc Feb 8, 2024
d544717
Revert "Fix/document `average` range values across the board"
bmgdc Feb 8, 2024
8a5877e
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 8, 2024
331a0a0
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 8, 2024
09d172f
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 9, 2024
ccd8b29
Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 9, 2024
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Merge branch 'main' into fix-sgdregressor-alpha-documentation
bmgdc Feb 9, 2024
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25 changes: 19 additions & 6 deletions sklearn/linear_model/_stochastic_gradient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1061,10 +1061,10 @@ class SGDClassifier(BaseSGDClassifier):
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.0 as eta0 is not used by
the default schedule 'optimal'.
Values must be in the range `(0.0, inf)`.
Values must be in the range `[0.0, inf)`.

power_t : float, default=0.5
The exponent for inverse scaling learning rate [default 0.5].
The exponent for inverse scaling learning rate.
Values must be in the range `(-inf, inf)`.

early_stopping : bool, default=False
Expand Down Expand Up @@ -1789,14 +1789,15 @@ class SGDRegressor(BaseSGDRegressor):

alpha : float, default=0.0001
Constant that multiplies the regularization term. The higher the
value, the stronger the regularization.
Also used to compute the learning rate when set to `learning_rate` is
set to 'optimal'.
value, the stronger the regularization. Also used to compute the
learning rate when `learning_rate` is set to 'optimal'.
Values must be in the range `[0.0, inf)`.

l1_ratio : float, default=0.15
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Only used if `penalty` is 'elasticnet'.
Values must be in the range `[0.0, 1.0]`.

fit_intercept : bool, default=True
Whether the intercept should be estimated or not. If False, the
Expand All @@ -1806,6 +1807,7 @@ class SGDRegressor(BaseSGDRegressor):
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.
Values must be in the range `[1, inf)`.

.. versionadded:: 0.19

Expand All @@ -1815,6 +1817,7 @@ class SGDRegressor(BaseSGDRegressor):
epochs.
Convergence is checked against the training loss or the
validation loss depending on the `early_stopping` parameter.
Values must be in the range `[0.0, inf)`.

.. versionadded:: 0.19

Expand All @@ -1823,6 +1826,7 @@ class SGDRegressor(BaseSGDRegressor):

verbose : int, default=0
The verbosity level.
Values must be in the range `[0, inf)`.

epsilon : float, default=0.1
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
Expand All @@ -1831,6 +1835,7 @@ class SGDRegressor(BaseSGDRegressor):
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
Values must be in the range `[0.0, inf)`.

random_state : int, RandomState instance, default=None
Used for shuffling the data, when ``shuffle`` is set to ``True``.
Expand All @@ -1855,9 +1860,11 @@ class SGDRegressor(BaseSGDRegressor):
eta0 : float, default=0.01
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.01.
Values must be in the range `[0.0, inf)`.

power_t : float, default=0.25
The exponent for inverse scaling learning rate.
Values must be in the range `(-inf, inf)`.

early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
Expand All @@ -1874,6 +1881,7 @@ class SGDRegressor(BaseSGDRegressor):
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if `early_stopping` is True.
Values must be in the range `(0.0, 1.0)`.

.. versionadded:: 0.20
Added 'validation_fraction' option
Expand All @@ -1883,6 +1891,7 @@ class SGDRegressor(BaseSGDRegressor):
fitting.
Convergence is checked against the training loss or the
validation loss depending on the `early_stopping` parameter.
Integer values must be in the range `[1, max_iter)`.

.. versionadded:: 0.20
Added 'n_iter_no_change' option
Expand Down Expand Up @@ -2058,10 +2067,12 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
`partial_fit`. Defaults to 1000.
Values must be in the range `[1, inf)`.

tol : float or None, default=1e-3
The stopping criterion. If it is not None, the iterations will stop
when (loss > previous_loss - tol). Defaults to 1e-3.
Values must be in the range `[0.0, inf)`.

shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.
Expand Down Expand Up @@ -2094,9 +2105,11 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.0 as eta0 is not used by
the default schedule 'optimal'.
Values must be in the range `[0.0, inf)`.

power_t : float, default=0.5
The exponent for inverse scaling learning rate [default 0.5].
The exponent for inverse scaling learning rate.
Values must be in the range `(-inf, inf)`.

warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as
Expand Down