DOC add interval range for parameter of SGDRegressor (scikit-learn#28373

)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

sklearn/linear_model/_stochastic_gradient.py

@@ -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
@@ -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
@@ -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
 
@@ -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
 
@@ -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
@@ -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``.
@@ -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
@@ -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
@@ -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
@@ -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.
@@ -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