8000 ENH change Ridge tol to 1e-4 (#24465) · glemaitre/scikit-learn@6be5770 · GitHub
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ENH change Ridge tol to 1e-4 (scikit-learn#24465)
* ENH set defaul tol of Ridge to 1e-4 instead of 1e-3 * MNT rename internal least square method in discrimant_analysis * DOC add versionchanged * DOC add whats_new entry * DOC add pr to whatsnew * DOC better versionchanged message
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‎doc/whats_new/v1.2.rst

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@@ -31,6 +31,11 @@ random sampling procedures.
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to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B.
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:pr:`23619` by :user:`Christian Lorentzen <lorentzenchr>`.
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- |API| The default value of `tol` was changed from `1e-3` to `1e-4` for
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:func:`linear_model.ridge_regression`, :class:`linear_model.Ridge` and
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:class:`linear_model.`RidgeClassifier`.
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:pr:`24465` by :user:`Christian Lorentzen <lorentzenchr>`.
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- |Fix| Make sign of `components_` deterministic in :class:`decomposition.SparsePCA`.
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:pr:`23935` by :user:`Guillaume Lemaitre <glemaitre>`.
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@@ -333,6 +338,11 @@ Changelog
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in :class:`linear_model.LogisticRegression`, and will be removed in version 1.4.
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Use `None` instead. :pr:`23877` by :user:`Zhehao Liu <MaxwellLZH>`.
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- |API| The default value of `tol` was changed from `1e-3` to `1e-4` for
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:func:`linear_model.ridge_regression`, :class:`linear_model.Ridge` and
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:class:`linear_model.`RidgeClassifier`.
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:pr:`24465` by :user:`Christian Lorentzen <lorentzenchr>`.
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- |Fix| :class:`linear_model.SGDOneClassSVM` no longer performs parameter
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validation in the constructor. All validation is now handled in `fit()` and
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`partial_fit()`.

‎sklearn/discriminant_analysis.py

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@@ -350,7 +350,7 @@ def __init__(
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self.tol = tol # used only in svd solver
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self.covariance_estimator = covariance_estimator
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def _solve_lsqr(self, X, y, shrinkage, covariance_estimator):
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def _solve_lstsq(self, X, y, shrinkage, covariance_estimator):
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"""Least squares solver.
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The least squares solver computes a straightforward solution of the
@@ -621,7 +621,7 @@ def fit(self, X, y):
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)
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self._solve_svd(X, y)
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elif self.solver == "lsqr":
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self._solve_lsqr(
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self._solve_lstsq(
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X,
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y,
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shrinkage=self.shrinkage,

‎sklearn/linear_model/_ridge.py

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@@ -66,7 +66,7 @@ def _solve_sparse_cg(
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y,
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alpha,
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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verbose=0,
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X_offset=None,
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X_scale=None,
@@ -153,7 +153,7 @@ def _solve_lsqr(
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alpha,
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fit_intercept=True,
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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X_offset=None,
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X_scale=None,
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sample_weight_sqrt=None,
@@ -303,7 +303,7 @@ def _solve_lbfgs(
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alpha,
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positive=True,
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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X_offset=None,
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X_scale=None,
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sample_weight_sqrt=None,
@@ -381,7 +381,7 @@ def ridge_regression(
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sample_weight=None,
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solver="auto",
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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verbose=0,
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positive=False,
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random_state=None,
@@ -471,8 +471,13 @@ def ridge_regression(
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by scipy.sparse.linalg. For 'sag' and saga solver, the default value is
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1000. For 'lbfgs' solver, the default value is 15000.
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tol : float, default=1e-3
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Precision of the solution.
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tol : float, default=1e-4
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Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
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'cholesky'.
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.. versionchanged:: 1.2
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Default value changed from 1e-3 to 1e-4 for consistency with other linear
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models.
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verbose : int, default=0
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Verbosity level. Setting verbose > 0 will display additional
@@ -556,7 +561,7 @@ def _ridge_regression(
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sample_weight=None,
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solver="auto",
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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verbose=0,
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positive=False,
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random_state=None,
@@ -803,7 +808,7 @@ def __init__(
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normalize="deprecated",
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copy_X=True,
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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solver="auto",
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positive=False,
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random_state=None,
@@ -977,8 +982,13 @@ class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
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by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.
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For 'lbfgs' solver, the default value is 15000.
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tol : float, default=1e-3
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Precision of the solution.
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tol : float, default=1e-4
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Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
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'cholesky'.
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.. versionchanged:: 1.2
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Default value changed from 1e-3 to 1e-4 for consistency with other linear
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models.
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solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
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'sag', 'saga', 'lbfgs'}, default='auto'
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normalize="deprecated",
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copy_X=True,
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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solver="auto",
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positive=False,
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random_state=None,
@@ -1276,8 +1286,13 @@ class RidgeClassifier(_RidgeClassifierMixin, _BaseRidge):
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Maximum number of iterations for conjugate gradient solver.
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The default value is determined by scipy.sparse.linalg.
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tol : float, default=1e-3
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Precision of the solution.
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tol : float, default=1e-4
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Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
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'cholesky'.
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.. versionchanged:: 1.2
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Default value changed from 1e-3 to 1e-4 for consistency with other linear
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models.
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class_weight : dict or 'balanced', default=None
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Weights associated with classes in the form ``{class_label: weight}``.
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normalize="deprecated",
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copy_X=True,
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max_iter=None,
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tol=1e-3,
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tol=1e-4,
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class_weight=None,
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solver="auto",
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positive=False,

‎sklearn/linear_model/tests/test_ridge.py

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@@ -1660,7 +1660,7 @@ def test_ridge_fit_intercept_sparse_sag(with_sample_weight, global_random_seed):
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assert_allclose(dense_ridge.intercept_, sparse_ridge.intercept_, rtol=1e-4)
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assert_allclose(dense_ridge.coef_, sparse_ridge.coef_, rtol=1e-4)
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with pytest.warns(UserWarning, match='"sag" solver requires.*'):
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Ridge(solver="sag").fit(X_csr, y)
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Ridge(solver="sag", fit_intercept=True, tol=1e-3, max_iter=None).fit(X_csr, y)
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@pytest.mark.parametrize("return_intercept", [False, True])

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