af17bfdedf
See https://github.com/scikit-learn/scikit-learn/pull/10723 This fixes the build of `scikitlearn` on master and nixos-unstable. The issue is originally an upstream issue (see https://github.com/scikit-learn/scikit-learn/issues/10619) which was fixed on master and was mainly caused by changes to the environment. Closes #43466
74 lines
3.2 KiB
Diff
74 lines
3.2 KiB
Diff
diff --git a/sklearn/linear_model/huber.py b/sklearn/linear_model/huber.py
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index e17dc1e..665654d 100644
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--- a/sklearn/linear_model/huber.py
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+++ b/sklearn/linear_model/huber.py
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@@ -181,7 +181,11 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
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n_iter_ : int
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Number of iterations that fmin_l_bfgs_b has run for.
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- Not available if SciPy version is 0.9 and below.
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+
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+ .. versionchanged:: 0.20
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+
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+ In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
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+ ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
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outliers_ : array, shape (n_samples,)
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A boolean mask which is set to True where the samples are identified
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@@ -272,7 +276,9 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
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raise ValueError("HuberRegressor convergence failed:"
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" l-BFGS-b solver terminated with %s"
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% dict_['task'].decode('ascii'))
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- self.n_iter_ = dict_.get('nit', None)
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+ # In scipy <= 1.0.0, nit may exceed maxiter.
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+ # See https://github.com/scipy/scipy/issues/7854.
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+ self.n_iter_ = min(dict_.get('nit', None), self.max_iter)
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self.scale_ = parameters[-1]
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if self.fit_intercept:
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self.intercept_ = parameters[-2]
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diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
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index 8646c9a..c72a7d9 100644
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--- a/sklearn/linear_model/logistic.py
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+++ b/sklearn/linear_model/logistic.py
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@@ -718,7 +718,9 @@ def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True,
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warnings.warn("lbfgs failed to converge. Increase the number "
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"of iterations.")
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try:
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- n_iter_i = info['nit'] - 1
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+ # In scipy <= 1.0.0, nit may exceed maxiter.
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+ # See https://github.com/scipy/scipy/issues/7854.
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+ n_iter_i = min(info['nit'], max_iter)
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except:
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n_iter_i = info['funcalls'] - 1
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elif solver == 'newton-cg':
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@@ -1115,6 +1117,11 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin,
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it returns only 1 element. For liblinear solver, only the maximum
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number of iteration across all classes is given.
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+ .. versionchanged:: 0.20
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+
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+ In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
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+ ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
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+
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See also
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--------
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SGDClassifier : incrementally trained logistic regression (when given
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diff --git a/sklearn/linear_model/tests/test_huber.py b/sklearn/linear_model/tests/test_huber.py
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index 08f4fdf..ca1092f 100644
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--- a/sklearn/linear_model/tests/test_huber.py
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+++ b/sklearn/linear_model/tests/test_huber.py
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@@ -42,6 +42,13 @@ def test_huber_equals_lr_for_high_epsilon():
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assert_almost_equal(huber.intercept_, lr.intercept_, 2)
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+def test_huber_max_iter():
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+ X, y = make_regression_with_outliers()
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+ huber = HuberRegressor(max_iter=1)
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+ huber.fit(X, y)
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+ assert huber.n_iter_ == huber.max_iter
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+
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+
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def test_huber_gradient():
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# Test that the gradient calculated by _huber_loss_and_gradient is correct
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rng = np.random.RandomState(1)
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