1 files changed, 0 insertions, 73 deletions
diff --git a/pkgs/development/python-modules/scikitlearn/n_iter-should-be-less-than-max_iter-using-lbgfs.patch b/pkgs/development/python-modules/scikitlearn/n_iter-should-be-less-than-max_iter-using-lbgfs.patch
deleted file mode 100644
index 67309a673d08..000000000000
--- a/pkgs/development/python-modules/scikitlearn/n_iter-should-be-less-than-max_iter-using-lbgfs.patch
+++ /dev/null
@@ -1,73 +0,0 @@
-diff --git a/sklearn/linear_model/huber.py b/sklearn/linear_model/huber.py
-index e17dc1e..665654d 100644
---- a/sklearn/linear_model/huber.py
-+++ b/sklearn/linear_model/huber.py
-@@ -181,7 +181,11 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
- 
-     n_iter_ : int
-         Number of iterations that fmin_l_bfgs_b has run for.
--        Not available if SciPy version is 0.9 and below.
-+
-+        .. versionchanged:: 0.20
-+
-+            In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
-+            ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
- 
-     outliers_ : array, shape (n_samples,)
-         A boolean mask which is set to True where the samples are identified
-@@ -272,7 +276,9 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
-             raise ValueError("HuberRegressor convergence failed:"
-                              " l-BFGS-b solver terminated with %s"
-                              % dict_['task'].decode('ascii'))
--        self.n_iter_ = dict_.get('nit', None)
-+        # In scipy <= 1.0.0, nit may exceed maxiter.
-+        # See https://github.com/scipy/scipy/issues/7854.
-+        self.n_iter_ = min(dict_.get('nit', None), self.max_iter)
-         self.scale_ = parameters[-1]
-         if self.fit_intercept:
-             self.intercept_ = parameters[-2]
-diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
-index 8646c9a..c72a7d9 100644
---- a/sklearn/linear_model/logistic.py
-+++ b/sklearn/linear_model/logistic.py
-@@ -718,7 +718,9 @@ def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True,
-                 warnings.warn("lbfgs failed to converge. Increase the number "
-                               "of iterations.")
-             try:
--                n_iter_i = info['nit'] - 1
-+                # In scipy <= 1.0.0, nit may exceed maxiter.
-+                # See https://github.com/scipy/scipy/issues/7854.
-+                n_iter_i = min(info['nit'], max_iter)
-             except:
-                 n_iter_i = info['funcalls'] - 1
-         elif solver == 'newton-cg':
-@@ -1115,6 +1117,11 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin,
-         it returns only 1 element. For liblinear solver, only the maximum
-         number of iteration across all classes is given.
- 
-+        .. versionchanged:: 0.20
-+
-+            In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
-+            ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
-+
-     See also
-     --------
-     SGDClassifier : incrementally trained logistic regression (when given
-diff --git a/sklearn/linear_model/tests/test_huber.py b/sklearn/linear_model/tests/test_huber.py
-index 08f4fdf..ca1092f 100644
---- a/sklearn/linear_model/tests/test_huber.py
-+++ b/sklearn/linear_model/tests/test_huber.py
-@@ -42,6 +42,13 @@ def test_huber_equals_lr_for_high_epsilon():
-     assert_almost_equal(huber.intercept_, lr.intercept_, 2)
- 
- 
-+def test_huber_max_iter():
-+    X, y = make_regression_with_outliers()
-+    huber = HuberRegressor(max_iter=1)
-+    huber.fit(X, y)
-+    assert huber.n_iter_ == huber.max_iter
-+
-+
- def test_huber_gradient():
-     # Test that the gradient calculated by _huber_loss_and_gradient is correct
-     rng = np.random.RandomState(1)