1 files changed, 0 insertions, 104 deletions
diff --git a/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch b/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch
deleted file mode 100644
index 8cf8cff9479f..000000000000
--- a/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch
+++ /dev/null
@@ -1,104 +0,0 @@
-diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
-index 096770c1d..ebde568f5 100644
---- a/skopt/learning/forest.py
-+++ b/skopt/learning/forest.py
-@@ -27,7 +27,7 @@ def _return_std(X, trees, predictions, min_variance):
-     -------
-     std : array-like, shape=(n_samples,)
-         Standard deviation of `y` at `X`. If criterion
--        is set to "mse", then `std[i] ~= std(y | X[i])`.
-+        is set to "squared_error", then `std[i] ~= std(y | X[i])`.
- 
-     """
-     # This derives std(y | x) as described in 4.3.2 of arXiv:1211.0906
-@@ -61,9 +61,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
-     n_estimators : integer, optional (default=10)
-         The number of trees in the forest.
- 
--    criterion : string, optional (default="mse")
-+    criterion : string, optional (default="squared_error")
-         The function to measure the quality of a split. Supported criteria
--        are "mse" for the mean squared error, which is equal to variance
-+        are "squared_error" for the mean squared error, which is equal to variance
-         reduction as feature selection criterion, and "mae" for the mean
-         absolute error.
- 
-@@ -194,7 +194,7 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
-     .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
- 
-     """
--    def __init__(self, n_estimators=10, criterion='mse', max_depth=None,
-+    def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
-                  min_samples_split=2, min_samples_leaf=1,
-                  min_weight_fraction_leaf=0.0, max_features='auto',
-                  max_leaf_nodes=None, min_impurity_decrease=0.,
-@@ -228,20 +228,20 @@ def predict(self, X, return_std=False):
-         Returns
-         -------
-         predictions : array-like of shape = (n_samples,)
--            Predicted values for X. If criterion is set to "mse",
-+            Predicted values for X. If criterion is set to "squared_error",
-             then `predictions[i] ~= mean(y | X[i])`.
- 
-         std : array-like of shape=(n_samples,)
-             Standard deviation of `y` at `X`. If criterion
--            is set to "mse", then `std[i] ~= std(y | X[i])`.
-+            is set to "squared_error", then `std[i] ~= std(y | X[i])`.
- 
-         """
-         mean = super(RandomForestRegressor, self).predict(X)
- 
-         if return_std:
--            if self.criterion != "mse":
-+            if self.criterion != "squared_error":
-                 raise ValueError(
--                    "Expected impurity to be 'mse', got %s instead"
-+                    "Expected impurity to be 'squared_error', got %s instead"
-                     % self.criterion)
-             std = _return_std(X, self.estimators_, mean, self.min_variance)
-             return mean, std
-@@ -257,9 +257,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
-     n_estimators : integer, optional (default=10)
-         The number of trees in the forest.
- 
--    criterion : string, optional (default="mse")
-+    criterion : string, optional (default="squared_error")
-         The function to measure the quality of a split. Supported criteria
--        are "mse" for the mean squared error, which is equal to variance
-+        are "squared_error" for the mean squared error, which is equal to variance
-         reduction as feature selection criterion, and "mae" for the mean
-         absolute error.
- 
-@@ -390,7 +390,7 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
-     .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
- 
-     """
--    def __init__(self, n_estimators=10, criterion='mse', max_depth=None,
-+    def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
-                  min_samples_split=2, min_samples_leaf=1,
-                  min_weight_fraction_leaf=0.0, max_features='auto',
-                  max_leaf_nodes=None, min_impurity_decrease=0.,
-@@ -425,19 +425,19 @@ def predict(self, X, return_std=False):
-         Returns
-         -------
-         predictions : array-like of shape=(n_samples,)
--            Predicted values for X. If criterion is set to "mse",
-+            Predicted values for X. If criterion is set to "squared_error",
-             then `predictions[i] ~= mean(y | X[i])`.
- 
-         std : array-like of shape=(n_samples,)
-             Standard deviation of `y` at `X`. If criterion
--            is set to "mse", then `std[i] ~= std(y | X[i])`.
-+            is set to "squared_error", then `std[i] ~= std(y | X[i])`.
-         """
-         mean = super(ExtraTreesRegressor, self).predict(X)
- 
-         if return_std:
--            if self.criterion != "mse":
-+            if self.criterion != "squared_error":
-                 raise ValueError(
--                    "Expected impurity to be 'mse', got %s instead"
-+                    "Expected impurity to be 'squared_error', got %s instead"
-                     % self.criterion)
-             std = _return_std(X, self.estimators_, mean, self.min_variance)
-             return mean, std