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-rw-r--r--gnu/packages/patches/python-scikit-optimize-1150.patch275
1 files changed, 0 insertions, 275 deletions
diff --git a/gnu/packages/patches/python-scikit-optimize-1150.patch b/gnu/packages/patches/python-scikit-optimize-1150.patch
deleted file mode 100644
index 0cdf361a80..0000000000
--- a/gnu/packages/patches/python-scikit-optimize-1150.patch
+++ /dev/null
@@ -1,275 +0,0 @@
-From cd74e00d0e4f435d548444e1a5edc20155e371d7 Mon Sep 17 00:00:00 2001
-From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
-Date: Wed, 15 Feb 2023 18:47:52 +0100
-Subject: [PATCH 1/5] Update RandomForesetRegressor criterion to be inline with
- scikit-learn change from mse to squared error this has the same funcitonality
-
----
- requirements.txt | 6 +++---
- setup.py | 6 +++---
- skopt/learning/forest.py | 30 +++++++++++++++---------------
- 3 files changed, 21 insertions(+), 21 deletions(-)
-
-diff --git a/requirements.txt b/requirements.txt
-index 1eaa3083a..23ab3d856 100644
---- a/requirements.txt
-+++ b/requirements.txt
-@@ -1,6 +1,6 @@
--numpy>=1.13.3
--scipy>=0.19.1
--scikit-learn>=0.20
-+numpy>=1.23.2
-+scipy>=1.10.0
-+scikit-learn>=1.2.1
- matplotlib>=2.0.0
- pytest
- pyaml>=16.9
-diff --git a/setup.py b/setup.py
-index 8879da880..e7f921765 100644
---- a/setup.py
-+++ b/setup.py
-@@ -42,9 +42,9 @@
- classifiers=CLASSIFIERS,
- packages=['skopt', 'skopt.learning', 'skopt.optimizer', 'skopt.space',
- 'skopt.learning.gaussian_process', 'skopt.sampler'],
-- install_requires=['joblib>=0.11', 'pyaml>=16.9', 'numpy>=1.13.3',
-- 'scipy>=0.19.1',
-- 'scikit-learn>=0.20.0'],
-+ install_requires=['joblib>=0.11', 'pyaml>=16.9', 'numpy>=1.23.2',
-+ 'scipy>=1.10.0',
-+ 'scikit-learn>=1.2.1'],
- extras_require={
- 'plots': ["matplotlib>=2.0.0"]
- }
-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
-
-From 6eb2d4ddaa299ae47d9a69ffb31ebc4ed366d1c1 Mon Sep 17 00:00:00 2001
-From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
-Date: Thu, 16 Feb 2023 11:34:58 +0100
-Subject: [PATCH 2/5] Change test to be consistent with code changes.
-
----
- skopt/learning/tests/test_forest.py | 4 ++--
- 1 file changed, 2 insertions(+), 2 deletions(-)
-
-diff --git a/skopt/learning/tests/test_forest.py b/skopt/learning/tests/test_forest.py
-index 0711cde9d..c6ed610f3 100644
---- a/skopt/learning/tests/test_forest.py
-+++ b/skopt/learning/tests/test_forest.py
-@@ -35,7 +35,7 @@ def test_random_forest():
- assert_array_equal(clf.predict(T), true_result)
- assert 10 == len(clf)
-
-- clf = RandomForestRegressor(n_estimators=10, criterion="mse",
-+ clf = RandomForestRegressor(n_estimators=10, criterion="squared_error",
- max_depth=None, min_samples_split=2,
- min_samples_leaf=1,
- min_weight_fraction_leaf=0.,
-@@ -80,7 +80,7 @@ def test_extra_forest():
- assert_array_equal(clf.predict(T), true_result)
- assert 10 == len(clf)
-
-- clf = ExtraTreesRegressor(n_estimators=10, criterion="mse",
-+ clf = ExtraTreesRegressor(n_estimators=10, criterion="squared_error",
- max_depth=None, min_samples_split=2,
- min_samples_leaf=1, min_weight_fraction_leaf=0.,
- max_features="auto", max_leaf_nodes=None,
-
-From 52c620add07d845debbaff2ce2b1c5faf3eae79b Mon Sep 17 00:00:00 2001
-From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
-Date: Wed, 22 Feb 2023 16:59:03 +0100
-Subject: [PATCH 3/5] Update skopt/learning/forest.py
-MIME-Version: 1.0
-Content-Type: text/plain; charset=UTF-8
-Content-Transfer-Encoding: 8bit
-
-Fix max line width
-
-Co-authored-by: Roland Laurès <roland@laures-valdivia.net>
----
- skopt/learning/forest.py | 4 ++--
- 1 file changed, 2 insertions(+), 2 deletions(-)
-
-diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
-index ebde568f5..07dc42664 100644
---- a/skopt/learning/forest.py
-+++ b/skopt/learning/forest.py
-@@ -194,8 +194,8 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
- .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
-
- """
-- def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
-- min_samples_split=2, min_samples_leaf=1,
-+ 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.,
- bootstrap=True, oob_score=False,
-
-From 52a7db95cb567186fb4e9003139fea4592bdbf05 Mon Sep 17 00:00:00 2001
-From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
-Date: Wed, 22 Feb 2023 17:03:25 +0100
-Subject: [PATCH 4/5] Fix line widht issues
-
----
- skopt/learning/forest.py | 4 ++--
- 1 file changed, 2 insertions(+), 2 deletions(-)
-
-diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
-index 07dc42664..d4c24456b 100644
---- a/skopt/learning/forest.py
-+++ b/skopt/learning/forest.py
-@@ -390,8 +390,8 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
- .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
-
- """
-- def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
-- min_samples_split=2, min_samples_leaf=1,
-+ 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.,
- bootstrap=False, oob_score=False,
-
-From 6b185e489fb4a56625e8505292a20c80434f0633 Mon Sep 17 00:00:00 2001
-From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
-Date: Wed, 22 Feb 2023 18:37:11 +0100
-Subject: [PATCH 5/5] Fix lin width issues for comments.
-
----
- skopt/learning/forest.py | 12 ++++++------
- 1 file changed, 6 insertions(+), 6 deletions(-)
-
-diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
-index d4c24456b..eb3bd6648 100644
---- a/skopt/learning/forest.py
-+++ b/skopt/learning/forest.py
-@@ -63,9 +63,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
-
- criterion : string, optional (default="squared_error")
- The function to measure the quality of a split. Supported criteria
-- 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.
-+ 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.
-
- max_features : int, float, string or None, optional (default="auto")
- The number of features to consider when looking for the best split:
-@@ -259,9 +259,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
-
- criterion : string, optional (default="squared_error")
- The function to measure the quality of a split. Supported criteria
-- 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.
-+ 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.
-
- max_features : int, float, string or None, optional (default="auto")
- The number of features to consider when looking for the best split:
generated by cgit v1.2.3 (git 2.44.0) at 2024-05-19 01:49:26 +0000