diff options
Diffstat (limited to 'gnu/packages/patches/python-scikit-optimize-1150.patch')
| -rw-r--r-- | gnu/packages/patches/python-scikit-optimize-1150.patch | 275 |
1 files changed, 275 insertions, 0 deletions
diff --git a/gnu/packages/patches/python-scikit-optimize-1150.patch b/gnu/packages/patches/python-scikit-optimize-1150.patch new file mode 100644 index 0000000000..0cdf361a80 --- /dev/null +++ b/gnu/packages/patches/python-scikit-optimize-1150.patch @@ -0,0 +1,275 @@ +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: |