commit: a7c3db5d998d752370dff2abf57c9515c9019461 Author: Andrew Ammerlaan <andrewammerlaan <AT> gentoo <DOT> org> AuthorDate: Mon Nov 6 11:40:00 2023 +0000 Commit: Andrew Ammerlaan <andrewammerlaan <AT> gentoo <DOT> org> CommitDate: Mon Nov 6 11:40:22 2023 +0000 URL: https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=a7c3db5d
sci-libs/scikit-optimize: pep517, numpy-1.24 compat Closes: https://bugs.gentoo.org/910028 Closes: https://bugs.gentoo.org/890666 Signed-off-by: Andrew Ammerlaan <andrewammerlaan <AT> gentoo.org> .../files/scikit-optimize-0.9.0-numpy-1.24.patch | 22 +++++ .../scikit-optimize-0.9.0-scikit-learn-1.2.0.patch | 104 +++++++++++++++++++++ .../scikit-optimize-0.9.0-r1.ebuild | 39 ++++++++ 3 files changed, 165 insertions(+) diff --git a/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-numpy-1.24.patch b/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-numpy-1.24.patch new file mode 100644 index 000000000000..65fc26f3eed1 --- /dev/null +++ b/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-numpy-1.24.patch @@ -0,0 +1,22 @@ +diff --git a/skopt/space/transformers.py b/skopt/space/transformers.py +index 68892952..87cc3b68 100644 +--- a/skopt/space/transformers.py ++++ b/skopt/space/transformers.py +@@ -259,7 +259,7 @@ def transform(self, X): + if (self.high - self.low) == 0.: + return X * 0. + if self.is_int: +- return (np.round(X).astype(np.int) - self.low) /\ ++ return (np.round(X).astype(np.int64) - self.low) /\ + (self.high - self.low) + else: + return (X - self.low) / (self.high - self.low) +@@ -272,7 +272,7 @@ def inverse_transform(self, X): + raise ValueError("All values should be greater than 0.0") + X_orig = X * (self.high - self.low) + self.low + if self.is_int: +- return np.round(X_orig).astype(np.int) ++ return np.round(X_orig).astype(np.int64) + return X_orig + + 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 new file mode 100644 index 000000000000..8cf8cff9479f --- /dev/null +++ b/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch @@ -0,0 +1,104 @@ +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 diff --git a/sci-libs/scikit-optimize/scikit-optimize-0.9.0-r1.ebuild b/sci-libs/scikit-optimize/scikit-optimize-0.9.0-r1.ebuild new file mode 100644 index 000000000000..694cd3ffafeb --- /dev/null +++ b/sci-libs/scikit-optimize/scikit-optimize-0.9.0-r1.ebuild @@ -0,0 +1,39 @@ +# Copyright 2020-2023 Gentoo Authors +# Distributed under the terms of the GNU General Public License v2 + +EAPI=8 + +DISTUTILS_USE_PEP517=setuptools +PYPI_NO_NORMALIZE=1 +PYTHON_COMPAT=( python3_{10..11} ) +inherit distutils-r1 pypi + +DESCRIPTION="Sequential model-based optimization library" +HOMEPAGE="https://scikit-optimize.github.io/"; + +LICENSE="BSD" +SLOT="0" +KEYWORDS="~amd64" + +RDEPEND=" + >=dev-python/joblib-0.11[${PYTHON_USEDEP}] + dev-python/pyyaml[${PYTHON_USEDEP}] + >=dev-python/matplotlib-2.0.0[${PYTHON_USEDEP}] + >=dev-python/numpy-1.13.3[${PYTHON_USEDEP}] + >=dev-python/scipy-0.19.1[${PYTHON_USEDEP}] + >=sci-libs/scikit-learn-0.20.0[${PYTHON_USEDEP}] +" + +PATCHES=( + # https://github.com/scikit-optimize/scikit-optimize/pull/1187 + "${FILESDIR}/${P}-numpy-1.24.patch" + # https://github.com/scikit-optimize/scikit-optimize/pull/1184/files + "${FILESDIR}/${P}-scikit-learn-1.2.0.patch" +) + +distutils_enable_tests pytest +# No such file or directory: image/logo.png +#distutils_enable_sphinx doc \ +# dev-python/numpydoc \ +# dev-python/sphinx-issues \ +# dev-python/sphinx-gallery
