From 9afce155a599e5f4518f3c7913b6424ac13be12e Mon Sep 17 00:00:00 2001 From: V3n3RiX Date: Mon, 6 Nov 2023 16:19:28 +0000 Subject: gentoo auto-resync : 06:11:2023 - 16:19:27 --- sci-libs/scikit-optimize/Manifest | 3 + .../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 ++++++++ 4 files changed, 168 insertions(+) create mode 100644 sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-numpy-1.24.patch create mode 100644 sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch create mode 100644 sci-libs/scikit-optimize/scikit-optimize-0.9.0-r1.ebuild (limited to 'sci-libs/scikit-optimize') diff --git a/sci-libs/scikit-optimize/Manifest b/sci-libs/scikit-optimize/Manifest index 2c0a66e4db19..b8d2f5462f1d 100644 --- a/sci-libs/scikit-optimize/Manifest +++ b/sci-libs/scikit-optimize/Manifest @@ -1,3 +1,6 @@ +AUX scikit-optimize-0.9.0-numpy-1.24.patch 892 BLAKE2B c06e68b47aa051546ede619ef5cb910b15ae2eb4f8b3a79058759ad6d7b0f29fe357670e2b6ec46d519e5e5dd1dce934336eee2dceec11cde471ed99d569049b SHA512 0d8d037b8a27e44709b27780f49089c17273d43bb90b102e62427c8847e3cd2b0020379e072c525540a3316d6fa7af0e9566880cb9826531213dda96cdded972 +AUX scikit-optimize-0.9.0-scikit-learn-1.2.0.patch 5047 BLAKE2B eb393b5a3f82478da2d58997dc0a8521a8c3f37c3de05df76d583b9bb6f0d18a149f14b90cc885cacd458c0aeb7e8de55cd1accfe8f16f85491423005fbc8830 SHA512 b501680cf6722ec60fea590f9ea966767108411c22b0ded6f3eb15e5f29d95e57f1f8842e91815b08403fb1e27424cbb2bcfc343ff7e5641a075e1217d8fb19e DIST scikit-optimize-0.9.0.tar.gz 275570 BLAKE2B ab481bf1cfc2b8c7cff213ae0ce2fa937de8f6269b491cf63ae115eea5c936c8a5c26b7fb339fa6cd2927c5105068635c008d6dc8b3f99b4b5d3abfed1a1c5a2 SHA512 a4c1bd589686dbbabcc5de38a4eb581c040cc2c3f83bc250ddcbe66314f03fc68b7b12d7679049da34c42445b446e1af3873f7ce90bec2a5361f0077ff3e9b74 +EBUILD scikit-optimize-0.9.0-r1.ebuild 1072 BLAKE2B 22c2666059968510416e9b3b8323829cddcc7107d94ba52e54ef5984b676f558067d103e75cc3741346c2f6e465700c061dd4eb8de0075b4f745166195a44323 SHA512 a5705395464c4c000ea9ef8a6fa917de831eb8d4339eabd7e7ef6073d737366c57e3c2a0db60051362107c3d5de89c3877aa5a36597d6a92c37d1dbf2bdc6beb EBUILD scikit-optimize-0.9.0.ebuild 810 BLAKE2B 4547d60f4efbb1a35da5b878ee2de8ba956dbb84ddad8d23f8deb9c61b9b47221aa169b42b96d582c6aca9ffdb8027ea99d1631704cc90aa458a45e5e166b73d SHA512 8c31bc0322d7ba807a3cf09a59d33b877e043cbf8be6aa841165166a8729d25b68e507a8525b51e47c617a74316e79bb1213a7e80db96c7f160cd3dcd835ebcd MISC metadata.xml 415 BLAKE2B 3bfa58da8f117a7b62399a17e5259dbfb0e74b9b9acd16e4515bcceaafc2928733f047f229c58bc437907cddf3b8a93c9576a9645e0c910129900072bed94aff SHA512 6343c76ca9a28f321c3fd8c94dfbb912f305ce43025ab6d666ed0aa5a496f08f258e1ab4e11c14844baa3c04c63a43c1d79bc8067a0d02a4eccf0e37c0c686f7 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 -- cgit v1.2.3