gentoo auto-resync : 11:04:2024 - 18:33:04

7 files changed, 0 insertions, 214 deletions

diff --git a/sci-libs/Manifest.gz b/sci-libs/Manifest.gz
index 0176dae1e290..61636c7fb650 100644
--- a/sci-libs/Manifest.gz
+++ b/sci-libs/Manifest.gz
diff --git a/sci-libs/scikit-optimize/Manifest b/sci-libs/scikit-optimize/Manifest
deleted file mode 100644
index 18610eb13f62..000000000000
--- a/sci-libs/scikit-optimize/Manifest
+++ /dev/null
@@ -1,6 +0,0 @@
-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 1074 BLAKE2B 3705e6e663546491a7cc05404d615045bc9558ee1a4df1efa1813d1f2ff69c67c5b6d1a40f8daffd64eb0e2506508e20ade14e93aedb66f56b6ba9d9bef9fccb SHA512 4866d5b725d5e2ac2fcef1288785d048afc647ee00eef3d13beabb75a720372b955af8d7a3e8190873fcda2a789872d5b0830af56275635b72f16566d88974f1
-EBUILD scikit-optimize-0.9.0.ebuild 813 BLAKE2B f3a355cd566b554578f6e8ce7c1e43afc07bd978b445bc1f77ae6afaa065ecc4cfa6b4973f5e0d3c1928fd5db8e929175780429f8518f237232356e4bf43fb4b SHA512 effb37ec8483a40617eacdc53a942012d417994f9eed7bc936cef72e5f8bbbf391e624b366aaf15c331e2f632231035e30464cd2ea7afae1c665654c1a928e0f
-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
deleted file mode 100644
index 65fc26f3eed1..000000000000
--- a/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-numpy-1.24.patch
+++ /dev/null
@@ -1,22 +0,0 @@
-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
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
diff --git a/sci-libs/scikit-optimize/metadata.xml b/sci-libs/scikit-optimize/metadata.xml
deleted file mode 100644
index d554e7c990fa..000000000000
--- a/sci-libs/scikit-optimize/metadata.xml
+++ /dev/null
@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE pkgmetadata SYSTEM "https://www.gentoo.org/dtd/metadata.dtd">
-<pkgmetadata>
-  <maintainer type="project">
-    <email>sci@gentoo.org</email>
-    <name>Gentoo Science Project</name>
-  </maintainer>
-  <upstream>
-    <remote-id type="pypi">scikit-optimize</remote-id>
-    <remote-id type="github">scikit-optimize/scikit-optimize</remote-id>
-  </upstream>
-</pkgmetadata>
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
deleted file mode 100644
index e908335940a8..000000000000
--- a/sci-libs/scikit-optimize/scikit-optimize-0.9.0-r1.ebuild
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright 2020-2024 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/scikit-learn-0.20.0[${PYTHON_USEDEP}]
-	>=dev-python/scipy-0.19.1[${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
diff --git a/sci-libs/scikit-optimize/scikit-optimize-0.9.0.ebuild b/sci-libs/scikit-optimize/scikit-optimize-0.9.0.ebuild
deleted file mode 100644
index b712b3f5252d..000000000000
--- a/sci-libs/scikit-optimize/scikit-optimize-0.9.0.ebuild
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright 2020-2024 Gentoo Authors
-# Distributed under the terms of the GNU General Public License v2
-
-EAPI=8
-
-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/scikit-learn-0.20.0[${PYTHON_USEDEP}]
-	>=dev-python/scipy-0.19.1[${PYTHON_USEDEP}]
-"
-
-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