* [gentoo-commits] repo/gentoo:master commit in: sci-libs/scikit-optimize/files/, sci-libs/scikit-optimize/, profiles/
@ 2024-04-11 14:36 Andrew Ammerlaan
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From: Andrew Ammerlaan @ 2024-04-11 14:36 UTC (permalink / raw
To: gentoo-commits
commit: 716a22e8cfcc8db49e96b764405648a6be1cc3a6
Author: Andrew Ammerlaan <andrewammerlaan <AT> gentoo <DOT> org>
AuthorDate: Thu Apr 11 14:36:00 2024 +0000
Commit: Andrew Ammerlaan <andrewammerlaan <AT> gentoo <DOT> org>
CommitDate: Thu Apr 11 14:36:00 2024 +0000
URL: https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=716a22e8
sci-libs/scikit-optimize: treeclean
Closes: https://bugs.gentoo.org/920439
Closes: https://bugs.gentoo.org/906565
Signed-off-by: Andrew Ammerlaan <andrewammerlaan <AT> gentoo.org>
profiles/package.mask | 6 --
sci-libs/scikit-optimize/Manifest | 1 -
.../files/scikit-optimize-0.9.0-numpy-1.24.patch | 22 -----
.../scikit-optimize-0.9.0-scikit-learn-1.2.0.patch | 104 ---------------------
sci-libs/scikit-optimize/metadata.xml | 12 ---
.../scikit-optimize-0.9.0-r1.ebuild | 39 --------
.../scikit-optimize/scikit-optimize-0.9.0.ebuild | 31 ------
7 files changed, 215 deletions(-)
diff --git a/profiles/package.mask b/profiles/package.mask
index e5309eeeb5b9..bef723653deb 100644
--- a/profiles/package.mask
+++ b/profiles/package.mask
@@ -328,12 +328,6 @@ games-engines/renpy
net-misc/econnman
sci-chemistry/mdtraj
-# Andrew Ammerlaan <andrewammerlaan@gentoo.org> (2024-03-10)
-# Archived upstream, latest release is 3 years old. One test
-# failure with python 3.11, more with python 3.12.
-# Removal on: 2024-04-10. Bug #920439
-sci-libs/scikit-optimize
-
# Eray Aslan <eras@gentoo.org> (2024-03-10)
# Mask experimental software
=mail-mta/postfix-3.10*
diff --git a/sci-libs/scikit-optimize/Manifest b/sci-libs/scikit-optimize/Manifest
deleted file mode 100644
index 460f16a85cb6..000000000000
--- a/sci-libs/scikit-optimize/Manifest
+++ /dev/null
@@ -1 +0,0 @@
-DIST scikit-optimize-0.9.0.tar.gz 275570 BLAKE2B ab481bf1cfc2b8c7cff213ae0ce2fa937de8f6269b491cf63ae115eea5c936c8a5c26b7fb339fa6cd2927c5105068635c008d6dc8b3f99b4b5d3abfed1a1c5a2 SHA512 a4c1bd589686dbbabcc5de38a4eb581c040cc2c3f83bc250ddcbe66314f03fc68b7b12d7679049da34c42445b446e1af3873f7ce90bec2a5361f0077ff3e9b74
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
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