automatic import of python-active-semi-supervised-clusteringopeneuler20.03

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+/active-semi-supervised-clustering-0.0.1.tar.gz
diff --git a/python-active-semi-supervised-clustering.spec b/python-active-semi-supervised-clustering.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-active-semi-supervised-clustering
+Version:	0.0.1
+Release:	1
+Summary:	Active semi-supervised clustering algorithms for scikit-learn
+License:	MIT License
+URL:		https://github.com/datamole-ai/active-semi-supervised-clustering
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/84/cc/8189ebe735cd7b6c53869775969d89c6fe2d68a872ddd1cc24df3a38d1ba/active-semi-supervised-clustering-0.0.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-metric-learn
+
+%description
+# active-semi-supervised-clustering
+
+Active semi-supervised clustering algorithms for scikit-learn.
+
+## Algorithms
+
+### Semi-supervised clustering
+
+* Seeded-KMeans
+* Constrainted-KMeans
+* COP-KMeans
+* Pairwise constrained K-Means (PCK-Means)
+* Metric K-Means (MK-Means)
+* Metric pairwise constrained K-Means (MPCK-Means)
+
+### Active learning of pairwise clustering
+
+* Explore & Consolidate
+* Min-max
+* Normalized point-based uncertainty (NPU) method
+
+## Installation
+
+```
+pip install active-semi-supervised-clustering
+```
+
+## Usage
+
+```python
+from sklearn import datasets, metrics
+from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
+from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
+```
+
+```python
+X, y = datasets.load_iris(return_X_y=True)
+```
+
+First, obtain some pairwise constraints from an oracle.
+
+```python
+# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
+oracle = ExampleOracle(y, max_queries_cnt=10)
+
+active_learner = MinMax(n_clusters=3)
+active_learner.fit(X, oracle=oracle)
+pairwise_constraints = active_learner.pairwise_constraints_
+```
+
+Then, use the constraints to do the clustering.
+
+```python
+clusterer = PCKMeans(n_clusters=3)
+clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
+```
+
+Evaluate the clustering using Adjusted Rand Score.
+
+```python
+metrics.adjusted_rand_score(y, clusterer.labels_)
+```
+
+
+
+
+%package -n python3-active-semi-supervised-clustering
+Summary:	Active semi-supervised clustering algorithms for scikit-learn
+Provides:	python-active-semi-supervised-clustering
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-active-semi-supervised-clustering
+# active-semi-supervised-clustering
+
+Active semi-supervised clustering algorithms for scikit-learn.
+
+## Algorithms
+
+### Semi-supervised clustering
+
+* Seeded-KMeans
+* Constrainted-KMeans
+* COP-KMeans
+* Pairwise constrained K-Means (PCK-Means)
+* Metric K-Means (MK-Means)
+* Metric pairwise constrained K-Means (MPCK-Means)
+
+### Active learning of pairwise clustering
+
+* Explore & Consolidate
+* Min-max
+* Normalized point-based uncertainty (NPU) method
+
+## Installation
+
+```
+pip install active-semi-supervised-clustering
+```
+
+## Usage
+
+```python
+from sklearn import datasets, metrics
+from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
+from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
+```
+
+```python
+X, y = datasets.load_iris(return_X_y=True)
+```
+
+First, obtain some pairwise constraints from an oracle.
+
+```python
+# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
+oracle = ExampleOracle(y, max_queries_cnt=10)
+
+active_learner = MinMax(n_clusters=3)
+active_learner.fit(X, oracle=oracle)
+pairwise_constraints = active_learner.pairwise_constraints_
+```
+
+Then, use the constraints to do the clustering.
+
+```python
+clusterer = PCKMeans(n_clusters=3)
+clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
+```
+
+Evaluate the clustering using Adjusted Rand Score.
+
+```python
+metrics.adjusted_rand_score(y, clusterer.labels_)
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for active-semi-supervised-clustering
+Provides:	python3-active-semi-supervised-clustering-doc
+%description help
+# active-semi-supervised-clustering
+
+Active semi-supervised clustering algorithms for scikit-learn.
+
+## Algorithms
+
+### Semi-supervised clustering
+
+* Seeded-KMeans
+* Constrainted-KMeans
+* COP-KMeans
+* Pairwise constrained K-Means (PCK-Means)
+* Metric K-Means (MK-Means)
+* Metric pairwise constrained K-Means (MPCK-Means)
+
+### Active learning of pairwise clustering
+
+* Explore & Consolidate
+* Min-max
+* Normalized point-based uncertainty (NPU) method
+
+## Installation
+
+```
+pip install active-semi-supervised-clustering
+```
+
+## Usage
+
+```python
+from sklearn import datasets, metrics
+from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
+from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
+```
+
+```python
+X, y = datasets.load_iris(return_X_y=True)
+```
+
+First, obtain some pairwise constraints from an oracle.
+
+```python
+# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
+oracle = ExampleOracle(y, max_queries_cnt=10)
+
+active_learner = MinMax(n_clusters=3)
+active_learner.fit(X, oracle=oracle)
+pairwise_constraints = active_learner.pairwise_constraints_
+```
+
+Then, use the constraints to do the clustering.
+
+```python
+clusterer = PCKMeans(n_clusters=3)
+clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
+```
+
+Evaluate the clustering using Adjusted Rand Score.
+
+```python
+metrics.adjusted_rand_score(y, clusterer.labels_)
+```
+
+
+
+
+%prep
+%autosetup -n active-semi-supervised-clustering-0.0.1
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-active-semi-supervised-clustering -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.1-1
+- Package Spec generated
diff --git a/sources b/sources
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+b7bf75e99c995593f831865fac6922bf  active-semi-supervised-clustering-0.0.1.tar.gz