%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 - 0.0.1-1 - Package Spec generated