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authorAndrew W Elble <aweits@rit.edu>2020-10-21 11:53:43 -0400
committerGitHub <noreply@github.com>2020-10-21 10:53:43 -0500
commite9eb9cdc410e4ae5c9c58f323370e699e7da10c7 (patch)
tree30f4d363664573442f4b4801662d49849bc7ec05
parentc678f119ba75956833b73069bcd69f8ec080a099 (diff)
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py-hdbscan (#19428)
-rw-r--r--var/spack/repos/builtin/packages/py-hdbscan/package.py31
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diff --git a/var/spack/repos/builtin/packages/py-hdbscan/package.py b/var/spack/repos/builtin/packages/py-hdbscan/package.py
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+# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
+# Spack Project Developers. See the top-level COPYRIGHT file for details.
+#
+# SPDX-License-Identifier: (Apache-2.0 OR MIT)
+
+
+class PyHdbscan(PythonPackage):
+ """HDBSCAN - Hierarchical Density-Based Spatial Clustering of
+ Applications with Noise. Performs DBSCAN over varying epsilon
+ values and integrates the result to find a clustering that gives
+ the best stability over epsilon. This allows HDBSCAN to find
+ clusters of varying densities (unlike DBSCAN), and be more robust
+ to parameter selection. In practice this means that HDBSCAN
+ returns a good clustering straight away with little or no
+ parameter tuning -- and the primary parameter, minimum cluster
+ size, is intuitive and easy to select. HDBSCAN is ideal for
+ exploratory data analysis; it's a fast and robust algorithm that
+ you can trust to return meaningful clusters (if there are any)."""
+
+ homepage = "https://github.com/scikit-learn-contrib/hdbscan"
+ url = "https://github.com/scikit-learn-contrib/hdbscan/archive/0.8.26.tar.gz"
+
+ version('0.8.26', sha256='2fd10906603b6565ee138656b6d59df3494c03c5e8099aede400d50b13af912b')
+
+ depends_on('py-setuptools', type='build')
+ depends_on('py-cython@0.27:', type='build')
+ depends_on('py-numpy@1.16.0:', type=('build', 'run'))
+ depends_on('py-scipy@0.9:', type=('build', 'run'))
+ depends_on('py-scikit-learn@0.17:', type=('build', 'run'))
+ depends_on('py-joblib', type=('build', 'run'))
+ depends_on('py-six', type=('build', 'run'))