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# Copyright 2013-2023 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)
from spack.package import *
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"
license("BSD-3-Clause")
version("0.8.29", sha256="67ba1c00b5ad7c0dca2d662d6036b6df235bd61522a785d68a8458b732555d76")
version("0.8.26", sha256="2fd10906603b6565ee138656b6d59df3494c03c5e8099aede400d50b13af912b")
depends_on("py-setuptools", type="build")
depends_on("py-cython@0.27:", type=("build", "run"))
depends_on("py-numpy@1.16.0:", type=("build", "run"))
depends_on("py-numpy@1.20:", type=("build", "run"), when="@0.8.29:")
depends_on("py-scipy@0.9:", type=("build", "run"))
depends_on("py-scipy@1.0:", type=("build", "run"), when="@0.8.29:")
depends_on("py-scikit-learn@0.17:", type=("build", "run"))
depends_on("py-scikit-learn@0.20:", type=("build", "run"), when="@0.8.29:")
depends_on("py-joblib", type=("build", "run"))
depends_on("py-joblib@1.0:", type=("build", "run"), when="@0.8.29:")
depends_on("py-six", type=("build", "run"))
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