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%global _empty_manifest_terminate_build 0
Name: python-spark-sklearn
Version: 0.3.0
Release: 1
Summary: Integration tools for running scikit-learn on Spark
License: Apache 2.0
URL: https://github.com/databricks/spark-sklearn
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b0/3f/34b8dec7d2cfcfe0ba99d637b4f2d306c1ca0b404107c07c829e085f6b38/spark-sklearn-0.3.0.tar.gz
BuildArch: noarch
%description
This package contains some tools to integrate the `Spark computing framework <https://spark.apache.org/>`_
with the popular `scikit-learn machine library <https://scikit-learn.org/stable/>`_. Among other things, it can:
- train and evaluate multiple scikit-learn models in parallel. It is a distributed analog to the
`multicore implementation <https://pythonhosted.org/joblib/parallel.html>`_ included by default in ``scikit-learn``
- convert Spark's Dataframes seamlessly into numpy ``ndarray`` or sparse matrices
- (experimental) distribute Scipy's sparse matrices as a dataset of sparse vectors
It focuses on problems that have a small amount of data and that can be run in parallel.
For small datasets, it distributes the search for estimator parameters (``GridSearchCV`` in scikit-learn),
using Spark. For datasets that do not fit in memory, we recommend using the `distributed implementation in
`Spark MLlib <https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html>`_.
This package distributes simple tasks like grid-search cross-validation.
It does not distribute individual learning algorithms (unlike Spark MLlib).
%package -n python3-spark-sklearn
Summary: Integration tools for running scikit-learn on Spark
Provides: python-spark-sklearn
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spark-sklearn
This package contains some tools to integrate the `Spark computing framework <https://spark.apache.org/>`_
with the popular `scikit-learn machine library <https://scikit-learn.org/stable/>`_. Among other things, it can:
- train and evaluate multiple scikit-learn models in parallel. It is a distributed analog to the
`multicore implementation <https://pythonhosted.org/joblib/parallel.html>`_ included by default in ``scikit-learn``
- convert Spark's Dataframes seamlessly into numpy ``ndarray`` or sparse matrices
- (experimental) distribute Scipy's sparse matrices as a dataset of sparse vectors
It focuses on problems that have a small amount of data and that can be run in parallel.
For small datasets, it distributes the search for estimator parameters (``GridSearchCV`` in scikit-learn),
using Spark. For datasets that do not fit in memory, we recommend using the `distributed implementation in
`Spark MLlib <https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html>`_.
This package distributes simple tasks like grid-search cross-validation.
It does not distribute individual learning algorithms (unlike Spark MLlib).
%package help
Summary: Development documents and examples for spark-sklearn
Provides: python3-spark-sklearn-doc
%description help
This package contains some tools to integrate the `Spark computing framework <https://spark.apache.org/>`_
with the popular `scikit-learn machine library <https://scikit-learn.org/stable/>`_. Among other things, it can:
- train and evaluate multiple scikit-learn models in parallel. It is a distributed analog to the
`multicore implementation <https://pythonhosted.org/joblib/parallel.html>`_ included by default in ``scikit-learn``
- convert Spark's Dataframes seamlessly into numpy ``ndarray`` or sparse matrices
- (experimental) distribute Scipy's sparse matrices as a dataset of sparse vectors
It focuses on problems that have a small amount of data and that can be run in parallel.
For small datasets, it distributes the search for estimator parameters (``GridSearchCV`` in scikit-learn),
using Spark. For datasets that do not fit in memory, we recommend using the `distributed implementation in
`Spark MLlib <https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html>`_.
This package distributes simple tasks like grid-search cross-validation.
It does not distribute individual learning algorithms (unlike Spark MLlib).
%prep
%autosetup -n spark-sklearn-0.3.0
%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-spark-sklearn -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.0-1
- Package Spec generated
|
