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|
%global _empty_manifest_terminate_build 0
Name: python-missingpy
Version: 0.2.0
Release: 1
Summary: Missing Data Imputation for Python
License: GNU General Public License v3 (GPLv3)
URL: https://github.com/epsilon-machine/missingpy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/20/ef/2c8b77dc55f0e1af9eb9b01ed220abf3957ae205c7b355951a02783416a0/missingpy-0.2.0.tar.gz
BuildArch: noarch
%description
missing_values : integer or "NaN", optional (default = "NaN")
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For missing values encoded as
``np.nan``, use the string value "NaN".
n_neighbors : int, optional (default = 5)
Number of neighboring samples to use for imputation.
weights : str or callable, optional (default = "uniform")
Weight function used in prediction. Possible values:
- 'uniform' : uniform weights. All points in each neighborhood
are weighted equally.
- 'distance' : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.
- [callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.
metric : str or callable, optional (default = "masked_euclidean")
Distance metric for searching neighbors. Possible values:
- 'masked_euclidean'
- [callable] : a user-defined function which conforms to the
definition of _pairwise_callable(X, Y, metric, **kwds). In other
words, the function accepts two arrays, X and Y, and a
``missing_values`` keyword in **kwds and returns a scalar distance
value.
row_max_missing : float, optional (default = 0.5)
The maximum fraction of columns (i.e. features) that can be missing
before the sample is excluded from nearest neighbor imputation. It
means that such rows will not be considered a potential donor in
``fit()``, and in ``transform()`` their missing feature values will be
imputed to be the column mean for the entire dataset.
col_max_missing : float, optional (default = 0.8)
The maximum fraction of rows (or samples) that can be missing
for any feature beyond which an error is raised.
copy : boolean, optional (default = True)
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible. Note that, if metric is
"masked_euclidean" and copy=False then missing_values in the
input matrix X will be overwritten with zeros.
%package -n python3-missingpy
Summary: Missing Data Imputation for Python
Provides: python-missingpy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-missingpy
missing_values : integer or "NaN", optional (default = "NaN")
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For missing values encoded as
``np.nan``, use the string value "NaN".
n_neighbors : int, optional (default = 5)
Number of neighboring samples to use for imputation.
weights : str or callable, optional (default = "uniform")
Weight function used in prediction. Possible values:
- 'uniform' : uniform weights. All points in each neighborhood
are weighted equally.
- 'distance' : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.
- [callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.
metric : str or callable, optional (default = "masked_euclidean")
Distance metric for searching neighbors. Possible values:
- 'masked_euclidean'
- [callable] : a user-defined function which conforms to the
definition of _pairwise_callable(X, Y, metric, **kwds). In other
words, the function accepts two arrays, X and Y, and a
``missing_values`` keyword in **kwds and returns a scalar distance
value.
row_max_missing : float, optional (default = 0.5)
The maximum fraction of columns (i.e. features) that can be missing
before the sample is excluded from nearest neighbor imputation. It
means that such rows will not be considered a potential donor in
``fit()``, and in ``transform()`` their missing feature values will be
imputed to be the column mean for the entire dataset.
col_max_missing : float, optional (default = 0.8)
The maximum fraction of rows (or samples) that can be missing
for any feature beyond which an error is raised.
copy : boolean, optional (default = True)
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible. Note that, if metric is
"masked_euclidean" and copy=False then missing_values in the
input matrix X will be overwritten with zeros.
%package help
Summary: Development documents and examples for missingpy
Provides: python3-missingpy-doc
%description help
missing_values : integer or "NaN", optional (default = "NaN")
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For missing values encoded as
``np.nan``, use the string value "NaN".
n_neighbors : int, optional (default = 5)
Number of neighboring samples to use for imputation.
weights : str or callable, optional (default = "uniform")
Weight function used in prediction. Possible values:
- 'uniform' : uniform weights. All points in each neighborhood
are weighted equally.
- 'distance' : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.
- [callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.
metric : str or callable, optional (default = "masked_euclidean")
Distance metric for searching neighbors. Possible values:
- 'masked_euclidean'
- [callable] : a user-defined function which conforms to the
definition of _pairwise_callable(X, Y, metric, **kwds). In other
words, the function accepts two arrays, X and Y, and a
``missing_values`` keyword in **kwds and returns a scalar distance
value.
row_max_missing : float, optional (default = 0.5)
The maximum fraction of columns (i.e. features) that can be missing
before the sample is excluded from nearest neighbor imputation. It
means that such rows will not be considered a potential donor in
``fit()``, and in ``transform()`` their missing feature values will be
imputed to be the column mean for the entire dataset.
col_max_missing : float, optional (default = 0.8)
The maximum fraction of rows (or samples) that can be missing
for any feature beyond which an error is raised.
copy : boolean, optional (default = True)
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible. Note that, if metric is
"masked_euclidean" and copy=False then missing_values in the
input matrix X will be overwritten with zeros.
%prep
%autosetup -n missingpy-0.2.0
%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-missingpy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.0-1
- Package Spec generated
|
