%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 * Fri Apr 21 2023 Python_Bot - 0.2.0-1 - Package Spec generated