automatic import of python-missingpy

3 files changed, 185 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/missingpy-0.2.0.tar.gz
diff --git a/python-missingpy.spec b/python-missingpy.spec
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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
diff --git a/sources b/sources
new file mode 100644
index 0000000..3beb798
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8ef4daf2e3626f80121308619cb05277  missingpy-0.2.0.tar.gz