automatic import of python-pypmml-sparkopeneuler20.03

3 files changed, 203 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..c1b6b1e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/pypmml-spark-0.9.16.tar.gz
diff --git a/python-pypmml-spark.spec b/python-pypmml-spark.spec
new file mode 100644
index 0000000..4621a4b
--- /dev/null
+++ b/python-pypmml-spark.spec
@@ -0,0 +1,201 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-pypmml-spark
+Version:	0.9.16
+Release:	1
+Summary:	Python PMML scoring library for PySpark as SparkML Transformer
+License:	Apache License 2.0
+URL:		https://github.com/autodeployai/pypmml-spark
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/93/18/a32277826a25ed1f8acc429d80e034d00c763066919118179433c738ea68/pypmml-spark-0.9.16.tar.gz
+BuildArch:	noarch
+
+
+%description
+[pypmml-spark](https://github.com/autodeployai/pypmml-spark/tree/master)              | PySpark >= 3.0.0
+[pypmml-spark2](https://github.com/autodeployai/pypmml-spark/tree/spark-2.x)          | PySpark >= 2.4.0, < 3.0.0
+## Installation
+```bash
+pip install pypmml-spark
+```
+Or install the latest version from github:
+```bash
+pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git
+```
+After that, you need to do more to use it in Spark that must know those jars in the package `pypmml_spark.jars`. There are several ways to do that:
+1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`:
+    ```bash
+    link_pmml4s_jars_into_spark.py
+    ```
+2. Use those config options to specify dependent jars properly. e.g. `--jars`, or `spark.executor.extraClassPath` and `spark.executor.extraClassPath`. See [Spark](http://spark.apache.org/docs/latest/configuration.html) for details about those parameters.
+## Usage
+1. Load model from various sources, e.g. filename, string, or array of bytes.
+    ```python
+    from pypmml_spark import ScoreModel
+    # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
+    model = ScoreModel.fromFile('single_iris_dectree.xml')
+    ```
+2. Call `transform(dataset)` to run a batch score against an input dataset.
+    ```python
+    # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv
+    df = spark.read.csv('Iris.csv', header='true')
+    score_df = model.transform(df)
+    ```
+## Use PMML in Scala or Java
+See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML.
+## Use PMML in Python
+See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S.
+## Use PMML in Spark
+See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer.
+## Deploy PMML as REST API
+See the [AI-Serving](https://github.com/autodeployai/ai-serving) project. _AI-Serving_ is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints.
+## Deploy and Manage AI/ML models at scale
+See the [DaaS](https://www.autodeploy.ai/) system that deploys AI/ML models in production at scale on Kubernetes.
+## Support
+If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository.
+Feedback and contributions to the project, no matter what kind, are always very welcome. 
+## License
+_PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0).
+
+%package -n python3-pypmml-spark
+Summary:	Python PMML scoring library for PySpark as SparkML Transformer
+Provides:	python-pypmml-spark
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pypmml-spark
+[pypmml-spark](https://github.com/autodeployai/pypmml-spark/tree/master)              | PySpark >= 3.0.0
+[pypmml-spark2](https://github.com/autodeployai/pypmml-spark/tree/spark-2.x)          | PySpark >= 2.4.0, < 3.0.0
+## Installation
+```bash
+pip install pypmml-spark
+```
+Or install the latest version from github:
+```bash
+pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git
+```
+After that, you need to do more to use it in Spark that must know those jars in the package `pypmml_spark.jars`. There are several ways to do that:
+1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`:
+    ```bash
+    link_pmml4s_jars_into_spark.py
+    ```
+2. Use those config options to specify dependent jars properly. e.g. `--jars`, or `spark.executor.extraClassPath` and `spark.executor.extraClassPath`. See [Spark](http://spark.apache.org/docs/latest/configuration.html) for details about those parameters.
+## Usage
+1. Load model from various sources, e.g. filename, string, or array of bytes.
+    ```python
+    from pypmml_spark import ScoreModel
+    # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
+    model = ScoreModel.fromFile('single_iris_dectree.xml')
+    ```
+2. Call `transform(dataset)` to run a batch score against an input dataset.
+    ```python
+    # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv
+    df = spark.read.csv('Iris.csv', header='true')
+    score_df = model.transform(df)
+    ```
+## Use PMML in Scala or Java
+See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML.
+## Use PMML in Python
+See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S.
+## Use PMML in Spark
+See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer.
+## Deploy PMML as REST API
+See the [AI-Serving](https://github.com/autodeployai/ai-serving) project. _AI-Serving_ is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints.
+## Deploy and Manage AI/ML models at scale
+See the [DaaS](https://www.autodeploy.ai/) system that deploys AI/ML models in production at scale on Kubernetes.
+## Support
+If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository.
+Feedback and contributions to the project, no matter what kind, are always very welcome. 
+## License
+_PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0).
+
+%package help
+Summary:	Development documents and examples for pypmml-spark
+Provides:	python3-pypmml-spark-doc
+%description help
+[pypmml-spark](https://github.com/autodeployai/pypmml-spark/tree/master)              | PySpark >= 3.0.0
+[pypmml-spark2](https://github.com/autodeployai/pypmml-spark/tree/spark-2.x)          | PySpark >= 2.4.0, < 3.0.0
+## Installation
+```bash
+pip install pypmml-spark
+```
+Or install the latest version from github:
+```bash
+pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git
+```
+After that, you need to do more to use it in Spark that must know those jars in the package `pypmml_spark.jars`. There are several ways to do that:
+1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`:
+    ```bash
+    link_pmml4s_jars_into_spark.py
+    ```
+2. Use those config options to specify dependent jars properly. e.g. `--jars`, or `spark.executor.extraClassPath` and `spark.executor.extraClassPath`. See [Spark](http://spark.apache.org/docs/latest/configuration.html) for details about those parameters.
+## Usage
+1. Load model from various sources, e.g. filename, string, or array of bytes.
+    ```python
+    from pypmml_spark import ScoreModel
+    # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
+    model = ScoreModel.fromFile('single_iris_dectree.xml')
+    ```
+2. Call `transform(dataset)` to run a batch score against an input dataset.
+    ```python
+    # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv
+    df = spark.read.csv('Iris.csv', header='true')
+    score_df = model.transform(df)
+    ```
+## Use PMML in Scala or Java
+See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML.
+## Use PMML in Python
+See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S.
+## Use PMML in Spark
+See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer.
+## Deploy PMML as REST API
+See the [AI-Serving](https://github.com/autodeployai/ai-serving) project. _AI-Serving_ is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints.
+## Deploy and Manage AI/ML models at scale
+See the [DaaS](https://www.autodeploy.ai/) system that deploys AI/ML models in production at scale on Kubernetes.
+## Support
+If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository.
+Feedback and contributions to the project, no matter what kind, are always very welcome. 
+## License
+_PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0).
+
+%prep
+%autosetup -n pypmml-spark-0.9.16
+
+%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-pypmml-spark -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.16-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..4602e8d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+09b67f27700adec0ee26ed6afb7c7afa  pypmml-spark-0.9.16.tar.gz