automatic import of python-hsfs

3 files changed, 518 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..ba2bb59 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/hsfs-3.0.7.tar.gz
diff --git a/python-hsfs.spec b/python-hsfs.spec
new file mode 100644
index 0000000..782e9f3
--- /dev/null
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+%global _empty_manifest_terminate_build 0
+Name:		python-hsfs
+Version:	3.0.7
+Release:	1
+Summary:	HSFS: An environment independent client to interact with the Hopsworks Featurestore
+License:	Apache License 2.0
+URL:		https://github.com/logicalclocks/feature-store-api
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/72/92/812b5e0cf4d49cf741102fec18c0ac9dd4624601013ad0f75827fc5d7ee2/hsfs-3.0.7.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Hopsworks Feature Store
+
+<p align="center">
+  <a href="https://community.hopsworks.ai"><img
+    src="https://img.shields.io/discourse/users?label=Hopsworks%20Community&server=https%3A%2F%2Fcommunity.hopsworks.ai"
+    alt="Hopsworks Community"
+  /></a>
+    <a href="https://docs.hopsworks.ai"><img
+    src="https://img.shields.io/badge/docs-HSFS-orange"
+    alt="Hopsworks Feature Store Documentation"
+  /></a>
+  <a href="https://pypi.org/project/hsfs/"><img
+    src="https://img.shields.io/pypi/v/hsfs?color=blue"
+    alt="PyPiStatus"
+  /></a>
+  <a href="https://archiva.hops.works/#artifact/com.logicalclocks/hsfs"><img
+    src="https://img.shields.io/badge/java-HSFS-green"
+    alt="Scala/Java Artifacts"
+  /></a>
+  <a href="https://pepy.tech/project/hsfs/month"><img
+    src="https://pepy.tech/badge/hsfs/month"
+    alt="Downloads"
+  /></a>
+  <a href="https://github.com/psf/black"><img
+    src="https://img.shields.io/badge/code%20style-black-000000.svg"
+    alt="CodeStyle"
+  /></a>
+  <a><img
+    src="https://img.shields.io/pypi/l/hsfs?color=green"
+    alt="License"
+  /></a>
+</p>
+
+HSFS is the library to interact with the Hopsworks Feature Store. The library makes creating new features, feature groups and training datasets easy.
+
+The library is environment independent and can be used in two modes:
+
+- Spark mode: For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides bindings both for Python and JVM languages.
+
+- Python mode: For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker or KubeFlow.
+
+The library automatically configures itself based on the environment it is run.
+However, to connect from an external environment such as Databricks or AWS Sagemaker,
+additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.
+
+## Getting Started On Hopsworks
+
+Instantiate a connection and get the project feature store handler
+```python
+import hsfs
+
+connection = hsfs.connection()
+fs = connection.get_feature_store()
+```
+
+Create a new feature group
+```python
+fg = fs.create_feature_group("rain",
+                        version=1,
+                        description="Rain features",
+                        primary_key=['date', 'location_id'],
+                        online_enabled=True)
+
+fg.save(dataframe)
+```
+
+Upsert new data in to the feature group with `time_travel_format="HUDI"`".
+```python
+fg.insert(upsert_df)
+```
+
+Retrieve commit timeline metdata of the feature group with `time_travel_format="HUDI"`".
+```python
+fg.commit_details()
+```
+
+"Reading feature group as of specific point in time".
+```python
+fg = fs.get_feature_group("rain", 1)
+fg.read("2020-10-20 07:34:11").show()
+```
+
+Read updates  that occurred between specified points in time.
+```python
+fg = fs.get_feature_group("rain", 1)
+fg.read_changes("2020-10-20 07:31:38", "2020-10-20 07:34:11").show()
+```
+
+Join features together
+```python
+feature_join = rain_fg.select_all()
+                    .join(temperature_fg.select_all(), on=["date", "location_id"])
+                    .join(location_fg.select_all())
+feature_join.show(5)
+```
+
+join feature groups that correspond to specific point in time
+```python
+feature_join = rain_fg.select_all()
+                    .join(temperature_fg.select_all(), on=["date", "location_id"])
+                    .join(location_fg.select_all())
+                    .as_of("2020-10-31")
+feature_join.show(5)
+```
+
+join feature groups that correspond to different time
+```python
+rain_fg_q = rain_fg.select_all().as_of("2020-10-20 07:41:43")
+temperature_fg_q = temperature_fg.select_all().as_of("2020-10-20 07:32:33")
+location_fg_q = location_fg.select_all().as_of("2020-10-20 07:33:08")
+joined_features_q = rain_fg_q.join(temperature_fg_q).join(location_fg_q)
+```
+
+Use the query object to create a training dataset:
+```python
+td = fs.create_training_dataset("rain_dataset",
+                                version=1,
+                                data_format="tfrecords",
+                                description="A test training dataset saved in TfRecords format",
+                                splits={'train': 0.7, 'test': 0.2, 'validate': 0.1})
+
+td.save(feature_join)
+```
+
+A short introduction to the Scala API:
+```scala
+import com.logicalclocks.hsfs._
+val connection = HopsworksConnection.builder().build()
+val fs = connection.getFeatureStore();
+val attendances_features_fg = fs.getFeatureGroup("games_features", 1);
+attendances_features_fg.show(1)
+```
+
+You can find more examples on how to use the library in our [hops-examples](https://github.com/logicalclocks/hops-examples) repository.
+
+## Documentation
+
+Documentation is available at [Hopsworks Feature Store Documentation](https://docs.hopsworks.ai/).
+
+## Issues
+
+For general questions about the usage of Hopsworks and the Feature Store please open a topic on [Hopsworks Community](https://community.hopsworks.ai/).
+
+Please report any issue using [Github issue tracking](https://github.com/logicalclocks/feature-store-api/issues).
+
+
+## Contributing
+
+If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).
+
+%package -n python3-hsfs
+Summary:	HSFS: An environment independent client to interact with the Hopsworks Featurestore
+Provides:	python-hsfs
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-hsfs
+# Hopsworks Feature Store
+
+<p align="center">
+  <a href="https://community.hopsworks.ai"><img
+    src="https://img.shields.io/discourse/users?label=Hopsworks%20Community&server=https%3A%2F%2Fcommunity.hopsworks.ai"
+    alt="Hopsworks Community"
+  /></a>
+    <a href="https://docs.hopsworks.ai"><img
+    src="https://img.shields.io/badge/docs-HSFS-orange"
+    alt="Hopsworks Feature Store Documentation"
+  /></a>
+  <a href="https://pypi.org/project/hsfs/"><img
+    src="https://img.shields.io/pypi/v/hsfs?color=blue"
+    alt="PyPiStatus"
+  /></a>
+  <a href="https://archiva.hops.works/#artifact/com.logicalclocks/hsfs"><img
+    src="https://img.shields.io/badge/java-HSFS-green"
+    alt="Scala/Java Artifacts"
+  /></a>
+  <a href="https://pepy.tech/project/hsfs/month"><img
+    src="https://pepy.tech/badge/hsfs/month"
+    alt="Downloads"
+  /></a>
+  <a href="https://github.com/psf/black"><img
+    src="https://img.shields.io/badge/code%20style-black-000000.svg"
+    alt="CodeStyle"
+  /></a>
+  <a><img
+    src="https://img.shields.io/pypi/l/hsfs?color=green"
+    alt="License"
+  /></a>
+</p>
+
+HSFS is the library to interact with the Hopsworks Feature Store. The library makes creating new features, feature groups and training datasets easy.
+
+The library is environment independent and can be used in two modes:
+
+- Spark mode: For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides bindings both for Python and JVM languages.
+
+- Python mode: For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker or KubeFlow.
+
+The library automatically configures itself based on the environment it is run.
+However, to connect from an external environment such as Databricks or AWS Sagemaker,
+additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.
+
+## Getting Started On Hopsworks
+
+Instantiate a connection and get the project feature store handler
+```python
+import hsfs
+
+connection = hsfs.connection()
+fs = connection.get_feature_store()
+```
+
+Create a new feature group
+```python
+fg = fs.create_feature_group("rain",
+                        version=1,
+                        description="Rain features",
+                        primary_key=['date', 'location_id'],
+                        online_enabled=True)
+
+fg.save(dataframe)
+```
+
+Upsert new data in to the feature group with `time_travel_format="HUDI"`".
+```python
+fg.insert(upsert_df)
+```
+
+Retrieve commit timeline metdata of the feature group with `time_travel_format="HUDI"`".
+```python
+fg.commit_details()
+```
+
+"Reading feature group as of specific point in time".
+```python
+fg = fs.get_feature_group("rain", 1)
+fg.read("2020-10-20 07:34:11").show()
+```
+
+Read updates  that occurred between specified points in time.
+```python
+fg = fs.get_feature_group("rain", 1)
+fg.read_changes("2020-10-20 07:31:38", "2020-10-20 07:34:11").show()
+```
+
+Join features together
+```python
+feature_join = rain_fg.select_all()
+                    .join(temperature_fg.select_all(), on=["date", "location_id"])
+                    .join(location_fg.select_all())
+feature_join.show(5)
+```
+
+join feature groups that correspond to specific point in time
+```python
+feature_join = rain_fg.select_all()
+                    .join(temperature_fg.select_all(), on=["date", "location_id"])
+                    .join(location_fg.select_all())
+                    .as_of("2020-10-31")
+feature_join.show(5)
+```
+
+join feature groups that correspond to different time
+```python
+rain_fg_q = rain_fg.select_all().as_of("2020-10-20 07:41:43")
+temperature_fg_q = temperature_fg.select_all().as_of("2020-10-20 07:32:33")
+location_fg_q = location_fg.select_all().as_of("2020-10-20 07:33:08")
+joined_features_q = rain_fg_q.join(temperature_fg_q).join(location_fg_q)
+```
+
+Use the query object to create a training dataset:
+```python
+td = fs.create_training_dataset("rain_dataset",
+                                version=1,
+                                data_format="tfrecords",
+                                description="A test training dataset saved in TfRecords format",
+                                splits={'train': 0.7, 'test': 0.2, 'validate': 0.1})
+
+td.save(feature_join)
+```
+
+A short introduction to the Scala API:
+```scala
+import com.logicalclocks.hsfs._
+val connection = HopsworksConnection.builder().build()
+val fs = connection.getFeatureStore();
+val attendances_features_fg = fs.getFeatureGroup("games_features", 1);
+attendances_features_fg.show(1)
+```
+
+You can find more examples on how to use the library in our [hops-examples](https://github.com/logicalclocks/hops-examples) repository.
+
+## Documentation
+
+Documentation is available at [Hopsworks Feature Store Documentation](https://docs.hopsworks.ai/).
+
+## Issues
+
+For general questions about the usage of Hopsworks and the Feature Store please open a topic on [Hopsworks Community](https://community.hopsworks.ai/).
+
+Please report any issue using [Github issue tracking](https://github.com/logicalclocks/feature-store-api/issues).
+
+
+## Contributing
+
+If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).
+
+%package help
+Summary:	Development documents and examples for hsfs
+Provides:	python3-hsfs-doc
+%description help
+# Hopsworks Feature Store
+
+<p align="center">
+  <a href="https://community.hopsworks.ai"><img
+    src="https://img.shields.io/discourse/users?label=Hopsworks%20Community&server=https%3A%2F%2Fcommunity.hopsworks.ai"
+    alt="Hopsworks Community"
+  /></a>
+    <a href="https://docs.hopsworks.ai"><img
+    src="https://img.shields.io/badge/docs-HSFS-orange"
+    alt="Hopsworks Feature Store Documentation"
+  /></a>
+  <a href="https://pypi.org/project/hsfs/"><img
+    src="https://img.shields.io/pypi/v/hsfs?color=blue"
+    alt="PyPiStatus"
+  /></a>
+  <a href="https://archiva.hops.works/#artifact/com.logicalclocks/hsfs"><img
+    src="https://img.shields.io/badge/java-HSFS-green"
+    alt="Scala/Java Artifacts"
+  /></a>
+  <a href="https://pepy.tech/project/hsfs/month"><img
+    src="https://pepy.tech/badge/hsfs/month"
+    alt="Downloads"
+  /></a>
+  <a href="https://github.com/psf/black"><img
+    src="https://img.shields.io/badge/code%20style-black-000000.svg"
+    alt="CodeStyle"
+  /></a>
+  <a><img
+    src="https://img.shields.io/pypi/l/hsfs?color=green"
+    alt="License"
+  /></a>
+</p>
+
+HSFS is the library to interact with the Hopsworks Feature Store. The library makes creating new features, feature groups and training datasets easy.
+
+The library is environment independent and can be used in two modes:
+
+- Spark mode: For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides bindings both for Python and JVM languages.
+
+- Python mode: For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker or KubeFlow.
+
+The library automatically configures itself based on the environment it is run.
+However, to connect from an external environment such as Databricks or AWS Sagemaker,
+additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.
+
+## Getting Started On Hopsworks
+
+Instantiate a connection and get the project feature store handler
+```python
+import hsfs
+
+connection = hsfs.connection()
+fs = connection.get_feature_store()
+```
+
+Create a new feature group
+```python
+fg = fs.create_feature_group("rain",
+                        version=1,
+                        description="Rain features",
+                        primary_key=['date', 'location_id'],
+                        online_enabled=True)
+
+fg.save(dataframe)
+```
+
+Upsert new data in to the feature group with `time_travel_format="HUDI"`".
+```python
+fg.insert(upsert_df)
+```
+
+Retrieve commit timeline metdata of the feature group with `time_travel_format="HUDI"`".
+```python
+fg.commit_details()
+```
+
+"Reading feature group as of specific point in time".
+```python
+fg = fs.get_feature_group("rain", 1)
+fg.read("2020-10-20 07:34:11").show()
+```
+
+Read updates  that occurred between specified points in time.
+```python
+fg = fs.get_feature_group("rain", 1)
+fg.read_changes("2020-10-20 07:31:38", "2020-10-20 07:34:11").show()
+```
+
+Join features together
+```python
+feature_join = rain_fg.select_all()
+                    .join(temperature_fg.select_all(), on=["date", "location_id"])
+                    .join(location_fg.select_all())
+feature_join.show(5)
+```
+
+join feature groups that correspond to specific point in time
+```python
+feature_join = rain_fg.select_all()
+                    .join(temperature_fg.select_all(), on=["date", "location_id"])
+                    .join(location_fg.select_all())
+                    .as_of("2020-10-31")
+feature_join.show(5)
+```
+
+join feature groups that correspond to different time
+```python
+rain_fg_q = rain_fg.select_all().as_of("2020-10-20 07:41:43")
+temperature_fg_q = temperature_fg.select_all().as_of("2020-10-20 07:32:33")
+location_fg_q = location_fg.select_all().as_of("2020-10-20 07:33:08")
+joined_features_q = rain_fg_q.join(temperature_fg_q).join(location_fg_q)
+```
+
+Use the query object to create a training dataset:
+```python
+td = fs.create_training_dataset("rain_dataset",
+                                version=1,
+                                data_format="tfrecords",
+                                description="A test training dataset saved in TfRecords format",
+                                splits={'train': 0.7, 'test': 0.2, 'validate': 0.1})
+
+td.save(feature_join)
+```
+
+A short introduction to the Scala API:
+```scala
+import com.logicalclocks.hsfs._
+val connection = HopsworksConnection.builder().build()
+val fs = connection.getFeatureStore();
+val attendances_features_fg = fs.getFeatureGroup("games_features", 1);
+attendances_features_fg.show(1)
+```
+
+You can find more examples on how to use the library in our [hops-examples](https://github.com/logicalclocks/hops-examples) repository.
+
+## Documentation
+
+Documentation is available at [Hopsworks Feature Store Documentation](https://docs.hopsworks.ai/).
+
+## Issues
+
+For general questions about the usage of Hopsworks and the Feature Store please open a topic on [Hopsworks Community](https://community.hopsworks.ai/).
+
+Please report any issue using [Github issue tracking](https://github.com/logicalclocks/feature-store-api/issues).
+
+
+## Contributing
+
+If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).
+
+%prep
+%autosetup -n hsfs-3.0.7
+
+%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-hsfs -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..375df85
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+532c133c083a6c12162b1948383d0ab6  hsfs-3.0.7.tar.gz