automatic import of python-instancelibopeneuler20.03

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+/instancelib-0.4.9.1.tar.gz
diff --git a/python-instancelib.spec b/python-instancelib.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-instancelib
+Version:	0.4.9.1
+Release:	1
+Summary:	A typed dataset abstraction toolkit for machine learning projects
+License:	GNU LGPL v3
+URL:		https://pypi.org/project/instancelib/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6e/3d/7ee9dccc7fa94386539a2f528f8f2916f5061dbf82cc41a18497345578f7/instancelib-0.4.9.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-h5py
+Requires:	python3-scikit-learn
+Requires:	python3-openpyxl
+Requires:	python3-xlrd
+Requires:	python3-tqdm
+Requires:	python3-more-itertools
+Requires:	python3-typing-extensions
+Requires:	python3-gensim
+Requires:	python3-tables
+
+%description
+`instancelib` provides a **generic architecture** for datasets. 
+© Michiel Bron, 2021
+## Quick tour
+**Load dataset**: Load the dataset in an environment
+```python
+import instancelib as il
+text_env = il.read_excel_dataset("./datasets/testdataset.xlsx",
+                                  data_cols=["fulltext"],
+                                  label_cols=["label"])
+ds = text_env.dataset # A `dict-like` interface for instances
+labels = text_env.labels # An object that stores all labels
+labelset = labels.labelset # All labels that can be given to instances
+ins = ds[20] # Get instance with identifier key  `20`
+ins_data = ins.data # Get the raw data for instance 20
+ins_vector = ins.vector # Get the vector representation for 20 if any
+ins_labels = labels.get_labels(ins)
+``` 
+**Dataset manipulation**: Divide the dataset in a train and test set
+```python
+train, test = text_env.train_test_split(ds, train_size=0.70)
+print(20 in train) # May be true or false, because of random sampling
+```
+**Train a model**:
+```python
+from sklearn.pipeline import Pipeline 
+from sklearn.naive_bayes import MultinomialNB 
+from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
+pipeline = Pipeline([
+     ('vect', CountVectorizer()),
+     ('tfidf', TfidfTransformer()),
+     ('clf', MultinomialNB()),
+     ])
+model = il.SkLearnDataClassifier.build(pipeline, text_env)
+model.fit_provider(train, labels)
+predictions = model.predict(test)
+```
+## Installation
+See [installation.md](docs/installation.md) for an extended installation guide.
+| Method | Instructions |
+|--------|--------------|
+| `pip` | Install from [PyPI](https://pypi.org/project/instancelib/) via `pip install instancelib`. |
+| Local | Clone this repository and install via `pip install -e .` or locally run `python setup.py install`.
+## Documentation
+Full documentation of the latest version is provided at [https://instancelib.readthedocs.org](https://instancelib.readthedocs.org).
+## Example usage
+See [usage.py](usage.py) to see an example of how the package can be used.
+## Releases
+`instancelib` is officially released through [PyPI](https://pypi.org/project/instancelib/).
+See [CHANGELOG.md](CHANGELOG.md) for a full overview of the changes for each version.
+## Citation
+```bibtex
+@misc{instancelib,
+  title = {Python package instancelib},
+  author = {Michiel Bron},
+  howpublished = {\url{https://github.com/mpbron/instancelib}},
+  year = {2021}
+}
+```
+## Library usage
+This library is used in the following projects:
+- [python-allib](https://github.com/mpbron/allib). A typed Active Learning framework for Python for both Classification and Technology-Assisted Review systems.
+- [text_explainability](https://marcelrobeer.github.io/text_explainability/). A generic explainability architecture for explaining text machine learning models
+- [text_sensitivity](https://marcelrobeer.github.io/text_sensitivity/). Sensitivity testing (fairness & robustness) for text machine learning models.
+## Maintenance
+### Contributors
+- [Michiel Bron](https://www.uu.nl/staff/MPBron) (`@mpbron`)
+### Todo
+Tasks yet to be done:
+* Implement support for ONNX models
+* Implement support for Python DataLoaders
+* Make the external dataset interface more user friendly
+* Redesign LabelProvider to support more attribute levels
+* CI/CD tests
+
+%package -n python3-instancelib
+Summary:	A typed dataset abstraction toolkit for machine learning projects
+Provides:	python-instancelib
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-instancelib
+`instancelib` provides a **generic architecture** for datasets. 
+© Michiel Bron, 2021
+## Quick tour
+**Load dataset**: Load the dataset in an environment
+```python
+import instancelib as il
+text_env = il.read_excel_dataset("./datasets/testdataset.xlsx",
+                                  data_cols=["fulltext"],
+                                  label_cols=["label"])
+ds = text_env.dataset # A `dict-like` interface for instances
+labels = text_env.labels # An object that stores all labels
+labelset = labels.labelset # All labels that can be given to instances
+ins = ds[20] # Get instance with identifier key  `20`
+ins_data = ins.data # Get the raw data for instance 20
+ins_vector = ins.vector # Get the vector representation for 20 if any
+ins_labels = labels.get_labels(ins)
+``` 
+**Dataset manipulation**: Divide the dataset in a train and test set
+```python
+train, test = text_env.train_test_split(ds, train_size=0.70)
+print(20 in train) # May be true or false, because of random sampling
+```
+**Train a model**:
+```python
+from sklearn.pipeline import Pipeline 
+from sklearn.naive_bayes import MultinomialNB 
+from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
+pipeline = Pipeline([
+     ('vect', CountVectorizer()),
+     ('tfidf', TfidfTransformer()),
+     ('clf', MultinomialNB()),
+     ])
+model = il.SkLearnDataClassifier.build(pipeline, text_env)
+model.fit_provider(train, labels)
+predictions = model.predict(test)
+```
+## Installation
+See [installation.md](docs/installation.md) for an extended installation guide.
+| Method | Instructions |
+|--------|--------------|
+| `pip` | Install from [PyPI](https://pypi.org/project/instancelib/) via `pip install instancelib`. |
+| Local | Clone this repository and install via `pip install -e .` or locally run `python setup.py install`.
+## Documentation
+Full documentation of the latest version is provided at [https://instancelib.readthedocs.org](https://instancelib.readthedocs.org).
+## Example usage
+See [usage.py](usage.py) to see an example of how the package can be used.
+## Releases
+`instancelib` is officially released through [PyPI](https://pypi.org/project/instancelib/).
+See [CHANGELOG.md](CHANGELOG.md) for a full overview of the changes for each version.
+## Citation
+```bibtex
+@misc{instancelib,
+  title = {Python package instancelib},
+  author = {Michiel Bron},
+  howpublished = {\url{https://github.com/mpbron/instancelib}},
+  year = {2021}
+}
+```
+## Library usage
+This library is used in the following projects:
+- [python-allib](https://github.com/mpbron/allib). A typed Active Learning framework for Python for both Classification and Technology-Assisted Review systems.
+- [text_explainability](https://marcelrobeer.github.io/text_explainability/). A generic explainability architecture for explaining text machine learning models
+- [text_sensitivity](https://marcelrobeer.github.io/text_sensitivity/). Sensitivity testing (fairness & robustness) for text machine learning models.
+## Maintenance
+### Contributors
+- [Michiel Bron](https://www.uu.nl/staff/MPBron) (`@mpbron`)
+### Todo
+Tasks yet to be done:
+* Implement support for ONNX models
+* Implement support for Python DataLoaders
+* Make the external dataset interface more user friendly
+* Redesign LabelProvider to support more attribute levels
+* CI/CD tests
+
+%package help
+Summary:	Development documents and examples for instancelib
+Provides:	python3-instancelib-doc
+%description help
+`instancelib` provides a **generic architecture** for datasets. 
+© Michiel Bron, 2021
+## Quick tour
+**Load dataset**: Load the dataset in an environment
+```python
+import instancelib as il
+text_env = il.read_excel_dataset("./datasets/testdataset.xlsx",
+                                  data_cols=["fulltext"],
+                                  label_cols=["label"])
+ds = text_env.dataset # A `dict-like` interface for instances
+labels = text_env.labels # An object that stores all labels
+labelset = labels.labelset # All labels that can be given to instances
+ins = ds[20] # Get instance with identifier key  `20`
+ins_data = ins.data # Get the raw data for instance 20
+ins_vector = ins.vector # Get the vector representation for 20 if any
+ins_labels = labels.get_labels(ins)
+``` 
+**Dataset manipulation**: Divide the dataset in a train and test set
+```python
+train, test = text_env.train_test_split(ds, train_size=0.70)
+print(20 in train) # May be true or false, because of random sampling
+```
+**Train a model**:
+```python
+from sklearn.pipeline import Pipeline 
+from sklearn.naive_bayes import MultinomialNB 
+from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
+pipeline = Pipeline([
+     ('vect', CountVectorizer()),
+     ('tfidf', TfidfTransformer()),
+     ('clf', MultinomialNB()),
+     ])
+model = il.SkLearnDataClassifier.build(pipeline, text_env)
+model.fit_provider(train, labels)
+predictions = model.predict(test)
+```
+## Installation
+See [installation.md](docs/installation.md) for an extended installation guide.
+| Method | Instructions |
+|--------|--------------|
+| `pip` | Install from [PyPI](https://pypi.org/project/instancelib/) via `pip install instancelib`. |
+| Local | Clone this repository and install via `pip install -e .` or locally run `python setup.py install`.
+## Documentation
+Full documentation of the latest version is provided at [https://instancelib.readthedocs.org](https://instancelib.readthedocs.org).
+## Example usage
+See [usage.py](usage.py) to see an example of how the package can be used.
+## Releases
+`instancelib` is officially released through [PyPI](https://pypi.org/project/instancelib/).
+See [CHANGELOG.md](CHANGELOG.md) for a full overview of the changes for each version.
+## Citation
+```bibtex
+@misc{instancelib,
+  title = {Python package instancelib},
+  author = {Michiel Bron},
+  howpublished = {\url{https://github.com/mpbron/instancelib}},
+  year = {2021}
+}
+```
+## Library usage
+This library is used in the following projects:
+- [python-allib](https://github.com/mpbron/allib). A typed Active Learning framework for Python for both Classification and Technology-Assisted Review systems.
+- [text_explainability](https://marcelrobeer.github.io/text_explainability/). A generic explainability architecture for explaining text machine learning models
+- [text_sensitivity](https://marcelrobeer.github.io/text_sensitivity/). Sensitivity testing (fairness & robustness) for text machine learning models.
+## Maintenance
+### Contributors
+- [Michiel Bron](https://www.uu.nl/staff/MPBron) (`@mpbron`)
+### Todo
+Tasks yet to be done:
+* Implement support for ONNX models
+* Implement support for Python DataLoaders
+* Make the external dataset interface more user friendly
+* Redesign LabelProvider to support more attribute levels
+* CI/CD tests
+
+%prep
+%autosetup -n instancelib-0.4.9.1
+
+%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-instancelib -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.9.1-1
+- Package Spec generated
diff --git a/sources b/sources
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+4fd63b3f8706dd366caf713ee0142ee4  instancelib-0.4.9.1.tar.gz