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+/aix360-0.2.1.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-aix360
+Version:	0.2.1
+Release:	1
+Summary:	IBM AI Explainability 360
+License:	Apache License 2.0
+URL:		https://github.com/Trusted-AI/AIX360
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e2/e6/a3dd79a986e3957cbafe5db1dae2d0daf8397d764a63e1767452e55c732a/aix360-0.2.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-joblib
+Requires:	python3-scikit-learn
+Requires:	python3-torch
+Requires:	python3-torchvision
+Requires:	python3-cvxpy
+Requires:	python3-cvxopt
+Requires:	python3-Image
+Requires:	python3-tensorflow
+Requires:	python3-keras
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-scipy
+Requires:	python3-xport
+Requires:	python3-scikit-image
+Requires:	python3-requests
+Requires:	python3-xgboost
+Requires:	python3-bleach
+Requires:	python3-docutils
+Requires:	python3-Pygments
+Requires:	python3-qpsolvers
+Requires:	python3-lime
+Requires:	python3-shap
+
+%description
+# AI Explainability 360 (v0.2.0)
+
+[![Build Status](https://travis-ci.com/Trusted-AI/AIX360.svg?branch=master)](https://travis-ci.com/Trusted-AI/AIX360)
+[![Documentation Status](https://readthedocs.org/projects/aix360/badge/?version=latest)](https://aix360.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/aix360.svg)](https://badge.fury.io/py/aix360)
+
+The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.           
+
+The [AI Explainability 360 interactive experience](http://aix360.mybluemix.net/data) provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The [tutorials and example notebooks](./examples) offer a deeper, data scientist-oriented introduction. The complete API is also available. 
+
+There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some [guidance material](http://aix360.mybluemix.net/resources#guidance) and a [chart](./aix360/algorithms/README.md) that can be consulted. 
+
+We have developed the package with extensibility in mind. This library is still in development. We encourage the contribution of your explainability algorithms and metrics. To get started as a contributor, please join the [AI Explainability 360 Community on Slack](https://aix360.slack.com) by requesting an invitation [here](https://join.slack.com/t/aix360/shared_invite/enQtNzEyOTAwOTk1NzY2LTM1ZTMwM2M4OWQzNjhmNGRiZjg3MmJiYTAzNDU1MTRiYTIyMjFhZTI4ZDUwM2M1MGYyODkwNzQ2OWQzMThlN2Q). Please review the instructions to contribute code [here](CONTRIBUTING.md).
+
+## Supported explainability algorithms
+
+### Data explanation
+
+- ProtoDash ([Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212))
+- Disentangled Inferred Prior VAE ([Kumar et al., 2018](https://openreview.net/forum?id=H1kG7GZAW))
+
+### Local post-hoc explanation 
+
+- ProtoDash ([Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212))
+- Contrastive Explanations Method ([Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))
+- Contrastive Explanations Method with Monotonic Attribute Functions ([Luss et al., 2019](https://arxiv.org/abs/1905.12698))
+- LIME ([Ribeiro et al. 2016](https://arxiv.org/abs/1602.04938),  [Github](https://github.com/marcotcr/lime))
+- SHAP ([Lundberg, et al. 2017](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions),  [Github](https://github.com/slundberg/shap))
+
+### Local direct explanation
+
+- Teaching AI to Explain its Decisions ([Hind et al., 2019](https://doi.org/10.1145/3306618.3314273)) 
+
+### Global direct explanation
+
+- Boolean Decision Rules via Column Generation (Light Edition) ([Dash et al., 2018](https://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation))
+- Generalized Linear Rule Models ([Wei et al., 2019](http://proceedings.mlr.press/v97/wei19a.html))
+
+### Global post-hoc explanation 
+
+- ProfWeight ([Dhurandhar et al., 2018](https://papers.nips.cc/paper/8231-improving-simple-models-with-confidence-profiles))
+
+
+## Supported explainability metrics
+- Faithfulness ([Alvarez-Melis and Jaakkola, 2018](https://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks))
+- Monotonicity ([Luss et al., 2019](https://arxiv.org/abs/1905.12698))
+
+## Setup
+
+Supported Configurations:
+
+| OS      | Python version |
+| ------- | -------------- |
+| macOS   | 3.6  |
+| Ubuntu  | 3.6  |
+| Windows | 3.6  |
+
+
+### (Optional) Create a virtual environment
+
+AI Explainability 360 requires specific versions of many Python packages which may conflict
+with other projects on your system. A virtual environment manager is strongly
+recommended to ensure dependencies may be installed safely. If you have trouble installing the toolkit, try this first.
+
+#### Conda
+
+Conda is recommended for all configurations though Virtualenv is generally
+interchangeable for our purposes. Miniconda is sufficient (see [the difference between Anaconda and
+Miniconda](https://conda.io/docs/user-guide/install/download.html#anaconda-or-miniconda)
+if you are curious) and can be installed from
+[here](https://conda.io/miniconda.html) if you do not already have it.
+
+Then, to create a new Python 3.6 environment, run:
+
+```bash
+conda create --name aix360 python=3.6
+conda activate aix360
+```
+
+The shell should now look like `(aix360) $`. To deactivate the environment, run:
+
+```bash
+(aix360)$ conda deactivate
+```
+
+The prompt will return back to `$ ` or `(base)$`.
+
+Note: Older versions of conda may use `source activate aix360` and `source
+deactivate` (`activate aix360` and `deactivate` on Windows).
+
+
+### Installation
+
+Clone the latest version of this repository:
+
+```bash
+(aix360)$ git clone https://github.com/Trusted-AI/AIX360
+```
+
+If you'd like to run the examples and tutorial notebooks, download the datasets now and place them in
+their respective folders as described in
+[aix360/data/README.md](aix360/data/README.md).
+
+Then, navigate to the root directory of the project which contains `setup.py` file and run:
+
+```bash
+(aix360)$ pip install -e .
+```
+
+## Using AI Explainability 360
+
+The `examples` directory contains a diverse collection of jupyter notebooks
+that use AI Explainability 360 in various ways. Both examples and tutorial notebooks illustrate
+working code using the toolkit. Tutorials provide additional discussion that walks
+the user through the various steps of the notebook. See the details about
+tutorials and examples [here](examples/README.md). 
+
+## Citing AI Explainability 360
+
+A technical description of AI Explainability 360 is available in this
+[paper](https://arxiv.org/abs/1909.03012). Below is the bibtex entry for this
+paper.
+
+```
+@misc{aix360-sept-2019,
+title = "One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques",
+author = {Vijay Arya and Rachel K. E. Bellamy and Pin-Yu Chen and Amit Dhurandhar and Michael Hind
+and Samuel C. Hoffman and Stephanie Houde and Q. Vera Liao and Ronny Luss and Aleksandra Mojsilovi\'c
+and Sami Mourad and Pablo Pedemonte and Ramya Raghavendra and John Richards and Prasanna Sattigeri
+and Karthikeyan Shanmugam and Moninder Singh and Kush R. Varshney and Dennis Wei and Yunfeng Zhang},
+month = sept,
+year = {2019},
+url = {https://arxiv.org/abs/1909.03012}
+}
+```
+
+## AIX360 Videos
+
+* Introductory [video](https://www.youtube.com/watch?v=Yn4yduyoQh4) to AI
+  Explainability 360 by Vijay Arya and Amit Dhurandhar, September 5, 2019 (35 mins)
+
+## Acknowledgements
+
+AIX360 is built with the help of several open source packages. All of these are listed in setup.py and some of these include: 
+* Tensorflow https://www.tensorflow.org/about/bib
+* Pytorch https://github.com/pytorch/pytorch
+* scikit-learn https://scikit-learn.org/stable/about.html
+
+## License Information
+
+Please view both the [LICENSE](https://github.com/vijay-arya/AIX360/blob/master/LICENSE) file and the folder [supplementary license](https://github.com/vijay-arya/AIX360/tree/master/supplementary%20license) present in the root directory for license information. 
+
+
+
+
+
+%package -n python3-aix360
+Summary:	IBM AI Explainability 360
+Provides:	python-aix360
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-aix360
+# AI Explainability 360 (v0.2.0)
+
+[![Build Status](https://travis-ci.com/Trusted-AI/AIX360.svg?branch=master)](https://travis-ci.com/Trusted-AI/AIX360)
+[![Documentation Status](https://readthedocs.org/projects/aix360/badge/?version=latest)](https://aix360.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/aix360.svg)](https://badge.fury.io/py/aix360)
+
+The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.           
+
+The [AI Explainability 360 interactive experience](http://aix360.mybluemix.net/data) provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The [tutorials and example notebooks](./examples) offer a deeper, data scientist-oriented introduction. The complete API is also available. 
+
+There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some [guidance material](http://aix360.mybluemix.net/resources#guidance) and a [chart](./aix360/algorithms/README.md) that can be consulted. 
+
+We have developed the package with extensibility in mind. This library is still in development. We encourage the contribution of your explainability algorithms and metrics. To get started as a contributor, please join the [AI Explainability 360 Community on Slack](https://aix360.slack.com) by requesting an invitation [here](https://join.slack.com/t/aix360/shared_invite/enQtNzEyOTAwOTk1NzY2LTM1ZTMwM2M4OWQzNjhmNGRiZjg3MmJiYTAzNDU1MTRiYTIyMjFhZTI4ZDUwM2M1MGYyODkwNzQ2OWQzMThlN2Q). Please review the instructions to contribute code [here](CONTRIBUTING.md).
+
+## Supported explainability algorithms
+
+### Data explanation
+
+- ProtoDash ([Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212))
+- Disentangled Inferred Prior VAE ([Kumar et al., 2018](https://openreview.net/forum?id=H1kG7GZAW))
+
+### Local post-hoc explanation 
+
+- ProtoDash ([Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212))
+- Contrastive Explanations Method ([Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))
+- Contrastive Explanations Method with Monotonic Attribute Functions ([Luss et al., 2019](https://arxiv.org/abs/1905.12698))
+- LIME ([Ribeiro et al. 2016](https://arxiv.org/abs/1602.04938),  [Github](https://github.com/marcotcr/lime))
+- SHAP ([Lundberg, et al. 2017](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions),  [Github](https://github.com/slundberg/shap))
+
+### Local direct explanation
+
+- Teaching AI to Explain its Decisions ([Hind et al., 2019](https://doi.org/10.1145/3306618.3314273)) 
+
+### Global direct explanation
+
+- Boolean Decision Rules via Column Generation (Light Edition) ([Dash et al., 2018](https://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation))
+- Generalized Linear Rule Models ([Wei et al., 2019](http://proceedings.mlr.press/v97/wei19a.html))
+
+### Global post-hoc explanation 
+
+- ProfWeight ([Dhurandhar et al., 2018](https://papers.nips.cc/paper/8231-improving-simple-models-with-confidence-profiles))
+
+
+## Supported explainability metrics
+- Faithfulness ([Alvarez-Melis and Jaakkola, 2018](https://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks))
+- Monotonicity ([Luss et al., 2019](https://arxiv.org/abs/1905.12698))
+
+## Setup
+
+Supported Configurations:
+
+| OS      | Python version |
+| ------- | -------------- |
+| macOS   | 3.6  |
+| Ubuntu  | 3.6  |
+| Windows | 3.6  |
+
+
+### (Optional) Create a virtual environment
+
+AI Explainability 360 requires specific versions of many Python packages which may conflict
+with other projects on your system. A virtual environment manager is strongly
+recommended to ensure dependencies may be installed safely. If you have trouble installing the toolkit, try this first.
+
+#### Conda
+
+Conda is recommended for all configurations though Virtualenv is generally
+interchangeable for our purposes. Miniconda is sufficient (see [the difference between Anaconda and
+Miniconda](https://conda.io/docs/user-guide/install/download.html#anaconda-or-miniconda)
+if you are curious) and can be installed from
+[here](https://conda.io/miniconda.html) if you do not already have it.
+
+Then, to create a new Python 3.6 environment, run:
+
+```bash
+conda create --name aix360 python=3.6
+conda activate aix360
+```
+
+The shell should now look like `(aix360) $`. To deactivate the environment, run:
+
+```bash
+(aix360)$ conda deactivate
+```
+
+The prompt will return back to `$ ` or `(base)$`.
+
+Note: Older versions of conda may use `source activate aix360` and `source
+deactivate` (`activate aix360` and `deactivate` on Windows).
+
+
+### Installation
+
+Clone the latest version of this repository:
+
+```bash
+(aix360)$ git clone https://github.com/Trusted-AI/AIX360
+```
+
+If you'd like to run the examples and tutorial notebooks, download the datasets now and place them in
+their respective folders as described in
+[aix360/data/README.md](aix360/data/README.md).
+
+Then, navigate to the root directory of the project which contains `setup.py` file and run:
+
+```bash
+(aix360)$ pip install -e .
+```
+
+## Using AI Explainability 360
+
+The `examples` directory contains a diverse collection of jupyter notebooks
+that use AI Explainability 360 in various ways. Both examples and tutorial notebooks illustrate
+working code using the toolkit. Tutorials provide additional discussion that walks
+the user through the various steps of the notebook. See the details about
+tutorials and examples [here](examples/README.md). 
+
+## Citing AI Explainability 360
+
+A technical description of AI Explainability 360 is available in this
+[paper](https://arxiv.org/abs/1909.03012). Below is the bibtex entry for this
+paper.
+
+```
+@misc{aix360-sept-2019,
+title = "One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques",
+author = {Vijay Arya and Rachel K. E. Bellamy and Pin-Yu Chen and Amit Dhurandhar and Michael Hind
+and Samuel C. Hoffman and Stephanie Houde and Q. Vera Liao and Ronny Luss and Aleksandra Mojsilovi\'c
+and Sami Mourad and Pablo Pedemonte and Ramya Raghavendra and John Richards and Prasanna Sattigeri
+and Karthikeyan Shanmugam and Moninder Singh and Kush R. Varshney and Dennis Wei and Yunfeng Zhang},
+month = sept,
+year = {2019},
+url = {https://arxiv.org/abs/1909.03012}
+}
+```
+
+## AIX360 Videos
+
+* Introductory [video](https://www.youtube.com/watch?v=Yn4yduyoQh4) to AI
+  Explainability 360 by Vijay Arya and Amit Dhurandhar, September 5, 2019 (35 mins)
+
+## Acknowledgements
+
+AIX360 is built with the help of several open source packages. All of these are listed in setup.py and some of these include: 
+* Tensorflow https://www.tensorflow.org/about/bib
+* Pytorch https://github.com/pytorch/pytorch
+* scikit-learn https://scikit-learn.org/stable/about.html
+
+## License Information
+
+Please view both the [LICENSE](https://github.com/vijay-arya/AIX360/blob/master/LICENSE) file and the folder [supplementary license](https://github.com/vijay-arya/AIX360/tree/master/supplementary%20license) present in the root directory for license information. 
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for aix360
+Provides:	python3-aix360-doc
+%description help
+# AI Explainability 360 (v0.2.0)
+
+[![Build Status](https://travis-ci.com/Trusted-AI/AIX360.svg?branch=master)](https://travis-ci.com/Trusted-AI/AIX360)
+[![Documentation Status](https://readthedocs.org/projects/aix360/badge/?version=latest)](https://aix360.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/aix360.svg)](https://badge.fury.io/py/aix360)
+
+The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.           
+
+The [AI Explainability 360 interactive experience](http://aix360.mybluemix.net/data) provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The [tutorials and example notebooks](./examples) offer a deeper, data scientist-oriented introduction. The complete API is also available. 
+
+There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some [guidance material](http://aix360.mybluemix.net/resources#guidance) and a [chart](./aix360/algorithms/README.md) that can be consulted. 
+
+We have developed the package with extensibility in mind. This library is still in development. We encourage the contribution of your explainability algorithms and metrics. To get started as a contributor, please join the [AI Explainability 360 Community on Slack](https://aix360.slack.com) by requesting an invitation [here](https://join.slack.com/t/aix360/shared_invite/enQtNzEyOTAwOTk1NzY2LTM1ZTMwM2M4OWQzNjhmNGRiZjg3MmJiYTAzNDU1MTRiYTIyMjFhZTI4ZDUwM2M1MGYyODkwNzQ2OWQzMThlN2Q). Please review the instructions to contribute code [here](CONTRIBUTING.md).
+
+## Supported explainability algorithms
+
+### Data explanation
+
+- ProtoDash ([Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212))
+- Disentangled Inferred Prior VAE ([Kumar et al., 2018](https://openreview.net/forum?id=H1kG7GZAW))
+
+### Local post-hoc explanation 
+
+- ProtoDash ([Gurumoorthy et al., 2019](https://arxiv.org/abs/1707.01212))
+- Contrastive Explanations Method ([Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))
+- Contrastive Explanations Method with Monotonic Attribute Functions ([Luss et al., 2019](https://arxiv.org/abs/1905.12698))
+- LIME ([Ribeiro et al. 2016](https://arxiv.org/abs/1602.04938),  [Github](https://github.com/marcotcr/lime))
+- SHAP ([Lundberg, et al. 2017](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions),  [Github](https://github.com/slundberg/shap))
+
+### Local direct explanation
+
+- Teaching AI to Explain its Decisions ([Hind et al., 2019](https://doi.org/10.1145/3306618.3314273)) 
+
+### Global direct explanation
+
+- Boolean Decision Rules via Column Generation (Light Edition) ([Dash et al., 2018](https://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation))
+- Generalized Linear Rule Models ([Wei et al., 2019](http://proceedings.mlr.press/v97/wei19a.html))
+
+### Global post-hoc explanation 
+
+- ProfWeight ([Dhurandhar et al., 2018](https://papers.nips.cc/paper/8231-improving-simple-models-with-confidence-profiles))
+
+
+## Supported explainability metrics
+- Faithfulness ([Alvarez-Melis and Jaakkola, 2018](https://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks))
+- Monotonicity ([Luss et al., 2019](https://arxiv.org/abs/1905.12698))
+
+## Setup
+
+Supported Configurations:
+
+| OS      | Python version |
+| ------- | -------------- |
+| macOS   | 3.6  |
+| Ubuntu  | 3.6  |
+| Windows | 3.6  |
+
+
+### (Optional) Create a virtual environment
+
+AI Explainability 360 requires specific versions of many Python packages which may conflict
+with other projects on your system. A virtual environment manager is strongly
+recommended to ensure dependencies may be installed safely. If you have trouble installing the toolkit, try this first.
+
+#### Conda
+
+Conda is recommended for all configurations though Virtualenv is generally
+interchangeable for our purposes. Miniconda is sufficient (see [the difference between Anaconda and
+Miniconda](https://conda.io/docs/user-guide/install/download.html#anaconda-or-miniconda)
+if you are curious) and can be installed from
+[here](https://conda.io/miniconda.html) if you do not already have it.
+
+Then, to create a new Python 3.6 environment, run:
+
+```bash
+conda create --name aix360 python=3.6
+conda activate aix360
+```
+
+The shell should now look like `(aix360) $`. To deactivate the environment, run:
+
+```bash
+(aix360)$ conda deactivate
+```
+
+The prompt will return back to `$ ` or `(base)$`.
+
+Note: Older versions of conda may use `source activate aix360` and `source
+deactivate` (`activate aix360` and `deactivate` on Windows).
+
+
+### Installation
+
+Clone the latest version of this repository:
+
+```bash
+(aix360)$ git clone https://github.com/Trusted-AI/AIX360
+```
+
+If you'd like to run the examples and tutorial notebooks, download the datasets now and place them in
+their respective folders as described in
+[aix360/data/README.md](aix360/data/README.md).
+
+Then, navigate to the root directory of the project which contains `setup.py` file and run:
+
+```bash
+(aix360)$ pip install -e .
+```
+
+## Using AI Explainability 360
+
+The `examples` directory contains a diverse collection of jupyter notebooks
+that use AI Explainability 360 in various ways. Both examples and tutorial notebooks illustrate
+working code using the toolkit. Tutorials provide additional discussion that walks
+the user through the various steps of the notebook. See the details about
+tutorials and examples [here](examples/README.md). 
+
+## Citing AI Explainability 360
+
+A technical description of AI Explainability 360 is available in this
+[paper](https://arxiv.org/abs/1909.03012). Below is the bibtex entry for this
+paper.
+
+```
+@misc{aix360-sept-2019,
+title = "One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques",
+author = {Vijay Arya and Rachel K. E. Bellamy and Pin-Yu Chen and Amit Dhurandhar and Michael Hind
+and Samuel C. Hoffman and Stephanie Houde and Q. Vera Liao and Ronny Luss and Aleksandra Mojsilovi\'c
+and Sami Mourad and Pablo Pedemonte and Ramya Raghavendra and John Richards and Prasanna Sattigeri
+and Karthikeyan Shanmugam and Moninder Singh and Kush R. Varshney and Dennis Wei and Yunfeng Zhang},
+month = sept,
+year = {2019},
+url = {https://arxiv.org/abs/1909.03012}
+}
+```
+
+## AIX360 Videos
+
+* Introductory [video](https://www.youtube.com/watch?v=Yn4yduyoQh4) to AI
+  Explainability 360 by Vijay Arya and Amit Dhurandhar, September 5, 2019 (35 mins)
+
+## Acknowledgements
+
+AIX360 is built with the help of several open source packages. All of these are listed in setup.py and some of these include: 
+* Tensorflow https://www.tensorflow.org/about/bib
+* Pytorch https://github.com/pytorch/pytorch
+* scikit-learn https://scikit-learn.org/stable/about.html
+
+## License Information
+
+Please view both the [LICENSE](https://github.com/vijay-arya/AIX360/blob/master/LICENSE) file and the folder [supplementary license](https://github.com/vijay-arya/AIX360/tree/master/supplementary%20license) present in the root directory for license information. 
+
+
+
+
+
+%prep
+%autosetup -n aix360-0.2.1
+
+%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-aix360 -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c5888f0
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+b78cb27d41614106048a18c70e6d5701  aix360-0.2.1.tar.gz