automatic import of python-recsim

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+/recsim-0.2.4.tar.gz
diff --git a/python-recsim.spec b/python-recsim.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-recsim
+Version:	0.2.4
+Release:	1
+Summary:	RecSim: A Configurable Recommender Systems Simulation Platform
+License:	Apache 2.0
+URL:		https://github.com/google-research/recsim
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/bb/5a/bbd19e986fd3448de90a2808010ddec29d048cff21cd940401c14c8666d6/recsim-0.2.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-absl-py
+Requires:	python3-dopamine-rl
+Requires:	python3-gin-config
+Requires:	python3-gym
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-tensorflow
+
+%description
+# RecSim: A Configurable Recommender Systems Simulation Platform
+
+RecSim is a configurable platform for authoring simulation environments for
+recommender systems (RSs) that naturally supports **sequential interaction**
+with users. RecSim allows the creation of new environments that reflect
+particular aspects of user behavior and item structure at a level of abstraction
+well-suited to pushing the limits of current reinforcement learning (RL) and RS
+techniques in sequential interactive recommendation problems. Environments can
+be easily configured that vary assumptions about: user preferences and item
+familiarity; user latent state and its dynamics; and choice models and other
+user response behavior. We outline how RecSim offers value to RL and RS
+researchers and practitioners, and how it can serve as a vehicle for
+academic-industrial collaboration. For a detailed description of the RecSim
+architecture please read [Ie et al](https://arxiv.org/abs/1909.04847). Please
+cite the paper if you use the code from this repository in your work.
+
+### Bibtex
+
+```
+@article{ie2019recsim,
+    title={RecSim: A Configurable Simulation Platform for Recommender Systems},
+    author={Eugene Ie and Chih-wei Hsu and Martin Mladenov and Vihan Jain and Sanmit Narvekar and Jing Wang and Rui Wu and Craig Boutilier},
+    year={2019},
+    eprint={1909.04847},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<a id='Disclaimer'></a>
+
+## Disclaimer
+
+This is not an officially supported Google product.
+
+## What's new
+
+*   **12/13/2019:** Added (abstract) classes for both multi-user environments
+    and agents. Added bandit algorithms for generalized linear models.
+
+## Installation and Sample Usage
+
+It is recommended to install RecSim using (https://pypi.org/project/recsim/):
+
+```shell
+pip install recsim
+```
+
+However, the latest version of Dopamine is not in PyPI as of December, 2019. We
+want to install the latest version from Dopamine's repository like the following
+before we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which is
+the final 1.x release including GPU support for Ubuntu and Windows.
+
+```
+pip install git+https://github.com/google/dopamine.git
+```
+
+Here are some sample commands you could use for testing the installation:
+
+```
+git clone https://github.com/google-research/recsim
+cd recsim/recsim
+python main.py --logtostderr \
+  --base_dir="/tmp/recsim/interest_exploration_full_slate_q" \
+  --agent_name=full_slate_q \
+  --environment_name=interest_exploration \
+  --episode_log_file='episode_logs.tfrecord' \
+  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \
+  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \
+  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \
+  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5
+```
+
+You could then start a tensorboard and view the output
+
+```
+tensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222
+```
+
+You could also find the simulated logs in /tmp/recsim/episode_logs.tfrecord
+
+## Tutorials
+
+To get started, please check out our Colab tutorials. In
+[**RecSim: Overview**](recsim/colab/RecSim_Overview.ipynb),
+we give a brief overview about RecSim. We then talk about each configurable
+component:
+[**environment**](recsim/colab/RecSim_Developing_an_Environment.ipynb)
+and
+[**recommender agent**](recsim/colab/RecSim_Developing_an_Agent.ipynb).
+
+## Documentation
+
+
+Please refer to the [white paper](http://arxiv.org/abs/1909.04847) for the
+high-level design.
+
+
+
+
+%package -n python3-recsim
+Summary:	RecSim: A Configurable Recommender Systems Simulation Platform
+Provides:	python-recsim
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-recsim
+# RecSim: A Configurable Recommender Systems Simulation Platform
+
+RecSim is a configurable platform for authoring simulation environments for
+recommender systems (RSs) that naturally supports **sequential interaction**
+with users. RecSim allows the creation of new environments that reflect
+particular aspects of user behavior and item structure at a level of abstraction
+well-suited to pushing the limits of current reinforcement learning (RL) and RS
+techniques in sequential interactive recommendation problems. Environments can
+be easily configured that vary assumptions about: user preferences and item
+familiarity; user latent state and its dynamics; and choice models and other
+user response behavior. We outline how RecSim offers value to RL and RS
+researchers and practitioners, and how it can serve as a vehicle for
+academic-industrial collaboration. For a detailed description of the RecSim
+architecture please read [Ie et al](https://arxiv.org/abs/1909.04847). Please
+cite the paper if you use the code from this repository in your work.
+
+### Bibtex
+
+```
+@article{ie2019recsim,
+    title={RecSim: A Configurable Simulation Platform for Recommender Systems},
+    author={Eugene Ie and Chih-wei Hsu and Martin Mladenov and Vihan Jain and Sanmit Narvekar and Jing Wang and Rui Wu and Craig Boutilier},
+    year={2019},
+    eprint={1909.04847},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<a id='Disclaimer'></a>
+
+## Disclaimer
+
+This is not an officially supported Google product.
+
+## What's new
+
+*   **12/13/2019:** Added (abstract) classes for both multi-user environments
+    and agents. Added bandit algorithms for generalized linear models.
+
+## Installation and Sample Usage
+
+It is recommended to install RecSim using (https://pypi.org/project/recsim/):
+
+```shell
+pip install recsim
+```
+
+However, the latest version of Dopamine is not in PyPI as of December, 2019. We
+want to install the latest version from Dopamine's repository like the following
+before we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which is
+the final 1.x release including GPU support for Ubuntu and Windows.
+
+```
+pip install git+https://github.com/google/dopamine.git
+```
+
+Here are some sample commands you could use for testing the installation:
+
+```
+git clone https://github.com/google-research/recsim
+cd recsim/recsim
+python main.py --logtostderr \
+  --base_dir="/tmp/recsim/interest_exploration_full_slate_q" \
+  --agent_name=full_slate_q \
+  --environment_name=interest_exploration \
+  --episode_log_file='episode_logs.tfrecord' \
+  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \
+  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \
+  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \
+  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5
+```
+
+You could then start a tensorboard and view the output
+
+```
+tensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222
+```
+
+You could also find the simulated logs in /tmp/recsim/episode_logs.tfrecord
+
+## Tutorials
+
+To get started, please check out our Colab tutorials. In
+[**RecSim: Overview**](recsim/colab/RecSim_Overview.ipynb),
+we give a brief overview about RecSim. We then talk about each configurable
+component:
+[**environment**](recsim/colab/RecSim_Developing_an_Environment.ipynb)
+and
+[**recommender agent**](recsim/colab/RecSim_Developing_an_Agent.ipynb).
+
+## Documentation
+
+
+Please refer to the [white paper](http://arxiv.org/abs/1909.04847) for the
+high-level design.
+
+
+
+
+%package help
+Summary:	Development documents and examples for recsim
+Provides:	python3-recsim-doc
+%description help
+# RecSim: A Configurable Recommender Systems Simulation Platform
+
+RecSim is a configurable platform for authoring simulation environments for
+recommender systems (RSs) that naturally supports **sequential interaction**
+with users. RecSim allows the creation of new environments that reflect
+particular aspects of user behavior and item structure at a level of abstraction
+well-suited to pushing the limits of current reinforcement learning (RL) and RS
+techniques in sequential interactive recommendation problems. Environments can
+be easily configured that vary assumptions about: user preferences and item
+familiarity; user latent state and its dynamics; and choice models and other
+user response behavior. We outline how RecSim offers value to RL and RS
+researchers and practitioners, and how it can serve as a vehicle for
+academic-industrial collaboration. For a detailed description of the RecSim
+architecture please read [Ie et al](https://arxiv.org/abs/1909.04847). Please
+cite the paper if you use the code from this repository in your work.
+
+### Bibtex
+
+```
+@article{ie2019recsim,
+    title={RecSim: A Configurable Simulation Platform for Recommender Systems},
+    author={Eugene Ie and Chih-wei Hsu and Martin Mladenov and Vihan Jain and Sanmit Narvekar and Jing Wang and Rui Wu and Craig Boutilier},
+    year={2019},
+    eprint={1909.04847},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<a id='Disclaimer'></a>
+
+## Disclaimer
+
+This is not an officially supported Google product.
+
+## What's new
+
+*   **12/13/2019:** Added (abstract) classes for both multi-user environments
+    and agents. Added bandit algorithms for generalized linear models.
+
+## Installation and Sample Usage
+
+It is recommended to install RecSim using (https://pypi.org/project/recsim/):
+
+```shell
+pip install recsim
+```
+
+However, the latest version of Dopamine is not in PyPI as of December, 2019. We
+want to install the latest version from Dopamine's repository like the following
+before we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which is
+the final 1.x release including GPU support for Ubuntu and Windows.
+
+```
+pip install git+https://github.com/google/dopamine.git
+```
+
+Here are some sample commands you could use for testing the installation:
+
+```
+git clone https://github.com/google-research/recsim
+cd recsim/recsim
+python main.py --logtostderr \
+  --base_dir="/tmp/recsim/interest_exploration_full_slate_q" \
+  --agent_name=full_slate_q \
+  --environment_name=interest_exploration \
+  --episode_log_file='episode_logs.tfrecord' \
+  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \
+  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \
+  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \
+  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5
+```
+
+You could then start a tensorboard and view the output
+
+```
+tensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222
+```
+
+You could also find the simulated logs in /tmp/recsim/episode_logs.tfrecord
+
+## Tutorials
+
+To get started, please check out our Colab tutorials. In
+[**RecSim: Overview**](recsim/colab/RecSim_Overview.ipynb),
+we give a brief overview about RecSim. We then talk about each configurable
+component:
+[**environment**](recsim/colab/RecSim_Developing_an_Environment.ipynb)
+and
+[**recommender agent**](recsim/colab/RecSim_Developing_an_Agent.ipynb).
+
+## Documentation
+
+
+Please refer to the [white paper](http://arxiv.org/abs/1909.04847) for the
+high-level design.
+
+
+
+
+%prep
+%autosetup -n recsim-0.2.4
+
+%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-recsim -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.4-1
+- Package Spec generated
diff --git a/sources b/sources
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+2b444f8e027cb86fae231e9034337492  recsim-0.2.4.tar.gz