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diff --git a/python-recsim-no-tf.spec b/python-recsim-no-tf.spec new file mode 100644 index 0000000..047c9ef --- /dev/null +++ b/python-recsim-no-tf.spec @@ -0,0 +1,314 @@ +%global _empty_manifest_terminate_build 0 +Name: python-recsim-no-tf +Version: 0.2.3 +Release: 1 +Summary: RecSim: A Configurable Recommender Systems Simulation Platform (w/o Tensorflow deps) +License: Apache 2.0 +URL: https://github.com/kittipatv/recsim-no-tf +Source0: https://mirrors.aliyun.com/pypi/web/packages/3f/93/18ab814aa409eb8af7c62e1ee64054d39dc23f12a948329c88c61af32150/recsim-no-tf-0.2.3.tar.gz +BuildArch: noarch + +Requires: python3-absl-py +Requires: python3-gin-config +Requires: python3-gym +Requires: python3-numpy +Requires: python3-scipy + +%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. + +<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-no-tf +Summary: RecSim: A Configurable Recommender Systems Simulation Platform (w/o Tensorflow deps) +Provides: python-recsim-no-tf +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-recsim-no-tf +# 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. + +<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-no-tf +Provides: python3-recsim-no-tf-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. + +<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-no-tf-0.2.3 + +%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-no-tf -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.3-1 +- Package Spec generated |