diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-04-10 14:58:22 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-10 14:58:22 +0000 |
| commit | 7253a103a1b376da93d966698cf50a3598c0f1ef (patch) | |
| tree | 4d578be96f4226c77bc64e41acfcb79a0a334368 | |
| parent | 79e1bcd56b1c085cd86d153a47b3ace160dede32 (diff) | |
automatic import of python-lightfm
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-lightfm.spec | 342 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 344 insertions, 0 deletions
@@ -0,0 +1 @@ +/lightfm-1.17.tar.gz diff --git a/python-lightfm.spec b/python-lightfm.spec new file mode 100644 index 0000000..f35d7f4 --- /dev/null +++ b/python-lightfm.spec @@ -0,0 +1,342 @@ +%global _empty_manifest_terminate_build 0 +Name: python-lightfm +Version: 1.17 +Release: 1 +Summary: LightFM recommendation model +License: MIT +URL: https://github.com/lyst/lightfm +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1f/96/5ec230f5c27811534af0faaa8525f11c1000ee1c24c8a82c0546d0724aea/lightfm-1.17.tar.gz +BuildArch: noarch + + +%description +# LightFM + + + +| Build status | | +|---|---| +| Linux |[](https://circleci.com/gh/lyst/lightfm)| +| OSX (OpenMP disabled)|[](https://travis-ci.org/lyst/lightfm)| +| Windows (OpenMP disabled) |[](https://ci.appveyor.com/project/maciejkula/lightfm/branch/master)| + +[](https://gitter.im/lightfm-rec/Lobby) [](https://pypi.python.org/pypi/lightfm/) +[](https://anaconda.org/conda-forge/lightfm) + +LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. + +It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features). + +For more details, see the [Documentation](http://lyst.github.io/lightfm/docs/home.html). + +Need help? Contact me via [email](mailto:lightfm@zoho.com), [Twitter](https://twitter.com/Maciej_Kula), or [Gitter](https://gitter.im/lightfm-rec/Lobby). + +## Installation +Install from `pip`: +``` +pip install lightfm +``` +or Conda: +``` +conda install -c conda-forge lightfm +``` + +## Quickstart +Fitting an implicit feedback model on the MovieLens 100k dataset is very easy: +```python +from lightfm import LightFM +from lightfm.datasets import fetch_movielens +from lightfm.evaluation import precision_at_k + +# Load the MovieLens 100k dataset. Only five +# star ratings are treated as positive. +data = fetch_movielens(min_rating=5.0) + +# Instantiate and train the model +model = LightFM(loss='warp') +model.fit(data['train'], epochs=30, num_threads=2) + +# Evaluate the trained model +test_precision = precision_at_k(model, data['test'], k=5).mean() +``` + +## Articles and tutorials on using LightFM +1. [Learning to Rank Sketchfab Models with LightFM](http://blog.ethanrosenthal.com/2016/11/07/implicit-mf-part-2/) +2. [Metadata Embeddings for User and Item Cold-start Recommendations](http://building-babylon.net/2016/01/26/metadata-embeddings-for-user-and-item-cold-start-recommendations/) +3. [Recommendation Systems - Learn Python for Data Science](https://www.youtube.com/watch?v=9gBC9R-msAk) +4. [Using LightFM to Recommend Projects to Consultants](https://medium.com/product-at-catalant-technologies/using-lightfm-to-recommend-projects-to-consultants-44084df7321c#.gu887ky51) + +## How to cite +Please cite LightFM if it helps your research. You can use the following BibTeX entry: +``` +@inproceedings{DBLP:conf/recsys/Kula15, + author = {Maciej Kula}, + editor = {Toine Bogers and + Marijn Koolen}, + title = {Metadata Embeddings for User and Item Cold-start Recommendations}, + booktitle = {Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender + Systems co-located with 9th {ACM} Conference on Recommender Systems + (RecSys 2015), Vienna, Austria, September 16-20, 2015.}, + series = {{CEUR} Workshop Proceedings}, + volume = {1448}, + pages = {14--21}, + publisher = {CEUR-WS.org}, + year = {2015}, + url = {http://ceur-ws.org/Vol-1448/paper4.pdf}, +} +``` + +## Development +Pull requests are welcome. To install for development: + +1. Clone the repository: `git clone git@github.com:lyst/lightfm.git` +2. Setup a virtual environment: `cd lightfm && python3 -m venv venv && source ./venv/bin/activate` +3. Install it for development using pip: `pip install -e . && pip install -r test-requirements.txt` +4. You can run tests by running `./venv/bin/py.test tests`. +5. LightFM uses [black](https://github.com/ambv/black) to enforce code formatting and flake8 for linting, see `lint-requirements.txt`. +6. [Optional]: You can install pre-commit to locally enfore formatting and linting. Install with: + ```bash + pip install pre-commit + pre-commit install + ``` + +When making changes to the `.pyx` extension files, you'll need to run `python setup.py cythonize` in order to produce the extension `.c` files before running `pip install -e .`. + +%package -n python3-lightfm +Summary: LightFM recommendation model +Provides: python-lightfm +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-lightfm +# LightFM + + + +| Build status | | +|---|---| +| Linux |[](https://circleci.com/gh/lyst/lightfm)| +| OSX (OpenMP disabled)|[](https://travis-ci.org/lyst/lightfm)| +| Windows (OpenMP disabled) |[](https://ci.appveyor.com/project/maciejkula/lightfm/branch/master)| + +[](https://gitter.im/lightfm-rec/Lobby) [](https://pypi.python.org/pypi/lightfm/) +[](https://anaconda.org/conda-forge/lightfm) + +LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. + +It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features). + +For more details, see the [Documentation](http://lyst.github.io/lightfm/docs/home.html). + +Need help? Contact me via [email](mailto:lightfm@zoho.com), [Twitter](https://twitter.com/Maciej_Kula), or [Gitter](https://gitter.im/lightfm-rec/Lobby). + +## Installation +Install from `pip`: +``` +pip install lightfm +``` +or Conda: +``` +conda install -c conda-forge lightfm +``` + +## Quickstart +Fitting an implicit feedback model on the MovieLens 100k dataset is very easy: +```python +from lightfm import LightFM +from lightfm.datasets import fetch_movielens +from lightfm.evaluation import precision_at_k + +# Load the MovieLens 100k dataset. Only five +# star ratings are treated as positive. +data = fetch_movielens(min_rating=5.0) + +# Instantiate and train the model +model = LightFM(loss='warp') +model.fit(data['train'], epochs=30, num_threads=2) + +# Evaluate the trained model +test_precision = precision_at_k(model, data['test'], k=5).mean() +``` + +## Articles and tutorials on using LightFM +1. [Learning to Rank Sketchfab Models with LightFM](http://blog.ethanrosenthal.com/2016/11/07/implicit-mf-part-2/) +2. [Metadata Embeddings for User and Item Cold-start Recommendations](http://building-babylon.net/2016/01/26/metadata-embeddings-for-user-and-item-cold-start-recommendations/) +3. [Recommendation Systems - Learn Python for Data Science](https://www.youtube.com/watch?v=9gBC9R-msAk) +4. [Using LightFM to Recommend Projects to Consultants](https://medium.com/product-at-catalant-technologies/using-lightfm-to-recommend-projects-to-consultants-44084df7321c#.gu887ky51) + +## How to cite +Please cite LightFM if it helps your research. You can use the following BibTeX entry: +``` +@inproceedings{DBLP:conf/recsys/Kula15, + author = {Maciej Kula}, + editor = {Toine Bogers and + Marijn Koolen}, + title = {Metadata Embeddings for User and Item Cold-start Recommendations}, + booktitle = {Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender + Systems co-located with 9th {ACM} Conference on Recommender Systems + (RecSys 2015), Vienna, Austria, September 16-20, 2015.}, + series = {{CEUR} Workshop Proceedings}, + volume = {1448}, + pages = {14--21}, + publisher = {CEUR-WS.org}, + year = {2015}, + url = {http://ceur-ws.org/Vol-1448/paper4.pdf}, +} +``` + +## Development +Pull requests are welcome. To install for development: + +1. Clone the repository: `git clone git@github.com:lyst/lightfm.git` +2. Setup a virtual environment: `cd lightfm && python3 -m venv venv && source ./venv/bin/activate` +3. Install it for development using pip: `pip install -e . && pip install -r test-requirements.txt` +4. You can run tests by running `./venv/bin/py.test tests`. +5. LightFM uses [black](https://github.com/ambv/black) to enforce code formatting and flake8 for linting, see `lint-requirements.txt`. +6. [Optional]: You can install pre-commit to locally enfore formatting and linting. Install with: + ```bash + pip install pre-commit + pre-commit install + ``` + +When making changes to the `.pyx` extension files, you'll need to run `python setup.py cythonize` in order to produce the extension `.c` files before running `pip install -e .`. + +%package help +Summary: Development documents and examples for lightfm +Provides: python3-lightfm-doc +%description help +# LightFM + + + +| Build status | | +|---|---| +| Linux |[](https://circleci.com/gh/lyst/lightfm)| +| OSX (OpenMP disabled)|[](https://travis-ci.org/lyst/lightfm)| +| Windows (OpenMP disabled) |[](https://ci.appveyor.com/project/maciejkula/lightfm/branch/master)| + +[](https://gitter.im/lightfm-rec/Lobby) [](https://pypi.python.org/pypi/lightfm/) +[](https://anaconda.org/conda-forge/lightfm) + +LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. + +It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features). + +For more details, see the [Documentation](http://lyst.github.io/lightfm/docs/home.html). + +Need help? Contact me via [email](mailto:lightfm@zoho.com), [Twitter](https://twitter.com/Maciej_Kula), or [Gitter](https://gitter.im/lightfm-rec/Lobby). + +## Installation +Install from `pip`: +``` +pip install lightfm +``` +or Conda: +``` +conda install -c conda-forge lightfm +``` + +## Quickstart +Fitting an implicit feedback model on the MovieLens 100k dataset is very easy: +```python +from lightfm import LightFM +from lightfm.datasets import fetch_movielens +from lightfm.evaluation import precision_at_k + +# Load the MovieLens 100k dataset. Only five +# star ratings are treated as positive. +data = fetch_movielens(min_rating=5.0) + +# Instantiate and train the model +model = LightFM(loss='warp') +model.fit(data['train'], epochs=30, num_threads=2) + +# Evaluate the trained model +test_precision = precision_at_k(model, data['test'], k=5).mean() +``` + +## Articles and tutorials on using LightFM +1. [Learning to Rank Sketchfab Models with LightFM](http://blog.ethanrosenthal.com/2016/11/07/implicit-mf-part-2/) +2. [Metadata Embeddings for User and Item Cold-start Recommendations](http://building-babylon.net/2016/01/26/metadata-embeddings-for-user-and-item-cold-start-recommendations/) +3. [Recommendation Systems - Learn Python for Data Science](https://www.youtube.com/watch?v=9gBC9R-msAk) +4. [Using LightFM to Recommend Projects to Consultants](https://medium.com/product-at-catalant-technologies/using-lightfm-to-recommend-projects-to-consultants-44084df7321c#.gu887ky51) + +## How to cite +Please cite LightFM if it helps your research. You can use the following BibTeX entry: +``` +@inproceedings{DBLP:conf/recsys/Kula15, + author = {Maciej Kula}, + editor = {Toine Bogers and + Marijn Koolen}, + title = {Metadata Embeddings for User and Item Cold-start Recommendations}, + booktitle = {Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender + Systems co-located with 9th {ACM} Conference on Recommender Systems + (RecSys 2015), Vienna, Austria, September 16-20, 2015.}, + series = {{CEUR} Workshop Proceedings}, + volume = {1448}, + pages = {14--21}, + publisher = {CEUR-WS.org}, + year = {2015}, + url = {http://ceur-ws.org/Vol-1448/paper4.pdf}, +} +``` + +## Development +Pull requests are welcome. To install for development: + +1. Clone the repository: `git clone git@github.com:lyst/lightfm.git` +2. Setup a virtual environment: `cd lightfm && python3 -m venv venv && source ./venv/bin/activate` +3. Install it for development using pip: `pip install -e . && pip install -r test-requirements.txt` +4. You can run tests by running `./venv/bin/py.test tests`. +5. LightFM uses [black](https://github.com/ambv/black) to enforce code formatting and flake8 for linting, see `lint-requirements.txt`. +6. [Optional]: You can install pre-commit to locally enfore formatting and linting. Install with: + ```bash + pip install pre-commit + pre-commit install + ``` + +When making changes to the `.pyx` extension files, you'll need to run `python setup.py cythonize` in order to produce the extension `.c` files before running `pip install -e .`. + +%prep +%autosetup -n lightfm-1.17 + +%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-lightfm -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.17-1 +- Package Spec generated @@ -0,0 +1 @@ +c714b5e83d4f25156ff1b8c89a49e2e6 lightfm-1.17.tar.gz |