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|
%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
|
