| Name | fastEASE JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | None |
| Summary | An easy-to-use Python library for building EASE recommendation systems with CUDA |
| upload_time | 2025-02-17 12:51:35 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2025 Hassan Abedi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
recsys
recommendation systems
machine learning
ai
personalization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
build
exceptiongroup
fastrlock
iniconfig
joblib
numpy
packaging
pluggy
pyproject_hooks
pytest
scikit-learn
scipy
threadpoolctl
tomli
tqdm
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
# fast EASE for CUDA
[](https://github.com/fkrasnov2/fastEASE)
##
**EASY** is a unique and decisive approach to recommendation for a limited number of users and items.
One challenge is that the matrix inversion process becomes computationally intensive, requiring significant processing time on the central processing unit (CPU).
This issue is addressed in the current project by leveraging CUDA, a powerful technology specifically designed for parallel processing. The key distinction is that this solution is intended not for research purposes but rather for deployment in production environments.
## Framework
## Structure
- `.github`: GitHub Actions workflows.
- `src`: Library's source code.
- `tests`: Unit tests.
- `.editorconfig`: Editor settings for consistent coding style.
- `.gitignore`: Excludes files generated by Python and Poetry.
- `LICENSE`: License file.
- `Makefile`: Manage tasks like testing, linting, and formatting.
- `pyproject.toml`: PyPi's configuration file.
## Getting Started
bash:
```console
pip install fastEASE
mkdir dataset
wget --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-1m.zip -O dataset/ml-1m.zip
unzip dataset/ml-1m.zip -d dataset/
```
python:
```python
k = 5
pipeline = PipelineEASE(
user_item_it=DatasetML1M.load_interactions(
"dataset/ml-1m"
),
min_item_freq=1,
min_user_interactions_len=5,
max_user_interactions_len=32,
calc_ndcg_at_k=True,
k=k,
predict_next_n=False,
)
print(f"nDCG@{k} = {pipeline.ndcg:.4}")
```
### Prerequisites
- `Python` >= 3.10
- `GNU Make`
Tested on `Ubuntu 24.04 LTS` and `Debian 12`. But the template should work on other operating systems as well.
### Setting Things Up
1. **Clone the repository**:
```sh
git clone https://github.com/fkrasnov2/fastEASE
cd fastEASE
```
2. **Install dependencies**:
```sh
make install
```
### Development Workflow Management
```sh
# Run the unit tests
make test
```
```sh
# Lint the code
make lint
```
Raw data
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"description": "# fast EASE for CUDA\n[](https://github.com/fkrasnov2/fastEASE)\n\n##\n**EASY** is a unique and decisive approach to recommendation for a limited number of users and items.\nOne challenge is that the matrix inversion process becomes computationally intensive, requiring significant processing time on the central processing unit (CPU).\nThis issue is addressed in the current project by leveraging CUDA, a powerful technology specifically designed for parallel processing. The key distinction is that this solution is intended not for research purposes but rather for deployment in production environments.\n\n## Framework\n\n\n\n## Structure\n\n- `.github`: GitHub Actions workflows.\n- `src`: Library's source code.\n- `tests`: Unit tests.\n- `.editorconfig`: Editor settings for consistent coding style.\n- `.gitignore`: Excludes files generated by Python and Poetry.\n- `LICENSE`: License file.\n- `Makefile`: Manage tasks like testing, linting, and formatting.\n- `pyproject.toml`: PyPi's configuration file.\n\n## Getting Started\nbash:\n```console\npip install fastEASE\nmkdir dataset\nwget --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-1m.zip -O dataset/ml-1m.zip\nunzip dataset/ml-1m.zip -d dataset/\n```\npython:\n```python\nk = 5\npipeline = PipelineEASE(\n user_item_it=DatasetML1M.load_interactions(\n \"dataset/ml-1m\"\n ),\n min_item_freq=1,\n min_user_interactions_len=5,\n max_user_interactions_len=32,\n calc_ndcg_at_k=True,\n k=k,\n predict_next_n=False,\n)\nprint(f\"nDCG@{k} = {pipeline.ndcg:.4}\")\n\n```\n\n### Prerequisites\n\n- `Python` >= 3.10\n- `GNU Make`\n\nTested on `Ubuntu 24.04 LTS` and `Debian 12`. But the template should work on other operating systems as well.\n\n### Setting Things Up\n\n1. **Clone the repository**:\n\n ```sh\n git clone https://github.com/fkrasnov2/fastEASE\n cd fastEASE\n ```\n\n2. **Install dependencies**:\n ```sh\n make install\n ```\n\n### Development Workflow Management\n```sh\n# Run the unit tests\nmake test\n```\n\n```sh\n# Lint the code\nmake lint\n```\n\n\n",
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