A toolkit for offline evaluation of Recommender Systems.
Project description
Streamsight
Streamsight is an offline Reccomender Systems (RecSys) evaluation toolkit that respects a global timeline. The aim is to partition the data into different windows where data is incrementally released for the programmer to fit, train and submit predictions. This aims to provide a close simulation of an online setting when evaluating RecSys algorithms.
Getting Started
Clone the repository
git clone https://github.com/hiiamtzekean/streamsight
cd streamsight
Dependencies can be installed with uv for ease of management.
uv sync
Alternatively, you may install dependencies locally with pip and venv
python3 -m venv venv
source venv/bin/activate
pip install -e .
The dependencies are listed in pyproject.toml.
Contributing
- We welcome all contributors, be it reporting an issue, or raising a pull request to fix an issue.
- When you make changes, rerun
pip install .to test your changes.
Documentation
The documentation can be found here and repository on Github.
Citation
If you use this library in any part of your work, please cite the following papers:
Ng, T. K. (2024). Streamsight: a toolkit for offline evaluation of recommender systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181114
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file streamsight-3.1.1.tar.gz.
File metadata
- Download URL: streamsight-3.1.1.tar.gz
- Upload date:
- Size: 86.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.9.21 {"installer":{"name":"uv","version":"0.9.21","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
421a9261d85565f5b137b5453793746bf4daaaa40313bb21fb833e56c937727a
|
|
| MD5 |
a80f4513098da5d977728a1deb1f4938
|
|
| BLAKE2b-256 |
0ecd20635b4d6caa4868a4799404ec09a36d86a01299b1a382043be1ea55f813
|
File details
Details for the file streamsight-3.1.1-py3-none-any.whl.
File metadata
- Download URL: streamsight-3.1.1-py3-none-any.whl
- Upload date:
- Size: 140.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.9.21 {"installer":{"name":"uv","version":"0.9.21","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
bcffcfc3971ac5ba9df7bb5040d1c786a4bf4c12ce8bfc08360ec513d6915f04
|
|
| MD5 |
5866e0eebe7440d4e4173e9d5acce508
|
|
| BLAKE2b-256 |
6a9b553c5e28cd537e9b591fb6e5ef915dc113e996bb6e9cc85872b698580364
|
