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https://github.com/katharineshapcott/rank-similarity
Rank Similarity is a set of nonlinear classification and transform tools for large datasets.
https://github.com/katharineshapcott/rank-similarity
classification machine-learning nonlinear scikit-learn transformer
Last synced: 27 days ago
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Rank Similarity is a set of nonlinear classification and transform tools for large datasets.
- Host: GitHub
- URL: https://github.com/katharineshapcott/rank-similarity
- Owner: KatharineShapcott
- License: bsd-3-clause
- Created: 2021-06-09T07:39:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-02T17:33:29.000Z (almost 3 years ago)
- Last Synced: 2024-09-27T20:22:41.726Z (about 1 month ago)
- Topics: classification, machine-learning, nonlinear, scikit-learn, transformer
- Language: Python
- Homepage: https://katharineshapcott.github.io/rank-similarity/
- Size: 3.22 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Rank Similarity
Rank Similarity is a set of non-linear classification and transform tools for large multi-dimensional datasets that use the scikit-learn API.
## Installation
### Dependencies
rank-similarity requires:
- Scikit-learn (>= 0.23)
- Python (>= 3.7)
- NumPy (>= 1.14.6)
- SciPy (>= 1.1.0)
Optionally for plotting examples:
- matplotlib (>= 2.2.2)
### Install via pip
```
pip install rank-similarity
```
### Development version
To get the latest development version, clone the GitHub repository:
```
git clone https://github.com/KatharineShapcott/rank-similarity.git
```
## Usage
``` python
from ranksim import RankSimilarityClassifier
X = [[0, 1], [1, 0]]
y = [0, 1]
clf = RankSimilarityClassifier()
clf.fit(X, y)
pred = clf.predict(X)
```
## More Information
### Documentation
More details and background information is available in the
[online documentation](https://katharineshapcott.github.io/rank-similarity/).
### License
The package is new BSD licensed.
### Citation
Please cite the following publication (in preparation) [[1]](#1).
[1]
Shapcott & Bird. Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data. arXiv [cs.LG]. 2021. Available from: http://arxiv.org/abs/2109.13610
## Contributors
