Metadata-Version: 2.1
Name: LANDMarkClassifier
Version: 2.1.0
Summary: LANDMark: An ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data
Project-URL: Homepage, https://github.com/jrudar/LANDMark
Project-URL: Repository, https://github.com/jrudar/LANDMark.git
Project-URL: Bug Tracker, https://github.com/jrudar/LANDMark/issues
Author: Teresita M. Porter, Michael Wright, G. Brian Golding
Author-email: Josip Rudar <rudarj@uoguelph.ca>, Mehrdad Hajibabaei <mhajibab@uoguelph.ca>
License: MIT License
        
        Copyright (c) 2022 Josip Rudar, Teresita M. Porter, Michael Wright, G.Brian Golding, Mehrdad Hajibabaei
        
        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
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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License-File: LICENSE
Keywords: classification,ecology,machine learning,multivariate statistics
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Requires-Dist: joblib>=1.2.0
Requires-Dist: numpy==1.23.5
Requires-Dist: pandas>=1.5.0
Requires-Dist: scikit-learn>=1.1.2
Requires-Dist: scipy>=1.8.1
Requires-Dist: skorch>=0.13.0
Requires-Dist: torch>=2.0.1
Provides-Extra: dev
Requires-Dist: black; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Requires-Dist: pytest-cov; extra == 'test'
Description-Content-Type: text/markdown

### LANDMark

[![CI](https://github.com/jrudar/LANDMark/actions/workflows/ci.yml/badge.svg)](https://github.com/jrudar/LANDMark/actions/workflows/ci.yml)

Implementation of a decision tree ensemble which splits each node using learned linear and non-linear functions.

### Install
From PyPI:

```bash
pip install LANDMarkClassifier
```

From source:

```bash
git clone https://github.com/jrudar/LANDMark.git
cd LANDMark
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install .
```

## Interface

An overview of the API can be found [here](docs/API.md).

## Usage and Examples

Examples of how to use `LANDMark` can be found [here](notebooks/README.md).

## Contributing

To contribute to the development of `LANDMark` please read our [contributing guide](docs/CONTRIBUTING.md)


### References

    Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble 
    approach to the supervised selection of biomarkers in high-throughput sequencing data. 
    BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z

    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: 
    Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30. 

    Kuncheva LI, Rodriguez JJ. Classifier ensembles with a random linear oracle. 
    IEEE Transactions on Knowledge and Data Engineering. 2007;19(4):500–8. 
    
    Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42. 

