Toolbox for reproducible research in machine learning.
Project description
research-learn
Toolbox to simplify the design, execution and analysis of machine learning experiments. It based on statsmodels, scikit-learn and imbalanced-learn.
Documentation
Installation documentation, API documentation, and examples can be found on the documentation.
Dependencies
research-learn is tested to work under Python 3.6+. The dependencies are the following:
numpy(>=1.1)
statsmodels(>=0.9.0)
scikit-learn(>=0.21)
imbalanced-learn(>=0.4.3)
Additionally, to run the examples, you need matplotlib(>=2.0.0) and pandas(>=0.22).
Installation
research-learn is currently available on the PyPi’s repository and you can install it via pip:
pip install -U research-learn
The package is released also in Anaconda Cloud platform:
conda install -c algowit research-learn
If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:
git clone https://github.com/AlgoWit/research-learn.git cd research-learn pip install .
Or install using pip and GitHub:
pip install -U git+https://github.com/AlgoWit/research-learn.git
Testing
After installation, you can use pytest to run the test suite:
make test
Project details
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
Hashes for research_learn-0.1.9-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b331be8e28c01a501d1ff21da0896c3e0fa7d94a0552f1fd335365e0dc8f6c14 |
|
MD5 | 0c255e4d907e321a9c07984e3f6a0f69 |
|
BLAKE2b-256 | 436484182a1224897900b0888d4589279bd87fb13ee779211a41f236a586b10c |