Similarity Forest : A similarity-based decision tree ensemble

This is my MS thesis project conducted at Poznan University of Technology under the supervision of prof. Mikołaj Morzy It aims to extend the work described in Similarity Forest by Sathe and Aggarwal and provide ease to use, Scikit-Learn compatible implementation. It can be used in Scikit-Learn pipelines and (hyper)parameter search, it includes testing (API compliance) and more. I will include necessary documentation.

Below you can find neccessary steps to install the project package. It assumes that you have Anaconda (or Miniconda) installed. If you don't, follow the steps from the docs: https://docs.conda.io/projects/conda/en/latest/user-guide/install/

Package instalation

1. Make sure that conda it's up to date

   conda update conda
   

2. Clone this repository

   git clone https://github.com/sfczekalski/similarity_forest
   

3. Go to the project folder

   cd similarity_forest
   

3. Create conda environment

   conda env create --file environment.yml
   

4. Activate conda environment

   conda activate similarity-forest
   

5. Install simforest package

   pip install .