Python implementation of Divisive iK-means (DiviK) algorithm.
Clustering at your command line with fit-clusters
Set of algorithm implementations for unsupervised analyses
- hands-free clustering method with built-in feature selection
for selecting the number of clusters
The recommended way to use this software is through . This is the most convenient way, if you want to use divik application.
To install latest stable version use:
Prerequisites for installation of base package:
Python 3.6 / 3.7 / 3.8
compiler capable of compiling the native C code and OpenMP support
You should have it already installed with GCC compiler, but if somehow not, try the following:
OpenMP is available as part of LLVM. You may need to install it with conda:
Having prerequisites installed, one can install latest base version of the package:
If you want to have compatibility with , you can install necessary extras with:
Note: Remember about \ before [ and ] in zsh shell.
You can install all extras with:
If you are using DiviK to run the analysis that could fail to fit RAM of your computer, consider disabling the default parallelism and switch to . It's easy to achieve through configuration:
set all parameters named n_jobs to 1;
set all parameters named allow_dask to True.
Note: Never set n_jobs>1 and allow_dask=True at the same time, the computations will freeze due to how multiprocessing and dask handle parallelism.
It can happen if the he gamred_native package (part of divik package) was compiled with different numpy ABI than scikit-learn. This could happen if you used different set of compilers than the developers of the scikit-learn package.
In such a case, a handler is defined to display the stack trace. If the trace comes from _matlab_legacy.py, the most probably this is the issue.
To resolve the issue, consider following the installation instructions once again. The exact versions get updated to avoid the issue.
Contribution guide will be developed soon.
Format the code with:
This software is part of contribution made by , rest of which is published .
Modular K-Means implementation with custom distance metrics and initializations
Two-step meta-clustering
Gaussian Mixture Model based data-driven feature selection
- allows you to select highly variant features above noise level, based on GMM-decomposition
- allows you to select highly variant features above noise level, based on outlier detection
- allows you to select highly variant features above noise level with your predefined thresholds for each
Stratified Sampler - generates samples of fixed number of rows from given dataset, preserving groups proportion
Uniform PCA Sampler - generates samples of random observations within boundaries of an original dataset, and preserving the rotation of the data
- generates samples of random observations within boundaries of an original dataset
docker pull gmrukwa/diviksudo apt-get install libgomp1conda install -c conda-forge "compilers>=1.0.4,!=1.1.0" llvm-openmppip install divikpip install divik[gin]pip install divik[all]isort -m 3 --fgw 3 --tc .
black -t py36 .