druhg 1.1.4

Basic Concept

How to use DRUHG

import sklearn.datasets as datasets
import druhg

iris = datasets.load_iris()
XX = iris['data']

clusterer = druhg.DRUHG(max_ranking=50)
labels = clusterer.fit(XX).labels_

It will build the tree and label the points. Now you can manipulate clusters by relabeling.

labels = dr.relabel(limit1=1, limit2=len(XX)/2, fix_outliers=1)
ari = adjusted_rand_score(iris['target'], labels)
print ('iris ari', ari)

It will relabel the clusters, by restricting their size.

from druhg import DRUHG
import matplotlib.pyplot as plt
import pandas as pd, numpy as np

XX = pd.read_csv('chameleon.csv', sep='\t', header=None)
XX = np.array(XX)
clusterer = DRUHG(max_ranking=200)
clusterer.fit(XX)

plt.figure(figsize=(30,16))
clusterer.minimum_spanning_tree_.plot(node_size=200)

It will draw mstree with druhg-edges.

Performance

Installing

PyPI install, presuming you have an up to date pip:

pip install druhg

Running the Tests

The package tests can be run after installation using the command:

pytest -s druhg

or

python -m pytest -s druhg

The tests may fail :-D

Python Version

The druhg library supports both Python 2 and Python 3.

Contributing

We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.

Licensing

The druhg package is 3-clause BSD licensed.