.. image:: https://img.shields.io/pypi/v/druhg.svg
:target: https://pypi.python.org/pypi/druhg/
:alt: PyPI Version
.. image:: https://img.shields.io/pypi/l/druhg.svg
:target: https://github.com/artamono1/druhg/blob/master/LICENSE
:alt: License
=====
DRUHG
=====
| DRUHG - Dialectical Reflection Universal Hierarchical Grouping (друг).
| Performs clustering based on densities and builds a minimum spanning tree.
| **Does not require parameters.** *(The parameter is space metric, e.x. euclidean)*
| The user can filter the size of the clusters with ``size_range``, for genuine result and genuine outliers set to [1,1].
| Parameter ``fix_outliers`` allows to label outliers to their closest clusters via mstree edges.
-------------
Basic Concept
-------------
| There are some optional tuning parameters but the actual algorithm requires none and is universal.
| It works by applying **the universal society rule: treat others how you want to be treated**.
| The core of the algorithm is to rank the subject's closest subjective similarities and amalgamate them accordingly.
| Parameter ``max_ranking`` controls precision vs productivity balance, after some value the precision and the result would not change.
| todo: Parameter ``algorithm`` can be set to 'slow' to further enhance the precision.
|
|
| The **dialectical distance** reflects the opposite density.
| Max( r/R d(r); d(R) ), where r and R are ranks from A to B and from B to A.
| This orders outliers last and equal densities first.
| It's great **replacement for DBSCAN** and **global outliers detection**.
|
| Those ordered connections become trees. Two trees reflect of each other in their totality and can transfrom into cluster.
| D N₂ K₁/(K₁+K₂) sum 1/dᵢ > N₁ - 1, where N is size of a tree, K is number of clusters in a tree.
| This allows newly formed clusters to resist the reshaping.
----------------
How to use DRUHG
----------------
.. code:: python
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.
.. code:: python
labels = dr.relabel(size_range==[1, 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.
.. code:: python
clusterer.plot(labels)
It will draw mstree with druhg-edges.
.. code:: python
clusterer.plot()
It will provide interactive sliders for an exploration.
.. image:: https://raw.githubusercontent.com/artamono1/druhg/master/docs/source/pics/chameleon-sliders.png
:width: 300px
:align: center
:height: 200px
:alt: chameleon-sliders
-----------
Performance
-----------
| It can be slow on a highly structural data.
| There is a parameters ``max_ranking`` that can be used to decrease for a better performance.
.. image:: https://raw.githubusercontent.com/artamono1/druhg/master/docs/source/pics/comparison_ver.png
:width: 300px
:align: center
:height: 200px
:alt: comparison
----------
Installing
----------
PyPI install, presuming you have an up to date pip:
.. code:: bash
pip install druhg
-----------------
Running the Tests
-----------------
The package tests can be run after installation using the command:
.. code:: bash
pytest -k "test_name"
The tests may fail :-D
--------------
Python Version
--------------
The druhg library supports Python 3.
------------
Contributing
------------
We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials,
is always welcome. To contribute please `fork the project <https://github.com/artamono1/druhg/issues#fork-destination-box>`_
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.
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