flexible-clustering-tree 0.21
easy interface for ensemble clustering

flexible-clustering-tree
========================

--------------

What’s this?
============

In the context of **clustering** task, ``flexible-clustering-tree``
provides you **easy** and **controllable** clustering framework.

|image0|

Background
==========

Let’s suppose, you have huge data. You’d like to observe data as easy as
possible.

Hierarchical clustering is a way to see clustering tree. However,
hierarchical clustering tends to fall into local optimization.

So, you need other clustering method. But at the same time, you wanna
observe your data with tree structure style.

here, ``flexible-clustering-tree`` could give you simple way from data
into tree viewer(d3 based)

You could set any kinds of clustering algorithm such as Kmeans, DBSCAN,
Spectral-Clustering.

Multi feature and Multi clustering
----------------------------------

During making a tree, you might want use various kind of clustering
algorithm. For example, you use Kmeans for the 1st later of a tree, and
DBSCAN for the 2nd layer of a tree.

And you might also use various kind of feature type depending on a layer
of a tree. For example, in the context of document clustering, “title”
of news for the 1st layer, and “whole text” for the 2nd layer.

The example below, this is a clustering tree of 20-news data set.

-  1st layer(red highlight) is done with HDBSCAN clustering, and feature
   is dense vector of ``Subject`` text, which is converted by word2vec
   model.
-  2nd layer(blue highlight) is done with Kmeans, and feature is sparse
   vector of whole text(BOW).

You could design your clustering tree as you want!

|image1|

Both are possible ``flexible-clustering-tree``!

Contribution
============

-  Easy interface(scikit-learn way) from data(feature matrix) into a
   tree viewer
-  Possible to make various clustering algorithms ensemble
-  Possible to set various feature types

How to use?
===========

.. code:: python

   from flexible_clustering_tree import FeatureMatrixObject, MultiFeatureMatrixObject
   from flexible_clustering_tree import ClusteringOperator, MultiClusteringOperator
   from flexible_clustering_tree import FlexibleClustering
   import numpy
   import codecs

   # set feature matrix
   # an input of 1st layer is list of string
   input_string = ['d'] * 10 + ['e'] * 10 + ['c'] * 10 + ['a'] * 10 + ['b'] * 10 + ['f'] * 50
   f_obj_1st = FeatureMatrixObject(0, feature_strings=input_string)
   # an input of 2nd layer is the dense matrix (100, 300)
   f_obj_2nd = FeatureMatrixObject(1, matrix_object=numpy.random.rand(100, 300))
   # an input of 3rd layer is the dense matrix (100, 50)
   f_obj_3rd = FeatureMatrixObject(2, matrix_object=numpy.random.rand(100, 50))
   dict_index2label = {i: "label-{}".format(i) for i in range(0, 100)}
   multi_feature_matrix = MultiFeatureMatrixObject(
       [f_obj_1st, f_obj_2nd, f_obj_3rd],
       dict_index2label=dict_index2label
   )

   # set clustering operation
   from sklearn.cluster.k_means_ import KMeans
   from hdbscan.hdbscan_ import HDBSCAN
   from flexible_clustering_tree import StringAggregation
   c_operation_1st = ClusteringOperator(0, -1, StringAggregation())
   c_operation_2nd = ClusteringOperator(1, 10, KMeans(10))
   c_operation_3rd = ClusteringOperator(2, -1, HDBSCAN())
   multi_clustering = MultiClusteringOperator([c_operation_1st, c_operation_2nd])

   # run flexible clustering
   clustering_runner = FlexibleClustering(max_depth=5)
   index2cluster_no = clustering_runner.fit_transform(multi_feature_matrix, multi_clustering)
   html = clustering_runner.clustering_tree.to_html()

   # output to html
   with codecs.open("out.html", "w", "utf-8") as f:
       f.write(html)

   # you're also able to generate tables via Pandas.
   import pandas
   table_objects = clustering_runner.clustering_tree.to_objects()
   print(pandas.DataFrame(table_objects['cluster_information']))
   print(pandas.DataFrame(table_objects['leaf_information']))

The output of pandas table is below.

The relation-table of clusters is in ``cluster_information``.

::

       cluster_id  parent_id  depth_level  clustering_method
   0            0         -1            1  StringAggregation
   1            1         -1            1  StringAggregation
   2            2         -1            1  StringAggregation
   3            3         -1            1  StringAggregation
   4            4         -1            1  StringAggregation
   5            5         -1            1  StringAggregation
   6            6          5            2             KMeans
   7            7          5            2             KMeans
   8            8          5            2             KMeans
   9            9          5            2             KMeans
   10          10          5            2             KMeans
   11          11          5            2             KMeans
   12          12          5            2             KMeans
   13          13          5            2             KMeans
   14          14          5            2             KMeans
   15          15          5            2             KMeans

The relation-table of leaf nodes is in ``leaf_information``.

::

       leaf_id  cluster_id     label  args
   0         0           0   label-0  None
   1         1           0   label-1  None
   2         2           0   label-2  None
   3         3           0   label-3  None
   4         4           0   label-4  None
   ..      ...         ...       ...   ...
   95       95          14  label-95  None
   96       96           8  label-96  None
   97       97          13  label-97  None
   98       98          10  label-98  None
   99       99          12  label-99  None
   [100 rows x 4 columns]

You could see examples at ``/examples``.

setup
=====

.. code:: bash

   pip install flexible_clustering_tree

or close this repository

.. code:: bash

   python setup.py install

For Developers
==============

Environment
-----------

-  Python >= 3.x

Dev/Test environment by Docker
------------------------------

You build dev/test environment with Docker container. Here is simple
procedure,

1. build docker image
2. start docker container
3. run test in the container

.. code:: bash

   $ cd tests
   $ docker-compose build
   $ docker-compose up
   $ docker run --name test-container -v `pwd`:/codes/flexible-clustering-tree/ -dt tests_dev_env
   $ docker exec -it test-container python /codes/flexible-clustering-tree/setup.py test

If you’re using pycharm professional edition, you could call a
docker-compose file as Python interpreter.

.. |image0| image:: https://user-images.githubusercontent.com/1772712/47308081-9980cd00-d66b-11e8-98c0-a275db021cd7.gif
.. |image1| image:: https://user-images.githubusercontent.com/1772712/47308468-abaf3b00-d66c-11e8-9a08-26facc39e80e.png