Metadata-Version: 1.0
Name: flexible-clustering-tree
Version: 0.11
Summary: easy interface for ensemble clustering
Home-page: https://github.com/Kensuke-Mitsuzawa/flexible_clustering_tree
Author: Kensuke Mitsuzawa
Author-email: kensuke.mit@gmail.com
License: MIT
Description: flexible-clustering-tree
        ========================
        
        --------------
        
        What's this?
        ============
        
        In the context of **clustering** task, ``flexible-clustering-tree``
        provides you **easy** and **controllable** clustering framework.
        
        .. figure:: https://user-images.githubusercontent.com/1772712/47308081-9980cd00-d66b-11e8-98c0-a275db021cd7.gif
           :alt: 
        
        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!
        
        .. figure:: https://user-images.githubusercontent.com/1772712/47308468-abaf3b00-d66c-11e8-9a08-26facc39e80e.png
           :alt: 
        
        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
        
            # set feature matrix
            f_obj_1st = FeatureMatrixObject(0, numpy.random.rand(500, 600))
            f_obj_2nd = FeatureMatrixObject(1, numpy.random.rand(500, 300))
            f_obj_3rd = FeatureMatrixObject(2, numpy.random.rand(500, 50))
            dict_index2label = {i: "label-{}".format(i) for i in range(0, 500)}
            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
            c_operation_1st = ClusteringOperator(0, 10, KMeans(10))
            c_operation_2nd = ClusteringOperator(1, 5, KMeans(5))
            multi_clustering = MultiClusteringOperator([c_operation_1st, c_operation_2nd])
        
            # run flexible clustering
            clustering_runner = FlexibleClustering(max_depth=3)
            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 could see examples at ``/examples``.
        
        setup
        =====
        
        ::
        
            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.
        
Platform: UNKNOWN
