HMC-loss
========

Abstruct
--------

Python-implemented hierarchical multi-class validation metrics: HMC-loss
Original paper is (Bi&Kwok, 2012)

Install
-------

::

    pip install hmc_loss

Requirement
-----------

-  numpy
-  Network X

How to use
----------

This metrics is implemented like scikit-learn metrics.

::

    from hmc_loss import hmc_loss, get_cost_list
    import numpy as np

    # Generate label data(2-D array of numpy)
    true_label = np.random.randint(2, size(100, 100))
    pred_label = np.random.randint(2, size(100, 100))

    # Generate test graph(Di-Graph of NetworkX)
    graph = nx.gnc_graph(100)
    # Generate element list of graph node
    label_list = list(range(100))
    # Calculate cost of each node in graph
    cost_list = get_cost_list(graph, 0, label_list)
    # Calculate HMC-loss
    hmc_loss(true_label, pred_label, graph, 0, label_list, cost_list, alpha=0.5, beta=1.5)

Licence
-------

`MIT <http://choosealicense.com/licenses/mit/>`__

Author
------

`Taske HAMANO <https://github.com/TaskeHAMANO>`__
