Metadata-Version: 1.1
Name: hmc-loss
Version: 0.4.0
Summary: UNKNOWN
Home-page: https://github.com/TaskeHAMANO/hmc_loss
Author: Shinya SUZUKI
Author-email: shinya.s.825@gmail.com
License: MIT
Description: 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>`__
        
Keywords: validation,metrics,machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Information Analysis
