Metadata-Version: 2.1
Name: hmeasure
Version: 0.1.6
Summary: H-Measure Classification Metric
Home-page: https://github.com/ldanov/pypkg_hmeasure
Author: Lyubomir Danov
License: UNKNOWN
Description: # README
        
        ## Description 
        
        A Python translation of the R package `hmeasure` ([GitHub](https://github.com/canagnos/hmeasure)) ([CRAN](https://cran.r-project.org/package=hmeasure)).
        
        ## Installation 
        
        To install the hmeasure library use pip:
        
        ```
        pip install hmeasure
        ```
        or install directly from source:
        
        ```
        python setup.py install
        ```
        
        ## Usage
        
        ``` 
        >>> import numpy
        >>> from hmeasure import h_score
        >>> rng = numpy.random.default_rng(66)
        >>> y_true = rng.integers(low=0, high=2, size=10)
        >>> y_true
        array([1, 1, 0, 1, 1, 0, 1, 1, 1, 0])
        >>> # y_pred random sampled in interval [0, 1)
        >>> y_pred = (1 - 0) * rng.random(10) + 0
        >>> y_pred
        array([0.84901876, 0.10282827, 0.43752488, 0.46004468, 0.90878931,
        ...    0.79177719, 0.5297229 , 0.13803906, 0.73166264, 0.22959056])
        >>> h_score(y_true, y_pred)
        0.18889596344769588
        >>> n1, n0 = y_true.sum(), y_true.shape[0]-y_true.sum()
        >>> h_score(y_true, y_pred, severity_ratio=(n1/n0))
        0.18889596344769588
        >>> h_score(y_true, y_pred, severity_ratio=0.7)
        0.13502616807120948
        >>> h_score(y_true, y_pred, severity_ratio=-0.7)
        0.18310946512079307
        >>> h_score(y_true, y_pred, severity_ratio=0.1)
        0.001212529211507385
        >>> h_score(y_true, y_pred, severity_ratio=0.5)
        0.10750123502531805
        ```
        ## Questions and comments
        In case of questions or comments, write an email:  
        `ldanov@users.noreply.github.com`
Platform: UNKNOWN
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