(y_true, y_pred, class_hierarchy, beta=1., root=ROOT)
return (1. + beta ** 2.) * hP * hR / (beta ** 2. * hP + hR)
Calculate the micro-averaged hierarchical F-beta ("hF_{\beta}") metric based on
    given set of true class labels and predicated class labels, and the
    class hierarchy graph.

    Note that the format expected here for `y_true` and `y_pred` is a
    binary multi-label matrix format, e.g. as can be generated by scikit-learn's
    MultiLabelBinarizer.

    For motivation and definition details, see:

        Functional Annotation of Genes Using Hierarchical Text
        Categorization, Kiritchenko et al 2008

        http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.5824&rep=rep1&type=pdf

    Parameters
    ----------
    y_true : array-like, shape = [n_samples, n_classes].
        Ground truth multi-class targets.

    y_pred : array-like, shape = [n_samples, n_classes].
        Predicted multi-class targets.

    class_hierarchy : the class hierarchy graph, given as a `networkx.DiGraph` instance
        Node ids must be integer and correspond to the indices into the y_true / y_pred matrices.

    beta: float
        the beta parameter for the F-beta score. Defaults to F1 score (beta=1).

    Returns
    -------
    hFscore : float
        The computed (micro-averaged) hierarchical F-score.
Calculate the micro-averaged hierarchical F-beta ("hF_{\beta}") metric based on
    given set of true class labels and predicated class labels, and the
    class hierarchy graph.
Calculate the micro - averaged hierarchical F - beta ( hF_ { \ beta } ) metric based on given set of true class labels and predicated class labels and the class hierarchy graph .
def h_fbeta_score(y_true, y_pred, class_hierarchy, beta=1., root=ROOT):
    """
    Calculate the micro-averaged hierarchical F-beta ("hF_{\beta}") metric based on
    given set of true class labels and predicated class labels, and the
    class hierarchy graph.

    Note that the format expected here for `y_true` and `y_pred` is a
    binary multi-label matrix format, e.g. as can be generated by scikit-learn's
    MultiLabelBinarizer.

    For motivation and definition details, see:

        Functional Annotation of Genes Using Hierarchical Text
        Categorization, Kiritchenko et al 2008

        http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.5824&rep=rep1&type=pdf

    Parameters
    ----------
    y_true : array-like, shape = [n_samples, n_classes].
        Ground truth multi-class targets.

    y_pred : array-like, shape = [n_samples, n_classes].
        Predicted multi-class targets.

    class_hierarchy : the class hierarchy graph, given as a `networkx.DiGraph` instance
        Node ids must be integer and correspond to the indices into the y_true / y_pred matrices.

    beta: float
        the beta parameter for the F-beta score. Defaults to F1 score (beta=1).

    Returns
    -------
    hFscore : float
        The computed (micro-averaged) hierarchical F-score.

    """
    hP = h_precision_score(y_true, y_pred, class_hierarchy, root=root)
    hR = h_recall_score(y_true, y_pred, class_hierarchy, root=root)
    return (1. + beta ** 2.) * hP * hR / (beta ** 2. * hP + hR)
def h_fbeta_score ( y_true , y_pred , class_hierarchy , beta = 1. , root = ROOT ) : hP = h_precision_score ( y_true , y_pred , class_hierarchy , root = root ) hR = h_recall_score ( y_true , y_pred , class_hierarchy , root = root ) return ( 1. + beta ** 2. ) * hP * hR / ( beta ** 2. * hP + hR )