cdt/utils/metrics.py
def precision_recall(target, prediction, low_confidence_undirected=False):
    r"""Compute precision-recall statistics for directed graphs.
    
    Precision recall statistics are useful to compare algorithms that make 
    predictions with a confidence score. Using these statistics, performance 
    of an algorithms given a set threshold (confidence score) can be
    approximated.
    Area under the precision-recall curve, as well as the coordinates of the 
    precision recall curve are computed, using the scikit-learn library tools.
    Note that unlike the AUROC metric, this metric does not account for class
    imbalance.

    Precision is defined by: :math:`Pr=tp/(tp+fp)` and directly denotes the
    total classification accuracy given a confidence threshold. On the other
    hand, Recall is defined by: :math:`Re=tp/(tp+fn)` and denotes  
    misclassification given a threshold.

    Args:
        target (numpy.ndarray or networkx.DiGraph): Target graph, must be of 
            ones and zeros.
        prediction (numpy.ndarray or networkx.DiGraph): Prediction made by the 
            algorithm to evaluate.
        low_confidence_undirected: Put the lowest confidence possible to 
            undirected edges (edges that are symmetric in the confidence score).
            Default: False
 
    Returns:
        tuple: tuple containing:
        
            + Area under the precision recall curve (float)
            + Tuple of data points of the precision-recall curve used in the computation of the score (tuple). 
            

    Examples:
        >>> import numpy as np
        >>> tar, pred = np.random.randint(2, size=(10, 10)), np.random.randn(10, 10)
        >>> # adjacency matrixes of size 10x10
        >>> aupr, curve = precision_recall(target, input) 
        >>> # leave low_confidence_undirected to False as the predictions are continuous
    """
    true_labels = retrieve_adjacency_matrix(target)
    pred = retrieve_adjacency_matrix(prediction, target.nodes()
                                            if isinstance(target, nx.DiGraph) else None,
                                            weight=True)
    
    if low_confidence_undirected:
        # Take account of undirected edges by putting them with low confidence
        pred[pred==pred.transpose()] *= min(min(pred[np.nonzero(pred)])*.5, .1)
    precision, recall, _ = precision_recall_curve(
        true_labels.ravel(), pred.ravel())
    aupr = auc(recall, precision, reorder=True)

    return aupr, list(zip(precision, recall))
def precision_recall(target, prediction, low_confidence_undirected=False):
    r"""Compute precision-recall statistics for directed graphs.
    
    Precision recall statistics are useful to compare algorithms that make 
    predictions with a confidence score. Using these statistics, performance 
    of an algorithms given a set threshold (confidence score) can be
    approximated.
    Area under the precision-recall curve, as well as the coordinates of the 
    precision recall curve are computed, using the scikit-learn library tools.
    Note that unlike the AUROC metric, this metric does not account for class
    imbalance.

    Precision is defined by: :math:`Pr=tp/(tp+fp)` and directly denotes the
    total classification accuracy given a confidence threshold. On the other
    hand, Recall is defined by: :math:`Re=tp/(tp+fn)` and denotes  
    misclassification given a threshold.

    Args:
        target (numpy.ndarray or networkx.DiGraph): Target graph, must be of 
            ones and zeros.
        prediction (numpy.ndarray or networkx.DiGraph): Prediction made by the 
            algorithm to evaluate.
        low_confidence_undirected: Put the lowest confidence possible to 
            undirected edges (edges that are symmetric in the confidence score).
            Default: False
 
    Returns:
        tuple: tuple containing:
        
            + Area under the precision recall curve (float)
            + Tuple of data points of the precision-recall curve used in the computation of the score (tuple). 
            

    Examples:
        >>> import numpy as np
        >>> tar, pred = np.random.randint(2, size=(10, 10)), np.random.randn(10, 10)
        >>> # adjacency matrixes of size 10x10
        >>> aupr, curve = precision_recall(target, input) 
        >>> # leave low_confidence_undirected to False as the predictions are continuous
    """
    true_labels = retrieve_adjacency_matrix(target)
    pred = retrieve_adjacency_matrix(prediction, target.nodes()
                                            if isinstance(target, nx.DiGraph) else None,
                                            weight=True)
    
    if low_confidence_undirected:
        # Take account of undirected edges by putting them with low confidence
        pred[pred==pred.transpose()] *= min(min(pred[np.nonzero(pred)])*.5, .1)
    precision, recall, _ = precision_recall_curve(
        true_labels.ravel(), pred.ravel())
    aupr = auc(recall, precision, reorder=True)

    return aupr, list(zip(precision, recall))
def precision_recall ( target , prediction , low_confidence_undirected = False ) : true_labels = retrieve_adjacency_matrix ( target ) pred = retrieve_adjacency_matrix ( prediction , target . nodes ( ) if isinstance ( target , nx . DiGraph ) else None , weight = True ) if low_confidence_undirected : # Take account of undirected edges by putting them with low confidence pred [ pred == pred . transpose ( ) ] *= min ( min ( pred [ np . nonzero ( pred ) ] ) * .5 , .1 ) precision , recall , _ = precision_recall_curve ( true_labels . ravel ( ) , pred . ravel ( ) ) aupr = auc ( recall , precision , reorder = True ) return aupr , list ( zip ( precision , recall ) )
r"""Compute precision-recall statistics for directed graphs.
    
    Precision recall statistics are useful to compare algorithms that make 
    predictions with a confidence score. Using these statistics, performance 
    of an algorithms given a set threshold (confidence score) can be
    approximated.
    Area under the precision-recall curve, as well as the coordinates of the 
    precision recall curve are computed, using the scikit-learn library tools.
    Note that unlike the AUROC metric, this metric does not account for class
    imbalance.

    Precision is defined by: :math:`Pr=tp/(tp+fp)` and directly denotes the
    total classification accuracy given a confidence threshold. On the other
    hand, Recall is defined by: :math:`Re=tp/(tp+fn)` and denotes  
    misclassification given a threshold.

    Args:
        target (numpy.ndarray or networkx.DiGraph): Target graph, must be of 
            ones and zeros.
        prediction (numpy.ndarray or networkx.DiGraph): Prediction made by the 
            algorithm to evaluate.
        low_confidence_undirected: Put the lowest confidence possible to 
            undirected edges (edges that are symmetric in the confidence score).
            Default: False
 
    Returns:
        tuple: tuple containing:
        
            + Area under the precision recall curve (float)
            + Tuple of data points of the precision-recall curve used in the computation of the score (tuple). 
            

    Examples:
        >>> import numpy as np
        >>> tar, pred = np.random.randint(2, size=(10, 10)), np.random.randn(10, 10)
        >>> # adjacency matrixes of size 10x10
        >>> aupr, curve = precision_recall(target, input) 
        >>> # leave low_confidence_undirected to False as the predictions are continuous
r Compute precision - recall statistics for directed graphs . Precision recall statistics are useful to compare algorithms that make predictions with a confidence score . Using these statistics performance of an algorithms given a set threshold ( confidence score ) can be approximated . Area under the precision - recall curve as well as the coordinates of the precision recall curve are computed using the scikit - learn library tools . Note that unlike the AUROC metric this metric does not account for class imbalance .