CausalityExperiment/TSPerformanceMeasure.py

def overlapintervals(i1, i2):
    """
    Calculate intersection of two lists of intervals.

    :param i1: list of intervals
    :param i2: list of intervals

    :return: list of intervals with overlap of both intervals only
    """
    ollist = []
    for i in i1:
        for j in i2:
            if j[0] < i[0]+i[1] and j[0]+j[1] > i[0]:
                ollist += [[max(i[0], j[0]),
                            min(i[0]+i[1], j[0]+j[1])-max(i[0], j[0])]]
    return ollist



def calcPrecisionRecall(detection, groundtruth, setlength):
    """
    Calculated precision and recall for intervals in time series

    :param detection: array with detected intervals
    :param groundtruth: array with ground truth
    :param setlength: length of time series

    :return: precision, recall
    """
    import numpy as np


    if len(groundtruth) == 0 and len(detection) == 0:
        precision = 1
        recall = 1
    else:
        # Turn detection into array
        detArray = IntervalsToBinary(detection, setlength)
        gtArray = IntervalsToBinary(groundtruth, setlength)
        if np.sum(gtArray) == 0:
            gtArray = -1 * (gtArray - 1)
            detArray = -1*(detArray-1)

        unique, counts = np.unique(detArray+gtArray, return_counts=True)
        countvalues = dict(zip(unique, counts))
        tp = countvalues[2] if 2 in countvalues else 0
        tn = countvalues[0] if 0 in countvalues else 0

        unique, counts = np.unique(detArray-gtArray, return_counts=True)
        countvalues = dict(zip(unique, counts))
        fp = countvalues[1] if 1 in countvalues else 0

        unique, counts = np.unique(gtArray-detArray, return_counts=True)
        countvalues = dict(zip(unique, counts))
        fn = countvalues[1] if 1 in countvalues else 0

        precision = tp / (tp + fp) if tp + fp > 0 else 0
        recall = tp / (fn + tp) if tp + fn > 0 else 0

    #print(detection, groundtruth, precision, recall)
    return precision, recall