Function test

pattern/metrics.py:125–142

Returns an (accuracy, precision, recall, F1-score)-tuple. With average=None, precision & recall are computed for the positive class (True). With average=MACRO, precision & recall for positive and negative class are macro-averaged.

(classify=lambda document:False, documents=[], average=None)

    return TP, TN, FP, FN

def test(classify=lambda document:False, documents=[], average=None):
    """ Returns an (accuracy, precision, recall, F1-score)-tuple.
        With average=None, precision & recall are computed for the positive class (True).
        With average=MACRO, precision & recall for positive and negative class are macro-averaged.
    """
    TP, TN, FP, FN = confusion_matrix(classify, documents)
    A  = float(TP + TN) / ((TP + TN + FP + FN) or 1)
    P1 = float(TP) / ((TP + FP) or 1) # positive class precision
    R1 = float(TP) / ((TP + FN) or 1) # positive class recall
    P0 = float(TN) / ((TN + FN) or 1) # negative class precision
    R0 = float(TN) / ((TN + FP) or 1) # negative class recall
    if average is None:
        P, R = (P1, R1)
    if average == MACRO:
        P, R = ((P1 + P0) / 2,
                (R1 + R0) / 2)
    F1 = 2 * P * R / ((P + R) or 1)
    return (A, P, R, F1)

def accuracy(classify=lambda document:False, documents=[], average=None):
    """ Returns the percentage of correct classifications (true positives + true negatives).