clusterROC (
        dataset,
        labeling,
        label,
        distanceMetric=DistanceMetrics.EuclideanDistance,
        )

returns the ROCarea, xcoords, ycoords for a ROC cuve given a dataset, labeling and a label from that labeling, return an ROC curve and area. DistanceMetric must be of the functional form:

[list of distances] = distanceMetric(vector, array)

Implimentations of several distance metrics can be found in compClust.util.DistanceMetrics

clusteringROC (
        dataset,
        labeling,
        distanceMetric=DistanceMetrics.EuclideanDistance,
        )

given a dataset and a labeling return a dictionary with ROC statistics for each cluster in the daaset.

interclusterROC (
        dataset,
        labeling1,
        label1,
        labeling2,
        label2,
        distanceMetric=DistanceMetrics.EuclideanDistance,
        )

Given a dataset and a label from labeling1 and label from labeling2 calculate the ROC curve between the two sets bound to labe1 and label2

rocCurve (
        orderedList,
        set1,
        set2,
        )

returns 2 lists: the X and Y coordanates for creating an ROC curve on the input data.

given an orderedList which contains items from set1 and set2, generate an ROC curve plotting list1 vs list2

The area contained under this curve is easily calculated using MLab.trapz

example:

orderedList = [1,1,0,0,1,1,0,1,0,0,0,1,0] set1 = [1] set2 = [0]

([0,1,2,2,3,4,],[0,0,0,])rocCurve(orderedList, set1, set2)