def computeRankMetrics(self, X, Y, indexList, bestLearners, standardiserY, labelIndex):
#Some code to do ranking using the learner predictors
i = 0
rankMetrics = numpy.zeros((len(indexList), self.boundsList[labelIndex].shape[0]-1))
for idxtr, idxts in indexList:
logging.info("Iteration " + str(i))
trainX, testX = X[idxtr, :], X[idxts, :]
trainY, testY = Y[idxtr], Y[idxts]
bestLearners[i].learnModel(trainX, trainY)
predY = bestLearners[i].predict(testX)
gc.collect()
#Now output 3 sets of ranked scores
predY = standardiserY.unstandardiseArray(predY)
testY = standardiserY.unstandardiseArray(testY)
YScores = MetabolomicsUtils.scoreLabels(predY, self.boundsList[labelIndex])
YIndList = MetabolomicsUtils.createIndicatorLabel(testY, self.boundsList[labelIndex])
for j in range(self.boundsList[labelIndex].shape[0]-1):
rankMetrics[i, j] = Evaluator.auc(YScores[:, j], YIndList[j])
i += 1
logging.debug(rankMetrics)
return rankMetrics
def meanAUC(self, predY, testY, labelIndex, standardiserY):
predY = standardiserY.unstandardiseArray(predY)
testY = standardiserY.unstandardiseArray(testY)
YScores = MetabolomicsUtils.scoreLabels(predY, self.boundsList[labelIndex])
YIndList = MetabolomicsUtils.createIndicatorLabel(testY, self.boundsList[labelIndex])
rankMetrics = numpy.zeros(self.boundsList[labelIndex].shape[0]-1)
for j in range(rankMetrics.shape[0]):
rankMetrics[j] = Evaluator.auc(YScores[:, j], YIndList[j])
return numpy.mean(rankMetrics)
