if saveResults:
trainObjectives = numpy.zeros((startAverages.shape[0], len(learningRateParams)))
testObjectives = numpy.zeros((startAverages.shape[0], len(learningRateParams)))
for trainX, testX in trainTestXs:
trainOmegaPtr = SparseUtils.getOmegaListPtr(trainX)
testOmegaPtr = SparseUtils.getOmegaListPtr(testX)
allOmegaPtr = SparseUtils.getOmegaListPtr(X)
logging.debug("Number of non-zero elements: " + str((trainX.nnz, testX.nnz)))
paramList = []
for j, startAverage in enumerate(startAverages):
for i, (alpha, t0) in enumerate(learningRateParams):
maxLocalAuc.startAverage = startAverage
maxLocalAuc.alpha = alpha
maxLocalAuc.t0 = t0
logging.debug(maxLocalAuc)
learner = maxLocalAuc.copy()
paramList.append((trainX, learner))
pool = multiprocessing.Pool(maxtasksperchild=100, processes=multiprocessing.cpu_count())
resultsIterator = pool.imap(computeTestObj, paramList, chunkSize)
for j, startAverage in enumerate(startAverages):
for i, (alpha, t0) in enumerate(learningRateParams):
U, V, trainObj, testObj = resultsIterator.next()
trainObjectives[j, i] += trainObj
