fprTrain, tprTrain = MCEvaluator.averageRocCurve(trainX, U, V)
fprTest, tprTest = MCEvaluator.averageRocCurve(testX, U, V)
return fprTrain, tprTrain, fprTest, tprTest
if saveResults:
paramList = []
chunkSize = 1
U, V = maxLocalAuc.initUV(X)
for loss, rho in losses:
for trainX, testX in trainTestXs:
maxLocalAuc.loss = loss
maxLocalAuc.rho = rho
paramList.append((trainX, testX, maxLocalAuc.copy(), U.copy(), V.copy()))
pool = multiprocessing.Pool(maxtasksperchild=100, processes=multiprocessing.cpu_count())
resultsIterator = pool.imap(computeTestAuc, paramList, chunkSize)
#import itertools
#resultsIterator = itertools.imap(computeTestAuc, paramList)
meanFprTrains = []
meanTprTrains = []
meanFprTests = []
meanTprTests = []
for loss in losses:
fprTrains = []
tprTrains = []
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
testObjectives[j, i] += testObj
pool.terminate()
trainObjectives /= folds
return fprTrain, tprTrain, fprTest, tprTest
if saveResults:
paramList = []
chunkSize = 1
U, V = maxLocalAuc.initUV(X)
for loss in losses:
for nnz in nnzs:
for trainX, testX in trainTestXs:
numpy.random.seed(21)
modelSelectX, userInds = Sampling.sampleUsers2(trainX, nnz*trainX.nnz)
maxLocalAuc.loss = loss
paramList.append((modelSelectX, trainX, testX, maxLocalAuc.copy(), U.copy(), V.copy()))
pool = multiprocessing.Pool(maxtasksperchild=100, processes=multiprocessing.cpu_count())
resultsIterator = pool.imap(computeTestAuc, paramList, chunkSize)
#import itertools
#resultsIterator = itertools.imap(computeTestAuc, paramList)
meanFprTrains = []
meanTprTrains = []
meanFprTests = []
meanTprTests = []
for loss in losses:
fprTrains = []
tprTrains = []
def testCopy(self):
u = 0.1
eps = 0.001
k = 10
maxLocalAuc = MaxLocalAUC(k, u, alpha=5.0, eps=eps)
maxLocalAuc.copy()
def testCopy(self):
u= 0.1
eps = 0.001
k = 10
maxLocalAuc = MaxLocalAUC(k, u, alpha=5.0, eps=eps)
maxLocalAuc.copy()
