def testLearningRateSelect(self):
m = 10
n = 20
k = 5
u = 0.5
w = 1 - u
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True)
eps = 0.001
maxLocalAuc = MaxLocalAUC(k, u, eps=eps, stochastic=True)
maxLocalAuc.rate = "optimal"
maxLocalAuc.maxIterations = 5
maxLocalAuc.numProcesses = 1
maxLocalAuc.learningRateSelect(X)
def testLearningRateSelect(self):
m = 10
n = 20
k = 5
u = 0.5
w = 1-u
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True)
eps = 0.001
maxLocalAuc = MaxLocalAUC(k, u, eps=eps, stochastic=True)
maxLocalAuc.rate = "optimal"
maxLocalAuc.maxIterations = 5
maxLocalAuc.numProcesses = 1
maxLocalAuc.learningRateSelect(X)
for normalise in [False, True]:
for initialAlg in initialAlgs:
for rate in rates:
maxLocalAuc.stochastic = stochastic
maxLocalAuc.initialAlg = initialAlg
if initialAlg == "rand":
maxLocalAuc.t0s = t0s
else:
maxLocalAuc.t0s = t0s
maxLocalAuc.normalise = normalise
maxLocalAuc.rate = rate
meanObjs, stdObjs = maxLocalAuc.learningRateSelect(X)
meanObjsList.append(meanObjs)
stdObjsList.append(stdObjs)
pickle.dump((meanObjsList, stdObjsList), open(outputFile, "w"))
else:
data = numpy.load(outputFile)
meanObjsList, stdObjsList = pickle.load(open(outputFile))
import matplotlib
matplotlib.use("GTK3Agg")
import matplotlib.pyplot as plt
"""
plotInd = 0
for stochastic in [False, True]:
for normalise in [False, True]:
maxLocalAuc.reg = False
maxLocalAuc.rho = 1.0
maxLocalAuc.startAverage = 100
maxLocalAuc.t0 = 1.0
maxLocalAuc.t0s = 2.0**-numpy.arange(1, 12, 2)
maxLocalAuc.validationSize = 5
maxLocalAuc.validationUsers = 0.0
if saveResults:
X = DatasetUtils.getDataset(dataset, nnz=100000)
print(X.shape, X.nnz)
print(maxLocalAuc)
maxLocalAuc.lmbdaU = 0.25
maxLocalAuc.lmbdaV = 0.25
meanObjs1, paramDict = maxLocalAuc.learningRateSelect(X)
maxLocalAuc.lmbdaU = 0.03125
maxLocalAuc.lmbdaV = 0.25
meanObjs2, paramDict = maxLocalAuc.learningRateSelect(X)
maxLocalAuc.lmbdaU = 0.25
maxLocalAuc.lmbdaV = 0.03125
meanObjs3, paramDict = maxLocalAuc.learningRateSelect(X)
maxLocalAuc.lmbdaU = 0.03125
maxLocalAuc.lmbdaV = 0.03125
meanObjs4, paramDict = maxLocalAuc.learningRateSelect(X)
numpy.savez(outputFile, meanObjs1, meanObjs2, meanObjs3, meanObjs4)
else:
