def getSetup(learnerName, dataDir, outputDir, numProcesses):
if learnerName=="SVM":
learner = LibSVM(kernel='gaussian', type="C_SVC", processes=numProcesses)
loadMethod = ModelSelectUtils.loadRatschDataset
dataDir += "benchmark/"
outputDir += "classification/" + learnerName + "/"
paramDict = {}
paramDict["setC"] = learner.getCs()
paramDict["setGamma"] = learner.getGammas()
elif learnerName=="SVR":
learner = LibSVM(kernel='gaussian', type="Epsilon_SVR", processes=numProcesses)
learner.normModelSelect = True
loadMethod = ModelSelectUtils.loadRegressDataset
dataDir += "regression/"
outputDir += "regression/" + learnerName + "/"
paramDict = {}
paramDict["setC"] = 2.0**numpy.arange(-10, 14, 2, dtype=numpy.float)
paramDict["setGamma"] = 2.0**numpy.arange(-10, 4, 2, dtype=numpy.float)
paramDict["setEpsilon"] = learner.getEpsilons()
elif learnerName=="CART":
learner = DecisionTreeLearner(criterion="mse", maxDepth=30, minSplit=1, pruneType="CART", processes=numProcesses)
learner.setChunkSize(2)
loadMethod = ModelSelectUtils.loadRegressDataset
dataDir += "regression/"
outputDir += "regression/" + learnerName + "/"
paramDict = {}
paramDict["setGamma"] = numpy.array(numpy.round(2**numpy.arange(1, 7.5, 0.5)-1), dtype=numpy.int)
else:
raise ValueError("Unknown learnerName: " + learnerName)
return learner, loadMethod, dataDir, outputDir, paramDict
numProcesses = multiprocessing.cpu_count()
learner = LibSVM(kernel="rbf", processes=numProcesses, type="Epsilon_SVR")
learner.setChunkSize(3)
Cs = 2.0**numpy.arange(-10, 14, 2, dtype=numpy.float)
gammas = 2.0**numpy.arange(-10, 4, 2, dtype=numpy.float)
epsilons = learner.getEpsilons()
gammaInd = 3
gamma = gammas[gammaInd]
learner.setGamma(gamma)
epsilonInd = 0
epsilon = epsilons[epsilonInd]
learner.setEpsilon(epsilon)
learner.normModelSelect = True
paramDict = {}
paramDict["setC"] = Cs
numParams = Cs.shape[0]
#datasets = [datasets[1]]
for datasetName, numRealisations in datasets:
logging.debug("Dataset " + datasetName)
errors = numpy.zeros(numRealisations)
sampleMethod = Sampling.crossValidation
alpha = 1.0
folds = 2
numRealisations = 5
