showCART = True showSVR = False from itertools import cycle lines = ["k-","k--","k-.","k:","k-x", "k-+"] linecycler = cycle(lines) if showSVR: outputDir = PathDefaults.getOutputDir() + "modelPenalisation/regression/SVR/" sampleSizes = numpy.array([50, 100, 200]) sampleMethods = ["CV"] cvScalings = numpy.arange(0.6, 1.61, 0.2) foldsSet = numpy.arange(2, 13, 2) datasetNames = ModelSelectUtils.getRegressionDatasets() fileNameSuffix = 'Results' summary(datasetNames, sampleSizes, foldsSet, cvScalings, sampleMethods, fileNameSuffix) plotDatasetNames = [datasetNames[7]] plotAlphas(plotDatasetNames, sampleSizes, foldsSet, cvScalings, sampleMethods, fileNameSuffix) sampleSizes = numpy.array([25, 50, 100]) sampleMethods = ["CV"] cvScalings = numpy.arange(0.6, 1.61, 0.2) foldsSet = numpy.arange(10, 51, 10) datasetNames = ModelSelectUtils.getRegressionDatasets() fileNameSuffix = "ResultsExt" summary(datasetNames, sampleSizes, foldsSet, cvScalings, sampleMethods, fileNameSuffix, "GridResultsExt")
from exp.modelselect.ModelSelectUtils import ModelSelectUtils from sandbox.util.Sampling import Sampling from apgl.predictors.LibSVM import LibSVM import matplotlib.pyplot as plt logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) numpy.seterr(all="raise") numpy.random.seed(21) dataDir = PathDefaults.getDataDir() dataDir += "modelPenalisation/regression/" outputDir = PathDefaults.getOutputDir() + "modelPenalisation/regression/SVR/" figInd = 0 loadMethod = ModelSelectUtils.loadRegressDataset datasets = ModelSelectUtils.getRegressionDatasets(True) 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]