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
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]
learner.setEpsilon(epsilon)
learner.normModelSelect = True
paramDict = {}
paramDict["setC"] = Cs
numParams = Cs.shape[0]
#datasets = [datasets[1]]
numProcesses = 8
dataDir = PathDefaults.getDataDir() + "modelPenalisation/regression/"
datasetName = datasets[9]
print(datasetName)
j = 0
trainX, trainY, testX, testY = ModelSelectUtils.loadRegressDataset(dataDir, datasetName, j)
learner = LibSVM(kernel='gaussian', type="Epsilon_SVR", processes=numProcesses)
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()
foldsSet = numpy.arange(2, 31, 2)
Cvs = numpy.array([1.0])
sampleMethod = Sampling.crossValidation
sampleSize = 100
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
"""
for i in range(foldsSet.shape[0]):
folds = foldsSet[i]
Cvs = numpy.array([folds-1.0])
def testModelSelect(self):
"""
We test the results on some data and compare to SVR.
"""
numExamples = 200
X, y = data.make_regression(numExamples, noise=0.5)
X = Standardiser().standardiseArray(X)
y = Standardiser().standardiseArray(y)
trainX = X[0:100, :]
trainY = y[0:100]
testX = X[100:, :]
testY = y[100:]
learner = DecisionTreeLearner(maxDepth=20, minSplit=10, pruneType="REP-CV")
learner.setPruneCV(8)
paramDict = {}
paramDict["setGamma"] = numpy.linspace(0.0, 1.0, 10)
paramDict["setPruneCV"] = numpy.arange(6, 11, 2, numpy.int)
folds = 5
idx = Sampling.crossValidation(folds, trainX.shape[0])
bestTree, cvGrid = learner.parallelModelSelect(trainX, trainY, idx, paramDict)
predY = bestTree.predict(testX)
error = Evaluator.rootMeanSqError(testY, predY)
print(error)
learner = DecisionTreeLearner(maxDepth=20, minSplit=5, pruneType="CART")
paramDict = {}
paramDict["setGamma"] = numpy.linspace(0.0, 1.0, 50)
folds = 5
idx = Sampling.crossValidation(folds, trainX.shape[0])
bestTree, cvGrid = learner.parallelModelSelect(trainX, trainY, idx, paramDict)
predY = bestTree.predict(testX)
error = Evaluator.rootMeanSqError(testY, predY)
print(error)
return
#Let's compare to the SVM
learner2 = LibSVM(kernel='gaussian', type="Epsilon_SVR")
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"] = learner2.getEpsilons()
idx = Sampling.crossValidation(folds, trainX.shape[0])
bestSVM, cvGrid = learner2.parallelModelSelect(trainX, trainY, idx, paramDict)
predY = bestSVM.predict(testX)
error = Evaluator.rootMeanSqError(testY, predY)
print(error)
