def testSaveParams(self):
try:
import sklearn
except ImportError as error:
return
svm = LibSVM()
svm.setC(10.5)
svm.setEpsilon(12.1)
svm.setErrorCost(1.8)
svm.setSvmType("Epsilon_SVR")
svm.setTermination(0.12)
svm.setKernel("gaussian", 0.43)
outputDir = PathDefaults.getOutputDir()
fileName = outputDir + "test/testSvmParams"
svm.saveParams(fileName)
svm2 = LibSVM()
svm2.loadParams(fileName)
self.assertEquals(svm.getC(), 10.5)
self.assertEquals(svm.getEpsilon(), 12.1)
self.assertEqual(svm.getErrorCost(), 1.8)
self.assertEqual(svm.getSvmType(), "Epsilon_SVR")
self.assertEqual(svm.getTermination(), 0.12)
self.assertEqual(svm.getKernel(), "gaussian")
self.assertEqual(svm.getKernelParams(), 0.43)
def testSaveParams(self):
try:
import sklearn
except ImportError as error:
return
svm = LibSVM()
svm.setC(10.5)
svm.setEpsilon(12.1)
svm.setErrorCost(1.8)
svm.setSvmType("Epsilon_SVR")
svm.setTermination(0.12)
svm.setKernel("gaussian", 0.43)
outputDir = PathDefaults.getOutputDir()
fileName = outputDir + "test/testSvmParams"
svm.saveParams(fileName)
svm2 = LibSVM()
svm2.loadParams(fileName)
self.assertEquals(svm.getC(), 10.5)
self.assertEquals(svm.getEpsilon(), 12.1)
self.assertEqual(svm.getErrorCost(), 1.8)
self.assertEqual(svm.getSvmType(), "Epsilon_SVR")
self.assertEqual(svm.getTermination(), 0.12)
self.assertEqual(svm.getKernel(), "gaussian")
self.assertEqual(svm.getKernelParams(), 0.43)
def testSetSvmType(self):
try:
import sklearn
except ImportError as error:
return
numExamples = 100
numFeatures = 10
X = numpy.random.randn(numExamples, numFeatures)
X = Standardiser().standardiseArray(X)
c = numpy.random.randn(numFeatures)
y = numpy.dot(X, numpy.array([c]).T).ravel() + 1
y2 = numpy.array(y > 0, numpy.int32) * 2 - 1
svm = LibSVM()
svm.setSvmType("Epsilon_SVR")
self.assertEquals(svm.getType(), "Epsilon_SVR")
#Try to get a good error
Cs = 2**numpy.arange(-6, 4, dtype=numpy.float)
epsilons = 2**numpy.arange(-6, 4, dtype=numpy.float)
bestError = 10
for C in Cs:
for epsilon in epsilons:
svm.setEpsilon(epsilon)
svm.setC(C)
svm.learnModel(X, y)
yp = svm.predict(X)
if Evaluator.rootMeanSqError(y, yp) < bestError:
bestError = Evaluator.rootMeanSqError(y, yp)
self.assertTrue(
bestError < Evaluator.rootMeanSqError(y, numpy.zeros(y.shape[0])))
svm.setSvmType("C_SVC")
svm.learnModel(X, y2)
yp2 = svm.predict(X)
self.assertTrue(0 <= Evaluator.binaryError(y2, yp2) <= 1)
def testSetSvmType(self):
try:
import sklearn
except ImportError as error:
return
numExamples = 100
numFeatures = 10
X = numpy.random.randn(numExamples, numFeatures)
X = Standardiser().standardiseArray(X)
c = numpy.random.randn(numFeatures)
y = numpy.dot(X, numpy.array([c]).T).ravel() + 1
y2 = numpy.array(y > 0, numpy.int32)*2 -1
svm = LibSVM()
svm.setSvmType("Epsilon_SVR")
self.assertEquals(svm.getType(), "Epsilon_SVR")
#Try to get a good error
Cs = 2**numpy.arange(-6, 4, dtype=numpy.float)
epsilons = 2**numpy.arange(-6, 4, dtype=numpy.float)
bestError = 10
for C in Cs:
for epsilon in epsilons:
svm.setEpsilon(epsilon)
svm.setC(C)
svm.learnModel(X, y)
yp = svm.predict(X)
if Evaluator.rootMeanSqError(y, yp) < bestError:
bestError = Evaluator.rootMeanSqError(y, yp)
self.assertTrue(bestError < Evaluator.rootMeanSqError(y, numpy.zeros(y.shape[0])))
svm.setSvmType("C_SVC")
svm.learnModel(X, y2)
yp2 = svm.predict(X)
self.assertTrue(0 <= Evaluator.binaryError(y2, yp2) <= 1)
def testSetEpsilon(self):
"""
Test out the parameter for the regressive SVM, vary epsilon and look at
number of support vectors.
"""
try:
import sklearn
except ImportError as error:
return
svm = LibSVM()
svm.setC(10.0)
svm.setEpsilon(0.1)
svm.setSvmType("Epsilon_SVR")
numExamples = 100
numFeatures = 10
X = numpy.random.randn(numExamples, numFeatures)
c = numpy.random.randn(numFeatures)
y = numpy.dot(X, numpy.array([c]).T).ravel() + numpy.random.randn(100)
svm.setEpsilon(1.0)
svm.learnModel(X, y)
numSV = svm.getModel().support_.shape
svm.setEpsilon(0.5)
svm.learnModel(X, y)
numSV2 = svm.getModel().support_.shape
svm.setEpsilon(0.01)
svm.learnModel(X, y)
numSV3 = svm.getModel().support_.shape
#There should be fewer SVs as epsilon increases
self.assertTrue(numSV < numSV2)
self.assertTrue(numSV2 < numSV3)
def testSetEpsilon(self):
"""
Test out the parameter for the regressive SVM, vary epsilon and look at
number of support vectors.
"""
try:
import sklearn
except ImportError as error:
return
svm = LibSVM()
svm.setC(10.0)
svm.setEpsilon(0.1)
svm.setSvmType("Epsilon_SVR")
numExamples = 100
numFeatures = 10
X = numpy.random.randn(numExamples, numFeatures)
c = numpy.random.randn(numFeatures)
y = numpy.dot(X, numpy.array([c]).T).ravel() + numpy.random.randn(100)
svm.setEpsilon(1.0)
svm.learnModel(X, y)
numSV = svm.getModel().support_.shape
svm.setEpsilon(0.5)
svm.learnModel(X, y)
numSV2 = svm.getModel().support_.shape
svm.setEpsilon(0.01)
svm.learnModel(X, y)
numSV3 = svm.getModel().support_.shape
#There should be fewer SVs as epsilon increases
self.assertTrue(numSV < numSV2)
self.assertTrue(numSV2 < numSV3)
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
for sampleSize in sampleSizes:
print("Sample size " +str(sampleSize))
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
#errors = learner.parallelPenaltyGrid(validX, validY, testX, testY, paramDict, computeTestError)
#errors = numpy.squeeze(errors)
errors = numpy.zeros((Cs.shape[0], gammas.shape[0]))
norms = numpy.zeros((Cs.shape[0], gammas.shape[0]))
for i, C in enumerate(Cs):
for j, gamma in enumerate(gammas):
learner.setEpsilon(epsilons[0])
learner.setC(C)
learner.setGamma(gamma)
learner.learnModel(validX, validY)
predY = learner.predict(testX)
errors[i, j] = Evaluator.meanAbsError(predY, testY)
norms[i, j] = learner.weightNorm()
for i in range(gammas.shape[0]):
plt.figure(i)
plt.plot(numpy.log(Cs), errors[:, i], label=str(sampleSize))
plt.legend(loc="upper left")
plt.xlabel("C")
plt.ylabel("Error")
