def TopVarImportanceTest(data, expectNone=False):
resA = []
resB = []
learner = AZorngCvSVM.CvSVMLearner(gamma=1.0,
svm_type=103,
C=1,
coef0=0,
degree=3,
epsR=0.001,
kernel_type=2,
nu=0.5,
p=0.1,
probability=0,
shrinking=1)
CvSVM = learner(data)
for ex in data:
resA.append(CvSVM.getTopImportantVars(ex, 1))
scratchdir = miscUtilities.createScratchDir(
desc="TopVarImportanceTest")
modelPath = os.path.join(scratchdir, "CvSVNModel")
CvSVM.write(modelPath)
LoadedCvSVM = AZorngCvSVM.CvSVMread(modelPath)
miscUtilities.removeDir(scratchdir)
for ex in data:
resB.append(LoadedCvSVM.getTopImportantVars(ex, 1))
if expectNone:
return resA == resB == [None] * len(data)
else:
return resA == resB and None not in resA and resA.count(
resA[0]) != len(resA)
def test_CreateLogicalExpressionConsensusLearner(self):
""" Test creation of logical expression consensus learner """
# Arrange
# Construct expression learner/classifier
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
discreteExpression = [
"firstLearner == Iris-setosa -> Iris-setosa", "-> Iris-virginica"
]
discreteLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=discreteExpression)
discreteClassifier = discreteLearner(self.irisData)
verifiedLearner = AZorngCvSVM.CvSVMLearner()
verifiedClassifier = verifiedLearner(self.irisData)
# Act
result = []
verifiedResult = []
for ex in self.irisData:
result.append(discreteClassifier(ex))
verifiedResult.append(verifiedClassifier(ex))
# Assert
for index, item in enumerate(result):
if not result[index].value == verifiedResult[index].value:
print "Not equal on index: ", index
self.assertEqual(result[index].value, verifiedResult[index].value)
def trainSVMOptParam(train, SVMparam):
# Optimize parameters
#SVMparam = [1.0, 0.05]
if not SVMparam:
trainDataFile = "/scratch/trainDataTmp.tab"
train.save(trainDataFile)
learner = AZorngCvSVM.CvSVMLearner()
param = paramOptUtilities.getOptParam(learner,
trainDataFile,
paramList=None,
useGrid=False,
verbose=1,
queueType="NoSGE",
runPath=None,
nExtFolds=None,
nFolds=10,
logFile="",
getTunedPars=True,
fixedParams={})
optC = float(param[1]["C"])
optGamma = float(param[1]["gamma"])
SVMparam = [optC, optGamma]
else:
optC = SVMparam[0]
optGamma = SVMparam[1]
#print "Optimal SVM parameters ", optC, optGamma
model = AZorngCvSVM.CvSVMLearner(train, C=optC, gamma=optGamma)
return model, SVMparam
def test_SVMC(self):
# Train a svm
svmL = AZorngCvSVM.CvSVMLearner(scaleData=False,
svm_type=103,
gamma=0.01,
C=1,
nu=0.5,
p=1,
eps=0.001,
coef0=0,
degree=3)
svm = svmL(self.inDataC)
trainedAcc = evalUtilities.getRMSE(self.inDataC, svm)
self.assertEqual(round(trainedAcc, 7), round(2.8525863999999999,
7)) # ver 0.3
# Save model
rc = svm.write(self.modelPath)
self.assertEqual(rc, True)
# Load the saved model
loadedsvm = AZorngCvSVM.CvSVMread(self.modelPath)
loadedAcc = evalUtilities.getRMSE(self.inDataC, loadedsvm)
# Assure equal accuracy
self.assertEqual(trainedAcc, loadedAcc)
svmLearner = AZorngCvSVM.CvSVMLearner(scaleData=False)
svmLearner.name = "CvSVMLearner"
svmLearner.eps = 0.001
svmLearner.p = 1
svmLearner.nu = 0.6
svmLearner.kernel_type = 2
svmLearner.svm_type = 103
svmLearner.gamma = 0.0033
svmLearner.C = 47
svmLearner.scaleData = True
svmLearner.scaleClass = False
Res = orngTest.crossValidation(
[svmLearner],
self.inDataC,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible)
RMSE = evalUtilities.RMSE(Res)[0]
self.assertEqual(round(RMSE, 2), round(2.96, 2)) #Ver 0.3
newSVM = svmLearner(self.inDataC)
trainedAcc = evalUtilities.getRMSE(self.inDataC, newSVM)
# Save model
rc = newSVM.write(self.modelPath)
self.assertEqual(rc, True)
# Load the saved model
loadedsvm = AZorngCvSVM.CvSVMread(self.modelPath)
loadedAcc = evalUtilities.getRMSE(self.inDataC, loadedsvm)
# Assure equal accuracy
self.assertEqual(round(trainedAcc, 4), round(2.8289, 4)) #Ver 0.3
self.assertEqual(round(trainedAcc, 4), round(loadedAcc, 4))
def test_Priors(self):
"""Test to assure that priors are set correcly."""
# Create a CvSVM model
CvSVMlearner = AZorngCvSVM.CvSVMLearner(C=3,
priors={
"Iris-versicolor": 2,
"Iris-virginica": 4,
"Iris-setosa": 6
})
CvSVMmodel = CvSVMlearner(self.inDataD)
# Calculate classification accuracy
Acc = evalUtilities.getClassificationAccuracy(self.inDataD, CvSVMmodel)
# Save the model
scratchdir = os.path.join(AZOC.SCRATCHDIR,
"scratchdirTest" + str(time.time()))
os.mkdir(scratchdir)
modelPath = os.path.join(scratchdir, "modelPriors.CvSVM")
CvSVMmodel.write(modelPath)
# Read in the model
newCvSVMmodel = AZorngCvSVM.CvSVMread(modelPath)
# Calculate classification accuracy
savedAcc = evalUtilities.getClassificationAccuracy(
self.inDataD, CvSVMmodel)
# Test that the accuracy of the two classifiers is the exact same
self.assertEqual(Acc, savedAcc)
#Check the priors saved in the model
file = open(os.path.join(modelPath, "model.svm"), "r")
lines = file.readlines()
file.close()
priors = [
round(x, 2) for x in eval((lines[18].strip()).replace("data:", ""))
]
self.assertEqual(len(priors), 3)
self.assertEqual(
priors[self.inDataD.domain.classVar.values.index("Iris-setosa")],
18.0)
self.assertEqual(
priors[self.inDataD.domain.classVar.values.index(
"Iris-versicolor")], 6.0)
self.assertEqual(
priors[self.inDataD.domain.classVar.values.index(
"Iris-virginica")], 12.0)
# Remove the scratch directory
os.system("/bin/rm -rf " + scratchdir)
def test_CreateDefaultClassifierUsingPreTrainedRegressionClassifiers(self):
""" Test the creation of custom Consensus Classifier using pre-trained regression classifiers. """
# Arrange
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
classifiers = {}
for k, v in learners.items():
classifiers[k] = v(self.getRegressionTrainingData())
expression = "a + b + c"
# Act
classifier = AZorngConsensus.ConsensusClassifier(
classifiers=classifiers, expression=expression)
# Assert
self.assertNotEqual(classifier, None)
self.assertEqual(len(classifier.classifiers), len(learners))
self.assertNotEqual(classifier.basicStat, None)
self.assertNotEqual(classifier.classVar, None)
self.assertNotEqual(classifier.domain, None)
self.assertEqual(classifier.expression, expression)
self.assertNotEqual(classifier.imputeData, None)
#self.assertEqual(classifier.NTrainEx, len(trainingData))
self.assertEqual(classifier.name, "Consensus classifier")
self.assertNotEqual(classifier.varNames, None)
self.assertEqual(classifier.verbose, 0)
self.assertEqual(classifier.weights, None)
def test_CreateDefaultClassifierUsingPreTrainedRegressionClassifiers(self):
""" Test the creation of default Consensus Classifier using pre-trained classification classifiers. """
# Arrange
learners = [
AZorngCvSVM.CvSVMLearner(),
AZorngCvANN.CvANNLearner(),
AZorngRF.RFLearner()
]
classifiers = [l(self.getRegressionTrainingData()) for l in learners]
# Act
classifier = AZorngConsensus.ConsensusClassifier(
classifiers=classifiers)
# Assert
self.assertNotEqual(classifier, None)
self.assertEqual(len(classifier.classifiers), len(learners))
self.assertNotEqual(classifier.basicStat, None)
self.assertNotEqual(classifier.classVar, None)
self.assertNotEqual(classifier.domain, None)
self.assertEqual(classifier.expression, None)
self.assertNotEqual(classifier.imputeData, None)
#self.assertEqual(classifier.NTrainEx, len(trainingData))
self.assertEqual(classifier.name, "Consensus classifier")
self.assertNotEqual(classifier.varNames, None)
self.assertEqual(classifier.verbose, 0)
self.assertEqual(classifier.weights, None)
def test_CreateCustomClassificationClassifierUsingTrainingData(self):
""" Test the creation of custom classification Classifier by calling learner with training data. """
# Arrange
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
expression = [
"firstLearner == Iris-setosa -> Iris-setosa", "-> Iris-virginica"
]
trainingData = self.getClassificationTrainingData()
learner = AZorngConsensus.ConsensusLearner(learners=learners,
expression=expression)
# Act
classifier = learner(trainingData)
# Assert
self.assertNotEqual(classifier, None)
self.assertEqual(len(classifier.classifiers), len(learners))
self.assertNotEqual(classifier.basicStat, None)
self.assertNotEqual(classifier.classVar, None)
self.assertNotEqual(classifier.domain, None)
self.assertEqual(classifier.expression, expression)
self.assertNotEqual(classifier.imputeData, None)
self.assertEqual(classifier.NTrainEx, len(trainingData))
self.assertEqual(classifier.name, "Consensus classifier")
self.assertNotEqual(classifier.varNames, None)
self.assertEqual(classifier.verbose, 0)
self.assertEqual(classifier.weights, None)
def test_PredictionWithIncompatibleDomain(self):
"""Test prediction with uncompatible domain
Test the non-prediction of examples with an incompatible domain
"""
expectedAcc1 = 0.7 #Ver 0.3
# Create a svm model
svm = AZorngCvSVM.CvSVMLearner(self.noBadDataTrain)
#using from index 3 o the end of data, because we know that from 0 to 2 the examples are not compatible
Acc1 = evalUtilities.getClassificationAccuracy(self.noBadDataTest, svm)
self.assertEqual(round(Acc1, 9), round(expectedAcc1, 9),
"The Accuracy is not the expected. Got: " + str(Acc1))
self.assertEqual(svm(self.badVarTypeData[0]), 'NEG',
"This example could still be predicted. Got: " +
str(svm(self.badVarTypeData[0]))) #Ver 0.3
self.assertEqual(
svm(self.badVarTypeData[1]), 'NEG',
"This example could still be predicted. Got: " +
str(svm(self.badVarTypeData[1])))
self.assertEqual(
svm(self.badVarNameData[0]), None,
"This example should NOT be predicted. Got: " +
str(svm(self.badVarNameData[0])))
self.assertEqual(
svm(self.badVarCountData[0]), None,
"This example should NOT be predicted. Got: " +
str(svm(self.badVarCountData[0])))
def test_PredictionWithDiffVarType(self):
"""Test prediction with diff. VarType
Test the prediction of examples with different varType
"""
expectedAcc = 0.666666666667
# Create a svm model
svm = AZorngCvSVM.CvSVMLearner(self.noBadDataTrain)
#using from index 3 o the end of data, because we know that from 0 to 2 the examples are not compatible
Acc2 = evalUtilities.getClassificationAccuracy(self.noBadDataTest[3:],
svm)
Acc1 = evalUtilities.getClassificationAccuracy(self.badVarTypeData[3:],
svm)
self.assertEqual(round(Acc1, 7), round(expectedAcc, 7),
"The Accuracy is not the expected. Got: " + str(Acc1))
self.assertEqual(round(Acc2, 7), round(expectedAcc, 7),
"The Accuracy is not the expected. Got: " + str(Acc2))
self.assert_(
('Fixed Types of variables' in svm.examplesFixedLog)
and (svm.examplesFixedLog['Fixed Types of variables'] == 27),
"No report of fixing in classifier class")
self.assert_(
('Vars needing type fix' in svm.examplesFixedLog)
and (svm.examplesFixedLog['Vars needing type fix']['[Br]([C])']
== "EnumVariable to FloatVariable"),
"No report of fixing in classifier class")
def test_CreateDefaultClassifierUsingTrainingData(self):
""" Test the creation of default Classifier by calling learner with training data. """
# Arrange
learners = [
AZorngCvSVM.CvSVMLearner(),
AZorngCvANN.CvANNLearner(),
AZorngRF.RFLearner()
]
trainingData = self.getRegressionTrainingData()
learner = AZorngConsensus.ConsensusLearner(learners=learners)
# Act
classifier = learner(trainingData)
# Assert
self.assertNotEqual(classifier, None)
self.assertEqual(len(classifier.classifiers), len(learners))
self.assertEqual(classifier.expression, None)
self.assertEqual(classifier.name, "Consensus classifier")
self.assertEqual(classifier.verbose, 0)
self.assertNotEqual(classifier.imputeData, None)
self.assertEqual(classifier.NTrainEx, len(trainingData))
self.assertNotEqual(classifier.basicStat, None)
self.assertEqual(classifier.weights, None)
def test_CreateModelWithLearnerDictionary(self):
""" Test the creation of Consensus Model using dictionary of learners """
# Arrange
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
expression = "a + b + c"
# Act
learner = AZorngConsensus.ConsensusLearner(learners=learners,
expression=expression)
# Assert
for k, v in learner.learners.items():
self.assertEqual(learner.learners[k], learners[k])
self.assertEqual(learner.expression, expression)
self.assertEqual(learner.name, "Consensus learner")
self.assertEqual(learner.verbose, 0)
self.assertEqual(learner.imputeData, None)
self.assertEqual(learner.NTrainEx, 0)
self.assertEqual(learner.basicStat, None)
self.assertEqual(learner.weights, None)
def test_MetaDataHandleForSavingModel(self):
"""Test the handling of SaveModel for Data with Meta Atributes
"""
#Test the save of a model created from a train data with meta attributes
self.assert_(
len(self.WMetaTest.domain.getmetas()) >= 1,
"The dataset WMetaTest should have Meta Attributes")
svmM = AZorngCvSVM.CvSVMLearner(self.WMetaTest)
AccNoMetaBefore = evalUtilities.getClassificationAccuracy(
self.NoMetaTrain, svmM)
AccWMetaBefore = evalUtilities.getClassificationAccuracy(
self.WMetaTest, svmM)
# Save the model
scratchdir = os.path.join(AZOC.SCRATCHDIR,
"scratchdirSVMtest" + str(time.time()))
os.mkdir(scratchdir)
modelPath = os.path.join(scratchdir, "CvSVMModel")
svmM.write(modelPath)
# Read in the model
svmR = AZorngCvSVM.CvSVMread(modelPath)
self.assert_(
len(svmR.imputer.defaults.domain.getmetas()) == 0,
"There shouldn't be any Meta data now!")
# Calculate classification accuracy
AccNoMetaAfter = evalUtilities.getClassificationAccuracy(
self.NoMetaTrain, svmR)
AccWMetaAfter = evalUtilities.getClassificationAccuracy(
self.WMetaTest, svmR)
# Test that the accuracy of the model before and after saved
self.assertEqual(
AccNoMetaBefore, AccNoMetaAfter,
"NoMeta: Predictions after loading saved model were different")
self.assertEqual(
AccWMetaBefore, AccWMetaAfter,
"WMeta: Predictions after loading saved model were different")
self.assertEqual(round(AccWMetaAfter, 9), round(0.7, 9),
"Accuracy was not the expected value!")
self.assertEqual(round(AccNoMetaAfter, 9), round(0.6, 9),
"Accuracy was not the expected value!")
# Remove the scratch directory
os.system("/bin/rm -rf " + scratchdir)
def test_CustomLogicalExpressionUsingOrAndStatement(self):
""" Test logical expression using OR/AND statements """
# Arrange
# Construct verification learners
a = AZorngCvSVM.CvSVMLearner()
a = a(self.irisData)
b = AZorngCvANN.CvANNLearner()
b = b(self.irisData)
c = AZorngRF.RFLearner()
c = c(self.irisData)
# Construct expression learner/classifier
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
discreteExpression = [
"a == Iris-setosa and c == Iris-virginica or b == Iris-setosa -> Iris-setosa",
"-> Iris-virginica"
]
discreteLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=discreteExpression)
discreteClassifier = discreteLearner(self.irisData)
# Act
result = []
for ex in self.irisData:
result.append(discreteClassifier(ex))
verifiedResult = []
for ex in self.irisData:
if a(ex).value == "Iris-setosa" and c(
ex).value == "Iris-virginica" or b(
ex).value == "Iris-setosa":
verifiedResult.append("Iris-setosa")
else:
verifiedResult.append("Iris-virginica")
# Assert
for index, item in enumerate(result):
if not result[index].value == verifiedResult[index]:
print "Not equal on index: ", index, " Predicted: ", result[
index].value, " Real: ", verifiedResult[index]
self.assertEqual(result[index].value, verifiedResult[index])
def test_ImputeTrain(self):
"""
Assure that imputation works for the svm models. Test on data with missing values
This test just assures the the model is trained. The correct imputation test is made on testImpute
"""
svm = AZorngCvSVM.CvSVMLearner(self.missingTrain)
Acc = evalUtilities.getClassificationAccuracy(self.missingTest, svm)
self.assertEqual(round(0.59999999999999998, 5), round(Acc,
5)) # Ver 0.3
def test_AverageNRegressionExpressionUsingObjMap(self):
""" Test regular expression using average N regression with object map """
# Arrange
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
# Construct expression learner/classifier
regressionExpression = "(firstLearner + secondLearner + thirdLearner) / 3"
expressionLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=regressionExpression)
expressionClassifier = expressionLearner(self.DataReg)
# Construct default learner/classifier
defaultLearners = [
AZorngRF.RFLearner(),
AZorngCvANN.CvANNLearner(),
AZorngCvSVM.CvSVMLearner()
]
defaultLearner = AZorngConsensus.ConsensusLearner(
learners=defaultLearners)
defaultClassifier = defaultLearner(self.DataReg)
# Act
expressionPredictions = []
for ex in self.DataReg:
expressionPredictions.append(expressionClassifier(ex))
defaultPredictions = []
for ex in self.DataReg:
defaultPredictions.append(defaultClassifier(ex))
# Assert
for index in range(len(expressionPredictions)):
self.assertEqual(
True,
float_compare(expressionPredictions[index],
defaultPredictions[index]))
def test_TwoWays(self):
"""Test two ways svm creation
Test that an svm created in one or two steps give the same results
"""
# One step svm creation
svm = AZorngCvSVM.CvSVMLearner(self.train_data)
# Calculate classification accuracy for the classifier trained in one step
oneStepAcc = evalUtilities.getClassificationAccuracy(
self.test_data, svm)
# Two step svm creation
learner = AZorngCvSVM.CvSVMLearner()
svm = learner(self.train_data)
# Calculate classification accuracy for the classifier trained in two steps
twoStepAcc = evalUtilities.getClassificationAccuracy(
self.test_data, svm)
# Test that the accuracy of the classifiers created in different ways is the exact same
self.assertEqual(oneStepAcc, twoStepAcc)
def test_CreateLearnerWithObjectMapping(self):
""" Test the creation of learners with an object map """
# Arrange
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
# Act
learner = AZorngConsensus.ConsensusLearner(learners=learners)
# Assert
self.assertEqual(len(learner.learners), len(learners))
def test_CustomRegressionExpressionUsingWeights(self):
""" Test regression expression using weights """
# Arrange
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
weights = {'a': lambda x: 1, 'b': lambda x: 2, 'c': lambda x: 3}
regressionExpression = "(a + b + c) / 3"
expressionLearner = AZorngConsensus.ConsensusLearner(
learners=learners,
expression=regressionExpression,
weights=weights)
classifier = expressionLearner(self.DataReg)
# Act
result = []
for ex in self.DataReg:
result.append(classifier(ex))
verifiedResult = []
for ex in self.DataReg:
a_value = classifier.classifiers['a'](ex)
a_weight_value = weights['a'](a_value)
b_value = classifier.classifiers['b'](ex)
b_weight_value = weights['b'](b_value)
c_value = classifier.classifiers['c'](ex)
c_weight_value = weights['c'](c_value)
prediction = (a_value * a_weight_value + b_value * b_weight_value +
c_value * c_weight_value) / 3
verifiedResult.append(prediction)
# Assert
for index, item in enumerate(result):
if float_compare(result[index].value,
verifiedResult[index]) == False:
print "Not equal on index: ", index
print "Result: ", result[
index].value, " Verified: ", verifiedResult[index]
print "Delta: ", abs(result[index].value -
verifiedResult[index])
self.assertEqual(
float_compare(result[index].value, verifiedResult[index]),
True)
def test_CreateLearnerWithObjectMappingWithoutExpression(self):
""" Test with name variable mapping defined but not expression given """
# Arrange
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
learner = AZorngConsensus.ConsensusLearner(learners=learners)
# Act
classifier = learner(self.DataReg)
# Assert
self.assertEqual(classifier, None)
def test_SVMreg(self):
CvSVMmodel = AZorngCvSVM.CvSVMLearner(self.regTrainData)
predList = [
5.482803,
4.889269,
5.188474,
5.528782,
6.224637,
4.679743,
2.062022,
7.878900,
5.603292,
5.905775,
] # Ver. 0.3
for idx, ex in enumerate(self.regTrainData[0:10]):
self.assertEqual(round(CvSVMmodel(ex), 4), round(predList[idx], 4))
def test_SaveLoadCustomRegressionExpression(self):
""" Test save/load custom expression using average N regression with object map """
# Arrange
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
# Construct expression learner/classifier
regressionExpression = "(firstLearner + secondLearner + thirdLearner) / 3"
expressionLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=regressionExpression)
expressionClassifier = expressionLearner(self.DataReg)
# Construct default learner/classifier
result = []
for ex in self.DataReg:
result.append(expressionClassifier(ex))
# Act
scratchdir = miscUtilities.createScratchDir(
desc="ConsensusSaveLoadTest")
expressionClassifier.write(os.path.join(scratchdir, "./CM.model"))
resultLoaded = []
loaded = AZorngConsensus.Consensusread(
os.path.join(scratchdir, "./CM.model"))
self.assertNotEqual(loaded, None)
for ex in self.DataReg:
resultLoaded.append(loaded(ex))
# Assert
for index, item in enumerate(result):
if not float_compare(result[index].value,
resultLoaded[index].value):
print "Not equal on index: ", index
self.assertEqual(
float_compare(result[index].value, resultLoaded[index].value),
True)
self.assertEqual(len(loaded.domain), len(self.DataReg.domain))
self.assertEqual(len(loaded.imputeData), len(loaded.domain))
self.assertEqual(len(loaded.basicStat), len(loaded.domain))
self.assertEqual(loaded.NTrainEx, len(self.DataReg))
miscUtilities.removeDir(scratchdir)
def test_PredictionWithDiffVarOrder(self):
"""Test Prediction with diff. VarOrder
Test the prediction examples with different varOrder
"""
expectedAcc = 0.7 # 0.59999999999999998 #0.7 # Ver 0.3
# Create a svm model
svm = AZorngCvSVM.CvSVMLearner(self.noBadDataTrain)
#using from index 3 o the end of data, because we know that from 0 to 2 the examples are not compatible
Acc1 = evalUtilities.getClassificationAccuracy(self.noBadDataTest, svm)
Acc2 = evalUtilities.getClassificationAccuracy(self.badVarOrderData,
svm)
self.assertEqual(round(Acc1, 9), round(expectedAcc, 9),
"The Accuracy is not the expected. Got: " +
str(Acc1)) #Ver 0.3
self.assertEqual(round(Acc2, 9), round(expectedAcc, 9),
"The Accuracy is not the expected. Got: " + str(Acc2))
def test_MetaDataHandle(self):
"""Test the handling of Data with Meta Atributes
"""
# Create an svm model
svm = AZorngCvSVM.CvSVMLearner(self.NoMetaTrain)
# Calculate classification accuracy (NoMetaTest and WMeta are the same appart from the meta atribute)
AccNoMeta = evalUtilities.getClassificationAccuracy(
self.NoMetaTest, svm)
AccWMeta = evalUtilities.getClassificationAccuracy(self.WMetaTest, svm)
self.assertEqual(
AccNoMeta, AccWMeta,
"Predictions with and without meta data were different!")
self.assertEqual(round(AccNoMeta, 9), round(
0.7, 9), "Accuracy was not the expected value! Got: ") #Ver 0.3
def test_SaveLoadCustomLogicalExpression(self):
""" Test save/load functionality with a custom logical expression """
# Arrange
# Construct expression learner/classifier
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
discreteExpression = [
"firstLearner == Iris-setosa -> Iris-setosa", "-> Iris-virginica"
]
discreteLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=discreteExpression)
discreteClassifier = discreteLearner(self.irisData)
result = []
for ex in self.irisData:
result.append(discreteClassifier(ex))
# Act
scratchdir = miscUtilities.createScratchDir(
desc="ConsensusSaveLoadTest")
discreteClassifier.write(os.path.join(scratchdir, "./CM.model"))
resultLoaded = []
loaded = AZorngConsensus.Consensusread(
os.path.join(scratchdir, "./CM.model"))
self.assertNotEqual(loaded, None)
for ex in self.irisData:
resultLoaded.append(loaded(ex))
# Assert
for index, item in enumerate(result):
if not result[index].value == resultLoaded[index].value:
print "Not equal on index: ", index
self.assertEqual(result[index].value, resultLoaded[index].value)
self.assertEqual(len(loaded.domain), len(self.irisData.domain))
self.assertEqual(len(loaded.imputeData), len(loaded.domain))
self.assertEqual(len(loaded.basicStat), len(loaded.domain))
self.assertEqual(loaded.NTrainEx, len(self.irisData))
miscUtilities.removeDir(scratchdir)
def test_InvalidCustomRegressionExpression(self):
""" Test invalid custom expression """
# Arrange
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
regressionExpression = "(a + b + 3cd45 + c) / 3"
expressionLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=regressionExpression)
# Act
classifier = expressionLearner(self.DataReg)
# Assert
self.assertEqual(classifier(self.DataReg[0]), None)
def applySettings(self):
self.error(0)
self.warning(0)
if self.priorsGUI:
self.priors = str(self.priorsGUI)
else:
self.priors = None
#Update the variables handling the learner specific values
self.setSVMTypeLearner()
self.setStopCritLearner()
self.changeKernel()
#Create the Learner
self.learner=AZorngCvSVM.CvSVMLearner()
#Set the several learner parameters
self.learner.priors = self.priors
for attr in ("nMin","nMax","nClassMin","nClassMax","scaleClass","scaleData"):
setattr(self.learner, attr, getattr(self, attr))
for attr in ("name", "kernel_type", "degree", "svm_type", "stopCrit", "maxIter"):
setattr(self.learner, attr, getattr(self, attr))
for attr in ("gamma", "coef0", "C", "p", "eps", "nu"):
setattr(self.learner, attr, float(getattr(self, attr)))
self.classifier=None
self.supportVectors=None
#Create the classifier
if self.data:
self.classifier=self.learner(self.data)
if self.learner:
self.learner.name=str(self.name)
self.send("Learner", self.learner)
if self.classifier:
self.classifier.name=str(self.name)
#self.supportVectors=self.classifier.classifier.supportVectors
self.send("Classifier", self.classifier)
#if self.supportVectors:
# self.send("Support Vectors", self.supportVectors)
self.refreshParams()
def test_CanPersistRegressionModelUsingClassifiers(self):
"""Test the save/load for a regression model - Using average of N classifiers"""
# Arrange
learners = [
AZorngRF.RFLearner(),
AZorngCvSVM.CvSVMLearner(),
AZorngCvANN.CvANNLearner()
]
learner = AZorngConsensus.ConsensusLearner(learners=learners)
classifier = learner(self.DataReg)
# Act
predictions = []
for ex in self.DataReg:
predictions.append(classifier(ex))
scratchdir = miscUtilities.createScratchDir(
desc="ConsensusSaveLoadTest")
classifier.write(os.path.join(scratchdir, "./CM.model"))
# Assert
predictionsL = []
Loaded = AZorngConsensus.Consensusread(
os.path.join(scratchdir, "./CM.model"))
for ex in self.DataReg:
predictionsL.append(Loaded(ex))
self.assertEqual(
[round(pred.value, 4) for pred in predictions],
[round(pred.value, 4) for pred in predictionsL],
"Loaded model predictions differ: Pred. 1 (saved/loaded):" +
str(predictions[0]) + " / " + str(predictionsL[0]))
self.assertEqual(len(Loaded.domain), len(self.DataReg.domain))
self.assertEqual(len(Loaded.imputeData), len(Loaded.domain))
self.assertEqual(len(Loaded.basicStat), len(Loaded.domain))
self.assertEqual(Loaded.NTrainEx, len(self.DataReg))
miscUtilities.removeDir(scratchdir)
def test_CanCreateClassifierUsingObjMapping(self):
""" Test with name variable mapping defined but not expression given """
# Arrange
learners = {
'firstLearner': AZorngCvSVM.CvSVMLearner(),
'secondLearner': AZorngCvANN.CvANNLearner(),
'thirdLearner': AZorngRF.RFLearner()
}
discreteExpression = ""
regressionExpression = "(firstLearner + secondLearner + thirdLearner) / 2"
learner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=regressionExpression)
# Act
classifier = learner(self.DataReg)
# Assert
self.assertNotEqual(classifier, None)
self.assertEqual(len(classifier.classifiers), 3)
self.assertEqual(classifier.expression, regressionExpression)
def test_InvalidCustomClassificationExpression(self):
""" Test invalid custom expression """
# Arrange
learners = {
'a': AZorngCvSVM.CvSVMLearner(),
'b': AZorngCvANN.CvANNLearner(),
'c': AZorngRF.RFLearner()
}
discreteExpression = [
"a == Iris-setosa and or c == Iris-virginica or b == Iris-setosa -> Iris-setosa",
"-> Iris-virginica"
]
expressionLearner = AZorngConsensus.ConsensusLearner(
learners=learners, expression=discreteExpression)
# Act
classifier = expressionLearner(self.getClassificationTrainingData())
# Assert
self.assertEqual(classifier(self.getClassificationTrainingData()[0]),
None)
