def test_CanPersistClassificationModelProbabilities(self):
"""Test the save/load for a classification model - Using probabilities average"""
# Arrange
learners = [AZorngRF.RFLearner(), AZorngCvANN.CvANNLearner()]
learner = AZorngConsensus.ConsensusLearner(learners=learners)
classifier = learner(self.irisData)
# Act
predictions = []
for ex in self.irisData:
predictions.append(classifier(ex))
scratchdir = miscUtilities.createScratchDir(
desc="ConsensusSaveLoadTest")
print scratchdir
classifier.write(os.path.join(scratchdir, "./CM.model"))
# Assert
predictionsL = []
Loaded = AZorngConsensus.Consensusread(
os.path.join(scratchdir, "./CM.model"))
for ex in self.irisData:
predictionsL.append(Loaded(ex))
self.assertEqual(predictions, predictionsL)
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_CanPersistClassificationModelMajority(self):
"""Test the save/load for a classification model - Using Majority"""
""" Arrange """
learners = self.createTestLearners()
learner = AZorngConsensus.ConsensusLearner(learners=learners)
classifier = learner(self.getClassificationTrainingData())
""" Act """
predictions = []
for ex in self.irisData:
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"))
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))
for ex in self.irisData:
predictionsL.append(Loaded(ex))
self.assertEqual(predictions, predictionsL)
miscUtilities.removeDir(scratchdir)
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 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_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_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_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_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_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_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_CreateDefaultModel(self):
""" Test the creation of Consensus Model using no learners """
# Arrange
# Act
learner = AZorngConsensus.ConsensusLearner()
# Assert
self.assertEqual(learner.learners, None)
self.assertEqual(learner.expression, None)
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_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 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_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)
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_CreateModelWithLearnerList(self):
""" Test the creation of Consensus Model using list of learners """
# Arrange
learners = [
AZorngCvSVM.CvSVMLearner(),
AZorngCvANN.CvANNLearner(),
AZorngRF.RFLearner()
]
# Act
learner = AZorngConsensus.ConsensusLearner(learners=learners)
# Assert
for i, l in enumerate(learner.learners):
self.assertEqual(learner.learners[i], learners[i])
self.assertEqual(learner.expression, None)
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 buildConsensus(trainData, learners, MLMethods, logFile=None):
log(
logFile, "Building a consensus model based on optimized MLmethods: " +
str([ml for ml in MLMethods]) + "...")
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
#Expression: If CAavg_{POS} ge CAavg_{NEG} -> POS else -> NEG
# where CAavg_{POS} is the average of classification accuracies of all models predicting POS.
CLASS0 = str(trainData.domain.classVar.values[0])
CLASS1 = str(trainData.domain.classVar.values[1])
#exprTest0
exprTest0 = "(0"
for ml in MLMethods:
exprTest0 += "+( " + ml + " == " + CLASS0 + " )*" + str(
MLMethods[ml]["optAcc"]) + " "
exprTest0 += ")/IF0(sum([False"
for ml in MLMethods:
exprTest0 += ", " + ml + " == " + CLASS0 + " "
exprTest0 += "]),1)"
# exprTest1
exprTest1 = "(0"
for ml in MLMethods:
exprTest1 += "+( " + ml + " == " + CLASS1 + " )*" + str(
MLMethods[ml]["optAcc"]) + " "
exprTest1 += ")/IF0(sum([False"
for ml in MLMethods:
exprTest1 += ", " + ml + " == " + CLASS1 + " "
exprTest1 += "]),1)"
# expression
expression = [
exprTest0 + " >= " + exprTest1 + " -> " + CLASS0, " -> " + CLASS1
]
else:
Q2sum = sum([MLMethods[ml]["optAcc"] for ml in MLMethods])
expression = "(1 / " + str(Q2sum) + ") * (0"
for ml in MLMethods:
expression += " + " + str(
MLMethods[ml]["optAcc"]) + " * " + ml + " "
expression += ")"
consensusLearners = {}
for learnerName in learners:
consensusLearners[learnerName] = learners[learnerName]
learner = AZorngConsensus.ConsensusLearner(learners=consensusLearners,
expression=expression)
log(logFile, " Training Consensus Learner")
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
log(logFile, "Found SMILES attribute:" + smilesAttr)
if learner.specialType == 1:
trainData = dataUtilities.attributeSelectionData(
trainData, [smilesAttr, trainData.domain.classVar.name])
log(
logFile, "Selected attrs: " +
str([attr.name for attr in trainData.domain]))
else:
trainData = dataUtilities.attributeDeselectionData(
trainData, [smilesAttr])
log(logFile,"Selected attrs: "+str([attr.name for attr in trainData.domain[0:3]] + ["..."] +\
[attr.name for attr in trainData.domain[len(trainData.domain)-3:]]))
return learner(trainData)
