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)