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_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_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_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 modelRead(modelFile=None, verbose=0, retrunClassifier=True):
"""Get the type of model saved in 'modelPath' and loads the respective model
Returns the Classifier saved in the respective model path
If called without parameters, it returns a list of known classifier types
It can returns the classifier, or just a string with the Type
modelRead (modelFile [, verbose = 0] [, retrunClassifier = True] )"""
if not modelFile:
return ("SignSVM", "CvSVM", "CvANN", "PLS", "CvRF", "CvBoost",
"CvBayes", "Consensus")
modelType = None
loadedModel = None
if os.path.isfile(os.path.join(modelFile, "model.svm")):
modelType = "CvSVM"
if not retrunClassifier: return modelType
from trainingMethods import AZorngCvSVM
loadedModel = AZorngCvSVM.CvSVMread(modelFile, verbose)
elif os.path.isdir(os.path.join(modelFile, "model.SignSvm")):
modelType = "SignSVM"
if not retrunClassifier: return modelType
from trainingMethods import AZorngSignSVM
loadedModel = AZorngSignSVM.SignSVMread(modelFile, verbose)
elif os.path.isfile(os.path.join(modelFile, "model.ann")):
modelType = "CvANN"
if not retrunClassifier: return modelType
from trainingMethods import AZorngCvANN
loadedModel = AZorngCvANN.CvANNread(modelFile, verbose)
elif os.path.isfile(os.path.join(modelFile, "Model.pls")):
modelType = "PLS"
if not retrunClassifier: return modelType
from trainingMethods import AZorngPLS
loadedModel = AZorngPLS.PLSread(modelFile, verbose)
elif os.path.isfile(os.path.join(modelFile, "model.rf")):
modelType = "RF"
if not retrunClassifier: return modelType
from trainingMethods import AZorngRF
loadedModel = AZorngRF.RFread(modelFile, verbose)
elif os.path.isdir(os.path.join(modelFile, "C0.model")):
modelType = "Consensus"
if not retrunClassifier: return modelType
from trainingMethods import AZorngConsensus
loadedModel = AZorngConsensus.Consensusread(modelFile, verbose)
elif os.path.isfile(os.path.join(modelFile, "model.boost")):
modelType = "CvBoost"
if not retrunClassifier: return modelType
from trainingMethods import AZorngCvBoost
loadedModel = AZorngCvBoost.CvBoostread(modelFile, verbose)
elif os.path.isfile(os.path.join(modelFile, "model.bayes")):
modelType = "CvBayes"
if not retrunClassifier: return modelType
from trainingMethods import AZorngCvBayes
loadedModel = AZorngCvBayes.CvBayesread(modelFile, verbose)
else: # Assuming an RF old format for backcompatibility
try:
if os.path.isdir(modelFile):
modelType = "RF"
if not retrunClassifier: return modelType
from trainingMethods import AZorngRF
loadedModel = AZorngRF.RFread(modelFile, verbose)
else:
modelType = None
loadedModel = None
except:
modelType = None
loadedModel = None
return loadedModel