def build(numberOfTimeUnits=24):
validLattices = set()
for data in FileIO.iterateJsonFromFile(hashtagsLatticeGraphFile%('world','%s_%s'%(2,11))): validLattices.add(data['id'])
documents, lattices = [], set()
for h in FileIO.iterateJsonFromFile(hashtagsFile%('training_world','%s_%s'%(2,11))):
hashtag, document = Hashtag(h), []
if hashtag.isValidObject():
for timeUnit, occs in enumerate(hashtag.getOccrancesEveryTimeWindowIterator(HashtagsClassifier.CLASSIFIER_TIME_UNIT_IN_SECONDS)):
occs = filter(lambda t: t[0] in validLattices, occs)
occs = sorted(occs, key=itemgetter(0))
if occs:
for lattice in zip(*occs)[0]: lattices.add(lattice)
document.append([timeUnit, [(k, len(list(i))) for k, i in groupby(occs, key=itemgetter(0))]])
if document: documents.append(document)
lattices = sorted(list(lattices))
print len(lattices)
documents = [(d, TargetSelectionRegressionClassifier.getPercentageDistributionInLattice(d)) for d in documents]
documents = documents[:int(len(documents)*0.80)]
for decisionTimeUnit in range(1, numberOfTimeUnits+1):
for latticeCount, predictingLattice in enumerate(lattices):
print decisionTimeUnit, latticeCount,
inputVectors, outputValues = [], []
for rawDocument, processedDocument in documents:
documentForTimeUnit = TargetSelectionRegressionClassifier.getPercentageDistributionInLattice(rawDocument[:decisionTimeUnit])
if documentForTimeUnit and processedDocument:
vector = [documentForTimeUnit.get(l, 0) for l in lattices]
inputVectors.append(vector), outputValues.append(float(processedDocument.get(predictingLattice, 0)))
# TargetSelectionRegressionClassifier(decisionTimeUnit=decisionTimeUnit, predictingLattice=predictingLattice).build(zip(inputVectors, outputValues))
TargetSelectionRegressionSVMRBFClassifier(decisionTimeUnit=decisionTimeUnit, predictingLattice=predictingLattice).build(zip(inputVectors, outputValues))
def testClassifierPerformance(numberOfTimeUnits=24):
validLattices = set()
for data in FileIO.iterateJsonFromFile(hashtagsLatticeGraphFile%('world','%s_%s'%(2,11))): validLattices.add(data['id'])
documents, lattices = [], set()
for h in FileIO.iterateJsonFromFile(hashtagsFile%('training_world','%s_%s'%(2,11))):
hashtag, document = Hashtag(h), []
if hashtag.isValidObject():
for timeUnit, occs in enumerate(hashtag.getOccrancesEveryTimeWindowIterator(HashtagsClassifier.CLASSIFIER_TIME_UNIT_IN_SECONDS)):
occs = filter(lambda t: t[0] in validLattices, occs)
occs = sorted(occs, key=itemgetter(0))
if occs:
for lattice in zip(*occs)[0]: lattices.add(lattice)
document.append([timeUnit, [(k, len(list(i))) for k, i in groupby(occs, key=itemgetter(0))]])
if document: documents.append(document)
lattices = sorted(list(lattices))
print len(lattices)
documents = [(d, TargetSelectionRegressionClassifier.getPercentageDistributionInLattice(d)) for d in documents]
documents = documents[-int(len(documents)*0.20):]
GeneralMethods.runCommand('rm -rf %s'%TargetSelectionRegressionClassifier.classifiersPerformanceFile)
for decisionTimeUnit in range(1, numberOfTimeUnits+1):
for classifierType in [TargetSelectionRegressionSVMRBFClassifier, TargetSelectionRegressionSVMLinearClassifier,
TargetSelectionRegressionSVMPolyClassifier, TargetSelectionRegressionClassifier]:
totalError = []
for latticeCount, predictingLattice in enumerate(lattices):
inputVectors, outputValues, tempError = [], [], []
for rawDocument, processedDocument in documents:
documentForTimeUnit = TargetSelectionRegressionClassifier.getPercentageDistributionInLattice(rawDocument[:decisionTimeUnit])
if documentForTimeUnit and processedDocument:
vector = [documentForTimeUnit.get(l, 0) for l in lattices]
inputVectors.append(vector), outputValues.append(float(processedDocument.get(predictingLattice, 0)))
classifier = classifierType(decisionTimeUnit=decisionTimeUnit, predictingLattice=predictingLattice)
for iv, ov in zip(inputVectors, outputValues):
if latticeCount==2: print ov, classifier.predict(iv), pow(ov-classifier.predict(iv), 2)
if ov!=0.0:
tempError.append(pow(ov-classifier.predict(iv), 2))
# print tempError, np.mean(tempError)
# exit()
totalError.append(np.mean(tempError))
print {'id': classifier.id, 'timeUnit': decisionTimeUnit-1, 'error': np.mean(totalError)}
FileIO.writeToFileAsJson({'id': classifier.id, 'timeUnit': decisionTimeUnit-1, 'error': np.mean(totalError)}, TargetSelectionRegressionClassifier.classifiersPerformanceFile)
