def getClusterForDocument(self, document):
UtilityMethods.updatePhraseTextAndDimensionsMap(document, self.phraseTextAndDimensionMap, **self.clustering_settings)
document.setSignatureUsingVectorPermutations(self.unitVector, self.vectorPermutations, self.phraseTextAndDimensionMap)
predictedCluster = None
possibleNearestNeighbors = reduce(lambda x,y:x.union(y), (permutation.getNearestDocuments(document) for permutation in self.signaturePermutations), set())
if possibleNearestNeighbors: predictedCluster = max(((clusterId, self.clusters[clusterId].cosineSimilarity(document)) for clusterId in possibleNearestNeighbors), key=itemgetter(1))
if predictedCluster and predictedCluster[1]>=self.thresholdForDocumentToBeInACluster:return predictedCluster[0]
def test_updatedPhraseObject_PhraseObjectScoresAreUpdatedCorrectly(self):
UtilityMethods.updatePhraseTextToPhraseObject(
self.phraseVector, test_time + timedelta(seconds=60),
self.phraseTextToPhraseObjectMap, **stream_settings)
self.assertEqual(5, len(self.phraseTextToPhraseObjectMap))
self.assertEqual(5, self.phraseTextToPhraseObjectMap['project'].score)
self.assertEqual(1, self.phraseTextToPhraseObjectMap['streams'].score)
def cluster(self, dataIterator):
i = 1
for data in dataIterator:
message = self.convertDataToMessageMethod(data,
**self.stream_settings)
# message = data
if DataStreamMethods.messageInOrder(message.timeStamp):
UtilityMethods.updatePhraseTextToPhraseObject(
message.vector, message.timeStamp,
self.phraseTextToPhraseObjectMap, **self.stream_settings)
if message.streamId not in self.streamIdToStreamObjectMap:
self.streamIdToStreamObjectMap[message.streamId] = Stream(
message.streamId, message)
else:
self.streamIdToStreamObjectMap[
message.streamId].updateForMessage(
message, VectorUpdateMethods.exponentialDecay,
**self.stream_settings)
streamObject = self.streamIdToStreamObjectMap[message.streamId]
self.updateDimensionsMethod.call(
message.timeStamp,
hdStreamClusteringObject=self,
currentMessageTime=message.timeStamp)
self.clusterFilteringMethod.call(
message.timeStamp,
hdStreamClusteringObject=self,
currentMessageTime=message.timeStamp)
self.clusterAnalysisMethod.call(
message.timeStamp,
hdStreamClusteringObject=self,
currentMessageTime=message.timeStamp)
self.getClusterAndUpdateExistingClusters(streamObject)
def cluster(self, dataIterator):
i = 1
for data in dataIterator:
message = self.convertDataToMessageMethod(data, **self.stream_settings)
# message = data
if DataStreamMethods.messageInOrder(message.timeStamp):
UtilityMethods.updatePhraseTextToPhraseObject(message.vector, message.timeStamp, self.phraseTextToPhraseObjectMap, **self.stream_settings)
if message.streamId not in self.streamIdToStreamObjectMap:
self.streamIdToStreamObjectMap[message.streamId] = Stream(message.streamId, message)
self.getClusterAndUpdateExistingClusters(self.streamIdToStreamObjectMap[message.streamId])
else:
previousStreamObject=Vector(vectorInitialValues=self.streamIdToStreamObjectMap[message.streamId])
self.streamIdToStreamObjectMap[message.streamId].updateForMessage(message, VectorUpdateMethods.exponentialDecay, **self.stream_settings )
streamObject=self.streamIdToStreamObjectMap[message.streamId]
distance = Vector.euclideanDistance(streamObject, previousStreamObject)
if distance>10:
# print i, len(self.clusters), distance
self.getClusterAndUpdateExistingClusters(self.streamIdToStreamObjectMap[message.streamId])
self.updateDimensionsMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
self.clusterFilteringMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
# self.clusterAnalysisMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
self.clusterAnalysisMethod.call(time.time(), hdStreamClusteringObject=self, currentMessageTime=message.timeStamp, numberOfMessages=i)
# print i, len(self.clusters)
i+=1
def test_updateDimensions_when_phrases_with_lower_id_are_removed_from_phraseTextToIdMap(self):
stream_settings['dimensions'] = 3
for phrase, score in zip(['new', 'phrases', 'are'], range(100,103)): self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase, test_time, score=score)
self.phraseTextAndDimensionMap.set(TwoWayMap.MAP_FORWARD, 'cluster', 2)
self.phraseTextToPhraseObjectMap['cluster'].score=100
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap, self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertEqual(range(3), sorted(self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD).values()))
def test_updateDimensions_when_dimensions_have_to_be_removed(self):
stream_settings['dimensions'] = 4
self.phraseTextAndDimensionMap.set(TwoWayMap.MAP_FORWARD, 'abcdx', 2)
self.phraseTextAndDimensionMap.set(TwoWayMap.MAP_FORWARD, 'abcdxy', 3)
for phrase, score in zip(['new_text'], range(7,8)): self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase, test_time, score=score)
self.phraseTextToPhraseObjectMap['cluster'].latestOccuranceTime=test_time-3*stream_settings['max_phrase_inactivity_time_in_seconds']
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap, self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertEqual(set({'project':0, 'new_text': 1}), set(self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD)))
def getNearestDocument(self, document):
UtilityMethods.updatePhraseTextAndDimensionsMap(document, self.phraseTextAndDimensionMap, **self.settings)
document.setSignatureUsingVectorPermutations(self.unitVector, self.vectorPermutations, self.phraseTextAndDimensionMap)
predictedNeighbor = None
possibleNearestNeighbors = reduce(lambda x,y:x.union(y), (permutation.getNearestDocuments(document) for permutation in self.signaturePermutations), set())
if possibleNearestNeighbors: predictedNeighbor = max(((docId, self.documentIdToDocumentMap[docId].cosineSimilarity(document)) for docId in possibleNearestNeighbors), key=itemgetter(1))
print predictedNeighbor
if predictedNeighbor and predictedNeighbor[1]>=self.nearestNeighborThreshold:return predictedNeighbor[0]
def update(self, newDocument):
UtilityMethods.updatePhraseTextAndDimensionsMap(newDocument, self.phraseTextAndDimensionMap, **self.settings)
currentDocument = self.documentIdToDocumentMap.get(newDocument.docId, None)
self.documentIdToDocumentMap[newDocument.docId] = newDocument
newDocument.setSignatureUsingVectorPermutations(self.unitVector, self.vectorPermutations, self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations:
if currentDocument!=None: permutation.removeDocument(currentDocument)
permutation.addDocument(newDocument)
def updateDimensions(hdStreamClusteringObject, currentMessageTime):
# Update dimensions.
UtilityMethods.updateDimensions(
hdStreamClusteringObject.phraseTextAndDimensionMap,
hdStreamClusteringObject.phraseTextToPhraseObjectMap,
currentMessageTime, **hdStreamClusteringObject.stream_settings)
DataStreamMethods._resetClustersInSignatureTries(
hdStreamClusteringObject, currentMessageTime)
def run(self, dataIterator, estimationMethod, parameterSpecificDataCollectionMethod=None):
estimationMethod = FixedIntervalMethod(estimationMethod, self.timeUnitInSeconds)
for data in dataIterator:
message = self.convertDataToMessageMethod(data, **self.stream_settings)
if CDA.messageInOrder(message.timeStamp):
if parameterSpecificDataCollectionMethod != None: parameterSpecificDataCollectionMethod(estimationObject=self, message=message)
UtilityMethods.updatePhraseTextToPhraseObject(message.vector, message.timeStamp, self.phraseTextToPhraseObjectMap, **self.stream_settings)
estimationMethod.call(message.timeStamp, estimationObject=self, currentMessageTime=message.timeStamp)
def test_updatePhraseTextAndDimensionsMap_PhraseMapHasLesserDimensions(
self):
settings['dimensions'] = 4
UtilityMethods.updatePhraseTextAndDimensionsMap(
self.phraseVector, self.phraseTextAndDimensionMap, **settings)
self.assertEqual(
self.finalPhraseToIdMap,
self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
def test_updateDimensions_remove_old_phrases(self):
originalTime=self.phraseTextToPhraseObjectMap['abcd'].latestOccuranceTime
self.phraseTextToPhraseObjectMap['abcd'].latestOccuranceTime=test_time
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap, self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertTrue('abcd' in self.phraseTextToPhraseObjectMap)
self.phraseTextToPhraseObjectMap['abcd'].latestOccuranceTime=originalTime
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap, self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertTrue('abcd' not in self.phraseTextToPhraseObjectMap)
def run(self, dataIterator, estimationMethod, parameterSpecificDataCollectionMethod=None):
estimationMethod = FixedIntervalMethod(estimationMethod, self.timeUnitInSeconds)
for data in dataIterator:
message = self.convertDataToMessageMethod(data, **self.stream_settings)
if CDA.messageInOrder(message.timeStamp):
if parameterSpecificDataCollectionMethod != None: parameterSpecificDataCollectionMethod(estimationObject=self, message=message)
UtilityMethods.updatePhraseTextToPhraseObject(message.vector, message.timeStamp, self.phraseTextToPhraseObjectMap, **self.stream_settings)
estimationMethod.call(message.timeStamp, estimationObject=self, currentMessageTime=message.timeStamp)
def test_updatedPhraseObject_phrase_does_not_exist_in_phraseToIdMap_but_exists_in_phraseTextToPhraseObjectMap_with_dimensions_full(self):
stream_settings['dimensions'] = 1
self.phraseTextAndDimensionMap.remove(TwoWayMap.MAP_FORWARD, 'cluster')
UtilityMethods.updatePhraseTextToPhraseObject(self.phraseVector, test_time+timedelta(seconds=60), self.phraseTextToPhraseObjectMap, **stream_settings)
self.assertEqual({'project':0}, self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
self.assertEqual(5, len(self.phraseTextToPhraseObjectMap))
self.assertEqual(5, self.phraseTextToPhraseObjectMap['project'].score)
self.assertEqual(5, self.phraseTextToPhraseObjectMap['cluster'].score)
self.assertEqual(1, self.phraseTextToPhraseObjectMap['streams'].score)
def test_updatePhraseTextAndDimensionsMap_PhraseMapHasMaximumDimensions(
self):
UtilityMethods.updatePhraseTextAndDimensionsMap(
self.phraseVector, self.phraseTextAndDimensionMap, **settings)
for k in ['streams', 'highdimensional']:
del self.finalPhraseToIdMap[k]
self.assertEqual(
self.finalPhraseToIdMap,
self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
def test_createOrAddNewPhraseObject(self):
UtilityMethods.createOrAddNewPhraseObject(
'new_phrase', self.phraseTextToPhraseObjectMap, test_time,
**stream_settings)
UtilityMethods.createOrAddNewPhraseObject(
'project', self.phraseTextToPhraseObjectMap, test_time,
**stream_settings)
self.assertEqual(4, len(self.phraseTextToPhraseObjectMap))
self.assertEqual(1,
self.phraseTextToPhraseObjectMap['new_phrase'].score)
self.assertEqual(9, self.phraseTextToPhraseObjectMap['project'].score)
def test_pruningConditionRandom(self):
phrase1 = Phrase(
'dsf', test_time -
3 * stream_settings['max_phrase_inactivity_time_in_seconds'], 1)
phrase2 = Phrase('dsf', test_time, 1)
self.assertTrue(
UtilityMethods.pruningConditionRandom(phrase1, test_time,
**stream_settings))
self.assertFalse(
UtilityMethods.pruningConditionRandom(phrase2, test_time,
**stream_settings))
def update(self, newDocument):
UtilityMethods.updatePhraseTextAndDimensionsMap(
newDocument, self.phraseTextAndDimensionMap, **self.settings)
currentDocument = self.documentIdToDocumentMap.get(
newDocument.docId, None)
self.documentIdToDocumentMap[newDocument.docId] = newDocument
newDocument.setSignatureUsingVectorPermutations(
self.unitVector, self.vectorPermutations,
self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations:
if currentDocument != None:
permutation.removeDocument(currentDocument)
permutation.addDocument(newDocument)
def test_updateDimensions_when_phraseTextToIdMap_is_filled_to_max_dimensions(
self):
for phrase, score in zip(['added'], range(10, 11)):
self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase,
test_time,
score=score)
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap,
self.phraseTextToPhraseObjectMap,
test_time, **stream_settings)
self.assertEqual({
'project': 0,
'added': 1
}, self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
def cluster(self, dataIterator):
i = 1
for data in dataIterator:
message = self.convertDataToMessageMethod(data,
**self.stream_settings)
# message = data
if DataStreamMethods.messageInOrder(message.timeStamp):
UtilityMethods.updatePhraseTextToPhraseObject(
message.vector, message.timeStamp,
self.phraseTextToPhraseObjectMap, **self.stream_settings)
if message.streamId not in self.streamIdToStreamObjectMap:
self.streamIdToStreamObjectMap[message.streamId] = Stream(
message.streamId, message)
self.getClusterAndUpdateExistingClusters(
self.streamIdToStreamObjectMap[message.streamId])
else:
previousStreamObject = Vector(
vectorInitialValues=self.streamIdToStreamObjectMap[
message.streamId])
self.streamIdToStreamObjectMap[
message.streamId].updateForMessage(
message, VectorUpdateMethods.exponentialDecay,
**self.stream_settings)
streamObject = self.streamIdToStreamObjectMap[
message.streamId]
distance = Vector.euclideanDistance(
streamObject, previousStreamObject)
if distance > 10:
# print i, len(self.clusters), distance
self.getClusterAndUpdateExistingClusters(
self.streamIdToStreamObjectMap[message.streamId])
self.updateDimensionsMethod.call(
message.timeStamp,
hdStreamClusteringObject=self,
currentMessageTime=message.timeStamp)
self.clusterFilteringMethod.call(
message.timeStamp,
hdStreamClusteringObject=self,
currentMessageTime=message.timeStamp)
# self.clusterAnalysisMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
self.clusterAnalysisMethod.call(
time.time(),
hdStreamClusteringObject=self,
currentMessageTime=message.timeStamp,
numberOfMessages=i)
# print i, len(self.clusters)
i += 1
def cluster(self, dataIterator):
i = 1
for data in dataIterator:
message = self.convertDataToMessageMethod(data, **self.stream_settings)
# message = data
if DataStreamMethods.messageInOrder(message.timeStamp):
UtilityMethods.updatePhraseTextToPhraseObject(message.vector, message.timeStamp, self.phraseTextToPhraseObjectMap, **self.stream_settings)
if message.streamId not in self.streamIdToStreamObjectMap: self.streamIdToStreamObjectMap[message.streamId] = Stream(message.streamId, message)
else: self.streamIdToStreamObjectMap[message.streamId].updateForMessage(message, VectorUpdateMethods.exponentialDecay, **self.stream_settings )
streamObject=self.streamIdToStreamObjectMap[message.streamId]
self.updateDimensionsMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
self.clusterFilteringMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
self.clusterAnalysisMethod.call(message.timeStamp, hdStreamClusteringObject=self, currentMessageTime=message.timeStamp)
self.getClusterAndUpdateExistingClusters(streamObject)
def test_updatedPhraseObject_phrase_does_not_exist_in_phraseToIdMap_but_exists_in_phraseTextToPhraseObjectMap_with_dimensions_full(
self):
stream_settings['dimensions'] = 1
self.phraseTextAndDimensionMap.remove(TwoWayMap.MAP_FORWARD, 'cluster')
UtilityMethods.updatePhraseTextToPhraseObject(
self.phraseVector, test_time + timedelta(seconds=60),
self.phraseTextToPhraseObjectMap, **stream_settings)
self.assertEqual({'project': 0},
self.phraseTextAndDimensionMap.getMap(
TwoWayMap.MAP_FORWARD))
self.assertEqual(5, len(self.phraseTextToPhraseObjectMap))
self.assertEqual(5, self.phraseTextToPhraseObjectMap['project'].score)
self.assertEqual(5, self.phraseTextToPhraseObjectMap['cluster'].score)
self.assertEqual(1, self.phraseTextToPhraseObjectMap['streams'].score)
def test_pruneUnnecessaryPhrases(self):
phraseTextToPhraseObjectMap = {
'dsf':
Phrase(
'dsf', test_time -
3 * stream_settings['max_phrase_inactivity_time_in_seconds'],
1),
'abc':
Phrase('abc', test_time, 1)
}
UtilityMethods.pruneUnnecessaryPhrases(
phraseTextToPhraseObjectMap, test_time,
UtilityMethods.pruningConditionRandom, **stream_settings)
self.assertTrue('dsf' not in phraseTextToPhraseObjectMap)
self.assertTrue('abc' in phraseTextToPhraseObjectMap)
def test_updateDimensions_remove_old_phrases(self):
originalTime = self.phraseTextToPhraseObjectMap[
'abcd'].latestOccuranceTime
self.phraseTextToPhraseObjectMap[
'abcd'].latestOccuranceTime = test_time
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap,
self.phraseTextToPhraseObjectMap,
test_time, **stream_settings)
self.assertTrue('abcd' in self.phraseTextToPhraseObjectMap)
self.phraseTextToPhraseObjectMap[
'abcd'].latestOccuranceTime = originalTime
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap,
self.phraseTextToPhraseObjectMap,
test_time, **stream_settings)
self.assertTrue('abcd' not in self.phraseTextToPhraseObjectMap)
def test_updateDimensions_when_phrases_with_lower_id_are_removed_from_phraseTextToIdMap(
self):
stream_settings['dimensions'] = 3
for phrase, score in zip(['new', 'phrases', 'are'], range(100, 103)):
self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase,
test_time,
score=score)
self.phraseTextAndDimensionMap.set(TwoWayMap.MAP_FORWARD, 'cluster', 2)
self.phraseTextToPhraseObjectMap['cluster'].score = 100
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap,
self.phraseTextToPhraseObjectMap,
test_time, **stream_settings)
self.assertEqual(
range(3),
sorted(
self.phraseTextAndDimensionMap.getMap(
TwoWayMap.MAP_FORWARD).values()))
def test_updateDimensions_when_dimensions_have_to_be_removed(self):
stream_settings['dimensions'] = 4
self.phraseTextAndDimensionMap.set(TwoWayMap.MAP_FORWARD, 'abcdx', 2)
self.phraseTextAndDimensionMap.set(TwoWayMap.MAP_FORWARD, 'abcdxy', 3)
for phrase, score in zip(['new_text'], range(7, 8)):
self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase,
test_time,
score=score)
self.phraseTextToPhraseObjectMap[
'cluster'].latestOccuranceTime = test_time - 3 * stream_settings[
'max_phrase_inactivity_time_in_seconds']
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap,
self.phraseTextToPhraseObjectMap,
test_time, **stream_settings)
self.assertEqual(
set({
'project': 0,
'new_text': 1
}),
set(self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD)))
def test_updateDimensions_when_phraseTextToIdMap_has_lesser_than_max_dimensions(
self):
stream_settings['dimensions'] = 4
for phrase, score in zip(['new', 'phrases', 'are', 'added'],
range(7, 11)):
self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase,
test_time,
score=score)
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap,
self.phraseTextToPhraseObjectMap,
test_time, **stream_settings)
self.assertEqual(
set({
'project': 0,
'phrases': 1,
'are': 2,
'added': 3
}),
set(self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD)))
self.assertEqual(4, len(self.phraseTextAndDimensionMap))
def getClusterForDocument(self, document):
UtilityMethods.updatePhraseTextAndDimensionsMap(
document, self.phraseTextAndDimensionMap,
**self.clustering_settings)
document.setSignatureUsingVectorPermutations(
self.unitVector, self.vectorPermutations,
self.phraseTextAndDimensionMap)
predictedCluster = None
possibleNearestNeighbors = reduce(
lambda x, y: x.union(y),
(permutation.getNearestDocuments(document)
for permutation in self.signaturePermutations), set())
if possibleNearestNeighbors:
predictedCluster = max(
((clusterId,
self.clusters[clusterId].cosineSimilarity(document))
for clusterId in possibleNearestNeighbors),
key=itemgetter(1))
if predictedCluster and predictedCluster[
1] >= self.thresholdForDocumentToBeInACluster:
return predictedCluster[0]
def getNearestDocument(self, document):
UtilityMethods.updatePhraseTextAndDimensionsMap(
document, self.phraseTextAndDimensionMap, **self.settings)
document.setSignatureUsingVectorPermutations(
self.unitVector, self.vectorPermutations,
self.phraseTextAndDimensionMap)
predictedNeighbor = None
possibleNearestNeighbors = reduce(
lambda x, y: x.union(y),
(permutation.getNearestDocuments(document)
for permutation in self.signaturePermutations), set())
if possibleNearestNeighbors:
predictedNeighbor = max(((
docId,
self.documentIdToDocumentMap[docId].cosineSimilarity(document))
for docId in possibleNearestNeighbors),
key=itemgetter(1))
print predictedNeighbor
if predictedNeighbor and predictedNeighbor[
1] >= self.nearestNeighborThreshold:
return predictedNeighbor[0]
def test_pruningConditionRandom(self):
phrase1 = Phrase('dsf', test_time-3*stream_settings['max_phrase_inactivity_time_in_seconds'], 1)
phrase2 = Phrase('dsf', test_time, 1)
self.assertTrue(UtilityMethods.pruningConditionRandom(phrase1, test_time, **stream_settings))
self.assertFalse(UtilityMethods.pruningConditionRandom(phrase2, test_time, **stream_settings))
def test_updatePhraseTextAndDimensionsMap_PhraseMapHasLesserDimensions(self):
settings['dimensions'] = 4
UtilityMethods.updatePhraseTextAndDimensionsMap(self.phraseVector, self.phraseTextAndDimensionMap, **settings)
self.assertEqual(self.finalPhraseToIdMap, self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
def test_updatePhraseTextAndDimensionsMap_PhraseMapHasMaximumDimensions(self):
UtilityMethods.updatePhraseTextAndDimensionsMap(self.phraseVector, self.phraseTextAndDimensionMap, **settings)
for k in ['streams', 'highdimensional']: del self.finalPhraseToIdMap[k]
self.assertEqual(self.finalPhraseToIdMap, self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
def test_updateDimensions_when_phraseTextToIdMap_has_lesser_than_max_dimensions(self):
stream_settings['dimensions'] = 4
for phrase, score in zip(['new', 'phrases', 'are', 'added'], range(7,11)): self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase, test_time, score=score)
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap, self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertEqual(set({'project':0, 'phrases': 1, 'are':2, 'added':3}), set(self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD)))
self.assertEqual(4, len(self.phraseTextAndDimensionMap))
def test_updateDimensions_when_phraseTextToIdMap_is_filled_to_max_dimensions_and_entire_map_is_changed(self):
for phrase, score in zip(['added', 'are'], range(10,12)): self.phraseTextToPhraseObjectMap[phrase] = Phrase(phrase, test_time, score=score)
UtilityMethods.updateDimensions(self.phraseTextAndDimensionMap, self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertEqual({'added':1, 'are': 0}, self.phraseTextAndDimensionMap.getMap(TwoWayMap.MAP_FORWARD))
def test_createOrAddNewPhraseObject(self):
UtilityMethods.createOrAddNewPhraseObject('new_phrase', self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
UtilityMethods.createOrAddNewPhraseObject('project', self.phraseTextToPhraseObjectMap, test_time, **stream_settings)
self.assertEqual(4, len(self.phraseTextToPhraseObjectMap))
self.assertEqual(1, self.phraseTextToPhraseObjectMap['new_phrase'].score)
self.assertEqual(9, self.phraseTextToPhraseObjectMap['project'].score)
def test_updatedPhraseObject_PhraseObjectScoresAreUpdatedCorrectly(self):
UtilityMethods.updatePhraseTextToPhraseObject(self.phraseVector, test_time+timedelta(seconds=60), self.phraseTextToPhraseObjectMap, **stream_settings)
self.assertEqual(5, len(self.phraseTextToPhraseObjectMap))
self.assertEqual(5, self.phraseTextToPhraseObjectMap['project'].score)
self.assertEqual(1, self.phraseTextToPhraseObjectMap['streams'].score)
def test_pruneUnnecessaryPhrases(self):
phraseTextToPhraseObjectMap = {'dsf': Phrase('dsf', test_time-3*stream_settings['max_phrase_inactivity_time_in_seconds'], 1), 'abc': Phrase('abc', test_time, 1)}
UtilityMethods.pruneUnnecessaryPhrases(phraseTextToPhraseObjectMap, test_time, UtilityMethods.pruningConditionRandom, **stream_settings)
self.assertTrue('dsf' not in phraseTextToPhraseObjectMap)
self.assertTrue('abc' in phraseTextToPhraseObjectMap)
def updateDimensions(hdStreamClusteringObject, currentMessageTime):
# Update dimensions.
UtilityMethods.updateDimensions(hdStreamClusteringObject.phraseTextAndDimensionMap, hdStreamClusteringObject.phraseTextToPhraseObjectMap, currentMessageTime, **hdStreamClusteringObject.stream_settings)
DataStreamMethods._resetClustersInSignatureTries(hdStreamClusteringObject, currentMessageTime)
