def clusterFilteringMethod(hdStreamClusteringObject, currentMessageTime):
for cluster in StreamCluster.getClustersByAttributeAndThreshold(hdStreamClusteringObject.clusters.values(), hdStreamClusteringObject.stream_settings['cluster_filter_attribute'],
hdStreamClusteringObject.stream_settings['cluster_filter_threshold'], StreamCluster.BELOW_THRESHOLD): del hdStreamClusteringObject.clusters[cluster.clusterId]
for cluster in StreamCluster.getClustersByAttributeAndThreshold(hdStreamClusteringObject.clusters.values(), 'lastStreamAddedTime',
currentMessageTime-hdStreamClusteringObject.stream_settings['cluster_inactivity_time_in_seconds'], StreamCluster.BELOW_THRESHOLD): del hdStreamClusteringObject.clusters[cluster.clusterId]
if hdStreamClusteringObject.combineClustersMethod!=None: hdStreamClusteringObject.clusters=hdStreamClusteringObject.combineClustersMethod(hdStreamClusteringObject.clusters, **hdStreamClusteringObject.stream_settings)
DataStreamMethods._resetClustersInSignatureTries(hdStreamClusteringObject, currentMessageTime)
def getClusterAndUpdateExistingClusters(self, stream):
predictedCluster = self.getClusterForDocument(stream)
if predictedCluster!=None: self.clusters[predictedCluster].addDocument(stream, **self.stream_settings)
else:
newCluster = StreamCluster(stream)
newCluster.setSignatureUsingVectorPermutations(self.unitVector, self.vectorPermutations, self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations: permutation.addDocument(newCluster)
self.clusters[newCluster.clusterId] = newCluster
def setUp(self):
self.m1 = Message(1, 'sdf',
'A project to cluster high-dimensional streams.',
test_time - timedelta(seconds=60))
self.m1.vector = Vector({1: 1., 2: 3.})
self.stream = Stream(1, self.m1)
self.cluster = StreamCluster(self.stream)
self.crowd = Crowd(self.cluster, test_time)
def test_clustersIteration(self):
clusters = [self.cluster1, self.cluster2, self.cluster3]
self.assertEqual([self.cluster1],
[cluster for cluster in StreamCluster.getClustersByAttributeAndThreshold(clusters, 'lastStreamAddedTime', test_time ,StreamCluster.BELOW_THRESHOLD)]
)
self.assertEqual([self.cluster1, self.cluster2],
[cluster for cluster in StreamCluster.getClustersByAttributeAndThreshold(clusters, 'lastStreamAddedTime', test_time+timedelta(seconds=60) ,StreamCluster.BELOW_THRESHOLD)]
)
def getClusterFromMapFormat(clusterMap):
dummyMessage = Message(1, '', '', datetime.now())
dummyMessage.vector=Vector({})
dummyStream=Stream(1, dummyMessage)
cluster = StreamCluster(dummyStream)
cluster.clusterId = clusterMap['clusterId']
cluster.lastStreamAddedTime = getDateTimeObjectFromTweetTimestamp(clusterMap['lastStreamAddedTime'])
cluster.mergedClustersList = clusterMap['mergedClustersList']
cluster.documentsInCluster = clusterMap['streams']
for k,v in clusterMap['dimensions'].iteritems(): cluster[k]=v
return cluster
def getClusterAndUpdateExistingClusters(self, stream):
predictedCluster = self.getClusterForDocument(stream)
'''
Do not remove this comment. Might need this if StreamCluster is used again in future.
if predictedCluster!=None: self.clusters[predictedCluster].addStream(stream, **self.stream_settings)
'''
if predictedCluster!=None: self.clusters[predictedCluster].addDocument(stream, **self.stream_settings)
else:
newCluster = StreamCluster(stream)
newCluster.setSignatureUsingVectorPermutations(self.unitVector, self.vectorPermutations, self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations: permutation.addDocument(newCluster)
self.clusters[newCluster.clusterId] = newCluster
class CrowdTests(unittest.TestCase):
def setUp(self):
self.m1 = Message(1, 'sdf',
'A project to cluster high-dimensional streams.',
test_time - timedelta(seconds=60))
self.m1.vector = Vector({1: 1., 2: 3.})
self.stream = Stream(1, self.m1)
self.cluster = StreamCluster(self.stream)
self.crowd = Crowd(self.cluster, test_time)
def test_intitialization(self):
self.assertEqual(self.cluster.clusterId, self.crowd.crowdId)
def test_append(self):
self.crowd.append(self.cluster, test_time + timedelta(days=1))
self.assertEqual([
GeneralMethods.getEpochFromDateTimeObject(test_time),
GeneralMethods.getEpochFromDateTimeObject(test_time +
timedelta(days=1))
], sorted(self.crowd.clusters.keys()))
self.assertEqual(
StreamCluster,
type(self.crowd.clusters[GeneralMethods.getEpochFromDateTimeObject(
test_time)]))
self.assertEqual(2, self.crowd.lifespan)
self.assertEqual(
getStringRepresentationForTweetTimestamp(test_time),
getStringRepresentationForTweetTimestamp(self.crowd.startTime))
self.assertEqual(
getStringRepresentationForTweetTimestamp(test_time +
timedelta(days=1)),
getStringRepresentationForTweetTimestamp(self.crowd.endTime))
def test_maxClusterSize(self):
self.assertEqual(1, self.crowd.maxClusterSize)
message2 = Message(4, 'sdf',
'A project to cluster high-dimensional streams.',
test_time)
message2.vector = Vector({2: 4})
stream2 = Stream(4, message2)
self.cluster.addDocument(stream2)
self.assertEqual(2, self.crowd.maxClusterSize)
def test_crowdSize(self):
self.assertEqual(1, self.crowd.crowdSize)
self.cluster.addDocument(Stream(2, self.m1))
self.cluster.addDocument(Stream(3, self.m1))
self.assertEqual(3, self.crowd.crowdSize)
cluster = StreamCluster(Stream(3, self.m1))
self.crowd.append(cluster, test_time + timedelta(days=2))
self.assertNotEqual(4, self.crowd.crowdSize)
self.assertEqual(3, self.crowd.crowdSize)
def getClusterAndUpdateExistingClusters(self, stream):
predictedCluster = self.getClusterForDocument(stream)
if predictedCluster != None:
self.clusters[predictedCluster].addDocument(
stream, **self.stream_settings)
else:
newCluster = StreamCluster(stream)
newCluster.setSignatureUsingVectorPermutations(
self.unitVector, self.vectorPermutations,
self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations:
permutation.addDocument(newCluster)
self.clusters[newCluster.clusterId] = newCluster
def test_clustersIteration(self):
clusters = [self.cluster1, self.cluster2, self.cluster3]
self.assertEqual([self.cluster1], [
cluster
for cluster in StreamCluster.getClustersByAttributeAndThreshold(
clusters, 'lastStreamAddedTime', test_time,
StreamCluster.BELOW_THRESHOLD)
])
self.assertEqual([self.cluster1, self.cluster2], [
cluster
for cluster in StreamCluster.getClustersByAttributeAndThreshold(
clusters, 'lastStreamAddedTime', test_time +
timedelta(seconds=60), StreamCluster.BELOW_THRESHOLD)
])
def combineClusters(clusters, **twitter_stream_settings):
def getHashtagSet(vector):
return set([
word for dimension in vector for word in dimension.split()
if word.startswith('#')
])
def getClusterInt(id):
return int(id.split('_')[1])
mergedClustersMap = {}
for cluster in [
clusters[v] for v in sorted(clusters, key=getClusterInt)
]:
mergedClusterId = None
for mergedCluster in mergedClustersMap.itervalues():
clusterHashtags, mergedClusterHashtags = getHashtagSet(
cluster), getHashtagSet(mergedCluster)
if len(
clusterHashtags.union(mergedClusterHashtags)
) and jaccard_distance(
clusterHashtags,
mergedClusterHashtags) <= 1 - twitter_stream_settings[
'cluster_merging_jaccard_distance_threshold']:
mergedCluster.mergeCluster(
cluster), mergedCluster.mergedClustersList.append(
cluster.clusterId)
mergedClusterId = mergedCluster.clusterId
break
if mergedClusterId == None:
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
cluster)
mergedCluster.mergedClustersList = [cluster.clusterId]
mergedClustersMap[mergedCluster.clusterId] = mergedCluster
return mergedClustersMap
def getClusterAndUpdateExistingClusters(self, stream):
predictedCluster = self.getClusterForDocument(stream)
'''
Do not remove this comment. Might need this if StreamCluster is used again in future.
if predictedCluster!=None: self.clusters[predictedCluster].addStream(stream, **self.stream_settings)
'''
if predictedCluster != None:
self.clusters[predictedCluster].addDocument(
stream, **self.stream_settings)
else:
newCluster = StreamCluster(stream)
newCluster.setSignatureUsingVectorPermutations(
self.unitVector, self.vectorPermutations,
self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations:
permutation.addDocument(newCluster)
self.clusters[newCluster.clusterId] = newCluster
def test_mergeCluster_compare_vector_lastStreamAddedTime_more_than_original_cluster(self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster1)
mergedCluster.mergeCluster(self.cluster2)
self.assertEqual([self.stream1, self.stream2], list(mergedCluster.iterateDocumentsInCluster()))
meanVectorForAllDocuments = Vector.getMeanVector([self.stream1, self.stream2])
self.assertEqual(meanVectorForAllDocuments, mergedCluster)
self.assertEqual([mergedCluster.docId, mergedCluster.docId], list(doc.clusterId for doc in mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster2.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
def setUp(self):
self.tweet = {'user':{'screen_name': 'abc'}, 'id':10, 'text':'A project to cluster high-dimensional streams.', 'created_at': 'Tue Mar 01 05:59:59 +0000 2011'}
m1 = Message(1, '', '', datetime.now())
m1.vector=Vector({'#tcot':2,'dsf':4})
self.cluster1 = StreamCluster(Stream(1, m1))
m2 = Message(2, '', '', datetime.now())
m2.vector=Vector({'#tcot':4})
self.cluster2 = StreamCluster(Stream(2, m2))
m3 = Message(3, '', '', datetime.now())
m3.vector=Vector(Vector({'#tcot':2}))
m4 = Message(4, '', '', datetime.now())
m4.vector=Vector(Vector({'#tcot':2}))
self.doc1 = Stream(1, m3)
self.doc2 = Stream(2, m4)
self.meanVectorForAllDocuments = Vector.getMeanVector([self.cluster1, self.cluster2, self.doc1, self.doc2])
self.cluster1.addDocument(self.doc1)
self.cluster2.addDocument(self.doc2)
def test_crowdSize(self):
self.assertEqual(1, self.crowd.crowdSize)
self.cluster.addDocument(Stream(2, self.m1))
self.cluster.addDocument(Stream(3, self.m1))
self.assertEqual(3, self.crowd.crowdSize)
cluster = StreamCluster(Stream(3, self.m1))
self.crowd.append(cluster, test_time + timedelta(days=2))
self.assertNotEqual(4, self.crowd.crowdSize)
self.assertEqual(3, self.crowd.crowdSize)
class TwitterCrowdsSpecificMethodsTests(unittest.TestCase):
def setUp(self):
self.tweet = {'user':{'screen_name': 'abc'}, 'id':10, 'text':'A project to cluster high-dimensional streams.', 'created_at': 'Tue Mar 01 05:59:59 +0000 2011'}
m1 = Message(1, '', '', datetime.now())
m1.vector=Vector({'#tcot':2,'dsf':4})
self.cluster1 = StreamCluster(Stream(1, m1))
m2 = Message(2, '', '', datetime.now())
m2.vector=Vector({'#tcot':4})
self.cluster2 = StreamCluster(Stream(2, m2))
m3 = Message(3, '', '', datetime.now())
m3.vector=Vector(Vector({'#tcot':2}))
m4 = Message(4, '', '', datetime.now())
m4.vector=Vector(Vector({'#tcot':2}))
self.doc1 = Stream(1, m3)
self.doc2 = Stream(2, m4)
self.meanVectorForAllDocuments = Vector.getMeanVector([self.cluster1, self.cluster2, self.doc1, self.doc2])
self.cluster1.addDocument(self.doc1)
self.cluster2.addDocument(self.doc2)
def test_convertTweetJSONToMessage(self):
message = TwitterCrowdsSpecificMethods.convertTweetJSONToMessage(self.tweet, **twitter_stream_settings)
self.assertEqual({'project': 1, 'cluster': 1, 'streams': 1, 'highdimensional': 1}, message.vector)
def test_combineClusters(self):
clustersMap = {self.cluster1.clusterId: self.cluster1, self.cluster2.clusterId: self.cluster2}
clustersMap = TwitterCrowdsSpecificMethods.combineClusters(clustersMap, **twitter_stream_settings)
self.assertEqual(1, len(clustersMap))
mergedCluster = clustersMap.values()[0]
self.assertEqual([self.doc1, self.doc2], list(mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.meanVectorForAllDocuments, mergedCluster)
self.assertEqual([mergedCluster.docId, mergedCluster.docId], list(doc.clusterId for doc in mergedCluster.iterateDocumentsInCluster()))
self.assertEqual([self.cluster1.clusterId, self.cluster2.clusterId], mergedCluster.mergedClustersList)
def test_getClusterInMapFormat(self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster1)
mergedCluster.mergedClustersList = [self.cluster1.clusterId]
mergedCluster.lastStreamAddedTime = test_time
mapReresentation = {'clusterId': mergedCluster.clusterId, 'lastStreamAddedTime':getStringRepresentationForTweetTimestamp(mergedCluster.lastStreamAddedTime), 'mergedClustersList': [self.cluster1.clusterId], 'streams': [self.doc1.docId], 'dimensions': {'#tcot':2, 'dsf':2}}
self.assertEqual(mapReresentation, TwitterCrowdsSpecificMethods.getClusterInMapFormat(mergedCluster))
def test_getClusterFromMapFormat(self):
mapReresentation = {'clusterId': 1, 'mergedClustersList': [self.cluster1.clusterId], 'lastStreamAddedTime': getStringRepresentationForTweetTimestamp(test_time), 'streams': [self.doc1.docId], 'dimensions': {'#tcot':2, 'dsf':2}}
cluster = TwitterCrowdsSpecificMethods.getClusterFromMapFormat(mapReresentation)
self.assertEqual(1, cluster.clusterId)
self.assertEqual([self.cluster1.clusterId], cluster.mergedClustersList)
self.assertEqual([self.doc1.docId], cluster.documentsInCluster)
self.assertEqual({'#tcot':2, 'dsf':2}, cluster)
self.assertEqual(getStringRepresentationForTweetTimestamp(test_time), getStringRepresentationForTweetTimestamp(cluster.lastStreamAddedTime))
def test_getClusterObjectToMergeFrom(self):
documentsInCluster=list(self.cluster1.iterateDocumentsInCluster())
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster1)
self.assertEqual(test_time-timedelta(seconds=60), mergedCluster.lastStreamAddedTime)
self.assertEqual(self.cluster1, mergedCluster)
self.assertNotEqual(self.cluster1.clusterId, mergedCluster.clusterId)
self.assertEqual(documentsInCluster, list(mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster1.aggregateVector, mergedCluster.aggregateVector)
self.assertEqual(self.cluster1.vectorWeights, mergedCluster.vectorWeights)
self.assertEqual(self.cluster1.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
def setUp(self):
self.m1 = Message(1, 'sdf',
'A project to cluster high-dimensional streams.',
test_time - timedelta(seconds=60))
self.m1.vector = Vector({1: 2, 2: 4})
self.stream1 = Stream(1, self.m1)
self.m2 = Message(2, 'sdf',
'A project to cluster high-dimensional streams.',
test_time)
self.m2.vector = Vector({2: 4})
self.stream2 = Stream(2, self.m2)
self.m3 = Message(3, 'sdf',
'A project to cluster high-dimensional streams.',
test_time + timedelta(seconds=60))
self.m3.vector = Vector({2: 4})
self.stream3 = Stream(3, self.m3)
self.cluster1 = StreamCluster(self.stream1)
self.cluster2 = StreamCluster(self.stream2)
self.cluster3 = StreamCluster(self.stream3)
def test_mergeCluster_lastStreamAddedTime_lesser_than_original_cluster(
self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
self.cluster3)
mergedCluster.mergeCluster(self.cluster1)
self.assertTrue(self.cluster1.lastStreamAddedTime <
self.cluster3.lastStreamAddedTime)
self.assertEqual(self.cluster3.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
self.assertNotEqual(self.cluster1.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
class CrowdTests(unittest.TestCase):
def setUp(self):
self.m1 = Message(1, 'sdf', 'A project to cluster high-dimensional streams.', test_time-timedelta(seconds=60))
self.m1.vector=Vector({1:1.,2:3.})
self.stream = Stream(1, self.m1)
self.cluster = StreamCluster(self.stream)
self.crowd = Crowd(self.cluster, test_time)
def test_intitialization(self):
self.assertEqual(self.cluster.clusterId, self.crowd.crowdId)
def test_append(self):
self.crowd.append(self.cluster, test_time+timedelta(days=1))
self.assertEqual([GeneralMethods.getEpochFromDateTimeObject(test_time), GeneralMethods.getEpochFromDateTimeObject(test_time+timedelta(days=1))], sorted(self.crowd.clusters.keys()))
self.assertEqual(StreamCluster, type(self.crowd.clusters[GeneralMethods.getEpochFromDateTimeObject(test_time)]))
self.assertEqual(2, self.crowd.lifespan)
self.assertEqual(getStringRepresentationForTweetTimestamp(test_time), getStringRepresentationForTweetTimestamp(self.crowd.startTime))
self.assertEqual(getStringRepresentationForTweetTimestamp(test_time+timedelta(days=1)), getStringRepresentationForTweetTimestamp(self.crowd.endTime))
def test_maxClusterSize(self):
self.assertEqual(1, self.crowd.maxClusterSize)
message2 = Message(4, 'sdf', 'A project to cluster high-dimensional streams.', test_time)
message2.vector=Vector({2:4})
stream2 = Stream(4, message2)
self.cluster.addDocument(stream2)
self.assertEqual(2, self.crowd.maxClusterSize)
def test_crowdSize(self):
self.assertEqual(1, self.crowd.crowdSize)
self.cluster.addDocument(Stream(2, self.m1));self.cluster.addDocument(Stream(3, self.m1))
self.assertEqual(3, self.crowd.crowdSize)
cluster = StreamCluster(Stream(3, self.m1))
self.crowd.append(cluster, test_time+timedelta(days=2))
self.assertNotEqual(4, self.crowd.crowdSize)
self.assertEqual(3, self.crowd.crowdSize)
def clusterFilteringMethod(hdStreamClusteringObject, currentMessageTime):
for cluster in StreamCluster.getClustersByAttributeAndThreshold(
hdStreamClusteringObject.clusters.values(),
hdStreamClusteringObject.
stream_settings['cluster_filter_attribute'],
hdStreamClusteringObject.
stream_settings['cluster_filter_threshold'],
StreamCluster.BELOW_THRESHOLD):
del hdStreamClusteringObject.clusters[cluster.clusterId]
for cluster in StreamCluster.getClustersByAttributeAndThreshold(
hdStreamClusteringObject.clusters.values(),
'lastStreamAddedTime',
currentMessageTime - hdStreamClusteringObject.
stream_settings['cluster_inactivity_time_in_seconds'],
StreamCluster.BELOW_THRESHOLD):
del hdStreamClusteringObject.clusters[cluster.clusterId]
if hdStreamClusteringObject.combineClustersMethod != None:
hdStreamClusteringObject.clusters = hdStreamClusteringObject.combineClustersMethod(
hdStreamClusteringObject.clusters,
**hdStreamClusteringObject.stream_settings)
DataStreamMethods._resetClustersInSignatureTries(
hdStreamClusteringObject, currentMessageTime)
def setUp(self):
self.m1 = Message(1, 'sdf', 'A project to cluster high-dimensional streams.', test_time-timedelta(seconds=60))
self.m1.vector=Vector({1:2,2:4})
self.stream1 = Stream(1, self.m1)
self.m2 = Message(2, 'sdf', 'A project to cluster high-dimensional streams.', test_time)
self.m2.vector=Vector({2:4})
self.stream2 = Stream(2, self.m2)
self.m3 = Message(3, 'sdf', 'A project to cluster high-dimensional streams.', test_time+timedelta(seconds=60))
self.m3.vector=Vector({2:4})
self.stream3 = Stream(3, self.m3)
self.cluster1 = StreamCluster(self.stream1)
self.cluster2 = StreamCluster(self.stream2)
self.cluster3 = StreamCluster(self.stream3)
def test_getClusterObjectToMergeFrom(self):
documentsInCluster = list(self.cluster1.iterateDocumentsInCluster())
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
self.cluster1)
self.assertEqual(test_time - timedelta(seconds=60),
mergedCluster.lastStreamAddedTime)
self.assertEqual(self.cluster1, mergedCluster)
self.assertNotEqual(self.cluster1.clusterId, mergedCluster.clusterId)
self.assertEqual(documentsInCluster,
list(mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster1.aggregateVector,
mergedCluster.aggregateVector)
self.assertEqual(self.cluster1.vectorWeights,
mergedCluster.vectorWeights)
self.assertEqual(self.cluster1.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
def test_mergeCluster_compare_vector_lastStreamAddedTime_more_than_original_cluster(
self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
self.cluster1)
mergedCluster.mergeCluster(self.cluster2)
self.assertEqual([self.stream1, self.stream2],
list(mergedCluster.iterateDocumentsInCluster()))
meanVectorForAllDocuments = Vector.getMeanVector(
[self.stream1, self.stream2])
self.assertEqual(meanVectorForAllDocuments, mergedCluster)
self.assertEqual(
[mergedCluster.docId, mergedCluster.docId],
list(doc.clusterId
for doc in mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster2.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
def clusterAnalysisMethod(hdStreamClusteringObject, currentMessageTime):
print currentMessageTime
print '\n\n\nEntering:', currentMessageTime, len(
hdStreamClusteringObject.phraseTextAndDimensionMap), len(
hdStreamClusteringObject.phraseTextToPhraseObjectMap), len(
hdStreamClusteringObject.clusters)
for cluster, _ in sorted(StreamCluster.iterateByAttribute(
hdStreamClusteringObject.clusters.values(), 'length'),
key=itemgetter(1),
reverse=True)[:1]:
print cluster.clusterId, cluster.length, [
stream.docId for stream in cluster.iterateDocumentsInCluster()
][:5], cluster.getTopDimensions(numberOfFeatures=5)
print 'Leaving: ', currentMessageTime, len(
hdStreamClusteringObject.phraseTextAndDimensionMap), len(
hdStreamClusteringObject.phraseTextToPhraseObjectMap), len(
hdStreamClusteringObject.clusters)
def combineClusters(clusters, **twitter_stream_settings):
def getHashtagSet(vector): return set([word for dimension in vector for word in dimension.split() if word.startswith('#')])
def getClusterInt(id): return int(id.split('_')[1])
mergedClustersMap = {}
for cluster in [clusters[v] for v in sorted(clusters, key=getClusterInt)]:
mergedClusterId = None
for mergedCluster in mergedClustersMap.itervalues():
clusterHashtags, mergedClusterHashtags = getHashtagSet(cluster), getHashtagSet(mergedCluster)
if len(clusterHashtags.union(mergedClusterHashtags)) and jaccard_distance(clusterHashtags, mergedClusterHashtags) <= 1-twitter_stream_settings['cluster_merging_jaccard_distance_threshold']:
mergedCluster.mergeCluster(cluster), mergedCluster.mergedClustersList.append(cluster.clusterId)
mergedClusterId = mergedCluster.clusterId
break
if mergedClusterId==None:
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(cluster)
mergedCluster.mergedClustersList = [cluster.clusterId]
mergedClustersMap[mergedCluster.clusterId]=mergedCluster
return mergedClustersMap
def getClusterFromMapFormat(clusterMap):
dummyMessage = Message(1, '', '', datetime.now())
dummyMessage.vector = Vector({})
dummyStream = Stream(1, dummyMessage)
cluster = StreamCluster(dummyStream)
cluster.clusterId = clusterMap['clusterId']
cluster.lastStreamAddedTime = getDateTimeObjectFromTweetTimestamp(
clusterMap['lastStreamAddedTime'])
cluster.mergedClustersList = clusterMap['mergedClustersList']
cluster.documentsInCluster = clusterMap['streams']
for k, v in clusterMap['dimensions'].iteritems():
cluster[k] = v
return cluster
def setUp(self):
self.m1 = Message(1, 'sdf', 'A project to cluster high-dimensional streams.', test_time-timedelta(seconds=60))
self.m1.vector=Vector({1:1.,2:3.})
self.stream = Stream(1, self.m1)
self.cluster = StreamCluster(self.stream)
self.crowd = Crowd(self.cluster, test_time)
def test_getClusterInMapFormat(self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster1)
mergedCluster.mergedClustersList = [self.cluster1.clusterId]
mergedCluster.lastStreamAddedTime = test_time
mapReresentation = {'clusterId': mergedCluster.clusterId, 'lastStreamAddedTime':getStringRepresentationForTweetTimestamp(mergedCluster.lastStreamAddedTime), 'mergedClustersList': [self.cluster1.clusterId], 'streams': [self.doc1.docId], 'dimensions': {'#tcot':2, 'dsf':2}}
self.assertEqual(mapReresentation, TwitterCrowdsSpecificMethods.getClusterInMapFormat(mergedCluster))
def test_mergeCluster_lastStreamAddedTime_lesser_than_original_cluster(self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster3)
mergedCluster.mergeCluster(self.cluster1)
self.assertTrue(self.cluster1.lastStreamAddedTime<self.cluster3.lastStreamAddedTime)
self.assertEqual(self.cluster3.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
self.assertNotEqual(self.cluster1.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
def writeClusters(hdStreamClusteringObject, currentMessageTime):
print '\n\n\nEntering:', currentMessageTime, len(hdStreamClusteringObject.phraseTextAndDimensionMap), len(hdStreamClusteringObject.phraseTextToPhraseObjectMap), len(hdStreamClusteringObject.clusters)
iterationData = {'time_stamp': getStringRepresentationForTweetTimestamp(currentMessageTime),
'clusters': map(TwitterCrowdsSpecificMethods.getClusterInMapFormat, [cluster for cluster, _ in sorted(StreamCluster.iterateByAttribute(hdStreamClusteringObject.clusters.values(), 'length'), key=itemgetter(1), reverse=True)]),
'settings': Settings.getSerialzedObject(hdStreamClusteringObject.stream_settings)
}
FileIO.writeToFileAsJson(iterationData, hdStreamClusteringObject.stream_settings['lsh_clusters_folder']+FileIO.getFileByDay(currentMessageTime))
print 'Leaving: ', currentMessageTime, len(hdStreamClusteringObject.phraseTextAndDimensionMap), len(hdStreamClusteringObject.phraseTextToPhraseObjectMap), len(hdStreamClusteringObject.clusters)
class StreamClusterTests(unittest.TestCase):
def setUp(self):
self.m1 = Message(1, 'sdf',
'A project to cluster high-dimensional streams.',
test_time - timedelta(seconds=60))
self.m1.vector = Vector({1: 2, 2: 4})
self.stream1 = Stream(1, self.m1)
self.m2 = Message(2, 'sdf',
'A project to cluster high-dimensional streams.',
test_time)
self.m2.vector = Vector({2: 4})
self.stream2 = Stream(2, self.m2)
self.m3 = Message(3, 'sdf',
'A project to cluster high-dimensional streams.',
test_time + timedelta(seconds=60))
self.m3.vector = Vector({2: 4})
self.stream3 = Stream(3, self.m3)
self.cluster1 = StreamCluster(self.stream1)
self.cluster2 = StreamCluster(self.stream2)
self.cluster3 = StreamCluster(self.stream3)
def test_initialization(self):
self.assertEqual(test_time - timedelta(seconds=60),
self.cluster1.lastStreamAddedTime)
self.assertEqual(test_time, self.cluster2.lastStreamAddedTime)
def test_getClusterObjectToMergeFrom(self):
documentsInCluster = list(self.cluster1.iterateDocumentsInCluster())
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
self.cluster1)
self.assertEqual(test_time - timedelta(seconds=60),
mergedCluster.lastStreamAddedTime)
self.assertEqual(self.cluster1, mergedCluster)
self.assertNotEqual(self.cluster1.clusterId, mergedCluster.clusterId)
self.assertEqual(documentsInCluster,
list(mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster1.aggregateVector,
mergedCluster.aggregateVector)
self.assertEqual(self.cluster1.vectorWeights,
mergedCluster.vectorWeights)
self.assertEqual(self.cluster1.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
def test_mergeCluster_compare_vector_lastStreamAddedTime_more_than_original_cluster(
self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
self.cluster1)
mergedCluster.mergeCluster(self.cluster2)
self.assertEqual([self.stream1, self.stream2],
list(mergedCluster.iterateDocumentsInCluster()))
meanVectorForAllDocuments = Vector.getMeanVector(
[self.stream1, self.stream2])
self.assertEqual(meanVectorForAllDocuments, mergedCluster)
self.assertEqual(
[mergedCluster.docId, mergedCluster.docId],
list(doc.clusterId
for doc in mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster2.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
def test_mergeCluster_lastStreamAddedTime_lesser_than_original_cluster(
self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(
self.cluster3)
mergedCluster.mergeCluster(self.cluster1)
self.assertTrue(self.cluster1.lastStreamAddedTime <
self.cluster3.lastStreamAddedTime)
self.assertEqual(self.cluster3.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
self.assertNotEqual(self.cluster1.lastStreamAddedTime,
mergedCluster.lastStreamAddedTime)
def test_addDocument(self):
message1 = Message(3, 'sdf',
'A project to cluster high-dimensional streams.',
test_time)
message1.vector = Vector({3: 4})
stream1 = Stream(3, message1)
message2 = Message(4, 'sdf',
'A project to cluster high-dimensional streams.',
test_time)
message2.vector = Vector({2: 4})
stream2 = Stream(4, message2)
self.assertNotEqual(test_time, self.cluster1.lastStreamAddedTime)
self.cluster1.addDocument(stream1)
self.assertEqual(test_time, self.cluster1.lastStreamAddedTime)
# Test if cluster id is set.
self.assertEqual(self.cluster1.clusterId, stream1.clusterId)
# Test that cluster mean is updated.
self.assertEqual({1: 2 / 2., 2: 2., 3: 2.}, self.cluster1)
# Test that cluster aggrefate is updated.
self.assertEqual({1: 2, 2: 4, 3: 4}, self.cluster1.aggregateVector)
# Test that document is added to cluster documents.
self.assertEqual(stream1,
self.cluster1.documentsInCluster[stream1.docId])
self.cluster1.addDocument(stream2)
self.assertEqual(3, self.cluster1.vectorWeights)
self.assertEqual({1: 2 / 3., 2: 8 / 3., 3: 4 / 3.}, self.cluster1)
self.assertEqual({1: 2, 2: 8, 3: 4}, self.cluster1.aggregateVector)
def test_clustersIteration(self):
clusters = [self.cluster1, self.cluster2, self.cluster3]
self.assertEqual([self.cluster1], [
cluster
for cluster in StreamCluster.getClustersByAttributeAndThreshold(
clusters, 'lastStreamAddedTime', test_time,
StreamCluster.BELOW_THRESHOLD)
])
self.assertEqual([self.cluster1, self.cluster2], [
cluster
for cluster in StreamCluster.getClustersByAttributeAndThreshold(
clusters, 'lastStreamAddedTime', test_time +
timedelta(seconds=60), StreamCluster.BELOW_THRESHOLD)
])
def clusterAnalysisMethod(hdStreamClusteringObject, currentMessageTime):
print currentMessageTime
print '\n\n\nEntering:', currentMessageTime, len(hdStreamClusteringObject.phraseTextAndDimensionMap), len(hdStreamClusteringObject.phraseTextToPhraseObjectMap), len(hdStreamClusteringObject.clusters)
for cluster, _ in sorted(StreamCluster.iterateByAttribute(hdStreamClusteringObject.clusters.values(), 'length'), key=itemgetter(1), reverse=True)[:1]:
print cluster.clusterId, cluster.length, [stream.docId for stream in cluster.iterateDocumentsInCluster()][:5], cluster.getTopDimensions(numberOfFeatures=5)
print 'Leaving: ', currentMessageTime, len(hdStreamClusteringObject.phraseTextAndDimensionMap), len(hdStreamClusteringObject.phraseTextToPhraseObjectMap), len(hdStreamClusteringObject.clusters)
class StreamClusterTests(unittest.TestCase):
def setUp(self):
self.m1 = Message(1, 'sdf', 'A project to cluster high-dimensional streams.', test_time-timedelta(seconds=60))
self.m1.vector=Vector({1:2,2:4})
self.stream1 = Stream(1, self.m1)
self.m2 = Message(2, 'sdf', 'A project to cluster high-dimensional streams.', test_time)
self.m2.vector=Vector({2:4})
self.stream2 = Stream(2, self.m2)
self.m3 = Message(3, 'sdf', 'A project to cluster high-dimensional streams.', test_time+timedelta(seconds=60))
self.m3.vector=Vector({2:4})
self.stream3 = Stream(3, self.m3)
self.cluster1 = StreamCluster(self.stream1)
self.cluster2 = StreamCluster(self.stream2)
self.cluster3 = StreamCluster(self.stream3)
def test_initialization(self):
self.assertEqual(test_time-timedelta(seconds=60), self.cluster1.lastStreamAddedTime)
self.assertEqual(test_time, self.cluster2.lastStreamAddedTime)
def test_getClusterObjectToMergeFrom(self):
documentsInCluster=list(self.cluster1.iterateDocumentsInCluster())
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster1)
self.assertEqual(test_time-timedelta(seconds=60), mergedCluster.lastStreamAddedTime)
self.assertEqual(self.cluster1, mergedCluster)
self.assertNotEqual(self.cluster1.clusterId, mergedCluster.clusterId)
self.assertEqual(documentsInCluster, list(mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster1.aggregateVector, mergedCluster.aggregateVector)
self.assertEqual(self.cluster1.vectorWeights, mergedCluster.vectorWeights)
self.assertEqual(self.cluster1.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
def test_mergeCluster_compare_vector_lastStreamAddedTime_more_than_original_cluster(self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster1)
mergedCluster.mergeCluster(self.cluster2)
self.assertEqual([self.stream1, self.stream2], list(mergedCluster.iterateDocumentsInCluster()))
meanVectorForAllDocuments = Vector.getMeanVector([self.stream1, self.stream2])
self.assertEqual(meanVectorForAllDocuments, mergedCluster)
self.assertEqual([mergedCluster.docId, mergedCluster.docId], list(doc.clusterId for doc in mergedCluster.iterateDocumentsInCluster()))
self.assertEqual(self.cluster2.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
def test_mergeCluster_lastStreamAddedTime_lesser_than_original_cluster(self):
mergedCluster = StreamCluster.getClusterObjectToMergeFrom(self.cluster3)
mergedCluster.mergeCluster(self.cluster1)
self.assertTrue(self.cluster1.lastStreamAddedTime<self.cluster3.lastStreamAddedTime)
self.assertEqual(self.cluster3.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
self.assertNotEqual(self.cluster1.lastStreamAddedTime, mergedCluster.lastStreamAddedTime)
def test_addDocument(self):
message1 = Message(3, 'sdf', 'A project to cluster high-dimensional streams.', test_time)
message1.vector=Vector({3:4})
stream1 = Stream(3, message1)
message2 = Message(4, 'sdf', 'A project to cluster high-dimensional streams.', test_time)
message2.vector=Vector({2:4})
stream2 = Stream(4, message2)
self.assertNotEqual(test_time, self.cluster1.lastStreamAddedTime)
self.cluster1.addDocument(stream1)
self.assertEqual(test_time, self.cluster1.lastStreamAddedTime)
# Test if cluster id is set.
self.assertEqual(self.cluster1.clusterId, stream1.clusterId)
# Test that cluster mean is updated.
self.assertEqual({1:2/2.,2:2.,3:2.}, self.cluster1)
# Test that cluster aggrefate is updated.
self.assertEqual({1:2,2:4,3:4}, self.cluster1.aggregateVector)
# Test that document is added to cluster documents.
self.assertEqual(stream1, self.cluster1.documentsInCluster[stream1.docId])
self.cluster1.addDocument(stream2)
self.assertEqual(3, self.cluster1.vectorWeights)
self.assertEqual({1:2/3.,2:8/3.,3:4/3.}, self.cluster1)
self.assertEqual({1:2,2:8,3:4}, self.cluster1.aggregateVector)
def test_clustersIteration(self):
clusters = [self.cluster1, self.cluster2, self.cluster3]
self.assertEqual([self.cluster1],
[cluster for cluster in StreamCluster.getClustersByAttributeAndThreshold(clusters, 'lastStreamAddedTime', test_time ,StreamCluster.BELOW_THRESHOLD)]
)
self.assertEqual([self.cluster1, self.cluster2],
[cluster for cluster in StreamCluster.getClustersByAttributeAndThreshold(clusters, 'lastStreamAddedTime', test_time+timedelta(seconds=60) ,StreamCluster.BELOW_THRESHOLD)]
)