def clusterLagDistributionMethod(hdStreamClusteringObject,
currentMessageTime):
lagDistribution = defaultdict(int)
for cluster in hdStreamClusteringObject.clusters.values():
lag = DateTimeAirthematic.getDifferenceInTimeUnits(
currentMessageTime, cluster.lastStreamAddedTime,
hdStreamClusteringObject.
stream_settings['time_unit_in_seconds'].seconds)
lagDistribution[str(lag)] += 1
print currentMessageTime, len(hdStreamClusteringObject.clusters)
iterationData = {
'time_stamp':
getStringRepresentationForTweetTimestamp(currentMessageTime),
'settings':
pprint.pformat(hdStreamClusteringObject.stream_settings),
ClusteringParametersEstimation.clusterLagDistributionId:
lagDistribution,
'lag_between_streams_added_to_cluster':
hdStreamClusteringObject.
stream_settings['lag_between_streams_added_to_cluster']
}
# print hdStreamClusteringObject.stream_settings['lag_between_streams_added_to_cluster']
FileIO.writeToFileAsJson(
iterationData, hdStreamClusteringObject.stream_settings[
'%s_file' %
ClusteringParametersEstimation.clusterLagDistributionId])
def updateScore(self, currentOccuranceTime, scoreToUpdate, **stream_settings):
timeDifference = DateTimeAirthematic.getDifferenceInTimeUnits(
currentOccuranceTime, self.latestOccuranceTime, stream_settings["time_unit_in_seconds"].seconds
)
self.score = (
exponentialDecay(self.score, stream_settings["phrase_decay_coefficient"], timeDifference) + scoreToUpdate
)
self.latestOccuranceTime = currentOccuranceTime
def updateScore(self, currentOccuranceTime, scoreToUpdate,
**stream_settings):
timeDifference = DateTimeAirthematic.getDifferenceInTimeUnits(
currentOccuranceTime, self.latestOccuranceTime,
stream_settings['time_unit_in_seconds'].seconds)
self.score = exponentialDecay(
self.score, stream_settings['phrase_decay_coefficient'],
timeDifference) + scoreToUpdate
self.latestOccuranceTime = currentOccuranceTime
def parameterSpecificDataCollectionMethod(estimationObject, message):
for phrase in message.vector:
if phrase in estimationObject.phraseTextToPhraseObjectMap:
phraseObject = estimationObject.phraseTextToPhraseObjectMap[
phrase]
lag = DateTimeAirthematic.getDifferenceInTimeUnits(
message.timeStamp, phraseObject.latestOccuranceTime,
estimationObject.
twitter_stream_settings['time_unit_in_seconds'].seconds)
estimationObject.lagBetweenMessagesDistribution[str(lag)] += 1
def updateForMessage(self, message, updateMethod, **stream_settings):
timeDifference = None
if stream_settings['time_unit_in_seconds'] != None:
timeDifference = DateTimeAirthematic.getDifferenceInTimeUnits(
message.timeStamp, self.lastMessageTime,
stream_settings['time_unit_in_seconds'].seconds)
updateMethod(
self,
message.vector,
decayCoefficient=stream_settings['stream_decay_coefficient'],
timeDifference=timeDifference)
self.lastMessageTime = message.timeStamp
def dimensionInActivityTimeEstimation(estimationObject, currentMessageTime):
phrasesLagDistribution = defaultdict(int)
for phraseObject in estimationObject.phraseTextToPhraseObjectMap.itervalues():
lag = DateTimeAirthematic.getDifferenceInTimeUnits(currentMessageTime, phraseObject.latestOccuranceTime, estimationObject.stream_settings['time_unit_in_seconds'].seconds)
phrasesLagDistribution[str(lag)] += 1
print currentMessageTime
iterationData = {
'time_stamp': getStringRepresentationForTweetTimestamp(currentMessageTime),
'settings': pprint.pformat(estimationObject.stream_settings),
ParameterEstimation.dimensionInActivityTimeId:estimationObject.lagBetweenMessagesDistribution,
'phrases_lag_distribution': phrasesLagDistribution
}
FileIO.writeToFileAsJson(iterationData, estimationObject.dimensionInActivityTimeFile)
def updateForMessage(self, message, updateMethod, **stream_settings):
timeDifference = None
if stream_settings["time_unit_in_seconds"] != None:
timeDifference = DateTimeAirthematic.getDifferenceInTimeUnits(
message.timeStamp, self.lastMessageTime, stream_settings["time_unit_in_seconds"].seconds
)
updateMethod(
self,
message.vector,
decayCoefficient=stream_settings["stream_decay_coefficient"],
timeDifference=timeDifference,
)
self.lastMessageTime = message.timeStamp
def dimensionInActivityTimeEstimation(estimationObject, currentMessageTime):
phrasesLagDistribution = defaultdict(int)
for phraseObject in estimationObject.phraseTextToPhraseObjectMap.itervalues():
lag = DateTimeAirthematic.getDifferenceInTimeUnits(currentMessageTime, phraseObject.latestOccuranceTime, estimationObject.stream_settings['time_unit_in_seconds'].seconds)
phrasesLagDistribution[str(lag)] += 1
print currentMessageTime
iterationData = {
'time_stamp': getStringRepresentationForTweetTimestamp(currentMessageTime),
'settings': pprint.pformat(estimationObject.stream_settings),
ParameterEstimation.dimensionInActivityTimeId:estimationObject.lagBetweenMessagesDistribution,
'phrases_lag_distribution': phrasesLagDistribution
}
FileIO.writeToFileAsJson(iterationData, estimationObject.dimensionInActivityTimeFile)
def clusterLagDistributionMethod(hdStreamClusteringObject, currentMessageTime):
lagDistribution = defaultdict(int)
for cluster in hdStreamClusteringObject.clusters.values():
lag = DateTimeAirthematic.getDifferenceInTimeUnits(currentMessageTime, cluster.lastStreamAddedTime, hdStreamClusteringObject.stream_settings['time_unit_in_seconds'].seconds)
lagDistribution[str(lag)] += 1
print currentMessageTime, len(hdStreamClusteringObject.clusters)
iterationData = {
'time_stamp': getStringRepresentationForTweetTimestamp(currentMessageTime),
'settings': pprint.pformat(hdStreamClusteringObject.stream_settings),
ClusteringParametersEstimation.clusterLagDistributionId: lagDistribution,
'lag_between_streams_added_to_cluster': hdStreamClusteringObject.stream_settings['lag_between_streams_added_to_cluster']
}
# print hdStreamClusteringObject.stream_settings['lag_between_streams_added_to_cluster']
FileIO.writeToFileAsJson(iterationData, hdStreamClusteringObject.stream_settings['%s_file' % ClusteringParametersEstimation.clusterLagDistributionId])
def analyzeClusterLag(streamCluster, stream, **stream_settings):
lag = DateTimeAirthematic.getDifferenceInTimeUnits(streamCluster.lastStreamAddedTime, stream.lastMessageTime, stream_settings['time_unit_in_seconds'].seconds)
stream_settings['lag_between_streams_added_to_cluster'][str(lag)] += 1
def parameterSpecificDataCollectionMethod(estimationObject, message):
for phrase in message.vector:
if phrase in estimationObject.phraseTextToPhraseObjectMap:
phraseObject = estimationObject.phraseTextToPhraseObjectMap[phrase]
lag = DateTimeAirthematic.getDifferenceInTimeUnits(message.timeStamp, phraseObject.latestOccuranceTime, estimationObject.twitter_stream_settings['time_unit_in_seconds'].seconds)
estimationObject.lagBetweenMessagesDistribution[str(lag)] += 1
def analyzeClusterLag(streamCluster, stream, **stream_settings):
lag = DateTimeAirthematic.getDifferenceInTimeUnits(
streamCluster.lastStreamAddedTime, stream.lastMessageTime,
stream_settings['time_unit_in_seconds'].seconds)
stream_settings['lag_between_streams_added_to_cluster'][str(lag)] += 1
