def detectEvents(limit=300,similarityType=MED_SIM,minimalTermPerTweet=MIN_TERM_OCCURENCE,remove_noise_with_poisson_Law=REMOVE_NOISE_WITH_POISSON_LAW,printEvents=True) :
mongoDBHandler=MongoDBHandler()
tweets=mongoDBHandler.getAllTweets(limit=limit)
if similarityType==LED_SIM :
s=LEDSimilarityMatrixBuilder(timeThreshold=TIME_RESOLUTION,distanceThreshold=DISTANCE_RESOLUTION)
eventDetector=EventDetector(tweets,s)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
elif similarityType==MED_SIM :
s=MEDSimilarityMatrixBuilder(timeResolution=TIME_RESOLUTION,distanceResolution=DISTANCE_RESOLUTION,scaleNumber=SCALE_NUMBER,minSimilarity=MIN_SIMILARITY)
eventDetector=EventDetector(tweets,s)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
else :
eventDetector=OptimisedEventDetectorMEDBased(tweets,timeResolution=TIME_RESOLUTION,distanceResolution=DISTANCE_RESOLUTION,scaleNumber=SCALE_NUMBER,minSimilarity=MIN_SIMILARITY)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
print ""
print "-"*40
print "{0} Event detected : ".format(len(events))
print "-"*40
if printEvents :
eventDetector.showTopEvents(top=10)
return events
def detectEvents(limit=300,
similarityType=MED_SIM,
minimalTermPerTweet=MIN_TERM_OCCURENCE,
remove_noise_with_poisson_Law=REMOVE_NOISE_WITH_POISSON_LAW,
printEvents=True):
mongoDBHandler = MongoDBHandler()
tweets = mongoDBHandler.getAllTweets(limit=limit)
if similarityType == LED_SIM:
s = LEDSimilarityMatrixBuilder(timeThreshold=TIME_RESOLUTION,
distanceThreshold=DISTANCE_RESOLUTION)
eventDetector = EventDetector(tweets, s)
events = eventDetector.getEvents(
minimalTermPerTweet=minimalTermPerTweet,
remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
elif similarityType == MED_SIM:
s = MEDSimilarityMatrixBuilder(timeResolution=TIME_RESOLUTION,
distanceResolution=DISTANCE_RESOLUTION,
scaleNumber=SCALE_NUMBER,
minSimilarity=MIN_SIMILARITY)
eventDetector = EventDetector(tweets, s)
events = eventDetector.getEvents(
minimalTermPerTweet=minimalTermPerTweet,
remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
else:
eventDetector = OptimisedEventDetectorMEDBased(
tweets,
timeResolution=TIME_RESOLUTION,
distanceResolution=DISTANCE_RESOLUTION,
scaleNumber=SCALE_NUMBER,
minSimilarity=MIN_SIMILARITY)
events = eventDetector.getEvents(
minimalTermPerTweet=minimalTermPerTweet,
remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
print ""
print "-" * 40
print "{0} Event detected : ".format(len(events))
print "-" * 40
if printEvents:
eventDetector.showTopEvents(top=10)
return events
def detectEvents(limit=300,similarityType=MED_SIM,minimalTermPerTweet=MIN_TERM_OCCURENCE,remove_noise_with_poisson_Law=REMOVE_NOISE_WITH_POISSON_LAW,printEvents=True,printInFile="events.txt",useOnlyHashtags=False,mongoDBName='Twitter',mongoCollectionName="tweets") :
staringTime=time.time()
mongoDBHandler=MongoDBHandler(database_name=mongoDBName,collection_name=mongoCollectionName)
tweets=mongoDBHandler.getAllTweets(limit=limit)
if similarityType==LED_SIM :
s=LEDSimilarityMatrixBuilder(timeThreshold=TIME_RESOLUTION,distanceThreshold=DISTANCE_RESOLUTION,useOnlyHashtags=useOnlyHashtags)
eventDetector=EventDetector(tweets,s)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
elif similarityType==MED_SIM :
s=MEDSimilarityMatrixBuilder(timeResolution=TIME_RESOLUTION,distanceResolution=DISTANCE_RESOLUTION,scaleNumber=SCALE_NUMBER,minSimilarity=MIN_SIMILARITY,useOnlyHashtags=useOnlyHashtags)
eventDetector=EventDetector(tweets,s)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
else :
eventDetector=OptimisedEventDetectorMEDBased(tweets,timeResolution=TIME_RESOLUTION,distanceResolution=DISTANCE_RESOLUTION,scaleNumber=SCALE_NUMBER,minSimilarity=MIN_SIMILARITY,useOnlyHashtags=useOnlyHashtags)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
print ""
print "-"*40
print "{0} Event detected : ".format(len(events))
print "-"*40
if printEvents :
eventDetector.showTopEvents(top=len(events))
if printInFile :
txtFile=open(printInFile, 'w')
elapsed_time=(time.time()-staringTime)
txtFile.write("Total elapsed time : {0}s\n".format(elapsed_time))
txtFile.write("-"*40+"\n")
SEPARATOR="\t|"
HEADER="|"+SEPARATOR.join(["Median time","estimated duration (s)","mean latitude","mean longitude","radius (m)","user number","tweets number","top hashtags"])+SEPARATOR+"\n"
txtFile.write(HEADER)
txtFile.write("-"*40+"\n")
for event in events :
line=eventDetector.getStringOfEvent(event)
txtFile.write(line+"\n")
txtFile.write("-"*40+"\n")
txtFile.close();
return events
def detectEvents(limit=300,similarityType=MED_SIM,minimalTermPerTweet=MIN_TERM_OCCURENCE,remove_noise_with_poisson_Law=REMOVE_NOISE_WITH_POISSON_LAW,printEvents=True,fileName="inputTweets.txt",printInFile="events.txt",useOnlyHashtags=False) :
staringTime=time.time()
#mongoDBHandler=MongoDBHandler(database_name=mongoDBName,collection_name=mongoCollectionName)
#tweets=mongoDBHandler.getAllTweets(limit=limit)
tweets=getTweetsFromFile(fileName);
if similarityType==LED_SIM :
s=LEDSimilarityMatrixBuilder(timeThreshold=TIME_RESOLUTION,distanceThreshold=DISTANCE_RESOLUTION,useOnlyHashtags=useOnlyHashtags)
eventDetector=EventDetector(tweets,s)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
elif similarityType==MED_SIM :
s=MEDSimilarityMatrixBuilder(timeResolution=TIME_RESOLUTION,distanceResolution=DISTANCE_RESOLUTION,scaleNumber=SCALE_NUMBER,minSimilarity=MIN_SIMILARITY,useOnlyHashtags=useOnlyHashtags)
eventDetector=EventDetector(tweets,s)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
else :
eventDetector=OptimisedEventDetectorMEDBased(tweets,timeResolution=TIME_RESOLUTION,distanceResolution=DISTANCE_RESOLUTION,scaleNumber=SCALE_NUMBER,minSimilarity=MIN_SIMILARITY,useOnlyHashtags=useOnlyHashtags)
events=eventDetector.getEvents(minimalTermPerTweet=minimalTermPerTweet,remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
print ""
print "-"*40
print "{0} Event detected : ".format(len(events))
print "-"*40
if printEvents :
eventDetector.showTopEvents(top=len(events))
if printInFile :
txtFile=open(printInFile, 'w')
elapsed_time=(time.time()-staringTime)
#txtFile.write("Total elapsed time : {0}s\n".format(elapsed_time))
#txtFile.write("-"*40+"\n")
SEPARATOR=","
HEADER=SEPARATOR.join(["key","event.type","eventDetector.endEpoch","event.strength","event.periodicity","event.period","mean_time","stddev_time","expression","dispersion","count","medoid","community_ID","nb_events_covered","dispersion","count","number_of_hashtags","covered_events"])+"\n"
txtFile.write(HEADER)
#txtFile.write("-"*40+"\n")
for event in events :
line=eventDetector.getStringOfEvent(event)
txtFile.write(line+"\n")
#txtFile.write("-"*40+"\n")
txtFile.close();
return events
def detectEvents(limit=300,
similarityType=MED_SIM,
minimalTermPerTweet=MIN_TERM_OCCURENCE,
remove_noise_with_poisson_Law=REMOVE_NOISE_WITH_POISSON_LAW,
printEvents=True,
printInFile="events.txt",
useOnlyHashtags=False,
mongoDBName='Twitter',
mongoCollectionName="tweets"):
staringTime = time.time()
mongoDBHandler = MongoDBHandler(database_name=mongoDBName,
collection_name=mongoCollectionName)
tweets = mongoDBHandler.getAllTweets(limit=limit)
if similarityType == LED_SIM:
s = LEDSimilarityMatrixBuilder(timeThreshold=TIME_RESOLUTION,
distanceThreshold=DISTANCE_RESOLUTION,
useOnlyHashtags=useOnlyHashtags)
eventDetector = EventDetector(tweets, s)
events = eventDetector.getEvents(
minimalTermPerTweet=minimalTermPerTweet,
remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
elif similarityType == MED_SIM:
s = MEDSimilarityMatrixBuilder(timeResolution=TIME_RESOLUTION,
distanceResolution=DISTANCE_RESOLUTION,
scaleNumber=SCALE_NUMBER,
minSimilarity=MIN_SIMILARITY,
useOnlyHashtags=useOnlyHashtags)
eventDetector = EventDetector(tweets, s)
events = eventDetector.getEvents(
minimalTermPerTweet=minimalTermPerTweet,
remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
else:
eventDetector = OptimisedEventDetectorMEDBased(
tweets,
timeResolution=TIME_RESOLUTION,
distanceResolution=DISTANCE_RESOLUTION,
scaleNumber=SCALE_NUMBER,
minSimilarity=MIN_SIMILARITY,
useOnlyHashtags=useOnlyHashtags)
events = eventDetector.getEvents(
minimalTermPerTweet=minimalTermPerTweet,
remove_noise_with_poisson_Law=remove_noise_with_poisson_Law)
print ""
print "-" * 40
print "{0} Event detected : ".format(len(events))
print "-" * 40
if printEvents:
eventDetector.showTopEvents(top=len(events))
if printInFile:
txtFile = open(printInFile, 'w')
elapsed_time = (time.time() - staringTime)
txtFile.write("Total elapsed time : {0}s\n".format(elapsed_time))
txtFile.write("-" * 40 + "\n")
SEPARATOR = "\t|"
HEADER = "|" + SEPARATOR.join([
"Median time", "estimated duration (s)", "mean latitude",
"mean longitude", "radius (m)", "user number", "tweets number",
"top hashtags"
]) + SEPARATOR + "\n"
txtFile.write(HEADER)
txtFile.write("-" * 40 + "\n")
for event in events:
line = eventDetector.getStringOfEvent(event)
txtFile.write(line + "\n")
txtFile.write("-" * 40 + "\n")
txtFile.close()
return events
