def generateExperimentData2(self, fixedType):
global previousTime
experts_twitter_stream_settings[
'cluster_analysis_method'] = JustifyDimensionsEstimation.modifiedClusterAnalysisMethod2
if fixedType:
experts_twitter_stream_settings[
'dimensions_performance_type'] = JustifyDimensionsEstimation.first_n_dimension
# experts_twitter_stream_settings['update_dimensions_method'] = emptyUpdateDimensionsMethod
experts_twitter_stream_settings['phrase_decay_coefficient'] = 1.0
experts_twitter_stream_settings['stream_decay_coefficient'] = 1.0
experts_twitter_stream_settings[
'stream_cluster_decay_coefficient'] = 1.0
for dimensions in range(10**4, 21 * 10**4, 10**4):
experts_twitter_stream_settings[
'dimensions'] = getLargestPrimeLesserThan(dimensions)
previousTime = time.time()
HDStreaminClustering(
**experts_twitter_stream_settings).cluster(
TwitterIterators.iterateTweetsFromExperts(
expertsDataStartTime=datetime(2011, 3, 19),
expertsDataEndTime=datetime(2011, 3, 20, 5)))
else:
experts_twitter_stream_settings[
'dimensions_performance_type'] = JustifyDimensionsEstimation.top_n_dimension
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(
TwitterIterators.iterateTweetsFromExperts(
expertsDataStartTime=datetime(2011, 3, 19),
expertsDataEndTime=datetime(2011, 3, 20, 5)))
def generateExperimentData2(self, fixedType):
global previousTime
experts_twitter_stream_settings[
"cluster_analysis_method"
] = JustifyDimensionsEstimation.modifiedClusterAnalysisMethod2
if fixedType:
experts_twitter_stream_settings[
"dimensions_performance_type"
] = JustifyDimensionsEstimation.first_n_dimension
# experts_twitter_stream_settings['update_dimensions_method'] = emptyUpdateDimensionsMethod
experts_twitter_stream_settings["phrase_decay_coefficient"] = 1.0
experts_twitter_stream_settings["stream_decay_coefficient"] = 1.0
experts_twitter_stream_settings["stream_cluster_decay_coefficient"] = 1.0
for dimensions in range(10 ** 4, 21 * 10 ** 4, 10 ** 4):
experts_twitter_stream_settings["dimensions"] = getLargestPrimeLesserThan(dimensions)
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(
TwitterIterators.iterateTweetsFromExperts(
expertsDataStartTime=datetime(2011, 3, 19), expertsDataEndTime=datetime(2011, 3, 20, 5)
)
)
else:
experts_twitter_stream_settings["dimensions_performance_type"] = JustifyDimensionsEstimation.top_n_dimension
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(
TwitterIterators.iterateTweetsFromExperts(
expertsDataStartTime=datetime(2011, 3, 19), expertsDataEndTime=datetime(2011, 3, 20, 5)
)
)
def generate(self):
i = 0
for tweet in TwitterIterators.iterateTweetsFromExperts():
FileIO.writeToFileAsJson(tweet, self.fileName)
i += 1
if i == self.length: break
os.system('gzip %s' % self.fileName)
def generateExperimentData(self, withOutPruning):
global previousTime
if withOutPruning: experts_twitter_stream_settings['cluster_filtering_method'] = emptyClusterFilteringMethod; experts_twitter_stream_settings['pruing_type'] = JustifyMemoryPruning.without_memory_pruning
else: experts_twitter_stream_settings['pruing_type'] = JustifyMemoryPruning.with_memory_pruning
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyMemoryPruning.modifiedClusterAnalysisMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,3,19), expertsDataEndTime=datetime(2011,3,27)))
def generateExperimentData(self, with_vanilla_lsh):
global previousTime
if with_vanilla_lsh:
experts_twitter_stream_settings[
'lsh_type'] = JustifyNotUsingVanillaLSH.with_vanilla_lsh
experts_twitter_stream_settings['phrase_decay_coefficient'] = 1.0
experts_twitter_stream_settings['stream_decay_coefficient'] = 1.0
experts_twitter_stream_settings[
'stream_cluster_decay_coefficient'] = 1.0
experts_twitter_stream_settings[
'cluster_filtering_method'] = emptyClusterFilteringMethod
experts_twitter_stream_settings[
'signature_type'] = 'signature_type_list'
experts_twitter_stream_settings[
'dimensions'] = getLargestPrimeLesserThan(10000)
experts_twitter_stream_settings[
'update_dimensions_method'] = emptyUpdateDimensionsMethod
else:
experts_twitter_stream_settings[
'lsh_type'] = JustifyNotUsingVanillaLSH.with_modified_lsh
experts_twitter_stream_settings[
'cluster_analysis_method'] = JustifyNotUsingVanillaLSH.modifiedClusterAnalysisMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(
TwitterIterators.iterateTweetsFromExperts(
expertsDataStartTime=datetime(2011, 3, 19),
expertsDataEndTime=datetime(2011, 3, 27)))
def generateExperimentData(self, withOutPruning):
global previousTime
if withOutPruning: experts_twitter_stream_settings['cluster_filtering_method'] = emptyClusterFilteringMethod; experts_twitter_stream_settings['pruing_type'] = JustifyMemoryPruning.without_memory_pruning
else: experts_twitter_stream_settings['pruing_type'] = JustifyMemoryPruning.with_memory_pruning
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyMemoryPruning.modifiedClusterAnalysisMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,3,19), expertsDataEndTime=datetime(2011,3,27)))
def generate(self):
i=0
for tweet in TwitterIterators.iterateTweetsFromExperts():
FileIO.writeToFileAsJson(tweet, self.fileName)
i+=1
if i==self.length: break;
os.system('gzip %s'%self.fileName)
def performanceForCDAAt(noOfTweets, fileName, **stream_settings):
clustering=HDStreaminClustering(**stream_settings)
ts = time.time()
clustering.cluster(TwitterIterators.iterateFromFile(fileName))
te = time.time()
documentClusters = [cluster.documentsInCluster.keys() for k, cluster in clustering.clusters.iteritems() if len(cluster.documentsInCluster.keys())>=stream_settings['cluster_filter_threshold']]
return Evaluation.getEvaluationMetrics(noOfTweets, documentClusters, te-ts)
def generateExperimentData(self, withOutDecay):
global previousTime
if withOutDecay:
experts_twitter_stream_settings['decay_type'] = JustifyExponentialDecay.without_decay
experts_twitter_stream_settings['phrase_decay_coefficient']=1.0; experts_twitter_stream_settings['stream_decay_coefficient']=1.0; experts_twitter_stream_settings['stream_cluster_decay_coefficient']=1.0;
else: experts_twitter_stream_settings['decay_type'] = JustifyExponentialDecay.with_decay
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyExponentialDecay.modifiedClusterAnalysisMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,4,1), expertsDataEndTime=datetime(2011,4,8)))
def _tweetIterator(self):
userMap = {}
for tweet in TwitterIterators.iterateFromFile(self.fileName+'.gz'):
user = tweet['user']['screen_name']
phrases = [phrase.replace(' ', unique_string) for phrase in getPhrases(getWordsFromRawEnglishMessage(tweet['text']), self.stream_settings['min_phrase_length'], self.stream_settings['max_phrase_length'])]
if phrases:
if user not in userMap: userMap[user] = ' '.join(phrases)
else: userMap[user]+= ' ' + ' '.join(phrases)
return userMap.iteritems()
def generateExperimentData(self, withOutDecay):
global previousTime
if withOutDecay:
experts_twitter_stream_settings['decay_type'] = JustifyExponentialDecay.without_decay
experts_twitter_stream_settings['phrase_decay_coefficient']=1.0; experts_twitter_stream_settings['stream_decay_coefficient']=1.0; experts_twitter_stream_settings['stream_cluster_decay_coefficient']=1.0;
else: experts_twitter_stream_settings['decay_type'] = JustifyExponentialDecay.with_decay
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyExponentialDecay.modifiedClusterAnalysisMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,4,1), expertsDataEndTime=datetime(2011,4,8)))
def generateExperimentData(self, withoutTrie):
global previousTime
if withoutTrie:
experts_twitter_stream_settings['trie_type'] = JustifyTrie.with_sorted_list
experts_twitter_stream_settings['signature_type']='signature_type_list'
else: experts_twitter_stream_settings['trie_type'] = JustifyTrie.with_trie
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyTrie.modifiedClusterAnalysisMethod
experts_twitter_stream_settings['cluster_filtering_method'] = emptyClusterFilteringMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,3,19), expertsDataEndTime=datetime(2011,3,27)))
def generateExperimentData(self, withoutTrie):
global previousTime
if withoutTrie:
experts_twitter_stream_settings['trie_type'] = JustifyTrie.with_sorted_list
experts_twitter_stream_settings['signature_type']='signature_type_list'
else: experts_twitter_stream_settings['trie_type'] = JustifyTrie.with_trie
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyTrie.modifiedClusterAnalysisMethod
experts_twitter_stream_settings['cluster_filtering_method'] = emptyClusterFilteringMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,3,19), expertsDataEndTime=datetime(2011,3,27)))
def generateDocsByLength():
for length in [1500000]:
# for length in [150]:
fileName = clustering_quality_experts_folder + 'data/%s' % str(
length)
print fileName
i = 0
for tweet in TwitterIterators.iterateTweetsFromExperts():
FileIO.writeToFileAsJson(tweet, fileName)
i += 1
if i == length: break
def _tweetWithTimestampIterator(self):
userMap = defaultdict(dict)
for tweet in TwitterIterators.iterateFromFile(self.fileName+'.gz'):
user = tweet['user']['screen_name']
userMap[user]['user'] = {'screen_name': user}
userMap[user]['id'] = tweet['id']
userMap[user]['created_at'] = tweet['created_at']
if 'text' not in userMap[user]: userMap[user]['text'] = ' '
phrases = [phrase.replace(' ', unique_string) for phrase in getPhrases(getWordsFromRawEnglishMessage(tweet['text']), self.stream_settings['min_phrase_length'], self.stream_settings['max_phrase_length'])]
if phrases: userMap[user]['text']+= ' ' + ' '.join(phrases)
return userMap.iteritems()
def generateExperimentData(self):
global previousTime
experts_twitter_stream_settings['dimensions_performance_type'] = JustifyDimensionsEstimation.first_n_dimension
experts_twitter_stream_settings['update_dimensions_method'] = emptyUpdateDimensionsMethod
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyDimensionsEstimation.modifiedClusterAnalysisMethod
for dimensions in range(10**4,21*10**4,10**4):
experts_twitter_stream_settings['dimensions'] = getLargestPrimeLesserThan(dimensions)
previousTime = time.time()
try:
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts())
except Exception as e: pass
def generateExperimentData(self):
global previousTime
experts_twitter_stream_settings['dimensions_performance_type'] = JustifyDimensionsEstimation.first_n_dimension
experts_twitter_stream_settings['update_dimensions_method'] = emptyUpdateDimensionsMethod
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyDimensionsEstimation.modifiedClusterAnalysisMethod
for dimensions in range(10**4,21*10**4,10**4):
experts_twitter_stream_settings['dimensions'] = getLargestPrimeLesserThan(dimensions)
previousTime = time.time()
try:
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts())
except Exception as e: pass
def generateDocsByLength():
for length in [1500000]:
# for length in [150]:
fileName = clustering_quality_experts_folder + "data/%s" % str(length)
print fileName
i = 0
for tweet in TwitterIterators.iterateTweetsFromExperts():
FileIO.writeToFileAsJson(tweet, fileName)
i += 1
if i == length:
break
def generateExperimentData(self, with_vanilla_lsh):
global previousTime
if with_vanilla_lsh:
experts_twitter_stream_settings['lsh_type'] = JustifyNotUsingVanillaLSH.with_vanilla_lsh
experts_twitter_stream_settings['phrase_decay_coefficient']=1.0; experts_twitter_stream_settings['stream_decay_coefficient']=1.0; experts_twitter_stream_settings['stream_cluster_decay_coefficient']=1.0;
experts_twitter_stream_settings['cluster_filtering_method'] = emptyClusterFilteringMethod;
experts_twitter_stream_settings['signature_type']='signature_type_list'
experts_twitter_stream_settings['dimensions'] = getLargestPrimeLesserThan(10000)
experts_twitter_stream_settings['update_dimensions_method'] = emptyUpdateDimensionsMethod
else: experts_twitter_stream_settings['lsh_type'] = JustifyNotUsingVanillaLSH.with_modified_lsh
experts_twitter_stream_settings['cluster_analysis_method'] = JustifyNotUsingVanillaLSH.modifiedClusterAnalysisMethod
previousTime = time.time()
HDStreaminClustering(**experts_twitter_stream_settings).cluster(TwitterIterators.iterateTweetsFromExperts(expertsDataStartTime=datetime(2011,3,19), expertsDataEndTime=datetime(2011,3,27)))
def performanceForCDAAt(noOfTweets, fileName, **stream_settings):
clustering = HDStreaminClustering(**stream_settings)
ts = time.time()
clustering.cluster(TwitterIterators.iterateFromFile(fileName))
te = time.time()
documentClusters = [
cluster.documentsInCluster.keys()
for k, cluster in clustering.clusters.iteritems()
if len(cluster.documentsInCluster.keys()) >=
stream_settings['cluster_filter_threshold']
]
return Evaluation.getEvaluationMetrics(noOfTweets, documentClusters,
te - ts)
def _tweetIterator(self):
userMap = {}
for tweet in TwitterIterators.iterateFromFile(self.fileName + '.gz'):
user = tweet['user']['screen_name']
phrases = [
phrase.replace(' ', unique_string) for phrase in getPhrases(
getWordsFromRawEnglishMessage(tweet['text']),
self.stream_settings['min_phrase_length'],
self.stream_settings['max_phrase_length'])
]
if phrases:
if user not in userMap: userMap[user] = ' '.join(phrases)
else: userMap[user] += ' ' + ' '.join(phrases)
return userMap.iteritems()
def _tweetWithTimestampIterator(self):
userMap = defaultdict(dict)
for tweet in TwitterIterators.iterateFromFile(self.fileName + '.gz'):
user = tweet['user']['screen_name']
userMap[user]['user'] = {'screen_name': user}
userMap[user]['id'] = tweet['id']
userMap[user]['created_at'] = tweet['created_at']
if 'text' not in userMap[user]: userMap[user]['text'] = ' '
phrases = [
phrase.replace(' ', unique_string) for phrase in getPhrases(
getWordsFromRawEnglishMessage(tweet['text']),
self.stream_settings['min_phrase_length'],
self.stream_settings['max_phrase_length'])
]
if phrases: userMap[user]['text'] += ' ' + ' '.join(phrases)
return userMap.iteritems()
def generateClusters():
TwitterStreamAnalysis(**experts_twitter_stream_settings).generateClusters(TwitterIterators.iterateTweetsFromExperts())
