def FitSpatialEntropy(self,word,no):
if self.TableON:
return [self.Feature[word][0],self.Feature[word][1]]
k = no
tokenize = T_Tokenizer().tokenize
#Store locations
ALLLOC = []
WORDLOC = []
while k<0:
ALLLOC += self.QueueStack[k]['LOC']
for order,text in enumerate(self.QueueStack[k]['TEXT']):
if word in tokenize(text):
WORDLOC.append(self.QueueStack[k]['LOC'][order])
k+=1
#Choose Cluster of max ALLLOC, C*
MakeCluster = GMM_clustering()
MakeCluster.Snap = {'LOC':ALLLOC}
MakeCluster.build_clusters()
WORDLABELS = Counter([MakeCluster.labels[ALLLOC.index(LOC)] for LOC in WORDLOC])
#Global entropy
GLOBAL_COUNTER = Counter(MakeCluster.labels)
G_D_pq = 0.0
for cl,number in WORDLABELS.items():
G_D_pq += -1*(number/float(GLOBAL_COUNTER[cl]))*np.log2(number/float(GLOBAL_COUNTER[cl]))
#G_D_pq += -1*((number/sum(WORDLABELS))/float(GLOBAL_COUNTER[cl]/sum(GLOBAL_COUNTER)))*np.log2(number/float(GLOBAL_COUNTER[cl]))
C_Star = WORDLABELS.most_common(1)[0][0]
C_Star_LOC = [ ALLLOC[No] for No,label in filter(lambda (enum,x): x==C_Star,enumerate(MakeCluster.labels)) ]
C_Star_WORD_LOC = [LOC for LOC in filter(lambda x:x in C_Star_LOC,WORDLOC)]
#Find D(p||q) of word inside C*
del MakeCluster
MakeLocalCluster = GMM_clustering(components=range(2,8))
MakeLocalCluster.Snap = {'LOC':C_Star_LOC}
MakeLocalCluster.build_clusters()
WORD_LOCAL_COUNTER = Counter([MakeLocalCluster.labels[C_Star_LOC.index(LOC)] for LOC in C_Star_WORD_LOC])
LOCAL_ALL_COUNTER = Counter( MakeLocalCluster.labels )
L_D_pq = 0.0
for cl,number in WORD_LOCAL_COUNTER.items():
L_D_pq += -1*(number/float(LOCAL_ALL_COUNTER[cl]))*np.log2(number/float(LOCAL_ALL_COUNTER[cl]))
#L_D_pq += -1*((number/sum(WORD_LOCAL_COUNTER.values()))/float(LOCAL_ALL_COUNTER[cl]/sum(LOCAL_ALL_COUNTER.values())))*np.log2(number/float(LOCAL_ALL_COUNTER[cl]))
return [G_D_pq,L_D_pq]
def ReportEventQueue(self, word, no, SampleLim=3):
# Find clusters at start point of event
gmm = GMM_clustering(components=range(4, 15))
gmm.Snap = self.SnapStack[no]
gmm.build_clusters()
Labels = []
tokenize = T_Tokenizer().tokenize
for k, text in enumerate(gmm.Snap["TEXT"]):
if word in tokenize(text):
Labels.append(gmm.labels[k])
Labels = Counter(Labels)
# Find cluster where word was most common
StarLabel = Labels.most_common(1)[0][0]
SampleSet = []
# Print a tweet from that cluster
for k, text in enumerate(gmm.Snap["TEXT"]):
if gmm.labels[k] == StarLabel and word in tokenize(text):
SampleSet.append((gmm.Snap["SCREEN_NAME"][k], gmm.Snap["CREATED_AT"][k], text, gmm.Snap["LOC"][k]))
if len(SampleSet) >= SampleLim:
break
return SampleSet
def visualize_timeframe(db,timeWindow,**kwargs):
print "COLLECTING TWEETS...."
TS = TweetSnap(db=db,timeWindow = timeWindow,Placename2Geocode=False)
print "COLLECTION OVER...."
TIME_START = kwargs.get("TIME_START",time.gmtime(0))
TIME_END = kwargs.get("TIME_END",time.gmtime(time.time()))
VocabSize = kwargs.get("VocabSize",500)
if isinstance(TIME_START,str):
TIME_START = time.gmtime(time.mktime(time.strptime(TIME_START,"%d %b %H:%M %Z %Y")))
if isinstance(TIME_END,str):
TIME_END = time.gmtime(time.mktime(time.strptime(TIME_END,"%d %b %H:%M %Z %Y")))
TIME_DIFF = time.mktime(TIME_START) - time.mktime(TS.time_start)
if TIME_DIFF>0:
TS.move_on(TIME_DIFF-timeWindow)
#Create Dataframe
df = pd.DataFrame(columns=['Virality','Locality','Volume','Words','TimeWindow'])
while (TS.time_start<TIME_END and not TS.end):
#InitializeColumns
Virality = {}
Volume = {}
Locality = {}
TimeWindow=[]
#Capture nextSnap and initialize time_start of next snap
snap = TS.next()
if len(snap['TEXT'])<100:
continue
gmm = GMM_clustering()
#1. Virality
Virality = te(snap)
#2. Locality
gmm.Snap = snap
gmm.build_clusters()
Locality = GeographicalEntropy(snap,gmm.labels)
#3. Volume
Volume = Count(snap)
#HotWords= set(dict(Virality.most_common(HotWordSize)).keys())&set(dict(Broadcast.most_common(HotWordSize)).keys())&set(dict(Locality.most_common(HotWordSize)).keys())&set(dict(Prevalence.most_common(HotWordSize)).keys())&set(dict(Volume.most_common(HotWordSize)).keys())
Words= list(set(dict(Virality.most_common(VocabSize)).keys())&set(dict(Locality.most_common(VocabSize)).keys())&set(dict(Volume.most_common(VocabSize)).keys()))
if not len(Words)>0:
continue
Virality= [Virality[key] if key in Virality.keys() else 0 for key in Words]
# Broadcast=[Broadcast[key] if key in Broadcast.keys() else 0 for key in HotWords]
Locality= [Locality[key] if key in Locality.keys() else 0 for key in Words]
# Prevalence=[Prevalence[key] if key in Prevalence.keys() else 0 for key in HotWords]
Volume=[Volume[key] if key in Volume.keys() else 0 for key in Words]
#4. TimeWindow
TimeWindow= [snap['TimeWindow'][0]]*len(Words)
#5. Words
Words= Words
#Append to Dataframe
df = df.append({'Virality':Virality,'Locality':Locality,'Volume':Volume,'Words':Words,'TimeWindow':TimeWindow},ignore_index=True)
return df
def newsworthy_words(db,timeWindow,**kwargs):
print "COLLECTING TWEETS...."
TS = TweetSnap(db=db,timeWindow = timeWindow,Placename2Geocode=False)
print "COLLECTION OVER...."
TIME_START = kwargs.get("TIME_START",time.gmtime(0))
TIME_END = kwargs.get("TIME_END",time.gmtime(time.time()))
HotWordSize = kwargs.get("HotWordSize",25)
if isinstance(TIME_START,str):
TIME_START = time.gmtime(time.mktime(time.strptime(TIME_START,"%d %b %H:%M %Z %Y")))
if isinstance(TIME_END,str):
TIME_END = time.gmtime(time.mktime(time.strptime(TIME_END,"%d %b %H:%M %Z %Y")))
TIME_DIFF = time.mktime(TIME_START) - time.mktime(TS.time_start)
if TIME_DIFF>0:
TS.move_on(TIME_DIFF-timeWindow)
Day = {}
while (TS.time_start<TIME_END and not TS.end):
#Capture nextSnap and initialize time_start of next snap
snap = TS.next()
if len(snap['TEXT'])<100:
continue
gmm = GMM_clustering()
#1. Virality
Virality = PoissonRate(snap)
#2. DeltaVolume
#Broadcast = DeltaVolume(snap0,snap)
#3. Locality
gmm.Snap = snap
gmm.build_clusters()
Locality = GeographicalEntropy(snap,gmm.labels)
#4. Prevalence
#Prevalence= Ttest(snap)
#5. Count
Volume = Count(snap)
#Prepare Dataframe
#Union
#HotWords= list(set(dict(Virality.most_common(HotWordSize)).keys()+dict(Broadcast.most_common(HotWordSize)).keys()+dict(Locality.most_common(HotWordSize)).keys()+dict(Prevalence.most_common(HotWordSize)).keys()+dict(Volume.most_common(HotWordSize)).keys()))
#Intersection
#print "Simmering words"
#print 'Virality',set(dict(Virality.most_common(HotWordSize)).keys())
#print 'Broadcast',set(dict(Broadcast.most_common(HotWordSize)).keys())
#print 'Locality',set(dict(Locality.most_common(HotWordSize)).keys())
#print set(dict(Prevalence.most_common(HotWordSize)).keys())
#print 'Volume',set(dict(Volume.most_common(HotWordSize)).keys())
#print "*"*5
#HotWords= set(dict(Virality.most_common(HotWordSize)).keys())&set(dict(Broadcast.most_common(HotWordSize)).keys())&set(dict(Locality.most_common(HotWordSize)).keys())&set(dict(Prevalence.most_common(HotWordSize)).keys())&set(dict(Volume.most_common(HotWordSize)).keys())
HotWords= list(set(dict(Virality.most_common(HotWordSize)).keys())&set(dict(Locality.most_common(HotWordSize)).keys())&set(dict(Volume.most_common(HotWordSize)).keys()))
if not len(HotWords)>0:
continue
Virality= [Virality[key] if key in Virality.keys() else 0 for key in HotWords]
# Broadcast=[Broadcast[key] if key in Broadcast.keys() else 0 for key in HotWords]
Locality= [Locality[key] if key in Locality.keys() else 0 for key in HotWords]
# Prevalence=[Prevalence[key] if key in Prevalence.keys() else 0 for key in HotWords]
Volume=[Volume[key] if key in Volume.keys() else 0 for key in HotWords]
#scaler = preprocessing.MinMaxScaler([0,100]).fit_transform
#scaledVirality = list(scaler(np.array([Virality]).T).flatten())
# scaledBroadcast = scaler(Broadcast)
#scaledLocality = list(scaler(np.array([Locality]).T).flatten())
# scaledPrevalence = scaler(Prevalence)
#scaledVolume = list(scaler(np.array([Volume],dtype=np.float16).T).flatten())
Score = [vi+lo+vo for vi,lo,vo in zip(Virality,Locality,Volume)]
df = pd.DataFrame({'Words':HotWords,'Virality':Virality,'Locality':Locality,'Volume':Volume,'Score':Score})
#df_scaled = pd.DataFrame({'Words':HotWords,'Virality':scaledVirality,'Locality':scaledLocality,'Volume':scaledVolume,'Score':Score})
Day['to'.join(snap['TimeWindow'])]=df
return Day
def SetFeatureTable(self):
tokenize = T_Tokenizer().tokenize
self.Feature = {}
k = -len(self.QueueStack)
#Store locations
ALL_LOC = []
WORD_LOC = {}
C_Star_LOC = {}
C_Star_Labels = {}
#Get List of locations of all tweets Collected : ALL_LOC
#Get List of locations where "word" appears in tweets posted after it was declared as an event
# : WORD_LOC[word]
while k<0:
ALL_LOC += self.QueueStack[k]['LOC']
for order,text in enumerate(self.QueueStack[k]['TEXT']):
for word,no in self.Candidates.items():
if word in tokenize(text) and order>=no:
WORD_LOC.setdefault(word,[]).append(self.QueueStack[k]['LOC'][order])
k+=1
#Global Clustering
MakeCluster = GMM_clustering(components=range(3,8))
MakeCluster.Snap = {'LOC':ALL_LOC}
MakeCluster.build_clusters()
#Input : ALL_LOC & Output : Global labels for locations of tweets
GLOBAL_LABELS = Counter(MakeCluster.labels)
#Local Clustering for each cluster in lists
for C_Star in GLOBAL_LABELS.keys():
#Input : C_Star_LOC ; All tweet locations withing C_Star cluster
C_Star_LOC[C_Star] = [ ALL_LOC[No] for No,label in filter(lambda (enum,x): x==C_Star,enumerate(MakeCluster.labels)) ]
if len(C_Star_LOC[C_Star])>=(self.MinWordSamples/3.0):
MakeLocalCluster = GMM_clustering(components=range(2,min(8,int(self.MinWordSamples/3))))
MakeLocalCluster.Snap = {'LOC':C_Star_LOC[C_Star]}
MakeLocalCluster.build_clusters()
#Output : C_Star_Labels ; Labels for All tweet locations withing C_Star cluster
C_Star_Labels[C_Star] = MakeLocalCluster.labels
#Set GlobalEntropy and LocalEntropy for each Candidate word
for word,no in self.Candidates.items():
#Global entropy
#1. Initialize to 0
G_D_pq = 0.0
#2. List of all non-zero counts for global clusters where 'word' appears in tweet
WORD_LABELS = Counter([MakeCluster.labels[ALL_LOC.index(LOC)] for LOC in WORD_LOC[word]])
#3. Calculate entropy by summing up over all clusters
for cl,number in WORD_LABELS.items():
G_D_pq += -1*(number/float(GLOBAL_LABELS[cl]))*np.log2(number/float(GLOBAL_LABELS[cl]))
#G_D_pq += -1*((number/sum(WORDLABELS))/float(GLOBAL_COUNTER[cl]/sum(GLOBAL_COUNTER)))*np.log2(number/float(GLOBAL_COUNTER[cl]))
#Local entropy
#1. Most populated cluster with 'word'
C_Star = WORD_LABELS.most_common(1)[0][0]
#2. List of all non-zero counts for global clusters where 'word' appears in tweet
WORD_LOCAL_LABELS = Counter([C_Star_Labels[C_Star][C_Star_LOC[C_Star].index(LOC)] for LOC in WORD_LOC[word] if LOC in C_Star_LOC[C_Star]])
LOCAL_LABELS = Counter( C_Star_Labels[C_Star] )
#3. Calculate entropy by summing up over all local clusters
L_D_pq = 0.0
for cl,number in WORD_LOCAL_LABELS.items():
L_D_pq += -1*(number/float(LOCAL_LABELS[cl]))*np.log2(number/float(LOCAL_LABELS[cl]))
#L_D_pq += -1*((number/sum(WORD_LOCAL_COUNTER.values()))/float(LOCAL_ALL_COUNTER[cl]/sum(LOCAL_ALL_COUNTER.values())))*np.log2(number/float(LOCAL_ALL_COUNTER[cl]))
self.Feature[word] = [G_D_pq,L_D_pq,self.GetPoissonRate(word,no)]
