def processLocs():
print 'Starting all locations search:'
logo = logger('AllLocs')
bd = BigData(logo, status_line_count=10000)
bd.obj = ka
#bd.processFile(open('/Users/gaurav/Documents/Work/Projects/DataMining/uncompressed/locations_cities/ny_11_1_to_11_15.data'), None)
bd.processFiles(BigData.GetInputFiles(input_dir), None)
return ka
def processLocs():
print 'Starting keyword search:'
logo = logger('Keywords')
bd = BigData(logo)
bd.obj = ka
#bd.processFile(open('/Users/gaurav/Documents/Work/Projects/DataMining/uncompressed/locations_cities/ny_11_1_to_11_15.data'), None)
bd.processFiles(BigData.GetInputFiles(input_dir), None)
return ka
class Cities(object):
'''
classdocs
'''
def __init__(self, params, outDir):
'''
Constructor
params: a list of city names
outDir: is the output directory path
'''
self.outDir = outDir
self.d = dict((x,City2(x, self.getOutFile(x))) for x in params)
self.logger = logger('Multiple_cities')
self.curCity = ''
def getOutFile(self, city_name):
# return the output file path for the input city
return os.path.join(self.outDir, city_name + '.data')
def generateTDF(self, input_files):
self.bd = BigData(self.logger)
self.bd.obj = self
self.bdCheckCondition = self.CheckCondition
self.bdDoSomething = self.DoSomething
self.bd.processFiles(input_files,None)
def CheckCondition(self, rec):
if 'user' in rec:
user_data = rec['user']
if 'location' in user_data:
if user_data['location'] != None:
loc = user_data['location'].lower()
for k in self.d:
if k in loc:
self.curCity = k
# print k + ':' + loc
return True
else:
return False
return False
def DoSomething(self, rec):
text = rec['text'].encode('utf-8')
loc = self.curCity
#print loc
sentences = nltk.sent_tokenize(text) # NLTK default sentence segmenter
sentences = [nltk.word_tokenize(sent) for sent in sentences] # NLTK word tokenizer
#sentences = post_tag(sentences)
#print sentences
for sent in sentences:
#print sent
#a = nltk.pos_tag(sent)
for w in sent:
if w in self.d[loc].tdf:
self.d[loc].tdf[w] +=1
else:
self.d[loc].tdf[w] = 1
# write output to outfile
self.d[loc].write(dumps(rec))
def getNounsTDF(self):
for k in self.d:
self.d[k].getNounsTDF()
def getTimeLine(self):
bd = BigData(self.logger)
for k in self.d:
self.d[k].getTimeLine(bd)
class Cities(object):
"""
classdocs
"""
def __init__(self, params, outDir):
"""
Constructor
params: a list of city names
outDir: is the output directory path
"""
self.outDir = outDir
self.d = dict((x, City2(x, self.getOutFile(x))) for x in params)
self.logger = logger("Multiple_cities")
self.curCity = ""
def getOutFile(self, city_name):
# return the output file path for the input city
return os.path.join(self.outDir, city_name + ".data")
def generateTDF(self, input_files):
self.bd = BigData(self.logger)
self.bd.obj = self
self.bdCheckCondition = self.CheckCondition
self.bdDoSomething = self.DoSomething
self.bd.processFiles(input_files, None)
def CheckCondition(self, rec):
if "user" in rec:
user_data = rec["user"]
if "location" in user_data:
if user_data["location"] != None:
loc = user_data["location"].lower()
for k in self.d:
if k in loc:
self.curCity = k
# print k + ':' + loc
return True
else:
return False
return False
def DoSomething(self, rec):
text = rec["text"].encode("utf-8")
loc = self.curCity
# print loc
sentences = nltk.sent_tokenize(text) # NLTK default sentence segmenter
sentences = [nltk.word_tokenize(sent) for sent in sentences] # NLTK word tokenizer
# sentences = post_tag(sentences)
# print sentences
for sent in sentences:
# print sent
# a = nltk.pos_tag(sent)
for w in sent:
if w in self.d[loc].tdf:
self.d[loc].tdf[w] += 1
else:
self.d[loc].tdf[w] = 1
# write output to outfile
self.d[loc].write(dumps(rec))
def getNounsTDF(self):
for k in self.d:
self.d[k].getNounsTDF()
def getTimeLine(self):
bd = BigData(self.logger)
for k in self.d:
self.d[k].getTimeLine(bd)