def __init__(self):
self.container = {}
self.is_xsmoother = None
if config.get('sketch', 'smoother') == 'XEWMASmoother':
print 'Using XEWMASmoother.'
self.is_xsmoother = True
if config.get('sketch', 'smoother') == 'EWMASmoother':
print 'Using EWMASmoother.'
self.is_xsmoother = False
class SparseSmootherContainer():
_THRESHOLD_FOR_CLEANING = eval(config.get('sketch', 'threshold_for_cleaning'))
_CAPACITY_FOR_CLEANING = eval(config.get('sketch', 'capacity_for_cleaning'))
def __init__(self):
self.container = {}
self.is_xsmoother = None
if config.get('sketch', 'smoother') == 'XEWMASmoother':
print 'Using XEWMASmoother.'
self.is_xsmoother = True
if config.get('sketch', 'smoother') == 'EWMASmoother':
print 'Using EWMASmoother.'
self.is_xsmoother = False
def close(self):
pass
def _clean(self, _timestamp):
to_be_cleaned_up = []
for key, value in self.container.iteritems():
tp = value.get(_timestamp)
if not tp:
print _timestamp, value.timestamp
print 'stream item seems out of time order!'
continue
t ,v ,a = tp
if v <= self._THRESHOLD_FOR_CLEANING: # check v
to_be_cleaned_up.append(key)
print 'cleaning', len(to_be_cleaned_up), 'items...'
for key in to_be_cleaned_up:
self.container.pop(key)
def get(self, _id, _timestamp):
# check for cleaning
if len(self.container) > self._CAPACITY_FOR_CLEANING:
self._clean(_timestamp)
# return
if _id in self.container:
return self.container[_id]
else:
if self.is_xsmoother:
_smoother = fast_smoother.XEWMASmoother()
else:
_smoother = fast_smoother.EWMASmoother()
self.container[_id] = _smoother
return _smoother
def analyse_topics(self, _probs):
words = set()
for term in self.active_terms:
for word in term[1]:
words.add(word)
print "size of words:", len(words)
high_prob_words = []
for _word in words:
word = stemmer.stem(_word)
hash_code = np.array(fast_hashing.hash_code(word)) % _SKETCH_BUCKET_SIZE
min_prob_list = []
for h in range(fast_hashing.HASH_NUMBER):
prob = _probs[h][hash_code[h]]
min_prob_list.append(prob)
min_prob_list.sort()
min_prob = min_prob_list[1] # !!!
if min_prob >= _PROBABILITY_THRESHOLD:
high_prob_words.append((word, min_prob))
# rescale
s_prob = sum([p for w, p in high_prob_words])
high_prob_words = [(w, p/s_prob) for w, p in high_prob_words]
high_prob_words.sort(key=lambda x: x[1], reverse=True)
# top 20
high_prob_words = high_prob_words[:20]
post_res = postprocessor.process(high_prob_words, self.active_terms)
if eval(config.get('output', 'debug_info')):
self.output.write('high_prob_words\n')
self.output.write(str(high_prob_words)) #debugging
self.output.write('\npost_res\n')
self.output.write(str(post_res)) #debugging
self.output.write('\n')
flag, word_level_results, _ = post_res
if flag:
event = dict()
event['detection_time'] = str(datetime.utcfromtimestamp(self.timestamp))
event_words = list()
for prob_word, word_flag in zip(high_prob_words, word_level_results):
_word = prob_word[0]
if word_flag:
event_words.append(_word)
event['key_words'] = event_words
self.output.write(json.dumps(event))
self.output.write('\n')
def test(s):
_uz = eval(exp_config.get('sketch', 'unit_size'))
print 'set unit size ' + str(fast_smoother.set_unit_size(_uz))
if exp_config.get('sketch', 'smoother') == 'XEWMASmoother':
_wz = eval(exp_config.get('sketch', 'window_size'))
print 'set XEWMASmoother window ' + str(
fast_smoother.XEWMASmoother.set_window_size(_wz))
if exp_config.get('sketch', 'smoother') == 'EWMASmoother':
_wz1 = eval(exp_config.get('sketch', 'window_size1'))
_wz2 = eval(exp_config.get('sketch', 'window_size2'))
print 'set EWMASmoother window ' + str(
fast_smoother.EWMASmoother.set_window_size(_wz1, _wz2))
sketch = None
if exp_config.get('sketch', 'type') == 'topicsketch':
sketch = tps.TopicSketch()
if exp_config.get('sketch', 'type') == 'topicsketchplus':
sketch = tpsp.TopicSketchPlus()
for item in s:
sketch.process(item)
if exp_config.get('sketch', 'type') == 'topicsketchplus':
print 'a', sketch.a
'''
m = sketch.plot_sketch('m2', 'a').toarray()
m = m[50:56, 50:56]
print m
p1 = np.array([[0.2, 0.1, 0.0, 0.7, 0.0, 0.0]])
p1 = p1.T
p2 = np.array([[0.0, 0.0, 0.0, 0.05, 0.25, 0.7]])
p2 = p2.T
#m_ = 18.5 * np.dot(p2, p2.T) + 13.5 * np.dot(p1, p1.T)# 11.6791335802, 0.284346678468
m_ = 11.6791335802 * np.dot(p2, p2.T) + 0.284346678468 * np.dot(p1, p1.T)
print m_'''
_sketch_status = sketch.get_sketch()
infer_result = examine.simplified_ex(None, _sketch_status, True)[0]
a = infer_result[0]
print a
a = map(lambda x: x.real, a)
_id = a.index(max(a))
print 'id', _id
#debugging
#print infer_result[2][:_N_words, 0]
#print infer_result[2][:_N_words, 1]
###############
return infer_result[2][:_N_words, _id]
from scipy.sparse import dok_matrix
from scipy.sparse import csr_matrix
import numpy as np
import json
import fast_hashing
import fast_smoother
import solver
import stemmer
import postprocessor
import experiment.exp_config as config
_SKETCH_BUCKET_SIZE = eval(config.get('sketch', 'sketch_bucket_size'))
_NUM_TOPICS = eval(config.get('sketch', 'num_topics'))
_PROBABILITY_THRESHOLD = eval(config.get('sketch', 'probability_threshold'))
if _PROBABILITY_THRESHOLD == 0.:
_PROBABILITY_THRESHOLD = 1./_SKETCH_BUCKET_SIZE
_ACTIVE_WINDOW_SIZE = eval(config.get('sketch', 'active_window_size'))
class SparseSmootherContainer():
_THRESHOLD_FOR_CLEANING = eval(config.get('sketch', 'threshold_for_cleaning'))
_CAPACITY_FOR_CLEANING = eval(config.get('sketch', 'capacity_for_cleaning'))
def __init__(self):
import stream
import experiment.exp_config as config
import clean_wb
_PUN_PATTERN = re.compile('^[' + punctuation + ']+$')
_SPACE_PATTERN = re.compile('^\s+')
_HTTP_PATTERN = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
_FILTERED_USERS = set([
'@girlposts', '@FreddyAmazin', '@ChelseaFC', '@ComedyPosts', '@13elieveSG',
'@ComedyTruth', '@ComedyPics'
])
_ACTIVE_WINDOW_SIZE = eval(config.get('sketch', 'active_window_size'))
class ActiveTermMaintainer:
def __init__(self):
self.active_terms = deque([])
def add(self, item):
self.active_terms.append(item)
while len(self.active_terms) > 0:
term = self.active_terms[0]
if term.timestamp < item.timestamp - _ACTIVE_WINDOW_SIZE * 60:
self.active_terms.popleft()
else:
break
__author__ = 'Wei Xie'
__email__ = '*****@*****.**'
__affiliation__ = 'Pinnacle Lab for Analytics, Singapore Management University'
__website__ = 'http://mysmu.edu/phdis2012/wei.xie.2012'
from nltk.stem.lancaster import *
from nltk.stem.snowball import *
from nltk.stem.porter import *
import experiment.exp_config as config
stemmer = None
if config.get('pre_process', 'stemmer') == 'Snowball':
stemmer = SnowballStemmer("english")
if config.get('pre_process', 'stemmer') == 'Porter':
stemmer = PorterStemmer()
if config.get('pre_process', 'stemmer') == 'Lancaster':
stemmer = LancasterStemmer()
def stem(word):
if stemmer is None:
return word
try:
ret = stemmer.stem(word)
ret = str(ret)
except:
__author__ = 'Wei Xie'
__email__ = '*****@*****.**'
__affiliation__ = 'Living Analytics Research Centre, Singapore Management University'
__website__ = 'http://mysmu.edu/phdis2012/wei.xie.2012'
import experiment.exp_config as config
_THRESHOLD_FOR_SIMILARITY = eval(
config.get('post_process', 'threshold_for_similarity'))
_THRESHOLD_TOPIC_LEVEL_NUMBER_RELATED_USERS = eval(
config.get('post_process', 'topic_number_related_users'))
_THRESHOLD_TOPIC_LEVEL_NUMBER_RELATED_TWEETS = eval(
config.get('post_process', 'topic_number_related_tweets'))
_THRESHOLD_WORD_LEVEL_NUMBER_RELATED_USERS = eval(
config.get('post_process', 'word_number_related_users'))
_THRESHOLD_WORD_LEVEL_NUMBER_RELATED_TWEETS = eval(
config.get('post_process', 'word_number_related_tweets'))
def similarity(set_words, tokens):
s = 0.
for token in tokens:
if token in set_words:
s += 1
return s
def process(
high_prob_words,
from scipy.sparse import dok_matrix
from scipy.sparse import csr_matrix
import numpy as np
import fast_hashing
import solver
import fast_smoother
import stemmer
import postprocessor
import experiment.exp_config as config
import experiment.event_output as event_output
_SKETCH_BUCKET_SIZE = eval(config.get('sketch', 'sketch_bucket_size'))
_NUM_TOPICS = eval(config.get('sketch', 'num_topics'))
_PROBABILITY_THRESHOLD = eval(config.get('sketch', 'probability_threshold'))
_ACTIVE_WINDOW_SIZE = eval(config.get('sketch', 'active_window_size'))
_CUT_TIMESTAMP = eval(config.get('sketch', 'cut_timestamp'))
_MAX_NUMBER_WORDS = eval(config.get('sketch', 'max_number_words'))
class SparseSmootherContainer():
_THRESHOLD_FOR_CLEANING = eval(config.get('sketch', 'threshold_for_cleaning'))
_CAPACITY_FOR_CLEANING = eval(config.get('sketch', 'capacity_for_cleaning'))
def simplified_ex(_fstr, _sketch_status=None, direct=False):
if _fstr:
_f = gzip.open(_fstr, 'rb')
sketch_status = cpickle.load(_f)
_f.close()
else:
sketch_status = _sketch_status
_t = datetime.datetime.utcfromtimestamp(sketch_status[0])
_words = sketch_status[1]
_m2 = sketch_status[2]
_m3 = sketch_status[3]
#######################
mat = _m2[0]
x = [] # for debugging
for i in xrange(_SKETCH_BUCKET_SIZE):
x.append(mat[i, i])
id = np.argmax(np.array(x))
for _w in _words:
w = stemmer.stem(_w)
if hashing.hash_code(w)[0] % _SKETCH_BUCKET_SIZE == id:
print 'significant', _w
#######################
H = fast_hashing.HASH_NUMBER
K = eval(config.get('sketch', 'num_topics')) #15
infer_results = map(
lambda _h: solver.solve(_m2[_h], _m3[_h], _SKETCH_BUCKET_SIZE, K),
range(H))
if direct:
return infer_results
### debugging
print 'Inference finished.'
############
transactions = []
topics_group = []
for h in xrange(H):
topics = dict()
a, r, v = infer_results[h]
a_max = max(np.array(a).real)
print a_max
for k in xrange(K):
s = set()
topic = set()
prob = v[:, k]
prob = remove_negative_terms(prob)
# filtering
if a[k].real < 0.1 * a_max: #1.0:
continue
if entropy(prob) > 6.0:
continue
_ranks = dict()
for _w in _words:
w = stemmer.stem(_w)
p = prob[hashing.hash_code(w)[h] % _SKETCH_BUCKET_SIZE]
_ranks[w] = p
if p >= 0.0100:
s.add(w)
if p >= 0.0075:
topic.add(w)
_tops = sorted(_ranks.keys(),
key=lambda x: _ranks[x],
reverse=True)
_top_n = 15
if len(s) > _top_n:
transactions.append(
apriori.Transaction(set(_tops[:_top_n]), h, k))
#print _top_n
else:
transactions.append(apriori.Transaction(s, h, k))
#print len(s)
topics[k] = topic
print h, k, a[k].real, map(lambda w, h: (w, h, _ranks[w]), s,
hash_code(s, h)) # for debugging
topics_group.append(topics)
### debugging
print 'starting apriori.'
#############
output = apriori.apriori(transactions, 4)
_result = dict()
_result['time'] = _t
_result['topics'] = list()
print _t
for ws in output:
'''
if support_distance(ws.support) > 5:
continue'''
_result['topics'].append((connect_words(recover(ws.words, _words)), connect_words(recover(join(map(lambda item: topics_group[item[0]][item[1]], ws.support.iteritems())), _words)), \
np.max(np.array(map(lambda item: infer_results[item[0]][0][item[1]].real, ws.support.iteritems()))), \
np.median(np.array(map(lambda item: infer_results[item[0]][0][item[1]].real, ws.support.iteritems())))))
if _fstr:
out_file = open('E:/experiment/results/' + _fstr.split('/')[-1], 'wb')
cpk.dump(_result, out_file)
out_file.close()
else:
return _result
__author__ = 'Wei Xie'
__email__ = '*****@*****.**'
__affiliation__ = 'Pinnacle Lab for Analytics, Singapore Management University'
__website__ = 'http://mysmu.edu/phdis2012/wei.xie.2012'
import numpy
import datetime
import gzip
import cPickle as cpickle
import topic_sketch.fast_hashing as hashing
import matplotlib.pyplot as plt
import numpy as np
import experiment.exp_config as config
_SKETCH_BUCKET_SIZE = eval(config.get('sketch', 'sketch_bucket_size'))
def pairs(mat, words):
shape = mat.shape
n = shape[1]
for w1 in words:
for w2 in words:
hashcode = numpy.array(hashing.hash_code(w1)) % n
h1 = hashcode[0]
hashcode = numpy.array(hashing.hash_code(w2)) % n
h2 = hashcode[0]
if h1 > h2:
__author__ = 'Wei Xie'
__email__ = '*****@*****.**'
__affiliation__ = 'Pinnacle Lab for Analytics, Singapore Management University'
__website__ = 'http://mysmu.edu/phdis2012/wei.xie.2012'
import experiment.exp_config as config
_THRESHOLD_FOR_SIMILARITY = eval(config.get('post_process', 'threshold_for_similarity'))
_THRESHOLD_TOPIC_LEVEL_NUMBER_RELATED_USERS = eval(config.get('post_process', 'topic_number_related_users'))
_THRESHOLD_TOPIC_LEVEL_NUMBER_RELATED_TWEETS = eval(config.get('post_process', 'topic_number_related_tweets'))
_THRESHOLD_WORD_LEVEL_NUMBER_RELATED_USERS = eval(config.get('post_process', 'word_number_related_users'))
_THRESHOLD_WORD_LEVEL_NUMBER_RELATED_TWEETS = eval(config.get('post_process', 'word_number_related_tweets'))
def similarity(set_words, tokens):
s = 0.
for token in tokens:
if token in set_words:
s += 1
return s
def process(high_prob_words, active_terms): # high_prob_words and active_terms from topic_sketch
set_words = set()
for prob_word in high_prob_words:
set_words.add(prob_word[0])
# for topic level
