def lexrank(sentences, build_matrix, target_wc_perc, tokenize_fn, min_score,
include_quot, damping_factor=0.85, error=0.001,
title=None):
assert isinstance(min_score, (int, float)), 'min_score needs to be float, not %s' % type(min_score)
assert isinstance(include_quot, bool)
include_sent = [(include_quot or not is_quotation(s.text)) and \
not extractive_summary.exclude_sentence(s.text) for s in sentences]
tokenized_sents = [s.map(text=lambda t:list(tokenize_fn(t)),
length=lambda t:len(space_split(s.text)))
for s in sentences]
tokenized_title = tokenize_fn(title) if title != None else None
M = build_matrix(sentences, tokenized_sents, include_sent,
tokenized_title=tokenized_title)
scores = pageRank(M, s=damping_factor, maxerr=error)
assert len(scores) == len(sentences)
## normalize scores: 1 is equal to average page rank
## round to avoid float precision issues
sentence_scores = [round(s*len(tokenized_sents), 5) for s in scores]
sentence_wc = extractive_summary.count_words(tokenized_sents)
summary = extractive_summary.build_summary(sentences, sentence_scores, sentence_wc,
target_wc_perc, min_score)
return summary, sentence_wc
def testDocMatrix(self):
links = [
[0, 2, 2, 3],
[0],
[3, 2],
[0],
]
result = pagerank.pageRank(links)
self.assertEqual(str(result), '[ 0.36723503 0.0375 0.33665007 0.25861487]')
def testOneCircle(self):
links = [
[1, 1, 1, 1],
[2],
[3],
[4],
[0]
]
result = pagerank.pageRank(links, alpha=1.0)
self.assertEqual(str(result), '[ 0.2 0.2 0.2 0.2 0.2]')
def testTwoCircles(self):
links = [
[1, 2],
[2],
[3],
[4],
[0]
]
result = pagerank.pageRank(links)
self.assertEqual(str(result), '[ 0.2116109 0.12411822 0.2296187 0.22099231 0.21365988]')
def format_data(self, dependencygroup):
header = 'Filename', 'PageRank', 'PageID', 'Outgoing Links'
converter = pagerank.DependenciesToLinkMatrix(dependencygroup.dependencies)
matrix = converter.create_matrix()
ranking = pagerank.pageRank(matrix)
ids = [idx for idx in range(len(matrix))]
filenames = [utils.prettify_path(converter.id_to_node_map[nid]) for nid in ids]
rowinfos = zip(filenames, ranking, ids, matrix)
rowinfos.sort(key=lambda item: item[1]) #sort by ranking
rowinfos.reverse()
rows = []
for rowi in rowinfos:
row = (rowi[0], self._fmt_rank(rowi[1]), str(rowi[2]), str(rowi[3]))
rows.append(row)
tbl = tableprint.Table(header, rows)
tbl.write(self._outstream)
def lexrank(sentences,
build_matrix,
target_wc_perc,
tokenize_fn,
min_score,
include_quot,
damping_factor=0.85,
error=0.001,
title=None):
assert isinstance(
min_score,
(int, float)), 'min_score needs to be float, not %s' % type(min_score)
assert isinstance(include_quot, bool)
include_sent = [(include_quot or not is_quotation(s.text)) and \
not extractive_summary.exclude_sentence(s.text) for s in sentences]
tokenized_sents = [
s.map(text=lambda t: list(tokenize_fn(t)),
length=lambda t: len(space_split(s.text))) for s in sentences
]
tokenized_title = tokenize_fn(title) if title != None else None
M = build_matrix(sentences,
tokenized_sents,
include_sent,
tokenized_title=tokenized_title)
scores = pageRank(M, s=damping_factor, maxerr=error)
assert len(scores) == len(sentences)
## normalize scores: 1 is equal to average page rank
## round to avoid float precision issues
sentence_scores = [round(s * len(tokenized_sents), 5) for s in scores]
sentence_wc = extractive_summary.count_words(tokenized_sents)
summary = extractive_summary.build_summary(sentences, sentence_scores,
sentence_wc, target_wc_perc,
min_score)
return summary, sentence_wc
def group_info():
algo_type = request.args.get('algo_type')
cur = get_db().cursor()
sec_since_start_of_day = time.localtime().tm_sec + \
time.localtime().tm_min*60 + time.localtime().tm_hour*3600
start_of_day_epoch = time.time() - sec_since_start_of_day
week_ago_epoch = start_of_day_epoch - 7*24*3600
ids = get_db_identifications(cur)
interact_dict = {}
for (full_name, uniqname, id) in ids:
interact_dict[id] = {}
start_time = week_ago_epoch
while start_time < time.time():
end_time = start_time + 24*3600
interactions = get_db_interactions(cur, id, start_time, end_time)
interact_dict[id][time.localtime(start_time).tm_wday] = interactions
start_time = end_time
data = []
print(str(ids))
# pagerank points allocation
if algo_type == 'pagerank':
# calculate pagerank on a day-by-day basis
results = {}
for day in range(7):
# create pagerank graph
pr_graph = []
for (_, _, my_id) in ids:
# find interactions with all other ids
pr_row = []
for (_, _, their_id) in ids:
# sum interactions with this id
interactions = 0
if their_id in interact_dict[my_id][day]:
interactions += interact_dict[my_id][day][their_id]
# insert into row
pr_row.append(interactions)
# insert into pagerank graph
pr_graph.append(pr_row)
# run pagerank algorithm
if 0:
pr_graph = np.array([[0,0,1,0,0,0,0,0],
[0,1,1,0,0,0,0,0],
[1,0,1,1,0,0,0,0],
[0,0,0,1,1,0,0,0],
[0,0,0,0,0,0,1,0],
[0,0,0,0,0,1,1,0],
[0,0,0,1,1,0,1,0],
[0,0,0,0,0,0,0,0]])
print("\nAt time: " + str(time.time()))
print(str(pr_graph))
results[day] = pagerank.pageRank(np.array(pr_graph), maxerr = 0.1)
print(str(results[day]))
for value in results[day]:
if math.isnan(value):
results[day] = [1/float(len(ids))]*len(ids)
break
# create data for graphing
id_index = 0
for (full_name, uniqname, id) in ids:
total_points = 0
point_dict = {}
point_dict['full_name'] = full_name
point_dict['uniqname'] = uniqname
point_counts = []
for day_num in range(7):
day_points = {}
day_points['day'] = days[day_num]
day_points['points'] = round(results[day_num][id_index]*100000)
total_points += day_points['points']
point_counts.append(day_points)
# point_counts needs to be rotated so that the current day is last
curr_day_num = time.localtime().tm_wday
point_counts = point_counts[curr_day_num+1:] + point_counts[:curr_day_num+1]
point_dict['point_counts'] = point_counts
point_dict['total_points'] = total_points
data.append(point_dict)
id_index += 1
# default is linear points
else:
for (full_name, uniqname, id) in ids:
total_points = 0
point_dict = {}
point_dict['full_name'] = full_name
point_dict['uniqname'] = uniqname
point_counts = []
for day_num in range(7):
day_points = {}
day_points['day'] = days[day_num]
points = 0
for pings in interact_dict[id][day_num].values():
points += pings
total_points += pings
day_points['points'] = points
point_counts.append(day_points)
# point_counts needs to be rotated so that the current day is last
curr_day_num = time.localtime().tm_wday
point_counts = point_counts[curr_day_num+1:] + point_counts[:curr_day_num+1]
point_dict['point_counts'] = point_counts
point_dict['total_points'] = total_points
data.append(point_dict)
# Sort data by total points
data = sorted(data, key=lambda record: record['total_points'], reverse=True)
return json.dumps(data)
intrain = []
print fileN
intrain = extractFailureIntrain(fileintrain, outtrain)
LOC = len(intrain[0])
numTest = len(intrain)
cover = []
for i in range(0, LOC):
for j in range(0, numTest):
temp.append(intrain[j][i])
cover.append(temp)
temp = []
print "start pagerank"
pr = pagerank.pageRank(cover, MM)
pr.calCompute()
pr.calNormalized()
save = 'C:/Users/Jeongsu Jang/Desktop/2018-1/paper/RBF+pageRank/experiment/tcas/Testv' + str(
version) + '.txt'
f = open(save, 'w+')
for i in range(0, numTest):
f.write(str(pr.norTest[i][0]) + "\n")
f.close()
save = 'C:/Users/Jeongsu Jang/Desktop/2018-1/paper/RBF+pageRank/experiment/tcas/Xv' + str(
version) + '.txt'
f = open(save, 'w+')
for i in range(0, LOC):
print "---------------------"
print "## computing histgrams"
all_word_histgrams = {}
for imagefname in all_features.keys():
word_histgram = computeHistograms(fcodebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print "---------------------"
print "## build matrix"
matrix, matrixOrder = buildMatrix(all_word_histgrams)
# randomWorkVector = [] : consider tfidf score of word in each photo
randomWorkVector = buildRWVector(matrixOrder, photoTfIdfScore)
print "---------------------"
print "## computing pagerank"
# refer https://gist.github.com/diogojc/1338222/download
# also refer https://github.com/timothyasp/PageRank
rank = pagerank.pageRank(matrix, s=.86, rwVector=randomWorkVector)
rankIndex = rank.argsort()[::-1]
for i in range(0,30):
print( str(i) + ": " + str(rank[rankIndex[i]]) + ' - ' + matrixOrder[rankIndex[i]])
print(photoTfIdfScore[matrixOrder[rankIndex[i]]])
for title, tfidf in (photoTfIdfScore[matrixOrder[rankIndex[i]]]).iteritems() :
print( title + ' - ' + str(tfidf))
# + ' - ' + all_files[rankIndex[i]])