def run(self):
filename = self.__dataset + '.tagged_graph'
tags_filename = filename + '.ranks/all.tags'
f = open(tags_filename)
lines = []
for i in range(self.__top_tags + 1000):
lines.append(f.readline())
f.close()
top_tags = map(lambda line: line.strip().split(' ')[0], lines)
top_tags = filter(lambda x:not x in self.__forb_tags, top_tags)[0:self.__top_tags]
#top_tags = tags.get_top_tags(self.__top_tags)
#top_tags = map(lambda x: x[0], top_tags)
self.__ranker = RankerByTags()
self.__ranker.load(filename)
# tag_sets = []
# for i in range(len(top_tags)):
# tag1 = top_tags[i]
# for tag2 in top_tags[:i-1]:
# tag_sets.append((tag1, tag2))
# empty result files
self.__empty_result_files()
count = 0
nro_exps = len(top_tags)*(len(top_tags)-1) / 2
len_top_tags = len(top_tags)
for i in range(1,len(top_tags)):
tag1 = top_tags[i]
for tag2 in top_tags[:i]:
ranks = self.__eggomatic.rank_by_tags([tag1, tag2])
type_rank = {}
type_rank['offline1'] = ranks[0]
type_rank['mono'] = ranks[1]
type_rank['online1'] = ranks[2]
type_rank['online2'] = ranks[3]
type_rank['online3'] = ranks[4]
type_rank['online4'] = ranks[5]
type_rank['offline2'] = ranks[6]
type_rank['online6'] = ranks[7]
if (tag1=='portugal' and tag2=='music') or (tag2=='portugal' and tag1=='music'):
f = open('ranks.txt','w')
for trank, rank in type_rank.iteritems():
f.write('----------------------------------------\n')
f.write('%s\n' % trank)
for t in rank:
f.write('%s\n' % str(t))
f.close()
for offline_type in self.__offline_list:
for online_type in self.__online_list:
for error_type in self.__error_list:
print 'for tags: %s %s' % (tag1,tag2)
self.__calc_error_save(offline_type, online_type, type_rank, error_type)
count += 1
print 'NUMBER OF EXPERIMENTS/PAIRS COMPLETED: %d of %d' % (count, nro_exps)
print 'EXPERIMENTS COMPLETED FOR THE TOP %d of %d TAGS (all pairs)' % (i,len_top_tags)
def __init__(self, dataset, compute_ranks=True, compute_mono_rank=False, build_index=False, max_per_rank=10000000000, top_tags=None):
self.__max_per_rank = max_per_rank
self.__dataset = dataset
self.__ranker = RankerByTags()
self.__ranker.load(dataset + '.tagged_graph')
self.__compute_ranks = compute_ranks
self.__compute_mono_rank = compute_mono_rank
filename = dataset + '.tagged_graph'
if self.__compute_ranks or self.__compute_mono_rank:
self.__ranker.all_ranks(filename, compute_ranks, compute_mono_rank, top_tags)
self.__build_user_index()
print 'loading map tag userrank'
self.__map_tag_userrank = self.__build_map_tag_userrank()
print 'loading rank'
self.__rank = self.__read_rank()
print 'loading tags'
self.__tags = self.__read_tags()
print 'loading map tag users'
self.__map_tag_users = self.__build_map_tag_users()
print 'loading map user tags'
self.__map_user_tags = self.__build_map_user_tags()
if build_index:
# print 'saving pickles'
# print 'saving tag userrank'
# self.__dump_obj('.eggomatic_map_tag_userrank', self.__map_tag_userrank)
# print 'saving rank'
# self.__dump_obj('.eggomatic_rank', self.__rank)
# print 'saving tags'
# self.__dump_obj('.eggomatic_tags', self.__tags)
# print 'saving map tag users'
# self.__dump_obj('.eggomatic_map_tag_users', self.__map_tag_users)
# print 'saving map user tags'
# self.__dump_obj('.tagged_graph.ranks/eggomatic_map.user_tags', self.__map_user_tags)
pass
else: # used pickled objects
# print 'loading pickles'
# print 'loading map user tags'
# self.__map_user_tags = self.__load_obj('.tagged_graph.ranks/eggomatic_map.user_tags')
pass
print 'finish EggOMatic initialization.'
class EggOMatic:
def __build_map_user_tags(self):
return EggOUserTagranksDict(self.__dataset)
# map = {}
#
# filename = self.__dataset + '.tagged_graph'
# f = open(filename, 'r')
# lines = f.readlines()
# f.close()
# total_tags = []
# pairs = 0
#
# i = 0
# for line in lines:
# if i % 10000 == 0:
# print '%d lines read of %d'%(i,len(lines))
# i += 1
# cols = line.split('\t\t')
# if len(cols) < 3:
# continue
# src = cols[0]
# dst = cols[1]
# cols[2] = cols[2].strip().lower()
# tags = cols[2].split('|')
#
# if not src in map:
# map[src] = copy.deepcopy(tags)
# else:
# map[src] += copy.deepcopy(tags)
#
# if not dst in map:
# map[dst] = tags
# else:
# map[dst] += tags
#
# total_tags += tags
# return map
def __build_map_tag_users(self):
return EggOTagUserDict(self.__dataset)
# map = {}
# i = 0
# for tag, userrank in self.__tag_userrank.iteritems():
# if i % 1000 == 0:
# print '%d users read of %d'%(i,len(self.__map_user_tags.keys()))
# i += 1
# #print 'user: %s tags: %s' % (user,tags)
# users = map(lambda x:x[0], userrank)
# map[tag] = set(users)
# return map
def __build_map_tag_userrank(self):
#return self.__load_obj('.tagged_graph.all_tags_ranks')
return EggOTagUserrankDict(self.__dataset)
def __read_rank(self):
return self.__map_tag_userrank['']
# f = open(self.__dataset+'.tagged_graph-.graph.rank', 'r')
# lines = f.readlines()
# f.close()
# return [(l.strip().split(' ')[0],float(l.strip().split(' ')[1])) for l in lines]
def __read_tags(self):
f = open(self.__dataset+ '.tagged_graph.ranks/all.tags', 'r')
lines = f.readlines()
f.close()
return map(lambda line:line.strip().split(' ')[0], lines)
def __read_clusters(self, clust_limit=5):
clusters = list(self.__tags)
return clusters
def __build_map_tag_clusters(self):
map = {}
map[''] = ['']
for tag in self.__tags:
map[tag] = [tag]
return map
def __dump_obj(self, extension, obj):
filename = self.__dataset + extension
f = open(filename, 'w')
pickle.dump(obj, f)
f.close()
def __load_obj(self, extension):
filename = self.__dataset + extension
f = open(filename, 'r')
obj = pickle.load(f)
f.close()
return obj
def __build_user_index(self):
filename = self.__dataset + '.tagged_graph'
tags_filename = filename + '.ranks/all.tags'
f = open(tags_filename)
lines = f.readlines()
f.close()
self.__users = set([])
for line in lines:
if line.strip() == '':
continue
tag = line.strip().split(' ')[0]
if len(tag) >= 64:
continue
try: # some tags may be missing if we only rank top_tags
u_lines = open(filename + '.ranks/%s.graph.rank' % tag)
users = map(lambda line: line.strip().split(' ')[0], u_lines)
for user, pos in zip(users,range(1,len(users)+1)):
u_file = open(filename + '.ranks/%s.user.ranks' % user, 'aw')
u_file.write('%s %d\n' % (tag,pos))
u_file.close()
self.__users.add(user)
except:
pass
# sort user better ranks by position.
users_file = open(filename + '.ranks/all.users','w')
for user in self.__users:
lines = open(filename + '.ranks/%s.user.ranks' % user).readlines()
user_ranks = map(lambda x: tuple(x.strip().split(' ')), filter(lambda x:len(x.strip())>0, lines))
user_ranks = map(lambda (tag,pos): (tag,int(pos)), user_ranks)
user_ranks.sort(snd_cmp)
u_file = open(filename + '.ranks/%s.user.ranks' % user, 'w')
for tag, pos in user_ranks:
u_file.write('%s %d\n' % (tag,pos))
u_file.close()
users_file.write('%s\n' % user)
users_file.close()
def __init__(self, dataset, compute_ranks=True, compute_mono_rank=False, build_index=False, max_per_rank=10000000000, top_tags=None):
self.__max_per_rank = max_per_rank
self.__dataset = dataset
self.__ranker = RankerByTags()
self.__ranker.load(dataset + '.tagged_graph')
self.__compute_ranks = compute_ranks
self.__compute_mono_rank = compute_mono_rank
filename = dataset + '.tagged_graph'
if self.__compute_ranks or self.__compute_mono_rank:
self.__ranker.all_ranks(filename, compute_ranks, compute_mono_rank, top_tags)
self.__build_user_index()
print 'loading map tag userrank'
self.__map_tag_userrank = self.__build_map_tag_userrank()
print 'loading rank'
self.__rank = self.__read_rank()
print 'loading tags'
self.__tags = self.__read_tags()
print 'loading map tag users'
self.__map_tag_users = self.__build_map_tag_users()
print 'loading map user tags'
self.__map_user_tags = self.__build_map_user_tags()
if build_index:
# print 'saving pickles'
# print 'saving tag userrank'
# self.__dump_obj('.eggomatic_map_tag_userrank', self.__map_tag_userrank)
# print 'saving rank'
# self.__dump_obj('.eggomatic_rank', self.__rank)
# print 'saving tags'
# self.__dump_obj('.eggomatic_tags', self.__tags)
# print 'saving map tag users'
# self.__dump_obj('.eggomatic_map_tag_users', self.__map_tag_users)
# print 'saving map user tags'
# self.__dump_obj('.tagged_graph.ranks/eggomatic_map.user_tags', self.__map_user_tags)
pass
else: # used pickled objects
# print 'loading pickles'
# print 'loading map user tags'
# self.__map_user_tags = self.__load_obj('.tagged_graph.ranks/eggomatic_map.user_tags')
pass
print 'finish EggOMatic initialization.'
def good_tag(self, tag):
return tag in self.__map_tag_userrank
#return tag in self.__tags
def best_tag(self, tag):
return self.__ranker.best_tag(tag)
def tag_weight(self, tag):
return self.__ranker.tag_weight(tag)
def has_bigger_cluster(self, tag):
return False
# for cluster in self.__map_tag_clusters[tag]:
# if len(cluster) > 1:
# return True
# return False
def has_many_clusters(self, tag):
return len(self.__map_tag_clusters[tag]) > 1
def clusters(self, tag):
return self.__map_tag_clusters[tag]
def rank_by_tag(self, tag='', use_cluster=False, cluster_number=0):
# if not tag in self.__tags:
# return []
if use_cluster:
cluster = self.clusters(tag)[cluster_number]
return self.rank_by_tags(cluster)
rank = self.__map_tag_userrank[tag][:self.__max_per_rank]
return rank, self.__merge_rank_and_monolitic([tag]), rank, rank, rank, rank, rank
def rank_by_tags(self, tags):
if len(tags)==0:
return []
tags = list(tags)
rank0, mono_rank, rank, rank2, rank3, rank4, rank6 = self.rank_by_tag(tags[0])
for tag in tags[1:]:
rank = self.__merge_rank_and(rank, self.rank_by_tag(tag)[2], '1')
rank2 = self.__merge_rank_and(rank2, self.rank_by_tag(tag)[3], '2')
rank3 = self.__merge_rank_and(rank3, self.rank_by_tag(tag)[4], '3')
rank4 = self.__merge_rank_and(rank4, self.rank_by_tag(tag)[5], '4', tag)
rank6 = self.__merge_rank_and(rank6, self.rank_by_tag(tag)[6], '6')
and_rank0 = add_pos(and_rank0_gold1(tags, self.__ranker))
and_rank5 = add_pos(and_rank5_gold2(tags, self.__ranker))
mono_rank = self.__merge_rank_and_monolitic(tags)
return and_rank0, mono_rank, rank, rank2, rank3, rank4, and_rank5, rank6
def rank_by_tag_fast(self, tag='', use_cluster=False, cluster_number=0):
rank = self.__map_tag_userrank[tag][:self.__max_per_rank]
return rank, rank
def rank_by_tags_fast(self, tags):
if len(tags)==0:
return []
tags = list(tags)
rank1, rank3 = self.rank_by_tag_fast(tags[0])
for tag in tags[1:]:
rank1 = self.__merge_rank_and(rank1, self.rank_by_tag_fast(tag)[0], '1')
rank3 = self.__merge_rank_and(rank3, self.rank_by_tag_fast(tag)[1], '3')
return rank1, rank3
def __merge_rank_and_monolitic(self, tags):
users = self.__map_tag_users[tags[0]]
for tag in tags[1:]:
users = users.intersection(self.__map_tag_users[tag])
mono_rank = []
for name, pagerank, pos in self.__rank:
if name in users:
mono_rank.append((name,pagerank))
if self.__max_per_rank <= len(mono_rank):
break
mono_rank = PageRank.normalize(mono_rank)
return add_pos(mono_rank)
def __merge_rank_and(self, rank1, rank2, and_type='1', tag=None):
rank1_no_pos = [(name,pagerank) for name, pagerank, pos in rank1]
rank2_no_pos = [(name,pagerank) for name, pagerank, pos in rank2]
if and_type=='1':
new_rank = and_rank(rank1_no_pos, rank2_no_pos)
elif and_type=='2':
new_rank = and_rank2(rank1_no_pos, rank2_no_pos)
elif and_type=='3':
new_rank = and_rank3(rank1_no_pos, rank2_no_pos)
elif and_type=='4':
new_rank = and_rank4(rank1_no_pos, rank2_no_pos, self.__ranker, tag, self.__max_per_rank)
elif and_type=='6':
new_rank = and_rank6(rank1_no_pos, rank2_no_pos)
else:
raise Exception('bad and_type in __merge_rank_and')
new_rank_pos = add_pos(new_rank)
return new_rank_pos
def __merge_rank_or(self, rank1, rank2):
pos = 0
present_pagerank = -3.0
rank = []
index1, index2 = 0, 0
while index1 < len(rank1) and index2 < len(rank2) and len(rank) < self.__max_per_rank:
if rank1[index1][1] > rank2[index2][1] or (rank1[index1][1] == rank2[index2][1] and rank1[index1][0] <= rank2[index2][0]):
name, pagerank, old_pos1 = rank1[index1]
index1 += 1
else:
name, pagerank, old_pos2 = rank2[index2]
index2 += 1
if pagerank != present_pagerank:
pos += 1
present_pagerank = pagerank
if len(rank)==0 or rank[-1][0] != name:
rank.append((name,pagerank,pos))
if index1 < len(rank1):
for name, pagerank, old_pos1 in rank1[index1:]:
if pagerank != present_pagerank:
pos += 1
present_pagerank = pagerank
if len(rank) >= self.__max_per_rank:
break
if len(rank)==0 or rank[-1][0] != name:
rank.append((name,pagerank,pos))
elif index2 < len(rank2):
for name, pagerank, old_pos2 in rank1[index2:]:
if pagerank != present_pagerank:
pos += 1
present_pagerank = pagerank
if len(rank) >= self.__max_per_rank:
break
if len(rank)==0 or rank[-1][0] != name:
rank.append((name,pagerank,pos))
return rank
def total_users(self):
return len(self.__map_user_tags)
def total_tags(self):
return len(self.__tags)
def good_user(self, user):
return user in self.__map_user_tags
def __cmp_fst(self, A, B):
if A[0][0] < B[0][0]:
return -1
elif A[0][0] > B[0][0]:
return 1
else:
return 0
def __sort_inside_pos(self, ranks):
if len(ranks) == 0:
return []
ret = []
aux, aux_pos = [ranks[0]], ranks[0][2]
for tags, pr, pos in ranks[1:]:
if pos == aux_pos:
aux.append((tags,pr,pos))
else:
aux.sort(self.__cmp_fst)
ret += aux
aux, aux_pos = [(tags,pr,pos)], pos
aux.sort()
ret += aux
return ret
def user_ranks(self, user, filter_set=None, max_per=500, sorting=True):
user_ranks = self.__map_user_tags.__getitem__(user, max_per)
ranks = map(lambda (tag, pos): ([tag], 0.0, int(pos)), user_ranks)
if sorting:
ranks = self.__sort_inside_pos(ranks)
if filter_set:
ranks = filter(lambda (tags,pr,pos): tags[0] in filter_set, ranks)
return ranks
# good_tags = []
# ranks = []
# for tag in tags:
# tag_rank = self.__map_tag_userrank[tag]
# for rank_user, pagerank, pos in tag_rank:
# if rank_user == user:
# ranks.append((tag, pagerank, pos))
# ranks = list(set(ranks))
# ranks = map(lambda (tag, pr, pos): ([tag], pr, pos), ranks)
# ranks.sort(thrd_fst_cmp)
# return ranks
def user_ranks_clustering(self, user):
good_tags = []
ranks = []
added_clusters = set([])
for cluster in self.__clusters:
rank = self.rank_by_tags(cluster)
for rank_user, pagerank, pos in rank:
if rank_user == user and not '|'.join(cluster) in added_clusters:
ranks.append((cluster, pagerank, pos))
added_clusters.add('|'.join(cluster))
ranks.sort(thrd_fst_cmp)
return ranks
def main():
# create and load
ranker = RankerByTags()
#filename = '../data/rpoland--1000.tagged_graph'
#filename = '../data/rpoland--2000.tagged_graph'
#filename = '../data/jcl5m--39370.tagged_graph'
#filename = '../data/MIX.tagged_graph'
#filename = '../data/flickr.tagged_graph'
#filename = '../data/jcl5m-cuantos.tagged_graph'
#filename = '../data/flickr_med.tagged_graph'
#filename = '../data/yt.tagged_graph'
#filename = '../data/fr.tagged_graph'
#filename = '../data/yt_nd.tagged_graph'
filename = '../data/fr_nd.tagged_graph'
top_tags_size = 20
ranker.load(filename)
# filter by tag boolean formula
tag_form = TagBooleanFormula()
# tag_form.run_tests()
# and1 = TagBooleanConjunction()
# and1.addAtom(TagBooleanAtom(True,'fun'))
# tag_form.addTagAnd(and1)
# and2 = TagBooleanConjunction()
# and2.addAtom(TagBooleanAtom(True,'fun'))
# tag_form.addTagAnd(and2)
print str(tag_form)
ranker.filter(tag_form)
# save new subgraph withou tags.
#outfilename = filename + '-%s.graph' % str(tag_form)
outfilename = filename
#ranker.save(outfilename)
ranker.save_edges(outfilename + '.edges')
ranker.save_nwb(outfilename + '.nwb')
# now save graphs of top tags
tags = Tags(filename)
top_tags = tags.get_top_tags(top_tags_size)
#top_tags = map(lambda x: x[0], top_tags)
#top_tags = ['music', 'funny']
top_tags = ['blue', 'flower']
for tag in top_tags:
# filter by tag boolean formula
tag_form = TagBooleanFormula()
# tag_form.run_tests()
and1 = TagBooleanConjunction()
and1.addAtom(TagBooleanAtom(True,tag))
tag_form.addTagAnd(and1)
# and2 = TagBooleanConjunction()
# and2.addAtom(TagBooleanAtom(True,'fun'))
# tag_form.addTagAnd(and2)
print str(tag_form)
ranker.filter(tag_form)
# save new subgraph withou tags.
#outfilename = filename + '-%s.graph' % str(tag_form)
outfilename = filename
#ranker.save(outfilename)
ranker.save_edges(outfilename + '.'+ str(tag_form) + '.edges')
ranker.save_nwb(outfilename + '.' + str(tag_form) + '.nwb')
# now save graphs of top tags by pairs, ANDed.
tags = Tags(filename)
top_tags = tags.get_top_tags(top_tags_size)
#top_tags = map(lambda x: x[0], top_tags)
#top_tags = ['music', 'funny']
top_tags = ['blue', 'flower']
for tag1, i in zip(top_tags,range(len(top_tags))):
for tag2 in top_tags[i+1:]:
# AND
# filter by tag boolean formula
tag_form = TagBooleanFormula()
# tag_form.run_tests()
and1 = TagBooleanConjunction()
and1.addAtom(TagBooleanAtom(True,tag1))
and1.addAtom(TagBooleanAtom(True,tag2))
tag_form.addTagAnd(and1)
# and2 = TagBooleanConjunction()
# and2.addAtom(TagBooleanAtom(True,tag2))
# tag_form.addTagAnd(and2)
print str(tag_form)
ranker.filter(tag_form)
# save new subgraph withou tags.
#outfilename = filename + '-%s.graph' % str(tag_form)
outfilename = filename
#ranker.save(outfilename)
ranker.save_edges(outfilename + '.'+ str(tag_form) + '.edges')
ranker.save_nwb(outfilename + '.' + str(tag_form) + '.nwb')
ranker.rank(10)
ranker.saveRank(outfilename + '.ranks/' + str(tag_form) + '.graph.rank' )
# OR
# filter by tag boolean formula
tag_form = TagBooleanFormula()
# tag_form.run_tests()
and1 = TagBooleanConjunction()
and1.addAtom(TagBooleanAtom(True,tag1))
tag_form.addTagAnd(and1)
and2 = TagBooleanConjunction()
and2.addAtom(TagBooleanAtom(True,tag2))
tag_form.addTagAnd(and2)
# and1.addAtom(TagBooleanAtom(True,tag2))
# tag_form.addTagAnd(and1)
# and2 = TagBooleanConjunction()
# and2.addAtom(TagBooleanAtom(True,tag2))
# tag_form.addTagAnd(and2)
print str(tag_form)
ranker.filter(tag_form)
# save new subgraph withou tags.
#outfilename = filename + '-%s.graph' % str(tag_form)
outfilename = filename
#ranker.save(outfilename)
ranker.save_edges(outfilename + '.'+ str(tag_form) + '.edges')
ranker.save_nwb(outfilename + '.' + str(tag_form) + '.nwb')
ranker.rank()
ranker.saveRank(outfilename + '.ranks/' + str(tag_form) + '.graph.rank' )
print 'finish.'
class EggOMaticExperiments:
__exp_OSim = True
__exp_KSim = True
__offline_list = ['offline1'] # or 'gold2'
__online_list = ['online1']
__top_tags = 20
__begin_top_many_users = 1
__end_top_many_users = 10
# exponential step 2, 4 ,8 16
__step_top_many_users = 2
def set_top_tags(self, top_tags=20):
self.__top_tags = top_tags
def set_forb_tags(self, forb_tags):
self.__forb_tags = forb_tags
def set_offline_list(self, type=['offline1']):
self.__offline_list = type
def set_online_list(self, list):
self.__online_list = list
def set_error_list(self, list=['osim','ksim']):
self.__error_list = list
def __init__(self, dataset, compute_ranks=True, compute_mono_rank=True, build_index=True, max_per_rank=10000000000, top_tags=None):
self.__dataset = dataset
self.__eggomatic = EggOMatic(dataset, compute_ranks, compute_mono_rank, build_index, max_per_rank, top_tags)
def __calc_error_save(self, offline_type, online_type, type_rank, error_type):
f = open(self.__dataset + '.' + offline_type + '_VS_' + online_type + '.' + error_type, 'a')
print 'writing ' + self.__dataset + '.' + offline_type + '_VS_' + online_type + '.' + error_type
offline_rank = type_rank[offline_type]
online_rank = type_rank[online_type]
for top_many_users in [self.__step_top_many_users**i for i in range(self.__begin_top_many_users,self.__end_top_many_users)]:
float_error, info_val = eval('rank_dist_%s(offline_rank, online_rank, top_many_users)' % error_type)
if info_val >= 0:
f.write('%d %d %f \n' % (top_many_users, len(offline_rank), float_error))
def __empty_result_files(self):
for offline_type in self.__offline_list:
for online_type in self.__online_list:
for error_type in self.__error_list:
f = open(self.__dataset + '.' + offline_type + '_VS_' + online_type + '.' + error_type, 'w')
f.close()
def run(self):
filename = self.__dataset + '.tagged_graph'
tags_filename = filename + '.ranks/all.tags'
f = open(tags_filename)
lines = []
for i in range(self.__top_tags + 1000):
lines.append(f.readline())
f.close()
top_tags = map(lambda line: line.strip().split(' ')[0], lines)
top_tags = filter(lambda x:not x in self.__forb_tags, top_tags)[0:self.__top_tags]
#top_tags = tags.get_top_tags(self.__top_tags)
#top_tags = map(lambda x: x[0], top_tags)
self.__ranker = RankerByTags()
self.__ranker.load(filename)
# tag_sets = []
# for i in range(len(top_tags)):
# tag1 = top_tags[i]
# for tag2 in top_tags[:i-1]:
# tag_sets.append((tag1, tag2))
# empty result files
self.__empty_result_files()
count = 0
nro_exps = len(top_tags)*(len(top_tags)-1) / 2
len_top_tags = len(top_tags)
for i in range(1,len(top_tags)):
tag1 = top_tags[i]
for tag2 in top_tags[:i]:
ranks = self.__eggomatic.rank_by_tags([tag1, tag2])
type_rank = {}
type_rank['offline1'] = ranks[0]
type_rank['mono'] = ranks[1]
type_rank['online1'] = ranks[2]
type_rank['online2'] = ranks[3]
type_rank['online3'] = ranks[4]
type_rank['online4'] = ranks[5]
type_rank['offline2'] = ranks[6]
type_rank['online6'] = ranks[7]
if (tag1=='portugal' and tag2=='music') or (tag2=='portugal' and tag1=='music'):
f = open('ranks.txt','w')
for trank, rank in type_rank.iteritems():
f.write('----------------------------------------\n')
f.write('%s\n' % trank)
for t in rank:
f.write('%s\n' % str(t))
f.close()
for offline_type in self.__offline_list:
for online_type in self.__online_list:
for error_type in self.__error_list:
print 'for tags: %s %s' % (tag1,tag2)
self.__calc_error_save(offline_type, online_type, type_rank, error_type)
count += 1
print 'NUMBER OF EXPERIMENTS/PAIRS COMPLETED: %d of %d' % (count, nro_exps)
print 'EXPERIMENTS COMPLETED FOR THE TOP %d of %d TAGS (all pairs)' % (i,len_top_tags)
