def single_run(city, cicnt=20, wl=100, wt=20, n_feature=128, new_run=False):
# city = "Brightkite"# ny la london Gowalla Brightkite
# cicnt = 20
folder_setup(city)
checkin, friends = data_process(city, cicnt)
ul_graph, lu_graph = ul_graph_build(checkin, 'locid')
model_name = str(cicnt) + '_locid'
print(model_name)
walk_len, walk_times = 100, 20 # maximal 100 walk_len, 20 walk_times
print('walking')
if new_run:
para_ul_random_batch(city, model_name, checkin.uid.unique(), ul_graph,
lu_graph, walk_len, walk_times)
print('walk done')
print('emb training')
emb_train(city, model_name, wl, wt, n_feature)
print('emb training done')
feature_construct(city, model_name, friends, wl, wt, n_feature)
unsuper_friends_predict(city, model_name, wl, wt, n_feature)
def single_ex_run(city, cicnt):
folder_setup(city)
checkin, friends = data_process(city, cicnt)
defense_name = str(cicnt) + '_ex'
print(defense_name)
checkin = extreme_balance(city, defense_name, checkin)
ul_graph, lu_graph = ul_graph_build(checkin, 'locid')
model_name = str(cicnt) + '_locid_ex'
print(model_name)
walk_len, walk_times = 100, 20 # maximal 100 walk_len, 20 walk_times
print('walking')
para_ul_random_batch(city, model_name, checkin.uid.unique(), ul_graph,
lu_graph, walk_len, walk_times)
print('walk done')
print('emb training')
emb_train(city, model_name)
print('emb training done')
feature_construct(city, model_name, friends)
unsuper_friends_predict(city, model_name)
def single_run(city, cicnt, ratio):
# ratio = int(sys.argv[3])# 10 20 30 40
ratio = ratio * 1.0 / 100
folder_setup(city)
checkin, friends = data_process(city, cicnt)
defense_name = str(cicnt) + '_hiding_' + str(int(ratio * 100))
print(defense_name)
checkin = para_hiding(city, defense_name, checkin, ratio)
ul_graph, lu_graph = ul_graph_build(checkin, 'locid')
model_name = str(cicnt) + '_locid_hiding_' + str(int(ratio * 100))
print(model_name)
walk_len, walk_times = 100, 20 # maximal 100 walk_len, 20 walk_times
print('walking')
para_ul_random_walk(city, model_name, checkin.uid.unique(), ul_graph,
lu_graph, walk_len, walk_times)
print('walk done')
print('emb training')
emb_train(city, model_name)
print('emb training done')
feature_construct(city, model_name, friends)
unsuper_friends_predict(city, model_name)
def single_replace(city, cicnt, ratio, step, fail_to_continue=False):
ratio = ratio * 1.0 / 100
folder_setup(city)
checkin, friends = data_process(city, cicnt)
defense_name = str(cicnt) + '_replace_' + str(int(
ratio * 100)) + '_' + str(int(step))
model_name = str(cicnt) + '_locid_replace_' + str(int(
ratio * 100)) + '_' + str(int(step))
if not fail_to_continue:
checkin = para_replace(city, defense_name, checkin, ratio, step)
else:
checkin = pd.read_csv('dataset/'+ city + '/defense/' + city + \
'_20_replace_'+
str(int(ratio * 100)) + '_' + str(int(step)) +
'.checkin')
ul_graph, lu_graph = ul_graph_build(checkin, 'locid')
walk_len, walk_times = 100, 20 # maximal 100 walk_len, 20 walk_times
print('walking')
if not fail_to_continue:
para_ul_random_batch(city, model_name, checkin.uid.unique(), ul_graph,
lu_graph, walk_len, walk_times)
print('walk done')
print('emb training')
emb_train(city, model_name)
print('emb training done')
feature_construct(city, model_name, friends)
unsuper_friends_predict(city, model_name)
ratio = int(sys.argv[3]) # 10 20 30 40
ratio = ratio * 1.0 / 100
step = int(sys.argv[4])
folder_setup(city)
checkin, friends = data_process(city, cicnt)
defense_name = str(cicnt) + '_replace_' + str(int(ratio * 100)) + '_' + str(
int(step))
checkin = para_replace(city, defense_name, checkin, ratio, step)
ul_graph, lu_graph = ul_graph_build(checkin, 'locid')
model_name = str(cicnt) + '_locid_replace_' + str(int(
ratio * 100)) + '_' + str(int(step))
walk_len, walk_times = 100, 20 # maximal 100 walk_len, 20 walk_times
print 'walking'
para_ul_random_walk(city, model_name, checkin.uid.unique(), ul_graph, lu_graph,\
walk_len, walk_times)
print 'walk done'
print 'emb training'
emb_train(city, model_name)
print 'emb training done'
feature_construct(city, model_name, friends)
unsuper_friends_predict(city, model_name)