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_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_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 batch_random_walk(num_user, num_location):
city = 'workload_' + str(num_user) + '_' + str(num_location)
folder_setup(city)
cicnt = 20
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 = 50, 10 # 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')
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)
import sys
from process import folder_setup, data_process
from defense import para_replace
from emb import ul_graph_build, para_ul_random_walk, emb_train
from predict import feature_construct, unsuper_friends_predict
city = sys.argv[1]
cicnt = int(sys.argv[2])
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)
import sys
from process import data_process
from utility import js_utility
city = sys.argv[1]
cicnt = int(sys.argv[2])
ratio = int(sys.argv[3]) # 10 20 30 40
ratio = ratio * 1.0 / 100
step = int(sys.argv[4])
checkin, _ = data_process(city, cicnt)
defense_name = str(cicnt) + '_replace_' + str(int(ratio * 100)) + '_' + str(
int(step))
js_utility(city, defense_name, checkin)
# classifier_hidden_layers = ''
# else:
# classifier_hidden_layers = [int(i) for i in (args.classifier_hidden_layers).split(',')]
# dropout = args.dropout
# building_weight = args.building_weight
# floor_weight = args.floor_weight
# N = args.neighbours
# scaling = args.scaling
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
if gpu_id >= 0:
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
else:
os.environ["CUDA_VISIBLE_DEVICES"] = ''
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
[x_trainval, y_trainval, x_train, y_train, x_val, y_val, x_test, y_test, dataset_name] = data_process()
OUTPUT_DIM = y_train.shape[1]
INPUT_DIM = x_train.shape[1]
# pd.DataFrame(x_train).to_csv('preprocessed_data//' + 'train' + '_' + '.csv')
# pd.DataFrame(x_val).to_csv('preprocessed_data//' + 'val' + '_' + '.csv')
# pd.DataFrame(x_test).to_csv('preprocessed_data//' + 'test' + '_' + '.csv')
for dropout in [0.1]:
#for LR_REDUCE_FACTOR in [0.05, 0.1, 0.5, 0.9]:
for LR_REDUCE_FACTOR in [1]:
hr_ls = []
for SIZE in range(6, 23, 2):
root_folder = 'd' + str(dropout) + 'f' + str(LR_REDUCE_FACTOR) + '//' + str(
SIZE) + 'x' + str(SIZE)
# true_index = []
# false_index = []