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geo_data_decoder.py
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import cPickle
from model.serm_models import geo_lprnn_model,geo_lprnn_text_model,geo_lprnn_trainable_text_model
from eval_tools import *
import config
TWEET_PATH = config.TWEET_PATH
POI_PATH = config.POI_PATH
LA_TWEETS = config.LA_TWEETS
GRID_COUNT = config.GRID_COUNT
BATCH_SIZE = config.batch_size
WINDOW_SIZE = config.window_size
MIN_SEQ = config.min_seq_num
MAX_SEQ = config.max_seq_num
MIN_TRAJ = config.min_traj_num
RECORD_TH = config.threshold
def decode_data_fs(threshold=RECORD_TH):
tsf = open(TWEET_PATH)
poif = open(POI_PATH)
pois = {}
index = []
x = []
y = []
for l in poif:
poifs = l.split(',')
# print len(poifs)
if len(poifs)>5:
print 'error'
pois[poifs[0]] = poifs
useful_poi = {}
useful_user_cis = {}
user_cis = {}
poi_cis = {}
poi_catecology_dict = {}
tsfls = tsf.readlines()
for l in tsfls:
cifs = l.replace('\n', '').split('')
if pois.has_key(cifs[8]):
if poi_cis.has_key(cifs[8]) :
poi_cis[cifs[8]].append(cifs)
else:
poi_cis[cifs[8]] = []
poi_cis[cifs[8]].append(cifs)
if user_cis.has_key(cifs[1]):
user_cis[cifs[1]].append(cifs)
else:
user_cis[cifs[1]] = []
user_cis[cifs[1]].append(cifs)
if poi_catecology_dict.has_key(pois[cifs[8]][3]):
poi_catecology_dict[pois[cifs[8]][3]].append(pois[cifs[8]])
else:
poi_catecology_dict[pois[cifs[8]][3]] = []
poi_catecology_dict[pois[cifs[8]][3]].append(pois[cifs[8]])
for u in user_cis.keys():
if len(user_cis[u])>= threshold:
useful_user_cis[u] = user_cis[u]
for r in user_cis[u]:
if not useful_poi.has_key(r[8]):
useful_poi[r[8]] = pois[r[8]]
for p in useful_poi.keys():
poifs = pois[p]
x.append(float(poifs[1]))
y.append(float(poifs[2]))
index.append(poifs[0])
print ('POI nums',len(useful_poi.keys()))
print ('User nums',len(useful_user_cis.keys()))
return useful_poi,useful_user_cis, poi_catecology_dict
def geo_data_clean_fs(w = WINDOW_SIZE, min_seq_num = MIN_SEQ, min_traj_num = MIN_TRAJ, locationtpye ='GRADE',
gridc = GRID_COUNT):
poi_attr, user_ci, poi_catecology_dict = decode_data_fs()
users = user_ci.keys()
user_record_sequence = {}
useful_poi_dict = {}
user_feature_sequence = {}
# use W and min_traj_num filter data
for user in users:
ci_records = user_ci[user]
ci_records.reverse()
clean_records = []
traj_records = []
perious_record = None
for record in ci_records:
try:
if perious_record == None:
perious_record = record
time = record[4]
if time_diff(time,perious_record[4])< w:
traj_records.append(record)
else:
if len(traj_records)>min_seq_num:
clean_records.append(traj_records)
traj_records = []
perious_record = record
except Exception as e:
print e
if (len(traj_records)>0) & (len(traj_records)>min_seq_num):
clean_records.append(traj_records)
if len(clean_records)>min_traj_num:
user_record_sequence[user] = clean_records
# generate useful pois
for user in user_record_sequence.keys():
trajs = user_record_sequence[user]
for traj in trajs:
for record in traj:
if not useful_poi_dict.has_key(record[8]):
useful_poi_dict[record[8]] = []
useful_poi_dict[record[8]].append(record)
# generate poi dict
if locationtpye == 'GRADE':
index,x,y = [],[],[]
for i in useful_poi_dict.keys():
poifs = poi_attr[i]
index.append(i)
x.append(float(poifs[1]))
y.append(float(poifs[2]))
poi_index_dict, center_location_list = geo_grade(index, x, y, m_nGridCount=gridc)
elif locationtpye == 'LOCS':
poi_index_dict = {}
locs = useful_poi_dict.keys()
for p in range(len(locs)):
poifs = locs[p]
poi_index_dict[poifs] = p
print ("POI Dim", len(poi_index_dict.keys()))
seg_max_record = 0
for user in user_record_sequence.keys():
all_sequ_features = []
for traj in user_record_sequence[user]:
pl_features = []
time_features = []
text_features = []
if seg_max_record < len(traj):
seg_max_record = len(traj)
for record in traj:
pl_features.append(poi_index_dict[record[8]]+1)
time_features.append(time_hour(record[4])+1)
text_features.append(record[6])
all_sequ_features.append([pl_features,time_features,text_features])
user_feature_sequence[user] = all_sequ_features
print 'seg_max_record, pois_num, user_num'
print seg_max_record, len(poi_index_dict.keys()),len(user_feature_sequence.keys())
user_feature_sequence_text, useful_vec= text_feature_generation(user_feature_sequence)
cPickle.dump((user_feature_sequence_text, poi_index_dict, seg_max_record, center_location_list, useful_vec),
open('./features/features&index_seg_gride_fs', 'w'))
return user_feature_sequence_text, poi_index_dict, seg_max_record, center_location_list, useful_vec
def decode_data_la(threshold = RECORD_TH):
tsf = open(LA_TWEETS)
tsfls = tsf.readlines()
print tsfls[0].split('')
x = []
y = []
for l in tsfls:
attrs = l.split('')
x.append(float(attrs[2]))
y.append(float(attrs[3]))
useful_user_cis = {}
user_cis = {}
user_poi = {}
for i in range(len(tsfls)):
l = tsfls[i]
cifs = l.replace('\n', '').split('')
if user_cis.has_key(cifs[1]):
user_cis[cifs[1]].append(cifs)
else:
user_cis[cifs[1]] = []
user_cis[cifs[1]].append(cifs)
user_poi[cifs[0]] = [float(cifs[2]), float(cifs[3])]
for u in user_cis.keys():
if (len(user_cis[u])>= threshold):
useful_user_cis[u] = user_cis[u]
print ("Num of users:", len(useful_user_cis.keys()))
print ("Num of pois:", len(user_poi.keys()))
return user_poi,useful_user_cis
def geo_data_clean_la(w = WINDOW_SIZE,min_seq_num = MIN_SEQ, max_seq_num = MAX_SEQ, min_traj_num = MIN_TRAJ,
locationtpye = 'GRADE', gridc = GRID_COUNT):
poi_attr, user_ci = decode_data_la()
users = user_ci.keys()
user_record_sequence = {}
useful_poi_dict = {}
user_feature_sequence = {}
# use W and min_traj_num filter data
for user in users:
ci_records = user_ci[user]
clean_records = []
traj_records = []
perious_record = None
for record in ci_records:
try:
if perious_record == None:
perious_record = record
time = record[4]
dif = time_diff_la(time,perious_record[4])
if dif<0: print "Fasle"
if (dif< w) & (dif>0):
traj_records.append(record)
else:
if (len(traj_records)>min_seq_num) & (len(traj_records)<max_seq_num):
if check_records_locations(traj_records):
clean_records.append(traj_records)
traj_records = []
perious_record = record
except Exception as e:
print e
if (len(traj_records)>0) & (len(traj_records)>min_seq_num) & (len(traj_records)<max_seq_num):
if check_records_locations(traj_records):
clean_records.append(traj_records)
if (len(clean_records)>min_traj_num):
user_record_sequence[user] = clean_records
# generate useful pois
for user in user_record_sequence.keys():
trajs = user_record_sequence[user]
for traj in trajs:
for record in traj:
if not useful_poi_dict.has_key(record[0]):
useful_poi_dict[record[0]] = []
useful_poi_dict[record[0]].append(record)
# generate poi dict
if locationtpye == 'GRADE':
index,x,y = [],[],[]
for i in useful_poi_dict.keys():
poifs = poi_attr[i]
index.append(i)
x.append(float(poifs[0]))
y.append(float(poifs[1]))
poi_index_dict, center_location_list = geo_grade(index, x, y, m_nGridCount=gridc)
elif locationtpye == 'LOCS':
poi_index_dict = {}
locs = useful_poi_dict.keys()
for p in range(len(locs)):
poifs = locs[p]
poi_index_dict[poifs] = p
print ("POI Dim", len(poi_index_dict.keys()))
seg_max_record = 0
for user in user_record_sequence.keys():
all_sequ_features = []
for traj in user_record_sequence[user]:
pl_features = []
time_features = []
text_features = []
if seg_max_record < len(traj):
seg_max_record = len(traj)
if len(traj) >100:
for r in traj:
print r
for record in traj:
pl_features.append(poi_index_dict[record[0]]+1)
time_features.append(time_hour_la(record[4])+1)
text_features.append(record[6])
all_sequ_features.append([pl_features,time_features,text_features])
user_feature_sequence[user] = all_sequ_features
print 'seg_max_record, pois_num, user_num'
print seg_max_record, len(poi_index_dict.keys()),len(user_feature_sequence.keys())
grid_count = {}
for poi in poi_index_dict.keys():
if not grid_count.has_key(poi_index_dict[poi]):
grid_count[poi_index_dict[poi]] = 1
else:
grid_count[poi_index_dict[poi]] += 1
print ("userful poi nums:",len(grid_count.keys()))
user_feature_sequence_text, useful_vec= text_feature_generation(user_feature_sequence,dataset='LA')
cPickle.dump((user_feature_sequence_text, poi_index_dict,seg_max_record, center_location_list, useful_vec),
open('./features/features&index_seg_gride_la', 'w'))
return user_feature_sequence_text, poi_index_dict, seg_max_record, center_location_list, useful_vec