def merge_per_time(dataset):
"""
merge the dateset which is seprated by the time
:dataset: target dataset
:return: none
"""
global data_mode, out_dir, per_day_path, valid_urls, dict_new_ts
write_log('Merging per_time Start')
time_files = get_files_under_path(per_day_path + '/per_time')
list_merged = []
write_log('Merging per_time : Load Start')
for time_path in time_files:
with open(time_path, 'r') as f_data:
list_per_time = json.load(f_data)
list_merged += list_per_time
list_per_time = None
write_log('Merging per_time : Load End')
write_log('Merging per_time : Sort Start')
# (timestamp, user_id, url)
list_merged = list(filter(lambda x: x[2] in valid_urls, list_merged))
list_merged.sort(key=lambda x: x[0])
# time interval compression
new_timestamp = 1
if dataset == 'glob_':
dict_new_ts = {}
prev_ts = -1
for ts in [x[0] for x in list_merged]:
if prev_ts < 0:
dict_new_ts[str(ts)] = new_timestamp
prev_ts = ts
continue
if prev_ts == ts:
continue
new_timestamp += min(ts - prev_ts, 60 * 60 * 3)
dict_new_ts[str(ts)] = new_timestamp
prev_ts = ts
list_merged = [(dict_new_ts[str(x[0])], x[1], x[2])
for x in list_merged]
write_log('Merging per_time : Sort End')
with open(out_dir + '/per_time.json', 'w') as f_time:
json.dump(list_merged, f_time)
list_merged = None
write_log('Merging per_time End')
def generate_simple_dataset():
global one_week_path, simple_path
for data_path in get_files_under_path(one_week_path):
simple_data = ''
with open(data_path, 'r') as f_data:
for i in range(1000):
simple_data += f_data.readline().strip() + '\n'
target_path = os.path.join(simple_path, os.path.basename(data_path))
with open(target_path, 'w') as f_simple:
f_simple.write(simple_data)
def merge_per_user(dataset):
"""
merge the dateset which is seprated by the user
:dataset: target dataset
:return: none
"""
global data_mode, out_dir, per_day_path, valid_urls, dict_new_ts
write_log('Merging per_user Start')
user_files = get_files_under_path(per_day_path + '/per_user')
dict_merged = {}
total_count = len(user_files)
count = 0
for user_path in user_files:
write_log('Merging per_user : {}/{}'.format(count, total_count))
count += 1
with open(user_path, 'r') as f_data:
dict_per_user = json.load(f_data)
write_log('Merging per_user Loaded: {}/{}'.format(count, total_count))
for key in dict_per_user.keys():
dict_merged[key] = dict_merged.get(key, []) + dict_per_user[key]
write_log('Merging per_user Merged: {}/{}'.format(count, total_count))
dict_per_user = None
write_log('Merging per_user : sorting start')
for user_id in dict_merged:
# (timestamp, url)
dict_merged[user_id] = list(
filter(lambda x: x[1] in valid_urls, dict_merged[user_id]))
# time interval compression
if dataset == 'glob_':
dict_merged[user_id] = [(dict_new_ts[str(x[0])], x[1])
for x in dict_merged[user_id]]
dict_merged[user_id].sort(key=lambda x: x[0])
write_log('Merging per_user : sorting end')
write_log('Merging per_user start to writing')
with open(out_dir + '/per_user.json', 'w') as f_user:
json.dump(dict_merged, f_user)
write_log('Merging per_user End')
dict_merged = None
def generate_merged_sequences():
"""
generate the merges sequences from the seperated inputs
:return: merges sequences for all users
"""
global separated_output_dir_path, merged_sequences, dict_per_user, dict_usr2idx
merged_sequences = []
separated_files = get_files_under_path(separated_output_dir_path)
for separated_file in separated_files:
with open(separated_file, 'r') as f_dict:
separated_dict = json.load(f_dict)
# separated_dict[user_id] = {
# 'start_time': start_time,
# 'end_time': end_time,
# 'sequence': idx_sequence,
# 'time_sequence': time_sequence,
# }
for user_id, dict_data in separated_dict.items():
seq_len = len(dict_data['sequence'])
if seq_len <= 1:
continue
sequence_entry = (dict_data['start_time'], dict_data['end_time'],
dict_usr2idx[user_id], dict_data['sequence'],
dict_data['time_sequence'])
merged_sequences.append(sequence_entry)
# st = 0
# st_step = max(1, int((seq_len - 20) / 5) + 1)
# while (st == 0) or (st + 20 <= seq_len):
# cur_seq = dict_data['sequence'][st:st+20]
# cur_t_seq = dict_data['time_sequence'][st:st+20]
#
# sequence_entry = (cur_t_seq[0], cur_t_seq[-1], dict_usr2idx[user_id],
# cur_seq, cur_t_seq)
#
# merged_sequences.append(sequence_entry)
#
# st += st_step
merged_sequences.sort(key=lambda x: x[0])
def main():
"""
main function
"""
global data_mode, out_dir, data_path, dict_url2id
options, args = parser.parse_args()
if (options.mode == None) or (options.output == None) or (options.dataset
== None):
return
data_mode = options.mode
out_dir = options.output
dataset = options.dataset
if dataset not in ['adressa', 'glob']:
print('Wrong dataset name : {}'.format(dataset))
return
if dataset == 'adressa':
data_path = 'data/' + data_mode
worker_fn = raw_to_per_day
elif dataset == 'glob':
data_path = 'data/glob'
if data_mode == 'simple':
data_path += '/simple'
else:
data_path += '/clicks'
worker_fn = raw_to_per_day_glob
os.system('mkdir -p {}'.format(out_dir + '/per_user'))
os.system('mkdir -p {}'.format(out_dir + '/per_time'))
works = get_files_under_path(data_path)
dict_url2id = {}
with ThreadPool(8) as pool:
pool.map(worker_fn, works)
with open(out_dir + '/url2id.json', 'w') as f_dict:
json.dump(dict_url2id, f_dict)
def generate_merged_sequences():
global separated_output_dir_path, merged_sequences
merged_sequences = []
separated_files = get_files_under_path(separated_output_dir_path)
for separated_file in separated_files:
with open(separated_file, 'r') as f_dict:
separated_dict = json.load(f_dict)
# separated_dict[user_id] = {
# 'start_time': start_time,
# 'end_time': end_time,
# 'sequence': idx_sequence,
# }
for user_id, dict_data in separated_dict.items():
sequence_entry = (dict_data['start_time'], dict_data['end_time'],
dict_data['sequence'])
merged_sequences.append(sequence_entry)
merged_sequences.sort(key=lambda x: x[0])
def generate_rnn_input(seperated_input_path=None, output_path=None):
"""
generate an RNN input of each task
:seperated_input_path: path of the input directory storing RNN input seperated by the user
:output_path: path of output to save RNN input
:return: none
"""
global dict_url_idx, dict_per_time
if (seperated_input_path == None) or (output_path == None):
return
merged_sequences = []
write_log('Merging seperated infos ...')
for seperated_path in get_files_under_path(seperated_input_path):
with open(seperated_path, 'r') as f_dict:
seperated_dict = json.load(f_dict)
# seperated_dict[user_id] = {
# 'start_time': start_time,
# 'end_time': end_time,
# 'sequence': idx_sequence,
# }
# dict_url_idx
for user_id, dict_data in seperated_dict.items():
sequence_entry = (dict_data['start_time'], dict_data['end_time'],
dict_data['sequence'])
merged_sequences.append(sequence_entry)
write_log('Merging seperated infos ... Done !')
write_log('Sort by time : start')
merged_sequences.sort(key=lambda x:x[0])
write_log('Sort by time : end')
timestamp_tuple = list(map(lambda x:tuple((x[0], x[1])), merged_sequences))
seq_len = list(map(lambda x:len(x[2]), merged_sequences))
sequence = list(map(lambda x:x[2], merged_sequences))
write_log('Generate idx2url : start')
merged_sequences = None
dict_idx2url = {idx:word for word, idx in dict_url_idx.items()}
write_log('Generate idx2url : end')
write_log('Generate candidate data structure : start')
dict_time_idx = {}
prev_timestamp = None
for (timestamp, user_id, url) in dict_per_time:
if prev_timestamp != timestamp:
if prev_timestamp != None:
dict_time_idx[prev_timestamp]['next_time'] = timestamp
dict_time_idx[timestamp] = {
'prev_time': prev_timestamp,
'next_time': None,
'indices': {},
}
idx_of_url = dict_url_idx[url]
dict_time_idx[timestamp]['indices'][idx_of_url] = dict_time_idx[timestamp]['indices'].get(idx_of_url, 0) + 1
prev_timestamp = timestamp
write_log('Generate candidate data structure : end')
write_log('Save rnn_inputs : start')
dict_rnn_input = {
'timestamp': timestamp_tuple,
'seq_len': seq_len,
'sequence': sequence,
'idx2url': dict_idx2url,
'time_idx': dict_time_idx,
}
with open(output_path, 'w') as f_input:
json.dump(dict_rnn_input, f_input)
write_log('Save rnn_inputs : end')
