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import pickle
import warnings
warnings.filterwarnings('ignore')
def read_data_step_1():
def read_pkl(file_dir):
res = []
taxi = pickle.load(open(file_dir, "rb" ))
count = 0
for seq in taxi['seqs']:
if len(seq) > 34:
count += 1
res.append(seq)
# print(np.max(seq['time_since_last_event']))
print(count)
return res
# from Mei et al 's paper on event imputation
train_res = read_pkl('pilottaxi/big/train.pkl')
dev_res = read_pkl('pilottaxi/big/dev.pkl')
test_res = read_pkl('pilottaxi/big/test1.pkl')
with open('../data/taxi/train.pkl', "wb") as f_out:
pickle.dump(
{
"dim_process": 10,
'train': train_res[:1500]
}, f_out
)
with open('../data/taxi/dev.pkl', "wb") as f_out:
pickle.dump(
{
"dim_process": 10,
'dev': dev_res[:200]
}, f_out
)
with open('../data/taxi/test.pkl', "wb") as f_out:
pickle.dump(
{
"dim_process": 10,
'test': test_res[:400]
}, f_out
)
return
if __name__ == '__main__':
read_data_step_1() |