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helpers.py
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helpers.py
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import torch
from torch.autograd import Variable
from math import ceil
import numpy as np
import random
import logging
from datetime import datetime
import os
from pathlib import Path
import json
import copy
CITY_SIM={
'New_York': 'NYC',
'geolife': 'GEO',
'yahoo_japan': 'JPN',
'Singapore': 'SGP'
}
def set_random_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def set_logger(log_dir='./logs/', log_prefix=''):
Path(log_dir).mkdir(parents=True, exist_ok=True)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s %(levelname)-8s %(message)s',
"%Y-%m-%d %H:%M:%S")
sh = logging.StreamHandler()
sh.setLevel(logging.INFO)
sh.setFormatter(formatter)
logger.addHandler(sh)
ts = datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
fh = logging.FileHandler(f'{log_dir}/{log_prefix}-{ts}.log')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
return logger
def read_meta_datasets(train_cities=[], test_city='', path='', dis_type='geo'):
params_map = {"Singapore":{'num_locs':11509, 'max_dist':285.42816162109375, 'max_dist_geo':4371.3525390625},
"New_York":{'num_locs':9387, 'max_dist':298.0574035644531, 'max_dist_geo':4454.46826171875},
"geolife":{'num_locs':32675, 'max_dist':350.427001953125, 'max_dist_geo':3943.664306640625},
"yahoo_japan": {'num_locs':16241, 'max_dist_geo':373.7079162597656}}
data_neural = {c:{} for c in train_cities + [test_city]}
meta_loc = 0
for c in train_cities + [test_city]:
with open(f'{path}/{c}/split.json', 'r') as fr:
data = json.load(fr)
data_neural[c]['tra_user'] = data['tra_user']
data_neural[c]['tra_path'] = data['tra_path']
data_neural[c]['tra_tim'] = data['tra_tim']
data_neural[c]['tra_len'] = [len(t) for t in data['tra_path']]
data_neural[c]['tes_user'] = data['tes_user']
data_neural[c]['tes_path'] = data['tes_path']
data_neural[c]['tes_tim'] = data['tes_tim']
data_neural[c]['tes_len'] = [len(t) for t in data['tes_path']]
data_neural[c]['val_user'] = data['val_user']
data_neural[c]['val_path'] = data['val_path']
data_neural[c]['val_tim'] = data['val_tim']
data_neural[c]['val_len'] = [len(t) for t in data['val_path']]
data_neural[c]['num_locs'] = params_map[c]['num_locs']
if dis_type == 'geo':
data_neural[c]['max_dist'] = params_map[c]['max_dist_geo']
else:
data_neural[c]['max_dist'] = params_map[c]['max_dist']
meta_loc = max(meta_loc, data_neural[c]['num_locs'])
merged_l = []
for traj in data_neural[c]['tra_path']:
merged_l.extend(traj)
unq, cnt = np.unique(merged_l, return_counts=True)
freqs = np.ones(data_neural[c]['num_locs'])
freqs[unq] = cnt
data_neural[c]['frequency'] = freqs
return data_neural, meta_loc
def add_eos_and_pad_seq(seqs, EOS = None, mode = 'no-eos'):
max_seq = 24
valid_len = [len(seq) for seq in seqs]
for i, seq in enumerate(seqs):
if valid_len[i] < max_seq:
if mode == 'add-eos':
seq.append(EOS)
valid_len[i] += 1
if valid_len[i] < max_seq:
seq.extend([0] * (max_seq - valid_len[i]))
else:
seq.extend([0] * (max_seq - valid_len[i]))
assert len(seq) == max_seq
return seqs, valid_len
def get_batch_city(split, args, city_neural, start_i, end_i, device):
if split == 'tra':
data_len, data_traj, data_tim = city_neural['tra_len'], city_neural['tra_path'], city_neural['tra_tim']
elif split == 'val':
data_len, data_traj, data_tim = city_neural['val_len'], city_neural['val_path'], city_neural['val_tim']
else:
data_len, data_traj, data_tim = city_neural['tes_len'], city_neural['tes_path'], city_neural['tes_tim']
data_traj = copy.deepcopy(data_traj[start_i:end_i])
data_traj, data_len = add_eos_and_pad_seq(data_traj)
data_traj, data_len = torch.tensor(data_traj).to(device), torch.tensor(data_len).to(device)
if args.use_start_letter:
x_seq, y_seq = torch.zeros_like(data_traj), torch.zeros_like(data_traj)
x_seq[:, 0] = args.start_letter
x_seq[:, 1:] = data_traj[:, :-1]
y_seq[:, :] = data_traj
mask = torch.arange(len(x_seq[0]), dtype=torch.float32, device=y_seq.device)[None, :] < data_len[:, None]
else:
b, t = data_traj.shape[0], data_traj.shape[1]-1
x_seq, y_seq = torch.zeros((b, t)).to(data_traj), torch.zeros((b, t)).to(data_traj)
x_seq[:, :] = data_traj[:, :-1]
y_seq[:, :] = data_traj[:, 1:]
mask = torch.arange(len(x_seq[0]), dtype=torch.float32, device=y_seq.device)[None, :] < data_len[:, None] - 1
y_seq[~mask] = -1
return x_seq, y_seq
def partial_metrics_sum(results):
acc_K = [1, 5, 10, 20]
result = {}
num_of_test = np.sum([r['num_of_test'] for r in results])
for K in acc_K:
result[K] = np.sum([r[K] for r in results])
result[K] /= num_of_test
result['mrr'] = np.sum([r['mrr'] for r in results])
result['mrr'] /= np.sum([r['mrr_num'] for r in results])
return result[acc_K[0]], result[acc_K[1]], result[acc_K[2]], result[acc_K[3]], result['mrr']
def estimate_loss(model, args, city_neural, device):
out = {}
model.eval()
for split in ['tra', 'val']:
start_i = 0
name = f'{split}_path'
data_traj = city_neural[name]
batch = int(np.ceil(len(data_traj) / args.batch_size))
res_l = []
losses = torch.zeros(batch)
for bt in range(batch):
end_i = min(start_i + args.batch_size, len(data_traj))
X, Y = get_batch_city(split, args, city_neural, start_i, end_i, device)
with args.ctx:
logits, loss, res = model(X, Y, freqs=city_neural['frequency'], use_acc=True)
losses[bt] = loss.item()
res_l.append(res)
start_i = start_i + args.batch_size
eval_res = partial_metrics_sum(res_l)
out[split+'_accs'] = eval_res
out[split+'_loss'] = losses.mean()
model.train()
return out
def read_data_from_file(fp):
path = []
with open(fp, 'r') as f:
lines = f.readlines()
for line in lines:
pois = line.split(' ')
path.append([int(poi) for poi in pois])
return path
def get_gps(gps_file):
gps = np.load(gps_file)
X, Y= gps[:,0], gps[:,1]
return X, Y