mirror of
https://github.com/wassname/torch-neuralpointprocess.git
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wassname
127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
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import numpy as np
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import tqdm
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import torch
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from argparse import ArgumentParser
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from torch.utils.data import DataLoader
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from utils import read_timeseries,generate_sequence, plt_lmbda
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from module import GTPP
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def get_parser():
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parser = ArgumentParser()
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parser.add_argument("--data", type=str, default='exponential_hawkes')
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# parser.add_argument("--model", type=str, default='GTPP')
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parser.add_argument("--seq_len", type=int, default=20)
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parser.add_argument("--emb_dim", type=int, default=10)
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parser.add_argument("--hid_dim", type=int, default=64)
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parser.add_argument("--mlp_layer", type=int, default=2)
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parser.add_argument("--mlp_dim", type=int, default=64)
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parser.add_argument("--event_class", type=int, default=1)
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parser.add_argument("--batch_size", type=int, default=128)
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parser.add_argument("--epochs", type=float, default=100)
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parser.add_argument("--lr", type=float, default=1e-3)
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parser.add_argument("--dropout", type=float, default=0.1)
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parser.add_argument("--prt_evry", type=int, default=15)
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# parser.add_argument("--early_stop", type=bool, default=True) # on by default
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## Alpha ??
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parser.add_argument("--alpha", type=float, default=0.05, help='future discount factor for display true event probability')
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# parser.add_argument("--importance_weight", action="store_true") # not used
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parser.add_argument("--log_mode", type=bool, default=False, help="generate sequence in log mode")
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parser.add_argument("--log_t", action="store_true", help="use log of time in model inputs")
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parser.add_argument("--mean_first", action="store_true", help="in model take mean first")
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return parser
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# if __name__ == "__main__":
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# parser = get_parser()
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# config = parser.parse_args()
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# path = 'data/'
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# if config.data == 'exponential_hawkes':
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# train_data = read_timeseries(path + config.data + '_training.csv')
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# val_data = read_timeseries(path + config.data + '_validation.csv')
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# test_data = read_timeseries(path + config.data + '_testing.csv')
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# else:
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# raise NotImplemented('only exponential_hawkes')
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# train_timeseq, train_eventseq = generate_sequence(train_data, config.seq_len, log_mode=config.log_mode)
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# train_loader = DataLoader(torch.utils.data.TensorDataset(train_timeseq, train_eventseq), shuffle=True, batch_size=config.batch_size)
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# val_timeseq, val_eventseq = generate_sequence(val_data, config.seq_len, log_mode=config.log_mode)
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# val_loader = DataLoader(torch.utils.data.TensorDataset(val_timeseq, val_eventseq), shuffle=False, batch_size=len(val_data))
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# model = GTPP(config)
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# best_loss = 1e3
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# patients = 0
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# tol = 30
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# for epoch in range(config.epochs):
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# model.train()
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# loss1 = loss2 = loss3 = 0
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# for batch in train_loader:
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# loss, log_lmbda, int_lmbda, lmbda = model.train_batch(batch)
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# loss1 += loss
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# loss2 += log_lmbda
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# loss3 += int_lmbda
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# model.eval()
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# for batch in val_loader:
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# val_loss, val_log_lmbda, val_int_lmbda, _ = model(batch)
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# if best_loss > val_loss:
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# best_loss = val_loss.item()
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# else:
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# patients += 1
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# if patients >= tol:
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# print("Early Stop")
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# print("epoch", epoch)
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# plt_lmbda(train_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)
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# break
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# if epoch % config.prt_evry == 0:
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# print("Epochs:{}".format(epoch))
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# print("Training Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(loss1/train_timeseq.size(0), -loss2 / train_timeseq.size(0), loss3 / train_timeseq.size(0)))
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# print("Validation Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(val_loss / val_timeseq.size(0),
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# -val_log_lmbda / val_timeseq.size(0),
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# val_int_lmbda/val_timeseq.size(0)))
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# plt_lmbda(train_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)
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# # plt_lmbda(test_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)
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# print("end")
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