From c0cfe2186ec2b7a11e49c56ed33832b72fb175cc Mon Sep 17 00:00:00 2001 From: kanghoon Date: Tue, 15 Sep 2020 15:25:43 +0900 Subject: [PATCH] Neural Temporal Point Process --- run.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/run.py b/run.py index 4008713..612ad7d 100644 --- a/run.py +++ b/run.py @@ -8,7 +8,7 @@ from argparse import ArgumentParser from torch.utils.data import DataLoader -from utils import read_timeseries, generate_sequence, plt_lmbda +from utils import read_timeseries,generate_sequence, plt_lmbda from module import GTPP @@ -48,6 +48,8 @@ if __name__ == "__main__": val_data = read_timeseries(path + config.data + '_validation.csv') test_data = read_timeseries(path + config.data + '_testing.csv') + + train_timeseq, train_eventseq = generate_sequence(train_data, config.seq_len, log_mode=config.log_mode) train_loader = DataLoader(torch.utils.data.TensorDataset(train_timeseq, train_eventseq), shuffle=True, batch_size=config.batch_size) val_timeseq, val_eventseq = generate_sequence(val_data, config.seq_len, log_mode=config.log_mode) @@ -57,7 +59,7 @@ if __name__ == "__main__": best_loss = 1e3 patients = 0 - tol = 20 + tol = 30 for epoch in range(config.epochs): @@ -90,10 +92,10 @@ if __name__ == "__main__": if epoch % config.prt_evry == 0: print("Epochs:{}".format(epoch)) - print("Training Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(loss1/config.batch_size, -loss2 / config.batch_size, loss3 / config.batch_size)) - print("Validation Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(val_loss / config.batch_size, - -val_log_lmbda / config.batch_size, - val_int_lmbda/ config.batch_size)) + 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))) + print("Validation Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(val_loss / val_timeseq.size(0), + -val_log_lmbda / val_timeseq.size(0), + val_int_lmbda/val_timeseq.size(0))) plt_lmbda(train_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode) # plt_lmbda(test_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)