mirror of
https://github.com/wassname/torch-neuralpointprocess.git
synced 2026-06-27 16:47:35 +08:00
wassname
154 lines
4.3 KiB
Python
154 lines
4.3 KiB
Python
# %reload_ext autoreload
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# %autoreload 2
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import matplotlib.pyplot as plt
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# %matplotlib inline
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plt.style.use('ggplot')
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plt.rcParams['figure.figsize'] = (12.0, 3)
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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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from run import get_parser
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# +
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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 = 333
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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:{:2.6f} Log Lambda:{:2.6f}: Integral Lambda:{:2.6f}".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:{:2.6f} Log Lambda:{:2.6f}: Integral Lambda:{:2.6f}".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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# -
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# +
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# class CryptoTraderPL_NLL(pl.LightningModule):
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# def __init__(self, config):
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# super().__init__()
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# self.config = config
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# self._model = GTPP(config)
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# def forward(self, x):
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# return self._model(x)
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# def training_step(self, batch, batch_idx, phase='train'):
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# """
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# Training step which runs for N steps, and get loss over all of them
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# """
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# x, l, r = batch
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# y_pred = self._model(x)
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# # we have multiple targets. So move them to batch
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# l2 = l.reshape(-1)
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# y_pred2 = y_pred.reshape((*l2.shape, 3))
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# loss = F.nll_loss(y_pred2, l2)
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# # record weights
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# self.log_dict({
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# f'loss/{phase}': loss,
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# }, prog_bar=True)
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# assert torch.isfinite(loss)
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# return loss
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# def validation_step(self, batch, batch_idx):
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# return self.training_step(batch, batch_idx, phase='val')
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# def predict_step(self, batch, batch_idx):
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# x, y, r = batch
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# y_pred = self.forward(x)
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# return y_pred, y, r
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# def configure_optimizers(self):
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# optim = Ranger21(self.parameters(),
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# lr=self.train_kwargs['lr'],
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# num_epochs=num_epochs,
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# num_batches_per_epoch=num_batches_per_epoch,
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# weight_decay=self.train_kwargs['weight_decay'])
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# return {'optimizer': optim, 'monitor': 'loss/val'}
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# -
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