mirror of
https://github.com/wassname/torch-neuralpointprocess.git
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wassname
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
import torch
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from torch import nn
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from torch.autograd import grad
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from torch.optim import Adam
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from torch.nn import functional as F
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from matplotlib import pyplot as plt
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class IntensityNet(nn.Module):
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def __init__(self, config):
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super(IntensityNet, self).__init__()
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self.linear1 = nn.Linear(in_features=1, out_features=1)
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self.linear2 = nn.Linear(in_features=config.hid_dim+1, out_features=config.mlp_dim)
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self.module_list = nn.ModuleList([nn.Linear(in_features=config.mlp_dim, out_features=config.mlp_dim) for _ in range(config.mlp_layer-1)])
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self.linear3 = nn.Sequential(nn.Linear(in_features=config.mlp_dim, out_features=1), nn.Softplus())
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self.mean_first = config.mean_first
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self.log_t = config.log_t
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self.init_weights_positive()
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def init_weights_positive(self):
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eps = 1e-10
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for p in self.parameters():
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p.data = torch.abs(p.data)
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p.data = torch.clamp(p.data, min=eps)
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def forward(self, hidden_state, target_time):
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eps = 1e-10
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for p in self.parameters():
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p.data = torch.clamp(p.data, min=eps)
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target_time.requires_grad_(True)
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if self.log_t:
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target_time = torch.log(target_time+eps)
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t = self.linear1(target_time.unsqueeze(dim=-1))
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out = torch.tanh(self.linear2(torch.cat([hidden_state[:,-1,:], t], dim=-1)))
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for layer in self.module_list:
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out = torch.tanh(layer(out))
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int_lmbda = F.softplus(self.linear3(out))
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int_lmbda_mean = int_lmbda.mean()
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lmbda = grad(
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int_lmbda.mean(),
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target_time,
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create_graph=True, retain_graph=True)[0]
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log_lmbda = (lmbda + eps).log()
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log_lmbda_mean = log_lmbda.mean()
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if self.mean_first:
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nll = int_lmbda_mean - log_lmbda_mean
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else:
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nll = (int_lmbda - log_lmbda).mean()
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return [nll, log_lmbda_mean, int_lmbda_mean, lmbda]
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class GTPP(nn.Module):
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def __init__(self, config):
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super(GTPP, self).__init__()
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self.batch_size = config.batch_size
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self.lr = config.lr
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self.log_mode = config.log_mode # TODO meant to be used here?
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self.embedding = nn.Embedding(num_embeddings=config.event_class, embedding_dim=config.emb_dim)
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self.emb_drop = nn.Dropout(p=config.dropout)
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self.lstm = nn.LSTM(input_size=1+config.emb_dim,
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hidden_size=config.hid_dim,
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batch_first=True,
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bidirectional=False)
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self.intensity_net = IntensityNet(config)
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def forward(self, batch):
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time_seq, event_seq = batch
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event_seq = event_seq.long()
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emb = self.embedding(event_seq)
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emb = self.emb_drop(emb)
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lstm_input = torch.cat([emb[:, :-1], time_seq[:, :-1].unsqueeze(-1)], dim=-1)
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hidden_state, _ = self.lstm(lstm_input)
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nll, log_lmbda, int_lmbda, lmbda = self.intensity_net(hidden_state, time_seq[:, -1])
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return [nll, log_lmbda.detach(), int_lmbda.detach(), lmbda.detach()]
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