import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import copy
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
print(device)
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
def clone_params(param, N):
return nn.ParameterList([copy.deepcopy(param) for _ in range(N)])
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
class GraphLayer(nn.Module):
def __init__(self, in_features, hidden_features, out_features, num_of_nodes,
num_of_heads, dropout, alpha, concat=True):
super(GraphLayer, self).__init__()
self.in_features = in_features
self.hidden_features = hidden_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.num_of_nodes = num_of_nodes
self.num_of_heads = num_of_heads
self.W = clones(nn.Linear(in_features, hidden_features), num_of_heads)
self.a = clone_params(nn.Parameter(torch.rand(size=(1, 2 * hidden_features)), requires_grad=True), num_of_heads)
self.ffn = nn.Sequential(
nn.Linear(out_features, out_features),
nn.ReLU()
)
if not concat:
self.V = nn.Linear(hidden_features, out_features)
else:
self.V = nn.Linear(num_of_heads * hidden_features, out_features)
self.dropout = nn.Dropout(dropout)
self.leakyrelu = nn.LeakyReLU(self.alpha)
if concat:
self.norm = LayerNorm(hidden_features)
else:
self.norm = LayerNorm(hidden_features)
def initialize(self):
for i in range(len(self.W)):
nn.init.xavier_normal_(self.W[i].weight.data)
for i in range(len(self.a)):
nn.init.xavier_normal_(self.a[i].data)
if not self.concat:
nn.init.xavier_normal_(self.V.weight.data)
nn.init.xavier_normal_(self.out_layer.weight.data)
def attention(self, linear, a, N, data, edge):
data = linear(data).unsqueeze(0)
assert not torch.isnan(data).any()
# edge: 2*D x E
h = torch.cat((data[:, edge[0, :], :], data[:, edge[1, :], :]), dim=0)
data = data.squeeze(0)
# h: N x out
assert not torch.isnan(h).any()
# edge_h: 2*D x E
edge_h = torch.cat((h[0, :, :], h[1, :, :]), dim=1).transpose(0, 1)
# edge: 2*D x E
edge_e = torch.exp(self.leakyrelu(a.mm(edge_h).squeeze()) / np.sqrt(self.hidden_features * self.num_of_heads))
assert not torch.isnan(edge_e).any()
# edge_e: E
edge_e = torch.sparse_coo_tensor(edge, edge_e, torch.Size([N, N]))
e_rowsum = torch.sparse.mm(edge_e, torch.ones(size=(N, 1)).to(device))
# e_rowsum: N x 1
row_check = (e_rowsum == 0)
e_rowsum[row_check] = 1
zero_idx = row_check.nonzero()[:, 0]
edge_e = edge_e.add(
torch.sparse.FloatTensor(zero_idx.repeat(2, 1), torch.ones(len(zero_idx)).to(device), torch.Size([N, N])))
# edge_e: E
h_prime = torch.sparse.mm(edge_e, data)
assert not torch.isnan(h_prime).any()
# h_prime: N x out
h_prime.div_(e_rowsum)
# h_prime: N x out
assert not torch.isnan(h_prime).any()
return h_prime
def forward(self, edge, data=None):
N = self.num_of_nodes
if self.concat:
h_prime = torch.cat([self.attention(l, a, N, data, edge) for l, a in zip(self.W, self.a)], dim=1)
else:
h_prime = torch.stack([self.attention(l, a, N, data, edge) for l, a in zip(self.W, self.a)], dim=0).mean(
dim=0)
h_prime = self.dropout(h_prime)
if self.concat:
return F.elu(self.norm(h_prime))
else:
return self.V(F.relu(self.norm(h_prime)))
class VariationalGNN(nn.Module):
def __init__(self, in_features, out_features, num_of_nodes, n_heads, n_layers,
dropout, alpha, variational=True, none_graph_features=0, concat=True):
super(VariationalGNN, self).__init__()
self.variational = variational
self.num_of_nodes = num_of_nodes + 1 - none_graph_features
self.embed = nn.Embedding(self.num_of_nodes, in_features, padding_idx=0)
self.in_att = clones(
GraphLayer(in_features, in_features, in_features, self.num_of_nodes,
n_heads, dropout, alpha, concat=True), n_layers)
self.out_features = out_features
self.out_att = GraphLayer(in_features, in_features, out_features, self.num_of_nodes,
n_heads, dropout, alpha, concat=False)
self.n_heads = n_heads
self.dropout = nn.Dropout(dropout)
self.parameterize = nn.Linear(out_features, out_features * 2)
self.out_layer = nn.Sequential(
nn.Linear(out_features, out_features),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(out_features, 1))
self.none_graph_features = none_graph_features
if none_graph_features > 0:
self.features_ffn = nn.Sequential(
nn.Linear(none_graph_features, out_features//2),
nn.ReLU(),
nn.Dropout(dropout))
self.out_layer = nn.Sequential(
nn.Linear(out_features + out_features//2, out_features),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(out_features, 1))
for i in range(n_layers):
self.in_att[i].initialize()
def data_to_edges(self, data):
data = data.bool()
length = data.size()[0]
nonzero = data.nonzero()
if nonzero.size()[0] == 0:
return torch.LongTensor([[0], [0]]), torch.LongTensor([[length + 1], [length + 1]])
if self.training:
mask = torch.rand(nonzero.size()[0])
mask = mask > 0.05
nonzero = nonzero[mask]
if nonzero.size()[0] == 0:
return torch.LongTensor([[0], [0]]), torch.LongTensor([[length + 1], [length + 1]])
nonzero = nonzero.transpose(0, 1) + 1
lengths = nonzero.size()[1]
input_edges = torch.cat((nonzero.repeat(1, lengths),
nonzero.repeat(lengths, 1).transpose(0, 1)
.contiguous().view((1, lengths ** 2))), dim=0)
nonzero = torch.cat((nonzero, torch.LongTensor([[length + 1]]).to(device)), dim=1)
lengths = nonzero.size()[1]
output_edges = torch.cat((nonzero.repeat(1, lengths),
nonzero.repeat(lengths, 1).transpose(0, 1)
.contiguous().view((1, lengths ** 2))), dim=0)
return input_edges.to(device), output_edges.to(device)
def reparameterise(self, mu, logvar):
if self.training:
std = logvar.mul(0.5).exp_()
eps = std.data.new(std.size()).normal_()
return eps.mul(std).add_(mu)
else:
return mu
def encoder_decoder(self, data):
N = self.num_of_nodes
input_edges, output_edges = self.data_to_edges(data)
h_prime = self.embed(torch.arange(N).long().to(device))
for attn in self.in_att:
h_prime = attn(input_edges, h_prime)
if self.variational:
h_prime = self.parameterize(h_prime).view(-1, 2, self.out_features)
h_prime = self.dropout(h_prime)
mu = h_prime[:, 0, :]
logvar = h_prime[:, 1, :]
h_prime = self.reparameterise(mu, logvar)
mu = mu[data, :]
logvar = logvar[data, :]
h_prime = self.out_att(output_edges, h_prime)
if self.variational:
return h_prime[-1], 0.5 * torch.sum(logvar.exp() - logvar - 1 + mu.pow(2)) / mu.size()[0]
else:
return h_prime[-1], torch.tensor(0.0).to(device)
def forward(self, data):
# Concate batches
batch_size = data.size()[0]
# In eicu data the first feature whether have be admitted before is not included in the graph
if self.none_graph_features == 0:
outputs = [self.encoder_decoder(data[i, :]) for i in range(batch_size)]
return self.out_layer(F.relu(torch.stack([out[0] for out in outputs]))), \
torch.sum(torch.stack([out[1] for out in outputs]))
else:
outputs = [(data[i, :self.none_graph_features],
self.encoder_decoder(data[i, self.none_graph_features:])) for i in range(batch_size)]
return self.out_layer(F.relu(
torch.stack([torch.cat((self.features_ffn(torch.FloatTensor([out[0]]).to(device)), out[1][0]))
for out in outputs]))), \
torch.sum(torch.stack([out[1][1] for out in outputs]), dim=-1)
