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model.py
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model.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 9 18:04:58 2020
"""
# Define model
import torch
import torch.nn as nn
import torch.nn.functional as F
import scipy.sparse as sp
import numpy as np
from utils import normalize_adj, normalize_features
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, weighted_adjacency_matrix, alpha, concat=True, in_edge_features=1):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.in_edge_features = in_edge_features
self.weighted_adjacency_matrix = weighted_adjacency_matrix
self.W = nn.Parameter(torch.empty(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
if weighted_adjacency_matrix==False:
self.a = nn.Parameter(torch.empty(size=(2*out_features, 1)))
else:
self.a = nn.Parameter(torch.empty(size=(3*out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.We = nn.Parameter(torch.empty(size=(in_edge_features, out_features)))
nn.init.xavier_uniform_(self.We.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, h, edge, adj):
Wh = torch.mm(h, self.W) # h.shape: (N, in_features), Wh.shape: (N, out_features)
if self.weighted_adjacency_matrix==True:
WeE = torch.mm(edge, self.We)
a_input = self._prepare_attentional_mechanism_input_with_edge_features(Wh, WeE)
else:
a_input = self._prepare_attentional_mechanism_input(Wh)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
zero_vec = -9e15*torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, Wh)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
#def _prepare_edge_attention(self, Eh):
def _prepare_attentional_mechanism_input(self, Wh):
N = Wh.size()[0] # number of nodes
# Below, two matrices are created that contain embeddings in their rows in different orders.
# (e stands for embedding)
# These are the rows of the first matrix (Wh_repeated_in_chunks):
# e1, e1, ..., e1, e2, e2, ..., e2, ..., eN, eN, ..., eN
# '-------------' -> N times '-------------' -> N times '-------------' -> N times
#
# These are the rows of the second matrix (Wh_repeated_alternating):
# e1, e2, ..., eN, e1, e2, ..., eN, ..., e1, e2, ..., eN
# '----------------------------------------------------' -> N times
#
Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=0)
Wh_repeated_alternating = Wh.repeat(N, 1)
# Wh_repeated_in_chunks.shape == Wh_repeated_alternating.shape == (N * N, out_features)
# The all_combination_matrix, created below, will look like this (|| denotes concatenation):
# e1 || e1in_edge_features,
# e1 || e2
# e1 || e3
# ...
# e1 || eN
# e2 || e1
# e2 || e2
# e2 || e3
# ...
# e2 || eN
# ...
# eN || e1
# eN || e2
# eN || e3
# ...
# eN || eNin_edge_features,
all_combinations_matrix = torch.cat([Wh_repeated_in_chunks, Wh_repeated_alternating], dim=1)
# all_combinations_matrix.shape == (N * N, 2 * out_features)
return all_combinations_matrix.view(N, N, 2 * self.out_features)
def _prepare_attentional_mechanism_input_with_edge_features(self, Wh, WeE):
N = Wh.size()[0] # number of nodes
Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=0)
Wh_repeated_alternating = Wh.repeat(N, 1)
WeE_repeated_in_chunks = WeE.repeat_interleave(N, dim=0)
all_combinations_matrix = torch.cat([Wh_repeated_in_chunks, Wh_repeated_alternating, WeE_repeated_in_chunks], dim=1)
return all_combinations_matrix.view(N, N, 3 * self.out_features)
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads, weighted_adjacency_matrix):
"""Dense version of GAT."""
super(GAT, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout, weighted_adjacency_matrix, alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout, weighted_adjacency_matrix, alpha=alpha, concat=True)
self.softmax = nn.Softmax(dim=0)
def forward(self, x, edge, adj):
x = F.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, edge, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
x = F.elu(self.out_att(x, edge, adj))
#return F.log_softmax(x, dim=1)
return self.softmax(x)
class GATES(nn.Module):
def __init__(self, pred2ix_size, entity2ix_size, pred_embedding_dim, entity_embedding_dim, device, dropout, hidden_layer, nheads, weighted_adjacency_matrix):
super(GATES, self).__init__()
self.pred2ix_size = pred2ix_size
self.entity2ix_size = entity2ix_size
self.pred_embedding_dim = pred_embedding_dim
self.entity_embedding_dim = entity_embedding_dim
self.input_size = self.entity_embedding_dim + self.pred_embedding_dim
self.hidden_layer = hidden_layer
self.nheads = nheads
self.dropout = dropout
self.weighted_adjacency_matrix = weighted_adjacency_matrix
self.device = device
self.gat = GAT(nfeat=self.input_size, nhid=self.hidden_layer, nclass=1, dropout=self.dropout, alpha=0.2, nheads=self.nheads, weighted_adjacency_matrix=self.weighted_adjacency_matrix)
self.device = device
def forward(self, input_tensor, adj):
pred_embedded = input_tensor[0]
obj_embedded = input_tensor[1]
embedded = torch.cat((pred_embedded, obj_embedded), 2)
embedded = torch.flatten(embedded, start_dim=1)
edge = adj.data
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
adj = normalize_adj(adj + sp.eye(adj.shape[0]))
adj = torch.FloatTensor(np.array(adj.todense()))
features = normalize_features(embedded.detach().numpy())
features = torch.FloatTensor(np.array(features))
edge = torch.FloatTensor(np.array(edge)).unsqueeze(1)
logits = self.gat(features, edge, adj)
return logits