1 changed files with 88 additions and 88 deletions
+ 88
- 88
GraphTransformer.py
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| @@ -1,17 +1,17 @@ | |||
| import math | |||
| import time | |||
| import torch | |||
| import numpy as np | |||
| import scipy as sp | |||
| import pandas as pd | |||
| import torch.nn as nn | |||
| import networkx as nx | |||
| import scipy as sp | |||
| import seaborn as sns | |||
| import time | |||
| import torch | |||
| import torch.nn as nn | |||
| from torch import Tensor | |||
| import torch.nn.init as init | |||
| import torch.nn.functional as F | |||
| from torch.nn.parameter import Parameter | |||
| from torch.nn.modules.module import Module | |||
| from torch import Tensor | |||
| if torch.cuda.is_available(): | |||
| torch.device('cuda') | |||
| @@ -28,54 +28,54 @@ def Graph_load_batch(min_num_nodes=20, max_num_nodes=1000, name='ENZYMES', node_ | |||
| load many graphs, e.g. enzymes | |||
| :return: a list of graphs | |||
| ''' | |||
| print('Loading graph dataset: ' + str(name)) | |||
| G = nx.Graph() | |||
| # load data | |||
| # path = '../dataset/' + name + '/' | |||
| path = '/content/gdrive/My Drive/' + name + '/' | |||
| data_adj = np.loadtxt(path + name + '_A.txt', delimiter=',').astype(int) | |||
| if node_attributes: | |||
| data_node_att = np.loadtxt(path + name + '_node_attributes.txt', delimiter=',') | |||
| data_node_label = np.loadtxt(path + name + '_node_labels.txt', delimiter=',').astype(int) | |||
| data_graph_indicator = np.loadtxt(path + name + '_graph_indicator.txt', delimiter=',').astype(int) | |||
| if graph_labels: | |||
| data_graph_labels = np.loadtxt(path + name + '_graph_labels.txt', delimiter=',').astype(int) | |||
| data_tuple = list(map(tuple, data_adj)) | |||
| G.add_edges_from(data_tuple) | |||
| for i in range(data_node_label.shape[0]): | |||
| print('Loading graph dataset: ' + str(name)) | |||
| G = nx.Graph() | |||
| # load data | |||
| # path = '../dataset/' + name + '/' | |||
| path = '/content/gdrive/My Drive/' + name + '/' | |||
| data_adj = np.loadtxt(path + name + '_A.txt', delimiter=',').astype(int) | |||
| if node_attributes: | |||
| G.add_node(i + 1, feature=data_node_att[i]) | |||
| G.add_node(i + 1, label=data_node_label[i]) | |||
| G.remove_nodes_from(list(nx.isolates(G))) | |||
| graph_num = data_graph_indicator.max() | |||
| node_list = np.arange(data_graph_indicator.shape[0]) + 1 | |||
| graphs = [] | |||
| max_nodes = 0 | |||
| for i in range(graph_num): | |||
| nodes = node_list[data_graph_indicator == i + 1] | |||
| G_sub = G.subgraph(nodes) | |||
| data_node_att = np.loadtxt(path + name + '_node_attributes.txt', delimiter=',') | |||
| data_node_label = np.loadtxt(path + name + '_node_labels.txt', delimiter=',').astype(int) | |||
| data_graph_indicator = np.loadtxt(path + name + '_graph_indicator.txt', delimiter=',').astype(int) | |||
| if graph_labels: | |||
| G_sub.graph['label'] = data_graph_labels[i] | |||
| if G_sub.number_of_nodes() >= min_num_nodes and G_sub.number_of_nodes() <= max_num_nodes: | |||
| graphs.append(G_sub) | |||
| if G_sub.number_of_nodes() > max_nodes: | |||
| max_nodes = G_sub.number_of_nodes() | |||
| print('Loaded') | |||
| return graphs | |||
| data_graph_labels = np.loadtxt(path + name + '_graph_labels.txt', delimiter=',').astype(int) | |||
| data_tuple = list(map(tuple, data_adj)) | |||
| G.add_edges_from(data_tuple) | |||
| for i in range(data_node_label.shape[0]): | |||
| if node_attributes: | |||
| G.add_node(i + 1, feature=data_node_att[i]) | |||
| G.add_node(i + 1, label=data_node_label[i]) | |||
| G.remove_nodes_from(list(nx.isolates(G))) | |||
| graph_num = data_graph_indicator.max() | |||
| node_list = np.arange(data_graph_indicator.shape[0]) + 1 | |||
| graphs = [] | |||
| max_nodes = 0 | |||
| for i in range(graph_num): | |||
| nodes = node_list[data_graph_indicator == i + 1] | |||
| G_sub = G.subgraph(nodes) | |||
| if graph_labels: | |||
| G_sub.graph['label'] = data_graph_labels[i] | |||
| if G_sub.number_of_nodes() >= min_num_nodes and G_sub.number_of_nodes() <= max_num_nodes: | |||
| graphs.append(G_sub) | |||
| if G_sub.number_of_nodes() > max_nodes: | |||
| max_nodes = G_sub.number_of_nodes() | |||
| print('Loaded') | |||
| return graphs | |||
| def feature_matrix(g): | |||
| ''' | |||
| constructs the feautre matrix (N x 3) for the enzymes datasets | |||
| ''' | |||
| esm = nx.get_node_attributes(g, 'label') | |||
| piazche = np.zeros((len(esm), 3)) | |||
| for i, (k, v) in enumerate(esm.items()): | |||
| piazche[i][v-1] = 1 | |||
| esm = nx.get_node_attributes(g, 'label') | |||
| piazche = np.zeros((len(esm), 3)) | |||
| for i, (k, v) in enumerate(esm.items()): | |||
| piazche[i][v-1] = 1 | |||
| return piazche | |||
| return piazche | |||
| # def remove_random_node(graph, max_size=40, min_size=10): | |||
| @@ -101,18 +101,18 @@ def prepare_graph_data(graph, max_size=40, min_size=10): | |||
| gets a graph as an input | |||
| returns a graph with a randomly removed node adj matrix [0], its feature matrix [1], the removed node true links [2] | |||
| ''' | |||
| if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size: | |||
| return None | |||
| relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph) | |||
| choice = np.random.choice(list(relabeled_graph.nodes())) | |||
| remaining_graph = relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes()))) | |||
| remaining_graph_adj = nx.to_numpy_matrix(remaining_graph) | |||
| graph_length = len(remaining_graph) | |||
| remaining_graph_adj = np.pad(remaining_graph_adj, [(0, max_size - graph_length), (0, max_size - graph_length)]) | |||
| removed_node = nx.to_numpy_matrix(relabeled_graph)[choice] | |||
| removed_node_row = np.asarray(removed_node)[0] | |||
| removed_node_row = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))]) | |||
| return remaining_graph_adj, feature_matrix(remaining_graph), removed_node_row | |||
| if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size: | |||
| return None | |||
| relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph) | |||
| choice = np.random.choice(list(relabeled_graph.nodes())) | |||
| remaining_graph = relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes()))) | |||
| remaining_graph_adj = nx.to_numpy_matrix(remaining_graph) | |||
| graph_length = len(remaining_graph) | |||
| remaining_graph_adj = np.pad(remaining_graph_adj, [(0, max_size - graph_length), (0, max_size - graph_length)]) | |||
| removed_node = nx.to_numpy_matrix(relabeled_graph)[choice] | |||
| removed_node_row = np.asarray(removed_node)[0] | |||
| removed_node_row = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))]) | |||
| return remaining_graph_adj, feature_matrix(remaining_graph), removed_node_row | |||
| """" | |||
| Layers: | |||
| @@ -144,36 +144,36 @@ class GraphConv(nn.Module): | |||
| class GraphAttn(nn.Module): | |||
| def __init__(self, heads, model_dim, dropout=0.1): | |||
| super().__init__() | |||
| self.model_dim = model_dim | |||
| self.key_dim = model_dim // heads | |||
| self.heads = heads | |||
| super().__init__() | |||
| self.model_dim = model_dim | |||
| self.key_dim = model_dim // heads | |||
| self.heads = heads | |||
| self.q_linear = nn.Linear(model_dim, model_dim).cuda() | |||
| self.v_linear = nn.Linear(model_dim, model_dim).cuda() | |||
| self.k_linear = nn.Linear(model_dim, model_dim).cuda() | |||
| self.q_linear = nn.Linear(model_dim, model_dim).cuda() | |||
| self.v_linear = nn.Linear(model_dim, model_dim).cuda() | |||
| self.k_linear = nn.Linear(model_dim, model_dim).cuda() | |||
| self.dropout = nn.Dropout(dropout) | |||
| self.out = nn.Linear(model_dim, model_dim).cuda() | |||
| self.dropout = nn.Dropout(dropout) | |||
| self.out = nn.Linear(model_dim, model_dim).cuda() | |||
| def forward(self, query, key, value): | |||
| # print(q, k, v) | |||
| bs = query.size(0) | |||
| # print(q, k, v) | |||
| bs = query.size(0) | |||
| key = self.k_linear(key.float()).view(bs, -1, self.heads, self.key_dim) | |||
| query = self.q_linear(query.float()).view(bs, -1, self.heads, self.key_dim) | |||
| value = self.v_linear(value.float()).view(bs, -1, self.heads, self.key_dim) | |||
| key = self.k_linear(key.float()).view(bs, -1, self.heads, self.key_dim) | |||
| query = self.q_linear(query.float()).view(bs, -1, self.heads, self.key_dim) | |||
| value = self.v_linear(value.float()).view(bs, -1, self.heads, self.key_dim) | |||
| key = key.transpose(1,2) | |||
| query = query.transpose(1,2) | |||
| value = value.transpose(1,2) | |||
| key = key.transpose(1,2) | |||
| query = query.transpose(1,2) | |||
| value = value.transpose(1,2) | |||
| scores = attention(query, key, value, self.key_dim) | |||
| concat = scores.transpose(1,2).contiguous().view(bs, -1, self.model_dim) | |||
| output = self.out(concat) | |||
| output = output.view(bs, self.model_dim) | |||
| scores = attention(query, key, value, self.key_dim) | |||
| concat = scores.transpose(1,2).contiguous().view(bs, -1, self.model_dim) | |||
| output = self.out(concat) | |||
| output = output.view(bs, self.model_dim) | |||
| return output | |||
| return output | |||
| class FeedForward(nn.Module): | |||
| @@ -196,16 +196,16 @@ class FeedForward(nn.Module): | |||
| class Hydra(nn.Module): | |||
| def __init__(self, gcn_input, model_dim, head): | |||
| super().__init__() | |||
| def __init__(self, gcn_input, model_dim, head): | |||
| super().__init__() | |||
| self.GCN = GraphConv(input_dim=gcn_input, output_dim=model_dim).cuda() | |||
| self.GAT = GraphAttn(heads=head, model_dim=model_dim).cuda() | |||
| self.MLP = FeedForward(input_size=model_dim, hidden_size=gcn_input).cuda() | |||
| self.GCN = GraphConv(input_dim=gcn_input, output_dim=model_dim).cuda() | |||
| self.GAT = GraphAttn(heads=head, model_dim=model_dim).cuda() | |||
| self.MLP = FeedForward(input_size=model_dim, hidden_size=gcn_input).cuda() | |||
| def forward(self, x, adj): | |||
| gcn_outputs = self.GCN(x, adj) | |||
| gat_output = self.GAT(gcn_outputs) | |||
| mlp_output = self.MLP(gat_output).reshape(1,-1) | |||
| def forward(self, x, adj): | |||
| gcn_outputs = self.GCN(x, adj) | |||
| gat_output = self.GAT(gcn_outputs) | |||
| mlp_output = self.MLP(gat_output).reshape(1,-1) | |||
| return mlp_output | |||
| return mlp_output | |||
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