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dblp-4.py
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import torch, pickle, sys, subprocess, random, copy, time, evaluate
import numpy as np
import importlib.util
from torch_geometric.data import Data, Batch
from data.handlers import subgraph_loader
from torch import nn
from layers.gnn import GraphSAGE
from external_libs.subgraph_utils import get_subgraph
from torch_geometric.nn import global_add_pool, global_mean_pool
from torch.nn import Sequential
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
""" srun -n 1 --gres=gpu:1 --cpus-per-task=15 --partition=ml-all-gpu --output=dblp-4.txt python -u dblp-4.py & """
""" srun -n 1 --gres=gpu:1 --cpus-per-task=15 --nice=1000 --partition=ml01-gpu,ml02-gpu,ml04-gpu,ml05-gpu,ml06-gpu,ml07-gpu,ml08-gpu --time=12:00:00 --pty bash """
""" Arguments: """
dataset = "dblp"
k = 4
hidden_size = 128
tol = 1e-2
patience = 5
num_cpus = 10
lr = 0.001
num_tours = 80
batch_size = 10
super_node_size = 50000
cuda = True
train = {"Hyperedge": torch.load("data/"+dataset+"/train.pt")[k]["Hyperedge"]}
test = {"Hyperedge": torch.load("data/"+dataset+"/test.pt")[k]["Hyperedge"]}
graph = torch.load("data/"+dataset+"/data.pt")["graph"]
dataset = torch.load("data/"+dataset+"/data.pt")["dataset"]
input_size = graph.x.size(1)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.gnn_layer = GraphSAGE( input_size, hidden_size, double_layer=False )
self.output_layer = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.LeakyReLU(), nn.Linear(hidden_size, 1))
def forward(self, data):
subgraph_data = subgraph_loader( data, k, super_node_size, num_tours, num_cpus )
subgraphs = [get_subgraph(data[subgraph_data.batch[i].item()], subgraph_data.subgraphs[i].squeeze()) for i in range(len(subgraph_data.subgraphs))]
subgraphs_lst = []
for i in range(0, len(subgraphs), 500):
subgraphs_b = Batch().from_data_list(subgraphs[i:i+min([500,len(subgraphs)-i])])
subgraphs_b = self.gnn_layer(subgraphs_b.x.cuda(), subgraphs_b.edge_index.cuda(), subgraphs_b.batch.cuda()) \
if next(self.parameters()).get_device() != -1 else self.gnn_layer(subgraphs_b.x, subgraphs_b.edge_index, subgraphs_b.batch)
subgraphs_lst.append(subgraphs_b)
subgraphs = torch.cat(subgraphs_lst,dim=0)
subgraphs = self.output_layer(subgraphs)
weights = subgraph_data.weights.cuda() if next(self.parameters()).get_device() != -1 else subgraph_data.weights
batch = subgraph_data.batch.cuda() if next(self.parameters()).get_device() != -1 else subgraph_data.batch
subgraphs = subgraphs*weights
norm = global_add_pool(weights, batch)
energy = global_add_pool(subgraphs, batch)
return energy/norm
def embedding(self, subgraphs):
with torch.no_grad():
subgraphs_lst = []
for i in range(0, len(subgraphs), 500):
subgraphs_b = Batch().from_data_list(subgraphs[i:i+min([500,len(subgraphs)-i])])
subgraphs_b = self.gnn_layer(subgraphs_b.x.cuda(), subgraphs_b.edge_index.cuda(), subgraphs_b.batch.cuda()) \
if next(self.parameters()).get_device() != -1 else self.gnn_layer(subgraphs_b.x, subgraphs_b.edge_index, subgraphs_b.batch)
subgraphs_lst.append(subgraphs_b)
subgraphs = torch.cat(subgraphs_lst,dim=0)
return subgraphs
def init_weights(m):
if type(m) == torch.nn.Linear:
torch.nn.init.xavier_uniform_(m.weight)
""" Creating samplers to parallelize over each minibatch """
for i in range(1,batch_size+1):
process = subprocess.Popen("cp external_libs/rgpm/tradicional external_libs/rgpm/tradicional-"+str(i), stdout=subprocess.PIPE, stderr=None, shell=True, stdin=subprocess.PIPE)
process.wait()
model = Net()
model.apply(init_weights)
best_model = copy.deepcopy(model)
best_loss = float("inf")
cn_wait = 0
step = 0
# print("===\t RANDOM MODEL RESULTS \t===")
# print("hyperedge:\t", evaluate.logit(train_x=best_model.embedding([ get_subgraph(graph, train["Hyperedge"]["x"][i].squeeze()) for i in range(len(train["Hyperedge"]["x"])) ]).detach().cpu(), train_y=train["Hyperedge"]["y"], \
# test_x=best_model.embedding([ get_subgraph(graph, test["Hyperedge"]["x"][i].squeeze()) for i in range(len(test["Hyperedge"]["x"])) ]).detach().cpu(), test_y=test["Hyperedge"]["y"] ) )
# print("===\t \t===")
if cuda: model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss_fn = torch.nn.BCEWithLogitsLoss()
train_subsample_ids = list( range( len( train["Hyperedge"]["x"] ) ) )
test_subsample_ids = list( range( len( test["Hyperedge"]["x"] ) ) )
random.shuffle(train_subsample_ids)
random.shuffle(test_subsample_ids)
train_subsample_ids = train_subsample_ids[:10000]
test_subsample_ids = test_subsample_ids[:10000]
random.shuffle(dataset)
stime = time.time()
for i in range(0, len(dataset), batch_size):
model.train()
optimizer.zero_grad()
y = torch.tensor([graph.y for graph in dataset[i:i+min([batch_size,len(dataset)-i])]]).float().unsqueeze(-1)
if cuda: y = y.cuda()
energy = model(dataset[i:i+min([batch_size,len(dataset)-i])])
loss = loss_fn(-energy, y)
loss.backward()
optimizer.step()
if loss.item() <= best_loss - tol:
best_loss, cn_wait = loss.item(), 1
best_model = copy.deepcopy(model)
else:
cn_wait += 1
print("loss:\t", loss.item(), "time:\t", time.time()-stime, "step:\t", step, "hyperedge:\t", evaluate.logit(train_x=model.embedding([ get_subgraph(graph, train["Hyperedge"]["x"][i].squeeze()) \
for i in train_subsample_ids ]).detach().cpu(), train_y=train["Hyperedge"]["y"][train_subsample_ids], \
test_x=model.embedding([ get_subgraph(graph, test["Hyperedge"]["x"][i].squeeze()) for i in test_subsample_ids ]).detach().cpu(), test_y=test["Hyperedge"]["y"][test_subsample_ids] ))
if cn_wait == patience and best_loss < 0.2: break
step += 1
print("===\t TRAINED MODEL RESULTS \t===")
print("hyperedge:\t", evaluate.logit(train_x=best_model.embedding([ get_subgraph(graph, train["Hyperedge"]["x"][i].squeeze()) for i in range(len(train["Hyperedge"]["x"])) ]).detach().cpu(), train_y=train["Hyperedge"]["y"], \
test_x=best_model.embedding([ get_subgraph(graph, test["Hyperedge"]["x"][i].squeeze()) for i in range(len(test["Hyperedge"]["x"])) ]).detach().cpu(), test_y=test["Hyperedge"]["y"] ) )
print("===\t \t===")