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run_script_pyg.py
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run_script_pyg.py
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import json
import pickle
import statistics
import time
import argparse
from os import listdir
import torch
import torch.nn.functional as F
from torch.nn import Linear, Sequential, ReLU
from torch_geometric.data import DataLoader
from torch_geometric.nn import GCNConv, avg_pool, global_mean_pool, JumpingKnowledge, global_add_pool, GINConv, SAGEConv
import numpy as np
from sklearn.model_selection import StratifiedKFold, train_test_split
from random import shuffle
# from tensorboardX import SummaryWriter
torch.set_default_tensor_type('torch.DoubleTensor')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# %%
class NetGCN(torch.nn.Module):
def __init__(self, num_features, num_classes, dim=10):
super(NetGCN, self).__init__()
self.conv1 = GCNConv(num_features, dim, normalize=False, cached=False, bias=False)
self.conv2 = GCNConv(dim, dim, normalize=False, cached=False, bias=False)
self.reg_params = self.conv1.parameters()
self.non_reg_params = self.conv2.parameters()
self.fc1 = Linear(dim, 1, bias=False)
def forward(self, x, edge_index, batch):
x = F.relu(self.conv1(x, edge_index))
x = self.conv2(x, edge_index)
x = global_mean_pool(x, batch)
x = self.fc1(x)
return torch.sigmoid(x)
class NetGraphSage(torch.nn.Module):
def __init__(self, num_features, num_classes, concat=False, dim=10):
super(NetGraphSage, self).__init__()
self.conv1 = SAGEConv(num_features, dim, normalize=False, concat=True, bias=False)
self.conv2 = SAGEConv(dim, dim, normalize=False, concat=True, bias=False)
self.fc1 = Linear(dim, 1, bias=False)
def forward(self, x, edge_index, batch):
x = F.relu(self.conv1(x, edge_index))
x = self.conv2(x, edge_index)
x = global_mean_pool(x, batch)
x = self.fc1(x)
return torch.sigmoid(x)
class NetGIN(torch.nn.Module):
def __init__(self, num_features, num_classes, dim=10):
super(NetGIN, self).__init__()
nn1 = Sequential(Linear(num_features, dim), ReLU(), Linear(dim, dim))
self.conv1 = GINConv(nn1)
nn2 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
self.conv2 = GINConv(nn2)
nn3 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
self.conv3 = GINConv(nn3)
nn4 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
self.conv4 = GINConv(nn4)
nn5 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
self.conv5 = GINConv(nn5)
self.l1 = Linear(dim, 1, bias=False)
self.l2 = Linear(dim, 1, bias=False)
self.l3 = Linear(dim, 1, bias=False)
self.l4 = Linear(dim, 1, bias=False)
self.l5 = Linear(dim, 1, bias=False)
def forward(self, x, edge_index, batch):
x1 = F.relu(self.conv1(x, edge_index))
x2 = F.relu(self.conv2(x1, edge_index))
x3 = F.relu(self.conv3(x2, edge_index))
x4 = F.relu(self.conv4(x3, edge_index))
x5 = F.relu(self.conv5(x4, edge_index))
m1 = global_mean_pool(x1, batch)
m2 = global_mean_pool(x2, batch)
m3 = global_mean_pool(x3, batch)
m4 = global_mean_pool(x4, batch)
m5 = global_mean_pool(x5, batch)
suma = torch.stack([self.l1(m1),
self.l2(m2),
self.l3(m3),
self.l4(m4),
self.l5(m5)], dim=0)
x = torch.sum(suma, dim=0)
return torch.sigmoid(x)
# %%
class Crossval:
def __init__(self, train_results, val_results, test_results, times=[]):
self.train_loss_mean, self.train_loss_std = self.aggregate_loss(train_results)
self.train_acc_mean, self.train_acc_std = self.aggregate_acc(train_results)
self.val_loss_mean, self.val_loss_std = self.aggregate_loss(val_results)
self.val_acc_mean, self.val_acc_std = self.aggregate_acc(val_results)
self.test_loss_mean, self.test_loss_std = self.aggregate_loss(test_results)
self.test_acc_mean, self.test_acc_std = self.aggregate_acc(test_results)
if times:
self.time_per_step = sum(times) / len(times)
def aggregate_loss(self, results):
losses = [res.loss for res in results]
return np.mean(losses), np.std(losses)
def aggregate_acc(self, results):
accuracies = [res.accuracy for res in results]
return statistics.mean(accuracies), statistics.stdev(accuracies)
class Results:
def __init__(self, pred=[], lab=[], loss_fcn=F.binary_cross_entropy):
self.loss = 0
self.accuracy = 0
if pred and lab:
# pred = [p.detach().numpy() for p in pred]
# lab = [l.detach().numpy() for l in lab]
for (p, l) in zip(pred, lab):
if p.ndim == 1: # batching changes this
self.loss += loss_fcn(p[0], l.double())
else:
self.loss += loss_fcn(p[0][0], l[0].double())
self.loss /= len(lab)
self.loss = float(self.loss)
self.accuracy = self.acc(pred, lab)
def acc(self, output, labels):
correct = 0
for out, lab in zip(output, labels):
if len(out) == 1:
out_ = out
else:
out_ = out[0][0]
pred = 1 if out_ > 0.5 else 0
if lab.ndim == 0:
lab_ = lab
else:
lab_ = lab[0]
if pred == int(lab_):
correct += 1
return correct / len(labels)
def increment(self, other):
self.loss = other.loss
self.accuracy = other.accuracy
class ResultList:
def __init__(self, results, times=None):
self.folds = [to_json(res) for res in results]
if times:
self.times = times
def load_obj(name):
with open(name, 'rb') as f:
return pickle.load(f)
def load_dataset_folds_external(path, suffix="_graphs.pkl", batch=1):
folds = []
for fold in sorted(listdir(path)):
if fold.startswith("fold"):
train_fold = load_obj(path + "/" + fold + "/train" + suffix)
val_fold = load_obj(path + "/" + fold + "/val" + suffix)
test_fold = load_obj(path + "/" + fold + "/test" + suffix)
folds.append([DataLoader(train_fold, batch_size=batch), DataLoader(val_fold, batch_size=batch),
DataLoader(test_fold, batch_size=batch)])
return folds
def load_dataset_folds(path, batch=1, folds=10):
dataset = load_obj(path)
num_features = dataset[0].num_node_features
shuffle(dataset)
skf = StratifiedKFold(n_splits=folds)
labels = [lab.y.numpy() for lab in dataset]
folds = []
for train_idx, test_idx in skf.split(np.zeros(len(dataset)), labels):
train_fold_tmp = [dataset[i] for i in train_idx]
y_train = [lab.y.numpy() for lab in train_fold_tmp]
train_fold, val_fold, _, _ = train_test_split(train_fold_tmp, y_train, stratify=y_train,
test_size=0.1, random_state=1)
test_fold = [dataset[i] for i in test_idx]
folds.append([DataLoader(train_fold, batch_size=batch), DataLoader(val_fold, batch_size=batch),
DataLoader(test_fold, batch_size=batch)])
return folds
def train(model, loader, optimizer, epoch):
model.train()
# if epoch % 51 == 0:
# for param_group in optimizer.param_groups:
# param_group['lr'] = 0.5 * param_group['lr']
loss_all = 0
outputs = []
labels = []
for data in loader:
data = data.to(device)
optimizer.zero_grad()
output = model(data.x, data.edge_index, data.batch)
# loss = F.nll_loss(output, data.y)
loss = F.binary_cross_entropy(output[0][0], data.y[0].double())
if len(data.y) == 1:
outputs.append(output)
labels.append(data.y)
else:
outputs.extend(output)
labels.extend(data.y)
loss.backward()
loss_all += loss.item() * data.num_graphs
optimizer.step()
return Results(outputs, labels)
def test(model, loader):
model.eval()
correct = 0
outputs = []
labels = []
for data in loader:
data = data.to(device)
output = model(data.x, data.edge_index, data.batch)
pred = output.max(dim=1)[1]
# correct += pred.eq(data.y).sum().item()
if len(data.y) == 1:
outputs.append(output)
labels.append(data.y)
else:
outputs.extend(output)
labels.extend(data.y)
return Results(outputs, labels)
def learn(model, train_loader, val_loader, test_loader, writer, steps=1000, lr=0.000015):
#
# input = [dataset[0].x, dataset[0].edge_index]
# # writer.add_graph(model, input)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
best_val_results = Results()
best_val_results.loss = 1e10
cumtime = 0
for epoch in range(0, steps):
start = time.time()
train_results = train(model, train_loader, optimizer, epoch)
# train_loss2, train_acc = test(model, train_loader)
val_results = test(model, val_loader)
test_results = test(model, test_loader)
if val_results.loss < best_val_results.loss:
print(f'improving validation loss to {val_results.loss} at epoch {epoch}')
best_val_results = val_results
best_test_results = test_results
best_train_results = train_results
print(f'storing respective test results with accuracy {best_test_results.accuracy}')
end = time.time()
elapsed = end - start
cumtime += elapsed
print('Epoch: {:03d}, Train Loss: {:.7f}, '
'Train Acc: {:.7f}, Val Acc: {:.7f}, Test Acc: {:.7f}'.format(epoch, train_results.loss,
train_results.accuracy,
val_results.accuracy,
test_results.accuracy) + " elapsed: " + str(
elapsed))
# writer.add_scalar('Loss/train', train_results.loss, epoch)
# writer.add_scalar('Loss/val', val_results.loss, epoch)
# writer.add_scalar('Loss/test', test_results.loss, epoch)
# writer.add_scalar('Accuracy/train', train_results.accuracy, epoch)
# writer.add_scalar('Accuracy/val', val_results.accuracy, epoch)
# writer.add_scalar('Accuracy/test', test_results.accuracy, epoch)
# writer.flush()
return best_train_results, best_val_results, best_test_results, cumtime / steps
def export_fold(content, outpath):
with open(outpath + ".json", "w") as f:
f.writelines(content)
f.close()
def crossvalidate(model_string, folds, outpath, steps=1000, lr=0.000015, dim=10):
# writer = SummaryWriter(outpath)
if outpath == None:
outpath = "./out" # + writer.logdir
train_results = []
val_results = []
test_results = []
times = []
counter = 0
for train_fold, val_fold, test_fold in folds:
model = get_model(model_string, dim)
best_train_results, best_val_results, best_test_results, elapsed = learn(model, train_fold, val_fold, test_fold,
None,
steps, lr)
train_results.append(best_train_results)
val_results.append(best_val_results)
test_results.append(best_test_results)
times.append(elapsed)
train = to_json(ResultList(train_results, times=times))
export_fold(train, outpath + "/train")
test = to_json(ResultList(test_results))
export_fold(test, outpath + "/test")
counter += 1
cross = Crossval(train_results, val_results, test_results, times)
return cross
def get_model(string, dim):
if string == "gcn":
model = NetGCN(num_node_features, num_classes, dim=dim).to(device)
elif string == "gin":
model = NetGIN(num_node_features, num_classes, dim=dim).to(device)
elif string == "gsage":
model = NetGraphSage(num_node_features, num_classes, dim=dim).to(device)
return model
def to_json(obj):
return json.dumps(obj.__dict__, indent=4)
if __name__ == '__main__':
num_classes = 2
parser = argparse.ArgumentParser()
parser.add_argument("-sd", help="path to dataset for learning", type=str)
parser.add_argument("-model", help="type of model (gcn,gin)", type=str)
parser.add_argument("-out", help="path to output folder", type=str)
parser.add_argument("-xval", nargs='?', help="number fo folds for crossval", type=int)
parser.add_argument("-lr", nargs='?', help="learning rate for Adam", type=float)
parser.add_argument("-ts", nargs='?', help="number of training steps", type=int)
parser.add_argument("-batch", nargs='?', help="size of minibatch", type=int)
parser.add_argument("-dim", nargs='?', help="dimension of hidden layers", type=int)
parser.add_argument("-filename", nargs='?', help="filename with example data", type=str)
parser.add_argument("-limit", nargs='?', help="dummy for compatibility wih lrnns", type=str) # dummy
args = parser.parse_args()
steps = args.ts or 1000
folds = args.xval or 10
dim = args.dim or 10
batch = args.batch or 1
print(str(args))
filename = args.filename or "_graphs.pkl"
dataset_folds = load_dataset_folds_external(args.sd, suffix=filename, batch=batch)
num_node_features = dataset_folds[0][0].dataset[0].num_node_features
cross = crossvalidate(args.model.lower(), dataset_folds, args.out, steps, dim=dim)
content = json.dumps(cross.__dict__, indent=4)
outp = args.out # or "./" + writer.logdir
with open(outp + "/crossval.json", "w") as f:
f.writelines(content)
f.close()