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train_mlp.py
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train_mlp.py
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from dataset import *
from model import *
from utils import *
from evaluation import *
import argparse
from tqdm import tqdm
from torch import tensor
import warnings
warnings.filterwarnings('ignore')
import math
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
from torch.optim.lr_scheduler import ExponentialLR
class MLP(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(MLP, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
def reset_parameters(self):
self.fc1.reset_parameters()
self.fc2.reset_parameters()
self.fc3.reset_parameters()
def run(data, args):
pbar = tqdm(range(args.runs), unit='run')
criterion = nn.BCELoss()
acc, f1, auc_roc, parity, equality = np.zeros(args.runs), np.zeros(
args.runs), np.zeros(args.runs), np.zeros(args.runs), np.zeros(args.runs)
data = data.to(args.device)
discriminator = MLP_discriminator(args).to(args.device)
optimizer_d = torch.optim.Adam([
dict(params=discriminator.lin.parameters(), weight_decay=args.d_wd)], lr=args.d_lr)
classifier = MLP_classifier(args).to(args.device)
optimizer_c = torch.optim.Adam([
dict(params=classifier.lin.parameters(), weight_decay=args.c_wd)], lr=args.c_lr)
if(args.encoder == 'MLP'):
encoder = MLP_encoder(args).to(args.device)
optimizer_e = torch.optim.Adam([
dict(params=encoder.lin.parameters(), weight_decay=args.e_wd)], lr=args.e_lr)
elif(args.encoder == 'GCN'):
if args.prop == 'scatter':
encoder = GCN_encoder_scatter(args).to(args.device)
else:
encoder = GCN_encoder_spmm(args).to(args.device)
optimizer_e = torch.optim.Adam([
dict(params=encoder.lin.parameters(), weight_decay=args.e_wd),
dict(params=encoder.bias, weight_decay=args.e_wd)], lr=args.e_lr)
elif(args.encoder == 'GIN'):
encoder = GIN_encoder(args).to(args.device)
optimizer_e = torch.optim.Adam([
dict(params=encoder.conv.parameters(), weight_decay=args.e_wd)], lr=args.e_lr)
elif(args.encoder == 'SAGE'):
encoder = SAGE_encoder(args).to(args.device)
optimizer_e = torch.optim.Adam([
dict(params=encoder.conv1.parameters(), weight_decay=args.e_wd),
dict(params=encoder.conv2.parameters(), weight_decay=args.e_wd)], lr=args.e_lr)
if os.path.isfile(args.dataset+'_hadj.pt'):
print('########## sample already done #############')
new_adj = torch.load(args.dataset+'_hadj.pt')
else:
data.adj = data.adj - sp.eye(data.adj.shape[0])
print('sample begin')
new_adj = torch.zeros((data.adj.shape[0], data.adj.shape[0])).int()
for i in tqdm(range(data.adj.shape[0])):
neighbor = torch.tensor(data.adj[i].nonzero()).to(args.device)
mask = (data.sens[neighbor[1]] != data.sens[i])
h_nei_idx = neighbor[1][mask]
new_adj[i, h_nei_idx] = 1
print('select done')
torch.save(new_adj, args.dataset+'_hadj.pt')
c_X = data.x
new_adj = new_adj.cpu()
adj = torch.from_numpy(data.adj.toarray()).cpu()
adj_new = adj - new_adj
num_homo_neighbors = adj_new.sum(dim=1)
num_hetero_neighbors = new_adj.sum(dim=1)
diffs = num_homo_neighbors - num_hetero_neighbors + 1
lam = diffs.reshape(-1, 1).to(args.device, dtype=torch.float)
deg = np.sum(new_adj.numpy(), axis=1)
deg = torch.from_numpy(deg).cpu()
indices = torch.nonzero(new_adj)
values = new_adj[indices[:, 0], indices[:, 1]]
mat = torch.sparse_coo_tensor(indices.t(), values, new_adj.shape).float().cpu()
h_X = torch.spmm(mat,(data.x).cpu()) / deg.unsqueeze(-1)
mask = torch.any(torch.isnan(h_X), dim=1)
h_X = h_X[~mask].to(args.device)
c_X = c_X[~mask].to(args.device)
print('node avg degree:',data.edge_index.shape[1]/data.adj.shape[0],' heteroneighbor degree mean:',deg.float().mean(),' node without heteroneghbor:',(deg == 0).sum())
deg_norm = deg
deg_norm[deg_norm == 0] = 1
deg_norm = deg_norm.to(args.device)
model = MLP(len(data.x[0]),args.hidden,len(data.x[0])).to(args.device)
optimizer = torch.optim.Adam([
dict(params=model.parameters(), weight_decay=0.005)], lr=0.1)
from sklearn.model_selection import train_test_split
indices = np.arange(c_X.shape[0]) # help for check the index after split
[indices_train, indices_test, y_train, y_test] = train_test_split(indices, indices, test_size=0.1)
X_train, X_test, y_train, y_test = c_X[indices_train], c_X[indices_test], h_X[indices_train], h_X[indices_test]
for count in pbar:
seed_everything(count + args.seed)
discriminator.reset_parameters()
classifier.reset_parameters()
encoder.reset_parameters()
model.reset_parameters()
best_val_tradeoff = 0
best_val_loss = math.inf
for epoch in range(0, args.epochs):
# train mlp
for m_epoch in range(0, args.m_epoch):
model.train() # prep model for training
optimizer.zero_grad()
output = model(X_train)
train_loss = torch.nn.functional.mse_loss(output, y_train)
# backward pass: compute gradient of the loss with respect to model parameters
train_loss.backward()
# perform a single optimization step (parameter update)
optimizer.step()
model.eval()
output = model(X_test)
valid_loss = torch.nn.functional.mse_loss(output, y_test)
if valid_loss < best_val_loss:
best_val_loss = valid_loss
best_mlp_state = model.state_dict()
# if m_epoch % 5 == 0:
# print('Epoch: {} \tTraining Loss: {:.6f}\tValidation Loss:{:.6f}'.format(
# m_epoch,
# train_loss,
# valid_loss
# ))
model.load_state_dict(best_mlp_state)
model.eval()
# train classifier
classifier.train()
encoder.train()
for epoch_c in range(0, args.c_epochs):
optimizer_c.zero_grad()
optimizer_e.zero_grad()
optimizer.zero_grad()
h = encoder(data.x + lam * args.delta * model(data.x), data.edge_index, data.adj_norm_sp)
# h = encoder(data.x + args.delta * model(data.x), data.edge_index, data.adj_norm_sp)
output = classifier(h)
loss_c = F.binary_cross_entropy_with_logits(
output[data.train_mask], data.y[data.train_mask].unsqueeze(1).to(args.device))
loss_c.backward()
optimizer_e.step()
optimizer_c.step()
# optimizer.step()
if args.d == 'yes':
# train discriminator to recognize the sensitive group
discriminator.train()
encoder.train()
for epoch_d in range(0, args.d_epochs):
optimizer_d.zero_grad()
optimizer_e.zero_grad()
optimizer.zero_grad()
h = encoder((data.x + lam * args.delta * model(data.x)) , data.edge_index, data.adj_norm_sp)
# h = encoder(data.x + args.delta * model(data.x), data.edge_index, data.adj_norm_sp)
output = discriminator(h)
loss_d = criterion(output.view(-1),
data.x[:, args.sens_idx])
loss_d.backward()
optimizer_d.step()
optimizer_e.step()
# evaluate classifier
accs, auc_rocs, F1s, tmp_parity, tmp_equality = evaluate(
data.x, classifier, discriminator, encoder, data, args)
# print('*****************')
# print(epoch, 'Acc:', accs['val'], 'AUC_ROC:', auc_rocs['val'], 'F1:', F1s['val'],
# 'Parity:', tmp_parity['val'], 'Equality:', tmp_equality['val'],'tradeoff:',auc_rocs['val']+F1s['val'] + accs['val'] - args.alpha * (tmp_parity['val'] + tmp_equality['val']))
print(epoch, 'Acc:', accs['test'], 'F1:', F1s['test'],
'Parity:', tmp_parity['test'], 'Equality:', tmp_equality['test'],'tradeoff:',auc_rocs['test']+F1s['test'] + accs['test'] - args.alpha * (tmp_parity['test'] + tmp_equality['test']))
# if auc_rocs['val'] + F1s['val'] + accs['val'] - args.alpha * (tmp_parity['val'] + tmp_equality['val']) > best_val_tradeoff:
# test_acc = accs['test']
# test_auc_roc = auc_rocs['test']
# test_f1 = F1s['test']
# test_parity, test_equality = tmp_parity['test'], tmp_equality['test']
# best_val_tradeoff = auc_rocs['val'] + F1s['val'] + \
# accs['val'] - (tmp_parity['val'] + tmp_equality['val'])
if auc_rocs['val'] + F1s['val'] + accs['val'] - args.alpha * (tmp_parity['val'] + tmp_equality['val']) > best_val_tradeoff:
test_acc = accs['test']
test_auc_roc = auc_rocs['test']
test_f1 = F1s['test']
test_parity, test_equality = tmp_parity['test'], tmp_equality['test']
print('best_val_tradeoff',epoch)
best_val_tradeoff = auc_rocs['val'] + F1s['val'] + accs['val'] - args.alpha * (tmp_parity['val'] + tmp_equality['val'])
# print('=====VALIDATION=====', epoch, epoch_g)
# print('Utility:', auc_rocs['val'] + F1s['val'] + accs['val'],
# 'Fairness:', tmp_parity['val'] + tmp_equality['val'])
# print('=====VALIDATION-BEST=====', epoch, epoch_g)
# print('Utility:', args.best_val_model_utility,
# 'Fairness:', args.best_val_fair)
# print('=====TEST=====', epoch)
# print('Acc:', test_acc, 'AUC_ROC:', test_auc_roc, 'F1:', test_f1,
# 'Parity:', test_parity, 'Equality:', test_equality)
# print('=====epoch:{}====='.format(epoch))
# print('sens_acc:', (((output.view(-1) > 0.5) & (data.x[:, args.sens_idx] == 1)).sum() + ((output.view(-1) < 0.5) &
# (data.x[:, args.sens_idx] == 0)).sum()).item() / len(data.y))
acc[count] = test_acc
f1[count] = test_f1
auc_roc[count] = test_auc_roc
parity[count] = test_parity
equality[count] = test_equality
# print('auc_roc:', np.mean(auc_roc[:(count + 1)]))
# print('f1:', np.mean(f1[:(count + 1)]))
# print('acc:', np.mean(acc[:(count + 1)]))
# print('Statistical parity:', np.mean(parity[:(count + 1)]))
# print('Equal Opportunity:', np.mean(equality[:(count + 1)]))
return acc, f1, auc_roc, parity, equality
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='german')
parser.add_argument('--runs', type=int, default=5)
parser.add_argument('--epochs', type=int, default=20)
parser.add_argument('--d_epochs', type=int, default=5)
parser.add_argument('--c_epochs', type=int, default=5)
parser.add_argument('--d_lr', type=float, default=0.1)
parser.add_argument('--d_wd', type=float, default=0)
parser.add_argument('--c_lr', type=float, default=0.1)
parser.add_argument('--c_wd', type=float, default=0)
parser.add_argument('--e_lr', type=float, default=0.1)
parser.add_argument('--e_wd', type=float, default=0)
parser.add_argument('--early_stopping', type=int, default=5)
parser.add_argument('--prop', type=str, default='scatter')
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--hidden', type=int, default=16)
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--encoder', type=str, default='GIN')
parser.add_argument('--alpha', type=float, default=1)
parser.add_argument('--delta', type=float, default=5)
parser.add_argument('--m_epoch', type=int, default=10)
parser.add_argument('--d', type=str, default='no')
args = parser.parse_args()
args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data, args.sens_idx, args.x_min, args.x_max = get_dataset(args.dataset)
args.num_features, args.num_classes = data.x.shape[1], 2-1 # binary classes are 0,1
acc, f1, auc_roc, parity, equality = run(data, args)
print('======' + args.dataset + args.encoder + '======')
print('Acc:', round(np.mean(acc) * 100,2), '±' ,round(np.std(acc) * 100,2), sep='')
print('f1:', round(np.mean(f1) * 100,2), '±' ,round(np.std(f1) * 100,2), sep='')
print('parity:', round(np.mean(parity) * 100,2), '±', round(np.std(parity) * 100,2), sep='')
print('equality:', round(np.mean(equality) * 100,2), '±', round(np.std(equality) * 100,2), sep='')