geniepath.py

import argparse
import os.path as osp
import time

import torch
from sklearn.metrics import f1_score

from torch_geometric.datasets import PPI
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GATConv

parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='GeniePathLazy')
args = parser.parse_args()
assert args.model in ['GeniePath', 'GeniePathLazy']

path = osp.join(osp.dirname(osp.realpath(__file__)), 'data', 'PPI')
train_dataset = PPI(path, split='train')
val_dataset = PPI(path, split='val')
test_dataset = PPI(path, split='test')
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False)

dim = 256
lstm_hidden = 256
layer_num = 4


class Breadth(torch.nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.gatconv = GATConv(in_dim, out_dim, heads=1)

    def forward(self, x, edge_index):
        x = torch.tanh(self.gatconv(x, edge_index))
        return x


class Depth(torch.nn.Module):
    def __init__(self, in_dim, hidden):
        super().__init__()
        self.lstm = torch.nn.LSTM(in_dim, hidden, 1, bias=False)

    def forward(self, x, h, c):
        x, (h, c) = self.lstm(x, (h, c))
        return x, (h, c)


class GeniePathLayer(torch.nn.Module):
    def __init__(self, in_dim):
        super().__init__()
        self.breadth_func = Breadth(in_dim, dim)
        self.depth_func = Depth(dim, lstm_hidden)

    def forward(self, x, edge_index, h, c):
        x = self.breadth_func(x, edge_index)
        x = x[None, :]
        x, (h, c) = self.depth_func(x, h, c)
        x = x[0]
        return x, (h, c)


class GeniePath(torch.nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.lin1 = torch.nn.Linear(in_dim, dim)
        self.gplayers = torch.nn.ModuleList(
            [GeniePathLayer(dim) for i in range(layer_num)])
        self.lin2 = torch.nn.Linear(dim, out_dim)

    def forward(self, x, edge_index):
        x = self.lin1(x)
        h = torch.zeros(1, x.shape[0], lstm_hidden, device=x.device)
        c = torch.zeros(1, x.shape[0], lstm_hidden, device=x.device)
        for i, l in enumerate(self.gplayers):
            x, (h, c) = self.gplayers[i](x, edge_index, h, c)
        x = self.lin2(x)
        return x


class GeniePathLazy(torch.nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.lin1 = torch.nn.Linear(in_dim, dim)
        self.breadths = torch.nn.ModuleList(
            [Breadth(dim, dim) for i in range(layer_num)])
        self.depths = torch.nn.ModuleList(
            [Depth(dim * 2, lstm_hidden) for i in range(layer_num)])
        self.lin2 = torch.nn.Linear(dim, out_dim)

    def forward(self, x, edge_index):
        x = self.lin1(x)
        h = torch.zeros(1, x.shape[0], lstm_hidden, device=x.device)
        c = torch.zeros(1, x.shape[0], lstm_hidden, device=x.device)
        h_tmps = []
        for i, l in enumerate(self.breadths):
            h_tmps.append(self.breadths[i](x, edge_index))
        x = x[None, :]
        for i, l in enumerate(self.depths):
            in_cat = torch.cat((h_tmps[i][None, :], x), -1)
            x, (h, c) = self.depths[i](in_cat, h, c)
        x = self.lin2(x[0])
        return x


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
kwargs = {'GeniePath': GeniePath, 'GeniePathLazy': GeniePathLazy}
model = kwargs[args.model](train_dataset.num_features,
                           train_dataset.num_classes).to(device)
loss_op = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.005)


def train():
    model.train()

    total_loss = 0
    for data in train_loader:
        num_graphs = data.num_graphs
        data.batch = None
        data = data.to(device)
        optimizer.zero_grad()
        loss = loss_op(model(data.x, data.edge_index), data.y)
        total_loss += loss.item() * num_graphs
        loss.backward()
        optimizer.step()
    return total_loss / len(train_loader.dataset)


def test(loader):
    model.eval()

    ys, preds = [], []
    for data in loader:
        ys.append(data.y)
        with torch.no_grad():
            out = model(data.x.to(device), data.edge_index.to(device))
        preds.append((out > 0).float().cpu())

    y, pred = torch.cat(ys, dim=0).numpy(), torch.cat(preds, dim=0).numpy()
    return f1_score(y, pred, average='micro') if pred.sum() > 0 else 0


times = []
for epoch in range(1, 101):
    start = time.time()
    loss = train()
    val_f1 = test(val_loader)
    test_f1 = test(test_loader)
    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Val: {val_f1:.4f}, '
          f'Test: {test_f1:.4f}')
    times.append(time.time() - start)
print(f"Median time per epoch: {torch.tensor(times).median():.4f}s")