Datawhale 图神经网络 Task04数据完整存储与内存的数据集类+节点预测与边预测任务实践
作者：互联网
学习课程：gitee_Datawhale_GNN
学习论坛：Datawhale CLUB
公众号：Datawhale
1.node_classfication
对于节点分类的任务，GAT的得分是0.765，GCN的得分是0.779。
不同的层数和不同的out_channels通过更改hidden_channels_list的数值。
2.edge_classification
由于之前的学习已经保存了"Cora"数据集，所以将Planetoild中的路径修改。
参考代码
<code: node_classification.ipynb>
import os.path as osp
import torch
import torch.nn.functional as F
from torch_geometric.data import (InMemoryDataset, download_url)
from torch_geometric.nn import GATConv, Sequential
from torch_geometric.transforms import NormalizeFeatures
from torch_geometric.io import read_planetoid_data
from torch.nn import Linear, ReLU
class PlanetoidPubMed(InMemoryDataset):
r"""The citation network datasets "PubMed" from the
`"Revisiting Semi-Supervised Learning with Graph Embeddings"
<https://arxiv.org/abs/1603.08861>`_ paper.
Nodes represent documents and edges represent citation links.
Training, validation and test splits are given by binary masks.
Args:
root (string): Root directory where the dataset should be saved.
split (string): The type of dataset split
(:obj:`"public"`, :obj:`"full"`, :obj:`"random"`).
If set to :obj:`"public"`, the split will be the public fixed split
from the
`"Revisiting Semi-Supervised Learning with Graph Embeddings"
<https://arxiv.org/abs/1603.08861>`_ paper.
If set to :obj:`"full"`, all nodes except those in the validation
and test sets will be used for training (as in the
`"FastGCN: Fast Learning with Graph Convolutional Networks via
Importance Sampling" <https://arxiv.org/abs/1801.10247>`_ paper).
If set to :obj:`"random"`, train, validation, and test sets will be
randomly generated, according to :obj:`num_train_per_class`,
:obj:`num_val` and :obj:`num_test`. (default: :obj:`"public"`)
num_train_per_class (int, optional): The number of training samples
per class in case of :obj:`"random"` split. (default: :obj:`20`)
num_val (int, optional): The number of validation samples in case of
:obj:`"random"` split. (default: :obj:`500`)
num_test (int, optional): The number of test samples in case of
:obj:`"random"` split. (default: :obj:`1000`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
"""
url = 'https://github.com/kimiyoung/planetoid/raw/master/data'
def __init__(self, root, split="public", num_train_per_class=20,
num_val=500, num_test=1000, transform=None,
pre_transform=None):
super(PlanetoidPubMed, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
self.split = split
assert self.split in ['public', 'full', 'random']
if split == 'full':
data = self.get(0)
data.train_mask.fill_(True)
data.train_mask[data.val_mask | data.test_mask] = False
self.data, self.slices = self.collate([data])
elif split == 'random':
data = self.get(0)
data.train_mask.fill_(False)
for c in range(self.num_classes):
idx = (data.y == c).nonzero(as_tuple=False).view(-1)
idx = idx[torch.randperm(idx.size(0))[:num_train_per_class]]
data.train_mask[idx] = True
remaining = (~data.train_mask).nonzero(as_tuple=False).view(-1)
remaining = remaining[torch.randperm(remaining.size(0))]
data.val_mask.fill_(False)
data.val_mask[remaining[:num_val]] = True
data.test_mask.fill_(False)
data.test_mask[remaining[num_val:num_val + num_test]] = True
self.data, self.slices = self.collate([data])
@property
def raw_dir(self):
return osp.join(self.root, 'raw')
@property
def processed_dir(self):
return osp.join(self.root, 'processed')
@property
def raw_file_names(self):
names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index']
return ['ind.pubmed.{}'.format(name) for name in names]
@property
def processed_file_names(self):
return 'data.pt'
def download(self):
for name in self.raw_file_names:
download_url('{}/{}'.format(self.url, name), self.raw_dir)
def process(self):
data = read_planetoid_data(self.raw_dir, 'pubmed')
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])
def __repr__(self):
return '{}()'.format(self.name)
dataset = PlanetoidPubMed(root='data/PlanetoidPubMed/', transform=NormalizeFeatures())
print('dataset.num_features:', dataset.num_features)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = dataset[0].to(device)
def train():
model.train()
optimizer.zero_grad() # Clear gradients.
out = model(data.x, data.edge_index) # Perform a single forward pass.
# Compute the loss solely based on the training nodes.
loss = criterion(out[data.train_mask], data.y[data.train_mask])
loss.backward() # Derive gradients.
optimizer.step() # Update parameters based on gradients.
return loss
def test():
model.eval()
out = model(data.x, data.edge_index)
pred = out.argmax(dim=1) # Use the class with highest probability.
test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels.
test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions.
return test_acc
class GAT(torch.nn.Module):
def __init__(self, num_features, hidden_channels_list, num_classes):
super(GAT, self).__init__()
torch.manual_seed(12345)
hns = [num_features] + hidden_channels_list
conv_list = []
for idx in range(len(hidden_channels_list)):
conv_list.append((GATConv(hns[idx], hns[idx+1]), 'x, edge_index -> x'))
conv_list.append(ReLU(inplace=True),)
self.convseq = Sequential('x, edge_index', conv_list)
self.linear = Linear(hidden_channels_list[-1], num_classes)
def forward(self, x, edge_index):
x = self.convseq(x, edge_index)
x = F.dropout(x, p=0.5, training=self.training)
x = self.linear(x)
return x
model = GAT(num_features=dataset.num_features, hidden_channels_list=[200, 100], num_classes=dataset.num_classes).to(device)
print(model)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
criterion = torch.nn.CrossEntropyLoss()
for epoch in range(1, 201):
loss = train()
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')
test_acc = test()
print(f'Test Accuracy: {test_acc:.4f}')
code:edge_classification.ipynb
import os.path as osp
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from sklearn.metrics import roc_auc_score
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv
from torch_geometric.utils import negative_sampling, train_test_split_edges
class Net(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super(Net, self).__init__()
self.conv1 = GCNConv(in_channels, 128)
self.conv2 = GCNConv(128, out_channels)
def encode(self, x, edge_index):
x = self.conv1(x, edge_index)
x = x.relu()
return self.conv2(x, edge_index)
def decode(self, z, pos_edge_index, neg_edge_index):
edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
return (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
def decode_all(self, z):
prob_adj = z @ z.t()
return (prob_adj > 0).nonzero(as_tuple=False).t()
def get_link_labels(pos_edge_index, neg_edge_index):
num_links = pos_edge_index.size(1) + neg_edge_index.size(1)
link_labels = torch.zeros(num_links, dtype=torch.float)
link_labels[:pos_edge_index.size(1)] = 1.
return link_labels
def train(data, model, optimizer):
model.train()
neg_edge_index = negative_sampling(
edge_index=data.train_pos_edge_index,
num_nodes=data.num_nodes,
num_neg_samples=data.train_pos_edge_index.size(1))
train_neg_edge_set = set(map(tuple, neg_edge_index.T.tolist()))
val_pos_edge_set = set(map(tuple, data.val_pos_edge_index.T.tolist()))
test_pos_edge_set = set(map(tuple, data.test_pos_edge_index.T.tolist()))
if (len(train_neg_edge_set & val_pos_edge_set) > 0) or (len(train_neg_edge_set & test_pos_edge_set) > 0):
print('wrong!')
optimizer.zero_grad()
z = model.encode(data.x, data.train_pos_edge_index)
link_logits = model.decode(z, data.train_pos_edge_index, neg_edge_index)
link_labels = get_link_labels(data.train_pos_edge_index, neg_edge_index).to(data.x.device)
loss = F.binary_cross_entropy_with_logits(link_logits, link_labels)
loss.backward()
optimizer.step()
return loss
@torch.no_grad()
def test(data, model):
model.eval()
z = model.encode(data.x, data.train_pos_edge_index)
results = []
for prefix in ['val', 'test']:
pos_edge_index = data[f'{prefix}_pos_edge_index']
neg_edge_index = data[f'{prefix}_neg_edge_index']
link_logits = model.decode(z, pos_edge_index, neg_edge_index)
link_probs = link_logits.sigmoid()
link_labels = get_link_labels(pos_edge_index, neg_edge_index)
results.append(roc_auc_score(link_labels.cpu(), link_probs.cpu()))
return results
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = 'Cora'
# path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
# dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())
dataset = Planetoid('data/Planetoid', dataset, transform=T.NormalizeFeatures())
data = dataset[0]
ground_truth_edge_index = data.edge_index.to(device)
data.train_mask = data.val_mask = data.test_mask = data.y = None
data = train_test_split_edges(data)
data = data.to(device)
model = Net(dataset.num_features, 64).to(device)
optimizer = torch.optim.Adam(params=model.parameters(), lr=0.01)
best_val_auc = test_auc = 0
for epoch in range(1, 101):
loss = train(data, model, optimizer)
val_auc, tmp_test_auc = test(data, model)
if val_auc > best_val_auc:
best_val_auc = val_auc
test_auc = tmp_test_auc
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Val: {val_auc:.4f}, '
f'Test: {test_auc:.4f}')
z = model.encode(data.x, data.train_pos_edge_index)
final_edge_index = model.decode_all(z)
if __name__ == "__main__":
main()
标签：index,torch,self,集类,Datawhale,edge,test,data,Task04 来源： https://blog.csdn.net/Andrew_zjc/article/details/118277303