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from data import load_mumin_graph
from model import HeteroGraphSAGE
from pathlib import Path
import torch
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as D
from torch.optim.lr_scheduler import LinearLR
import torchmetrics as tm
from dgl.dataloading.neighbor import MultiLayerNeighborSampler
from dgl.dataloading.pytorch import NodeDataLoader
import dgl
import logging
import datetime as dt
from tqdm.auto import tqdm
from mumin import load_dgl_graph, save_dgl_graph
from typing import Dict
logger = logging.getLogger(__name__)
def train_graph_model(task: str,
size: str,
num_epochs: int = 300,
random_split: bool = False,
**_) -> Dict[str, Dict[str, float]]:
'''Train a heterogeneous GraphConv model on the MuMiN dataset.
Args:
task (str):
The task to consider, which can be either 'tweet' or 'claim',
corresponding to doing thread-level or claim-level node
classification.
size (str):
The size of the dataset to use.
num_epochs (int, optional):
The number of epochs to train for. Defaults to 300.
random_split (bool, optional):
Whether a random train/val/test split of the data should be
performed (with a fixed random seed). If not then the claim cluster
splits will be used. Defaults to False.
dict:
The results of the training, with keys 'train', 'val' and 'split',
with dictionaries with the split scores as values.
'''
# Set random seeds
torch.manual_seed(4242)
dgl.seed(4242)
# Set config
config = dict(hidden_dim=1024,
input_dropout=0.2,
dropout=0.2,
size=size,
task=task,
lr=3e-4,
betas=(0.9, 0.999),
pos_weight=20.)
# Set up PyTorch device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Set up graph path
graph_path = Path(f'dgl-graph-{size}.bin')
# Load the graph if it exists
if graph_path.exists():
graph = load_dgl_graph(graph_path)
# Otherwise, build the graph and save it
else:
# Build the graph
graph = load_mumin_graph(size=size)
# Save the graph
save_dgl_graph(graph, graph_path)
# Store labels and masks
train_mask = graph.nodes[task].data['train_mask'].bool()
val_mask = graph.nodes[task].data['val_mask'].bool()
test_mask = graph.nodes[task].data['test_mask'].bool()
# Initialise dictionary with feature dimensions
dims = {ntype: graph.nodes[ntype].data['feat'].shape[-1]
for ntype in graph.ntypes}
feat_dict = {rel: (dims[rel[0]], dims[rel[2]])
for rel in graph.canonical_etypes}
# Initialise model
model = HeteroGraphSAGE(input_dropout=0.2,
dropout=0.2,
hidden_dim=1024,
feat_dict=feat_dict,
task=task)
model.to(device)
model.train()
# Enumerate the nodes with the labels, for performing train/val/test splits
node_enum = torch.arange(graph.num_nodes(task))
# If we are performing a random split then split the dataset into a
# 80/10/10 train/val/test split, with a fixed random seed
if random_split:
# Set a random seed through a PyTorch Generator
torch_gen = torch.Generator().manual_seed(4242)
# Compute the number of train/val/test samples
num_train = int(0.8 * graph.num_nodes(task))
num_val = int(0.1 * graph.num_nodes(task))
num_test = graph.num_nodes(task) - (num_train + num_val)
nums = [num_train, num_val, num_test]
# Split the data, using the PyTorch generator for reproducibility
train_nids, val_nids, test_nids = D.random_split(dataset=node_enum,
lengths=nums,
generator=torch_gen)
# Store the resulting node IDs
train_nids = {task: train_nids}
val_nids = {task: val_nids}
test_nids = {task: test_nids}
# If we are not performing a random split we're performing a split based on
# the claim clusters of the data. This means that the different splits will
# belong to different events, thus making the task harder.
else:
train_nids = {task: node_enum[train_mask].int()}
val_nids = {task: node_enum[val_mask].int()}
test_nids = {task: node_enum[test_mask].int()}
# Set up the sampler
sampler = MultiLayerNeighborSampler([100, 100], replace=False)
# Set up the dataloaders
train_dataloader = NodeDataLoader(g=graph,
nids=train_nids,
block_sampler=sampler,
batch_size=32,
shuffle=True,
drop_last=False,
num_workers=1)
val_dataloader = NodeDataLoader(g=graph,
nids=val_nids,
block_sampler=sampler,
batch_size=1000000,
shuffle=False,
drop_last=False,
num_workers=1)
test_dataloader = NodeDataLoader(g=graph,
nids=test_nids,
block_sampler=sampler,
batch_size=1000000,
shuffle=False,
drop_last=False,
num_workers=1)
# Set up pos_weight
pos_weight_tensor = torch.tensor(20.).to(device)
# Set up path to state dict
datetime = dt.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
Path('models').mkdir(exist_ok=True)
model_dir = Path('models') / f'{datetime}-{task}-model-{size}'
model_dir.mkdir(exist_ok=True)
# Initialise optimiser
opt = optim.AdamW(model.parameters(), lr=3e-4, betas=(0.9, 0.999))
# Initialise learning rate scheduler
scheduler = LinearLR(optimizer=opt,
start_factor=1.,
end_factor=1e-7 / 3e-4,
total_iters=100)
# Initialise scorer
scorer = tm.F1Score(num_classes=2, average='none').to(device)
# Initialise progress bar
epoch_pbar = tqdm(range(num_epochs), desc='Training')
for epoch in epoch_pbar:
# Reset metrics
train_loss = 0.0
train_misinformation_f1 = 0.0
train_factual_f1 = 0.0
val_loss = 0.0
val_misinformation_f1 = 0.0
val_factual_f1 = 0.0
# Reset metrics
scorer.reset()
# Train model
model.train()
for _, _, blocks in train_dataloader:
# Reset the gradients
opt.zero_grad()
# Ensure that `blocks` are on the correct device
blocks = [block.to(device) for block in blocks]
# Get the input features and the output labels
input_feats = {n: feat.float()
for n, feat in blocks[0].srcdata['feat'].items()}
output_labels = blocks[-1].dstdata['label'][task].to(device)
# Forward propagation
logits = model(blocks, input_feats).squeeze()
# Compute loss
loss = F.binary_cross_entropy_with_logits(
input=logits,
target=output_labels.float(),
pos_weight=pos_weight_tensor
)
# Compute training metrics
scorer(logits.ge(0), output_labels)
# Backward propagation
loss.backward()
# Update gradients
opt.step()
# Store the training loss
train_loss += float(loss)
# Divide the training loss by the number of batches
train_loss /= len(train_dataloader)
# Compute the training metrics
train_f1s = scorer.compute()
train_misinformation_f1 = train_f1s[0].item()
train_factual_f1 = train_f1s[1].item()
# Reset the metrics
scorer.reset()
# Evaluate model
model.eval()
for _, _, blocks in val_dataloader:
with torch.no_grad():
# Ensure that `blocks` are on the correct device
blocks = [block.to(device) for block in blocks]
# Get the input features and the output labels
input_feats = {n: f.float()
for n, f in blocks[0].srcdata['feat'].items()}
output_labels = blocks[-1].dstdata['label'][task].to(device)
# Forward propagation
logits = model(blocks, input_feats).squeeze()
# Compute validation loss
loss = F.binary_cross_entropy_with_logits(
input=logits,
target=output_labels.float(),
pos_weight=pos_weight_tensor
)
# Compute validation metrics
scorer(logits.ge(0), output_labels)
# Store the validation loss
val_loss += float(loss)
# Divide the validation loss by the number of batches
val_loss /= len(val_dataloader)
# Compute the validation metrics
val_f1s = scorer.compute()
val_misinformation_f1 = val_f1s[0].item()
val_factual_f1 = val_f1s[1].item()
# Gather statistics to be logged
stats = [
('train_loss', train_loss),
('train_misinformation_f1', train_misinformation_f1),
('train_factual_f1', train_factual_f1),
('val_loss', val_loss),
('val_misinformation_f1', val_misinformation_f1),
('val_factual_f1', val_factual_f1),
('learning_rate', opt.param_groups[0]['lr'])
]
# Report and log statistics
config['epoch'] = epoch
for statistic, value in stats:
config[statistic] = value
# Update progress bar description
desc = (f'Training - '
f'loss {train_loss:.3f} - '
f'factual_f1 {train_factual_f1:.3f} - '
f'misinfo_f1 {train_misinformation_f1:.3f} - '
f'val_loss {val_loss:.3f} - '
f'val_factual_f1 {val_factual_f1:.3f} - '
f'val_misinfo_f1 {val_misinformation_f1:.3f}')
epoch_pbar.set_description(desc)
# Update learning rate
scheduler.step()
# Close progress bar
epoch_pbar.close()
# Reset loss
val_loss = 0.0
test_loss = 0.0
# Reset metrics
scorer.reset()
# Final evaluation on the validation set
model.eval()
for _, _, blocks in tqdm(val_dataloader, desc='Evaluating'):
with torch.no_grad():
# Ensure that `blocks` are on the correct device
blocks = [block.to(device) for block in blocks]
# Get the input features and the output labels
input_feats = {n: f.float()
for n, f in blocks[0].srcdata['feat'].items()}
output_labels = blocks[-1].dstdata['label'][task].to(device)
# Forward propagation
logits = model(blocks, input_feats).squeeze()
# Compute validation loss
loss = F.binary_cross_entropy_with_logits(
input=logits,
target=output_labels.float(),
pos_weight=pos_weight_tensor
)
# Compute validation metrics
scorer(logits.ge(0), output_labels)
# Store the validation loss
val_loss += float(loss)
# Divide the validation loss by the number of batches
val_loss /= len(val_dataloader)
# Compute the validation metrics
val_f1s = scorer.compute()
val_misinformation_f1 = val_f1s[0].item()
val_factual_f1 = val_f1s[1].item()
# Reset the metrics
scorer.reset()
# Final evaluation on the test set
model.eval()
for _, _, blocks in tqdm(test_dataloader, desc='Evaluating'):
with torch.no_grad():
# Ensure that `blocks` are on the correct device
blocks = [block.to(device) for block in blocks]
# Get the input features and the output labels
input_feats = {n: f.float()
for n, f in blocks[0].srcdata['feat'].items()}
output_labels = blocks[-1].dstdata['label'][task].to(device)
# Forward propagation
logits = model(blocks, input_feats).squeeze()
# Compute test loss
loss = F.binary_cross_entropy_with_logits(
input=logits,
target=output_labels.float(),
pos_weight=pos_weight_tensor
)
# Compute test metrics
scorer(logits.ge(0), output_labels)
# Store the test loss
test_loss += float(loss)
# Divide the test loss by the number of batches
test_loss /= len(test_dataloader)
# Compute the test metrics
test_f1s = scorer.compute()
test_misinformation_f1 = test_f1s[0].item()
test_factual_f1 = test_f1s[1].item()
# Gather statistics to be logged
results = {
'train': {
'loss': train_loss,
'factual_f1': train_factual_f1,
'misinformation_f1': train_misinformation_f1
},
'val': {
'loss': val_loss,
'factual_f1': val_factual_f1,
'misinformation_f1': val_misinformation_f1
},
'test': {
'loss': test_loss,
'factual_f1': test_factual_f1,
'misinformation_f1': test_misinformation_f1
}
}
return results
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