import torch.nn.functional as F
import torch.nn as nn
from . import BaseTask, register_task
from ..dataset import build_dataset
from ..utils import Evaluator
[文档]@register_task("edge_classification")
class EdgeClassification(BaseTask):
r"""
Edge classification tasks.
Attributes
-----------
dataset : EdgeClassificationDataset
Task-related dataset
evaluator : Evaluator
offer evaluation metric
Methods
---------
get_graph :
return a graph
get_loss_fn :
return a loss function
"""
def __init__(self, args):
super(EdgeClassification, self).__init__()
self.logger = args.logger
self.dataset = build_dataset(args.dataset, 'edge_classification', logger=self.logger)
# self.evaluator = Evaluator()
self.logger = args.logger
if hasattr(args, 'validation'):
self.train_idx, self.val_idx, self.test_idx = self.dataset.get_split(args.validation)
else:
self.train_idx, self.val_idx, self.test_idx = self.dataset.get_split(validation=False)
self.evaluator = Evaluator(args.seed)
self.labels = self.dataset.get_labels()
self.multi_label = self.dataset.multi_label
if hasattr(args, 'evaluation_metric'):
self.evaluation_metric = args.evaluation_metric
else:
if args.dataset in ['dblp4Mg2vec']:
self.evaluation_metric = 'acc'
else:
self.evaluation_metric = 'f1'
def get_graph(self):
return self.dataset.g
def get_loss_fn(self):
if self.multi_label:
return nn.BCEWithLogitsLoss()
return F.cross_entropy
def get_evaluator(self, name):
if name == 'acc':
return self.evaluator.cal_acc
elif name == 'f1_lr':
return self.evaluator.nc_with_LR
elif name == 'f1':
return self.evaluator.f1_node_classification
def evaluate(self, logits, mode='test', info=True):
if mode == 'test':
mask = self.test_idx
elif mode == 'valid':
mask = self.val_idx
elif mode == 'train':
mask = self.train_idx
if self.multi_label:
pred = (logits[mask].cpu().numpy() > 0).astype(int)
else:
pred = logits[mask].argmax(dim=1).to('cpu')
if self.evaluation_metric == 'acc':
acc = self.evaluator.cal_acc(self.labels[mask], pred)
return dict(Accuracy=acc)
elif self.evaluation_metric == 'f1':
f1_dict = self.evaluator.f1_node_classification(self.labels[mask], pred)
return f1_dict
else:
raise ValueError('The evaluation metric is not supported!')
def downstream_evaluate(self, logits, evaluation_metric):
if evaluation_metric == 'acc_f1':
micro_f1, macro_f1, acc = self.evaluator.ec_with_SVC(C=100, gamma='auto', emd=logits, labels=self.labels,
train_idx=self.train_idx, test_idx=self.test_idx)
return dict(Macro_f1=macro_f1, Mirco_f1=micro_f1, Accuracy=acc)
def get_split(self):
return self.train_idx, self.val_idx, self.test_idx
def get_labels(self):
return self.labels