def test_average_precision(pos_label):
avg_prec = AveragePrecision(pos_label=pos_label)
assert avg_prec.name == 'AP'
pred, target = torch.tensor([1, 2, 3, 4]), torch.tensor([1, 2, 0, 1])
ap = avg_prec(pred=pred, target=target, sample_weight=[0.1, 0.2, 0.3, 0.4])
assert isinstance(ap, torch.Tensor)
def prepare(self):
(self.train_transforms,
self.infer_transforms) = self._build_transforms(
**self.config.transforms)
(
self._train_dataloader,
self._val_dataloader,
self._test_dataloader,
) = self._build_dataloaders(**self.config.data)
num_slots = len(
self.train_transforms["utterance&slots"][0].vocab.idx.keys())
num_intents = len(self.train_transforms["intent"][0].vocab.idx.keys())
add_feat_len = 0
if self.config.model.use_intent:
add_feat_len += num_intents
self.joint_model = build_intent_joint_model(
**self.config.model,
num_slots=num_slots,
num_intents=num_intents,
vocab=self.train_transforms["utterance"][1].vocab,
add_feat_len=add_feat_len,
)
self.slot_accuracy_metric = Accuracy(num_classes=num_slots)
self.doc_accuracy_metric = Accuracy(num_classes=num_intents)
self.precision_metric = AveragePrecision()
self.slot_f1_metric = F1(num_classes=num_slots)
self.doc_f1_metric = F1(num_classes=num_intents)
self.optimizer = FairSeqAdam(self.joint_model.parameters(),
**self.config.optimizer)