decagon-pytorch/src/triacontagon/loop.py

from .model import Model
from .batch import Batcher
from .sampling import negative_sample_data
from .data import Data


def _merge_pos_neg_batches(pos_batch, neg_batch):
    assert len(pos_batch.edges) == len(neg_batch.edges)
    assert pos_batch.vertex_type_row == neg_batch.vertex_type_row
    assert pos_batch.vertex_type_column == neg_batch.vertex_type_column
    assert pos_batch.relation_type_index == neg_batch.relation_type_index

    batch = TrainingBatch(pos_batch.vertex_type_row,
        pos_batch.vertex_type_column,
        pos_batch.relation_type_index,
        torch.cat([ pos_batch.edges, neg_batch.edges ]),
        torch.cat([
            torch.ones(len(pos_batch.edges)),
            torch.zeros(len(neg_batch.edges))
        ]))
    return batch


class TrainLoop(object):
    def __init__(self, model: Model,
        val_data: Data, test_data: Data,
        initial_repr: List[torch.Tensor],
        max_epochs: int = 50,
        batch_size: int = 512,
        loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = \
            torch.nn.functional.binary_cross_entropy_with_logits,
        lr: float = 0.001) -> None:

        assert isinstance(model, Model)
        assert isinstance(val_data, Data)
        assert isinstance(test_data, Data)
        assert callable(loss)

        self.model = model
        self.test_data = test_data
        self.initial_repr = list(initial_repr)
        self.max_epochs = int(num_epochs)
        self.batch_size = int(batch_size)
        self.loss = loss
        self.lr = float(lr)

        self.pos_data = model.data
        self.neg_data = negative_sample_data(model.data)

        self.pos_val_data = val_data
        self.neg_val_data = negative_sample_data(val_data)

        self.batcher = DualBatcher(self.pos_data, self.neg_data,
            batch_size=batch_size)
        self.val_batcher = DualBatcher(self.pos_val_data, self.neg_val_data)

        self.opt = torch.optim.Adam(self.model.parameters(), lr=self.lr)

    def run_epoch(self) -> None:
        loss_sum = 0.
        for pos_batch, neg_batch in self.batcher:
            batch = _merge_pos_neg_batches(pos_batch, neg_batch)

            self.opt.zero_grad()
            last_layer_repr = self.model.convolve(self.initial_repr)
            pred = self.model.decode(last_layer_repr, batch)
            loss = self.loss(pred, batch.target_values)
            loss.backward()
            self.opt.step()

            loss = loss.detach().cpu().item()
            loss_sum += loss
            print('loss:', loss)
        return loss_sum

    def validate_epoch(self):
        loss_sum = 0.
        for pos_batch, neg_batch in self.val_batcher:
            batch = _merge_pos_neg_batches(pos_batch, neg_batch)
            with torch.no_grad():
                last_layer_repr = self.model.convolve(self.initial_repr, eval_mode=True)
                pred = self.model.decode(last_layer_repr, batch, eval_mode=True)
                loss = self.loss(pred, batch.target_values)
                loss = loss.detach().cpu().item()
                loss_sum += loss
        return loss_sum

    def run(self) -> None:
        best_loss = float('inf')
        epochs_without_improvement = 0
        for epoch in range(self.max_epochs):
            print('Epoch', epoch)
            loss_sum = self.run_epoch()
            print('train loss_sum:', loss_sum)
            loss_sum = self.validate_epoch()
            print('val loss_sum:', loss_sum)
            if loss_sum >= best_loss:
                epochs_without_improvement += 1
            else:
                epochs_without_improvement = 0
                best_loss = loss_sum
            if epochs_without_improvement == 2:
                print('Early stopping after epoch', epoch, 'due to no improvement')

        return (epoch, best_loss, loss_sum)