Function test_early_stopping_paths

tests/callbacks_test.py:76–116

(capsys)

def test_early_stopping_paths(capsys):
    with pytest.raises(ValueError):
        EarlyStopping(mode="unsupported")

    # Cover baseline/min branch + _is_improvement(min)
    es_min = EarlyStopping(monitor="val_loss", mode="min", baseline=0.5)
    es_min.on_train_begin()
    assert es_min.best == 0.5
    assert es_min._is_improvement(0.4, es_min.best)

    # Cover auto/max branch + restore-best-weights path
    model = TinyModel()
    with torch.no_grad():
        model.linear.weight.fill_(1.0)

    es = EarlyStopping(
        monitor="val_auc",
        mode="auto",
        patience=1,
        verbose=1,
        restore_best_weights=True,
    )
    es.set_model(model)
    es.on_train_begin()

    # Missing metric should be ignored.
    es.on_epoch_end(0, {})

    # Improvement stores best weights.
    es.on_epoch_end(0, {"val_auc": 0.9})
    with torch.no_grad():
        model.linear.weight.fill_(2.0)

    # No improvement triggers early stop and restores best weights.
    es.on_epoch_end(1, {"val_auc": 0.8})
    es.on_train_end()
    out = capsys.readouterr().out

    assert model.stop_training is True
    assert torch.allclose(model.linear.weight, torch.tensor([[1.0]]))
    assert "early stopping" in out


def test_model_checkpoint_paths(tmp_path, capsys):