def test_custom_metric_class(): n_in, n_out = 3, 2 data = fake_data(n_in=n_in, n_out=n_out) model = nn.Linear(n_in, n_out) learn = Learner(data, model, metrics=[accuracy, DummyMetric()]) buffer = StringIO() with redirect_stdout(buffer): learn.fit_one_cycle(2) out = apply_print_resets(buffer.getvalue()) # expecting column header 'dummy', and the metrics per class definition for s in ['dummy', f'{dummy_base_val}.00', f'{dummy_base_val**2}.00']: assert s in out, f"{s} is in the output:\n{out}"
def data(): return fake_data(n_in=n_in, n_out=n_out)
def data(): return fake_data(n_in=n_in, n_out=n_out) @pytest.fixture(scope="module")