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")
def data(): return fake_data(n_in=n_in, n_out=n_out) @pytest.fixture(scope="module")