def test_custom_metric_class():
learn = fake_learner(3,2)
learn.metrics.append(DummyMetric())
with CaptureStdout() as cs: learn.fit_one_cycle(2)
# 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 cs.out, f"{s} is in the output:\n{cs.out}"
def test_custom_metric_class():
this_tests('na')
learn = fake_learner(3,2)
learn.metrics.append(DummyMetric())
with CaptureStdout() as cs: learn.fit_one_cycle(2)
# 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 cs.out, f"{s} is in the output:\n{cs.out}"
def test_average_metric_naming():
this_tests(AverageMetric)
top2_accuracy = partial(top_k_accuracy, k=2)
top3_accuracy = partial(top_k_accuracy, k=3)
top4_accuracy = partial(top_k_accuracy, k=4)
# give top2_accuracy and top4_accuracy a custom name
top2_accuracy.__name__ = "top2_accuracy"
top4_accuracy.__name__ = "top4_accuracy"
# prewrap top4_accuracy
top4_accuracy = AverageMetric(top4_accuracy)
learn = fake_learner()
learn.metrics = [accuracy, top2_accuracy, top3_accuracy, top4_accuracy]
learn.fit(1)
assert learn.recorder.names[3:7] == ["accuracy", "top2_accuracy", "top_k_accuracy", "top4_accuracy"]
