def test_add_foreground_dice() -> None:
g1 = "Liver"
g2 = "Lung"
ground_truth_ids = [BACKGROUND_CLASS_NAME, g1, g2]
dice = [0.85, 0.75, 0.55]
m = MetricsDict(hues=ground_truth_ids)
for j, ground_truth_id in enumerate(ground_truth_ids):
m.add_metric(MetricType.DICE, dice[j], hue=ground_truth_id)
metrics.add_average_foreground_dice(m)
assert m.get_single_metric(MetricType.DICE) == 0.5 * (dice[1] + dice[2])
def add_average_foreground_dice(metrics: MetricsDict) -> None:
"""
If the given metrics dictionary contains an entry for Dice score, and only one value for the Dice score per class,
then add an average Dice score for all foreground classes to the metrics dictionary (modified in place).
:param metrics: The object that holds metrics. The average Dice score will be written back into this object.
"""
all_dice = []
for structure_name in metrics.get_hue_names(include_default=False):
if structure_name != BACKGROUND_CLASS_NAME:
all_dice.append(metrics.get_single_metric(MetricType.DICE, hue=structure_name))
metrics.add_metric(MetricType.DICE, np.nanmean(all_dice).item())
def test_get_single_metric() -> None:
h1 = "a"
m = MetricsDict(hues=[h1])
m1, v1 = ("foo", 1.0)
m2, v2 = (MetricType.LOSS, 2.0)
m.add_metric(m1, v1, hue=h1)
m.add_metric(m2, v2)
assert m.get_single_metric(m1, h1) == v1
assert m.get_single_metric(m2) == v2
with pytest.raises(KeyError) as ex1:
m.get_single_metric(m1, "no such hue")
assert "no such hue" in str(ex1)
with pytest.raises(KeyError) as ex2:
m.get_single_metric("no such metric", h1)
assert "no such metric" in str(ex2)
m.add_metric(m2, v2)
with pytest.raises(ValueError) as ex3:
m.get_single_metric(m2)
assert "Expected a single entry" in str(ex3)