def test_metrics_dict_add_integer() -> None:
"""
Adding a scalar metric where the value is an integer by accident should still store the metric.
"""
m = MetricsDict()
m.add_metric("foo", 1)
assert "foo" in m.values()
assert m.values()["foo"] == [1.0]
def test_delete_metric() -> None:
"""
Deleting a set of metrics from the dictionary.
"""
m = MetricsDict()
m.add_metric(MetricType.LOSS, 1)
assert m.values()[MetricType.LOSS.value] == [1.0]
m.delete_metric(MetricType.LOSS)
assert MetricType.LOSS.value not in m.values()
def test_metrics_dict1() -> None:
"""
Test insertion of scalar values into a MetricsDict.
"""
m = MetricsDict()
assert m.get_hue_names() == [MetricsDict.DEFAULT_HUE_KEY]
name = "foo"
v1 = 2.7
v2 = 3.14
m.add_metric(name, v1)
m.add_metric(name, v2)
assert m.values()[name] == [v1, v2]
with pytest.raises(ValueError) as ex:
# noinspection PyTypeChecker
m.add_metric(name, [1.0]) # type: ignore
assert "Expected the metric to be a scalar" in str(ex)
assert m.skip_nan_when_averaging[name] is False
v3 = 3.0
name2 = "bar"
m.add_metric(name2, v3, skip_nan_when_averaging=True)
assert m.skip_nan_when_averaging[name2] is True
# Expected average: Metric "foo" averages over two values v1 and v2. For "bar", we only inserted one value anyhow
average = m.average()
mean_v1_v2 = mean([v1, v2])
assert average.values() == {name: [mean_v1_v2], name2: [v3]}
num_entries = m.num_entries()
assert num_entries == {name: 2, name2: 1}