def test_metrics_dict_roc() -> None:
"""
Test if adding ROC entries to a MetricsDict instance works, and returns the correct AUC.
"""
# Prepare a vector of predictions and labels. We can compute AUC off those to compare.
# MetricsDict will get that supplied in 3 chunks, and should return the same AUC value.
predictions = np.array([0.5, 0.6, 0.1, 0.8, 0.2, 0.9])
labels = np.array([0, 1.0, 0, 0, 1, 1], dtype=np.float)
split_length = [3, 2, 1]
assert sum(split_length) == len(predictions)
summed = np.cumsum(split_length)
m = MetricsDict()
for i, end in enumerate(summed):
start = 0 if i == 0 else summed[i - 1]
pred = predictions[start:end]
label = labels[start:end]
subject_ids = list(range(len(pred)))
m.add_predictions(subject_ids, pred, label)
assert m.has_prediction_entries
actual_auc = m.get_roc_auc()
expected_auc = roc_auc_score(labels, predictions)
assert actual_auc == pytest.approx(expected_auc, 1e-6)
actual_pr_auc = m.get_pr_auc()
expected_pr_auc = 0.7111111
assert actual_pr_auc == pytest.approx(expected_pr_auc, 1e-6)
def test_metrics_dict_roc_degenerate() -> None:
"""
Test if adding ROC entries to a MetricsDict instance works, if there is only 1 class present.
"""
# Prepare a vector of predictions and labels. We can compute AUC off those to compare.
# MetricsDict will get that supplied in 3 chunks, and should return the same AUC value.
predictions = np.array([0.5, 0.6, 0.1, 0.8, 0.2, 0.9])
m = MetricsDict()
subject_ids = list(range(len(predictions)))
m.add_predictions(subject_ids, predictions, np.ones_like(predictions))
assert m.has_prediction_entries
assert m.get_roc_auc() == 1.0
assert m.get_pr_auc() == 1.0