def test_integration_precision_recall_curve_with_activated_output_transform():
np.random.seed(1)
size = 100
np_y_pred = np.random.rand(size, 1)
np_y_pred_sigmoid = torch.sigmoid(torch.from_numpy(np_y_pred)).numpy()
np_y = np.zeros((size,), dtype=np.long)
np_y[size // 2 :] = 1
np.random.shuffle(np_y)
sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred_sigmoid)
batch_size = 10
def update_fn(engine, batch):
idx = (engine.state.iteration - 1) * batch_size
y_true_batch = np_y[idx : idx + batch_size]
y_pred_batch = np_y_pred[idx : idx + batch_size]
return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
engine = Engine(update_fn)
precision_recall_curve_metric = PrecisionRecallCurve(output_transform=lambda x: (torch.sigmoid(x[1]), x[2]))
precision_recall_curve_metric.attach(engine, "precision_recall_curve")
data = list(range(size // batch_size))
precision, recall, thresholds = engine.run(data, max_epochs=1).metrics["precision_recall_curve"]
assert np.array_equal(precision, sk_precision)
assert np.array_equal(recall, sk_recall)
# assert thresholds almost equal, due to numpy->torch->numpy conversion
np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
def _test(y_pred, y, batch_size, metric_device):
metric_device = torch.device(metric_device)
prc = PrecisionRecallCurve(device=metric_device)
torch.manual_seed(10 + rank)
prc.reset()
if batch_size > 1:
n_iters = y.shape[0] // batch_size + 1
for i in range(n_iters):
idx = i * batch_size
prc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
else:
prc.update((y_pred, y))
# gather y_pred, y
y_pred = idist.all_gather(y_pred)
y = idist.all_gather(y)
np_y = y.cpu().numpy()
np_y_pred = y_pred.cpu().numpy()
res = prc.compute()
assert isinstance(res, Tuple)
assert precision_recall_curve(np_y, np_y_pred)[0] == pytest.approx(res[0].cpu().numpy())
assert precision_recall_curve(np_y, np_y_pred)[1] == pytest.approx(res[1].cpu().numpy())
assert precision_recall_curve(np_y, np_y_pred)[2] == pytest.approx(res[2].cpu().numpy())
def test_check_compute_fn():
y_pred = torch.zeros((8, 13))
y_pred[:, 1] = 1
y_true = torch.zeros_like(y_pred)
output = (y_pred, y_true)
em = PrecisionRecallCurve(check_compute_fn=True)
em.reset()
with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
em.update(output)
em = PrecisionRecallCurve(check_compute_fn=False)
em.update(output)
def test_precision_recall_curve():
size = 100
np_y_pred = np.random.rand(size, 1)
np_y = np.zeros((size,), dtype=np.long)
np_y[size // 2 :] = 1
sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred)
precision_recall_curve_metric = PrecisionRecallCurve()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
precision_recall_curve_metric.update((y_pred, y))
precision, recall, thresholds = precision_recall_curve_metric.compute()
assert np.array_equal(precision, sk_precision)
assert np.array_equal(recall, sk_recall)
# assert thresholds almost equal, due to numpy->torch->numpy conversion
np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
def test_precision_recall_curve():
size = 100
np_y_pred = np.random.rand(size, 1)
np_y = np.zeros((size,))
np_y[size // 2 :] = 1
sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred)
precision_recall_curve_metric = PrecisionRecallCurve()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
precision_recall_curve_metric.update((y_pred, y))
precision, recall, thresholds = precision_recall_curve_metric.compute()
precision = precision.numpy()
recall = recall.numpy()
thresholds = thresholds.numpy()
assert pytest.approx(precision) == sk_precision
assert pytest.approx(recall) == sk_recall
# assert thresholds almost equal, due to numpy->torch->numpy conversion
np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
def test_no_sklearn(mock_no_sklearn):
with pytest.raises(
RuntimeError,
match=r"This contrib module requires sklearn to be installed."):
y = torch.tensor([1, 1])
pr_curve = PrecisionRecallCurve()
pr_curve.update((y, y))
pr_curve.compute()
def _test(n_epochs, metric_device):
metric_device = torch.device(metric_device)
n_iters = 80
size = 151
y_true = torch.randint(0, 2, (size,)).to(device)
y_preds = torch.randint(0, 2, (size,)).to(device)
def update(engine, i):
return (
y_preds[i * size : (i + 1) * size],
y_true[i * size : (i + 1) * size],
)
engine = Engine(update)
prc = PrecisionRecallCurve(device=metric_device)
prc.attach(engine, "prc")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "prc" in engine.state.metrics
precision, recall, thresholds = engine.state.metrics["prc"]
np_y_true = y_true.cpu().numpy().ravel()
np_y_preds = y_preds.cpu().numpy().ravel()
sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y_true, np_y_preds)
assert precision.shape == sk_precision.shape
assert recall.shape == sk_recall.shape
assert thresholds.shape == sk_thresholds.shape
assert pytest.approx(precision.cpu().numpy()) == sk_precision
assert pytest.approx(recall.cpu().numpy()) == sk_recall
assert pytest.approx(thresholds.cpu().numpy()) == sk_thresholds