def test_shape_accuracy4():
x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
y = Tensor(np.array(1))
metric = Accuracy('classification')
metric.clear()
with pytest.raises(ValueError):
metric.update(x, y)
def test_shape_accuracy2():
x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
y = Tensor(np.array([0, 1, 1, 1]))
metric = Accuracy('multilabel')
metric.clear()
with pytest.raises(ValueError):
metric.update(x, y)
def test_multilabel_accuracy():
x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
y = Tensor(np.array([[0, 1, 1, 1], [0, 1, 1, 1], [0, 0, 0, 1]]))
metric = Accuracy('multilabel')
metric.clear()
metric.update(x, y)
accuracy = metric.eval()
assert accuracy == 1 / 3
def test_classification_accuracy_indexes_awareness():
"""A indexes aware version of test_classification_accuracy"""
x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
y = Tensor(np.array([1, 0, 1]))
y2 = Tensor(np.array([0, 0, 1]))
metric = Accuracy('classification').set_indexes([0, 2])
metric.clear()
metric.update(x, y, y2)
accuracy = metric.eval()
assert math.isclose(accuracy, 1 / 3)
def test_classification_accuracy():
"""test_classification_accuracy"""
x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
y = Tensor(np.array([1, 0, 1]))
y2 = Tensor(np.array([[0, 1], [1, 0], [0, 1]]))
metric = Accuracy('classification')
metric.clear()
metric.update(x, y)
accuracy = metric.eval()
accuracy2 = metric(x, y2)
assert math.isclose(accuracy, 2 / 3)
assert math.isclose(accuracy2, 2 / 3)