def test_all_positives():
y_true = np.ones((5), dtype=bool)
y_pred = np.random.randn(y_true.size)
y_pred -= y_pred.min()
y_pred += 1.
acc = accuracy(y_true, y_pred)
assert acc == 1.0
acc = accuracy(y_pred, y_true)
assert acc == 1.0
def test_all_negatives():
y_true = np.zeros((5), dtype=bool)
y_pred = ~y_true
acc = accuracy(y_true, y_pred)
assert acc == 0.0
def test_basic_balanced():
y_true = np.array([True, True, True, False, False])
y_pred = np.array([True, True, False, True, False])
acc = accuracy(y_true, y_pred, balanced=True)
reference = ((2. / 3.) + (1. / 2.)) / 2.
assert acc == reference
def test_basic():
y_true = np.array([True, False, True, True, False])
y_pred = np.array([True, True, False, True, False])
acc = accuracy(y_true, y_pred)
reference = 3. / 5.
assert acc == reference
