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