def test_matthews_correlation():
y_true = K.variable(np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0]))
y_pred = K.variable(np.array([1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]))
# Calculated using sklearn.metrics.matthews_corrcoef
actual = -0.14907119849998601
calc = K.eval(metrics.matthews_correlation(y_true, y_pred))
epsilon = 1e-05
assert actual - epsilon <= calc <= actual + epsilon
def test_matthews_correlation():
y_true = K.variable(np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0]))
y_pred = K.variable(np.array([1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]))
# Calculated using sklearn.metrics.matthews_corrcoef
expected = -0.14907119849998601
actual = K.eval(metrics.matthews_correlation(y_true, y_pred))
epsilon = 1e-05
assert expected - epsilon <= actual <= expected + epsilon