def test_fbeta_score(): 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.f1_score expected = 0.33333333333333331 actual = K.eval(metrics.fbeta_score(y_true, y_pred)) epsilon = 1e-05 assert expected - epsilon <= actual <= expected + epsilon
def test_fbeta_score(): 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.fbeta_score expected = 0.30303030303030304 actual = K.eval(metrics.fbeta_score(y_true, y_pred, beta=2)) epsilon = 1e-05 assert expected - epsilon <= actual <= expected + epsilon