def test_regression_metrics_multioutput_array():
y_true = np.array([[1, 2], [2.5, -1], [4.5, 3], [5, 7]], dtype=np.float)
y_pred = np.array([[1, 1], [2, -1], [5, 4], [5, 6.5]], dtype=np.float)
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
cp.testing.assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
cp.testing.assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
weights = np.array([0.4, 0.6], dtype=np.float)
msew = mean_squared_error(y_true, y_pred, multioutput=weights)
rmsew = mean_squared_error(y_true, y_pred, multioutput=weights,
squared=False)
assert_almost_equal(msew, 0.39, decimal=2)
assert_almost_equal(rmsew, 0.62, decimal=2)
y_true = np.array([[0, 0]] * 4, dtype=np.int)
y_pred = np.array([[1, 1]] * 4, dtype=np.int)
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
cp.testing.assert_array_almost_equal(mse, [1., 1.], decimal=2)
cp.testing.assert_array_almost_equal(mae, [1., 1.], decimal=2)
y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
multioutput='raw_values')
cp.testing.assert_array_almost_equal(msle, msle2, decimal=2)
def test_mse_vs_msle_custom_weights():
y_true = np.array([0.5, 2, 7, 6], dtype=np.float)
y_pred = np.array([0.5, 1, 8, 8], dtype=np.float)
weights = np.array([0.2, 0.25, 0.4, 0.15], dtype=np.float)
msle = mean_squared_log_error(y_true, y_pred, sample_weight=weights)
msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
sample_weight=weights)
assert_almost_equal(msle, msle2, decimal=2)
def test_regression_metrics():
y_true = np.arange(50, dtype=np.int)
y_pred = y_true + 1
assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
assert_almost_equal(mean_squared_log_error(y_true, y_pred),
mean_squared_error(np.log(1 + y_true),
np.log(1 + y_pred)))
assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
def test_multioutput_regression():
y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])
error = mean_squared_error(y_true, y_pred)
assert_almost_equal(error, (1. + 2. / 3) / 4.)
error = mean_squared_error(y_true, y_pred, squared=False)
assert_almost_equal(error, 0.645, decimal=2)
error = mean_squared_log_error(y_true, y_pred)
assert_almost_equal(error, 0.200, decimal=2)
# mean_absolute_error and mean_squared_error are equal because
# it is a binary problem.
error = mean_absolute_error(y_true, y_pred)
assert_almost_equal(error, (1. + 2. / 3) / 4.)
def test_mean_squared_log_error_exceptions(inputs):
with pytest.raises(ValueError):
mean_squared_log_error(np.array(inputs[0]), np.array(inputs[1]))
