def test_mse_random_arrays_finite_values(self, y_true, y_pred):
mse_value = mse(y_true, y_pred)
expected_mse = self._correct_mse(y_true, y_pred)
print(y_true)
print(y_pred)
assert expected_mse == mse_value
def test_mse_dataframe(self):
y_true = pd.DataFrame([0, 1, 2, 3, 4, 5])
y_pred = pd.DataFrame([-1, 4, 5, 10, 4, 1])
mse_value = np.round(mse(y_true, y_pred), decimals=2)
expected_mse = 14
assert expected_mse == mse_value
def test_mse_array(self):
y_true = np.array([0, 1, 2, 3, 4, 5])
y_pred = np.array([-1, 4, 5, 10, 4, 1])
mse_value = np.round(mse(y_true, y_pred), decimals=2)
expected_mse = 14
assert expected_mse == mse_value
def test_mse_list(self):
y_true = [0, 1, 2, 3, 4, 5]
y_pred = [-1, 4, 5, 10, 4, 1]
mse_value = np.round(mse(y_true, y_pred), decimals=2)
expected_mse = 14
assert expected_mse == mse_value
def _correct_log_mse(self, y_true, y_pred):
y_true = np.array(y_true)
y_pred = np.array(y_pred)
if (np.any(y_true < 0)) or (np.any(y_pred < 0)):
raise ValueError(
"MSLE can not be used when inputs contain Negative values")
log_y_true = np.log(y_true + 1)
log_y_pred = np.log(y_pred + 1)
log_mse = mse(log_y_true, log_y_pred)
return log_mse
def _correct_rmse(self, y_true, y_pred):
y_true = np.array(y_true)
y_pred = np.array(y_pred)
return np.sqrt(mse(y_true, y_pred))
def test_infinite_values(self):
y_true = np.random.random(4)
y_pred = [0, np.inf, 2, 3]
with pytest.raises(ValueError):
mse(y_true, y_pred)
def test_nan_values(self):
y_true = [np.nan, 1, 2, 3]
y_pred = np.random.random(4)
with pytest.raises(ValueError):
mse(y_true, y_pred)
def test_wrong_vector_length(self):
y_true = np.random.random(5)
y_pred = np.random.random(4)
with pytest.raises(ValueError):
mse(y_true, y_pred)