def test_log_mse_random_arrays_finite_values(self, y_true, y_pred):
log_mse_value = log_mse(y_true, y_pred)
expected_log_mse = self._correct_log_mse(y_true, y_pred)
print(y_true)
print(y_pred)
assert expected_log_mse == log_mse_value
def test_log_mse_dataframe(self):
y_true = pd.DataFrame([0, 1, 2, 3, 4, 5])
y_pred = pd.DataFrame([1, 4, 5, 10, 4, 1])
log_mse_value = np.round(log_mse(y_true, y_pred), decimals=2)
expected_log_mse = 0.67
assert expected_log_mse == log_mse_value
def test_log_mse_array(self):
y_true = np.array([0, 1, 2, 3, 4, 5])
y_pred = np.array([1, 4, 5, 10, 4, 1])
log_mse_value = np.round(log_mse(y_true, y_pred), decimals=2)
expected_log_mse = 0.67
assert expected_log_mse == log_mse_value
def test_log_mse_list(self):
y_true = [0, 1, 2, 3, 4, 5]
y_pred = [1, 4, 5, 10, 4, 1]
log_mse_value = np.round(log_mse(y_true, y_pred), decimals=2)
expected_log_mse = 0.67
assert expected_log_mse == log_mse_value
def _correct_rmsle(self, y_true, y_pred):
y_true = np.array(y_true)
y_pred = np.array(y_pred)
return np.sqrt(log_mse(y_true, y_pred))
def test_log_mse_negative_values(self):
y_true = np.array([0, 1, 2, 3, 4, -5])
y_pred = np.array([0, 0, 0, 0, 0, 0])
with pytest.raises(ValueError):
log_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):
log_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):
log_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):
log_mse(y_true, y_pred)