def _build_model(self, X, y):
Validation.check_dataset(X, y)
if Validation.is_variable_length(X):
raise ValueError("Variable length inputs to CXPlain are currently not supported.")
n, p = Validation.get_input_dimension(X)
output_dim = Validation.get_output_dimension(y)
if self.model is None:
if self.num_models == 1:
build_fun = self._build_single
else:
build_fun = self._build_ensemble
self.model, self.prediction_model = build_fun(input_dim=p, output_dim=output_dim)
def test_is_variable_length_padded_false(self):
(x, _), _ = TestUtil.get_random_variable_length_dataset(max_value=1024)
x = pad_sequences(x, padding="post", truncating="post", dtype=int)
return_value = Validation.is_variable_length(x)
self.assertEqual(return_value, False)
def test_is_variable_length_ndarray_true(self):
(x, _), _ = TestUtil.get_random_variable_length_dataset(max_value=1024)
x = np.array(x)
return_value = Validation.is_variable_length(x)
self.assertEqual(return_value, True)