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)