def test_propose_without_add(self, randint_mock): index = 0 randint_mock.return_value = index proposed = np.argmax(self.dpp_matrix[0]) # 14 recommender = Recommender(self.dpp_matrix, self.n_components) np.testing.assert_array_equal( recommender.matching_dataset, self.dpp_matrix[index, :], ) pipeline_index = recommender.propose() randint_mock.assert_called_once_with(4) # 4 is self.dpp_matrix length assert pipeline_index == proposed
def test_propose_done(self, predict_mock, get_candidates_mock): # Set-up recommender = Recommender(self.dpp_matrix, self.n_components) get_candidates_mock.return_value = None # Run params = recommender.propose() # Assert expected_params = None assert params == expected_params
def test_propose_empty_add(self, randint_mock): index = 0 randint_mock.return_value = index proposed = np.argmax(self.dpp_matrix[0]) # 14 recommender = Recommender(self.dpp_matrix, self.n_components) recommender.add({}) np.testing.assert_array_equal( recommender.matching_dataset, self.dpp_matrix[index, :], ) pipeline_index = recommender.propose() assert randint_mock.call_count == 2 assert pipeline_index == proposed
def test_propose_once(self, predict_mock, get_candidates_mock): """n == 1""" # Set-up recommender = Recommender(self.dpp_matrix, self.n_components) get_candidates_mock.return_value = [1, 3, 7, 8] predict_mock.return_value = np.array([1, 4, 2, 3]) # Run pipeline_index = recommender.propose() # Assert expected_pipeline_index = 3 assert pipeline_index == expected_pipeline_index