def test_add_none(self, fit_mock):
recommender = Recommender(self.dpp_matrix, self.n_components)
recommender.add({})
expected_x = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
np.testing.assert_array_equal(recommender.dpp_vector, expected_x)
np.testing.assert_array_equal(
fit_mock.call_args[0][0],
expected_x.reshape(1, -1)
)
fit_mock.assert_called_once()
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_add_twice(self, fit_mock):
recommender = Recommender(self.dpp_matrix, self.n_components)
recommender.add({1: 2, 3: 4, 5: 1})
expected_x_1 = np.array(
[0, 2, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
)
np.testing.assert_array_equal(recommender.dpp_vector, expected_x_1)
np.testing.assert_array_equal(
fit_mock.call_args[0][0],
expected_x_1.reshape(1, -1)
)
recommender.add({1: 1, 6: 3, 9: 4})
expected_x_2 = np.array(
[0, 1, 0, 4, 0, 1, 3, 0, 0, 4, 0, 0, 0, 0, 0, 0]
)
np.testing.assert_array_equal(recommender.dpp_vector, expected_x_2)
np.testing.assert_array_equal(
fit_mock.call_args[0][0],
expected_x_2.reshape(1, -1)
)
self.assertEqual(fit_mock.call_count, 2)