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