def test__find_parent_id_all_good(self, choice_mock):
"""If all are good, use the likelihoods unmodified."""
likelihoods = pd.Series([0.5, 1, 1.5, 2])
num_rows = pd.Series([1, 2, 3, 4])
Sampler._find_parent_id(likelihoods, num_rows)
expected_weights = np.array([0.5 / 5, 1 / 5, 1.5 / 5, 2 / 5])
assert choice_mock.call_count == 1
assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
np.testing.assert_array_equal(choice_mock.call_args[1]['p'],
expected_weights)
def test__find_parent_id_all_0_or_singlar_matrix(self, choice_mock):
"""If likehoods are either 0 or NaN, fill the gaps with num_rows."""
likelihoods = pd.Series([0, None, 0, None])
num_rows = pd.Series([1, 2, 3, 4])
Sampler._find_parent_id(likelihoods, num_rows)
expected_weights = np.array([0, 2 / 6, 0, 4 / 6])
assert choice_mock.call_count == 1
assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
np.testing.assert_array_equal(choice_mock.call_args[1]['p'],
expected_weights)
def test__find_parent_id_some_good(self, choice_mock):
"""If some likehoods are good, fill the gaps with num_rows."""
likelihoods = pd.Series([0.5, None, 1.5, None])
num_rows = pd.Series([1, 2, 3, 4])
Sampler._find_parent_id(likelihoods, num_rows)
expected_weights = np.array([0.5 / 4, 1 / 4, 1.5 / 4, 1 / 4])
assert choice_mock.call_count == 1
assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
np.testing.assert_array_equal(choice_mock.call_args[1]['p'],
expected_weights)
def test__find_parent_id_all_singlar_matrix(self, choice_mock):
"""If all likelihoods got singular matrix, use num_rows."""
likelihoods = pd.Series([None, None, None, None])
num_rows = pd.Series([1, 2, 3, 4])
Sampler._find_parent_id(likelihoods, num_rows)
expected_weights = np.array([1 / 10, 2 / 10, 3 / 10, 4 / 10])
assert choice_mock.call_count == 1
assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
np.testing.assert_array_equal(choice_mock.call_args[1]['p'],
expected_weights)
