make distributional parameters optional

tests/fixtures.py

@@ -30,13 +30,15 @@ def __init__(
  data_mapping: Dict[str, str] = {},
  data_mapped_to_observable: bool = False,
  optimize_kwargs: Dict[str, Any] = None,
+ has_distributional_parameters: bool = True,
  ):
  self.url = url
  self.important_parameter = important_parameter
  self.data_path = data_path
  self.data_mapping = data_mapping
  self.data_mapped_to_observable = data_mapped_to_observable
  self.optimize_kwargs = optimize_kwargs
+ self.has_distributional_parameters = has_distributional_parameters
 
 
 # See https://github.com/DARPA-ASKEM/Model-Representations/issues/62 for discussion of valid models.
@@ -85,7 +87,7 @@ def __init__(
  ModelFixture(
  os.path.join(MODELS_PATH, "LV_rabbits_wolves_model03_regnet.json"), "beta"
  ),
- ModelFixture(os.path.join(MODELS_PATH, "LacOperon.json")),
+ ModelFixture(os.path.join(MODELS_PATH, "LacOperon.json"), "k_1", has_distributional_parameters=False),
 ]
 
 STOCKFLOW_MODELS = [

tests/test_interfaces.py

@@ -89,14 +89,16 @@ def setup_calibrate(model_fixture, start_time, end_time, logging_step_size):
 
 
 @pytest.mark.parametrize("sample_method", SAMPLE_METHODS)
-@pytest.mark.parametrize("model_url", MODEL_URLS)
+@pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("start_time", START_TIMES)
 @pytest.mark.parametrize("end_time", END_TIMES)
 @pytest.mark.parametrize("logging_step_size", LOGGING_STEP_SIZES)
 @pytest.mark.parametrize("num_samples", NUM_SAMPLES)
 def test_sample_no_interventions(
- sample_method, model_url, start_time, end_time, logging_step_size, num_samples
+ sample_method, model, start_time, end_time, logging_step_size, num_samples
 ):
+ model_url = model.url
+
  with pyro.poutine.seed(rng_seed=0):
  result1 = sample_method(
  model_url, end_time, logging_step_size, num_samples, start_time=start_time
@@ -115,7 +117,8 @@ def test_sample_no_interventions(
  check_result_sizes(result, start_time, end_time, logging_step_size, num_samples)
 
  check_states_match(result1, result2)
- check_states_match_in_all_but_values(result1, result3)
+ if model.has_distributional_parameters:
+ check_states_match_in_all_but_values(result1, result3)
 
  if sample_method.__name__ == "dummy_ensemble_sample":
  assert "total_state" in result1.keys()