added tests for optimize and calibration

tests/test_interfaces.py

@@ -87,43 +87,8 @@ def setup_calibrate(model_fixture, start_time, end_time, logging_step_size):
     "num_iterations": 2,
 }
 
-RTOL = [1e-6, 1e-5, 1e-4]
-ATOL = [1e-8, 1e-7, 1e-6]
-
-@pytest.mark.parametrize("sample_method", SAMPLE_METHODS)
-@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, 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
-        )["unprocessed_result"]
-    with pyro.poutine.seed(rng_seed=0):
-        result2 = sample_method(
-            model_url, end_time, logging_step_size, num_samples, start_time=start_time
-        )["unprocessed_result"]
-
-    result3 = sample_method(
-        model_url, end_time, logging_step_size, num_samples, start_time=start_time
-    )["unprocessed_result"]
-
-    for result in [result1, result2, result3]:
-        assert isinstance(result, dict)
-        check_result_sizes(result, start_time, end_time, logging_step_size, num_samples)
-
-    check_states_match(result1, result2)
-    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()
+RTOL = [1e-6, 1e-4]
+ATOL = [1e-8, 1e-6]
 
 
 @pytest.mark.parametrize("sample_method", SAMPLE_METHODS)
@@ -134,7 +99,7 @@ def test_sample_no_interventions(
 @pytest.mark.parametrize("num_samples", NUM_SAMPLES)
 @pytest.mark.parametrize("rtol", RTOL)
 @pytest.mark.parametrize("atol", ATOL)
-def test_sample_with_tolerance(
+def test_sample_no_interventions(
     sample_method, model, start_time, end_time, logging_step_size, num_samples, rtol, atol
 ):
     model_url = model.url
@@ -404,8 +369,10 @@ def test_calibrate_no_kwargs(
 @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("rtol", RTOL)
+@pytest.mark.parametrize("atol", ATOL)
 def test_calibrate_deterministic(
-    model_fixture, start_time, end_time, logging_step_size
+    model_fixture, start_time, end_time, logging_step_size, rtol, atol
 ):
     model_url = model_fixture.url
     (
@@ -421,6 +388,8 @@ def test_calibrate_deterministic(
         "data_mapping": model_fixture.data_mapping,
         "start_time": start_time,
         "deterministic_learnable_parameters": deterministic_learnable_parameters,
+        "rtol": rtol, 
+        "atol": atol,
         **CALIBRATE_KWARGS,
     }
 
@@ -440,7 +409,7 @@ def test_calibrate_deterministic(
         assert torch.allclose(param_value, param_sample_2[param_name])
 
     result = sample(
-        *sample_args, **sample_kwargs, inferred_parameters=inferred_parameters
+        *sample_args, **sample_kwargs, inferred_parameters=inferred_parameters, rtol=rtol, atol=atol
     )["unprocessed_result"]
 
     check_result_sizes(result, start_time, end_time, logging_step_size, 1)
@@ -603,7 +572,9 @@ def test_output_format(
 @pytest.mark.parametrize("start_time", START_TIMES)
 @pytest.mark.parametrize("end_time", END_TIMES)
 @pytest.mark.parametrize("num_samples", NUM_SAMPLES)
-def test_optimize(model_fixture, start_time, end_time, num_samples):
+@pytest.mark.parametrize("rtol", RTOL)
+@pytest.mark.parametrize("atol", ATOL)
+def test_optimize(model_fixture, start_time, end_time, num_samples, rtol, atol):
     logging_step_size = 1.0
     model_url = model_fixture.url
 
@@ -627,6 +598,8 @@ def __call__(self, x):
         "maxiter": 1,
         "maxfeval": 2,
         "progress_hook": progress_hook,
+        "rtol": rtol, 
+        "atol": atol
     }
     bounds_interventions = optimize_kwargs["bounds_interventions"]
     opt_result = optimize(
@@ -665,6 +638,8 @@ def __call__(self, x):
         static_parameter_interventions=opt_intervention,
         solver_method=optimize_kwargs["solver_method"],
         solver_options=optimize_kwargs["solver_options"],
+        rtol=rtol, 
+        atol=atol
     )["unprocessed_result"]
 
     intervened_result_subset = {