Update optimize api (#103)

* updating converter to correctly type.

* remove initial_guess_interventions

* update test.

* adding start_time_param_value

* updating version of pyciemss

pyproject.toml

@@ -40,7 +40,7 @@ httpx = "^0.24.1"
 
 
 [tool.poe.tasks]
-install-pyciemss = "pip install --no-cache-dir git+https://github.com/ciemss/pyciemss.git@7967dfaede3136dfec4b8e08dd11b272a6190677 --use-pep517"
+install-pyciemss = "pip install --no-cache-dir git+https://github.com/ciemss/pyciemss.git@672201f44752e40ed3a92a335ada136bc30f776e --use-pep517"
 
 
 [tool.pytest.ini_options]

service/models/operations/optimize.py

@@ -10,6 +10,7 @@
 from pyciemss.integration_utils.intervention_builder import (
     param_value_objective,
     start_time_objective,
+    start_time_param_value_objective,
 )
 
 from pyciemss.ouu.qoi import obs_nday_average_qoi, obs_max_qoi
@@ -90,7 +91,6 @@ class Optimize(OperationRequest):
     step_size: float = 1.0
     qoi: QOI
     risk_bound: float
-    initial_guess_interventions: List[float]
     bounds_interventions: List[List[float]]
     extra: OptimizeExtra = Field(
         None,
@@ -117,7 +117,7 @@ def gen_pyciemss_args(self, job_id):
                 param_name=self.optimize_interventions.param_names,
                 param_value=param_value,
             )
-        else:
+        if intervention_type == "start_time":
             assert self.optimize_interventions.param_values is not None
             param_value = [
                 torch.tensor(value)
@@ -127,6 +127,10 @@ def gen_pyciemss_args(self, job_id):
                 param_name=self.optimize_interventions.param_names,
                 param_value=param_value,
             )
+        if intervention_type == "start_time_param_value":
+            optimize_interventions = start_time_param_value_objective(
+                param_name=self.optimize_interventions.param_names
+            )
 
         extra_options = self.extra.dict()
         inferred_parameters = fetch_inferred_parameters(
@@ -146,7 +150,7 @@ def gen_pyciemss_args(self, job_id):
             ),
             "qoi": self.qoi.gen_call(),
             "risk_bound": self.risk_bound,
-            "initial_guess_interventions": self.initial_guess_interventions,
+            "initial_guess_interventions": self.optimize_interventions.initial_guess,
             "bounds_interventions": self.bounds_interventions,
             "static_parameter_interventions": optimize_interventions,
             "fixed_static_parameter_interventions": fixed_static_parameter_interventions,

tests/examples/optimize/input/request.json

@@ -19,8 +19,7 @@
 		"method": "day_average"
 	},
 	"risk_bound": 10.0,
-  	"initial_guess_interventions": [1.0],
-  	"bounds_interventions": [[0.0], [3.0]],
+  "bounds_interventions": [[0.0], [3.0]],
 	"extra": {
 		"num_samples": 4,
 		"is_minimized": true