Fix missing estimated parameters in exported PEtab (#166)

* closes #164

Author: dilpath

.github/dependabot.yml

@@ -8,4 +8,4 @@ updates:
     ignore:
       - dependency-name: "*"
         update-types: ["version-update:semver-patch", "version-update:semver-minor"]
-    target-branch: "develop"
+    target-branch: "main"

doc/examples/model_selection/calibrated_M1_4.yaml

@@ -10,6 +10,7 @@ model_subspace_petab_yaml: ../model_selection/petab_problem.yaml
 estimated_parameters:
   k2: 0.15
   k3: 0.0
+  sigma_x2: 0.01
 iteration: 1
 model_id: M1_4-000
 parameters:

doc/examples/model_selection/calibrated_M1_7.yaml

@@ -11,6 +11,7 @@ estimated_parameters:
   k1: 0.25
   k2: 0.1
   k3: 0.0
+  sigma_x2: 0.01
 iteration: 2
 model_id: M1_7-000
 parameters:

petab_select/model.py

@@ -561,13 +561,17 @@ def to_petab(
             set_estimated_parameters = True
 
         if set_estimated_parameters:
-            required_estimates = {
-                parameter_id
-                for parameter_id, value in self.parameters.items()
-                if value == ESTIMATE
-            }
-            missing_estimates = required_estimates.difference(
-                self.estimated_parameters
+            # Check that estimates are provided for all estimated parameters
+            required_estimates = set()
+            for parameter_id in petab_problem.x_ids:
+                if parameter_id in self.parameters:
+                    if self.parameters[parameter_id] == ESTIMATE:
+                        required_estimates.add(parameter_id)
+                elif parameter_id in petab_problem.x_free_ids:
+                    required_estimates.add(parameter_id)
+
+            missing_estimates = sorted(
+                required_estimates.difference(self.estimated_parameters)
             )
             if missing_estimates:
                 raise ValueError(
@@ -580,17 +584,20 @@ def to_petab(
             # If the parameter is to be estimated.
             if parameter_value == ESTIMATE:
                 petab_problem.parameter_df.loc[parameter_id, ESTIMATE] = 1
-                if set_estimated_parameters:
-                    petab_problem.parameter_df.loc[
-                        parameter_id, NOMINAL_VALUE
-                    ] = self.estimated_parameters[parameter_id]
             # Else the parameter is to be fixed.
             else:
                 petab_problem.parameter_df.loc[parameter_id, ESTIMATE] = 0
                 petab_problem.parameter_df.loc[parameter_id, NOMINAL_VALUE] = (
                     parameter_string_to_value(parameter_value)
                 )
 
+        if set_estimated_parameters:
+            petab_problem.parameter_df.update(
+                {
+                    NOMINAL_VALUE: self.estimated_parameters,
+                }
+            )
+
         petab_yaml = None
         if output_path is not None:
             output_path = Path(output_path)

test/cli/expected_output/model/parameters.tsv

@@ -4,4 +4,4 @@ k2	k_{2}	lin	0.0	1000.0	0.15	1
 k3	k_{3}	lin	0.0	1000.0	0.0	1
 k4	k_{4}	lin	0.0	1000.0	0.0	0
 k5	k_{5}	lin	0.0	1000.0	0.0	0
-sigma_x2	\sigma_{x2}	lin	1e-05	1000.0	0.15	1
+sigma_x2	\sigma_{x2}	lin	1e-05	1000.0	0.01	1

test/cli/expected_output/models/model_1/parameters.tsv

@@ -4,4 +4,4 @@ k2	k_{2}	lin	0.0	1000.0	0.15	1
 k3	k_{3}	lin	0.0	1000.0	0.0	1
 k4	k_{4}	lin	0.0	1000.0	0.0	0
 k5	k_{5}	lin	0.0	1000.0	0.0	0
-sigma_x2	\sigma_{x2}	lin	1e-05	1000.0	0.15	1
+sigma_x2	\sigma_{x2}	lin	1e-05	1000.0	0.01	1

test/cli/input/model.yaml

@@ -9,6 +9,7 @@
   estimated_parameters:
     k2: 0.15
     k3: 0.0
+    sigma_x2: 0.01
   model_id: model
   parameters:
     k1: 0.2

test/cli/input/models.yaml

@@ -9,6 +9,7 @@
   estimated_parameters:
     k2: 0.15
     k3: 0.0
+    sigma_x2: 0.01
   model_id: model_1
   parameters:
     k1: 0.2

test/model/expected_output/petab/parameters.tsv

@@ -4,4 +4,4 @@ k2	k_{2}	lin	0.0	1000.0	0.15	1
 k3	k_{3}	lin	0.0	1000.0	0.0	1
 k4	k_{4}	lin	0.0	1000.0	0.0	0
 k5	k_{5}	lin	0.0	1000.0	0.0	0
-sigma_x2	\sigma_{x2}	lin	1e-05	1000.0	0.15	1
+sigma_x2	\sigma_{x2}	lin	1e-05	1000.0	0.01	1

test/model/input/model.yaml

@@ -13,3 +13,4 @@ parameters:
 estimated_parameters:
   k2: 0.15
   k3: 0.0
+  sigma_x2: 0.01