Set OptimizerResult.optimizer in Optimizer.minimize

Fixes ICB-DCM#1524.

pypesto/optimize/ess/ess.py

@@ -401,7 +401,6 @@ def _create_result(self) -> pypesto.Result:
         for i, optimizer_result in enumerate(self.local_solutions):
             i_result += 1
             optimizer_result.id = f"Local solution {i}"
-            optimizer_result.optimizer = str(self.local_optimizer)
             result.optimize_result.append(optimizer_result)
 
         if self._result_includes_refset:

pypesto/optimize/optimizer.py

@@ -39,7 +39,7 @@ def __init__(self, optimizer: str):
 def hierarchical_decorator(minimize):
     """Add inner parameters to the optimizer result.
 
-    Default decorator for the minimize() method.
+    Default decorator for the :meth:`Optimizer.minimize` method.
     """
 
     @wraps(minimize)
@@ -81,7 +81,7 @@ def wrapped_minimize(
 def history_decorator(minimize):
     """Initialize and extract information stored in the history.
 
-    Default decorator for the minimize() method.
+    Default decorator for the :meth:`Optimizer.minimize` method.
     """
 
     @wraps(minimize)
@@ -140,7 +140,11 @@ def wrapped_minimize(
 
                 logger.error(f"start {id} failed:\n{trace}")
                 result = OptimizerResult(
-                    x0=x0, exitflag=-1, message=str(err), id=id
+                    x0=x0,
+                    exitflag=-1,
+                    message=str(err),
+                    id=id,
+                    optimizer=str(self),
                 )
             else:
                 raise
@@ -163,7 +167,7 @@ def wrapped_minimize(
 def time_decorator(minimize):
     """Measure time of optimization.
 
-    Default decorator for the minimize() method to take time.
+    Default decorator for the :meth:`Optimizer.minimize` method to take time.
     Currently, the method time.time() is used, which measures
     the wall-clock time.
     """
@@ -196,8 +200,8 @@ def wrapped_minimize(
 def fix_decorator(minimize):
     """Include also fixed parameters in the result arrays of minimize().
 
-    Default decorator for the minimize() method (nans will be inserted in the
-    derivatives).
+    Default decorator for the :meth:`Optimizer.minimize` method (nans will be
+    inserted in the derivatives).
     """
 
     @wraps(minimize)
@@ -523,6 +527,7 @@ def fun(x):
             hess=getattr(res, "hess", None),
             exitflag=res.status,
             message=res.message,
+            optimizer=str(self),
         )
 
         return optimizer_result
@@ -612,7 +617,10 @@ def minimize(
 
         # the ipopt return object is a scipy.optimize.OptimizeResult
         return OptimizerResult(
-            x=ret.x, exitflag=ret.status, message=ret.message
+            x=ret.x,
+            exitflag=ret.status,
+            message=ret.message,
+            optimizer=str(self),
         )
 
     def is_least_squares(self):
@@ -630,7 +638,7 @@ def __init__(self, options: dict = None):
         if self.options is None:
             self.options = DlibOptimizer.get_default_options(self)
         elif "maxiter" not in self.options:
-            raise KeyError("Dlib options are missing the key word " "maxiter.")
+            raise KeyError("Dlib options are missing the keyword maxiter.")
 
     def __repr__(self) -> str:
         rep = f"<{self.__class__.__name__}"
@@ -677,7 +685,7 @@ def get_fval_vararg(*x):
             0.002,
         )
 
-        optimizer_result = OptimizerResult()
+        optimizer_result = OptimizerResult(optimizer=str(self))
 
         return optimizer_result
 
@@ -737,7 +745,9 @@ def minimize(
             problem.objective.get_fval, lb, ub, **self.options
         )
 
-        optimizer_result = OptimizerResult(x=np.array(xopt), fval=fopt)
+        optimizer_result = OptimizerResult(
+            x=np.array(xopt), fval=fopt, optimizer=str(self)
+        )
 
         return optimizer_result
 
@@ -821,7 +831,7 @@ def minimize(
         )
 
         optimizer_result = OptimizerResult(
-            x=np.array(result[0]), fval=result[1]
+            x=np.array(result[0]), fval=result[1], optimizer=str(self)
         )
 
         return optimizer_result
@@ -901,7 +911,7 @@ def minimize(
         )
 
         optimizer_result = OptimizerResult(
-            x=np.array(result.x), fval=result.fun
+            x=np.array(result.x), fval=result.fun, optimizer=str(self)
         )
 
         return optimizer_result
@@ -1019,6 +1029,7 @@ def successively_working_fval(swarm: np.ndarray) -> np.ndarray:
         optimizer_result = OptimizerResult(
             x=pos,
             fval=float(cost),
+            optimizer=str(self),
         )
 
         return optimizer_result
@@ -1249,6 +1260,7 @@ def nlopt_objective(x, grad):
             fval=opt.last_optimum_value(),
             message=msg,
             exitflag=opt.last_optimize_result(),
+            optimizer=str(self),
         )
 
         return optimizer_result
@@ -1433,6 +1445,7 @@ def minimize(
             hess=opt.hess,
             message=msg,
             exitflag=opt.exitflag,
+            optimizer=str(self),
         )
 
         return optimizer_result

pypesto/optimize/task.py

@@ -63,7 +63,6 @@ def execute(self) -> OptimizerResult:
             history_options=self.history_options,
             optimize_options=self.optimize_options,
         )
-        optimizer_result.optimizer = str(self.optimizer)
 
         if not self.optimize_options.report_hess:
             optimizer_result.hess = None

test/optimize/test_optimize.py

@@ -308,6 +308,7 @@ def check_minimize(problem, library, solver, allow_failed_starts=False):
     ]:
         assert np.isfinite(result.optimize_result.list[0]["fval"])
         assert result.optimize_result.list[0]["x"] is not None
+        assert result.optimize_result.list[0]["optimizer"] is not None
 
 
 def test_trim_results(problem):