Change solution inform into a dataclass (#437)

* revert sol_inform back to emtpy dict, update Solution class

* rename variable to camelCase

* solution inform as dataclass

* whoops leftover from local branch

* fix for python < 3.12

* fix bad merge

* isort

* update testing utils

* update tests

* update type hints to support python 3.9

* reason -> message

* added alternate constructor

* rebase confusion

* version bump

* solInform -> sol_inform

---------

Co-authored-by: Ella Wu <[email protected]>

Author: eirikurj

pyoptsparse/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.14.0"
+__version__ = "2.14.1"
 
 from .pyOpt_history import History
 from .pyOpt_variable import Variable

pyoptsparse/pyALPSO/pyALPSO.py

@@ -190,8 +190,8 @@ def objconfunc(x):
             # Broadcast a -1 to indcate NSGA2 has finished
             self.optProb.comm.bcast(-1, root=0)
 
-            # Store Results
-            sol_inform = {"value": "", "text": ""}
+            # Optimizer has no exit conditions, so nothing to set
+            sol_inform = None
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, opt_f, opt_x)

pyoptsparse/pyCONMIN/pyCONMIN.py

@@ -239,8 +239,8 @@ def cnmngrad(n1, n2, x, f, g, ct, df, a, ic, nac):
             # Broadcast a -1 to indcate SLSQP has finished
             self.optProb.comm.bcast(-1, root=0)
 
-            # Store Results
-            sol_inform = {"value": "", "text": ""}
+            # Optimizer has no exit conditions, so nothing to set
+            sol_inform = None
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, ff, xs)

pyoptsparse/pyIPOPT/pyIPOPT.py

@@ -18,6 +18,7 @@
 
 # Local modules
 from ..pyOpt_optimizer import Optimizer
+from ..pyOpt_solution import SolutionInform
 from ..pyOpt_utils import ICOL, INFINITY, IROW, convertToCOO, extractRows, scaleRows
 
 
@@ -274,10 +275,8 @@ def intermediate(_, *args, **kwargs):
                 self.hist.writeData("metadata", self.metadata)
                 self.hist.close()
 
-            # Store Results
-            sol_inform = {}
-            sol_inform["value"] = info["status"]
-            sol_inform["text"] = self.informs[info["status"]]
+            # Store optimizer exit condition and message
+            sol_inform = SolutionInform.from_informs(self.informs, info["status"])
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, info["obj_val"], x, multipliers=info["mult_g"])

pyoptsparse/pyNLPQLP/pyNLPQLP.py

@@ -13,6 +13,7 @@
 
 # Local modules
 from ..pyOpt_optimizer import Optimizer
+from ..pyOpt_solution import SolutionInform
 from ..pyOpt_utils import try_import_compiled_module_from_path
 
 # import the compiled module
@@ -254,11 +255,9 @@ def nlgrad(m, me, mmax, n, f, g, df, dg, x, active, wa):
                 self.hist.writeData("metadata", self.metadata)
                 self.hist.close()
 
-            # Store Results
+            # Store optimizer exit condition and message
             inform = ifail.item()
-            sol_inform = {}
-            sol_inform["value"] = inform
-            sol_inform["text"] = self.informs[inform]
+            sol_inform = SolutionInform.from_informs(self.informs, inform)
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, f, xs)

pyoptsparse/pyNSGA2/pyNSGA2.py

@@ -189,8 +189,8 @@ def objconfunc(nreal, nobj, ncon, x, f, g):
             # Broadcast a -1 to indcate NSGA2 has finished
             self.optProb.comm.bcast(-1, root=0)
 
-            # Store Results
-            sol_inform = {"value": "", "text": ""}
+            # Optimizer has no exit conditions, so nothing to set
+            sol_inform = None
 
             xstar = [0.0] * n
             for i in range(n):

pyoptsparse/pyOpt_history.py

@@ -221,7 +221,7 @@ def _processDB(self):
 
         if self.metadata["version"] != __version__:
             pyOptSparseWarning(
-                "The version of pyoptsparse used to generate the history file does not match the one being run right now. There may be compatibility issues."
+                f"The version of pyoptsparse used to generate the history file (v{self.metadata['version']}) does not match the one being run right now (v{__version__}). There may be compatibility issues."
             )
 
     def getIterKeys(self):

pyoptsparse/pyOpt_optimizer.py

@@ -794,7 +794,7 @@ def _assembleObjective(self):
 
         return np.real(np.squeeze(ff))
 
-    def _createSolution(self, optTime, sol_inform, obj, xopt, multipliers=None) -> Solution:
+    def _createSolution(self, optTime, solInform, obj, xopt, multipliers=None) -> Solution:
         """
         Generic routine to create the solution after an optimizer
         finishes.
@@ -824,7 +824,7 @@ def _createSolution(self, optTime, sol_inform, obj, xopt, multipliers=None) -> S
             "interfaceTime": self.interfaceTime - self.userSensTime - self.userObjTime,
             "optCodeTime": optTime - self.interfaceTime,
         }
-        sol = Solution(self.optProb, xStar, fStar, multipliers, sol_inform, info)
+        sol = Solution(self.optProb, xStar, fStar, multipliers, solInform, info)
 
         return sol
 

pyoptsparse/pyOpt_solution.py

@@ -1,5 +1,7 @@
 # Standard Python modules
 import copy
+from dataclasses import dataclass
+from typing import Optional
 
 # External modules
 import numpy as np
@@ -8,8 +10,22 @@
 from .pyOpt_optimization import Optimization
 
 
+@dataclass(frozen=True)
+class SolutionInform:
+    """Data class that contains the optimizer solution value and message"""
+
+    value: int
+    """The integer return code"""
+    message: str
+    """The message string accompanying the return code"""
+
+    @classmethod
+    def from_informs(cls, informs: dict[int, str], value: int):
+        return cls(value=value, message=informs[value])
+
+
 class Solution(Optimization):
-    def __init__(self, optProb, xStar, fStar, lambdaStar, optInform, info):
+    def __init__(self, optProb, xStar, fStar, lambdaStar, optInform: Optional[SolutionInform], info):
         """
         This class is used to describe the solution of an optimization
         problem. This class inherits from Optimization which enables a
@@ -29,8 +45,9 @@ def __init__(self, optProb, xStar, fStar, lambdaStar, optInform, info):
         lambdaStar : dict
             The final Lagrange multipliers
 
-        optInform : int
-            The inform code returned by the optimizer
+        optInform : SolutionInform or None
+            Object containing the inform code and message returned by the optimizer.
+            Optimizers that do not have inform exit codes do not set this variable.
 
         info : dict
             A dictionary containing timing and call counter info to be stored
@@ -95,12 +112,14 @@ def __str__(self) -> str:
         for i in range(5, len(lines)):
             text1 += lines[i] + "\n"
 
-        inform_val = self.optInform["value"]
-        inform_text = self.optInform["text"]
-        text1 += "\n"
-        text1 += "   Exit Status\n"
-        text1 += "      Inform  Description\n"
-        text1 += f"      {inform_val:>6}  {inform_text:<0}\n"
+        # Only print exit status, inform, and description if the optimizer provides informs
+        if self.optInform:
+            inform_val = self.optInform.value
+            inform_text = self.optInform.message
+            text1 += "\n"
+            text1 += "   Exit Status\n"
+            text1 += "      Inform  Description\n"
+            text1 += f"      {inform_val:>6}  {inform_text:<0}\n"
 
         text1 += ("-" * 80) + "\n"
 

pyoptsparse/pyPSQP/pyPSQP.py

@@ -12,6 +12,7 @@
 
 # Local modules
 from ..pyOpt_optimizer import Optimizer
+from ..pyOpt_solution import SolutionInform
 from ..pyOpt_utils import try_import_compiled_module_from_path
 
 # import the compiled module
@@ -259,9 +260,7 @@ def pdcon(n, k, x, dg):
             inform = iterm.item()
             if inform < 0 and inform not in self.informs:
                 inform = -10
-            sol_inform = {}
-            sol_inform["value"] = inform
-            sol_inform["text"] = self.informs[inform]
+            sol_inform = SolutionInform.from_informs(self.informs, inform)
             if self.storeHistory:
                 self.metadata["endTime"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                 self.metadata["optTime"] = optTime