Add startpoint_method to Problem

It's not intuitive that `x_guesses` is part of `Problem`, but `startpoint_method`s are handled separately. See also discussion in #1017.

This patch
* adds `startpoint_method` to `Problem`
* during PEtab import, sets `Problem.startpoint_method` based on the PEtab problem

To be discussed: Do we want to keep the existing `startpoint_method` argument in the long term or should it be removed (deprecated)?

pypesto/optimize/ess/cess.py

@@ -107,7 +107,8 @@ def _initialize(self):
     def minimize(
         self,
         problem: Problem,
-        startpoint_method: StartpointMethod,
+        # TODO: deprecate?
+        startpoint_method: StartpointMethod = None,
     ) -> pypesto.Result:
         """Minimize the given objective using CESS.
 
@@ -122,7 +123,7 @@ def minimize(
 
         evaluator = FunctionEvaluator(
             problem=problem,
-            startpoint_method=startpoint_method,
+            startpoint_method=startpoint_method or problem.startpoint_method,
         )
 
         refsets = [

pypesto/optimize/ess/ess.py

@@ -189,15 +189,11 @@ def minimize(
         self._initialize()
         self.starttime = time.time()
 
-        if (
-            refset is None and (problem is None or startpoint_method is None)
-        ) or (
-            refset is not None
-            and (problem is not None or startpoint_method is not None)
+        if (refset is None and problem is None) or (
+            refset is not None and problem is not None
         ):
             raise ValueError(
-                "Either `refset` or `problem` and `startpoint_method` "
-                "has to be provided."
+                "Either `refset` or `problem` has to be provided."
             )
         # generate initial RefSet if not provided
         if refset is None:
@@ -210,13 +206,13 @@ def minimize(
             if self.n_procs:
                 self.evaluator = FunctionEvaluatorMP(
                     problem=problem,
-                    startpoint_method=startpoint_method,
+                    startpoint_method=startpoint_method or problem.startpoint,
                     n_procs=self.n_procs,
                 )
             else:
                 self.evaluator = FunctionEvaluatorMT(
                     problem=problem,
-                    startpoint_method=startpoint_method,
+                    startpoint_method=startpoint_method or problem.startpoint,
                     n_threads=self.n_threads or 1,
                 )
 

pypesto/optimize/ess/function_evaluator.py

@@ -31,7 +31,8 @@ class FunctionEvaluator:
     def __init__(
         self,
         problem: Problem,
-        startpoint_method: StartpointMethod,
+        # TODO deprecate?
+        startpoint_method: StartpointMethod = None,
     ):
         """Construct.
 
@@ -41,7 +42,9 @@ def __init__(
         startpoint_method: Method for choosing feasible parameters
         """
         self.problem: Problem = problem
-        self.startpoint_method: StartpointMethod = startpoint_method
+        self.startpoint_method: StartpointMethod = (
+            startpoint_method or problem.startpoint_method
+        )
         self.n_eval: int = 0
         self.n_eval_round: int = 0
 

pypesto/optimize/ess/sacess.py

@@ -96,7 +96,8 @@ def __init__(
     def minimize(
         self,
         problem: Problem,
-        startpoint_method: StartpointMethod,
+        # TODO: deprecate?
+        startpoint_method: StartpointMethod = None,
     ):
         """Solve the given optimization problem."""
         start_time = time.time()
@@ -135,7 +136,7 @@ def minimize(
                     args=(
                         worker,
                         problem,
-                        startpoint_method,
+                        startpoint_method or problem.startpoint_method,
                         self.sacess_loglevel,
                     ),
                 )

pypesto/optimize/optimize.py

@@ -87,8 +87,12 @@ def minimize(
     n_starts = bound_n_starts_from_env(n_starts)
 
     # startpoint method
+    # TODO: deprecate or support both, `problem.startpoint_method` and `startpoint_method`?
     if startpoint_method is None:
-        startpoint_method = uniform
+        if problem.startpoint_method is None:
+            startpoint_method = uniform
+        else:
+            startpoint_method = problem.startpoint_method
     # convert startpoint method to class instance
     startpoint_method = to_startpoint_method(startpoint_method)
 

pypesto/petab/importer.py

@@ -740,6 +740,7 @@ def create_problem(
             x_names=x_ids,
             x_scales=x_scales,
             x_priors_defs=prior,
+            startpoint_method=self.create_startpoint_method(),
             **problem_kwargs,
         )
 

pypesto/problem/base.py

@@ -1,12 +1,21 @@
 import copy
 import logging
-from typing import Iterable, List, Optional, SupportsFloat, SupportsInt, Union
+from typing import (
+    Callable,
+    Iterable,
+    List,
+    Optional,
+    SupportsFloat,
+    SupportsInt,
+    Union,
+)
 
 import numpy as np
 import pandas as pd
 
 from ..objective import ObjectiveBase
 from ..objective.priors import NegLogParameterPriors
+from ..startpoint import StartpointMethod, to_startpoint_method, uniform
 
 SupportsFloatIterableOrValue = Union[Iterable[SupportsFloat], SupportsFloat]
 SupportsIntIterableOrValue = Union[Iterable[SupportsInt], SupportsInt]
@@ -90,6 +99,7 @@ def __init__(
         lb_init: Union[np.ndarray, List[float], None] = None,
         ub_init: Union[np.ndarray, List[float], None] = None,
         copy_objective: bool = True,
+        startpoint_method: Union[StartpointMethod, Callable, bool] = None,
     ):
         if copy_objective:
             objective = copy.deepcopy(objective)
@@ -147,6 +157,12 @@ def __init__(
         self.normalize()
         self._check_x_guesses()
 
+        # startpoint method
+        if startpoint_method is None:
+            startpoint_method = uniform
+        # convert startpoint method to class instance
+        self.startpoint_method = to_startpoint_method(startpoint_method)
+
     @property
     def lb(self) -> np.ndarray:
         """Return lower bounds of free parameters."""

pypesto/startpoint/base.py

@@ -1,13 +1,16 @@
 """Startpoint base classes."""
+from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Callable, Union
+from typing import TYPE_CHECKING, Callable, Union
 
 import numpy as np
 
 from ..C import FVAL, GRAD
 from ..objective import ObjectiveBase
-from ..problem import Problem
+
+if TYPE_CHECKING:
+    from ..problem import Problem
 
 
 class StartpointMethod(ABC):