Add startpoint_method to Problem

pypesto/optimize/ess/cess.py

@@ -4,6 +4,7 @@
 import os
 import time
 from typing import Dict, List, Optional
+from warnings import warn
 
 import numpy as np
 
@@ -107,7 +108,7 @@ def _initialize(self):
     def minimize(
         self,
         problem: Problem,
-        startpoint_method: StartpointMethod,
+        startpoint_method: StartpointMethod = None,
     ) -> pypesto.Result:
         """Minimize the given objective using CESS.
 
@@ -117,7 +118,14 @@ def minimize(
             Problem to run ESS on.
         startpoint_method:
             Method for choosing starting points.
+            **Deprecated. Use ``problem.startpoint_method`` instead.**
         """
+        if startpoint_method is not None:
+            warn(
+                "Passing `startpoint_method` directly is deprecated, use `problem.startpoint_method` instead.",
+                DeprecationWarning,
+            )
+
         self._initialize()
 
         evaluator = FunctionEvaluator(

pypesto/optimize/ess/ess.py

@@ -28,6 +28,7 @@
 import logging
 import time
 from typing import List, Optional, Tuple
+from warnings import warn
 
 import numpy as np
 
@@ -183,21 +184,24 @@ def minimize(
             Problem to run ESS on.
         startpoint_method:
             Method for choosing starting points.
+            **Deprecated. Use ``problem.startpoint_method`` instead.**
         refset:
             The initial RefSet or ``None`` to auto-generate.
         """
+        if startpoint_method is not None:
+            warn(
+                "Passing `startpoint_method` directly is deprecated, use `problem.startpoint_method` instead.",
+                DeprecationWarning,
+            )
+
         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:

pypesto/optimize/ess/function_evaluator.py

@@ -5,6 +5,7 @@
 from concurrent.futures import ThreadPoolExecutor
 from copy import deepcopy
 from typing import Optional, Sequence, Tuple
+from warnings import warn
 
 import numpy as np
 
@@ -31,17 +32,28 @@ class FunctionEvaluator:
     def __init__(
         self,
         problem: Problem,
-        startpoint_method: StartpointMethod,
+        startpoint_method: StartpointMethod = None,
     ):
         """Construct.
 
         Parameters
         ----------
-        problem: The problem
-        startpoint_method: Method for choosing feasible parameters
+        problem:
+            The problem
+        startpoint_method:
+            Method for choosing feasible parameters
+            **Deprecated. Use ``problem.startpoint_method`` instead.**
         """
+        if startpoint_method is not None:
+            warn(
+                "Passing `startpoint_method` directly is deprecated, use `problem.startpoint_method` instead.",
+                DeprecationWarning,
+            )
+
         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

@@ -6,6 +6,7 @@
 from multiprocessing import Manager, Process
 from multiprocessing.managers import SyncManager
 from typing import Any, Dict, List, Optional, Tuple
+from warnings import warn
 
 import numpy as np
 
@@ -96,9 +97,15 @@ def __init__(
     def minimize(
         self,
         problem: Problem,
-        startpoint_method: StartpointMethod,
+        startpoint_method: StartpointMethod = None,
     ):
         """Solve the given optimization problem."""
+        if startpoint_method is not None:
+            warn(
+                "Passing `startpoint_method` directly is deprecated, use `problem.startpoint_method` instead.",
+                DeprecationWarning,
+            )
+
         start_time = time.time()
         logger.debug(
             f"Running sacess with {self.num_workers} "

pypesto/optimize/optimize.py

@@ -1,5 +1,6 @@
 import logging
 from typing import Callable, Iterable, Union
+from warnings import warn
 
 from ..engine import Engine, SingleCoreEngine
 from ..history import HistoryOptions
@@ -50,6 +51,7 @@ def minimize(
     startpoint_method:
         Method for how to choose start points. False means the optimizer does
         not require start points, e.g. for the 'PyswarmOptimizer'.
+        **Deprecated. Use ``problem.startpoint_method`` instead.**
     result:
         A result object to append the optimization results to. For example,
         one might append more runs to a previous optimization. If None,
@@ -88,7 +90,16 @@ def minimize(
 
     # 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
+    else:
+        warn(
+            "Passing `startpoint_method` directly is deprecated, use `problem.startpoint_method` instead.",
+            DeprecationWarning,
+        )
+
     # 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]
@@ -60,6 +69,9 @@ class Problem:
     copy_objective:
         Whethter to generate a deep copy of the objective function before
         potential modification the problem class performs on it.
+    startpoint_method:
+        Method for how to choose start points. ``False`` means the optimizer
+        does not require start points, e.g. for the ``PyswarmOptimizer``.
 
     Notes
     -----
@@ -90,6 +102,7 @@ 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 +160,12 @@ 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:
+    import pypesto
 
 
 class StartpointMethod(ABC):
@@ -21,7 +24,7 @@ class StartpointMethod(ABC):
     def __call__(
         self,
         n_starts: int,
-        problem: Problem,
+        problem: pypesto.problem.Problem,
     ) -> np.ndarray:
         """Generate startpoints.
 
@@ -42,7 +45,7 @@ class NoStartpoints(StartpointMethod):
     def __call__(
         self,
         n_starts: int,
-        problem: Problem,
+        problem: pypesto.problem.Problem,
     ) -> np.ndarray:
         """Generate a (n_starts, dim) nan matrix."""
         startpoints = np.full(shape=(n_starts, problem.dim), fill_value=np.nan)
@@ -78,7 +81,7 @@ def __init__(
     def __call__(
         self,
         n_starts: int,
-        problem: Problem,
+        problem: pypesto.problem.Problem,
     ) -> np.ndarray:
         """Generate checked startpoints."""
         # shape: (n_guesses, dim)