Polish the algo selection tool

.tools/create_algo_selection_code.py

@@ -109,7 +109,7 @@ def _get_algorithms_in_module(module: ModuleType) -> dict[str, Type[Algorithm]]:
     }
     algos = {}
     for candidate in candidate_dict.values():
-        name = candidate.__algo_info__.name
+        name = candidate.algo_info.name
         if issubclass(candidate, Algorithm) and candidate is not Algorithm:
             algos[name] = candidate
     return algos
@@ -119,47 +119,47 @@ def _get_algorithms_in_module(module: ModuleType) -> dict[str, Type[Algorithm]]:
 # Functions to filter algorithms by selectors
 # ======================================================================================
 def _is_gradient_based(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.needs_jac  # type: ignore
+    return algo.algo_info.needs_jac  # type: ignore
 
 
 def _is_gradient_free(algo: Type[Algorithm]) -> bool:
     return not _is_gradient_based(algo)
 
 
 def _is_global(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.is_global  # type: ignore
+    return algo.algo_info.is_global  # type: ignore
 
 
 def _is_local(algo: Type[Algorithm]) -> bool:
     return not _is_global(algo)
 
 
 def _is_bounded(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.supports_bounds  # type: ignore
+    return algo.algo_info.supports_bounds  # type: ignore
 
 
 def _is_linear_constrained(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.supports_linear_constraints  # type: ignore
+    return algo.algo_info.supports_linear_constraints  # type: ignore
 
 
 def _is_nonlinear_constrained(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.supports_nonlinear_constraints  # type: ignore
+    return algo.algo_info.supports_nonlinear_constraints  # type: ignore
 
 
 def _is_scalar(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.solver_type == AggregationLevel.SCALAR  # type: ignore
+    return algo.algo_info.solver_type == AggregationLevel.SCALAR  # type: ignore
 
 
 def _is_least_squares(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.solver_type == AggregationLevel.LEAST_SQUARES  # type: ignore
+    return algo.algo_info.solver_type == AggregationLevel.LEAST_SQUARES  # type: ignore
 
 
 def _is_likelihood(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.solver_type == AggregationLevel.LIKELIHOOD  # type: ignore
+    return algo.algo_info.solver_type == AggregationLevel.LIKELIHOOD  # type: ignore
 
 
 def _is_parallel(algo: Type[Algorithm]) -> bool:
-    return algo.__algo_info__.supports_parallelism  # type: ignore
+    return algo.algo_info.supports_parallelism  # type: ignore
 
 
 def _get_filters() -> dict[str, Callable[[Type[Algorithm]], bool]]:
@@ -385,27 +385,32 @@ def _all(self) -> list[Type[Algorithm]]:
             def _available(self) -> list[Type[Algorithm]]:
                 _all = self._all()
                 return [
-                    a for a in _all if a.__algo_info__.is_available # type: ignore
+                    a for a in _all if a.algo_info.is_available # type: ignore
                 ]
 
             @property
-            def All(self) -> list[str]:
-                return [a.__algo_info__.name for a in self._all()]  # type: ignore
+            def All(self) -> list[Type[Algorithm]]:
+                return self._all()
 
             @property
-            def Available(self) -> list[str]:
-                return [a.__algo_info__.name for a in self._available()]  # type: ignore
+            def Available(self) -> list[Type[Algorithm]]:
+                return self._available()
+
+            @property
+            def AllNames(self) -> list[str]:
+                return [str(a.name) for a in self._all()]
+
+            @property
+            def AvailableNames(self) -> list[str]:
+                return [str(a.name) for a in self._available()]
 
             @property
             def _all_algorithms_dict(self) -> dict[str, Type[Algorithm]]:
-                return {a.__algo_info__.name: a for a in self._all()} # type: ignore
+                return {str(a.name): a for a in self._all()}
 
             @property
             def _available_algorithms_dict(self) -> dict[str, Type[Algorithm]]:
-                return {
-                    a.__algo_info__.name: a # type: ignore
-                    for a in self._available()
-                }
+                return {str(a.name): a for a in self._available()}
 
     """)
     return out

CHANGES.md

@@ -4,9 +4,27 @@ This is a record of all past optimagic releases and what went into them in rever
 chronological order. We follow [semantic versioning](https://semver.org/) and all
 releases are available on [Anaconda.org](https://anaconda.org/optimagic-dev/optimagic).
 
-Following the [scientific python guidelines](https://scientific-python.org/specs/spec-0000/)
-we drop the official support for Python 3.9.
 
+## 0.5.1
+
+This is a minor release that introduces the new algorithm selection tool and several
+small improvements.
+
+To learn more about the algorithm selection feature check out the following resources:
+
+- [How to specify and configure algorithms](https://optimagic.readthedocs.io/en/latest/how_to/how_to_specify_algorithm_and_algo_options.html)
+- [How to select local optimizers](https://optimagic.readthedocs.io/en/latest/how_to/how_to_algorithm_selection.html)
+
+- {gh}`549` Add support for Python 3.13 ({ghuser}`timmens`)
+- {gh}`550` and {gh}`534` implement the new algorithm selection tool ({ghuser}`janosg`)
+- {gh}`548` and {gh}`531` improve the documentation ({ghuser}`ChristianZimpelmann`)
+- {gh}`544` Adjusts the results processing of the nag optimizers to be compatible
+  with the latest releases ({ghuser}`timmens`)
+- {gh}`543` Adds support for numpy 2.x ({ghuser}`timmens`)
+- {gh}`536` Adds a how-to guide for choosing local optimizers ({ghuser}`mpetrosian`)
+- {gh}`535` Allows algorithm classes and instances in estimation functions
+  ({ghuser}`timmens`)
+- {gh}`532` Makes several small improvements to the documentation.
 
 ## 0.5.0
 

src/estimagic/__init__.py

@@ -1,12 +1,6 @@
-import contextlib
 import warnings
 from dataclasses import dataclass
 
-try:
-    import pdbp  # noqa: F401
-except ImportError:
-    contextlib.suppress(Exception)
-
 from estimagic import utilities
 from estimagic.bootstrap import BootstrapResult, bootstrap
 from estimagic.estimate_ml import LikelihoodResult, estimate_ml

src/optimagic/__init__.py

@@ -1,12 +1,5 @@
 from __future__ import annotations
 
-import contextlib
-
-try:
-    import pdbp  # noqa: F401
-except ImportError:
-    contextlib.suppress(Exception)
-
 from optimagic import constraints, mark, utilities
 from optimagic.algorithms import algos
 from optimagic.benchmarking.benchmark_reports import (

src/optimagic/algorithms.py

@@ -90,27 +90,32 @@ def _available(self) -> list[Type[Algorithm]]:
         return [
             a
             for a in _all
-            if a.__algo_info__.is_available  # type: ignore
+            if a.algo_info.is_available  # type: ignore
         ]
 
     @property
-    def All(self) -> list[str]:
-        return [a.__algo_info__.name for a in self._all()]  # type: ignore
+    def All(self) -> list[Type[Algorithm]]:
+        return self._all()
 
     @property
-    def Available(self) -> list[str]:
-        return [a.__algo_info__.name for a in self._available()]  # type: ignore
+    def Available(self) -> list[Type[Algorithm]]:
+        return self._available()
+
+    @property
+    def AllNames(self) -> list[str]:
+        return [str(a.name) for a in self._all()]
+
+    @property
+    def AvailableNames(self) -> list[str]:
+        return [str(a.name) for a in self._available()]
 
     @property
     def _all_algorithms_dict(self) -> dict[str, Type[Algorithm]]:
-        return {a.__algo_info__.name: a for a in self._all()}  # type: ignore
+        return {str(a.name): a for a in self._all()}
 
     @property
     def _available_algorithms_dict(self) -> dict[str, Type[Algorithm]]:
-        return {
-            a.__algo_info__.name: a  # type: ignore
-            for a in self._available()
-        }
+        return {str(a.name): a for a in self._available()}
 
 
 @dataclass(frozen=True)

src/optimagic/optimization/algorithm.py

@@ -1,6 +1,6 @@
 import typing
 import warnings
-from abc import ABC, abstractmethod
+from abc import ABC, ABCMeta, abstractmethod
 from dataclasses import dataclass, replace
 from typing import Any
 
@@ -143,8 +143,38 @@
             raise TypeError(msg)
 
 
+class AlgorithmMeta(ABCMeta):
+    """Metaclass to get repr, algo_info and name for classes, not just instances."""
+
+    def __repr__(self) -> str:
+        if hasattr(self, "__algo_info__") and self.__algo_info__ is not None:
+            out = f"om.algos.{self.__algo_info__.name}"
+        else:
+            out = self.__class__.__name__
+        return out
+
+    @property
+    def name(self) -> str:
+        if hasattr(self, "__algo_info__") and self.__algo_info__ is not None:
+            out = self.__algo_info__.name
+        else:
+            out = self.__class__.__name__
+        return out
+
+    @property
+    def algo_info(self) -> AlgoInfo:
+        if not hasattr(self, "__algo_info__") or self.__algo_info__ is None:
+            msg = (
+                f"The algorithm {self.name} does not have have the __algo_info__ "
+                "attribute. Use the `mark.minimizer` decorator to add this attribute."
+            )
+            raise AttributeError(msg)
+
+        return self.__algo_info__
+
+
 @dataclass(frozen=True)
-class Algorithm(ABC):
+class Algorithm(ABC, metaclass=AlgorithmMeta):
     @abstractmethod
     def _solve_internal_problem(
         self, problem: InternalOptimizationProblem, x0: NDArray[np.float64]

src/optimagic/optimizers/pygmo_optimizers.py

@@ -19,7 +19,6 @@
 
 from __future__ import annotations
 
-import contextlib
 import warnings
 from dataclasses import dataclass
 from typing import Any, List, Literal
@@ -48,8 +47,10 @@
 
 STOPPING_MAX_ITERATIONS_GENETIC = 250
 
-with contextlib.suppress(ImportError):
+try:
     import pygmo as pg
+except ImportError:
+    pass
 
 
 @mark.minimizer(

src/optimagic/optimizers/tao_optimizers.py

@@ -1,6 +1,5 @@
 """This module implements the POUNDERs algorithm."""
 
-import contextlib
 import functools
 from dataclasses import dataclass
 
@@ -23,8 +22,10 @@
 from optimagic.typing import AggregationLevel, NonNegativeFloat, PositiveInt
 from optimagic.utilities import calculate_trustregion_initial_radius
 
-with contextlib.suppress(ImportError):
+try:
     from petsc4py import PETSc
+except ImportError:
+    pass
 
 
 @mark.minimizer(

src/optimagic/visualization/history_plots.py

@@ -1,12 +1,15 @@
+import inspect
 import itertools
 from pathlib import Path
+from typing import Any
 
 import numpy as np
 import plotly.graph_objects as go
 from pybaum import leaf_names, tree_flatten, tree_just_flatten, tree_unflatten
 
 from optimagic.config import PLOTLY_PALETTE, PLOTLY_TEMPLATE
 from optimagic.logging.logger import LogReader, SQLiteLogOptions
+from optimagic.optimization.algorithm import Algorithm
 from optimagic.optimization.history_tools import get_history_arrays
 from optimagic.optimization.optimize_result import OptimizeResult
 from optimagic.parameters.tree_registry import get_registry
@@ -50,23 +53,7 @@ def criterion_plot(
     # Process inputs
     # ==================================================================================
 
-    if not isinstance(names, list) and names is not None:
-        names = [names]
-
-    if not isinstance(results, dict):
-        if isinstance(results, list):
-            names = range(len(results)) if names is None else names
-            if len(names) != len(results):
-                raise ValueError("len(results) needs to be equal to len(names).")
-            results = dict(zip(names, results, strict=False))
-        else:
-            name = 0 if names is None else names
-            if isinstance(name, list):
-                if len(name) > 1:
-                    raise ValueError("len(results) needs to be equal to len(names).")
-                else:
-                    name = name[0]
-            results = {name: results}
+    results = _harmonize_inputs_to_dict(results, names)
 
     if not isinstance(palette, list):
         palette = [palette]
@@ -180,6 +167,46 @@ def criterion_plot(
     return fig
 
 
+def _harmonize_inputs_to_dict(results, names):
+    """Convert all valid inputs for results and names to dict[str, OptimizeResult]."""
+    # convert scalar case to list case
+    if not isinstance(names, list) and names is not None:
+        names = [names]
+
+    if isinstance(results, OptimizeResult):
+        results = [results]
+
+    if names is not None and len(names) != len(results):
+        raise ValueError("len(results) needs to be equal to len(names).")
+
+    # handle dict case
+    if isinstance(results, dict):
+        if names is not None:
+            results_dict = dict(zip(names, list(results.values()), strict=False))
+        else:
+            results_dict = results
+
+    # unlabeled iterable of results
+    else:
+        names = range(len(results)) if names is None else names
+        results_dict = dict(zip(names, results, strict=False))
+
+    # convert keys to strings
+    results_dict = {_convert_key_to_str(k): v for k, v in results_dict.items()}
+
+    return results_dict
+
+
+def _convert_key_to_str(key: Any) -> str:
+    if inspect.isclass(key) and issubclass(key, Algorithm):
+        out = str(key.name)
+    elif isinstance(key, Algorithm):
+        out = str(key.name)
+    else:
+        out = str(key)
+    return out
+
+
 def params_plot(
     result,
     selector=None,

tests/optimagic/optimization/test_history_collection.py

@@ -17,7 +17,7 @@
 OPTIMIZERS = []
 BOUNDED = []
 for name, algo in AVAILABLE_ALGORITHMS.items():
-    info = algo.__algo_info__
+    info = algo.algo_info
     if not info.disable_history:
         if info.supports_parallelism:
             OPTIMIZERS.append(name)