Fix cma library dependency, update pymoo (#1711)

* Fix cma library dependency: drop numpy downgrade, fix seed type, bump cma

- Remove forced numpy==1.26.4 downgrade in CI that broke cma import
- Wrap randint result with int() for random.seed() compatibility on numpy 2.x
- Bump cma requirement from >=2.6.0 to >=3.4.0

* Update pymoo from 0.5.0 to >=0.6.1 and fix removed factory API

- Bump pymoo requirement from ==0.5.0 to >=0.6.1 (fixes cma==2.7 pin conflict)
- Replace removed pymoo.factory.get_algorithm with _get_pymoo_algorithm helper
- Remove pymoo < 0.5.0 fallback in _create_pymoo_problem

Author: bottler

.github/actions/ubuntu_restore_all/action.yml

@@ -19,6 +19,6 @@ runs:
         ( unset GITHUB_REF ; pip install --progress-bar off IOHexperimenter>=0.2.9.2 )
         pip install --progress-bar off -e .[all]
         #pip install --progress-bar off --use-deprecated=legacy-resolver -e .[all]
-        pip install --progress-bar off -U numpy==1.26.4 matplotlib==3.9.0 pyomo==5.7.1
+        pip install --progress-bar off -U matplotlib==3.9.0 pyomo==5.7.1
         pip install keras==2.6.0  # issue on Nov 4th 2021
       shell: bash

nevergrad/benchmark/xpbase.py

@@ -27,7 +27,6 @@
 )  # import from optimizerlib so as to fill it
 from . import execution
 
-
 registry: decorators.Registry[tp.Callable[..., tp.Iterator["Experiment"]]] = decorators.Registry()
 
 
@@ -131,7 +130,7 @@ def create_seed_generator(seed: tp.Optional[int]) -> tp.Iterator[tp.Optional[int
     """
     generator = None if seed is None else np.random.RandomState(seed=seed)
     while True:
-        yield None if generator is None else generator.randint(2**32, dtype=np.uint32)  # type: ignore
+        yield None if generator is None else int(generator.randint(2**32, dtype=np.uint32))  # type: ignore
 
 
 class Experiment:

nevergrad/optimization/recastlib.py

@@ -599,23 +599,6 @@ def _optimization_function(
         # pylint:disable=unused-argument, import-outside-toplevel
         from pymoo import optimize as pymoooptimize
 
-        from pymoo.factory import get_algorithm as get_pymoo_algorithm
-
-        # from pymoo.factory import get_reference_directions
-
-        # reference direction code for when we want to use the other MOO optimizers in Pymoo
-        # if self.algorithm in [
-        #     "rnsga2",
-        #     "nsga3",
-        #     "unsga3",
-        #     "rnsga3",
-        #     "moead",
-        #     "ctaea",
-        # ]:  # algorithms that require reference points or reference directions
-        #     the appropriate n_partitions must be looked into
-        #     ref_dirs = get_reference_directions("das-dennis", self.num_objectives, n_partitions=12)
-        #     algorithm = get_pymoo_algorithm(self.algorithm, ref_dirs)
-        # else:
         problem = _create_pymoo_problem(weakself, objective_function)
         if weakself.algorithm == "CMAES":
             from pymoo.algorithms.soo.nonconvex.cmaes import CMAES
@@ -635,7 +618,7 @@ def _optimization_function(
                 bipop=True,
             )
         else:
-            algorithm = get_pymoo_algorithm(weakself.algorithm)
+            algorithm = _get_pymoo_algorithm(weakself.algorithm)
         pymoooptimize.minimize(problem, algorithm, seed=weakself._initial_seed)
         return None
 
@@ -745,24 +728,7 @@ def _optimization_function(
         # pylint:disable=unused-argument, import-outside-toplevel
         from pymoo import optimize as pymoooptimize
 
-        from pymoo.factory import get_algorithm as get_pymoo_algorithm
-
-        # from pymoo.factory import get_reference_directions
-
-        # reference direction code for when we want to use the other MOO optimizers in Pymoo
-        # if self.algorithm in [
-        #     "rnsga2",
-        #     "nsga3",
-        #     "unsga3",
-        #     "rnsga3",
-        #     "moead",
-        #     "ctaea",
-        # ]:  # algorithms that require reference points or reference directions
-        #     the appropriate n_partitions must be looked into
-        #     ref_dirs = get_reference_directions("das-dennis", self.num_objectives, n_partitions=12)
-        #     algorithm = get_pymoo_algorithm(self.algorithm, ref_dirs)
-        # else:
-        algorithm = get_pymoo_algorithm(weakself.algorithm)
+        algorithm = _get_pymoo_algorithm(weakself.algorithm)
         problem = _create_pymoo_problem(weakself, objective_function, False)
         pymoooptimize.minimize(problem, algorithm, seed=weakself._initial_seed)
         return None
@@ -834,23 +800,41 @@ def __init__(self, *, algorithm: str) -> None:
         super().__init__(_PymooBatchMinimizeBase, locals())
 
 
+def _get_pymoo_algorithm(name: str):
+    """Instantiate a pymoo algorithm by name (replaces removed pymoo.factory.get_algorithm)."""
+    # pylint:disable=import-outside-toplevel
+    _PYMOO_ALGORITHMS = {
+        "de": "pymoo.algorithms.soo.nonconvex.de.DE",
+        "ga": "pymoo.algorithms.soo.nonconvex.ga.GA",
+        "brkga": "pymoo.algorithms.soo.nonconvex.brkga.BRKGA",
+        "nelder-mead": "pymoo.algorithms.soo.nonconvex.nelder.NelderMead",
+        "pattern-search": "pymoo.algorithms.soo.nonconvex.pattern.PatternSearch",
+        "cmaes": "pymoo.algorithms.soo.nonconvex.cmaes.CMAES",
+        "nsga2": "pymoo.algorithms.moo.nsga2.NSGA2",
+        "rnsga2": "pymoo.algorithms.moo.rnsga2.RNSGA2",
+        "nsga3": "pymoo.algorithms.moo.nsga3.NSGA3",
+        "unsga3": "pymoo.algorithms.moo.unsga3.UNSGA3",
+        "rnsga3": "pymoo.algorithms.moo.rnsga3.RNSGA3",
+        "moead": "pymoo.algorithms.moo.moead.MOEAD",
+        "ctaea": "pymoo.algorithms.moo.ctaea.CTAEA",
+    }
+    import importlib
+
+    qualname = _PYMOO_ALGORITHMS[name]
+    module_path, class_name = qualname.rsplit(".", 1)
+    module = importlib.import_module(module_path)
+    return getattr(module, class_name)()
+
+
 def _create_pymoo_problem(
     optimizer: base.Optimizer,
     objective_function: tp.Callable[[tp.ArrayLike], float],
     elementwise: bool = True,
 ):
-    kwargs = {}
-    try:
-        # pylint:disable=import-outside-toplevel
-        from pymoo.core.problem import ElementwiseProblem, Problem  # type: ignore
-
-        Base = ElementwiseProblem if elementwise else Problem
-    except ImportError:
-        # Used if pymoo < 0.5.0
-        # pylint:disable=import-outside-toplevel
-        from pymoo.model.problem import Problem as Base  # type: ignore
+    # pylint:disable=import-outside-toplevel
+    from pymoo.core.problem import ElementwiseProblem, Problem  # type: ignore
 
-        kwargs = {"elementwise_evaluation": elementwise}
+    Base = ElementwiseProblem if elementwise else Problem
 
     class _PymooProblem(Base):  # type: ignore
         def __init__(self, optimizer, objective_function):
@@ -861,7 +845,6 @@ def __init__(self, optimizer, objective_function):
                 n_constr=0,  # constraints handled already by nevergrad
                 xl=-math.pi * 0.5,
                 xu=math.pi * 0.5,
-                **kwargs,
             )
 
         def _evaluate(self, X, out, *args, **kwargs):

requirements/bench.txt

@@ -40,7 +40,7 @@ scikit-image>=0.18.3
 image-quality>=1.2.7
 #keras>=2.4.3
 #tensorflow>=2.7.0
-pymoo==0.5.0
+pymoo>=0.6.1
 olymp>=0.0.1b0 ; sys_platform == "linux"
 Keras-Preprocessing
 silence_tensorflow  # for olymp

requirements/main.txt

@@ -1,5 +1,5 @@
 numpy>=1.26.4
-cma>=2.6.0
+cma>=3.4.0
 bayesian-optimization==1.4.0
 typing_extensions>=3.6.6
 pandas