Error checking for #1461

nevergrad/benchmark/test_experiments.py

@@ -29,9 +29,11 @@
 @testing.parametrized(**{name: (name, maker) for name, maker in experiments.registry.items()})
 def test_experiments_registry(name: str, maker: tp.Callable[[], tp.Iterator[experiments.Experiment]]) -> None:
     # "mav" is not availablefor now.
-    if name == "conformant_planning" or name == "neuro_planning":
+    if name == "conformant_planning" or name == "neuro_planning" or "compiler" in name:
         raise SkipTest("This is user parametric and can not be tested.")
-
+    if "emulator" in name:
+        raise SkipTest("Emulators not included in CI.")
+
     # Our PGAN is not well accepted by circleci.
     if "_pgan" in name and os.environ.get("CIRCLECI", False):
         raise SkipTest("Too slow in CircleCI")

nevergrad/optimization/differentialevolution.py

@@ -101,7 +101,8 @@ def __init__(
         if isinstance(self._config.popsize, int):
             self.llambda = self._config.popsize
         else:
-            self.llambda = max(30, self.num_workers, pop_choice[self._config.popsize])
+            self.llambda = max(30, pop_choice[self._config.popsize])
+        self.llambda = max(self.llambda, self.num_workers)
         # internals
         if budget is not None and budget < 60:
             warnings.warn(
@@ -197,7 +198,9 @@ def _internal_tell_candidate(self, candidate: p.Parameter, loss: tp.FloatLoss) -
         if uid not in self.population:  # parent was removed, revert to tell_not_asked
             self._internal_tell_not_asked(candidate, loss)
             return
-        self._uid_queue.tell(uid)  # only add to queue if not a "tell_not_asked" (from a removed parent)
+        if uid in self._uid_queue.asked:  # if taken from queue in multiple asks, add back only once
+            self._uid_queue.asked.discard(uid)
+            self._uid_queue.tell(uid)
         parent = self.population[uid]
         mo_adapt = self._config.multiobjective_adaptation and self.num_objectives > 1
         mo_adapt &= candidate._losses is not None  # can happen with bad constraints
@@ -275,8 +278,8 @@ class DifferentialEvolution(base.ConfiguredOptimizer):
     F2: float
         differential weight #2
     popsize: int, "standard", "dimension", "large"
-        size of the population to use. "standard" is max(num_workers, 30), "dimension" max(num_workers, 30, dimension +1)
-        and "large" max(num_workers, 30, 7 * dimension).
+        size of the population to use. "standard" is 30, "dimension" max(30, dimension +1) and "large"
+        max(30, 7 * dimension). Set to be at least num_workers so that base vectors are distinct.
     multiobjective_adaptation: bool
         Automatically adapts to handle multiobjective case.  This is a very basic **experimental** version,
         activated by default because the non-multiobjective implementation is performing very badly.

nevergrad/optimization/experimentalvariants.py

@@ -357,6 +357,7 @@
 MixDeterministicRL = ConfPortfolio(optimizers=[DiagonalCMA, PSO, GeneticDE]).set_name(
     "MixDeterministicRL", register=True
 )
+
 SpecialRL = Chaining([MixDeterministicRL, TBPSA], ["half"]).set_name("SpecialRL", register=True)
 NoisyRL1 = Chaining([MixDeterministicRL, NoisyOnePlusOne], ["half"]).set_name("NoisyRL1", register=True)
 NoisyRL2 = Chaining(

nevergrad/optimization/optimizerlib.py

@@ -1596,7 +1596,7 @@ def __init__(
         num = len(optimizers)
         self.optims: tp.List[base.Optimizer] = []
         sub_budget = None if budget is None else budget // num + (budget % num > 0)
-        sub_workers = 1
+        sub_workers = num_workers
         if distribute_workers:
             sub_workers = num_workers // num + (num_workers % num > 0)
         for opt in optimizers:
@@ -1673,7 +1673,7 @@ def enable_pickling(self) -> None:
             opt.enable_pickling()
 
 
-ParaPortfolio = ConfPortfolio(optimizers=[CMA, TwoPointsDE, PSO, SQP, ScrHammersleySearch]).set_name(
+ParaPortfolio = ConfPortfolio(optimizers=[CMA, TwoPointsDE, PSO, ScrHammersleySearch]).set_name(
     "ParaPortfolio", register=True
 )
 ASCMADEthird = ConfPortfolio(optimizers=[CMA, LhsDE], warmup_ratio=0.33).set_name(