From b88578d66ed62ce439a32a851038ce67106ff092 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 12:48:44 +0100
Subject: [PATCH 01/17] [WIP] Add MOO mode for 1+1

---
 nevergrad/optimization/optimizerlib.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/nevergrad/optimization/optimizerlib.py b/nevergrad/optimization/optimizerlib.py
index 0c6835561..a8fc78a2e 100644
--- a/nevergrad/optimization/optimizerlib.py
+++ b/nevergrad/optimization/optimizerlib.py
@@ -69,9 +69,11 @@ def __init__(
         noise_handling: tp.Optional[tp.Union[str, tp.Tuple[str, float]]] = None,
         mutation: str = "gaussian",
         crossover: bool = False,
+        use_pareto: bool = True,
     ) -> None:
         super().__init__(parametrization, budget=budget, num_workers=num_workers)
         self._sigma: float = 1
+        self.use_pareto = use_pareto
         all_params = paramhelpers.flatten_parameter(self.parametrization)
         arity = max(
             len(param.choices) if isinstance(param, p.TransitionChoice) else 500
@@ -153,6 +155,9 @@ def _internal_ask_candidate(self) -> p.Parameter:
         # crossover
         mutator = mutations.Mutator(self._rng)
         pessimistic = self.current_bests["pessimistic"].parameter.spawn_child()
+        if self.num_objectives > 1 and self.use_pareto:  # multiobjective
+            # revert to using a sample of the pareto front (not "pessimistic" though)
+            pessimistic = self._rng.choice(self.pareto_front())
         ref = self.parametrization
         if self.crossover and self._num_ask % 2 == 1 and len(self.archive) > 2:
             data = mutator.crossover(
@@ -293,6 +298,8 @@ class ParametrizedOnePlusOne(base.ConfiguredOptimizer):
         - `"lengler"`: specific mutation rate chosen as a function of the dimension and iteration index.
     crossover: bool
         whether to add a genetic crossover step every other iteration.
+    use_pareto: bool
+        whether to restart from a random pareto element in multiobjective mode, instead of the last one added
 
     Notes
     -----
@@ -310,6 +317,7 @@ def __init__(
         noise_handling: tp.Optional[tp.Union[str, tp.Tuple[str, float]]] = None,
         mutation: str = "gaussian",
         crossover: bool = False,
+        use_pareto: bool = True,
     ) -> None:
         super().__init__(_OnePlusOne, locals())
 

From e164c27a3ba76883b360cb24843563d0424ac51a Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 12:57:50 +0100
Subject: [PATCH 02/17] fix

---
 nevergrad/optimization/optimizerlib.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nevergrad/optimization/optimizerlib.py b/nevergrad/optimization/optimizerlib.py
index a8fc78a2e..3e891d526 100644
--- a/nevergrad/optimization/optimizerlib.py
+++ b/nevergrad/optimization/optimizerlib.py
@@ -157,7 +157,7 @@ def _internal_ask_candidate(self) -> p.Parameter:
         pessimistic = self.current_bests["pessimistic"].parameter.spawn_child()
         if self.num_objectives > 1 and self.use_pareto:  # multiobjective
             # revert to using a sample of the pareto front (not "pessimistic" though)
-            pessimistic = self._rng.choice(self.pareto_front())
+            pessimistic = self._rng.choice(self.pareto_front()).spawn_child()
         ref = self.parametrization
         if self.crossover and self._num_ask % 2 == 1 and len(self.archive) > 2:
             data = mutator.crossover(

From f7e0e73e3593ec8b0c3b37a8a25849ba8f6da67f Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 14:21:50 +0100
Subject: [PATCH 03/17] fix

---
 nevergrad/optimization/base.py              | 11 +++++++----
 nevergrad/optimization/test_optimizerlib.py | 10 +++++++++-
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/nevergrad/optimization/base.py b/nevergrad/optimization/base.py
index f56fb95a4..5bc07ba57 100644
--- a/nevergrad/optimization/base.py
+++ b/nevergrad/optimization/base.py
@@ -213,11 +213,14 @@ def pareto_front(
         ----
         During non-multiobjective optimization, this returns the current pessimistic best
         """
-        if self._hypervolume_pareto is None:
-            return [self.provide_recommendation()]
-        return self._hypervolume_pareto.pareto_front(
-            size=size, subset=subset, subset_tentatives=subset_tentatives
+        pareto = (
+            []
+            if self._hypervolume_pareto is None
+            else self._hypervolume_pareto.pareto_front(
+                size=size, subset=subset, subset_tentatives=subset_tentatives
+            )
         )
+        return pareto if pareto else [self.provide_recommendation()]
 
     def dump(self, filepath: tp.Union[str, Path]) -> None:
         """Pickles the optimizer into a file."""
diff --git a/nevergrad/optimization/test_optimizerlib.py b/nevergrad/optimization/test_optimizerlib.py
index 9f6e15fa2..f120638d5 100644
--- a/nevergrad/optimization/test_optimizerlib.py
+++ b/nevergrad/optimization/test_optimizerlib.py
@@ -715,7 +715,7 @@ def _multiobjective(z: np.ndarray) -> tp.Tuple[float, float, float]:
     return (abs(x - 1), abs(y + 1), abs(x - y))
 
 
-@pytest.mark.parametrize("name", ["DE", "ES"])  # type: ignore
+@pytest.mark.parametrize("name", ["DE", "ES", "OnePlusOne"])  # type: ignore
 @testing.suppress_nevergrad_warnings()  # hides bad loss
 def test_mo_constrained(name: str) -> None:
     optimizer = optlib.registry[name](2, budget=60)
@@ -733,6 +733,14 @@ def constraint(arg: tp.Any) -> bool:  # pylint: disable=unused-argument
         assert optimizer._rank_method is not None  # make sure the nsga2 ranker is used
 
 
+def test_mo_one_plus_one_with_nan() -> None:
+    optimizer = optlib.OnePlusOne(2, budget=30)
+    optimizer.tell(ng.p.MultiobjectiveReference(), [10, 10, 10])
+    for _ in range(3):
+        cand = optimizer.ask()
+        optimizer.tell(cand, [-38, 0, np.nan])
+
+
 def test_paraportfolio_de() -> None:
     workers = 40
     opt = optlib.ParaPortfolio(12, budget=100 * workers, num_workers=workers)

From 0ffc33577a999e0af34b7c9036a074a9adc527df Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 16:01:59 +0100
Subject: [PATCH 04/17] num

---
 nevergrad/benchmark/test_xpbase.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/nevergrad/benchmark/test_xpbase.py b/nevergrad/benchmark/test_xpbase.py
index d4c99ac52..da5de8835 100644
--- a/nevergrad/benchmark/test_xpbase.py
+++ b/nevergrad/benchmark/test_xpbase.py
@@ -48,9 +48,7 @@ def test_run_packed_artificial_function() -> None:
     )
     xp = xpbase.Experiment(func, optimizer="OnePlusOne", budget=24, num_workers=2, batch_mode=True, seed=14)
     summary = xp.run()
-    np.testing.assert_almost_equal(
-        summary["loss"], -9784.829729792353, decimal=1
-    )  # makes sure seeding works!
+    np.testing.assert_almost_equal(summary["loss"], -9726.2, decimal=1)  # makes sure seeding works!
 
 
 def test_noisy_artificial_function_loss() -> None:

From 1651d7039c783aaafcc5ed4a22b082b96ab31abf Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 17:55:15 +0100
Subject: [PATCH 05/17] fixes

---
 .../optimization/differentialevolution.py     | 31 +++++++++++--------
 nevergrad/optimization/optimizerlib.py        |  5 ++-
 nevergrad/optimization/test_optimizerlib.py   |  9 ++++--
 3 files changed, 28 insertions(+), 17 deletions(-)

diff --git a/nevergrad/optimization/differentialevolution.py b/nevergrad/optimization/differentialevolution.py
index 4baf975d7..2a1a1e9e1 100644
--- a/nevergrad/optimization/differentialevolution.py
+++ b/nevergrad/optimization/differentialevolution.py
@@ -117,18 +117,21 @@ def recommend(self) -> p.Parameter:  # This is NOT the naive version. We deal wi
     def _internal_ask_candidate(self) -> p.Parameter:
         if len(self.population) < self.llambda:  # initialization phase
             init = self._config.initialization
-            if self.sampler is None and init != "gaussian":
+            if self.sampler is None and init not in ["gaussian", "parametrization"]:
                 assert init in ["LHS", "QR"]
                 sampler_cls = sequences.LHSSampler if init == "LHS" else sequences.HammersleySampler
                 self.sampler = sampler_cls(
                     self.dimension, budget=self.llambda, scrambling=init == "QR", random_state=self._rng
                 )
-            new_guy = self.scale * (
-                self._rng.normal(0, 1, self.dimension)
-                if self.sampler is None
-                else stats.norm.ppf(self.sampler())
-            )
-            candidate = self.parametrization.spawn_child().set_standardized_data(new_guy)
+            if self.sampler == "parametrization":
+                candidate = self.parametrization.sample()
+            else:
+                new_guy = self.scale * (
+                    self._rng.normal(0, 1, self.dimension)
+                    if self.sampler is None
+                    else stats.norm.ppf(self.sampler())
+                )
+                candidate = self.parametrization.spawn_child().set_standardized_data(new_guy)
             candidate.heritage["lineage"] = candidate.uid  # new lineage
             self.population[candidate.uid] = candidate
             self._uid_queue.asked.add(candidate.uid)
@@ -146,10 +149,11 @@ def _internal_ask_candidate(self) -> p.Parameter:
         # redefine the different parents in case of multiobjective optimization
         if self._config.multiobjective_adaptation and self.num_objectives > 1:
             pareto = self.pareto_front()
+            # can't use choice directly on pareto, because parametrization can be iterable
             if pareto:
-                best = parent if parent in pareto else self._rng.choice(pareto)
+                best = parent if parent in pareto else pareto[self._rng.choice(len(pareto))]
             if len(pareto) > 2:  # otherwise, not enough diversity
-                a, b = self._rng.choice(pareto, size=2, replace=False)
+                a, b = (pareto[idx] for idx in self._rng.choice(len(pareto), size=2, replace=False))
         # define donor
         data_a, data_b, data_best = (
             indiv.get_standardized_data(reference=self.parametrization) for indiv in (a, b, best)
@@ -228,8 +232,9 @@ class DifferentialEvolution(base.ConfiguredOptimizer):
 
     Parameters
     ----------
-    initialization: "LHS", "QR" or "gaussian"
-        algorithm/distribution used for the initialization phase
+    initialization: "parametrization", "LHS" or "QR"
+        algorithm/distribution used for the initialization phase. If "parametrization", this uses the
+        sample method of the parametrization.
     scale: float or str
         scale of random component of the updates
     recommendation: "pessimistic", "optimistic", "mean" or "noisy"
@@ -256,7 +261,7 @@ class DifferentialEvolution(base.ConfiguredOptimizer):
     def __init__(
         self,
         *,
-        initialization: str = "gaussian",
+        initialization: str = "parametrization",
         scale: tp.Union[str, float] = 1.0,
         recommendation: str = "optimistic",
         crossover: tp.Union[str, float] = 0.5,
@@ -268,7 +273,7 @@ def __init__(
     ) -> None:
         super().__init__(_DE, locals(), as_config=True)
         assert recommendation in ["optimistic", "pessimistic", "noisy", "mean"]
-        assert initialization in ["gaussian", "LHS", "QR"]
+        assert initialization in ["gaussian", "LHS", "QR", "parametrization"]
         assert isinstance(scale, float) or scale == "mini"
         if not isinstance(popsize, int):
             assert popsize in ["large", "dimension", "standard"]
diff --git a/nevergrad/optimization/optimizerlib.py b/nevergrad/optimization/optimizerlib.py
index 3e891d526..e85c95b7d 100644
--- a/nevergrad/optimization/optimizerlib.py
+++ b/nevergrad/optimization/optimizerlib.py
@@ -157,7 +157,10 @@ def _internal_ask_candidate(self) -> p.Parameter:
         pessimistic = self.current_bests["pessimistic"].parameter.spawn_child()
         if self.num_objectives > 1 and self.use_pareto:  # multiobjective
             # revert to using a sample of the pareto front (not "pessimistic" though)
-            pessimistic = self._rng.choice(self.pareto_front()).spawn_child()
+            pareto = (
+                self.pareto_front()
+            )  # we can't use choice directly, because numpy does not like iterables
+            pessimistic = pareto[self._rng.choice(len(pareto))].spawn_child()
         ref = self.parametrization
         if self.crossover and self._num_ask % 2 == 1 and len(self.archive) > 2:
             data = mutator.crossover(
diff --git a/nevergrad/optimization/test_optimizerlib.py b/nevergrad/optimization/test_optimizerlib.py
index f120638d5..b26ddb277 100644
--- a/nevergrad/optimization/test_optimizerlib.py
+++ b/nevergrad/optimization/test_optimizerlib.py
@@ -733,10 +733,13 @@ def constraint(arg: tp.Any) -> bool:  # pylint: disable=unused-argument
         assert optimizer._rank_method is not None  # make sure the nsga2 ranker is used
 
 
-def test_mo_one_plus_one_with_nan() -> None:
-    optimizer = optlib.OnePlusOne(2, budget=30)
+@pytest.mark.parametrize("name", ["DE", "ES", "OnePlusOne"])  # type: ignore
+@testing.suppress_nevergrad_warnings()  # hides bad loss
+def test_mo_with_nan(name: str) -> None:
+    param = ng.p.Instrumentation(x=ng.p.Scalar(lower=0, upper=5), y=ng.p.Scalar(lower=0, upper=3))
+    optimizer = optlib.registry[name](param, budget=60)
     optimizer.tell(ng.p.MultiobjectiveReference(), [10, 10, 10])
-    for _ in range(3):
+    for _ in range(50):
         cand = optimizer.ask()
         optimizer.tell(cand, [-38, 0, np.nan])
 

From 361b8b47c765fb6cfded23d5757a543bdf35358b Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 18:06:24 +0100
Subject: [PATCH 06/17] fix

---
 nevergrad/optimization/differentialevolution.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nevergrad/optimization/differentialevolution.py b/nevergrad/optimization/differentialevolution.py
index 2a1a1e9e1..c79f2918b 100644
--- a/nevergrad/optimization/differentialevolution.py
+++ b/nevergrad/optimization/differentialevolution.py
@@ -123,7 +123,7 @@ def _internal_ask_candidate(self) -> p.Parameter:
                 self.sampler = sampler_cls(
                     self.dimension, budget=self.llambda, scrambling=init == "QR", random_state=self._rng
                 )
-            if self.sampler == "parametrization":
+            if init == "parametrization":
                 candidate = self.parametrization.sample()
             else:
                 new_guy = self.scale * (

From 8f6886b0b3a299cbb664f71f33c6676b9254f1e9 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 18:50:37 +0100
Subject: [PATCH 07/17] fix

---
 .../optimization/differentialevolution.py     | 20 ++++++++-----------
 nevergrad/optimization/test_optimizerlib.py   |  9 +++++++++
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/nevergrad/optimization/differentialevolution.py b/nevergrad/optimization/differentialevolution.py
index c79f2918b..a96913c04 100644
--- a/nevergrad/optimization/differentialevolution.py
+++ b/nevergrad/optimization/differentialevolution.py
@@ -5,11 +5,10 @@
 
 import warnings
 import numpy as np
-from scipy import stats
 import nevergrad.common.typing as tp
 from nevergrad.parametrization import parameter as p
 from . import base
-from . import sequences
+from . import oneshot
 
 
 class Crossover:
@@ -100,7 +99,7 @@ def __init__(
         self._penalize_cheap_violations = True
         self._uid_queue = base.utils.UidQueue()
         self.population: tp.Dict[str, p.Parameter] = {}
-        self.sampler: tp.Optional[sequences.Sampler] = None
+        self.sampler: tp.Optional[base.Optimizer] = None
 
     def recommend(self) -> p.Parameter:  # This is NOT the naive version. We deal with noise.
         if self._config.recommendation != "noisy":
@@ -119,18 +118,15 @@ def _internal_ask_candidate(self) -> p.Parameter:
             init = self._config.initialization
             if self.sampler is None and init not in ["gaussian", "parametrization"]:
                 assert init in ["LHS", "QR"]
-                sampler_cls = sequences.LHSSampler if init == "LHS" else sequences.HammersleySampler
-                self.sampler = sampler_cls(
-                    self.dimension, budget=self.llambda, scrambling=init == "QR", random_state=self._rng
-                )
+                self.sampler = oneshot.SamplingSearch(
+                    sampler=init if init == "LHS" else "Hammersley", scrambled=init == "QR"
+                )(self.parametrization, budget=self.llambda)
             if init == "parametrization":
                 candidate = self.parametrization.sample()
+            elif self.sampler is not None:
+                candidate = self.sampler.ask()
             else:
-                new_guy = self.scale * (
-                    self._rng.normal(0, 1, self.dimension)
-                    if self.sampler is None
-                    else stats.norm.ppf(self.sampler())
-                )
+                new_guy = self.scale * self._rng.normal(0, 1, self.dimension)
                 candidate = self.parametrization.spawn_child().set_standardized_data(new_guy)
             candidate.heritage["lineage"] = candidate.uid  # new lineage
             self.population[candidate.uid] = candidate
diff --git a/nevergrad/optimization/test_optimizerlib.py b/nevergrad/optimization/test_optimizerlib.py
index b26ddb277..647837bd0 100644
--- a/nevergrad/optimization/test_optimizerlib.py
+++ b/nevergrad/optimization/test_optimizerlib.py
@@ -744,6 +744,15 @@ def test_mo_with_nan(name: str) -> None:
         optimizer.tell(cand, [-38, 0, np.nan])
 
 
+def test_de_sampling() -> None:
+    param = ng.p.Scalar(lower=-100, upper=100).set_mutation(sigma=1)
+    opt = optlib.LhsDE(param, budget=600, num_workers=100)
+    above_50 = 0
+    for _ in range(100):
+        above_50 += abs(opt.ask().value) > 50
+    assert above_50 > 20  # should be around 50
+
+
 def test_paraportfolio_de() -> None:
     workers = 40
     opt = optlib.ParaPortfolio(12, budget=100 * workers, num_workers=workers)

From 8544074d1d0628d7ad5da307cdaea24da278f436 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 19:25:17 +0100
Subject: [PATCH 08/17] skip

---
 nevergrad/benchmark/experiments.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nevergrad/benchmark/experiments.py b/nevergrad/benchmark/experiments.py
index ec2012316..9ce60e62d 100644
--- a/nevergrad/benchmark/experiments.py
+++ b/nevergrad/benchmark/experiments.py
@@ -1272,6 +1272,7 @@ def image_similarity(
     """Optimizing images: artificial criterion for now."""
     seedg = create_seed_generator(seed)
     optims = get_optimizers("structured_moo", seed=next(seedg))
+    skip_ci(reason="too slow")
     funcs: tp.List[ExperimentFunction] = [
         imagesxp.Image(loss=loss, with_pgan=with_pgan)
         for loss in imagesxp.imagelosses.registry.values()
@@ -1281,7 +1282,6 @@ def image_similarity(
         for func in funcs:
             for algo in optims:
                 xp = Experiment(func, algo, budget, num_workers=1, seed=next(seedg))
-                skip_ci(reason="too slow")
                 if not xp.is_incoherent:
                     yield xp
 

From e8a13c63d045d4cc0c738f8ea437c0e3ed766080 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Mon, 22 Feb 2021 19:30:02 +0100
Subject: [PATCH 09/17] skip

---
 nevergrad/benchmark/experiments.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nevergrad/benchmark/experiments.py b/nevergrad/benchmark/experiments.py
index 9ce60e62d..43b430f2c 100644
--- a/nevergrad/benchmark/experiments.py
+++ b/nevergrad/benchmark/experiments.py
@@ -1272,7 +1272,6 @@ def image_similarity(
     """Optimizing images: artificial criterion for now."""
     seedg = create_seed_generator(seed)
     optims = get_optimizers("structured_moo", seed=next(seedg))
-    skip_ci(reason="too slow")
     funcs: tp.List[ExperimentFunction] = [
         imagesxp.Image(loss=loss, with_pgan=with_pgan)
         for loss in imagesxp.imagelosses.registry.values()
@@ -1282,6 +1281,7 @@ def image_similarity(
         for func in funcs:
             for algo in optims:
                 xp = Experiment(func, algo, budget, num_workers=1, seed=next(seedg))
+                skip_ci(reason="too slow")
                 if not xp.is_incoherent:
                     yield xp
 
@@ -1491,6 +1491,7 @@ def images_using_gan(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Optimizing an image using koncept512 and a GAN"""
     seedg = create_seed_generator(seed)
     optims = get_optimizers("structured_moo", seed=next(seedg))
+    skip_ci(reason="too slow")
     func = imagesxp.ImageFromPGAN()
     num_workers = 1
     for budget in [100 * 5 ** k for k in range(3)]:

From e4d217365d07bb06676deb8ac07e8fb51cddc3bc Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Tue, 23 Feb 2021 11:55:48 +0100
Subject: [PATCH 10/17] skip

---
 nevergrad/functions/images/core.py              | 8 ++++++--
 nevergrad/optimization/differentialevolution.py | 7 +++++--
 nevergrad/optimization/oneshot.py               | 8 +++-----
 3 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/nevergrad/functions/images/core.py b/nevergrad/functions/images/core.py
index 4c4a4430f..0ede7b5cd 100644
--- a/nevergrad/functions/images/core.py
+++ b/nevergrad/functions/images/core.py
@@ -3,10 +3,11 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import cv2
-from pathlib import Path
+import os
 import itertools
+from pathlib import Path
 
+import cv2
 import numpy as np
 import PIL.Image
 import torch.nn as nn
@@ -17,6 +18,7 @@
 
 import nevergrad as ng
 import nevergrad.common.typing as tp
+from nevergrad.common import errors
 from .. import base
 from . import imagelosses
 
@@ -259,6 +261,8 @@ def __init__(
         if not torch.cuda.is_available():
             use_gpu = False
         # Storing high level information..
+        if os.environ("CIRCLECI", False):
+            raise errors.UnsupportedExperiment("ImageFromPGAN is not well supported in CircleCI")
         self.pgan_model = torch.hub.load(
             "facebookresearch/pytorch_GAN_zoo:hub",
             "PGAN",
diff --git a/nevergrad/optimization/differentialevolution.py b/nevergrad/optimization/differentialevolution.py
index a96913c04..abdfc2798 100644
--- a/nevergrad/optimization/differentialevolution.py
+++ b/nevergrad/optimization/differentialevolution.py
@@ -119,8 +119,11 @@ def _internal_ask_candidate(self) -> p.Parameter:
             if self.sampler is None and init not in ["gaussian", "parametrization"]:
                 assert init in ["LHS", "QR"]
                 self.sampler = oneshot.SamplingSearch(
-                    sampler=init if init == "LHS" else "Hammersley", scrambled=init == "QR"
-                )(self.parametrization, budget=self.llambda)
+                    sampler=init if init == "LHS" else "Hammersley", scrambled=init == "QR", scale=self.scale
+                )(
+                    self.parametrization,
+                    budget=self.llambda,
+                )
             if init == "parametrization":
                 candidate = self.parametrization.sample()
             elif self.sampler is not None:
diff --git a/nevergrad/optimization/oneshot.py b/nevergrad/optimization/oneshot.py
index eb4892ebc..8696386b3 100644
--- a/nevergrad/optimization/oneshot.py
+++ b/nevergrad/optimization/oneshot.py
@@ -75,14 +75,12 @@ def avg_of_k_best(archive: utils.Archive[utils.MultiValue], method: str = "dimfo
         raise ValueError(f"{method} not implemented as a method for choosing k in avg_of_k_best.")
     k = 1 if k < 1 else int(k)
     # Wasted time.
-    first_k_individuals = [
-        k for k in sorted(items, key=lambda indiv: archive[indiv[0]].get_estimation("pessimistic"))[:k]
-    ]
+    first_k_individuals = sorted(items, key=lambda indiv: archive[indiv[0]].get_estimation("pessimistic"))[:k]
     assert len(first_k_individuals) == k
     return np.array(sum(p[0] for p in first_k_individuals) / k)
 
 
-# # # # # classes of optimizers # # # # #
+# # # # # classes of optimizers # # # # #
 
 
 class OneShotOptimizer(base.Optimizer):
@@ -99,7 +97,7 @@ class OneShotOptimizer(base.Optimizer):
 # - Some variants use a rescaling depending on the budget and the dimension.
 
 
-# # # # # One-shot optimizers: all fitness evaluations are in parallel. # # # # #
+# # # # # One-shot optimizers: all fitness evaluations are in parallel. # # # # #
 
 
 # pylint: disable=too-many-arguments,too-many-instance-attributes

From 85ba54119c231f904b2277252aea313e310c51b9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9my=20Rapin?= <jrapin.github@gmail.com>
Date: Tue, 23 Feb 2021 11:56:47 +0100
Subject: [PATCH 11/17] Update nevergrad/benchmark/experiments.py

---
 nevergrad/benchmark/experiments.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nevergrad/benchmark/experiments.py b/nevergrad/benchmark/experiments.py
index 43b430f2c..ec2012316 100644
--- a/nevergrad/benchmark/experiments.py
+++ b/nevergrad/benchmark/experiments.py
@@ -1491,7 +1491,6 @@ def images_using_gan(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Optimizing an image using koncept512 and a GAN"""
     seedg = create_seed_generator(seed)
     optims = get_optimizers("structured_moo", seed=next(seedg))
-    skip_ci(reason="too slow")
     func = imagesxp.ImageFromPGAN()
     num_workers = 1
     for budget in [100 * 5 ** k for k in range(3)]:

From bb8fe038f40f6f9fae998021f7d1169410da88a1 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Tue, 23 Feb 2021 12:02:28 +0100
Subject: [PATCH 12/17] fix

---
 nevergrad/functions/images/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nevergrad/functions/images/core.py b/nevergrad/functions/images/core.py
index 0ede7b5cd..16d1b87b7 100644
--- a/nevergrad/functions/images/core.py
+++ b/nevergrad/functions/images/core.py
@@ -261,7 +261,7 @@ def __init__(
         if not torch.cuda.is_available():
             use_gpu = False
         # Storing high level information..
-        if os.environ("CIRCLECI", False):
+        if os.environ.get("CIRCLECI", False):
             raise errors.UnsupportedExperiment("ImageFromPGAN is not well supported in CircleCI")
         self.pgan_model = torch.hub.load(
             "facebookresearch/pytorch_GAN_zoo:hub",

From 6811a4cadc0e8781d7e081eb0511672c874d1cb0 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Tue, 23 Feb 2021 12:14:47 +0100
Subject: [PATCH 13/17] fix

---
 nevergrad/optimization/experimentalvariants.py | 2 +-
 nevergrad/optimization/optimizerlib.py         | 4 ++--
 nevergrad/optimization/test_callbacks.py       | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/nevergrad/optimization/experimentalvariants.py b/nevergrad/optimization/experimentalvariants.py
index 72a31d75f..14f13009c 100644
--- a/nevergrad/optimization/experimentalvariants.py
+++ b/nevergrad/optimization/experimentalvariants.py
@@ -22,7 +22,7 @@
 ParametrizationDE = DifferentialEvolution(crossover="parametrization").set_name(
     "ParametrizationDE", register=True
 )
-MiniDE = DifferentialEvolution(scale="mini").set_name("MiniDE", register=True)
+MiniDE = DifferentialEvolution(initialization="gaussian", scale="mini").set_name("MiniDE", register=True)
 MiniLhsDE = DifferentialEvolution(initialization="LHS", scale="mini").set_name("MiniLhsDE", register=True)
 MiniQrDE = DifferentialEvolution(initialization="QR", scale="mini").set_name("MiniQrDE", register=True)
 AlmostRotationInvariantDEAndBigPop = DifferentialEvolution(crossover=0.9, popsize="dimension").set_name(
diff --git a/nevergrad/optimization/optimizerlib.py b/nevergrad/optimization/optimizerlib.py
index e85c95b7d..cec42244c 100644
--- a/nevergrad/optimization/optimizerlib.py
+++ b/nevergrad/optimization/optimizerlib.py
@@ -69,7 +69,7 @@ def __init__(
         noise_handling: tp.Optional[tp.Union[str, tp.Tuple[str, float]]] = None,
         mutation: str = "gaussian",
         crossover: bool = False,
-        use_pareto: bool = True,
+        use_pareto: bool = False,
     ) -> None:
         super().__init__(parametrization, budget=budget, num_workers=num_workers)
         self._sigma: float = 1
@@ -320,7 +320,7 @@ def __init__(
         noise_handling: tp.Optional[tp.Union[str, tp.Tuple[str, float]]] = None,
         mutation: str = "gaussian",
         crossover: bool = False,
-        use_pareto: bool = True,
+        use_pareto: bool = False,
     ) -> None:
         super().__init__(_OnePlusOne, locals())
 
diff --git a/nevergrad/optimization/test_callbacks.py b/nevergrad/optimization/test_callbacks.py
index 33f1b0ab6..178118795 100644
--- a/nevergrad/optimization/test_callbacks.py
+++ b/nevergrad/optimization/test_callbacks.py
@@ -33,9 +33,9 @@ def test_log_parameters(tmp_path: Path) -> None:
     logs = logger.load_flattened()
     assert len(logs) == 32
     assert isinstance(logs[-1]["1"], float)
-    assert len(logs[-1]) == 35
+    assert len(logs[-1]) == 36
     logs = logger.load_flattened(max_list_elements=2)
-    assert len(logs[-1]) == 27
+    assert len(logs[-1]) == 28
     # deletion
     logger = callbacks.ParametersLogger(filepath, append=False)
     assert not logger.load()

From a81b6ba139e5cb31a4064c92de771ebd28b6100c Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Tue, 23 Feb 2021 12:34:18 +0100
Subject: [PATCH 14/17] fix

---
 nevergrad/benchmark/test_xpbase.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nevergrad/benchmark/test_xpbase.py b/nevergrad/benchmark/test_xpbase.py
index da5de8835..dbf35ed45 100644
--- a/nevergrad/benchmark/test_xpbase.py
+++ b/nevergrad/benchmark/test_xpbase.py
@@ -48,7 +48,7 @@ def test_run_packed_artificial_function() -> None:
     )
     xp = xpbase.Experiment(func, optimizer="OnePlusOne", budget=24, num_workers=2, batch_mode=True, seed=14)
     summary = xp.run()
-    np.testing.assert_almost_equal(summary["loss"], -9726.2, decimal=1)  # makes sure seeding works!
+    np.testing.assert_almost_equal(summary["loss"], -9784.8, decimal=1)  # makes sure seeding works!
 
 
 def test_noisy_artificial_function_loss() -> None:

From 449539cea4a1bc95f776d84d9f1177122daa662f Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Tue, 23 Feb 2021 12:38:34 +0100
Subject: [PATCH 15/17] changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d95abc5b1..c12a89f37 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -21,6 +21,7 @@
 - `Parameter` classes have now a layer structure [#1045](https://github.com/facebookresearch/nevergrad/pull/1045)
   which simplifies changing their behavior. In future PRs this system will take charge of bounds, other constraints,
   sampling etc.
+- `DE` initial sampling as been updated to take bounds into accounts [#1058](https://github.com/facebookresearch/nevergrad/pull/1058)
 
 ### Other changes
 

From e9c477fcd2b604da2e19e3b1026ff8454b3dd563 Mon Sep 17 00:00:00 2001
From: Jeremy Rapin <jrapin@fb.com>
Date: Tue, 23 Feb 2021 12:52:55 +0100
Subject: [PATCH 16/17] fix

---
 nevergrad/optimization/base.py                | 2 ++
 nevergrad/optimization/multiobjective/core.py | 6 +++---
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/nevergrad/optimization/base.py b/nevergrad/optimization/base.py
index 5bc07ba57..a1cec4aef 100644
--- a/nevergrad/optimization/base.py
+++ b/nevergrad/optimization/base.py
@@ -220,6 +220,8 @@ def pareto_front(
                 size=size, subset=subset, subset_tentatives=subset_tentatives
             )
         )
+        print(pareto)
+        print(type(pareto))
         return pareto if pareto else [self.provide_recommendation()]
 
     def dump(self, filepath: tp.Union[str, Path]) -> None:
diff --git a/nevergrad/optimization/multiobjective/core.py b/nevergrad/optimization/multiobjective/core.py
index 88ffc4d55..ba4ef4c27 100644
--- a/nevergrad/optimization/multiobjective/core.py
+++ b/nevergrad/optimization/multiobjective/core.py
@@ -166,8 +166,8 @@ def pareto_front(
         if size is None or size >= len(self._pareto):  # No limit: we return the full set.
             return self._pareto
         if subset == "random":
-            return self._rng.choice(self._pareto, size)  # type: ignore
-        tentatives = [self._rng.choice(self._pareto, size) for _ in range(subset_tentatives)]
+            return self._rng.choice(self._pareto, size).tolist()  # type: ignore
+        tentatives = [self._rng.choice(self._pareto, size).tolist() for _ in range(subset_tentatives)]
         if self._hypervolume is None:
             raise RuntimeError("Hypervolume not initialized, not supported")  # TODO fix
         hypervolume = self._hypervolume
@@ -192,4 +192,4 @@ def pareto_front(
                             raise ValueError(f'Unknown subset for Pareto-Set subsampling: "{subset}"')
                     score += best_score ** 2 if subset != "EPS" else max(score, best_score)
                 scores += [score]
-        return tentatives[scores.index(min(scores))]
+        return tentatives[scores.index(min(scores))]  # type: ignore

From 917eea876f0545e6b8f009d024a13f2fd73443de Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9my=20Rapin?= <jrapin.github@gmail.com>
Date: Tue, 23 Feb 2021 13:06:41 +0100
Subject: [PATCH 17/17] Update nevergrad/optimization/base.py

---
 nevergrad/optimization/base.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/nevergrad/optimization/base.py b/nevergrad/optimization/base.py
index a1cec4aef..5bc07ba57 100644
--- a/nevergrad/optimization/base.py
+++ b/nevergrad/optimization/base.py
@@ -220,8 +220,6 @@ def pareto_front(
                 size=size, subset=subset, subset_tentatives=subset_tentatives
             )
         )
-        print(pareto)
-        print(type(pareto))
         return pareto if pareto else [self.provide_recommendation()]
 
     def dump(self, filepath: tp.Union[str, Path]) -> None: