updating branch

CHANGELOG.md

@@ -2,6 +2,8 @@
 
 ## main
 
+## 0.5.0 (2022-03-08)
+
 ### Breaking changes
 
 - `copy()` method of a `Parameter` does not change the parameters's random state anymore (it used to reset it to `None` [#1048](https://github.com/facebookresearch/nevergrad/pull/1048)
@@ -70,6 +72,11 @@
   [#1197](https://github.com/facebookresearch/nevergrad/pull/1197).
 - An interface with [BayesOptim](https://github.com/wangronin/Bayesian-Optimization) optimizers has been added
   [#1179](https://github.com/facebookresearch/nevergrad/pull/1179).
+- Fix for abnormally slow iterations for large budgets using CMA in a portfolio
+  [#1350](https://github.com/facebookresearch/nevergrad/pull/1350).
+- A new `enable_pickling` option was added to optimizers. This is only necessary for some of them (among which `scipy`-based optimizer), and comes at the cost of additional memory usage
+  [#1356](https://github.com/facebookresearch/nevergrad/pull/1356)
+  [#1358](https://github.com/facebookresearch/nevergrad/pull/1358).
 
 ## 0.4.3 (2021-01-28)
 

README.md

@@ -1,4 +1,4 @@
-[![CircleCI](https://circleci.com/gh/facebookresearch/nevergrad/tree/main.svg?style=svg)](https://circleci.com/gh/facebookresearch/nevergrad/tree/main)
+[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine) [![CircleCI](https://circleci.com/gh/facebookresearch/nevergrad/tree/main.svg?style=svg)](https://circleci.com/gh/facebookresearch/nevergrad/tree/main)
 
 # Nevergrad - A gradient-free optimization platform
 

docs/benchmarking.rst

@@ -74,4 +74,4 @@ Functions used for the experiments must derive from :code:`nevergrad.functions.E
 
 See the docstrings for more information, and `arcoating/core.py <https://github.com/facebookresearch/nevergrad/blob/main/nevergrad/functions/arcoating/core.py>`_ and `example.py <https://github.com/facebookresearch/nevergrad/blob/main/nevergrad/benchmark/additional/example.py>`_ for examples.
 
-If you want your experiment plan to be seedable, be extra careful as to how you handle randomness in the experiment generator, since each individual experiment may be run in any order. See `experiments.py <https://github.com/facebookresearch/nevergrad/blob/main/nevergrad/benchmark/experiments.py>`_ for examples of seedable experiment plans. If you do not care for it. For simplicity's sake, the experiment plan generator is however not required to have a seed parameter (but will not be reproducible in this case).
+If you want your experiment plan to be seedable, be extra careful as to how you handle randomness in the experiment generator, since each individual experiment may be run in any order. See `experiments.py <https://github.com/facebookresearch/nevergrad/blob/main/nevergrad/benchmark/experiments.py>`_ for examples of seedable experiment plans. For simplicity's sake, the experiment plan generator is however not required to have a seed parameter (but will not be reproducible in this case).

docs/index.rst

@@ -3,6 +3,10 @@ Nevergrad - A gradient-free optimization platform
 
 .. image:: ./resources/Nevergrad-LogoMark.png
 
+.. image:: https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB
+   :alt: Support Ukraine - Help Provide Humanitarian Aid to Ukraine.
+   :target: https://opensource.fb.com/support-ukraine
+
 This documentation is a work in progress, feel free to help us update/improve/restucture it!
 
 Quick start
@@ -82,6 +86,7 @@ License
 -------
 
 :code:`nevergrad` is released under the MIT license. See `LICENSE <https://github.com/facebookresearch/nevergrad/blob/main/LICENSE>`_ for additional details about it, as well as our `Terms of Use <https://opensource.facebook.com/legal/terms>`_ and `Privacy Policy <https://opensource.facebook.com/legal/privacy>`_.
+Copyright © Meta Platforms, Inc.
 
 Indices and tables
 ------------------

docs/machinelearning.rst

@@ -250,6 +250,7 @@ Optimization of parameters for reinforcement learning
 We do not average evaluations over multiple episodes - the algorithm is in charge of averaging, if need be.
 :code:`TBPSA`, based on population-control mechanisms, performs quite well in this case.
 
+If you want to run Open AI Gym, see `One-line for learning state-of-the-art OpenAI Gym controllers with Nevergrad <https://docs.google.com/document/d/1noubQ_ZTZ4PZeQ1St7Asi1Af02q7k0nRoX_Pipu9ZKs/edit?usp=sharing/>`_
 
 .. code-block:: python
 

docs/optimization.rst

@@ -185,6 +185,14 @@ Or if you want something more aimed at robustly outperforming random search in h
 - Use :code:`ScrHammersleySearchPlusMiddlePoint` (:code:`PlusMiddlePoint` only if you have continuous parameters or good default values for discrete parameters).
 
 
+Example with permutation
+------------------------
+
+SimpleTSP and ComplexTSP are two cases of optimization on a domain of permutations:
+`example here. <https://docs.google.com/document/d/1B5yVOx1H1nnjY3EOf14487hAr8CzwJ9zEkDwQnZ5nbE/edit?usp=sharing>`_
+This is relevant when you optimize a single big permutation.
+Also includes cases with many small permutations.
+
 Example of chaining, or inoculation, or initialization of an evolutionary algorithm
 -----------------------------------------------------------------------------------
 

mypy.ini

@@ -1,9 +1,9 @@
 [mypy]
 
-[mypy-scipy.*,requests,pandas,compiler_gym,compiler_gym.*,gym,gym.*,gym_anm,matplotlib.*,pytest,cma,bayes_opt.*,torchvision.models,torch.*,mpl_toolkits.*,fcmaes.*,tqdm,pillow,PIL,PIL.Image,sklearn.*,pyomo.*,pyproj,IOHexperimenter.*,tensorflow,koncept.models,cv2,imquality,imquality.brisque,lpips,mixsimulator.*,networkx.*,cdt.*,pymoo,pymoo.*,bayes_optim.*]
+[mypy-scipy.*,requests,pandas,compiler_gym,compiler_gym.*,gym,gym.*,gym_anm,matplotlib.*,pytest,cma,bayes_opt.*,torchvision.models,torch.*,mpl_toolkits.*,fcmaes.*,tqdm,pillow,PIL,PIL.Image,sklearn.*,pyomo.*,pyproj,IOHexperimenter.*,tensorflow,koncept.models,cv2,imquality,imquality.brisque,lpips,mixsimulator.*,networkx.*,cdt.*,pymoo,pymoo.*,bayes_optim.*,olympus.*]
 ignore_missing_imports = True
 
-[mypy-nevergrad.functions.rl.agents,torchvision,torchvision.*,nevergrad.functions.games.*,nevergrad.functions.multiobjective.pyhv,nevergrad.optimization.test_doc,,pymoo,pymoo.*,pybullet,pybullet_envs,pybulletgym,pyvirtualdisplay]
+[mypy-nevergrad.functions.rl.agents,torchvision,torchvision.*,nevergrad.functions.games.*,nevergrad.functions.multiobjective.pyhv,nevergrad.optimization.test_doc,,pymoo,pymoo.*,pybullet,pybullet_envs,pybulletgym,pyvirtualdisplay,nlopt,aquacrop.*]
 ignore_missing_imports = True
 ignore_errors = True
 

nevergrad/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
@@ -15,4 +15,4 @@
 __all__ = ["optimizers", "families", "callbacks", "p", "typing", "errors", "ops"]
 
 
-__version__ = "0.4.3.post9"
+__version__ = "0.5.0"

nevergrad/benchmark/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.

nevergrad/benchmark/__main__.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.

nevergrad/benchmark/additional/example.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.

nevergrad/benchmark/core.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.

nevergrad/benchmark/execution.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.

nevergrad/benchmark/experiments.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
@@ -21,14 +21,14 @@
 from nevergrad.functions.arcoating import ARCoating
 from nevergrad.functions import images as imagesxp
 from nevergrad.functions.powersystems import PowerSystem
+from nevergrad.functions.ac import NgAquacrop
 from nevergrad.functions.stsp import STSP
 from nevergrad.functions.rocket import Rocket
 from nevergrad.functions.mixsimulator import OptimizeMix
 from nevergrad.functions.unitcommitment import UnitCommitmentProblem
 from nevergrad.functions import control
 from nevergrad.functions import rl
 from nevergrad.functions.games import game
-from nevergrad.functions.causaldiscovery import CausalDiscovery
 from nevergrad.functions import iohprofiler
 from nevergrad.functions import helpers
 from .xpbase import Experiment as Experiment
@@ -619,8 +619,8 @@ def yabbob(
     hd: bool = False,
     constraint_case: int = 0,
     split: bool = False,
-    tiny: bool = False,
     tuning: bool = False,
+    reduction_factor: int = 1,
     bounded: bool = False,
     box: bool = False,
 ) -> tp.Iterator[Experiment]:
@@ -689,8 +689,9 @@ def yabbob(
             [100, 1000, 3000] if hd else ([2, 5, 10, 15] if tuning else ([40] if bounded else [2, 10, 50]))
         )
     ]
-    if tiny:
-        functions = functions[::13]
+
+    assert reduction_factor in [1, 7, 13, 17]  # needs to be a cofactor
+    functions = functions[::reduction_factor]
 
     # We possibly add constraints.
     max_num_constraints = 4
@@ -735,6 +736,12 @@ def yahdlbbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     return yabbob(seed, hd=True, small=True)
 
 
+@registry.register
+def reduced_yahdlbbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Counterpart of yabbob with HD and low budget."""
+    return yabbob(seed, hd=True, small=True, reduction_factor=17)
+
+
 @registry.register
 def yanoisysplitbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Counterpart of yabbob with more budget."""
@@ -782,13 +789,13 @@ def yahdsplitbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
 @registry.register
 def yatuningbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Counterpart of yabbob with less budget."""
-    return yabbob(seed, parallel=False, big=False, small=True, tiny=True, tuning=True)
+    return yabbob(seed, parallel=False, big=False, small=True, reduction_factor=13, tuning=True)
 
 
 @registry.register
 def yatinybbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Counterpart of yabbob with less budget."""
-    return yabbob(seed, parallel=False, big=False, small=True, tiny=True)
+    return yabbob(seed, parallel=False, big=False, small=True, reduction_factor=13)
 
 
 @registry.register
@@ -1151,6 +1158,23 @@ def realworld(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
                             yield xp
 
 
+@registry.register
+def aquacrop_fao(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """FAO Crop simulator. Maximize yield."""
+
+    funcs = [NgAquacrop(i, 300.0 + 150.0 * np.cos(i)) for i in range(3, 7)]
+    seedg = create_seed_generator(seed)
+    optims = get_optimizers("basics", seed=next(seedg))
+    for budget in [25, 50, 100, 200, 400, 800, 1600]:
+        for num_workers in [1, 30]:
+            if num_workers < budget:
+                for algo in optims:
+                    for fu in funcs:
+                        xp = Experiment(fu, algo, budget, num_workers=num_workers, seed=next(seedg))
+                        if not xp.is_incoherent:
+                            yield xp
+
+
 @registry.register
 def rocket(seed: tp.Optional[int] = None, seq: bool = False) -> tp.Iterator[Experiment]:
     """Rocket simulator. Maximize max altitude by choosing the thrust schedule, given a total thrust.
@@ -1263,6 +1287,52 @@ def neuro_control_problem(seed: tp.Optional[int] = None) -> tp.Iterator[Experime
                     yield xp
 
 
+@registry.register
+def olympus_surfaces(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Olympus surfaces"""
+    from nevergrad.functions.olympussurfaces import OlympusSurface
+
+    funcs = []
+    for kind in OlympusSurface.SURFACE_KINDS:
+        for k in range(2, 5):
+            for noise in ["GaussianNoise", "UniformNoise", "GammaNoise"]:
+                for noise_scale in [0.5, 1]:
+                    funcs.append(OlympusSurface(kind, 10 ** k, noise, noise_scale))
+
+    seedg = create_seed_generator(seed)
+    optims = get_optimizers("basics", "noisy", seed=next(seedg))
+    for budget in [25, 50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600]:
+        for num_workers in [1]:  # , 10, 100]:
+            if num_workers < budget:
+                for algo in optims:
+                    for fu in funcs:
+                        xp = Experiment(fu, algo, budget, num_workers=num_workers, seed=next(seedg))
+                        if not xp.is_incoherent:
+                            yield xp
+
+
+@registry.register
+def olympus_emulators(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Olympus emulators"""
+    from nevergrad.functions.olympussurfaces import OlympusEmulator
+
+    funcs = []
+    for dataset_kind in OlympusEmulator.DATASETS:
+        for model_kind in ["BayesNeuralNet", "NeuralNet"]:
+            funcs.append(OlympusEmulator(dataset_kind, model_kind))
+
+    seedg = create_seed_generator(seed)
+    optims = get_optimizers("basics", "noisy", seed=next(seedg))
+    for budget in [25, 50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600]:
+        for num_workers in [1]:  # , 10, 100]:
+            if num_workers < budget:
+                for algo in optims:
+                    for fu in funcs:
+                        xp = Experiment(fu, algo, budget, num_workers=num_workers, seed=next(seedg))
+                        if not xp.is_incoherent:
+                            yield xp
+
+
 @registry.register
 def simple_tsp(seed: tp.Optional[int] = None, complex_tsp: bool = False) -> tp.Iterator[Experiment]:
     """Simple TSP problems. Please note that the methods we use could be applied or complex variants, whereas
@@ -1274,7 +1344,22 @@ def simple_tsp(seed: tp.Optional[int] = None, complex_tsp: bool = False) -> tp.I
     """
     funcs = [STSP(10 ** k, complex_tsp) for k in range(2, 6)]
     seedg = create_seed_generator(seed)
-    optims = get_optimizers("basics", "noisy", seed=next(seedg))
+    optims = [
+        "RotatedTwoPointsDE",
+        "DiscreteLenglerOnePlusOne",
+        "DiscreteDoerrOnePlusOne",
+        "DiscreteBSOOnePlusOne",
+        "AdaptiveDiscreteOnePlusOne",
+        "GeneticDE",
+        "RotatedTwoPointsDE",
+        "DE",
+        "TwoPointsDE",
+        "DiscreteOnePlusOne",
+        "NGOpt38",
+        "CMA",
+        "MetaModel",
+        "DiagonalCMA",
+    ]
     for budget in [25, 50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600]:
         for num_workers in [1]:  # , 10, 100]:
             if num_workers < budget:
@@ -1851,6 +1936,9 @@ def pbo_suite(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
 
 def causal_similarity(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Finding the best causal graph"""
+    # pylint: disable=import-outside-toplevel
+    from nevergrad.functions.causaldiscovery import CausalDiscovery
+
     seedg = create_seed_generator(seed)
     optims = ["CMA", "NGOpt8", "DE", "PSO", "RecES", "RecMixES", "RecMutDE", "ParametrizationDE"]
     func = CausalDiscovery()

nevergrad/benchmark/exporttable.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.

nevergrad/benchmark/frozenexperiments.py

@@ -1,4 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.