[WIP] Use new Portfolio class

.circleci/config.yml

@@ -27,20 +27,18 @@ commands:
       - restore_cache:
           name: "[all] Restore cache"
           keys:
-          - v6-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
+          - v7-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
           # fallback to installing main requirements
-          - v6-dependencies-main-{{ checksum "requirements/main.txt"}}
+          - v7-dependencies-main-{{ checksum "requirements/main.txt"}}
 
       - run:
           name: "[all] Install dependencies"
           command: |
             python3 -m venv venv
             . venv/bin/activate
-            pip install -U pip
-            pip install -e .[all]
-            # master numpy-stubs adds a lot of messy warnings :s
-            pip install git+https://github.com/numpy/numpy-stubs@2c8d8f26adb1eef54571ccc85ef8ad4f2fb90c4c
-
+            pip install --progress-bar off pip==21.0.1  # TODO update after 21.1 which bugs
+            pip install --progress-bar off -e .[all]
+            pip install --progress-bar off -U numpy>=1.20.0
 
 jobs:
   install:
@@ -53,16 +51,16 @@ jobs:
       - restore_cache:
           name: "[no-extra] Restore cache"
           keys:
-          - v6-dependencies-main-{{ checksum "requirements/main.txt"}}
+          - v7-dependencies-main-{{ checksum "requirements/main.txt"}}
           # fallback to using the latest cache if no exact match is found
 
       - run:
           name: "[no-extra] Install dependencies"
           command: |
             python3 -m venv venv
             . venv/bin/activate
-            pip install --upgrade pip
-            pip install -e .
+            pip install --progress-bar off pip==21.0.1
+            pip install --progress-bar off -e .
 
       - run:
           name: "[no-extra] Print installation"
@@ -80,7 +78,7 @@ jobs:
           name: "[no-extra] Save cache"
           paths:
             - ./venv
-          key: v6-dependencies-main-{{ checksum "requirements/main.txt"}}
+          key: v7-dependencies-main-{{ checksum "requirements/main.txt"}}
 
       - run:
           name: "[no-extra] Run basic tests (checking dependencies)"
@@ -95,7 +93,7 @@ jobs:
           name: "[all] Save cache"
           paths:
             - ./venv
-          key: v6-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
+          key: v7-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
 
       - run:
           name: "[all] Print installation"
@@ -118,7 +116,7 @@ jobs:
             python3 -m venv test_wheel
             . test_wheel/bin/activate
             cd ..  # don't use nevergrad locally
-            pip install repo/nevergrad-build/dist/nevergrad-*any.whl
+            pip install --progress-bar off repo/nevergrad-build/dist/nevergrad-*any.whl
             python -c "from nevergrad import functions;f = functions.ArtificialFunction(name='sphere', block_dimension=2);f([2, 3])"
 
       - persist_to_workspace:
@@ -172,7 +170,7 @@ jobs:
           command: |
             . venv/bin/activate
             pylint --version
-            pylint nevergrad --disable=all --enable=unused-import,unused-argument,unused-variable,undefined-loop-variable,redefined-builtin,used-before-assignment,super-init-not-called,useless-super-delegation,dangerous-default-value,unnecessary-pass
+            pylint nevergrad --disable=all --enable=unused-import,unused-argument,unused-variable,undefined-loop-variable,redefined-builtin,used-before-assignment,super-init-not-called,useless-super-delegation,dangerous-default-value,unnecessary-pass,attribute-defined-outside-init
 
       - run:
           name: "Run black"
@@ -240,22 +238,22 @@ jobs:
       - restore_cache:
           name: "[all] Restore cache"
           keys:
-          - v6-win-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
+          - v7-win-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
 
       - run:
           name: Setup nevergrad in virtualenv
           command: |
             py -3 -m venv venv
             . ./venv/Scripts/activate
-            pip install -U pip
-            pip install torch==1.4.0+cpu torchvision==0.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
-            pip install .[all]
+            pip install --progress-bar off pip==21.0.1
+            pip install --progress-bar off torch==1.4.0+cpu torchvision==0.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+            pip install --progress-bar off .[all]
 
       - save_cache:
           name: "[all] Save cache"
           paths:
             - ./venv
-          key: v6-win-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
+          key: v7-win-dependencies-{{ checksum "requirements/dev.txt" }}-{{ checksum "requirements/main.txt"}}-{{ checksum "requirements/bench.txt"}}
 
       - run:
           name: pytest

CHANGELOG.md

@@ -26,6 +26,9 @@
 - Half the budget alloted to solve cheap constrained is now used by a sub-optimizer
   [#1047](https://github.com/facebookresearch/nevergrad/pull/1047). More changes of constraint management will land
   in the near future.
+- Experimental methods `Array.set_recombination` and `Array.set_mutation(custom=.)` are removed in favor of
+  layers changing `Array` behaviors [#1086](https://github.com/facebookresearch/nevergrad/pull/1086).
+  Caution: this is still very experimental (and undocumented).
 
 ### Important changes
 
@@ -56,6 +59,8 @@
 - `EvolutionStrategy` now defaults to NSGA2 selection in the multiobjective case
 - A new experimental callback adds an early stopping mechanism
   [#1054](https://github.com/facebookresearch/nevergrad/pull/1054).
+- `Choice`-like parameters now accept integers are inputs instead of a list, as a shortcut for `range(num)`
+  [#1106](https://github.com/facebookresearch/nevergrad/pull/1106).
 
 ## 0.4.3 (2021-01-28)
 

docs/getting_started.rst

@@ -47,7 +47,7 @@ Basic optimization example
 
 **By default all optimizers assume a centered and reduced prior at the beginning of the optimization (i.e. 0 mean and unitary standard deviation).**
 
-Optimizing (minimizing!) a function using an optimizer (here :code:`OnePlusOne`) can be easily run with:
+Optimizing (minimizing!) a function using an optimizer (here :code:`NGOpt`, our adaptative optimization algorithm) can be easily run with:
 
 .. literalinclude:: ../nevergrad/optimization/test_doc.py
     :language: python

docs/index.rst

@@ -18,7 +18,7 @@ You can find other installation options (including for Windows users) in the :re
 
 Feel free to join `Nevergrad users Facebook group <https://www.facebook.com/groups/nevergradusers/>`_.
 
-Minimizing a function using an optimizer (here :code:`OnePlusOne`) can be easily run with:
+Minimizing a function using an optimizer (here :code:`NGOpt`, our adaptative optimization algorithm) can be easily run with:
 
 .. literalinclude:: ../nevergrad/optimization/test_doc.py
     :language: python

docs/optimization.rst

@@ -6,7 +6,7 @@ How to perform optimization
 Basic example
 -------------
 
-Minimizing a function using an optimizer (here :code:`OnePlusOne`) can be easily run with:
+Minimizing a function using an optimizer (here :code:`NGOpt`, our adaptative optimization algorithm) can be easily run with:
 
 .. literalinclude:: ../nevergrad/optimization/test_doc.py
     :language: python
@@ -80,7 +80,7 @@ Please make sure that your function returns a float, and that you indeed want to
 Choosing an optimizer
 ---------------------
 
-:code:`ng.optimizers.registry` is a :code:`dict` of all optimizers, so you :code:`ng.optimizers.OnePlusOne` is equivalent to :code:`ng.optimizers.registry["OnePlusOne"]`.
+:code:`ng.optimizers.registry` is a :code:`dict` of all optimizers, so you :code:`ng.optimizers.NGOpt` is equivalent to :code:`ng.optimizers.registry["NGOpt"]`.
 Also, **you can print the full list of optimizers** with:
 
 

nevergrad/__init__.py

@@ -9,9 +9,10 @@
 from .optimization import families as families
 from .optimization import callbacks as callbacks
 from .common import errors as errors
+from . import ops as ops
 
 
-__all__ = ["optimizers", "families", "callbacks", "p", "typing", "errors"]
+__all__ = ["optimizers", "families", "callbacks", "p", "typing", "errors", "ops"]
 
 
-__version__ = "0.4.3.post2"
+__version__ = "0.4.3.post3"

nevergrad/benchmark/experiments.py

@@ -103,12 +103,16 @@ def keras_tuning(
 
 
 def mltuning(
-    seed: tp.Optional[int] = None, overfitter: bool = False, seq: bool = False
+    seed: tp.Optional[int] = None,
+    overfitter: bool = False,
+    seq: bool = False,
+    nano: bool = False,
 ) -> tp.Iterator[Experiment]:
     """Machine learning hyperparameter tuning experiment. Based on scikit models."""
     seedg = create_seed_generator(seed)
     optims: tp.List[str] = get_optimizers("basics", seed=next(seedg))  # type: ignore
-
+    if not seq:
+        optims = get_optimizers("oneshot", seed=next(seedg))  # type: ignore
     for dimension in [None, 1, 2, 3]:
         if dimension is None:
             datasets = ["boston", "diabetes", "auto-mpg", "red-wine", "white-wine"]
@@ -119,7 +123,7 @@ def mltuning(
                 function = MLTuning(
                     regressor=regressor, data_dimension=dimension, dataset=dataset, overfitter=overfitter
                 )
-                for budget in [50, 150, 500]:
+                for budget in [50, 150, 500] if not nano else [20, 40, 80, 160]:
                     # Seq for sequential optimization experiments.
                     parallelization = [1, budget // 4] if seq else [budget]
                     for num_workers in parallelization:
@@ -152,13 +156,31 @@ def naive_seq_keras_tuning(seed: tp.Optional[int] = None) -> tp.Iterator[Experim
     return keras_tuning(seed, overfitter=True, seq=True)
 
 
-# We register only the sequential counterparts for the moment.
+@registry.register
+def oneshot_mltuning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """One-shot counterpart of Scikit tuning."""
+    return mltuning(seed, overfitter=False, seq=False)
+
+
+# We register only the (almost) sequential counterparts for the moment.
 @registry.register
 def seq_mltuning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Sequential counterpart of mltuning."""
     return mltuning(seed, overfitter=False, seq=True)
 
 
+@registry.register
+def nano_seq_mltuning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Sequential counterpart of mltuning."""
+    return mltuning(seed, overfitter=False, seq=True, nano=True)
+
+
+@registry.register
+def nano_naive_seq_mltuning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Sequential counterpart of mltuning with overfitting of valid loss, i.e. train/valid/valid instead of train/valid/test."""
+    return mltuning(seed, overfitter=True, seq=True, nano=True)
+
+
 @registry.register
 def naive_seq_mltuning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Sequential counterpart of mltuning with overfitting of valid loss, i.e. train/valid/valid instead of train/valid/test."""
@@ -446,13 +468,16 @@ def oneshot(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     functions = [
         ArtificialFunction(name, block_dimension=bd, useless_variables=bd * uv_factor)
         for name in names
-        for bd in [3, 25]
-        for uv_factor in [0, 5]
+        for bd in [3, 10, 30, 100, 300, 1000, 3000]
+        for uv_factor in [0]  # , 5]
     ]
     for func in functions:
         for optim in optims:
-            for budget in [30, 100, 3000]:
-                yield Experiment(func, optim, budget=budget, num_workers=budget, seed=next(seedg))
+            # if not any(x in str(optim) for x in ["Tune", "Large", "Cauchy"]):
+            # if "Meta" in str(optim):
+            for budget in [100000, 30, 100, 300, 1000, 3000, 10000]:
+                if func.dimension < 3000 or budget < 100000:
+                    yield Experiment(func, optim, budget=budget, num_workers=budget, seed=next(seedg))
 
 
 @registry.register
@@ -586,6 +611,8 @@ def yabbob(
     hd: bool = False,
     constraint_case: int = 0,
     split: bool = False,
+    tiny: bool = False,
+    tuning: bool = False,
 ) -> tp.Iterator[Experiment]:
     """Yet Another Black-Box Optimization Benchmark.
     Related to, but without special effort for exactly sticking to, the BBOB/COCO dataset.
@@ -628,14 +655,18 @@ def yabbob(
         optims += ["OnePlusOne"]
         optims += get_optimizers("splitters", seed=next(seedg))  # type: ignore
 
+    if hd and small:
+        optims = ["BO", "BOSplit", "CMA", "PSO", "DE"]
     # List of objective functions.
     functions = [
         ArtificialFunction(name, block_dimension=d, rotation=rotation, noise_level=noise_level, split=split)
         for name in names
         for rotation in [True, False]
         for num_blocks in ([1] if not split else [7, 12])
-        for d in ([100, 1000, 3000] if hd else [2, 10, 50])
+        for d in ([100, 1000, 3000] if hd else ([2, 5, 10, 15] if tuning else [2, 10, 50]))
     ]
+    if tiny:
+        functions = functions[::13]
 
     # We possibly add constraints.
     max_num_constraints = 4
@@ -668,6 +699,12 @@ def yabbob(
                     yield xp
 
 
+@registry.register
+def yahdlbbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Counterpart of yabbob with HD and low budget."""
+    return yabbob(seed, hd=True, small=True)
+
+
 @registry.register
 def yanoisysplitbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Counterpart of yabbob with more budget."""
@@ -712,6 +749,18 @@ def yahdsplitbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     return yabbob(seed, hd=True, split=True)
 
 
+@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)
+
+
+@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)
+
+
 @registry.register
 def yasmallbbob(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """Counterpart of yabbob with less budget."""
@@ -1021,7 +1070,7 @@ def realworld(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     funcs += [
         _mlda.SammonMapping.from_mlda("Virus", rescale=False),
         _mlda.SammonMapping.from_mlda("Virus", rescale=True),
-        _mlda.SammonMapping.from_mlda("Employees"),
+        # _mlda.SammonMapping.from_mlda("Employees"),
     ]
     funcs += [_mlda.Landscape(transform) for transform in [None, "square", "gaussian"]]
 
@@ -1061,15 +1110,15 @@ def realworld(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
 
 
 @registry.register
-def rocket(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+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.
     Budget 25, 50, ..., 1600.
     Sequential or 30 workers."""
     funcs = [Rocket(i) for i in range(17)]
     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]:
+        for num_workers in [1] if seq else [1, 30]:
             if num_workers < budget:
                 for algo in optims:
                     for fu in funcs:
@@ -1079,6 +1128,12 @@ def rocket(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
                             yield xp
 
 
+@registry.register
+def mono_rocket(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
+    """Sequential counterpart of the rocket problem."""
+    return rocket(seed, seq=True)
+
+
 @registry.register
 def mixsimulator(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """MixSimulator of power plants
@@ -1088,7 +1143,7 @@ def mixsimulator(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     seedg = create_seed_generator(seed)
     optims: tp.List[str] = get_optimizers("basics", seed=next(seedg))  # type: ignore
 
-    seq = np.arange(0, 1601, 20)
+    seq = np.arange(0, 1601, 50)
     for budget in seq:
         for num_workers in [1, 30]:
             if num_workers < budget:
@@ -1236,7 +1291,7 @@ def mlda(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     funcs += [
         _mlda.SammonMapping.from_mlda("Virus", rescale=False),
         _mlda.SammonMapping.from_mlda("Virus", rescale=True),
-        _mlda.SammonMapping.from_mlda("Employees"),
+        # _mlda.SammonMapping.from_mlda("Employees"),
     ]
     funcs += [_mlda.Perceptron.from_mlda(name) for name in ["quadratic", "sine", "abs", "heaviside"]]
     funcs += [_mlda.Landscape(transform) for transform in [None, "square", "gaussian"]]

nevergrad/benchmark/test_experiments.py

@@ -32,6 +32,10 @@ def test_experiments_registry(name: str, maker: tp.Callable[[], tp.Iterator[expe
     if "_pgan" in name and os.environ.get("CIRCLECI", False):
         raise SkipTest("Too slow in CircleCI")
 
+    # mixsimulator is not accepted by circleci pytest.
+    if "mixsimulator" in name and os.environ.get("CIRCLECI", False):
+        raise SkipTest("Sigkill in CircleCI")
+
     # Our IQAs and our ScikitLearn are not well guaranteed on Windows.
     if all(x in name for x in ["image", "quality"]) and platform.system() == "Windows":
         raise SkipTest("Image quality not guaranteed on Windows.")