New wizard for real-world problems (#1464)

* New wizard for real-world problems

because real-world problems are really very different

* fix

* fix

* Update bench.txt

* Update bench.txt

* Update bench.txt

* Update bench.txt

* fix

* Update test_experiments.py

* Update bench.txt

* fix

* fix

* Remove compiler Gym (#1465)

* remove_cg

* remove_cg

* fix

* fix

* fix

* fix

* recorded_hyperopt

* Update test_suggest.py

nevergrad/benchmark/gymexperiments.py

@@ -6,7 +6,6 @@
 import os
 import typing as tp
 from nevergrad.functions import gym as nevergrad_gym
-from nevergrad.functions import ExperimentFunction
 from .xpbase import registry
 from .xpbase import create_seed_generator
 from .xpbase import Experiment
@@ -266,8 +265,6 @@ def gym_problem(
     seed: tp.Optional[int] = None,
     specific_problem: str = "LANM",
     conformant: bool = False,
-    compiler_gym_pb_index: tp.Optional[int] = None,
-    limited_compiler_gym: tp.Optional[bool] = None,
     big_noise: bool = False,
     multi_scale: bool = False,
     greedy_bias: bool = False,
@@ -280,53 +277,35 @@ def gym_problem(
         name of the problem we are working on
     conformant: bool
         do we focus on conformant planning
-    compiler_gym_pb_index: integer
-        index of Uris problem we work on.
-    limited_compiler_gym: boolean
-        for compiler-gyn, whether we use a restricted action space
     big_noise: bool
         do we switch to specific optimizers, dedicated to noise
     multi_scale: boolean
         do we check multiple scales
     greedy_bias: boolean
         do we use greedy reward estimates for biasing the decisions.
     """
-    if "directcompilergym" in specific_problem:
-        assert compiler_gym_pb_index is not None
-        assert limited_compiler_gym is not None
-        assert compiler_gym_pb_index >= 0
-        assert greedy_bias is False
-        funcs: tp.List[ExperimentFunction] = [
-            nevergrad_gym.CompilerGym(
-                compiler_gym_pb_index=compiler_gym_pb_index, limited_compiler_gym=limited_compiler_gym
+    if conformant:
+        funcs = [
+            nevergrad_gym.GymMulti(
+                specific_problem,
+                control="conformant",
+                neural_factor=None,
             )
         ]
     else:
-        if conformant:
-            funcs = [
-                nevergrad_gym.GymMulti(
-                    specific_problem,
-                    control="conformant",
-                    limited_compiler_gym=limited_compiler_gym,
-                    compiler_gym_pb_index=compiler_gym_pb_index,
-                    neural_factor=None,
-                )
-            ]
-        else:
-            funcs = [
-                nevergrad_gym.GymMulti(
-                    specific_problem,
-                    control=control,
-                    neural_factor=1 if control != "linear" else None,
-                    limited_compiler_gym=limited_compiler_gym,
-                    optimization_scale=scale,
-                    greedy_bias=greedy_bias,
-                )
-                for scale in ([-6, -4, -2, 0] if multi_scale else [0])
-                for control in (
-                    ["deep_neural", "semideep_neural", "neural", "linear"] if not greedy_bias else ["neural"]
-                )
-            ]
+        funcs = [
+            nevergrad_gym.GymMulti(
+                specific_problem,
+                control=control,
+                neural_factor=1 if control != "linear" else None,
+                optimization_scale=scale,
+                greedy_bias=greedy_bias,
+            )
+            for scale in ([-6, -4, -2, 0] if multi_scale else [0])
+            for control in (
+                ["deep_neural", "semideep_neural", "neural", "linear"] if not greedy_bias else ["neural"]
+            )
+        ]
     seedg = create_seed_generator(seed)
     optims = [
         "TwoPointsDE",
@@ -346,9 +325,7 @@ def gym_problem(
     ]
     if "stochastic" in specific_problem:
         optims = ["DiagonalCMA", "TBPSA"] if big_noise else ["DiagonalCMA"]
-    if "directcompilergym" in specific_problem or (
-        specific_problem == "EnergySavingsGym-v0" and conformant
-    ):  # Do this for all conformant discrete ?
+    if specific_problem == "EnergySavingsGym-v0" and conformant:  # Do this for all conformant discrete ?
         optims = [
             "DiscreteOnePlusOne",
             "PortfolioDiscreteOnePlusOne",
@@ -380,28 +357,6 @@ def gym_problem(
                             yield xp
 
 
-@registry.register
-def limited_stochastic_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. Stochastic problem: we are optimizing a net for driving compilation."""
-    return gym_problem(seed, specific_problem="stochasticcompilergym", limited_compiler_gym=True)
-
-
-@registry.register
-def multiscale_limited_stochastic_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. Stochastic problem: we are optimizing a net for driving compilation."""
-    return gym_problem(
-        seed, specific_problem="stochasticcompilergym", limited_compiler_gym=True, multi_scale=True
-    )
-
-
-@registry.register
-def unlimited_hardcore_stochastic_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. Stochastic problem: we are optimizing a net for driving compilation."""
-    return gym_problem(
-        seed, specific_problem="stochasticcompilergym", limited_compiler_gym=False, big_noise=True
-    )
-
-
 @registry.register
 def conformant_planning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     # You might modify this problem by specifying an environment variable.
@@ -425,53 +380,3 @@ def neuro_planning(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
         conformant=False,
         big_noise=False,
     )
-
-
-@registry.register
-def limited_hardcore_stochastic_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. Stochastic problem: we are optimizing a net for driving compilation."""
-    return gym_problem(
-        seed, specific_problem="stochasticcompilergym", limited_compiler_gym=True, big_noise=True
-    )
-
-
-@registry.register
-def greedy_limited_stochastic_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. Stochastic problem: we are optimizing a net for driving compilation."""
-    return gym_problem(
-        seed, specific_problem="stochasticcompilergym", limited_compiler_gym=True, greedy_bias=True
-    )
-
-
-@registry.register
-def unlimited_stochastic_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. Stochastic problem: we are optimizing a net for driving compilation."""
-    return gym_problem(seed, specific_problem="stochasticcompilergym", limited_compiler_gym=False)
-
-
-@registry.register
-def unlimited_direct_problems23_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. All 23 problems."""
-    for compiler_gym_pb_index in range(23):
-        pb = gym_problem(
-            seed,
-            specific_problem="directcompilergym" + str(compiler_gym_pb_index),
-            compiler_gym_pb_index=compiler_gym_pb_index,
-            limited_compiler_gym=False,
-        )
-        for xp in pb:
-            yield xp
-
-
-@registry.register
-def limited_direct_problems23_compiler_gym(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
-    """Working on CompilerGym. All 23 problems."""
-    for compiler_gym_pb_index in range(23):
-        pb = gym_problem(
-            seed,
-            specific_problem="directcompilergym" + str(compiler_gym_pb_index),
-            compiler_gym_pb_index=compiler_gym_pb_index,
-            limited_compiler_gym=True,
-        )
-        for xp in pb:
-            yield xp

nevergrad/benchmark/test_experiments.py

@@ -29,9 +29,12 @@
 @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 "conformant" in name or name == "neuro_planning":
         raise SkipTest("This is user parametric and can not be tested.")
 
+    if "compiler" in name or "emulators" in name:
+        raise SkipTest("Compiler/emulator stuff too heavy for CircleCI.")
+
     # 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/functions/gym/__init__.py

@@ -4,5 +4,4 @@
 # LICENSE file in the root directory of this source tree.
 
 from .multigym import GymMulti as GymMulti
-from .multigym import CompilerGym as CompilerGym
 from .tuple_gym_env import TupleActionSpace