Update mixsimulator benchmark (for v0.4.3)

nevergrad/benchmark/experiments.py

@@ -1263,7 +1263,7 @@ def mono_rocket(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
 @registry.register
 def mixsimulator(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     """MixSimulator of power plants
-    Budget 20, 40, ..., 1600.
+    Budget 20, 40, ..., 160.
     Sequential or 30 workers."""
     funcs = [OptimizeMix()]
     seedg = create_seed_generator(seed)
@@ -1356,7 +1356,7 @@ def olympus_surfaces(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
         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))
+                    funcs.append(OlympusSurface(kind, 10 ** k, noise, noise_scale))
 
     seedg = create_seed_generator(seed)
     optims = get_optimizers("basics", "noisy", seed=next(seedg))
@@ -1401,7 +1401,7 @@ def simple_tsp(seed: tp.Optional[int] = None, complex_tsp: bool = False) -> tp.I
     Budgets doubling from 25, 50, 100, 200, ... up  to 25600
 
     """
-    funcs = [STSP(10**k, complex_tsp) for k in range(2, 6)]
+    funcs = [STSP(10 ** k, complex_tsp) for k in range(2, 6)]
     seedg = create_seed_generator(seed)
     optims = [
         "RotatedTwoPointsDE",
@@ -1530,7 +1530,7 @@ def image_similarity(
         for loss in imagesxp.imagelosses.registry.values()
         if loss.REQUIRES_REFERENCE == similarity
     ]
-    for budget in [100 * 5**k for k in range(3)]:
+    for budget in [100 * 5 ** k for k in range(3)]:
         for func in funcs:
             for algo in optims:
                 xp = Experiment(func, algo, budget, num_workers=1, seed=next(seedg))
@@ -1577,7 +1577,7 @@ def image_multi_similarity(
         )
     else:
         mofuncs = [fbase.MultiExperiment(funcs, upper_bounds=base_values)]
-    for budget in [100 * 5**k for k in range(3)]:
+    for budget in [100 * 5 ** k for k in range(3)]:
         for num_workers in [1]:
             for algo in optims:
                 for mofunc in mofuncs:
@@ -1613,7 +1613,7 @@ def image_quality_proxy(seed: tp.Optional[int] = None, with_pgan: bool = False)
         for loss in (imagesxp.imagelosses.Koncept512, imagesxp.imagelosses.Blur, imagesxp.imagelosses.Brisque)
     ]
     # TODO: add the proxy info in the parametrization.
-    for budget in [100 * 5**k for k in range(3)]:
+    for budget in [100 * 5 ** k for k in range(3)]:
         for algo in optims:
             for func in [blur, brisque]:
                 # We optimize on blur or brisque and check performance on iqa.
@@ -1660,7 +1660,7 @@ def image_quality(
     else:
         upper_bounds = [func(func.parametrization.value) for func in funcs]
         mofuncs = [fbase.MultiExperiment(funcs, upper_bounds=upper_bounds)]  # type: ignore
-    for budget in [100 * 5**k for k in range(3)]:
+    for budget in [100 * 5 ** k for k in range(3)]:
         for num_workers in [1]:
             for algo in optims:
                 for func in mofuncs:
@@ -1722,7 +1722,7 @@ def image_similarity_and_quality(
                     [func, func_blur, func_iqa], upper_bounds=[base_value, base_blur_value, 100.0]
                 )
             ]
-        for budget in [100 * 5**k for k in range(3)]:
+        for budget in [100 * 5 ** k for k in range(3)]:
             for algo in optims:
                 for mofunc in mofuncs:
                     xp = Experiment(mofunc, algo, budget, num_workers=1, seed=next(seedg))
@@ -2016,7 +2016,7 @@ def causal_similarity(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     seedg = create_seed_generator(seed)
     optims = ["CMA", "NGOpt8", "DE", "PSO", "RecES", "RecMixES", "RecMutDE", "ParametrizationDE"]
     func = CausalDiscovery()
-    for budget in [100 * 5**k for k in range(3)]:
+    for budget in [100 * 5 ** k for k in range(3)]:
         for num_workers in [1]:
             for algo in optims:
                 xp = Experiment(func, algo, budget, num_workers=num_workers, seed=next(seedg))
@@ -2031,7 +2031,7 @@ def unit_commitment(seed: tp.Optional[int] = None) -> tp.Iterator[Experiment]:
     for num_timepoint in [5, 10, 20]:
         for num_generator in [3, 8]:
             func = UnitCommitmentProblem(num_timepoints=num_timepoint, num_generators=num_generator)
-            for budget in [100 * 5**k for k in range(3)]:
+            for budget in [100 * 5 ** k for k in range(3)]:
                 for algo in optims:
                     xp = Experiment(func, algo, budget, num_workers=1, seed=next(seedg))
                     if not xp.is_incoherent:

nevergrad/functions/mixsimulator/core.py

@@ -13,8 +13,7 @@ class OptimizeMix(base.ExperimentFunction):
     MixSimulator is an application with an optimization model for calculating
     and simulating the least cost of an energy mix under certain constraints.
 
-    For now, it uses a default dataset (more will be added soon).
-
+    For now, it uses a default dataset (10 interconnected power generators).
     For more information, visit : https://github.com/Foloso/MixSimulator
 
     Parameters
@@ -24,12 +23,12 @@ class OptimizeMix(base.ExperimentFunction):
 
     """
 
-    def __init__(self, time: int = 8760) -> None:
+    def __init__(self, time: int = 120) -> None:
         try:
-            from mixsimulator.MixSimulator import MixSimulator  # pylint: disable=import-outside-toplevel
-            from mixsimulator.Demand import Demand
+            from mixsimulator import ElectricityMix  # pylint: disable=import-outside-toplevel
+            from mixsimulator.demand.classic.Demand import Demand
 
-            self._mix = MixSimulator()
+            self._mix = ElectricityMix().mix(method="classic", carbon_cost=10, penalisation_cost=100)
             self._mix.set_data_to("Toamasina")
             self._demand = Demand()
             self._demand.set_data_to("Toamasina", delimiter=",")
@@ -38,8 +37,11 @@ def __init__(self, time: int = 8760) -> None:
         except (KeyError, AttributeError, ModuleNotFoundError) as e:
             # send a skip error so that this does not break the test suit
             raise base.UnsupportedExperiment("mixsimulator dependency issue") from e
-        self._mix.set_penalisation_cost(100)
-        self._mix.set_carbon_cost(10)
+
+        # penalizations setting
+        # self._mix.set_penalisation_cost(100)
+        # self._mix.set_carbon_cost(10)
+
         parameters = self._mix.get_opt_params(time)
         parameters.set_name("dims")
         super().__init__(self._mix.loss_function, parameters)

requirements/bench.txt

@@ -17,7 +17,7 @@ Pillow>=8.4.0
 tqdm
 torchvision>=0.11.1
 pyomo==5.7.1
-mixsimulator>=0.3.3
+mixsimulator>=0.4.3
 hyperopt>=0.2.5
 IOHexperimenter>=0.2.8.7
 cdt>=0.5.23