diff --git a/.flake8 b/.flake8 new file mode 100644 index 00000000..611a6bcb --- /dev/null +++ b/.flake8 @@ -0,0 +1,3 @@ +[flake8] +exclude = .git,__pycache__,.env,venv,env,ENV,env.bak,venv.bak,build,dist +max-line-length = 88 \ No newline at end of file diff --git a/ProcessOptimizer/XpyriMentor/suggestors/__init__.py b/ProcessOptimizer/XpyriMentor/suggestors/__init__.py index af821f18..00d1ef4a 100644 --- a/ProcessOptimizer/XpyriMentor/suggestors/__init__.py +++ b/ProcessOptimizer/XpyriMentor/suggestors/__init__.py @@ -1,4 +1,6 @@ +from .constant_suggestor import ConstantSuggestor from .default_suggestor import DefaultSuggestor +from .golden_ratio_suggestor import GoldenRatioSuggestor from .lhs_suggestor import LHSSuggestor from .po_suggestor import POSuggestor from .random_strategizer import RandomStragegizer @@ -7,7 +9,9 @@ from .suggestor_factory import suggestor_factory __all__ = [ + "ConstantSuggestor", "DefaultSuggestor", + "GoldenRatioSuggestor", "IncompatibleNumberAsked", "LHSSuggestor", "POSuggestor", diff --git a/ProcessOptimizer/XpyriMentor/suggestors/constant_suggestor.py b/ProcessOptimizer/XpyriMentor/suggestors/constant_suggestor.py new file mode 100644 index 00000000..2428b424 --- /dev/null +++ b/ProcessOptimizer/XpyriMentor/suggestors/constant_suggestor.py @@ -0,0 +1,37 @@ +from typing import Union, Iterable + +import numpy as np +from ProcessOptimizer.space import Space +from ProcessOptimizer.utils import is_listlike + + +class ConstantSuggestor(): + """ + A suggestor that always returns the same point. + """ + def __init__(self, space: Space, point: Union[list, float] = 0.5, convert: bool = True): + """ + Initialize the suggestor. + + Parameters: + * `space` [Space]: + The search space. + * `point` [list or float]: + The point to suggest. If not a list, it is converted to a list with + `space.n_dims` identical elements. Default is 0.5, which, if `convert` is + `True`, makes the suggestor return the center point of `space`. + * `convert` [bool]: + If `True` (default), the point is converted to a point in the space with + `space.sample` + """ + self.space = space + if not is_listlike(point): + point = [point] * space.n_dims + if convert: + point = space.sample(point) + self.point = np.array(point) + + def suggest( + self, Xi: Iterable[Iterable], Yi: Iterable, n_asked: int = 1 + ) -> np.ndarray: + return np.tile(self.point, (n_asked, 1)) \ No newline at end of file diff --git a/ProcessOptimizer/XpyriMentor/suggestors/golden_ratio_suggestor.py b/ProcessOptimizer/XpyriMentor/suggestors/golden_ratio_suggestor.py new file mode 100644 index 00000000..30a5e986 --- /dev/null +++ b/ProcessOptimizer/XpyriMentor/suggestors/golden_ratio_suggestor.py @@ -0,0 +1,42 @@ +from typing import Iterable + +import numpy as np +from ProcessOptimizer.space import Space + +class GoldenRatioSuggestor(): + """ + A quasi-random, low-discrepancy sequence suggestor based on the generalized golden + ratio. + + From https://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/ + """ + def __init__(self,space: Space, rng: np.random.Generator): + self.space = space + self.rng = rng + self.offset = self.rng.random() + + @staticmethod + def phi(d): + """ + Calculate the generalized golden ratio for a given dimensionality d. + + phi(d) is the unique positive real root of the polynomial equation + x**(d+1) = 1 + x. If follows that phi(d)**(d+1) = 1 + phi(d). + """ + x = 2.0 + for _ in range(10): + x = pow(1+x,1/(d+1)) + return x + + def suggest( + self, Xi: Iterable[Iterable], Yi: Iterable, n_asked: int = 1 + ) -> np.ndarray: + d = self.space.n_dims + g = self.phi(d) + alpha = np.fromiter((pow(1/g, j+1) %1 for j in range(d)), dtype=float) + offset = np.array([self.offset]*d + len(Xi)*alpha) + x = np.fromiter( + ((offset + alpha*(i+1)) %1 for i in range(n_asked)), + dtype = np.dtype((float,d)), + ) + return self.space.sample(x) diff --git a/ProcessOptimizer/XpyriMentor/suggestors/suggestor_factory.py b/ProcessOptimizer/XpyriMentor/suggestors/suggestor_factory.py index ad13fe73..2ac2b46d 100644 --- a/ProcessOptimizer/XpyriMentor/suggestors/suggestor_factory.py +++ b/ProcessOptimizer/XpyriMentor/suggestors/suggestor_factory.py @@ -4,7 +4,9 @@ import numpy as np from ProcessOptimizer.space import Space +from .constant_suggestor import ConstantSuggestor from .default_suggestor import DefaultSuggestor +from .golden_ratio_suggestor import GoldenRatioSuggestor from .lhs_suggestor import LHSSuggestor from .po_suggestor import POSuggestor from .random_strategizer import RandomStragegizer @@ -46,6 +48,7 @@ def suggestor_factory( logger.debug("Creating DefaultSuggestor") return DefaultSuggestor(space, n_objectives, rng) try: + definition = definition.copy() suggestor_type = definition.pop("suggestor_name") except KeyError as e: raise ValueError( @@ -96,6 +99,7 @@ def suggestor_factory( logger.debug("Creating SequentialStrategizer") suggestors = [] for suggestor in definition["suggestors"]: + suggestor: dict = suggestor.copy() n = suggestor.pop("suggestor_budget") if "suggestor" in suggestor: if len(suggestor) > 1: @@ -111,5 +115,15 @@ def suggestor_factory( suggestor_factory(space, suggestor, n_objectives, rng, n_points = n) )) return SequentialStrategizer(suggestors) + elif suggestor_type == "GoldenRatio": + logger.debug("Creating GoldenRatioSuggestor") + return GoldenRatioSuggestor( + space=space, + rng=rng, + **definition, + ) + elif suggestor_type == "Constant": + logger.debug("Creating ConstantSuggestor") + return ConstantSuggestor(space=space,**definition) else: raise ValueError(f"Unknown suggestor name: {suggestor_type}") diff --git a/ProcessOptimizer/XpyriMentor/xpyrimentor.py b/ProcessOptimizer/XpyriMentor/xpyrimentor.py index 70e91cfc..8d32f5f9 100644 --- a/ProcessOptimizer/XpyriMentor/xpyrimentor.py +++ b/ProcessOptimizer/XpyriMentor/xpyrimentor.py @@ -71,6 +71,7 @@ def ask(self, n: int = 1) -> np.ndarray: Ask the suggestor for new points to evaluate. The number of points to ask is specified by the argument n. The method returns a list of new points to evaluate. """ + n = int(n) # Ensure that n is an integer return self.suggestor.suggest(Xi=self.Xi, Yi=self.yi, n_asked=n) def tell(self, x: Iterable, y: Any) -> None: diff --git a/ProcessOptimizer/benchmarking/__init__.py b/ProcessOptimizer/benchmarking/__init__.py new file mode 100644 index 00000000..24bfd7cd --- /dev/null +++ b/ProcessOptimizer/benchmarking/__init__.py @@ -0,0 +1,6 @@ +from .benchmark import BenchmarkInstance, run_benchmark + +__all__ = [ + "BenchmarkInstance", + "run_benchmark", +] \ No newline at end of file diff --git a/ProcessOptimizer/benchmarking/benchmark.py b/ProcessOptimizer/benchmarking/benchmark.py new file mode 100644 index 00000000..1e694741 --- /dev/null +++ b/ProcessOptimizer/benchmarking/benchmark.py @@ -0,0 +1,153 @@ +from __future__ import annotations +import functools +from dataclasses import dataclass, field +from typing import Iterable + +from ..model_systems import get_model_system, ModelSystem +from ProcessOptimizer import Optimizer, XpyriMentor +from ProcessOptimizer.utils import expected_minimum + + +@dataclass +class BenchmarkInstance: + model_system_name: str + xpyrimentor_definition: dict + experimental_budget: int + expected_random_runtime: float + seed: int + noise_level: float + validate: bool = False + # Results: + number_of_evaluations: int | None = None + success: bool | None = None + # Internal variables: + success_level: float = field(init=False, repr=False) + model: ModelSystem = field(init=False, repr=False) + xpyrimentor: XpyriMentor = field(init=False, repr=False) + + def __init__( + self, + model_system_name: str, + xpyrimentor_definition: dict, + experimental_budget: int, + expected_random_runtime: float, + seed: int, + noise_level: float = 1.0, + **kwargs + ): + """ + Initialize the benchmark instance. + + Needs the following parameters: + * `model_system_name` [str]: + Name of the model system to use. + * `xpyrimentor_definition` [dict]: + Definition of the XpyriMentor object to use. + * `experimental_budget` [int]: + Maximum number of evaluations to run before stopping. + * `expected_random_runtime` [float]: + How "hard" the system is to optimize. This is the expected number of random + parameter sets you need to evaluate to find a good one. + * `seed` [int]: + Random seed to use. + """ + self.__dict__.update({ + "model_system_name": model_system_name, + "xpyrimentor_definition":xpyrimentor_definition, + "experimental_budget":experimental_budget, + "expected_random_runtime":expected_random_runtime, + "seed":seed, + "noise_level":noise_level, + }) + self.__dict__.update(kwargs) + self.success_level = find_limits( + model_system_name, expected_random_runtime, self.noise_level + ) + self.model = get_model_system(model_system_name, seed=seed) + self.xpyrimentor = XpyriMentor(self.model.space, self.xpyrimentor_definition, seed=seed) + + @property + def model_system(self) -> ModelSystem: + model_system = get_model_system(self.model_system_name, seed=self.seed) + model_system.noise_size = model_system.noise_size*self.noise_level + return model_system + + def find_estimated_optimum(self) -> float: + """ + Find the parameter set that is estimated to be the optimum, and the value that is + 2 standard deviations above the true value at that point. + """ + # This is a bit of a hack, but it works for now. The optimizer is the last + # suggestor in the suggestor list of the sequential strategizer. We need the + # optimizer since we need to find the expected minimum and the model uncertainty + # there. + optimizer: Optimizer = self.xpyrimentor.suggestor.suggestors[-1][1].optimizer + optimizer.Xi = self.xpyrimentor.Xi + optimizer.yi = self.xpyrimentor.yi + optimizer.update_next() + optimizer.add_observational_noise() + result = optimizer.get_result() + result_location, [result_value, result_std] = expected_minimum(result, return_std=True) + optimizer.remove_observational_noise() + return (result_location, result_value + 2*result_std) + +def run_benchmark(benchmark_instance: BenchmarkInstance) -> BenchmarkInstance: + """ + Run the benchmark instance, save the number of evaluations and whether the + success level was reached, and return the instance. + """ + success = False + while len(benchmark_instance.xpyrimentor.Xi) < benchmark_instance.experimental_budget: + x = benchmark_instance.xpyrimentor.ask() + y = benchmark_instance.model.get_score(x) + benchmark_instance.xpyrimentor.tell(x, [y]) + # We could restrict testing to only if the point is considered good, but it + # doesn't seem to matter much for the runtime. + minimum_location, minimum_value = benchmark_instance.find_estimated_optimum() + if minimum_value ModelSystem: +def create_hart3(noise=bool, **kwargs) -> ModelSystem: hart3 = ModelSystem( hart3_score, [(0.0, 1.0), (0.0, 1.0), (0.0, 1.0)], noise_model="constant", true_max=0.0, true_min=-3.863, + **kwargs, ) if noise: return hart3 diff --git a/ProcessOptimizer/model_systems/hart6.py b/ProcessOptimizer/model_systems/hart6.py index db4bb8ff..3a631aca 100644 --- a/ProcessOptimizer/model_systems/hart6.py +++ b/ProcessOptimizer/model_systems/hart6.py @@ -31,6 +31,10 @@ def hart6_score(x): The score of the system at x. """ # Define the constants that are canonically used with this function. + if isinstance(x, np.ndarray): + x=x.astype(dtype=float) # Ensure that x is an array of floats to support the math. + else: + x = np.asarray(x, dtype=float) alpha = np.asarray([1.0, 1.2, 3.0, 3.2]) P = 10**-4 * np.asarray( [ @@ -51,7 +55,7 @@ def hart6_score(x): return -np.sum(alpha * np.exp(-np.sum(A * (np.array(x) - P) ** 2, axis=1))) -def create_hart6(noise: bool = True) -> ModelSystem: +def create_hart6(noise: bool = True, **kwargs) -> ModelSystem: noise_model = "constant" if noise else None return ModelSystem( hart6_score, @@ -59,4 +63,5 @@ def create_hart6(noise: bool = True) -> ModelSystem: noise_model=noise_model, true_max=0.0, true_min=-3.3224, + **kwargs, ) diff --git a/ProcessOptimizer/model_systems/model_system.py b/ProcessOptimizer/model_systems/model_system.py index 5c5d5241..715d4838 100644 --- a/ProcessOptimizer/model_systems/model_system.py +++ b/ProcessOptimizer/model_systems/model_system.py @@ -1,4 +1,4 @@ -from typing import Callable, List, Union +from typing import Callable, List, Optional, Union import numpy as np from ..space import Space, space_factory @@ -11,29 +11,19 @@ class ModelSystem: Model System for testing ProcessOptimizer. Instances of this class are used in benchmarks and the example notebooks. - Parameters + Attributes ---------- - * `score` [Callable]: - Function for calculating the noiseless score of the system at a given - point in the parameter space. - - * `space` [List or Space]: + * `space` [Space]: A list of dimension defintions or the parameter space as a Space object. * `true_min` [float]: The true minimum value of the score function within the parameter space. - * `noise_model` [str, dict, or NoiseModel]: + * `true_max` [float]: + The true maximum value of the score function within the parameter space. + + * `noise_model` [NoiseModel]: Noise model to apply to the score. - If str, it should be the name of the noise model type. In this case, - further arguments can be given (e.g. `noise_size`). - If dict, one key should be `model_type`. - If NoiseModel, this NoiseModel will be used. - - Possible model type strings are: - "constant": The noise level is constant. - "proportional": Tne noise level is proportional to the score. - "zero": No noise is applied. """ def __init__( @@ -41,12 +31,52 @@ def __init__( score: Callable[..., float], space: Union[Space, List], noise_model: Union[str, dict, NoiseModel, None], - true_min=None, - true_max=None, + true_min: Optional[float] = None, + true_max: Optional[float] = None, + seed: Union[int, np.random.RandomState, np.random.Generator, None] = 42, ): + """ + Initialize the model system. + + Parameters + ---------- + * `score` [Callable]: + Function for calculating the noiseless score of the system at a given + point in the parameter space. + + * `space` [List or Space]: + A list of dimension defintions or the parameter space as a Space object. + + * `noise_model` [str, dict, or NoiseModel]: + Noise model to apply to the score. + If str, it should be the name of the noise model type. In this case, + further arguments can be given (e.g. `noise_size`). + If dict, one key should be `model_type`. + If NoiseModel, this NoiseModel will be used. + + Possible model type strings are: + "constant": The noise level is constant. + "proportional": Tne noise level is proportional to the score. + "zero": No noise is applied. + + * `true_min` [float]: + The true minimum value of the score function within the parameter space. If + not given, it will be estimated by evaluating the score function at a set of + points in the parameter space. + + * `true_max` [float]: + The true maximum value of the score function within the parameter space. If + not given, it will be estimated by evaluating the score function at a set of + points in the parameter space. + + * `seed` [int, RandomState, Generator, or None]: + Seed for the random number generator. If None, the ModelSystem will give + random results, otherwise the results will be deterministic. Default behavior + is deterministic. + """ self.score = score self.space = space_factory(space) - self.noise_model = parse_noise_model(noise_model) + self.noise_model = parse_noise_model(noise_model, seed=seed) if true_min is None: ndims = self.space.n_dims points = self.space.lhs( @@ -131,6 +161,21 @@ def set_noise_model(self, noise_model: Union[str, dict, NoiseModel, None]): """ self.noise_model = parse_noise_model(noise_model) + def copy(self): + """ + Returns a copy of the model system. + + The rng of the copy will a spawn of the original, that is, different in a + deterministic way. + """ + return self.__class__( + score=self.score, + space=self.space, + noise_model=self.noise_model.copy(), + true_min=self.true_min, + true_max=self.true_max, + ) + @property def noise_size(self): return self.noise_model.noise_size diff --git a/ProcessOptimizer/model_systems/noise_models.py b/ProcessOptimizer/model_systems/noise_models.py index 242e7753..41b536eb 100644 --- a/ProcessOptimizer/model_systems/noise_models.py +++ b/ProcessOptimizer/model_systems/noise_models.py @@ -3,6 +3,8 @@ import numpy as np +from ProcessOptimizer.utils import get_random_generator + class NoiseModel(ABC): """ @@ -12,7 +14,7 @@ class NoiseModel(ABC): def __init__( self, noise_size: Optional[float], - seed: Optional[int] = 42, + seed: Union[int, np.random.RandomState, np.random.Generator, None] = 42, ): """ Parameters @@ -32,17 +34,30 @@ def __init__( # directly set the size is more intuitive, and that would be complicated if it # was just one variable. # Note that this has the potential for problems if _noise_distribution does not - # have "size" 1, but as long as it is only set by set_noise_type(), it should be - # safe. + # have "size" 1, but as long as it is only use the ones defined here, you should + # be fine. self.noise_size = noise_size - self._rng = np.random.default_rng(seed) - # Change this to ..utils.get_random_genertor once the pull request with that have been merged - self.set_noise_type("normal") + self._rng = get_random_generator(seed) + self.noise_types = { + "normal": self.normal, + "Gaussian": self.normal, + "norm": self.normal, + "uniform": self.uniform, + } + self.noise_type = "normal" @abstractmethod def get_noise(self, X, Y: float) -> float: pass + def uniform(self): + """Convinience function to get a uniform distributed noise value.""" + return self._rng.uniform(low=-1, high=1) + + def normal(self): + """Convinience function to get a normal distributed noise value.""" + return self._rng.normal() + @property def _sample_noise(self) -> float: """A raw noise value, to be used in the get_noise() function.""" @@ -52,26 +67,33 @@ def _sample_noise(self) -> float: f"{self.__class__.__name__} is not supposed to be called." ) - return self._noise_distribution() * self.noise_size + return self.noise_types[self.noise_type]() * self.noise_size + + @property + def noise_type(self) -> str: + return self._noise_type - def set_noise_type(self, noise_type: str): - if noise_type in ["normal", "Gaussian", "norm", "uniform"]: - self.noise_type = noise_type + @noise_type.setter + def noise_type(self, value: str): + if value in self.noise_types: + self._noise_type = value else: - raise ValueError(f'Noise distribution "{noise_type}" not recognised.') + raise ValueError(f'Noise distribution "{value}" not recognised.') def set_seed(self, seed: Optional[int]): # Instantiate the random number generator again self._rng = np.random.default_rng(seed) - @property - def _noise_distribution(self) -> Callable[[], float]: - if self.noise_type in ["normal", "Gaussian", "norm"]: - return self._rng.normal - elif self.noise_type == "uniform": - return lambda: self._rng.uniform(low=-1, high=1) - else: - raise ValueError(f'Noise distribution "{self.noise_type}" not recognised.') + def copy(self) -> "NoiseModel": + """ + Create a copy of the noise model. This is necessary to avoid the same random + seed being used in multiple noise models, which would make the noise correlated. + """ + copy = self.__class__(noise_size=self.noise_size, seed=self._rng.spawn(1)[0]) + # np.random.Generator.spawn() returns a new generator based on the old one, but + # with a different seed. It is deterministic, but not identical to the old one. + copy.noise_type = self.noise_type + return copy class ConstantNoise(NoiseModel): diff --git a/ProcessOptimizer/optimizer/optimizer.py b/ProcessOptimizer/optimizer/optimizer.py index 85017641..1cb193f1 100644 --- a/ProcessOptimizer/optimizer/optimizer.py +++ b/ProcessOptimizer/optimizer/optimizer.py @@ -306,7 +306,7 @@ def __init__( self._lhs = lhs if lhs: - self._lhs_samples = self.space.lhs(n_initial_points) + self._lhs_samples = self.space.lhs(n_initial_points, seed=self.rng) # Default is no constraints self._constraints = None diff --git a/ProcessOptimizer/space/space.py b/ProcessOptimizer/space/space.py index a9f3490a..cefe94aa 100644 --- a/ProcessOptimizer/space/space.py +++ b/ProcessOptimizer/space/space.py @@ -456,8 +456,9 @@ def distance(self, a, b): def _sample(self, point_list: Iterable[float]) -> np.ndarray: point_list = [ - point * (self.high + 1 - self.low) + self.low for point in point_list + min(point * (self.high + 1 - self.low) + self.low,self.high) for point in point_list ] + return np.floor(point_list).astype(int) diff --git a/ProcessOptimizer/tests/test_noise_model.py b/ProcessOptimizer/tests/test_noise_model.py index 5b29b9f0..a4ecf9f6 100644 --- a/ProcessOptimizer/tests/test_noise_model.py +++ b/ProcessOptimizer/tests/test_noise_model.py @@ -112,7 +112,8 @@ def test_zero_noise(signal_list): @pytest.mark.parametrize("magnitude", (1, 2, 3)) def test_noise_model_example_1(long_signal_list, magnitude): # the following two lines are taken from the docstring of DataDependentNoise - noise_choice = lambda X: ConstantNoise(noise_size=X) + def noise_choice(X): + return ConstantNoise(noise_size=X) noise_model = DataDependentNoise(noise_function=noise_choice) data = [magnitude] * len(long_signal_list) noise_list = [ @@ -123,7 +124,11 @@ def test_noise_model_example_1(long_signal_list, magnitude): def test_noise_model_example_2(long_signal_list): # the following two lines are taken from the docstring of DataDependentNoise - noise_choice = lambda X: ZeroNoise() if X[0] == 0 else ConstantNoise() + def noise_choice(X): + if X[0] == 0: + return ZeroNoise() + else: + return ConstantNoise() noise_model = DataDependentNoise(noise_function=noise_choice) X = [0, 10, 5] noise_list = [noise_model.get_noise(X, signal) for signal in long_signal_list] @@ -143,7 +148,12 @@ def test_not_reseeding_data_dependent_noise(): # are identical. noise_model_one = ConstantNoise() noise_model_two = ConstantNoise() - noise_choice = lambda X: noise_model_one if X == 0 else noise_model_two + + def noise_choice(X): + if X == 0: + return noise_model_one + else: + return noise_model_two data_dependent_noise_model = DataDependentNoise( noise_function=noise_choice, overwrite_rng=False ) @@ -159,7 +169,12 @@ def test_reseeding_data_dependent_noise(): # reseeded. noise_model_one = ConstantNoise() noise_model_two = ConstantNoise() - noise_choice = lambda X: noise_model_one if X == 0 else noise_model_two + + def noise_choice(X): + if X == 0: + return noise_model_one + else: + return noise_model_two data_dependent_noise_model = DataDependentNoise( noise_function=noise_choice, overwrite_rng=True ) @@ -192,7 +207,8 @@ def test_sum_noise_raw_noise_error(): # _sample_noise of DataDependentNoise should not be accesible, since it is a composite # NoiseModel, and should ot have "its own" noise. def test_data_dependent_raw_noise_error(): - noise_choise = lambda: ConstantNoise() + def noise_choise(): + return ConstantNoise() noise_model = DataDependentNoise(noise_function=noise_choise) with pytest.raises(TypeError): noise_model._sample_noise @@ -255,7 +271,7 @@ def test_parse_zero(): def test_uniform_noise(long_signal_list): noise_model = ConstantNoise() - noise_model.set_noise_type("uniform") + noise_model.noise_type = "uniform" noise_list = [noise_model.get_noise(None, Y) for Y in long_signal_list] (start, width) = uniform.fit(noise_list) assert np.allclose(start, -1, atol=0.1) @@ -265,7 +281,15 @@ def test_uniform_noise(long_signal_list): def test_unknown_distribution(): noise_model = ConstantNoise() with pytest.raises(ValueError): - noise_model.set_noise_type("not_implemented") + noise_model.noise_type = "not_implemented" + +def test_new_distribution(): + noise_model = ConstantNoise() + noise_model.noise_types["new_distribution"] = lambda : 2 + noise_model.noise_type = "new_distribution" + assert noise_model.get_noise(None, 0) == 2 + noise_model.noise_size = 10 + assert noise_model.get_noise(None, 0) == 20 # set_noise_model_list did not reset the list. This test verifies that this bug has been diff --git a/ProcessOptimizer/tests/test_suggestors/test_constant_suggestor.py b/ProcessOptimizer/tests/test_suggestors/test_constant_suggestor.py new file mode 100644 index 00000000..5e082bb8 --- /dev/null +++ b/ProcessOptimizer/tests/test_suggestors/test_constant_suggestor.py @@ -0,0 +1,41 @@ +import numpy as np + +from ProcessOptimizer.space import space_factory +from ProcessOptimizer.XpyriMentor.suggestors import ConstantSuggestor, Suggestor, suggestor_factory + +def test_initializaton(): + suggestor = ConstantSuggestor(space=space_factory([[1, 2], [1, 2]])) + assert isinstance(suggestor, Suggestor) + +def test_factory(): + space = space_factory([[1, 2], [1, 2]]) + suggestor = suggestor_factory( + space=space, + definition={"suggestor_name": "Constant"}, + ) + assert isinstance(suggestor, ConstantSuggestor) + +def test_factory_settings(): + space = space_factory([[1, 2], [1, 2]]) + suggestor = suggestor_factory( + space=space, + definition={"suggestor_name": "Constant", "point": [1, 1]}, + ) + assert isinstance(suggestor, ConstantSuggestor) + assert (suggestor.suggest([1,2], []) == np.array([[2,2]])).all() + suggestor = suggestor_factory( + space=space, + definition={"suggestor_name": "Constant", "convert": False, "point": [1, 1]}, + ) + assert isinstance(suggestor, ConstantSuggestor) + assert (suggestor.suggest([1,2], []) == np.array([[1,1]])).all() + +def test_ask_multiple(): + space = space_factory([[1, 10], [1, 10]]) + suggestor = ConstantSuggestor(space) + suggestions = suggestor.suggest([], [], n_asked=5) + assert len(suggestions) == 5 + for suggestion in suggestions: + assert suggestion in space + assert len(suggestion) == 2 + assert (suggestion == suggestions[0]).all() \ No newline at end of file diff --git a/ProcessOptimizer/tests/test_suggestors/test_golden_ratio_suggestor.py b/ProcessOptimizer/tests/test_suggestors/test_golden_ratio_suggestor.py new file mode 100644 index 00000000..dbcc23fb --- /dev/null +++ b/ProcessOptimizer/tests/test_suggestors/test_golden_ratio_suggestor.py @@ -0,0 +1,32 @@ +import numpy as np +from ProcessOptimizer.space import space_factory +from ProcessOptimizer.XpyriMentor.suggestors import GoldenRatioSuggestor, Suggestor, suggestor_factory + +def test_initializaton(): + suggestor = GoldenRatioSuggestor( + space=space_factory([[1, 2], [1, 2]]), + rng=np.random.default_rng(1), + ) + assert isinstance(suggestor, Suggestor) + +def test_factory(): + space = space_factory([[1, 2], [1, 2]]) + suggestor = suggestor_factory( + space=space, + definition={"suggestor_name": "GoldenRatio"}, + ) + assert isinstance(suggestor, GoldenRatioSuggestor) + +def test_suggest(): + space = space_factory([[0, 10], [0.0, 1.0], ["cat", "dog"]]) + suggestor = GoldenRatioSuggestor( + space, rng=np.random.default_rng(1) + ) + suggestions = suggestor.suggest([], []) + assert len(suggestions) == 1 + assert suggestions[0] in space + suggestions = suggestor.suggest([], [], n_asked=5) + assert len(suggestions) == 5 + for suggestion in suggestions: + assert suggestion in space + assert len(suggestion) == 3 \ No newline at end of file diff --git a/examples/XpyriMentor.ipynb b/examples/XpyriMentor.ipynb index a684d56b..06cae2d0 100644 --- a/examples/XpyriMentor.ipynb +++ b/examples/XpyriMentor.ipynb @@ -19,10 +19,10 @@ "output_type": "stream", "text": [ "XpyriMentor with a SequentialStrategizer suggestor\n", - "[[90.10000000000001 190 'fish']]\n", - "[[50.5 150 'dog']]\n", - "[[70.3 170 'fish']\n", - " [30.7 130 'cat']]\n" + "[[90.10000000000001 170 'dog']]\n", + "[[50.5 110 'fish']]\n", + "[[70.3 130 'cat']\n", + " [30.7 150 'cat']]\n" ] } ], @@ -63,23 +63,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[78.78571428571428 179 'fish']\n", - " [8.071428571428571 107 'cat']\n", - " [2.819163230542185 101 'fish']\n", - " [72.5913762383257 114 'cat']\n", - " [15.47387860203124 172 'cat']\n", - " [86.40148481213906 143 'fish']\n", - " [43.333608793143426 186 'cat']\n", - " [56.15180835969205 105 'dog']\n", - " [65.42827321276842 152 'fish']\n", - " [12.56621714545117 176 'cat']]\n" + "[[78.78571428571428 150 'dog']\n", + " [8.071428571428571 179 'cat']\n", + " [99.60513691124697 160 'fish']\n", + " [71.94781457104274 175 'cat']\n", + " [11.98224033589071 115 'cat']\n", + " [81.988274338197 143 'dog']\n", + " [41.1196020302332 184 'cat']\n", + " [47.94812293786112 152 'dog']\n", + " [100.0 200 'dog']\n", + " [78.87276505955953 153 'cat']]\n" ] } ], @@ -139,9 +139,9 @@ "output_type": "stream", "text": [ "ConstantSuggestor is a Suggestor: True\n", - "[[83.5 184 'fish']\n", - " [50.5 150 'dog']\n", - " [17.5 116 'cat']\n", + "[[83.5 116 'dog']\n", + " [50.5 184 'fish']\n", + " [17.5 150 'cat']\n", " [50.5 150 'dog']\n", " [50.5 150 'dog']\n", " [90.10000000000001 190 'fish']\n", diff --git a/examples/design_qualifications/benchmarking.ipynb b/examples/design_qualifications/benchmarking.ipynb new file mode 100644 index 00000000..5fc750b2 --- /dev/null +++ b/examples/design_qualifications/benchmarking.ipynb @@ -0,0 +1,363 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Benchmarking\n", + "\n", + "We want to test the behavior of ProcessOptimizer on different model systems with\n", + "different settings. \n", + "\n", + "Benchmarking optimization algorithms requires comparing both the attained solution quality\n", + "and the resources used.\n", + "\n", + "We use constant target benchmarking, since the results are easy to compare. The target is\n", + "to find a parameter set (a point in `Space`) that gives acceptable results 95% of the\n", + "time. We define \"acceptable\" so that this is a certain proportion of the space. This\n", + "way, we can vary how \"hard\" to problem is, by varying how big a percentage of proportion\n", + "of the space is acceptable. The proportion is measured by its inverse, which is equivalent\n", + "to how many randomly chosen parameter sets you would have to test to find a parameter set\n", + "that fulfilled the target.\n", + "\n", + "For resources, we are going to count function\n", + "evaluations, that is, how many times `ask()` have been called an the returned parameter\n", + "set evaluated. This is done because we emulate physical experimentation, where evaluating\n", + "a parameter set is by far the most expensive part of the process.\n", + "\n", + "There is a `BenchmarkInstance` class which encapsulates all that characterizes a single\n", + "run. We will make a script that constructs a list of such objects, run them all, and\n", + "analyze the results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "instance.success_level=-2.7120793739341136\n" + ] + }, + { + "data": { + "text/plain": [ + "BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=180, expected_random_runtime=3000.0, seed=1, noise_level=2.0, validate=True, number_of_evaluations=20, success=True)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ProcessOptimizer.benchmarking import BenchmarkInstance, run_benchmark\n", + "\n", + "suggestor_definition = {\"suggestor_name\": \"Sequential\",\n", + " \"suggestors\": [\n", + " {\"suggestor_name\": \"GoldenRatio\", \"suggestor_budget\": 5},\n", + " {\"suggestor_name\": \"PO\", \"suggestor_budget\": float(\"inf\")},\n", + " ]\n", + "}\n", + "\n", + "instance = BenchmarkInstance(\n", + " model_system_name=\"hart6\",\n", + " experimental_budget=180,\n", + " xpyrimentor_definition=suggestor_definition,\n", + " seed=1,\n", + " validate=True,\n", + " expected_random_runtime=3000.0,\n", + " noise_level=2.0\n", + ")\n", + "print(f\"{instance.success_level=}\")\n", + "run_benchmark(instance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now make a matrix `BenchmarkInstances` with different settings." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from itertools import product\n", + "\n", + "from ProcessOptimizer.benchmarking import BenchmarkInstance, run_benchmark\n", + "from ProcessOptimizer.model_systems import get_model_system\n", + "\n", + "MODEL_SYSTEM_NAMES = [\"hart3\", \"hart6\"]\n", + "EXPECTED_RANDOM_RUNTIME = [300.0 ]#, 1000.0]\n", + "EXPERIMENT_BUDGET_PER_DIMENSION = [30] # When to discard the optimization\n", + "NOISE_LEVELS = [0.0, 1.0, 5.0]#, 0.2, 10.0] # What to multiply the noise of the modelsystem by\n", + "N_INITIAL_POINTS = [\"n+1\", \"2n\", \"3n\"]# Should be dependent on number of diemnsions\n", + "VALIDATE = [True] # Whether to validate the model system\n", + "NOISE_BOUNDS = [(1e-5, 1e5)]#, (0.01, 0.5)]# Relevan since we are relying on the modelled noise\n", + "NUM_REPLICATES = [0,5] # Whether to do replicate of the center point\n", + "ACQ_FUNC_SETTINGS = [{}]#,{\"n_restarts_optimizer\": 50}]\n", + "LENGTH_SCALES_BOUNDS = [(1e-5, 1e5), (0.01, 0.5)]\n", + "NUM_REPLICATIONS = 1 # How many times to perform each optimization, at least 30\n", + "\n", + "# Acquisition function strategy is missing, should be a new strategizer\n", + "\n", + "seed = 0 # Seed for \"randomly\" making noise, ensures reproducibility\n", + "tests : list[dict] = [] # Consider making a \"test\" dataclass for better typing\n", + "for (model_system_name, expected_random_runtime, experimental_budget_per_dimension, noise_level, n_initial_points, validate, noise_bounds, length_scale_bounds, num_replicates, acq_func_settings) in product(\n", + " MODEL_SYSTEM_NAMES,\n", + " EXPECTED_RANDOM_RUNTIME,\n", + " EXPERIMENT_BUDGET_PER_DIMENSION, \n", + " NOISE_LEVELS, \n", + " N_INITIAL_POINTS,\n", + " VALIDATE,\n", + " NOISE_BOUNDS,\n", + " LENGTH_SCALES_BOUNDS,\n", + " NUM_REPLICATES,\n", + " ACQ_FUNC_SETTINGS\n", + "):\n", + " for _ in range(NUM_REPLICATIONS): # Adding NUM_REPLICATIONS tests for each combination\n", + " seed += 1 # Each test should have a different seed\n", + " test = {\"model_system_name\": model_system_name,\n", + " \"expected_random_runtime\": expected_random_runtime,\n", + " \"experimental_budget_per_dimension\": experimental_budget_per_dimension,\n", + " \"noise_level\": noise_level,\n", + " \"n_initial_points\": n_initial_points,\n", + " \"validate\": validate,\n", + " \"noise_bounds\": noise_bounds,\n", + " \"num_replicates\": num_replicates,\n", + " \"acq_func_settings\": acq_func_settings,\n", + " \"seed\": seed}\n", + " model_system = get_model_system(model_system_name)\n", + " num_dimensions = model_system.space.n_dims\n", + " experimental_budget = num_dimensions * experimental_budget_per_dimension\n", + " if n_initial_points == \"n+1\":\n", + " n_initial_points = num_dimensions + 1\n", + " elif n_initial_points == \"2n\":\n", + " n_initial_points = 2*num_dimensions\n", + " elif n_initial_points == \"3n\":\n", + " n_initial_points = 3*num_dimensions\n", + " length_scale_bounds = [length_scale_bounds]*num_dimensions\n", + " suggestor_definition = {\n", + " \"suggestor_name\": \"Sequential\", \"suggestors\": [\n", + " {\"suggestor_name\": \"GoldenRatio\", \"suggestor_budget\": n_initial_points},\n", + " {\"suggestor_name\": \"Constant\", \"suggestor_budget\": num_replicates},\n", + " {\n", + " \"suggestor_name\": \"PO\",\n", + " \"suggestor_budget\": float(\"inf\"),\n", + " \"acq_optimizer_kwargs\": {\"length_scale_bounds\": length_scale_bounds}\n", + " },\n", + " ]\n", + "}\n", + " test[\"xpyrimentor_definition\"] = suggestor_definition\n", + " # noise_bounds=noise_bounds, Noise bounds need to be a new Suggetor\n", + " acq_func_settings=acq_func_settings,\n", + " test[\"benchmark_instance\"] = BenchmarkInstance(\n", + " model_system_name=model_system_name,\n", + " xpyrimentor_definition=suggestor_definition,\n", + " experimental_budget=experimental_budget,\n", + " expected_random_runtime=expected_random_runtime,\n", + " noise_level=noise_level,\n", + " validate=validate,\n", + " seed=seed\n", + " )\n", + " tests.append(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "72\n" + ] + } + ], + "source": [ + "#print(tests)\n", + "print(len(tests))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can run one of the tests." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 1, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=1, noise_level=0.0, validate=True, number_of_evaluations=16, success=True)} used 16 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 2, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=2, noise_level=0.0, validate=True, number_of_evaluations=19, success=True)} used 19 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 3, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=3, noise_level=0.0, validate=True, number_of_evaluations=40, success=True)} used 40 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 4, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=4, noise_level=0.0, validate=True, number_of_evaluations=27, success=True)} used 27 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 5, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=5, noise_level=0.0, validate=True, number_of_evaluations=29, success=True)} used 29 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 6, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=6, noise_level=0.0, validate=True, number_of_evaluations=18, success=True)} used 18 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 7, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=7, noise_level=0.0, validate=True, number_of_evaluations=15, success=True)} used 15 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 8, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=8, noise_level=0.0, validate=True, number_of_evaluations=46, success=True)} used 46 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 9, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=9, noise_level=0.0, validate=True, number_of_evaluations=33, success=True)} used 33 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 10, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=10, noise_level=0.0, validate=True, number_of_evaluations=25, success=True)} used 25 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 11, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=11, noise_level=0.0, validate=True, number_of_evaluations=16, success=True)} used 16 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 12, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=12, noise_level=0.0, validate=True, number_of_evaluations=31, success=True)} used 31 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 13, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=13, noise_level=1.0, validate=True, number_of_evaluations=28, success=True)} used 28 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 14, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=14, noise_level=1.0, validate=True, number_of_evaluations=34, success=True)} used 34 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 15, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=15, noise_level=1.0, validate=True, number_of_evaluations=21, success=True)} used 21 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 16, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=16, noise_level=1.0, validate=True, number_of_evaluations=32, success=True)} used 32 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 17, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=17, noise_level=1.0, validate=True, number_of_evaluations=29, success=True)} used 29 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 18, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=18, noise_level=1.0, validate=True, number_of_evaluations=28, success=True)} used 28 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 19, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=19, noise_level=1.0, validate=True, number_of_evaluations=22, success=True)} used 22 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 20, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=20, noise_level=1.0, validate=True, number_of_evaluations=21, success=True)} used 21 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 21, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=21, noise_level=1.0, validate=True, number_of_evaluations=21, success=True)} used 21 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 22, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=22, noise_level=1.0, validate=True, number_of_evaluations=23, success=True)} used 23 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 23, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=23, noise_level=1.0, validate=True, number_of_evaluations=33, success=True)} used 33 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 24, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=24, noise_level=1.0, validate=True, number_of_evaluations=30, success=True)} used 30 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 25, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=25, noise_level=5.0, validate=True, number_of_evaluations=18, success=True)} used 18 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 26, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=26, noise_level=5.0, validate=True, number_of_evaluations=29, success=True)} used 29 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 27, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=27, noise_level=5.0, validate=True, number_of_evaluations=11, success=True)} used 11 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 28, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=28, noise_level=5.0, validate=True, number_of_evaluations=31, success=True)} used 31 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 29, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=29, noise_level=5.0, validate=True, number_of_evaluations=14, success=True)} used 14 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 30, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=30, noise_level=5.0, validate=True, number_of_evaluations=17, success=True)} used 17 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 31, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=31, noise_level=5.0, validate=True, number_of_evaluations=34, success=True)} used 34 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 32, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=32, noise_level=5.0, validate=True, number_of_evaluations=19, success=True)} used 19 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 33, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=33, noise_level=5.0, validate=True, number_of_evaluations=15, success=True)} used 15 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 34, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=34, noise_level=5.0, validate=True, number_of_evaluations=20, success=True)} used 20 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 35, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=35, noise_level=5.0, validate=True, number_of_evaluations=19, success=True)} used 19 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 36, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=90, expected_random_runtime=300.0, seed=36, noise_level=5.0, validate=True, number_of_evaluations=17, success=True)} used 17 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 37, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=37, noise_level=0.0, validate=True, number_of_evaluations=27, success=True)} used 27 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 38, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=38, noise_level=0.0, validate=True, number_of_evaluations=24, success=True)} used 24 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 39, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=39, noise_level=0.0, validate=True, number_of_evaluations=11, success=True)} used 11 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 40, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=40, noise_level=0.0, validate=True, number_of_evaluations=32, success=True)} used 32 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 41, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=41, noise_level=0.0, validate=True, number_of_evaluations=26, success=True)} used 26 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 42, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=42, noise_level=0.0, validate=True, number_of_evaluations=38, success=True)} used 38 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 43, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=43, noise_level=0.0, validate=True, number_of_evaluations=26, success=True)} used 26 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 44, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=44, noise_level=0.0, validate=True, number_of_evaluations=33, success=True)} used 33 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 45, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=45, noise_level=0.0, validate=True, number_of_evaluations=36, success=True)} used 36 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 46, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=46, noise_level=0.0, validate=True, number_of_evaluations=35, success=True)} used 35 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 47, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=47, noise_level=0.0, validate=True, number_of_evaluations=25, success=True)} used 25 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 48, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=48, noise_level=0.0, validate=True, number_of_evaluations=30, success=True)} used 30 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 49, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=49, noise_level=1.0, validate=True, number_of_evaluations=22, success=True)} used 22 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 50, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=50, noise_level=1.0, validate=True, number_of_evaluations=21, success=True)} used 21 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 51, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=51, noise_level=1.0, validate=True, number_of_evaluations=20, success=True)} used 20 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 52, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=52, noise_level=1.0, validate=True, number_of_evaluations=28, success=True)} used 28 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 53, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=53, noise_level=1.0, validate=True, number_of_evaluations=20, success=False)} used 20 evaluations and finished unsuccessfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 54, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=54, noise_level=1.0, validate=True, number_of_evaluations=57, success=True)} used 57 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 55, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=55, noise_level=1.0, validate=True, number_of_evaluations=26, success=True)} used 26 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 56, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=56, noise_level=1.0, validate=True, number_of_evaluations=28, success=True)} used 28 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 57, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=57, noise_level=1.0, validate=True, number_of_evaluations=35, success=True)} used 35 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 58, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=58, noise_level=1.0, validate=True, number_of_evaluations=63, success=True)} used 63 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 59, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=59, noise_level=1.0, validate=True, number_of_evaluations=33, success=True)} used 33 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 60, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=60, noise_level=1.0, validate=True, number_of_evaluations=31, success=True)} used 31 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 61, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=61, noise_level=5.0, validate=True, number_of_evaluations=27, success=True)} used 27 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 62, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=62, noise_level=5.0, validate=True, number_of_evaluations=58, success=True)} used 58 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 63, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=63, noise_level=5.0, validate=True, number_of_evaluations=13, success=True)} used 13 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 64, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 7}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=64, noise_level=5.0, validate=True, number_of_evaluations=20, success=True)} used 20 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 65, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=65, noise_level=5.0, validate=True, number_of_evaluations=18, success=True)} used 18 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 66, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=66, noise_level=5.0, validate=True, number_of_evaluations=25, success=True)} used 25 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 67, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=67, noise_level=5.0, validate=True, number_of_evaluations=15, success=True)} used 15 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 68, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 12}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=68, noise_level=5.0, validate=True, number_of_evaluations=24, success=True)} used 24 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 69, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=69, noise_level=5.0, validate=True, number_of_evaluations=43, success=True)} used 43 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 70, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0), (1e-05, 100000.0)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=70, noise_level=5.0, validate=True, number_of_evaluations=28, success=False)} used 28 evaluations and finished unsuccessfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 71, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=71, noise_level=5.0, validate=True, number_of_evaluations=37, success=True)} used 37 evaluations and finished successfully.\n", + "Test {'model_system_name': 'hart6', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 72, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf, 'acq_optimizer_kwargs': {'length_scale_bounds': [(0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5), (0.01, 0.5)]}}]}, experimental_budget=180, expected_random_runtime=300.0, seed=72, noise_level=5.0, validate=True, number_of_evaluations=27, success=True)} used 27 evaluations and finished successfully.\n" + ] + } + ], + "source": [ + "for test in tests:\n", + " instance : BenchmarkInstance = test[\"benchmark_instance\"]\n", + " benchmark = run_benchmark(instance)\n", + " print(f\"Test {test} used {benchmark.number_of_evaluations} evaluations and finished {'successfully' if benchmark.success else 'unsuccessfully'}.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can run all of the tests in parallel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from multiprocessing import Pool\n", + "\n", + "def run_from_dict(test: dict):\n", + " instance = test.pop(\"benchmark_instance\")\n", + " instance[\"benchmark_instance\"] = run_benchmark(instance)\n", + " return instance\n", + "\n", + "if __name__ == '__main__':\n", + " with Pool() as p:\n", + " result = p.map(run_from_dict, tests)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 1, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=1, noise_level=0.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 2, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=2, noise_level=0.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 3, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=3, noise_level=0.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 4, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=4, noise_level=0.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 5, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=5, noise_level=0.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 0.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 6, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=6, noise_level=0.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 7, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=7, noise_level=1.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 8, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=8, noise_level=1.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 9, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=9, noise_level=1.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 10, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=10, noise_level=1.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 11, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=11, noise_level=1.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 1.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 12, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 9}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=12, noise_level=1.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 13, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=13, noise_level=5.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': 'n+1', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 14, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 4}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=14, noise_level=5.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 0, 'acq_func_settings': {}, 'seed': 15, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 0}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=90, expected_random_runtime=300.0, seed=15, noise_level=5.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart3', 'expected_random_runtime': 300.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '2n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 16, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 6}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart3', xpyrimentor_definition={'suggestor_name': 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noise_level=5.0, validate=True, number_of_evaluations=None, success=None)}, {'model_system_name': 'hart6', 'expected_random_runtime': 1000.0, 'experimental_budget_per_dimension': 30, 'noise_level': 5.0, 'n_initial_points': '3n', 'validate': True, 'noise_bounds': (1e-05, 100000.0), 'num_replicates': 5, 'acq_func_settings': {}, 'seed': 72, 'xpyrimentor_definition': {'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, 'benchmark_instance': BenchmarkInstance(model_system_name='hart6', xpyrimentor_definition={'suggestor_name': 'Sequential', 'suggestors': [{'suggestor_name': 'GoldenRatio', 'suggestor_budget': 18}, {'suggestor_name': 'Constant', 'suggestor_budget': 5}, {'suggestor_name': 'PO', 'suggestor_budget': inf}]}, experimental_budget=180, expected_random_runtime=1000.0, seed=72, noise_level=5.0, validate=True, number_of_evaluations=None, success=None)}]\n" + ] + } + ], + "source": [ + "\n", + "print(tests)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vi ønsker et sæt indstillinger som lever op til kravet 95% af tiden, inkl. process-støj\n", + "\n", + "Dette kan bestemmes teoretisk" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/design_qualifications/golden_ratio_sampling.ipynb b/examples/design_qualifications/golden_ratio_sampling.ipynb new file mode 100644 index 00000000..b005e970 --- /dev/null +++ b/examples/design_qualifications/golden_ratio_sampling.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jxaQdMO1gXR/V3JDYorkhf0V0bkhhpJgimkgjjXVfBc0N2aEAmKEt0P0U0f2GtExTTBFNpJHGuD6quSE7tFQnEtm5IYWRYkon0tEPgLRYDEgk6BJppLG1UZobsoM5AGqwWsIWwbkhhZFiimgijTS2NoqxqXEdcwBUWyO2NDUBx48Hu+22tQXfe3oogwigMFJ8EUykkcbWRrE1NT5gDYDMbY3L1ERlpOeGVq4MvhO/EVYYKYWIJdJIY2uj2JoaHzAGQOa2xmVqoiJLYaRUIpRII4+pjWJranzAGABZ2xqXMTdRamvGpTAibmBpo9iaGh8wBkDGtsZlzE2U2pqcKIxEnRJ3BksbxdTU+IAxADK2NS5jbaKY2xoyCiNRpsTNi6Wp8QVbAGRsa1zG2EQxtzWEtANrVGkLdH7aATNcTFugp9ua5cuD4DH891TLdcXH2ETp40HyomYkilgTt5oasY1lqQ7ga2tcxthEMbY1xBRGoohxfVRro/ZoboiXluvCobmhyFMYiSK2xM3a1PhAbRQ/prbGZWxNFGNbQ0xhJIrYEjdjU+MD1jZKTY3YwtREMbY1xBRGoogtcbM1NT5gbaPU1NihAJjB1ESxtTXEFEaiiC1xszU1PmBso1ibGtcpAHJjamuIKYxEFVPiZmtqfMDWRrE2Na5jDoBqazKY2hpSCiNRxpK42ZoaH7C1UYxNjeuYA6DaGsmTwkjUsSRupqbGB2xtFFtT4wPWAMjc1rgs4k2UwogUD0tT4wO2NoqtqfEBYwBkbmtc5kATpTBSDBFPpEXF0tT4gKmNYmtqfMAYAFnbGpc50kQpjEyUA4lUIoyljWJranzAGAAZ2xqXOdREKYxMhCOJNJLURmWwtFFMTY0PGAMgY1vjMoeaKIWRQjmUSCNHbRQvlqbGF2wBkLGtcZlDTdQk2wcQWfp4aDvSbdToEJhuo2y9A0+lOD66nkG6qZFwNDUBjY0cj790W7N8eRA8hv+earmu+BxqotSMFMqhRBoZrG2UmhqxjWWpDuBra1zmUBOlMFIohxJpZDCuj2puSORCWq4LB+PcUIEURgrlUCKNDLY2irWpEWHA1Na4zJEmSjMjhdLaaPjY2ijNDclomh0SG5jmhgqkZmQiHEmkkcHWRrE1NWKXZofEpog3UQojE6W10fCwrY+yNTVij2aHRCYkZky2BW8uyWQS1dXV6O/vR1VVle3DEdv27g1mNYY/8ScSQRAJMwSmUsE731Onss+NxGJBk9PTE7l3KZKH9ONgrCU7PQ78oqW6EXJ9/VYzItHD0kaxNTViB+NZXmKHluoKpjAi0cSyPqq5IdHskABaqpsgnU0jMlEOTLLLBGh2SMY7zT8WC07zb2zU88IYFEakcFobzdAW6P5Kn+U13uyQ9hxyl07znzAt00hhtDYqEtDskGipbsIURiR/WhsVGUmzQ37TUt2E6dReyY9OYxQZm5Yu/aTT/MeU6+u3ZkYkP1obleH04juSZof8pI8HmTAt00h+tDYqaZobEsnQUt2EqBmR/GhtVIDM3NDoSjo9N2TzyVdtjdii0/wLppkRyY/WRoV5bijbRwXU1gYVut6ZioRO28FLaeg0RmHd/lxnedmTSgGdncCuXcH3VMr2EUnEKIxI/rQ26jfGuaHxdsAEgh0w9SJZfJodkiLQzIgURmuj/mKcG9JZXnawzg5pbihyFEakcDqN0U+M258ztjWuY/08Fs0NRZKWafKltVHxHePcEGNb4zrG2SHNDUWWwkg+tDYqEmCbG0q3NaPDUVosBiQS+rC6YmJrozQ3FGkKI7liTtxqa8SGpibg+HGgowNoawu+9/TYqcIZ2xrXsbVRjE2N5KygMLJt2zbMmjULlZWVqKurw4EDBy56/Y8//hjr1q3DjBkzEI/HceWVV2Lfvn0FHbAVzIlbbU34FP4y0nNDK1cG322+2LO1Na5ja6PYmhrJS95hZM+ePWhubkZLSwsOHTqEuXPnYsmSJfjwww+zXn9gYAB/8zd/g+PHj+Oll17C0aNHsWPHDlwx+gmDGWviZm5rXKXwx42prXEdWxvF1tRIfkyeFixYYNatWzf036lUysycOdO0trZmvf6zzz5rZs+ebQYGBvL9UUP6+/sNANPf31/w3zEhbW3GBJHj4l9tbeEd0/nzxtTWjn0ssZgxiURwPSmOl18O7tds93UsFvy5LefPG9PRETwGOzr0/13C8/LLFz4XJRLh/z6knxOz/Y7qOdGaXF+/82pGBgYGcPDgQTQ0NAxdVlZWhoaGBnR1dWW9zc9//nPU19dj3bp1qKmpwTXXXIPHHnsMqYtU2+fOnUMymRzxZRVj4mZta1ylpTqR7FjaKLamRvKSVxg5ffo0UqkUampqRlxeU1OD3t7erLc5duwYXnrpJaRSKezbtw+bNm3CD3/4QzzyyCNj/pzW1lZUV1cPfSUSiXwOs/jY1kYBrY+GjTX8aanOHs0OZbDMDmluKLJKfjbN4OAgpk2bhueeew7z58/HihUrsHHjRmzfvn3M22zYsAH9/f1DXydPniz1YV4cY+JmbGtcxhj+mNsa16mN4sXS1Ehe8tqBderUqSgvL0dfX9+Iy/v6+jB9+vSst5kxYwYuueQSlA97ob766qvR29uLgYEBVFRUXHCbeDyOeDyez6GVXjpxZ9vZb+tWe/sqMO2C6TLG8Kct0O3QFuj8tDt05OTVjFRUVGD+/Plob28fumxwcBDt7e2or6/PepsbbrgB7733HgYHB4cue/fddzFjxoysQYQaU+JmbGtcpqU6AXjbKDU1EnX5Tsbu3r3bxONx8/zzz5u3337b3HXXXebyyy83vb29xhhjVq1aZdavXz90/RMnTpjJkyebu+++2xw9etT84he/MNOmTTOPPPJI0adxvcQyye6D9Nk0o6f1bZ1N09GR21leHR3hHpfLGO9z5rO8XKez2MaV6+t33mHEGGOeeuop8/nPf95UVFSYBQsWmN/+9rdDf7Z48WKzZs2aEdd/8803TV1dnYnH42b27Nnm0UcfNefz+J+mMDIO/UKEhyn86VTG8LGd5q9T/O3J9lxQW6vwN0qur98xY7L1jVySySSqq6vR39+Pqqoq24cjvmNam0/PLwAjlw7Sy0k6g6C4OjuDJZDxdHSEM7PAdjy+GGtuyPbvHdNz02dyff3WZ9OI5IvlNEZApzKGjW12SHND4dPcUEkojEhutKcCL6bBatexDY4znuXlOsY9hxzYb0hhRMYX8cTtBaa2xnVMbRRbU+MDtjaKtanJk8KIXBxz4lZbI7awtFFsTY0P2NooxqamAAojMjbmxK22JnwKfyOxtFFMTY0P2NootqamQAojMjbWxM3c1rhK4Y8bS1PjA7Y2iq2pKZDCiIyNMXEztzWuYg5/amsyWJoaHzC1UWxNTYEURmRsjImbta1xFXP4U1sjNrG0UWxNTYEURmRsjImbsa1xGWv4Y25rXKc2KoOljWJqagqkMCJjY0zcjG2NyxjDH3Nb4zq1UbxYmpoCKYzIxbElbsa2xmWM4Y+1rXEdaxulpiaDpakpgMKIjI8pcTO2NS5jDH+MbY3rWNsoNTXOUBiR3DAlbra2xmWM4Y+xrXEdYxvF2tRIQRRGJJqY2hrXsYU/xrbGdWxtFGtTIwWbZPsAJE+EHxFtTbqtkdJragIaGzkee+m2ZvnyIHgMf0HSUl1psLVR+TQ1eo6IBDUjUaL1UbFJS3X+Ymuj2JoamTCFkajQ+qjISFqqCw/b7BBbUyMTFjMm26Ibl2QyierqavT396Oqqsr24YQvlQoakLFqyVgseNfS06NqWkRKZ+/eYFZj+HNRIhEEkTBDYPo58dSp7HMjek6kkevrt2ZGokDrozKc5obEFpbZIc0NOUdhJAq0Pipp2d6Z1tYGT8xanpAwsAyOp+eGsv0+hN3UyIQpjESB1kcFyMwNja6l03NDGtwU37A0NTJhmhmJAq2PiuaGZDgt1UlE5Pr6rbNpooBtkl3Cx7gDptihU/zFQQojUaF9FfymuSEBdIq/OEszI1Gi9VF/aW5IxtsCPRYLtkBvbNRzgkSOwkjUsEyyS7jSO2CONzekz2Nxl07xl+EcmxvSMo1IFGhuSLRUJ2kOzg0pjBQilQI6O4Fdu4Lv+mRICYPmhvympToBnJ0b0qm9+dKmU2KbY/Ws5Ein+EsET/HXqb2l4GgijQS1URlMn54r4dFSnTh8ir/CSK7Gm2QHgkl2n18kS8XB9VGRgmipzm8Ozw3pbJpcaZLdDtYt0LVUIrboFH9/OTw3pDCSK4cTKS3WfRU0NyS26RR/Pzl8ir+WaXLlcCKlxbg+qrkhezQ3JL5zeG5IYSRX6UQ6+gGQFosBiUQkEykttjZKc0P2aG5IJODo3JDCSK4cTqS02NooxqbGB8xtlNoasaGpCTh+HOjoANragu89PZENIoDCSH4cTaS02NootqbGB8xtlNoascmxU/wVRvLlYCKlxdZGsTU1PmBto5jbGpepiXKWwkghHEuk1JjaKLamxgeMbRRzW+MyNVFOUxgRfixtFFtT4wPGNoq1rXEZcxOltqYoFEaY6UGewdJGMTU1PmBsoxjbGpcxN1Fqa4pGYYSVHuS8WJoaHzC2UYxtjctYmyjmtiaCFEYYsT7I1dRksDQ1PmBroxjbGpcxNlHMbU1EKYywYX2Qq6kRm5jaKMa2xmWMTRRrWxNhCiNsGB/krE2ND9RGZTC1UWxtjcsYmyjGtibiFEbYsD3IWZsaH6iN4sbU1riMsYlibGsiTmGEDduDnLGp8QFzG6W2JoOprXEZWxPF2NZEnMIIG7YHOVtT4wPmNkptjdjC1EQxtjURpzDChu1BztbU+IC1jWJua1ymJiqDqYlia2siTmGEEdODnK2p8QFjG8Xc1rhMTRQ3prYm4ibZPgAZQ1MT0NgYvPv94IOgeVi0KPx3AummZvnyIHgMfzFSHVkajG1UPm3NjTeGdlhOSzdRowNguomy+e47lbL/3MQi3dbIhKgZYcZSSTI1NT5gbKMY2xqXMTdRamukBBRGJDeqI8PDNjcEcLY1LtPckAznwdyQwojkjqWp8QFbG8XY1riMsYlibmtc5kkTpTAyHg8SqZBiaqMY2xqXMTZRrG2NyzxqohRGLsaTREpHATCDqY1ia2tcxthEMbY1LvOsiSoojGzbtg2zZs1CZWUl6urqcODAgZxut3v3bsRiMSxbtqyQHxsujxIpFQVAbkxtjcsYmyjGtsZlnjVReYeRPXv2oLm5GS0tLTh06BDmzp2LJUuW4MMPP7zo7Y4fP45/+qd/wqIorCl7lkhpsAZANTUjMbU1LmNrohjbGpd51kTlHUaefPJJ3HnnnVi7di2+9KUvYfv27bjsssuwc+fOMW+TSqXwzW9+E//8z/+M2bNnT+iAQ+FZIqXAGgDV1IhNTE0UY1vjMs+aqLzCyMDAAA4ePIiGhobMX1BWhoaGBnR1dY15u4cffhjTpk3D7bffXviRhsmzREqBMQCyNjXiF6Ymiq2tcZlnTVReO7CePn0aqVQKNTU1Iy6vqanBO++8k/U2b7zxBn784x+ju7s7559z7tw5nDt3bui/k8lkPoc5cZ4lUgpsAXC8piYWC5qaxka9ExS/sOwO7TrPdr8u6dk0Z86cwapVq7Bjxw5MnTo159u1traiurp66CuRSJTwKLPwLJFSYAuAjE2N2KXZoQymtsZlHjVReTUjU6dORXl5Ofr6+kZc3tfXh+nTp19w/d///vc4fvw4li5dOnTZ4OBg8IMnTcLRo0cxZ86cC263YcMGNDc3D/13MpkMN5B4lkgppAPgqVPZ24hYLPjzsAIgW1Mjdu3dGzRlwwNqbW3wPOHQC4IQ8qSJyqsZqaiowPz589He3j502eDgINrb21FfX3/B9a+66iq89dZb6O7uHvr6+te/jptuugnd3d1jBox4PI6qqqoRX6HzKJFSYBuOY2tqxB7NDoltHjRRMWOyvQ0d2549e7BmzRr867/+KxYsWICtW7fixRdfxDvvvIOamhqsXr0aV1xxBVpbW7Pe/rbbbsPHH3+MV199NeefmUwmUV1djf7+/vCDiT6dMlzZ3oEmEkEQCTMAplLBWTPjNTU9PXo8uCz9OBhryU6PA5GLyvX1O69lGgBYsWIFPvroIzz00EPo7e3FvHnzsH///qGh1hMnTqCszKGNXfXx0OFiqSS1VCdAfrNDep5wn96clkzezYgNVpsR8RtLUyN27NoV7C8znra2oEIXd2luqCAla0ZEvMLS1Igdmh0SIDM3NPq9e3puSHOEE6ZmRERkLJodEs0NTUiur98ODXdIUWlPBRG+s7wkfNpzKBQKI3IhfR6LSIZO8/eb9hwKhWZGZCStjYpcSLND/tLcUCg0MyIZWhuV0XQqo/hOc0MTopkRyZ/WRmU4LdeJaG4oJAojkqG1UUnTFugiGZobKjnNjEiG1kYFCGrpe+7JXkkbE7wbvPfeYIYi7HeDWjYSWzQ3VFIKI5LB9sm5YgfrFujaAdMOBcAMfTxIyWiZRjK0NioA53Kdlo3s0NyQhERhREbS2qiwLdeNt2wEBMtG2pivuFgDoDZkdJJO7ZXsVM36i+1Uxs7O4B35eDo6VKEXC+tp/lqqixyd2lsIJe6M9NroypXBdwURf7At1zEuG7mO8TR/1qZGikJhJE1royIZTMt1bMtGPmALgFqqc57OpgG4t0DXconYwnIqo87yCh9bAGQ9w0uKRs0Ic+JWWyO2MSzXsS0b+SAdAEff32mxGJBIhBcA2ZoaKTqFEca1UUDro7ZobogT07KRD9gCIFtTI0WnMMKYuJnbGpepieLW1AQcPx6cNdPWFnzv6VEQKRWmAMjW1EjRaWaEMXFrfTR8mhuKBu2AGS6WuaF0U7N8eRA8hv+eaqnOCWpGGBM3Y1vjMuYmSm2N2MYwNwRwNTVSdAojbGujAGdb4zLNDclomh3ipKU6ZymMAHyJm7GtcRljE8Xc1rhObRQ3lqZGikphJI0pcTO2NS5jbKJY2xrXsbZRamrEcQojwzElbra2xmWMTRRjW+M61jZKTY0dCoChUhhhxtTWuIyxiWJsa1zH2EaxNjWuUwAMncIIO6a2xmVsTRRjW+M6tjaKtalxHXMAdLitURgRSWNqohjbGtextVGMTY3rmAOg422Nwog4nbbzxtREsbU1rmNro9iaGh+wBkDmtqZIFEZ853jajjymtsZ1bG0UW1PjA8YAyNzWFJHCiM+Y07bamgymtsZ1TG0UW1PjA8YAyNrWFJnCiK+Y07baGrGJpY1ia2p8wBgAGduaElAY8RVr2mZua1ynNiqDpY1iamp8wBgAGduaElAY8RVj2mZua1ynNooXS1PjC7YAyNjWlMAk2wcgljCm7XzaGn2MfPGk26jRITDdRtl6B55K2f/oehbppkbC0dQENDZyPP7Sbc3y5UHwGP576tBynZoRXzGmbca2xnWsbZSaGrGNZakO4GtrSkBhxFdaGxWAc3ZIc0P2aG6Il+PLdQojPmNL24xtjevY2ijWpsYHaqP4MbU1RaYw4jumtM3Y1riOrY1ibGp8wNpGqanxhr9hRA/yDKa0zdbWuI6tjWJranzA2kapqfGKn2FED3JuTG2N69jaKLamxgeMbRRrUyMl418YYX6Qq63JYGprXMfURrE1NT5ga6NYmxopKb/CCPODXG2N2MTSRrE1NT5ga6MYmxopOb/CCOuDnLmtEX+wtFFMTY0P2NootqZGQuFXGGF8kDO3NSK2sDQ1PmBro9iaGgmFX2GE8UHO2taIHZobymBpanzA1EaxNTUSCr8+myb9ID91KnsTEYsFf64t0MWGvXuDlmx4OK2tDd61qhGQUmP5PBZPPotFRvKrGWGrIwHOtkbCp7khYcDSRjE1NRKKmDHZKgIuyWQS1dXV6O/vR1VV1cT/wmzvQBOJIIiE/SBPpYKzZsZra3p69E7AVenHwFjLdXoMiK/0yc2Rl+vrt59hBOB6kKffFQPZK0m9E3BbZ2dwKvd4Ojr0MfI+YHpuEpmgXF+//ZoZGS5dRzJIV5LZ5gVstDUSLs0NSZrmhsRT/oYRNizDYxI+zQ0JkGlIR5fV6bkhNaTiMH+XaURYaG5INDckjsr19duvs2lEGDGe5SXh0n5DMppnew4pjIgw0KmMftPckAzn4WeVaWZEhIXmhvyluSFJ83R2SDMjYp9OZRTfaW5IACdnh0o6M7Jt2zbMmjULlZWVqKurw4EDB8a87o4dO7Bo0SJMmTIFU6ZMQUNDw0WvL57xsI4UuYDmhgTwenYo7zCyZ88eNDc3o6WlBYcOHcLcuXOxZMkSfPjhh1mv39nZiZUrV6KjowNdXV1IJBL427/9W5w6dWrCBy8Rpy3QRTI0NyQezw7lvUxTV1eHr371q3j66acBAIODg0gkEvjud7+L9evXj3v7VCqFKVOm4Omnn8bq1atz+plapnEQex2ppSOxRY89fzm4G3NJdmAdGBjAwYMHsWHDhqHLysrK0NDQgK6urpz+jk8++QSffvopPve5z415nXPnzuHcuXND/51MJvM5TImCfOrIsH/ptAtm+PQCnMG0O7SEi/GT5UOS1zLN6dOnkUqlUFNTM+Lympoa9Pb25vR33H///Zg5cyYaGhrGvE5rayuqq6uHvhKJRD6HKVHAWkdq6Sh8mhsSCXg8OxTqPiNbtmzB7t278corr6CysnLM623YsAH9/f1DXydPngzxKCUUjKcyplJBI5LtHUn6snvvdX7zoVAxhz/PNp0SEp7ODuW1TDN16lSUl5ejr69vxOV9fX2YPn36RW/7xBNPYMuWLfjVr36Fr3zlKxe9bjweRzwez+fQJGoY60jmpSMXjRf+YrEg/DU2hv9OUEt1YpOHew7l1YxUVFRg/vz5aG9vH7pscHAQ7e3tqK+vH/N2jz/+ODZv3oz9+/fj+uuvL/xoxR2MdSTr0pGrWE9jZG5rXKYmaqT07NDKlcF3h4MIUMAyTXNzM3bs2IEXXngBR44cwbe//W2cPXsWa9euBQCsXr16xIDrv/zLv2DTpk3YuXMnZs2ahd7eXvT29uKPf/xj8f4VEk1sdSTj0pHLGMOflurs0NyQ9/LeDn7FihX46KOP8NBDD6G3txfz5s3D/v37h4ZaT5w4gbKyTMZ59tlnMTAwgOXLl4/4e1paWvCDH/xgYkcv0cdURzIuHbmMMfxpqS58zNuf6yyv0Gg7eJHh0k+MwMgnx/TSkcMDZKFj3AJ9167gnfl42tqC+lwmhnm/Ic0NFUVJt4OXItD6KCe2pSOXMc4NMbY1LtPckHxGYcQGrY9ya2oCjh8Pdjlsawu+9/QoiJQCW/hLL9WNDkdpsRiQSGiprlg0NySfyXtmRCZI66PRoF0ww8M0N5Rua5YvD4JHtqU6RzedsoKxidLckBVqRsLEnLjV1oRPS3UZTKcxsrU1LmNsohjbGg8ojIRJ66OSpvDHTUt14dDckHxGYSRMjImbua1xlcJfNDC1NS5ja6IY2xoPKIyEiTFxs7Y1rmIPf1o6EhuYmijGtsYDCiNhYkzcjG2Ny5jDn5aOwqfwl8HURLG1NR5QGAkTY+JmbGtcxhr+tHQUPoU/bkxtjQe0A6sN2Xb2SySCIBL2A51xF0yXdXYGLzrj6egI77RB5l0wXTXWKf4MO/3qFH8polxfvxVGbGH6hdcW6OFhDH+MAcllzOFPW6BLkWk7eHZaH/UT41Id69KRq1jnhrRUZ49mhxRG5DNaHw0PW/jT3FC4GMMf+1leLtPsEADfl2mYlkrEPyyPP8alI5cxLosxHpMPmGeHikTLNONRGhXbWJbqGJeOXKZT/AVQGzWKn2FEa6P2aG2UE9vSkcsYw5+W6sLHOjtkiX9hRGnUHrVR3DQ3FB628MfY1rhObdQIk2wfQOj08dB2jLU2mm6jtK8Ch/TSkZReUxPQ2Mjx2Eu3NcuXB8Ej2yn+WqorLrVRI/jXjCiNho+5jVJbIzaxzA0BfG2N69RGjeBfGFEaDR/r2qhmh+zQ3BAvLdWFh3F2yCL/wojSaPgY2yjmtsZlaqL4MbU1rlMbNcS/MKI0Gj7GNoq1rXEZcxOltkZsURsFwMcwAiiNho2xjWJsa1zG3ESprRHb1EZ5GkYApdEwMbZRjG2Ny1ibKOa2RsQj/oYRQGk0TGxtFGNb4zLGJoq5rZHwaanOKr/DiISLqY1ibGtcxthEsbY1Ej4t1VmnMCLhYmqj2NoalzE2UYxtjYRPS3UUFEbEb0xtjcsYmyjGtkbCpaU6GjFjsv1f4JLrRxBLHrQFutiwd2/w5D/8XWgiEQSRsANgKhVU8adOZX8xisWCNqenR78brursDJZkxtPRoY9JKFCur9/+fTaNZH9BqK0N3rmqEZBS0uexCBMt1dHQMo1vtD4qtmluSFhoqY6Glml8kq6lxzqDQLW0+ErLln7SUl3JaZlGLpTPqYxaH3WfXoAz0m2N+EVLdTS0TOMTrY9KmvZVEAloqY6CmhGfaH1UgMzc0OhaOj03pCdg8Q3TYLWnNDPiE62PiuaGRCREub5+a5nGJ4wbT0m4tAW6DKfPYxESCiO+0fqo3zQ3JGmaGxIimhnxkdZH/aW5IQG454Z0lpeXNDMi4hPNDQnz3JB2hw5ficOfZkZE5EKaGxLWuSHtDh0+oqU6hRER32huyG+Mc0P69NzwkYU/zYzYpLVRsUVzQ/5inBvS7tDhGi/8xWJB+GtsDO05QWHEFq2N2qEAmKEt0P20aFHwXDPe3NCiReEdE2Nb4zLC8KdlGhvI6jFvEK2PiljDODfE2Na4jDD8KYyETWujdrAGQG06JTawzQ2l25rR4SgtFgMSiXDbGpcRhj+d2hu2zs7gHfl4OjpUoRcL66mMWqoT25iWLdNvGIDsn56r4eriCfEUf53ay4qwHnMe46mMrE2ND9RGZaTnhlauDL7bnJ9ia2tcRrhUpzASNsJ6zHlsAVBLdfZobohbUxNw/HjQDLe1Bd97ehRESoEs/GmZJmzaATN8bEtjbMfji7G2QLe9DMC0VCL+0Q6sniKsx5zHNhzH1tT4gLWNUlMjtpEs1SmM2EBWjzmPLQBqqS58mhuS4TQ3REdhxBatjYaLKQCyNTU+YGujWJsaH6iNoqQdWG3SDpjhYtkCPd3ULF8eBI9spzFqqa642Noowh0wvTDW3FC6jbLZTHs+O6RmxAeqJDNI1kepmhofsLVRbE2ND5jbKLU1CiPO04Ocl5bqwqO5IWGcGwI0O/QZhRGX6UHOj6Wp8QFTG8XW1PiAsY1ibmtCVlAY2bZtG2bNmoXKykrU1dXhwIEDF73+z372M1x11VWorKzEtddei3379hV0sJIHPchFLsTSRrE1NT5gbKNY2xoL8g4je/bsQXNzM1paWnDo0CHMnTsXS5YswYcffpj1+m+++SZWrlyJ22+/HYcPH8ayZcuwbNky/O53v5vwwctF6EEukh1LG8XU1PiAsY1ibGssyTuMPPnkk7jzzjuxdu1afOlLX8L27dtx2WWXYefOnVmv/6Mf/Qh/93d/h+9973u4+uqrsXnzZlx33XV4+umnJ3zwchF6kIvwY2lqfMDYRjG2NZbkFUYGBgZw8OBBNDQ0ZP6CsjI0NDSgq6sr6226urpGXB8AlixZMub1AeDcuXNIJpMjviRPepCLRANLU+MDtjaKsa2xJK8wcvr0aaRSKdTU1Iy4vKamBr29vVlv09vbm9f1AaC1tRXV1dVDX4lEIp/DFEAPchGRbJjaKMa2xhLKs2k2bNiA/v7+oa+TJ0/aPqTo0YNcRCQ7pjaKra2xJK8dWKdOnYry8nL09fWNuLyvrw/Tp0/Pepvp06fndX0AiMfjiMfj+RyaZJN+kN9zz8hh1traIIh48iAXEaHGsju0RXk1IxUVFZg/fz7a29uHLhscHER7ezvq6+uz3qa+vn7E9QHg9ddfH/P6UmRMlaSIiGTH1NZYkPdn0zQ3N2PNmjW4/vrrsWDBAmzduhVnz57F2rVrAQCrV6/GFVdcgdbWVgDAPffcg8WLF+OHP/whbrnlFuzevRv//d//jeeee664/xIZmz4DR0REiOUdRlasWIGPPvoIDz30EHp7ezFv3jzs379/aEj1xIkTKCvLFC4LFy5EW1sbHnzwQTzwwAP4q7/6K7z66qu45pprivevEBERkciKGZNti04uyWQS1dXV6O/vR1VVle3DERERkRzk+vpNeTaNiIiI+ENhRERERKxSGBERERGrFEZERETEKoURERERsUphRERERKxSGBERERGr8t70zIb0VijJZNLykYiIiEiu0q/b421pFokwcubMGQBAIpGwfCQiIiKSrzNnzqC6unrMP4/EDqyDg4N4//33MXnyZMRisQn/fclkEolEAidPntSOriWm+zo8uq/Do/s6PLqvw1OK+9oYgzNnzmDmzJkjPipmtEg0I2VlZaitrS3631tVVaUHd0h0X4dH93V4dF+HR/d1eIp9X1+sEUnTAKuIiIhYpTAiIiIiVnkZRuLxOFpaWhCPx20fivN0X4dH93V4dF+HR/d1eGze15EYYBURERF3edmMiIiICA+FEREREbFKYURERESsUhgRERERq5wNI9u2bcOsWbNQWVmJuro6HDhw4KLX/9nPfoarrroKlZWVuPbaa7Fv376QjjT68rmvd+zYgUWLFmHKlCmYMmUKGhoaxv1/Ixn5Pq7Tdu/ejVgshmXLlpX2AB2R7/388ccfY926dZgxYwbi8TiuvPJKPYfkKN/7euvWrfjiF7+ISy+9FIlEAvfddx/+/Oc/h3S00fXrX/8aS5cuxcyZMxGLxfDqq6+Oe5vOzk5cd911iMfj+MIXvoDnn3++dAdoHLR7925TUVFhdu7caf7nf/7H3Hnnnebyyy83fX19Wa//m9/8xpSXl5vHH3/cvP322+bBBx80l1xyiXnrrbdCPvLoyfe+vvXWW822bdvM4cOHzZEjR8xtt91mqqurzR/+8IeQjzx68r2v03p6eswVV1xhFi1aZBobG8M52AjL934+d+6cuf76683NN99s3njjDdPT02M6OztNd3d3yEcePfne1z/96U9NPB43P/3pT01PT4/55S9/aWbMmGHuu+++kI88evbt22c2btxo9u7dawCYV1555aLXP3bsmLnssstMc3Ozefvtt81TTz1lysvLzf79+0tyfE6GkQULFph169YN/XcqlTIzZ840ra2tWa//jW98w9xyyy0jLqurqzP/8A//UNLjdEG+9/Vo58+fN5MnTzYvvPBCqQ7RGYXc1+fPnzcLFy40//Zv/2bWrFmjMJKDfO/nZ5991syePdsMDAyEdYjOyPe+Xrdunfnrv/7rEZc1NzebG264oaTH6Zpcwsj3v/998+Uvf3nEZStWrDBLliwpyTE5t0wzMDCAgwcPoqGhYeiysrIyNDQ0oKurK+tturq6RlwfAJYsWTLm9SVQyH092ieffIJPP/0Un/vc50p1mE4o9L5++OGHMW3aNNx+++1hHGbkFXI///znP0d9fT3WrVuHmpoaXHPNNXjssceQSqXCOuxIKuS+XrhwIQ4ePDi0lHPs2DHs27cPN998cyjH7JOwXxcj8UF5+Th9+jRSqRRqampGXF5TU4N33nkn6216e3uzXr+3t7dkx+mCQu7r0e6//37MnDnzgge9jFTIff3GG2/gxz/+Mbq7u0M4QjcUcj8fO3YM//Ef/4FvfvOb2LdvH9577z185zvfwaeffoqWlpYwDjuSCrmvb731Vpw+fRpf+9rXYIzB+fPn8a1vfQsPPPBAGIfslbFeF5PJJP70pz/h0ksvLerPc64ZkejYsmULdu/ejVdeeQWVlZW2D8cpZ86cwapVq7Bjxw5MnTrV9uE4bXBwENOmTcNzzz2H+fPnY8WKFdi4cSO2b99u+9Cc09nZicceewzPPPMMDh06hL179+K1117D5s2bbR+aTJBzzcjUqVNRXl6Ovr6+EZf39fVh+vTpWW8zffr0vK4vgULu67QnnngCW7Zswa9+9St85StfKeVhOiHf+/r3v/89jh8/jqVLlw5dNjg4CACYNGkSjh49ijlz5pT2oCOokMf0jBkzcMkll6C8vHzosquvvhq9vb0YGBhARUVFSY85qgq5rzdt2oRVq1bhjjvuAABce+21OHv2LO666y5s3LgRZWV6f10sY70uVlVVFb0VARxsRioqKjB//ny0t7cPXTY4OIj29nbU19dnvU19ff2I6wPA66+/Pub1JVDIfQ0Ajz/+ODZv3oz9+/fj+uuvD+NQIy/f+/qqq67CW2+9he7u7qGvr3/967jpppvQ3d2NRCIR5uFHRiGP6RtuuAHvvffeUNgDgHfffRczZsxQELmIQu7rTz755ILAkQ6BRh+zVlShvy6WZCzWst27d5t4PG6ef/558/bbb5u77rrLXH755aa3t9cYY8yqVavM+vXrh67/m9/8xkyaNMk88cQT5siRI6alpUWn9uYo3/t6y5YtpqKiwrz00kvmgw8+GPo6c+aMrX9CZOR7X4+ms2lyk+/9fOLECTN58mRz9913m6NHj5pf/OIXZtq0aeaRRx6x9U+IjHzv65aWFjN58mSza9cuc+zYMfPv//7vZs6cOeYb3/iGrX9CZJw5c8YcPnzYHD582AAwTz75pDl8+LD53//9X2OMMevXrzerVq0aun761N7vfe975siRI2bbtm06tbcQTz31lPn85z9vKioqzIIFC8xvf/vboT9bvHixWbNmzYjrv/jii+bKK680FRUV5stf/rJ57bXXQj7i6Mrnvv7Lv/xLA+CCr5aWlvAPPILyfVwPpzCSu3zv5zfffNPU1dWZeDxuZs+ebR599FFz/vz5kI86mvK5rz/99FPzgx/8wMyZM8dUVlaaRCJhvvOd75j/+7//C//AI6ajoyPrc2/6/l2zZo1ZvHjxBbeZN2+eqaioMLNnzzY/+clPSnZ8MWPUbYmIiIg9zs2MiIiISLQojIiIiIhVCiMiIiJilcKIiIiIWKUwIiIiIlYpjIiIiIhVCiMiIiJilcKIiIiIWKUwIiIiIlYpjIiIiIhVCiMiIiJilcKIiIiIWPX/moRdqesKD5UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from XpyriMentor import XpyriMentor\n", + "\n", + "# Create a new instance of the XpyriMentor class\n", + "xpyrimentor = XpyriMentor(space = [(0.0, 1.0) for _ in range(2)], suggestor={\"suggestor_name\": \"GoldenRatio\"})\n", + "points = xpyrimentor.ask(150)\n", + "plt.plot([point[0] for point in points], [point[1] for point in points], 'ro')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/features/control_parameters.ipynb b/examples/features/control_parameters.ipynb index c248e78a..35da5152 100644 --- a/examples/features/control_parameters.ipynb +++ b/examples/features/control_parameters.ipynb @@ -601,7 +601,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.1" } }, "nbformat": 4,