Source code for orion.algo.hebo.hebo_algo

:mod:`orion.algo.HEBO.hebo -- Orion adapter for the HEBO algorithm.

The HEBO algorithm implementation can be found at
from __future__ import annotations

import copy
import typing
import warnings
from dataclasses import dataclass
from logging import getLogger as get_logger
from typing import Any, ClassVar

import numpy as np
import pandas as pd
from typing_extensions import Literal, TypedDict  # type: ignore

from orion.algo.base import BaseAlgorithm
from import Dimension, Fidelity, Space
from orion.core.utils.format_trials import dict_to_trial
from orion.core.utils.module_import import ImportOptional
from orion.core.utils.random_state import RandomState, control_randomness
from orion.core.worker.transformer import TransformedDimension
from orion.core.worker.trial import Trial

with ImportOptional("HEBO") as import_optional:
    import hebo
    from hebo.acquisitions.acq import MACE, Acquisition
    from hebo.design_space import DesignSpace
    from hebo.design_space.param import Parameter
    from torch.quasirandom import SobolEngine

if import_optional.failed:
    MACE = object  # noqa: F811

if typing.TYPE_CHECKING and import_optional.failed:
    Acquisition = object  # noqa
    DesignSpace = object  # noqa
    Parameter = object  # noqa
    SobolEngine = object  # noqa

logger = get_logger(__name__)

ModelName = Literal[
properly_seeded_models: set[ModelName] = {"gp", "gpy", "gpy_mlp", "rf", "catboost"}
EvolutionStrategyName = Literal[

class HeboModelState(TypedDict):
    """Typed dict for the state of the HEBO class from `hebo.optimizers.hebo`."""

    space: DesignSpace
    # pylint: disable=invalid-name
    es: str
    X: pd.DataFrame
    y: np.ndarray
    model_name: str
    rand_sample: int
    sobol: SobolEngine
    acq_cls: type[Acquisition]
    _model_config: dict | None

[docs]class HEBO(BaseAlgorithm): """Adapter for the HEBO algorithm from Parameters ---------- :param space: Optimisation space with priors for each dimension. :param seed: Base seed for the random number generators. Defaults to `None`, in which case the randomness is not seeded. :param parameters: Parameters for the HEBO algorithm. """ requires_type: ClassVar[str | None] = None requires_shape: ClassVar[str | None] = "flattened" requires_dist: ClassVar[str | None] = None
[docs] @dataclass(frozen=True) class Parameters: """Parameters of the HEBO algorithm.""" model_name: ModelName = "gpy" """ Name of the model to use. See `ModelName` for the available values. """ random_samples: int | None = None """ Number of random samples to suggest before optimization begins. If `None`, the number of dimensions in the space is used as the default. Otherwise, the max of the value and `2` is used. """ acquisition_class: type[Acquisition] = MACE """ Acquisition class to use. """ evolutionary_strategy: EvolutionStrategyName = "nsga2" """ Name of the evolutionary strategy to use. See `EvolutionStrategyName` for the list of possible values. """ model_config: dict | None = None """ Keyword argument to be passed to the constructor of the model class that is selected with `model_name`. """
def __init__( self, space: Space, seed: int | None = None, parameters: Parameters | dict | None = None, ): import_optional.ensure() super().__init__(space) if isinstance(parameters, dict): parameters = self.Parameters(**parameters) self.parameters: HEBO.Parameters = parameters or self.Parameters() self.seed = seed self.random_state: RandomState | None = None if ( self.parameters.model_name not in properly_seeded_models and seed is not None ): warnings.warn( UserWarning( f"The randomness used by the chosen model '{self.parameters.model_name}' " f"cannot be properly seeded. The model will still work, but the results may " f"not be reproducible, and the random state will not be properly " f"saved/restored during checkpointing." ) ) # NOTE: Need to seed the randomness here, since creating the model affects the global torch # RNG state. This way, we always get the same model for the same seed. if self.seed is not None: self.seed_rng(self.seed) self.hebo_space: DesignSpace = orion_space_to_hebo_space( with control_randomness(self): self.model = hebo.optimizers.hebo.HEBO( space=self.hebo_space, model_name=self.parameters.model_name, rand_sample=self.parameters.random_samples, acq_cls=self.parameters.acquisition_class, es=self.parameters.evolutionary_strategy, model_config=self.parameters.model_config, )
[docs] def seed_rng(self, seed: int | None) -> None: """Seed the random number generators.""" logger.debug("Using a base seed of %s.", seed) self.random_state = RandomState.seed(seed)
@property def state_dict(self) -> dict: """Return a state dict that can be used to reset the state of the algorithm.""" base_state_dict = super().state_dict model_state = HeboModelState(, X=self.model.X, y=self.model.y,, model_name=self.model.model_name, acq_cls=self.model.acq_cls, rand_sample=self.model.rand_sample, _model_config=self.model._model_config, # pylint:disable=protected-access sobol=self.model.sobol, ) return copy.deepcopy( dict( **base_state_dict, model=model_state, random_state=self.random_state or RandomState.current(), parameters=self.parameters, ) )
[docs] def set_state(self, state_dict: dict) -> None: """Reset the state of the algorithm based on the given state_dict :param state_dict: Dictionary representing state of an algorithm """ super().set_state(state_dict) self.random_state = state_dict["random_state"] self.parameters = state_dict["parameters"] model_state = state_dict["model"] # NOTE: For now assuming that we can just store anything into the state dict for key, value in model_state.items(): if not hasattr(self.model, key): raise RuntimeError( f"The state dict has attribute {key} that is not in the model!" ) setattr(self.model, key, value)
[docs] def suggest(self, num: int) -> list[Trial]: """Suggest `num` new sets of hyper-parameters to try. Parameters ---------- num: int Number of trials to suggest. The algorithm may return less than the number of trials requested. Returns ------- A list of trials representing values suggested by the algorithm. """ trials: list[Trial] = [] with control_randomness(self): v: pd.DataFrame = self.model.suggest(n_suggestions=num) point_dicts: dict[int, dict] = v.to_dict(orient="index") # type: ignore for point_index, params_dict in point_dicts.items(): if self.is_done: break params_dict = self._hebo_params_to_orion_params(params_dict) new_trial = self._params_to_trial(params_dict) if not self.has_suggested(new_trial): self.register(new_trial) trials.append(new_trial) logger.debug("Suggestion %s: %s", point_index, new_trial) return trials
[docs] def observe(self, trials: list[Trial]) -> None: """Observe the `trials` new state of result. Parameters ---------- :param trials: New trials with their objectives. """ new_xs: list[dict] = [] new_ys: list[float] = [] assert len(self.model.X) == self.n_observed for trial in trials: if not self.has_observed(trial): self.register(trial) new_x = trial.params if trial.objective is None: # Trial is broken: ignore it. continue new_y = trial.objective.value new_x = self._orion_params_to_hebo_params(new_x) new_xs.append(new_x) new_ys.append(new_y) x_df = pd.DataFrame(new_xs) y_array = np.array(new_ys).reshape([-1, 1]) with control_randomness(self): self.model.observe(X=x_df, y=y_array)
def _hebo_params_to_orion_params(self, hebo_params: dict) -> dict: """Fix any issues with the outputs of the HEBO algo so they fit ``.""" orion_params = {} for name, value in hebo_params.items(): dim: Dimension =[name] from orion.core.worker.transformer import ReshapedDimension if ( dim.type == "categorical" and dim.prior_name == "choices" and value not in dim ): potential_vals = [v for v in dim.interval() if str(v) == value] if len(potential_vals) == 1: value = potential_vals[0] else: raise RuntimeError( f"Value {value} is not contained in the dimension {dim}, and " f"{len(potential_vals)} could match it." ) elif isinstance(dim, ReshapedDimension): # BUG: if isinstance(value, (int, float)) and not isinstance(value, bool): # note: need to make sure `value` isn't a bool, since issubclass(bool, int). value = np.array(value) # assert value in dim # NOTE: Doesn't work! Raises an issue (index is None + int). orion_params[name] = value if self._params_to_trial(orion_params) not in raise RuntimeError( f"Unable to fix all the issues: params {orion_params} still isn't in space " f"{}!" ) return orion_params def _orion_params_to_hebo_params(self, orion_params: dict) -> dict: """Fix any issues with the trials from Orion so they fit the `self.hebo_space`.""" assert self.hebo_space is not None # NOTE: Remove the extra stuff (e.g. Fidelity dimension), before passing it to the # model. It's just a precaution, since the Hebo model probably fetches data using # the keys of its space, which doesn't have the Fidelity dimension anyway. params = {} for name, value in orion_params.items(): orion_dim: Dimension =[name] if orion_dim.type == "fidelity": continue from hebo.design_space.categorical_param import CategoricalPara hebo_dim: Parameter = self.hebo_space.paras[name] if isinstance(hebo_dim, CategoricalPara): if ( value not in hebo_dim.categories and str(value) in hebo_dim.categories ): value = str(value) assert value in hebo_dim.categories, (value, hebo_dim.categories) params[name] = value return params def _params_to_trial(self, orion_params: dict) -> Trial: """Create a Trial from a dict of hyper-parameters.""" # Need to convert the {name: value} of point_dict into this format for Orion's Trial. # Add the max value for the Fidelity dimensions, if any. if self.fidelity_index is not None: fidelity_dim =[self.fidelity_index] while isinstance(fidelity_dim, TransformedDimension): fidelity_dim = fidelity_dim.original_dimension assert isinstance(fidelity_dim, Fidelity) orion_params[self.fidelity_index] = float(fidelity_dim.high) trial: Trial = dict_to_trial(orion_params, return trial
def orion_space_to_hebo_space(space: Space) -> DesignSpace: """Get the HEBO-equivalent space for the `Space` `space`. Parameters ---------- :param space: `Space` instance. Returns ------- a `DesignSpace` from the `hebo` package. Raises ------ NotImplementedError If there is an unsupported dimension or prior type in `space`. """ specs = [] ds = DesignSpace() name: str dimension: Dimension for name, dimension in space.items(): spec: dict[str, Any] = {"name": name} prior_name = dimension.prior_name bounds = dimension.interval() if dimension.shape: raise NotImplementedError( f"HEBO algorithm doesn't support dimension {dimension} since it has a shape." ) if dimension.type == "fidelity": # Ignore that dimension: Don't include it in the space for Hebo to optimize. continue # BUG: bounds = tuple(b.item() if isinstance(b, np.ndarray) else b for b in bounds) if prior_name == "choices": categories = [str(b) for b in bounds] spec.update(type="cat", categories=categories) elif prior_name == "uniform": spec.update(type="num", lb=bounds[0], ub=bounds[1]) elif prior_name == "reciprocal": spec.update(type="pow", lb=bounds[0], ub=bounds[1]) elif prior_name == "int_uniform": spec.update(type="int", lb=bounds[0], ub=bounds[1]) elif prior_name == "int_reciprocal": spec.update(type="pow_int", lb=bounds[0], ub=bounds[1]) else: raise NotImplementedError(prior_name, dimension) specs.append(spec) ds.parse(specs) return ds