Source code for smac.initial_design.abstract_initial_design

from __future__ import annotations

from abc import abstractmethod
from typing import Any

from collections import OrderedDict

import numpy as np
from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
from ConfigSpace.hyperparameters import (
from ConfigSpace.util import ForbiddenValueError, deactivate_inactive_hyperparameters

from smac.scenario import Scenario
from smac.utils.logging import get_logger

__copyright__ = "Copyright 2022,"
__license__ = "3-clause BSD"

logger = get_logger(__name__)

[docs]class AbstractInitialDesign: """Base class for initial design strategies that evaluates multiple configurations. Parameters ---------- scenario : Scenario n_configs : int | None, defaults to None Number of initial configurations (disables the arguments ``n_configs_per_hyperparameter``). n_configs_per_hyperparameter: int, defaults to 10 Number of initial configurations per hyperparameter. For example, if my configuration space covers five hyperparameters and ``n_configs_per_hyperparameter`` is set to 10, then 50 initial configurations will be samples. max_ratio: float, defaults to 0.25 Use at most ``scenario.n_trials`` * ``max_ratio`` number of configurations in the initial design. Additional configurations are not affected by this parameter. additional_configs: list[Configuration], defaults to [] Adds additional configurations to the initial design. seed : int | None, default to None """ def __init__( self, scenario: Scenario, n_configs: int | None = None, n_configs_per_hyperparameter: int | None = 10, max_ratio: float = 0.25, additional_configs: list[Configuration] = None, seed: int | None = None, ): self._configspace = scenario.configspace if seed is None: seed = scenario.seed self.use_default_config = scenario.use_default_config self._seed = seed self._rng = np.random.RandomState(seed) self._n_configs_per_hyperparameter = n_configs_per_hyperparameter # make sure that additional configs is not a mutable default value # this avoids issues if additional_configs is None: additional_configs = [] if self.use_default_config: default_config = self._configspace.get_default_configuration() default_config.origin = "Initial Design: Default configuration" additional_configs.append(default_config) self._additional_configs = additional_configs n_params = len(self._configspace.get_hyperparameters()) if n_configs is not None:"Using `n_configs` and ignoring `n_configs_per_hyperparameter`.") self._n_configs = n_configs elif n_configs_per_hyperparameter is not None: self._n_configs = n_configs_per_hyperparameter * n_params else: raise ValueError( "Need to provide either argument `n_configs` or " "`n_configs_per_hyperparameter` but provided none of them." ) # If the number of configurations is too large, we reduce it _n_configs = int(max(1, min(self._n_configs, (max_ratio * scenario.n_trials)))) if self._n_configs != _n_configs: f"Reducing the number of initial configurations from {self._n_configs} to " f"{_n_configs} (max_ratio == {max_ratio})." ) self._n_configs = _n_configs # We allow no configs if we have additional configs if n_configs is not None and n_configs == 0 and len(additional_configs) > 0: self._n_configs = 0 if self._n_configs + len(additional_configs) > scenario.n_trials: raise ValueError( f"Initial budget {self._n_configs} cannot be higher than the number of trials {scenario.n_trials}." ) @property def meta(self) -> dict[str, Any]: """Returns the meta data of the created object.""" return { "name": self.__class__.__name__, "n_configs": self._n_configs, "n_configs_per_hyperparameter": self._n_configs_per_hyperparameter, "additional_configs": [c.get_dictionary() for c in self._additional_configs], "seed": self._seed, }
[docs] def select_configurations(self) -> list[Configuration]: """Selects the initial configurations. Internally, `_select_configurations` is called, which has to be implemented by the child class. Returns ------- configs : list[Configuration] Configurations from the child class. """ configs: list[Configuration] = [] if self._n_configs == 0:"No initial configurations are used.") else: configs += self._select_configurations() # Adding additional configs configs += self._additional_configs for config in configs: if config.origin is None: config.origin = "Initial design" # Removing duplicates # (Reference: configs = list(OrderedDict.fromkeys(configs)) f"Using {len(configs) - len(self._additional_configs)} initial design configurations " f"and {len(self._additional_configs)} additional configurations." ) return configs
@abstractmethod def _select_configurations(self) -> list[Configuration]: """Selects the initial configurations, depending on the implementation of the initial design.""" raise NotImplementedError def _transform_continuous_designs( self, design: np.ndarray, origin: str, configspace: ConfigurationSpace ) -> list[Configuration]: """Transforms the continuous designs into a discrete list of configurations. Parameters ---------- design : np.ndarray Array of hyperparameters originating from the initial design strategy. origin : str | None, defaults to None Label for a configuration where it originated from. configspace : ConfigurationSpace Returns ------- configs : list[Configuration] Continuous transformed configs. """ params = configspace.get_hyperparameters() for idx, param in enumerate(params): if isinstance(param, IntegerHyperparameter): design[:, idx] = param._inverse_transform(param._transform(design[:, idx])) elif isinstance(param, NumericalHyperparameter): continue elif isinstance(param, Constant): design_ = np.zeros(np.array(design.shape) + np.array((0, 1))) design_[:, :idx] = design[:, :idx] design_[:, idx + 1 :] = design[:, idx:] design = design_ elif isinstance(param, CategoricalHyperparameter): v_design = design[:, idx] v_design[v_design == 1] = 1 - 10**-10 design[:, idx] = np.array(v_design * len(param.choices), dtype=int) elif isinstance(param, OrdinalHyperparameter): v_design = design[:, idx] v_design[v_design == 1] = 1 - 10**-10 design[:, idx] = np.array(v_design * len(param.sequence), dtype=int) else: raise ValueError("Hyperparameter not supported when transforming a continuous design.") configs = [] for vector in design: try: conf = deactivate_inactive_hyperparameters( configuration=None, configuration_space=configspace, vector=vector ) except ForbiddenValueError: continue conf.origin = origin configs.append(conf) return configs