Source code for smac.runhistory.encoder.eips_encoder

from __future__ import annotations

from typing import Mapping

import numpy as np

from smac.runhistory.encoder import AbstractRunHistoryEncoder
from smac.runhistory.runhistory import TrialKey, TrialValue
from smac.utils.configspace import convert_configurations_to_array
from smac.utils.logging import get_logger
from smac.utils.multi_objective import normalize_costs

__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"


logger = get_logger(__name__)


[docs]class RunHistoryEIPSEncoder(AbstractRunHistoryEncoder): """Encoder specifically for the EIPS (expected improvement per second) acquisition function.""" def _build_matrix( self, trials: Mapping[TrialKey, TrialValue], store_statistics: bool = False, ) -> tuple[np.ndarray, np.ndarray]: if store_statistics: # store_statistics is currently not necessary pass # First build nan-matrix of size #configs x #params+1 n_rows = len(trials) n_cols = self._n_params X = np.ones([n_rows, n_cols + self._n_features]) * np.nan y = np.ones([n_rows, 2]) # Then populate matrix for row, (key, run) in enumerate(trials.items()): # Scaling is automatically done in configSpace conf = self.runhistory.ids_config[key.config_id] conf_vector = convert_configurations_to_array([conf])[0] if self._n_features > 0 and self._instance_features is not None: assert isinstance(key.instance, str) feats = self._instance_features[key.instance] X[row, :] = np.hstack((conf_vector, feats)) else: X[row, :] = conf_vector if self._n_objectives > 1: assert self._multi_objective_algorithm is not None assert isinstance(run.cost, list) # Let's normalize y here # We use the objective_bounds calculated by the runhistory y_ = normalize_costs(run.cost, self.runhistory.objective_bounds) y_agg = self._multi_objective_algorithm(y_) y[row, 0] = y_agg else: y[row, 0] = run.cost y[row, 1] = run.time y_transformed = self.transform_response_values(values=y) return X, y_transformed
[docs] def transform_response_values(self, values: np.ndarray) -> np.ndarray: """Transform function response values. Transform the runtimes by a log transformation log(1. + runtime). Parameters ---------- values : np.ndarray Response values to be transformed. Returns ------- np.ndarray """ # We need to ensure that time remains positive after the log transform. values[:, 1] = np.log(1 + values[:, 1]) return values