Source code for smac.runhistory.encoder.log_scaled_encoder

from __future__ import annotations

import warnings

import numpy as np

from smac import constants
from smac.runhistory.encoder.encoder import RunHistoryEncoder
from smac.utils.logging import get_logger

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


logger = get_logger(__name__)


[docs]class RunHistoryLogScaledEncoder(RunHistoryEncoder):
[docs] def transform_response_values(self, values: np.ndarray) -> np.ndarray: """Transform the response values by linearly scaling them between zero and one and then using the log transformation. """ min_y = self._min_y - ( self._percentile - self._min_y ) # Subtract the difference between the percentile and the minimum min_y -= constants.VERY_SMALL_NUMBER # Minimal value to avoid numerical issues in the log scaling below # Linear scaling # prevent diving by zero min_y[np.where(min_y == self._max_y)] *= 1 - 10**-10 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=RuntimeWarning) values = (values - min_y) / (self._max_y - min_y) return np.log(values)