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__)




class RunHistoryLogScaledEncoder(RunHistoryEncoder):


    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)