from __future__ import annotations
from typing import Any
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 RunHistoryInverseScaledEncoder(RunHistoryEncoder):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
if self._instances is not None and len(self._instances) > 1:
raise NotImplementedError("Handling more than one instance is not supported for inverse scaled cost.")
[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 use inverse scaling.
"""
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
values = (values - min_y) / (self._max_y - min_y)
values = 1 - 1 / values
return values