Source code for smac.model.random_forest.abstract_random_forest

from __future__ import annotations

from typing import Any

import numpy as np
from ConfigSpace import (
    CategoricalHyperparameter,
    Constant,
    UniformFloatHyperparameter,
    UniformIntegerHyperparameter,
)

from smac.model.abstract_model import AbstractModel

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


[docs]class AbstractRandomForest(AbstractModel): """Abstract base class for all random forest models.""" def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) self._conditional: dict[int, bool] = dict() self._impute_values: dict[int, float] = dict() def _impute_inactive(self, X: np.ndarray) -> np.ndarray: X = X.copy() for idx, hp in enumerate(self._configspace.get_hyperparameters()): if idx not in self._conditional: parents = self._configspace.get_parents_of(hp.name) if len(parents) == 0: self._conditional[idx] = False else: self._conditional[idx] = True if isinstance(hp, CategoricalHyperparameter): self._impute_values[idx] = len(hp.choices) elif isinstance(hp, (UniformFloatHyperparameter, UniformIntegerHyperparameter)): self._impute_values[idx] = -1 elif isinstance(hp, Constant): self._impute_values[idx] = 1 else: raise ValueError if self._conditional[idx] is True: nonfinite_mask = ~np.isfinite(X[:, idx]) X[nonfinite_mask, idx] = self._impute_values[idx] return X