# Copyright 2021-2024 The DeepCAVE Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# noqa: D400
"""
# RandomForest Surrogate
This module provides a RandomForest Surrogate model.
Mean and standard deviation values can be predicted for a given input with this module.
## Classes
- RandomForestSurrogate: Random forest surrogate for the pyPDP package.
"""
from typing import Optional, Tuple
import ConfigSpace as CS
import numpy as np
from pyPDP.surrogate_models import SurrogateModel
from deepcave.evaluators.epm.random_forest import RandomForest
[docs]
class RandomForestSurrogate(SurrogateModel):
"""
Random forest surrogate for the pyPDP package.
Predict deviations and fit the model.
"""
def __init__(
self,
configspace: CS.ConfigurationSpace,
seed: Optional[int] = None,
n_trees: int = 16,
):
super().__init__(configspace, seed=seed)
self._model = RandomForest(configspace=configspace, seed=seed, n_trees=n_trees)
[docs]
def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""
Predict the deviations.
Parameters
----------
X : np.ndarray
The data points on which to predict.
Returns
-------
Tuple[np.ndarray, np.ndarray]
The means and standard deviation.
"""
means, stds = self._model.predict(X)
return means[:, 0], stds[:, 0]
def _fit(self, X: np.ndarray, y: np.ndarray) -> None:
"""
Train the surrogate model.
Parameters
----------
X : np.ndarray
Input data points.
y : np.ndarray
Corresponding target values.
"""
self._model.train(X, y)