# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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.
from typing import Dict, Any
import numpy as np
from syne_tune.optimizer.schedulers.searchers.bayesopt.sklearn.predictor import (
SKLearnPredictor,
)
[docs]
class SKLearnEstimator:
"""
Base class scikit-learn based estimators, giving rise to surrogate models
for Bayesian optimization.
"""
[docs]
def fit(
self, X: np.ndarray, y: np.ndarray, update_params: bool
) -> SKLearnPredictor:
"""
Implements :meth:`fit_from_state`, given transformed data. Here,
``y`` is normalized (zero mean, unit variance) iff
``normalize_targets == True``.
:param X: Feature matrix, shape ``(n_samples, n_features)``
:param y: Target values, shape ``(n_samples,)``
:param update_params: Should model (hyper)parameters be updated?
Ignored if estimator has no hyperparameters
:return: Predictor, wrapping the posterior state
"""
raise NotImplementedError
[docs]
def get_params(self) -> Dict[str, Any]:
"""
:return: Current model hyperparameters
"""
return dict() # Default (estimator has no hyperparameters)
[docs]
def set_params(self, param_dict: Dict[str, Any]):
"""
:param param_dict: New model hyperparameters
"""
pass # Default (estimator has no hyperparameters)
@property
def normalize_targets(self) -> bool:
"""
:return: Should targets in ``state`` be normalized before calling
:meth:`fit`?
"""
return False