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#
# 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
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from typing import Tuple, Dict
import numpy as np
[docs]
class SKLearnPredictor:
"""
Base class for predictors generated by scikit-learn based estimators of
:class:`~syne_tune.optimizer.schedulers.searchers.bayesopt.sklearn.estimator.SKLearnEstimator`.
This is only for predictors who return means and stddevs in :meth:`predict`.
"""
[docs]
def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""
Returns signals which are statistics of the predictive distribution at
input points ``inputs``.
:param inputs: Input points, shape ``(n, d)``
:return: ``(means, stds)``, where predictive means ``means`` and
predictive stddevs ``stds`` have shape ``(n,)``
"""
raise NotImplementedError
[docs]
def backward_gradient(
self, input: np.ndarray, head_gradients: Dict[str, np.ndarray]
) -> np.ndarray:
r"""
Needs to be implemented only if gradient-based local optimization of
an acquisition function is supported.
Computes the gradient :math:`\nabla f(x)` for an acquisition
function :math:`f(x)`, where :math:`x` is a single input point. This
is using reverse mode differentiation, the head gradients are passed
by the acquisition function. The head gradients are
:math:`\partial_k f`, where :math:`k` runs over the statistics
returned by :meth:`predict` for the single input point :math:`x`.
The shape of head gradients is the same as the shape of the
statistics.
:param input: Single input point :math:`x`, shape ``(d,)``
:param head_gradients: See above
:return: Gradient :math:`\nabla f(x)`
"""
raise NotImplementedError