kjappelbaum/pyepal

# -*- coding: utf-8 -*-
# Copyright 2020 PyePAL 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.


"""PAL for coregionalized GPR models"""

import numpy as np

from .pal_base import PALBase
from .schedules import linear

__all__ = ["PALCoregionalized"]


class PALCoregionalized(PALBase):
    """PAL class for a coregionalized GPR model"""

    def __init__(self, *args, **kwargs):
        """Construct the PALCoregionalized instance

        Args:
            X_design (np.array): Design space (feature matrix)
            models (list): Machine learning models
            ndim (int): Number of objectives
            epsilon (Union[list, float], optional): Epsilon hyperparameter.
                Defaults to 0.01.
            delta (float, optional): Delta hyperparameter. Defaults to 0.05.
            beta_scale (float, optional): Scaling parameter for beta.
                If not equal to 1, the theoretical guarantees do not necessarily hold.
                Also note that the parametrization depends on the kernel type.
                Defaults to 1/9.
            goals (List[str], optional): If a list, provide "min" for every objective
                that shall be minimized and "max" for every objective
                that shall be maximized. Defaults to None, which means
                that the code maximizes all objectives.
            coef_var_threshold (float, optional): Use only points with
                a coefficient of variation below this threshold
                in the classification step. Defaults to 3.
            restarts (int): Number of random restarts that are used for hyperparameter
                optimization. Defaults to 20.
            parallel (bool): If true, model hyperparameters are optimized in parallel,
                using the GPy implementation. Defaults to False.
        """
        from .validate_inputs import (  # pylint:disable=import-outside-toplevel
            validate_coregionalized_gpy,
        )

        self.restarts = kwargs.pop("restarts", 20)
        self.parallel = kwargs.pop("parallel", False)
        assert isinstance(self.parallel, bool), "the parallel keyword must be of type bool"
        assert isinstance(self.restarts, int), "the restarts keyword must be of type int"
        super().__init__(*args, **kwargs)
        validate_coregionalized_gpy(self.models)

    def _set_data(self):
        from ..models.gpr import set_xy_coregionalized  # pylint:disable=import-outside-toplevel

        self.models[0] = set_xy_coregionalized(
            self.models[0],
            self.design_space[self.sampled_indices],
            self.y[self.sampled_indices],
            self.sampled[self.sampled_indices],
        )

    def _train(self):
        pass

    def _predict(self):
        from ..models.gpr import predict_coregionalized  # pylint:disable=import-outside-toplevel

        means, stds = [], []
        for i in range(self.ndim):
            mean, std = predict_coregionalized(self.models[0], self.design_space, i)
            means.append(mean.reshape(-1, 1))
            stds.append(std.reshape(-1, 1))

        self._means = np.hstack(means)
        self.std = np.hstack(stds)

    def _set_hyperparameters(self):
        self.models[0].optimize_restarts(self.restarts, parallel=self.parallel)

    def _should_optimize_hyperparameters(self) -> bool:
        return linear(self.iteration, 10)