Source code for gemseo.mlearning.regression.gpr

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Francois Gallard, Matthias De Lozzo
r"""The Gaussian process algorithm for regression.


The Gaussian process regression (GPR) surrogate model
expresses the model output as a weighted sum of kernel functions
centered on the learning input data:

.. math::

    y = \mu
        + w_1\kappa(\|x-x_1\|;\epsilon)
        + w_2\kappa(\|x-x_2\|;\epsilon)
        + ...
        + w_N\kappa(\|x-x_N\|;\epsilon)


The GPR model relies on the assumption
that the original model :math:`f` to replace
is an instance of a Gaussian process (GP) with mean :math:`\mu`
and covariance :math:`\sigma^2\kappa(\|x-x'\|;\epsilon)`.

Then, the GP conditioned by the learning set
:math:`(x_i,y_i)_{1\leq i \leq N}`
is entirely defined by its expectation:

.. math::

    \hat{f}(x) = \hat{\mu} + \hat{w}^T k(x)

and its covariance:

.. math::

    \hat{c}(x,x') = \hat{\sigma}^2 - k(x)^T K^{-1} k(x')

where :math:`[\hat{\mu};\hat{w}]=([1_N~K]^T[1_N~K])^{-1}[1_N~K]^TY` with
and :math:`Y_i=y_i`.

The correlation length vector :math:`\epsilon`
is estimated by numerical non-linear optimization.

Surrogate model

The expectation :math:`\hat{f}` is the GPR surrogate model of :math:`f`.

Error measure

The standard deviation :math:`\hat{s}` is a local error measure
of :math:`\hat{f}`:

.. math::


Interpolation or regression

The GPR surrogate model can be regressive or interpolative
according to the value of the nugget effect :math:`\\alpha\geq 0`
which is a regularization term
applied to the correlation matrix :math:`K`.
When :math:`\alpha = 0`,
the surrogate model interpolates the learning data.

The GPR model relies on the GaussianProcessRegressor class
of the `scikit-learn library <
from __future__ import division, unicode_literals

import logging
from typing import Callable, Iterable, Optional, Union

import openturns
from numpy import atleast_2d, ndarray
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern

from gemseo.core.dataset import Dataset
from gemseo.mlearning.core.ml_algo import DataType, TransformerType
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from gemseo.utils.data_conversion import DataConversion

LOGGER = logging.getLogger(__name__)

[docs]class GaussianProcessRegression(MLRegressionAlgo): """Gaussian process regression.""" LIBRARY = "scikit-learn" ABBR = "GPR" def __init__( self, data, # type: Dataset transformer=None, # type: Optional[TransformerType] input_names=None, # type: Optional[Iterable[str]] output_names=None, # type: Optional[Iterable[str]] kernel=None, # type: Optional[openturns.CovarianceModel] alpha=1e-10, # type: Union[float,ndarray] optimizer="fmin_l_bfgs_b", # type: Union[str,Callable] n_restarts_optimizer=10, # type: int random_state=None, # type: Optional[int] ): # type: (...) -> None """ Args: kernel: The kernel function. If None, use a ``Matern(2.5)``. alpha: The nugget effect to regularize the model. optimizer: The optimization algorithm to find the hyperparameters. n_restarts_optimizer: The number of restarts of the optimizer. random_state: The seed used to initialize the centers. If None, the random number generator is the RandomState instance used by `numpy.random`. """ super(GaussianProcessRegression, self).__init__( data, transformer=transformer, input_names=input_names, output_names=output_names, kernel=kernel, alpha=alpha, optimizer=optimizer, n_restarts_optimizer=n_restarts_optimizer, random_state=random_state, ) if kernel is None: raw_input_shape, _ = self._get_raw_shapes() self.kernel = Matern( (1.0,) * raw_input_shape, [(0.01, 100)] * raw_input_shape, nu=2.5 ) else: self.kernel = kernel nro = n_restarts_optimizer self.algo = GaussianProcessRegressor( normalize_y=False, kernel=self.kernel, copy_X_train=True, alpha=alpha, optimizer=optimizer, n_restarts_optimizer=nro, random_state=random_state, ) self.parameters["kernel"] = self.kernel.__class__.__name__ def _fit( self, input_data, # type: ndarray output_data, # type: ndarray ): # type: (...) -> None, output_data) def _predict( self, input_data, # type: ndarray ): # type: (...) -> ndarray output_pred = self.algo.predict(input_data, False) return output_pred
[docs] def predict_std( self, input_data, # type:DataType ): # type: (...) -> ndarray """Predict the standard deviation from input data. Args: input_data: The input data with shape (n_samples, n_inputs). Returns: output_data: The output data with shape (n_samples, n_outputs). """ as_dict = isinstance(input_data, dict) if as_dict: input_data = DataConversion.dict_to_array(input_data, self.input_names) input_data = atleast_2d(input_data) inputs = self.learning_set.INPUT_GROUP if inputs in self.transformer: input_data = self.transformer[inputs].transform(input_data) _, output_std = self.algo.predict(input_data, True) return sum(output_std) / len(output_std)