Source code for openmdao.surrogate_models.surrogate_model

"""
Class definition for SurrogateModel, the base class for all surrogate models.
"""
from openmdao.utils.options_dictionary import OptionsDictionary


class SurrogateModel(object):
    """
    Base class for surrogate models.

    Parameters
    ----------
    **kwargs : dict
        Options dictionary.

    Attributes
    ----------
    options : <OptionsDictionary>
        Dictionary with general pyoptsparse options.
    trained : bool
        True when surrogate has been trained.
    """

    def __init__(self, **kwargs):
        """
        Initialize all attributes.
        """
        self.trained = False

        self.options = OptionsDictionary(parent_name=type(self).__name__)
        self._declare_options()
        self.options.update(kwargs)

    def train(self, x, y):
        """
        Train the surrogate model with the given set of inputs and outputs.

        Parameters
        ----------
        x : array-like
            Training input locations..
        y : array-like
            Model responses at given inputs.
        """
        self.trained = True

    def predict(self, x):
        """
        Calculate a predicted value of the response based on the current trained model.

        Parameters
        ----------
        x : array-like
            Point(s) at which the surrogate is evaluated.
        """
        if not self.trained:
            msg = "{0} has not been trained, so no prediction can be made."\
                .format(type(self).__name__)
            raise RuntimeError(msg)

    def vectorized_predict(self, x):
        """
        Calculate predicted values of the response based on the current trained model.

        Parameters
        ----------
        x : array-like
            Vectorized point(s) at which the surrogate is evaluated.
        """
        pass

    def linearize(self, x):
        """
        Calculate the jacobian of the interpolant at the requested point.

        Parameters
        ----------
        x : array-like
            Point at which the surrogate Jacobian is evaluated.
        """
        pass

    def _declare_options(self):
        """
        Declare options before kwargs are processed in the init method.
        """
        pass


class MultiFiSurrogateModel(SurrogateModel):
    """
    Base class for surrogate models using multi-fidelity training data.

    Parameters
    ----------
    **kwargs : dict
        Options dictionary.
    """

    def train(self, x, y):
        """
        Train the surrogate model with the given set of inputs and outputs.

        Parameters
        ----------
        x : array-like
            Point(s) at which the surrogate is evaluated.
        y : array-like
            Model responses at given inputs.
        """
        super().train(x, y)
        self.train_multifi([x], [y])

    def train_multifi(self, x, y):
        """
        Train the surrogate model, based on the given multi-fidelity training data.

        Parameters
        ----------
        x : list of double array_like elements
            A list of arrays with the input at which observations were made, from highest
            fidelity to lowest fidelity. Designs must be nested
            with X[i] = np.vstack([..., X[i+1]).
        y : list of double array_like elements
            A list of arrays with the observations of the scalar output to be predicted,
            from highest fidelity to lowest fidelity.
        """
        pass