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Base Model Constructor

ModelConstructor

Bases: XoptBaseModel, ABC

Abstract class that defines instructions for building heterogeneous botorch models used in Xopt Bayesian generators.

Attributes:

Name Type Description
name str

The name of the model.

Methods:

Name Description
build_model

Build and return a trained botorch model for objectives and constraints.
build_model_from_vocs

Convenience wrapper around build_model for use with VOCs (Variables, Objectives, Constraints, Statics).
build_single_task_gp

Utility method for creating and training simple SingleTaskGP models.
build_heteroskedastic_gp

Utility method for creating and training heteroskedastic SingleTaskGP models.
Source code in xopt/generators/bayesian/base_model.py 
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class ModelConstructor(XoptBaseModel, ABC):
    """
    Abstract class that defines instructions for building heterogeneous botorch models
    used in Xopt Bayesian generators.

    Attributes
    ----------
    name : str
        The name of the model.


    Methods
    -------
    build_model(input_names, outcome_names, data, input_bounds=None, dtype=torch.double, device='cpu')
        Build and return a trained botorch model for objectives and constraints.

    build_model_from_vocs(vocs, data, dtype=torch.double, device='cpu')
        Convenience wrapper around `build_model` for use with VOCs (Variables, Objectives,
        Constraints, Statics).

    build_single_task_gp(X, Y, train=True, **kwargs)
        Utility method for creating and training simple SingleTaskGP models.

    build_heteroskedastic_gp(X, Y, Yvar, train=True, **kwargs)
        Utility method for creating and training heteroskedastic SingleTaskGP models.

    """

    name: str

    model_config = ConfigDict(validate_assignment=True)

    def __init__(self, **kwargs: Any):
        super().__init__(**kwargs)

    @abstractmethod
    def build_model(
        self,
        input_names: List[str],
        outcome_names: List[str],
        data: pd.DataFrame,
        input_bounds: Dict[str, List] = None,
        dtype: torch.dtype = torch.double,
        device: Union[torch.device, str] = "cpu",
    ) -> ModelListGP:
        """
        Build and return a trained botorch model for objectives and constraints.

        Parameters
        ----------
        input_names : List[str]
            Names of input variables.
        outcome_names : List[str]
            Names of outcome variables.
        data : pd.DataFrame
            Data used for training the model.
        input_bounds : Dict[str, List], optional
            Bounds for input variables.
        dtype : torch.dtype, optional
            Data type for the model (default is torch.double).
        device : Union[torch.device, str], optional
            Device on which to perform computations (default is "cpu").

        Returns
        -------
        ModelListGP
            The trained botorch model.

        """
        pass  # pragma: no cover

    def build_model_from_vocs(
        self,
        vocs: VOCS,
        data: pd.DataFrame,
        dtype: torch.dtype = torch.double,
        device: Union[torch.device, str] = "cpu",
    ):
        """
        Convenience wrapper around `build_model` for use with VOCS (Variables,
        Objectives, Constraints, Statics).

        Parameters
        ----------
        vocs : VOCS
            The VOCS object for defining the problem's variables, objectives,
            constraints, and statics.
        data : pd.DataFrame
            Data used for training the model.
        dtype : torch.dtype, optional
            Data type for the model (default is torch.double).
        device : Union[torch.device, str], optional
            Device on which to perform computations (default is "cpu").

        Returns
        -------
        ModelListGP
            The trained botorch model.

        """
        variable_bounds = {name: ele.domain for name, ele in vocs.variables.items()}

        return self.build_model(
            vocs.variable_names, vocs.output_names, data, variable_bounds, dtype, device
        )

    @staticmethod
    def build_single_task_gp(X: Tensor, Y: Tensor, train=True, **kwargs) -> Model:
        """
        Utility method for creating and training simple SingleTaskGP models.

        Parameters
        ----------
        X : Tensor
            Training data for input variables.
        Y : Tensor
            Training data for outcome variables.
        train : bool, True
            Flag to specify if hyperparameter training should take place
        **kwargs
            Additional keyword arguments for model configuration.

        Returns
        -------
        Model
            The trained SingleTaskGP model.

        """
        if X.shape[0] == 0 or Y.shape[0] == 0:
            raise ValueError("no data found to train model!")
        model = SingleTaskGP(X, Y, **kwargs)

        if train:
            mll = ExactMarginalLogLikelihood(model.likelihood, model)
            fit_gpytorch_mll(mll)
        return model

    @staticmethod
    def build_heteroskedastic_gp(
        X: Tensor, Y: Tensor, Yvar: Tensor, train: bool = True, **kwargs
    ) -> Model:
        """
        Utility method for creating and training heteroskedastic SingleTaskGP models.

        Parameters
        ----------
        X : Tensor
            Training data for input variables.
        Y : Tensor
            Training data for outcome variables.
        Yvar : Tensor
            Training data for outcome variable variances.
        train : bool, True
            Flag to specify if hyperparameter training should take place
        **kwargs
            Additional keyword arguments for model configuration.

        Returns
        -------
        Model
            The trained heteroskedastic SingleTaskGP model.

        Notes
        -----
        Heteroskedastic modeling incurs a number of warnings from botorch, which are
        suppressed within this method.

        """
        warnings.warn(
            "Heteroskedastic modeling has been removed from botorch due "
            "to numerical stability issues. A copy of the implementation "
            "is included in Xopt, however it may be unstable / buggy. "
            "Your results may vary and keep an eye on warnings."
        )

        if X.shape[0] == 0 or Y.shape[0] == 0 or Yvar.shape[0] == 0:
            raise ValueError("no data found to train model!")

        with warnings.catch_warnings():
            warnings.filterwarnings("ignore")
            model = XoptHeteroskedasticSingleTaskGP(X, Y, Yvar, **kwargs)

        if train:
            try:
                with warnings.catch_warnings():
                    warnings.filterwarnings("ignore")
                    mll = ExactMarginalLogLikelihood(model.likelihood, model)
                    fit_gpytorch_mll(mll)
            except ModelFittingError:
                warnings.warn(
                    "Model fitting failed for heteroskedastic GP. Returning untrained model."
                )
        return model

build_heteroskedastic_gp(X, Y, Yvar, train=True, **kwargs) staticmethod

Utility method for creating and training heteroskedastic SingleTaskGP models.

Parameters:

Name Type Description Default
X Tensor

Training data for input variables.

 required
Y Tensor

Training data for outcome variables.

 required
Yvar Tensor

Training data for outcome variable variances.

 required
train (bool, True)

Flag to specify if hyperparameter training should take place

 True
**kwargs

Additional keyword arguments for model configuration.

 {}

Returns:

Type Description
Model

The trained heteroskedastic SingleTaskGP model.
Notes

Heteroskedastic modeling incurs a number of warnings from botorch, which are suppressed within this method.

Source code in xopt/generators/bayesian/base_model.py 
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@staticmethod
def build_heteroskedastic_gp(
    X: Tensor, Y: Tensor, Yvar: Tensor, train: bool = True, **kwargs
) -> Model:
    """
    Utility method for creating and training heteroskedastic SingleTaskGP models.

    Parameters
    ----------
    X : Tensor
        Training data for input variables.
    Y : Tensor
        Training data for outcome variables.
    Yvar : Tensor
        Training data for outcome variable variances.
    train : bool, True
        Flag to specify if hyperparameter training should take place
    **kwargs
        Additional keyword arguments for model configuration.

    Returns
    -------
    Model
        The trained heteroskedastic SingleTaskGP model.

    Notes
    -----
    Heteroskedastic modeling incurs a number of warnings from botorch, which are
    suppressed within this method.

    """
    warnings.warn(
        "Heteroskedastic modeling has been removed from botorch due "
        "to numerical stability issues. A copy of the implementation "
        "is included in Xopt, however it may be unstable / buggy. "
        "Your results may vary and keep an eye on warnings."
    )

    if X.shape[0] == 0 or Y.shape[0] == 0 or Yvar.shape[0] == 0:
        raise ValueError("no data found to train model!")

    with warnings.catch_warnings():
        warnings.filterwarnings("ignore")
        model = XoptHeteroskedasticSingleTaskGP(X, Y, Yvar, **kwargs)

    if train:
        try:
            with warnings.catch_warnings():
                warnings.filterwarnings("ignore")
                mll = ExactMarginalLogLikelihood(model.likelihood, model)
                fit_gpytorch_mll(mll)
        except ModelFittingError:
            warnings.warn(
                "Model fitting failed for heteroskedastic GP. Returning untrained model."
            )
    return model

build_model(input_names, outcome_names, data, input_bounds=None, dtype=torch.double, device='cpu') abstractmethod

Build and return a trained botorch model for objectives and constraints.

Parameters:

Name Type Description Default
input_names List[str]

Names of input variables.

 required
outcome_names List[str]

Names of outcome variables.

 required
data DataFrame

Data used for training the model.

 required
input_bounds Dict[str, List]

Bounds for input variables.

 None
dtype dtype

Data type for the model (default is torch.double).

 double
device Union[device, str]

Device on which to perform computations (default is "cpu").

 'cpu'

Returns:

Type Description
ModelListGP

The trained botorch model.
Source code in xopt/generators/bayesian/base_model.py 
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@abstractmethod
def build_model(
    self,
    input_names: List[str],
    outcome_names: List[str],
    data: pd.DataFrame,
    input_bounds: Dict[str, List] = None,
    dtype: torch.dtype = torch.double,
    device: Union[torch.device, str] = "cpu",
) -> ModelListGP:
    """
    Build and return a trained botorch model for objectives and constraints.

    Parameters
    ----------
    input_names : List[str]
        Names of input variables.
    outcome_names : List[str]
        Names of outcome variables.
    data : pd.DataFrame
        Data used for training the model.
    input_bounds : Dict[str, List], optional
        Bounds for input variables.
    dtype : torch.dtype, optional
        Data type for the model (default is torch.double).
    device : Union[torch.device, str], optional
        Device on which to perform computations (default is "cpu").

    Returns
    -------
    ModelListGP
        The trained botorch model.

    """
    pass  # pragma: no cover

build_model_from_vocs(vocs, data, dtype=torch.double, device='cpu')

Convenience wrapper around build_model for use with VOCS (Variables, Objectives, Constraints, Statics).

Parameters:

Name Type Description Default
vocs VOCS

The VOCS object for defining the problem's variables, objectives, constraints, and statics.

 required
data DataFrame

Data used for training the model.

 required
dtype dtype

Data type for the model (default is torch.double).

 double
device Union[device, str]

Device on which to perform computations (default is "cpu").

 'cpu'

Returns:

Type Description
ModelListGP

The trained botorch model.
Source code in xopt/generators/bayesian/base_model.py 
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def build_model_from_vocs(
    self,
    vocs: VOCS,
    data: pd.DataFrame,
    dtype: torch.dtype = torch.double,
    device: Union[torch.device, str] = "cpu",
):
    """
    Convenience wrapper around `build_model` for use with VOCS (Variables,
    Objectives, Constraints, Statics).

    Parameters
    ----------
    vocs : VOCS
        The VOCS object for defining the problem's variables, objectives,
        constraints, and statics.
    data : pd.DataFrame
        Data used for training the model.
    dtype : torch.dtype, optional
        Data type for the model (default is torch.double).
    device : Union[torch.device, str], optional
        Device on which to perform computations (default is "cpu").

    Returns
    -------
    ModelListGP
        The trained botorch model.

    """
    variable_bounds = {name: ele.domain for name, ele in vocs.variables.items()}

    return self.build_model(
        vocs.variable_names, vocs.output_names, data, variable_bounds, dtype, device
    )

build_single_task_gp(X, Y, train=True, **kwargs) staticmethod

Utility method for creating and training simple SingleTaskGP models.

Parameters:

Name Type Description Default
X Tensor

Training data for input variables.

 required
Y Tensor

Training data for outcome variables.

 required
train (bool, True)

Flag to specify if hyperparameter training should take place

 True
**kwargs

Additional keyword arguments for model configuration.

 {}

Returns:

Type Description
Model

The trained SingleTaskGP model.
Source code in xopt/generators/bayesian/base_model.py 
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@staticmethod
def build_single_task_gp(X: Tensor, Y: Tensor, train=True, **kwargs) -> Model:
    """
    Utility method for creating and training simple SingleTaskGP models.

    Parameters
    ----------
    X : Tensor
        Training data for input variables.
    Y : Tensor
        Training data for outcome variables.
    train : bool, True
        Flag to specify if hyperparameter training should take place
    **kwargs
        Additional keyword arguments for model configuration.

    Returns
    -------
    Model
        The trained SingleTaskGP model.

    """
    if X.shape[0] == 0 or Y.shape[0] == 0:
        raise ValueError("no data found to train model!")
    model = SingleTaskGP(X, Y, **kwargs)

    if train:
        mll = ExactMarginalLogLikelihood(model.likelihood, model)
        fit_gpytorch_mll(mll)
    return model

yaml(**kwargs)

serialize first then dump to yaml string

Source code in xopt/pydantic.py 
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def yaml(self, **kwargs):
    """serialize first then dump to yaml string"""
    output = json.loads(
        self.to_json(
            **kwargs,
        )
    )
    return yaml.dump(output)