Multi-Generation Gaussian Process Optimization (MGGPO)

MGGPOGenerator

Bases: MultiObjectiveBayesianGenerator

Multi-Generation Gaussian Process Optimization (MGGPO) generator. Combines multi-objective bayesian optimization with genetic algorithms to do highly-parallelized multi-objective optimization.

Attributes:

Name	Type	Description
name	str	The name of the generator.
population_size	int	The population size for the genetic algorithm.
supports_batch_generation	bool	Indicates if the generator supports batch candidate generation.
ga_generator	Optional[CNSGAGenerator]	The CNSGA generator used to generate candidates.

Methods:

Name	Description
propose_candidates	Propose candidates for Bayesian Optimization.
add_data	Add new data to the generator.
get_acquisition	Get the acquisition function for Bayesian Optimization.
_get_objective	Create the multi-objective Bayesian optimization objective.
_get_acquisition	Create the Log Expected Hypervolume Improvement acquisition function.

Source code in xopt/generators/bayesian/mggpo.py

class MGGPOGenerator(MultiObjectiveBayesianGenerator):
    """
    Multi-Generation Gaussian Process Optimization (MGGPO) generator.
    Combines multi-objective bayesian optimization with genetic algorithms
    to do highly-parallelized multi-objective optimization.

    Attributes
    ----------
    name : str
        The name of the generator.
    population_size : int
        The population size for the genetic algorithm.
    supports_batch_generation : bool
        Indicates if the generator supports batch candidate generation.
    ga_generator : Optional[CNSGAGenerator]
        The CNSGA generator used to generate candidates.

    Methods
    -------
    propose_candidates(self, model: torch.nn.Module, n_candidates: int = 1) -> torch.Tensor
        Propose candidates for Bayesian Optimization.
    add_data(self, new_data: pd.DataFrame)
        Add new data to the generator.
    get_acquisition(self, model: torch.nn.Module) -> Callable
        Get the acquisition function for Bayesian Optimization.
    _get_objective(self) -> Callable
        Create the multi-objective Bayesian optimization objective.
    _get_acquisition(self, model: torch.nn.Module) -> qLogNoisyExpectedHypervolumeImprovement
        Create the Log Expected Hypervolume Improvement acquisition function.
    """

    name = "mggpo"
    population_size: int = Field(64, description="population size for ga")
    supports_batch_generation: bool = True
    supports_constraints: bool = True
    supports_discrete_variables: bool = False

    ga_generator: Optional[CNSGAGenerator] = Field(
        None, description="CNSGA generator used to generate candidates"
    )

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

        # create GA generator
        self.ga_generator = CNSGAGenerator(
            vocs=self.vocs,
            population_size=self.population_size,
        )

    def propose_candidates(
        self, model: torch.nn.Module, n_candidates: int = 1
    ) -> torch.Tensor:
        """
        Propose candidates for Bayesian Optimization.

        Parameters
        ----------
        model : torch.nn.Module
            The model used for Bayesian Optimization.
        n_candidates : int, optional
            The number of candidates to propose, by default 1.

        Returns
        -------
        torch.Tensor
            The proposed candidates.

        Raises
        ------
        RuntimeError
            If not enough unique solutions are generated by the GA.
        """
        ga_candidates = self.ga_generator.generate(n_candidates * 10)
        ga_candidates = pd.DataFrame(ga_candidates)[self.vocs.variable_names].to_numpy()
        ga_candidates = torch.unique(
            torch.tensor(ga_candidates, **self.tkwargs), dim=0
        ).reshape(-1, 1, self.vocs.n_variables)

        if ga_candidates.shape[0] < n_candidates:
            raise RuntimeError("not enough unique solutions generated by the GA!")

        acq_funct = self.get_acquisition(self.model)
        acq_funct_vals = acq_funct(ga_candidates)
        best_idxs = torch.argsort(acq_funct_vals, descending=True)[:n_candidates]

        candidates = ga_candidates[best_idxs]
        return candidates.reshape(n_candidates, self.vocs.n_variables)

    def add_data(self, new_data: pd.DataFrame):
        """
        Add new data to the generator.

        Parameters
        ----------
        new_data : pd.DataFrame
            The new data to be added.
        """
        super().add_data(new_data)
        self.ga_generator.add_data(self.data)

    def get_acquisition(self, model: torch.nn.Module) -> Callable:
        """
        Get the acquisition function for Bayesian Optimization.

        Parameters
        ----------
        model : torch.nn.Module
            The model used for Bayesian Optimization.

        Returns
        -------
        Callable
            The acquisition function.
        """
        # TODO: add error if fixed features - why is this not supported?
        if model is None:
            raise ValueError("model cannot be None")

        # get base acquisition function
        acq = self._get_acquisition(model)
        acq = acq.to(**self.tkwargs)
        return acq

    def _get_objective(self) -> MCMultiOutputObjective:
        """
        Create the multi-objective Bayesian optimization objective.
        """
        return create_mobo_objective(self.vocs).to(**self.tkwargs)

    def _get_acquisition(
        self, model: torch.nn.Module
    ) -> qLogNoisyExpectedHypervolumeImprovement:
        """
        Create the Log Expected Hypervolume Improvement acquisition function.

        Parameters
        ----------
        model : torch.nn.Module
            The model used for Bayesian Optimization.

        Returns
        -------
        qLogNoisyExpectedHypervolumeImprovement
            The Log Expected Hypervolume Improvement acquisition function.
        """
        # get reference point from data
        inputs = self.get_input_data(self.data)
        sampler = self._get_sampler(model)

        acq = qLogNoisyExpectedHypervolumeImprovement(
            model,
            X_baseline=inputs,
            prune_baseline=True,
            constraints=self._get_constraint_callables(),
            ref_point=self.torch_reference_point,
            sampler=sampler,
            objective=self._get_objective(),
            cache_root=False,
        )

        return acq

model_input_names property

variable names corresponding to trained model

model_output_names property

output names corresponding to trained model

add_data(new_data)

Add new data to the generator.

Parameters:

Name	Type	Description	Default
new_data	DataFrame	The new data to be added.	required

Source code in xopt/generators/bayesian/mggpo.py

def add_data(self, new_data: pd.DataFrame):
    """
    Add new data to the generator.

    Parameters
    ----------
    new_data : pd.DataFrame
        The new data to be added.
    """
    super().add_data(new_data)
    self.ga_generator.add_data(self.data)

generate(n_candidates)

Generate candidates using Bayesian Optimization.

Parameters:

Name	Type	Description	Default
n_candidates	int	The number of candidates to generate in each optimization step.	required

Returns:

Type	Description
list[dict[Hashable, Any]]	A list of dictionaries containing the generated candidates.

Raises:

Type	Description
NotImplementedError	If the number of candidates is greater than 1, and the generator does not support batch candidate generation.
RuntimeError	If no data is contained in the generator, the 'add_data' method should be called to add data before generating candidates.

Notes

This method generates candidates for Bayesian Optimization based on the provided number of candidates. It updates the internal model with the current data and calculates the candidates by optimizing the acquisition function. The method returns the generated candidates in the form of a list of dictionaries.

Source code in xopt/generators/bayesian/bayesian_generator.py

def generate(self, n_candidates: int) -> list[dict[Hashable, Any]]:
    """
    Generate candidates using Bayesian Optimization.

    Parameters
    ----------
    n_candidates : int
        The number of candidates to generate in each optimization step.

    Returns
    -------
    list[dict[Hashable, Any]]
        A list of dictionaries containing the generated candidates.

    Raises
    ------
    NotImplementedError
        If the number of candidates is greater than 1, and the generator does not
        support batch candidate generation.

    RuntimeError
        If no data is contained in the generator, the 'add_data' method should be
        called to add data before generating candidates.

    Notes
    -----
    This method generates candidates for Bayesian Optimization based on the
    provided number of candidates. It updates the internal model with the current
    data and calculates the candidates by optimizing the acquisition function.
    The method returns the generated candidates in the form of a list of dictionaries.
    """

    self.n_candidates = n_candidates
    if n_candidates > 1 and not self.supports_batch_generation:
        raise NotImplementedError(
            "This Bayesian algorithm does not currently support parallel candidate "
            "generation"
        )

    # if no data exists raise error
    if self.data is None:
        raise RuntimeError(
            "no data contained in generator, call `add_data` "
            "method to add data, see also `Xopt.random_evaluate()`"
        )

    else:
        # dict to track runtimes
        timing_results = {}

        training_data = self.get_training_data(self.data)
        self._validate_contextual_variables_no_nan(training_data)

        # update internal model with internal data
        start_time = time.perf_counter()
        model = self.train_model(training_data)
        timing_results["training"] = time.perf_counter() - start_time

        # propose candidates given model
        start_time = time.perf_counter()
        candidates = self.propose_candidates(model, n_candidates=n_candidates)
        timing_results["acquisition_optimization"] = (
            time.perf_counter() - start_time
        )

        # post process candidates
        result = self._process_candidates(candidates)

        # append timing results to dataframe (if it exists)
        if self.computation_time is not None:
            self.computation_time = pd.concat(
                (
                    self.computation_time,
                    pd.DataFrame(timing_results, index=[0]),
                ),
                ignore_index=True,
            )
        else:
            self.computation_time = pd.DataFrame(timing_results, index=[0])

        if self.n_interpolate_points is not None:
            if has_discrete_variables(self.vocs):
                raise RuntimeError(
                    "cannot generate interpolated points for discrete variables"
                )

            if self.n_candidates > 1:
                raise RuntimeError(
                    "cannot generate interpolated points for "
                    "multiple candidate generation"
                )
            else:
                assert len(result) == 1
                result = interpolate_points(
                    pd.concat(
                        (self.data.iloc[-1:][self.vocs.variable_names], result),
                        axis=0,
                        ignore_index=True,
                    ),
                    num_points=self.n_interpolate_points,
                )

        return result.to_dict("records")

get_acquisition(model)

Get the acquisition function for Bayesian Optimization.

Parameters:

Name	Type	Description	Default
model	Module	The model used for Bayesian Optimization.	required

Returns:

Type	Description
Callable	The acquisition function.

Source code in xopt/generators/bayesian/mggpo.py

def get_acquisition(self, model: torch.nn.Module) -> Callable:
    """
    Get the acquisition function for Bayesian Optimization.

    Parameters
    ----------
    model : torch.nn.Module
        The model used for Bayesian Optimization.

    Returns
    -------
    Callable
        The acquisition function.
    """
    # TODO: add error if fixed features - why is this not supported?
    if model is None:
        raise ValueError("model cannot be None")

    # get base acquisition function
    acq = self._get_acquisition(model)
    acq = acq.to(**self.tkwargs)
    return acq

get_input_data(data)

Convert input data to a torch tensor.

Parameters:

Name	Type	Description	Default
data	DataFrame	The input data in the form of a pandas DataFrame.	required

Returns:

Type	Description
Tensor	A torch tensor containing the input data.

Notes

This method takes a pandas DataFrame as input data and converts it into a torch tensor. It specifically selects columns corresponding to the model's input names (variables), and the resulting tensor is configured with the data type and device settings from the generator.

Source code in xopt/generators/bayesian/bayesian_generator.py

def get_input_data(self, data: pd.DataFrame) -> torch.Tensor:
    """
    Convert input data to a torch tensor.

    Parameters
    ----------
    data : pd.DataFrame
        The input data in the form of a pandas DataFrame.

    Returns
    -------
    torch.Tensor
        A torch tensor containing the input data.

    Notes
    -----
    This method takes a pandas DataFrame as input data and converts it into a
    torch tensor. It specifically selects columns corresponding to the model's
    input names (variables), and the resulting tensor is configured with the data
    type and device settings from the generator.
    """
    return torch.tensor(
        data[self.model_input_names].to_numpy().copy(), **self.tkwargs
    )

get_model_input_bounds(data)

This will create a dictionary of variable bounds for the model input variables. It starts with the bounds from vocs, and then updates them based on the turbo trust region, fixed features, and contextual variables if they are specified.

Parameters:

Name	Type	Description	Default
data	DataFrame	The data in the form of a pandas DataFrame.	required

Returns:

Name	Type	Description
variable_bounds	Dict[str, List[float]]	A dictionary containing the variable bounds for the model input variables.

Source code in xopt/generators/bayesian/bayesian_generator.py

def get_model_input_bounds(self, data: pd.DataFrame) -> Dict[str, List[float]]:
    """
    This will create a dictionary of variable bounds for the model input variables. It starts with the
    bounds from vocs, and then updates them based on the turbo trust region, fixed features, and
    contextual variables if they are specified.

    Parameters
    ----------
    data : pd.DataFrame
        The data in the form of a pandas DataFrame.

    Returns
    -------
    variable_bounds : Dict[str, List[float]]
        A dictionary containing the variable bounds for the model input variables.

    """
    variable_bounds = deepcopy(get_variable_bounds(self.vocs, data=data))

    # if turbo restrict points is true then set the bounds to the trust region
    # bounds
    if self.turbo_controller is not None:
        if self.turbo_controller.restrict_model_data:
            trust_region_bounds = self.turbo_controller.get_trust_region(self)
            for idx, name in enumerate(self._candidate_names):
                variable_bounds[name] = trust_region_bounds[:, idx].numpy()

    # add fixed feature bounds if requested
    if self.fixed_features is not None:
        # get bounds for each fixed_feature (vocs bounds take precedent)
        for key in self.fixed_features:
            # if the fixed feature is not in the variable bounds, then we need to add it based on the data
            if key not in variable_bounds:
                if key not in data:
                    raise KeyError(
                        "generator data needs to contain fixed feature "
                        f"column name `{key}`"
                    )
                f_data = data[key]
                bounds = [f_data.min(), f_data.max()]
                if bounds[1] - bounds[0] < 1e-8:
                    bounds[1] = bounds[0] + 1e-8
                variable_bounds[key] = bounds

    return variable_bounds

get_optimum()

select the best point(s) given by the model using the Posterior mean

Source code in xopt/generators/bayesian/bayesian_generator.py

def get_optimum(self):
    """select the best point(s) given by the
    model using the Posterior mean"""
    acq = qUpperConfidenceBound(
        model=self.model, beta=0.0, objective=self._get_objective()
    )
    if len(self.vocs.constraints):
        acq = ConstrainedMCAcquisitionFunction(
            self.model,
            acq,
            self._get_constraint_callables(),
            sampler=self._get_sampler(self.model),
        )
    bounds = self._get_torch_bounds()

    if self.fixed_features is not None or self.contextual_variables:
        acq = self._apply_fixed_features_and_contextual_variables(acq)

    bounds = bounds.to(**self.tkwargs)
    acq = acq.to(**self.tkwargs)

    # use default initial conditions for a global search
    optimization_kwargs = self._get_discrete_optimization_kwargs()
    if isinstance(self.numerical_optimizer, GridOptimizer) and optimization_kwargs:
        raise ValueError(
            "grid optimizer does not support discrete variable optimization; "
            "use LBFGS optimizer"
        )

    result = self.numerical_optimizer.optimize(
        acq, bounds, 1, **optimization_kwargs
    )

    return self._process_candidates(result)

get_pareto_front_and_hypervolume()

Get the pareto front and hypervolume of the current data.

Source code in xopt/generators/bayesian/bayesian_generator.py

def get_pareto_front_and_hypervolume(
    self,
) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None, float]:
    """
    Get the pareto front and hypervolume of the current data.
    """
    variable_data, objective_data, weights = self._get_scaled_data(data=self.data)

    # if there are no valid points skip PF calculation and return None
    if len(variable_data) == 0:
        return None, None, None, 0.0

    pareto_front_variables, pareto_front_objectives, pareto_mask, hv = (
        compute_hypervolume_and_pf(
            variable_data,
            objective_data,
            self.torch_reference_point,
        )
    )

    # scale the pareto front objectives back to original space
    if pareto_front_objectives is not None:
        pareto_front_objectives = pareto_front_objectives / weights

    return (
        pareto_front_variables,
        pareto_front_objectives,
        pareto_mask,
        hv,
    )

get_training_data(data)

Get training data used to train the GP model.

If a turbo controller is specified with the flag restrict_model_data this will return a subset of data that is inside the trust region.

Parameters:

Name	Type	Description	Default
data	DataFrame	The data in the form of a pandas DataFrame.	required

Returns:

Name	Type	Description
data	DataFrame	A subset of data used to train the model form of a pandas DataFrame.

Source code in xopt/generators/bayesian/bayesian_generator.py

def get_training_data(self, data: pd.DataFrame) -> pd.DataFrame:
    """
    Get training data used to train the GP model.

    If a turbo controller is specified with the flag `restrict_model_data` this
    will return a subset of data that is inside the trust region.

    Parameters
    ----------
    data : pd.DataFrame
        The data in the form of a pandas DataFrame.

    Returns
    -------
    data : pd.DataFrame
        A subset of data used to train the model form of a pandas DataFrame.

    """
    if self.turbo_controller is not None:
        if self.turbo_controller.restrict_model_data:
            data = self.turbo_controller.get_data_in_trust_region(data, self)
            if data.empty:
                raise FeasibilityError(
                    "No training data available to build model, because ",
                    "no points in the dataset are within the TuRBO trust region. ",
                )
    return data

model_dump(*args, **kwargs)

overwrite model dump to remove faux class attrs

Source code in xopt/generator.py

def model_dump(self, *args: Any, **kwargs: Any) -> dict[str, Any]:
    """overwrite model dump to remove faux class attrs"""

    res = super().model_dump(*args, **kwargs)

    res.pop("supports_batch_generation", None)
    res.pop("supports_multi_objective", None)

    return res

propose_candidates(model, n_candidates=1)

Propose candidates for Bayesian Optimization.

Parameters:

Name	Type	Description	Default
model	Module	The model used for Bayesian Optimization.	required
n_candidates	int	The number of candidates to propose, by default 1.	1

Returns:

Type	Description
Tensor	The proposed candidates.

Raises:

Type	Description
RuntimeError	If not enough unique solutions are generated by the GA.

Source code in xopt/generators/bayesian/mggpo.py

def propose_candidates(
    self, model: torch.nn.Module, n_candidates: int = 1
) -> torch.Tensor:
    """
    Propose candidates for Bayesian Optimization.

    Parameters
    ----------
    model : torch.nn.Module
        The model used for Bayesian Optimization.
    n_candidates : int, optional
        The number of candidates to propose, by default 1.

    Returns
    -------
    torch.Tensor
        The proposed candidates.

    Raises
    ------
    RuntimeError
        If not enough unique solutions are generated by the GA.
    """
    ga_candidates = self.ga_generator.generate(n_candidates * 10)
    ga_candidates = pd.DataFrame(ga_candidates)[self.vocs.variable_names].to_numpy()
    ga_candidates = torch.unique(
        torch.tensor(ga_candidates, **self.tkwargs), dim=0
    ).reshape(-1, 1, self.vocs.n_variables)

    if ga_candidates.shape[0] < n_candidates:
        raise RuntimeError("not enough unique solutions generated by the GA!")

    acq_funct = self.get_acquisition(self.model)
    acq_funct_vals = acq_funct(ga_candidates)
    best_idxs = torch.argsort(acq_funct_vals, descending=True)[:n_candidates]

    candidates = ga_candidates[best_idxs]
    return candidates.reshape(n_candidates, self.vocs.n_variables)

train_model(data=None, update_internal=True)

Train a Bayesian model for Bayesian Optimization.

Parameters:

Name	Type	Description	Default
data	DataFrame	The data to be used for training the model. If not provided, the internal data of the generator is used.	None
update_internal	bool	Flag to indicate whether to update the internal model of the generator with the trained model (default is True).	True

Returns:

Type	Description
Module	The trained Bayesian model.

Raises:

Type	Description
ValueError	If no data is available to build the model.

Notes

This method trains a Bayesian model using the provided data or the internal data of the generator. It updates the internal model with the trained model if the 'update_internal' flag is set to True.

Source code in xopt/generators/bayesian/bayesian_generator.py

def train_model(
    self, data: pd.DataFrame | None = None, update_internal: bool = True
) -> Module:
    """
    Train a Bayesian model for Bayesian Optimization.

    Parameters
    ----------
    data : pd.DataFrame, optional
        The data to be used for training the model. If not provided, the internal
        data of the generator is used.
    update_internal : bool, optional
        Flag to indicate whether to update the internal model of the generator
        with the trained model (default is True).

    Returns
    -------
    Module
        The trained Bayesian model.

    Raises
    ------
    ValueError
        If no data is available to build the model.

    Notes
    -----
    This method trains a Bayesian model using the provided data or the internal
    data of the generator. It updates the internal model with the trained model
    if the 'update_internal' flag is set to True.
    """
    if data is None:
        data = self.get_training_data(self.data)
        if data is None:
            raise ValueError("no data available to build model")

    if data.empty:
        raise ValueError("no data available to build model")

    # get input bounds
    variable_bounds = self.get_model_input_bounds(data)

    _model = self.gp_constructor.build_model(
        self.model_input_names,
        self.model_output_names,
        data,
        variable_bounds,
        **self.tkwargs,
    )

    if update_internal:
        self.model = _model

    return _model

update_pareto_front_history()

Update the historical pareto front statistics in the generator.

For each row of data in self.data, compute the pareto front stats (hypervolume, number of non-dominated points) if there is no corresponding entry exists in the self.pareto_front_history DataFrame.

Source code in xopt/generators/bayesian/bayesian_generator.py

def update_pareto_front_history(self):
    """
    Update the historical pareto front statistics in the generator.

    For each row of data in self.data, compute the pareto front stats
    (hypervolume, number of non-dominated points) if there is no
    corresponding entry exists in the `self.pareto_front_history` DataFrame.
    """
    # TODO: make sure this works when manually changing the data frame
    if self.pareto_front_history is None:
        self.pareto_front_history = pd.DataFrame()

    # for each row of data, compute the cumulative pareto front stats
    for i in self.data.index:
        # check if the pareto front stats already exist
        if i in self.pareto_front_history.index:
            continue

        # get scaled data
        variable_data, objective_data, _ = self._get_scaled_data(
            data=self.data.loc[:i]
        )

        # compute the pareto front stats
        _, pareto_front_variables, _, hv = compute_hypervolume_and_pf(
            variable_data,
            objective_data,
            self.torch_reference_point,
        )

        # get the number of non-dominated points
        n_non_dominated = (
            len(pareto_front_variables) if pareto_front_variables is not None else 0
        )

        # create a new row for the pareto front stats
        new_row: dict[str, Any] = {
            "iteration": i,
            "hypervolume": hv,
            "n_non_dominated": n_non_dominated,
        }
        # add the new row to the pareto front history
        self.pareto_front_history = pd.concat(
            [
                self.pareto_front_history,
                pd.DataFrame(new_row, index=[i]),
            ],
            ignore_index=False,
        )

validate_turbo_controller(value, info) classmethod

note default behavior is no use of turbo

Source code in xopt/generators/bayesian/bayesian_generator.py

@field_validator("turbo_controller", mode="before")
@classmethod
def validate_turbo_controller(cls, value: Any, info: ValidationInfo) -> Any:
    """note default behavior is no use of turbo"""
    if value is None:
        return value

    compatible_turbo_controllers = [
        turbo_controller
        for turbo_controller in cls.get_compatible_turbo_controllers()
        if turbo_controller is not None
    ]

    if len(compatible_turbo_controllers) == 0:
        raise ValueError("no turbo controllers are compatible with this generator")
    else:
        return validate_turbo_controller_base(
            value, compatible_turbo_controllers, info
        )

visualize_model(**kwargs)

Display GP model predictions for the selected output(s).

The GP models are displayed with respect to the named variables. If None are given, the list of variables in vocs is used. Feasible samples are indicated with a filled orange "o", infeasible samples with a hollow red "o". Feasibility is calculated with respect to all constraints unless the selected output is a constraint itself, in which case only that one is considered.

Parameters:

Name	Type	Description	Default
**kwargs		Supported keyword arguments: - output_names : List[str] Outputs for which the GP models are displayed. Defaults to all outputs in vocs. - variable_names : List[str] The variables with respect to which the GP models are displayed (maximum of 2). Defaults to vocs.variable_names. Contextual variables are allowed for GP model visualization axes. - idx : int Index of the last sample to use. This also selects the point of reference in higher dimensions unless an explicit reference_point is given. - reference_point : dict Reference point determining the value of variables in vocs.variable_names, but not in variable_names (slice plots in higher dimensions). Defaults to last used sample. - show_samples : bool, optional Whether samples are shown. - show_prior_mean : bool, optional Whether the prior mean is shown. - show_feasibility : bool, optional Whether the feasibility region is shown. - show_acquisition : bool, optional Whether the acquisition function is computed and shown (only if acquisition function is not None). If contextual variables are selected as plot axes, the acquisition subplot is replaced with warning text because the acquisition is conditioned on contextual values. - n_grid : int, optional Number of grid points per dimension used to display the model predictions. - axes : Axes, optional Axes object used for plotting. - exponentiate : bool, optional Flag to exponentiate acquisition function before plotting.	{}

Returns:

Name	Type	Description
result	tuple	The matplotlib figure and axes objects.

Source code in xopt/generators/bayesian/bayesian_generator.py

def visualize_model(self, **kwargs):
    """Display GP model predictions for the selected output(s).

    The GP models are displayed with respect to the named variables. If None are given, the list of variables in
    vocs is used. Feasible samples are indicated with a filled orange "o", infeasible samples with a hollow
    red "o". Feasibility is calculated with respect to all constraints unless the selected output is a
    constraint itself, in which case only that one is considered.

    Parameters
    ----------
    **kwargs: dict, optional
        Supported keyword arguments:
        - output_names : List[str]
            Outputs for which the GP models are displayed. Defaults to all outputs in vocs.
        - variable_names : List[str]
            The variables with respect to which the GP models are displayed (maximum of 2).
            Defaults to vocs.variable_names.
            Contextual variables are allowed for GP model visualization axes.
        - idx : int
            Index of the last sample to use. This also selects the point of reference in
            higher dimensions unless an explicit reference_point is given.
        - reference_point : dict
            Reference point determining the value of variables in vocs.variable_names, but not in variable_names
            (slice plots in higher dimensions). Defaults to last used sample.
        - show_samples : bool, optional
            Whether samples are shown.
        - show_prior_mean : bool, optional
            Whether the prior mean is shown.
        - show_feasibility : bool, optional
            Whether the feasibility region is shown.
        - show_acquisition : bool, optional
            Whether the acquisition function is computed and shown (only if acquisition function is not None).
            If contextual variables are selected as plot axes, the acquisition subplot is replaced
            with warning text because the acquisition is conditioned on contextual values.
        - n_grid : int, optional
            Number of grid points per dimension used to display the model predictions.
        - axes : Axes, optional
            Axes object used for plotting.
        - exponentiate : bool, optional
            Flag to exponentiate acquisition function before plotting.

    Returns
    -------
    result : tuple
        The matplotlib figure and axes objects.
    """
    return visualize_generator_model(self, **kwargs)

yaml(**kwargs)

serialize first then dump to yaml string

Source code in xopt/pydantic.py

def yaml(self, **kwargs: Any) -> str:
    """serialize first then dump to yaml string"""
    output = json.loads(
        self.to_json(
            **kwargs,
        )
    )
    return yaml.dump(output)