Domain

`categorical`

`ConstrainedCategoricalObjective (ConstrainedObjective, Objective)`

Compute the categorical objective value as:

Po where P is an [n, c] matrix where each row is a probability vector
(P[i, :].sum()=1 for all i) and o is a vector of size [c] of objective values

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective.
`desirability`	`list`	list of values of size c (c is number of categories) such that the i-th entry is in {True, False}

Source code in bofire/data_models/objectives/categorical.py

class ConstrainedCategoricalObjective(ConstrainedObjective, Objective):
    """Compute the categorical objective value as:

        Po where P is an [n, c] matrix where each row is a probability vector
        (P[i, :].sum()=1 for all i) and o is a vector of size [c] of objective values

    Attributes:
        w (float): float between zero and one for weighting the objective.
        desirability (list): list of values of size c (c is number of categories) such that the i-th entry is in {True, False}

    """

    w: TWeight = 1.0
    categories: CategoryVals
    desirability: List[bool]
    type: Literal["ConstrainedCategoricalObjective"] = "ConstrainedCategoricalObjective"

    @model_validator(mode="after")
    def validate_desireability(self):
        """Validates that categories have unique names

        Args:
            categories (List[str]): List or tuple of category names

        Raises:
            ValueError: when categories do not match objective categories

        Returns:
            Tuple[str]: Tuple of the categories

        """
        if len(self.desirability) != len(self.categories):
            raise ValueError(
                "number of categories differs from number of desirabilities",
            )
        return self

    def to_dict(self) -> Dict:
        """Returns the categories and corresponding objective values as dictionary"""
        return dict(zip(self.categories, self.desirability))

    def to_dict_label(self) -> Dict:
        """Returns the categories and label location of categories"""
        return {c: i for i, c in enumerate(self.categories)}

    def from_dict_label(self) -> Dict:
        """Returns the label location and the categories"""
        d = self.to_dict_label()
        return dict(zip(d.values(), d.keys()))

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray, float]:
        """The call function returning a probabilistic reward for x.

        Args:
            x (np.ndarray): A matrix of x values
            x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
                update the objective parameters on the fly. Defaults to None.

        Returns:
            np.ndarray: A reward calculated as inner product of probabilities and feasible objectives.

        """
        return np.dot(x, np.array(self.desirability))

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

The call function returning a probabilistic reward for x.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	A matrix of x values	required
`x_adapt`	`Optional[np.ndarray]`	An array of x values which are used to update the objective parameters on the fly. Defaults to None.	`None`

Returns:

Type	Description
`np.ndarray`	A reward calculated as inner product of probabilities and feasible objectives.

Source code in bofire/data_models/objectives/categorical.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray, float]:
    """The call function returning a probabilistic reward for x.

    Args:
        x (np.ndarray): A matrix of x values
        x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
            update the objective parameters on the fly. Defaults to None.

    Returns:
        np.ndarray: A reward calculated as inner product of probabilities and feasible objectives.

    """
    return np.dot(x, np.array(self.desirability))

`from_dict_label(self)`

Returns the label location and the categories

Source code in bofire/data_models/objectives/categorical.py

def from_dict_label(self) -> Dict:
    """Returns the label location and the categories"""
    d = self.to_dict_label()
    return dict(zip(d.values(), d.keys()))

`to_dict(self)`

Returns the categories and corresponding objective values as dictionary

Source code in bofire/data_models/objectives/categorical.py

def to_dict(self) -> Dict:
    """Returns the categories and corresponding objective values as dictionary"""
    return dict(zip(self.categories, self.desirability))

`to_dict_label(self)`

Returns the categories and label location of categories

Source code in bofire/data_models/objectives/categorical.py

def to_dict_label(self) -> Dict:
    """Returns the categories and label location of categories"""
    return {c: i for i, c in enumerate(self.categories)}

`validate_desireability(self)`

Validates that categories have unique names

Parameters:

Name	Type	Description	Default
`categories`	`List[str]`	List or tuple of category names	required

Exceptions:

Type	Description
`ValueError`	when categories do not match objective categories

Returns:

Type	Description
`Tuple[str]`	Tuple of the categories

Source code in bofire/data_models/objectives/categorical.py

@model_validator(mode="after")
def validate_desireability(self):
    """Validates that categories have unique names

    Args:
        categories (List[str]): List or tuple of category names

    Raises:
        ValueError: when categories do not match objective categories

    Returns:
        Tuple[str]: Tuple of the categories

    """
    if len(self.desirability) != len(self.categories):
        raise ValueError(
            "number of categories differs from number of desirabilities",
        )
    return self

`desirabilities`

`DecreasingDesirabilityObjective (DesirabilityObjective)`

An objective returning a reward the negative, shifted scaled identity, but trimmed at the bounds:

d = ((upper_bound - x) / (upper_bound - lower_bound))^t

where:

t = exp(log_shape_factor)

Note, that with clipping the reward is always between zero and one.

Attributes:

Name	Type	Description
`clip`	`bool`	Whether to clip the values below/above the lower/upper bound, by default True.
`log_shape_factor`	`float`	Logarithm of the shape factor: Whether the interpolation between the lower bound and the upper is linear (=0), convex (>0) or concave (<0) , by default 0.0.
`w`	`float`	relative weight, by default = 1.
`bounds`	`tuple[float]`	lower and upper bound of the desirability. Below bounds[0] the desirability is =1 (if clip=True) or >1 (if clip=False). Above bounds[1] the desirability is =0 (if clip=True) or <0 (if clip=False). Defaults to (0, 1).

Source code in bofire/data_models/objectives/desirabilities.py

class DecreasingDesirabilityObjective(DesirabilityObjective):
    """An objective returning a reward the negative, shifted scaled identity, but trimmed at the bounds:

        d = ((upper_bound - x) / (upper_bound - lower_bound))^t

    where:

        t = exp(log_shape_factor)

    Note, that with clipping the reward is always between zero and one.

    Attributes:
        clip (bool): Whether to clip the values below/above the lower/upper bound, by
            default True.
        log_shape_factor (float): Logarithm of the shape factor:
            Whether the interpolation between the lower bound and the upper is linear (=0),
            convex (>0) or concave (<0) , by default 0.0.
        w (float): relative weight, by default = 1.
        bounds (tuple[float]): lower and upper bound of the desirability. Below
            bounds[0] the desirability is =1 (if clip=True) or >1 (if clip=False). Above
            bounds[1] the desirability is =0  (if clip=True) or <0 (if clip=False).
            Defaults to (0, 1).
    """

    type: Literal["DecreasingDesirabilityObjective"] = "DecreasingDesirabilityObjective"  # type: ignore
    log_shape_factor: float = 0.0

    def call_numpy(
        self,
        x: np.ndarray,
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> np.ndarray:
        y = np.zeros(x.shape)
        if self.clip:
            y[x < self.lower_bound] = 1.0
            y[x > self.upper_bound] = 0.0
            between = (x >= self.lower_bound) & (x <= self.upper_bound)
        else:
            between = np.full(x.shape, True)

        t = np.exp(self.log_shape_factor)

        y[between] = np.power(
            (self.upper_bound - x[between]) / (self.upper_bound - self.lower_bound), t
        )

        return y

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`DesirabilityObjective (IdentityObjective)`

Abstract class for desirability objectives. Works as Identity Objective

Source code in bofire/data_models/objectives/desirabilities.py

class DesirabilityObjective(IdentityObjective):
    """Abstract class for desirability objectives. Works as Identity Objective"""

    type: Literal["DesirabilityObjective"] = "DesirabilityObjective"  # type: ignore
    clip: bool = True

    @pydantic.model_validator(mode="after")
    def validate_clip(self):
        if self.clip:
            return self

        log_shapes = {
            key: val
            for (key, val) in self.__dict__.items()
            if key.startswith("log_shape_factor")
        }
        for key, log_shape_ in log_shapes.items():
            if log_shape_ != 0:
                raise ValueError(
                    f"Log shape factor {key} must be zero if clip is False."
                )
        return self

    def __call__(
        self, x: Union[pd.Series, np.ndarray], x_adapt
    ) -> Union[pd.Series, np.ndarray]:
        """Wrapper function for to call numpy and torch functions with series or numpy arrays. matches __call__
        signature of objectives."""

        convert_to_series = False
        if isinstance(x, pd.Series):
            convert_to_series = True
            name = x.name
            x = x.values

        y = self.call_numpy(x)

        if convert_to_series:
            return pd.Series(y, name=name)

        return y

    @abstractmethod
    def call_numpy(self, x: np.ndarray) -> np.ndarray:
        raise NotImplementedError()

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt)` `special`

Wrapper function for to call numpy and torch functions with series or numpy arrays. matches call signature of objectives.

Source code in bofire/data_models/objectives/desirabilities.py

def __call__(
    self, x: Union[pd.Series, np.ndarray], x_adapt
) -> Union[pd.Series, np.ndarray]:
    """Wrapper function for to call numpy and torch functions with series or numpy arrays. matches __call__
    signature of objectives."""

    convert_to_series = False
    if isinstance(x, pd.Series):
        convert_to_series = True
        name = x.name
        x = x.values

    y = self.call_numpy(x)

    if convert_to_series:
        return pd.Series(y, name=name)

    return y

`IncreasingDesirabilityObjective (DesirabilityObjective)`

An objective returning a reward the scaled identity, but trimmed at the bounds:

d = ((x - lower_bound) / (upper_bound - lower_bound))^t

if clip is True, the reward is zero for x < lower_bound and one for x > upper_bound.

where:

t = exp(log_shape_factor)

Note, that with clipping the reward is always between zero and one.

Attributes:

Name	Type	Description
`clip`	`bool`	Whether to clip the values below/above the lower/upper bound, by default True.
`log_shape_factor`	`float`	Logarithm of the shape factor: Whether the interpolation between the lower bound and the upper is linear (=0), convex (>0) or concave (<0) , by default 0.0.
`w`	`float`	relative weight, by default = 1.
`bounds`	`tuple[float]`	lower and upper bound of the desirability. Below bounds[0] the desirability is =0 (if clip=True) or <0 (if clip=False). Above bounds[1] the desirability is =1 (if clip=True) or >1 (if clip=False). Defaults to (0, 1).

Source code in bofire/data_models/objectives/desirabilities.py

class IncreasingDesirabilityObjective(DesirabilityObjective):
    """An objective returning a reward the scaled identity, but trimmed at the bounds:

        d = ((x - lower_bound) / (upper_bound - lower_bound))^t

    if clip is True, the reward is zero for x < lower_bound and one for x > upper_bound.

    where:

        t = exp(log_shape_factor)

    Note, that with clipping the reward is always between zero and one.

    Attributes:
        clip (bool): Whether to clip the values below/above the lower/upper bound, by
            default True.
        log_shape_factor (float): Logarithm of the shape factor:
            Whether the interpolation between the lower bound and the upper is linear (=0),
            convex (>0) or concave (<0) , by default 0.0.
        w (float): relative weight, by default = 1.
        bounds (tuple[float]): lower and upper bound of the desirability. Below
            bounds[0] the desirability is =0 (if clip=True) or <0 (if clip=False). Above
            bounds[1] the desirability is =1  (if clip=True) or >1 (if clip=False).
            Defaults to (0, 1).
    """

    type: Literal["IncreasingDesirabilityObjective"] = "IncreasingDesirabilityObjective"  # type: ignore
    log_shape_factor: float = 0.0

    def call_numpy(
        self,
        x: np.ndarray,
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> np.ndarray:
        y = np.zeros(x.shape)
        if self.clip:
            y[x < self.lower_bound] = 0.0
            y[x > self.upper_bound] = 1.0
            between = (x >= self.lower_bound) & (x <= self.upper_bound)
        else:
            between = np.full(x.shape, True)

        t = np.exp(self.log_shape_factor)

        y[between] = np.power(
            (x[between] - self.lower_bound) / (self.upper_bound - self.lower_bound), t
        )

        return y

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`PeakDesirabilityObjective (DesirabilityObjective)`

A piecewise (linear or convex/concave) objective that increases from the lower bound to the peak position and decreases from the peak position to the upper bound.

Attributes:

Name	Type	Description
`clip`	`bool`	Whether to clip the values below/above the lower/upper bound, by default True.
`log_shape_factor`	`float`	Logarithm of the shape factor for the increasing part: Whether the interpolation between the lower bound and the peak is linear (=0), convex (>1) or concave (<1) , by default 0.0.
`log_shape_factor_decreasing`	`float`	Logarithm of the shape factor for the decreasing part. Whether the interpolation between the peak and the upper bound is linear (=0), convex (>0) or concave (<0), by default 0.0.
`peak_position`	`float`	Position of the peak, by default 0.5.
`w`	`float`	relative weight: desirability, when x=peak_position, by default = 1.
`bounds`	`tuple[float]`	lower and upper bound of the desirability. Below bounds[0] the desirability is =0 (if clip=True) or <0 (if clip=False). Above bounds[1] the desirability is =0 (if clip=True) or <0 (if clip=False). Defaults to (0, 1).

Source code in bofire/data_models/objectives/desirabilities.py

class PeakDesirabilityObjective(DesirabilityObjective):
    """
    A piecewise (linear or convex/concave) objective that increases from the lower bound
    to the peak position and decreases from the peak position to the upper bound.

    Attributes:
        clip (bool): Whether to clip the values below/above the lower/upper bound, by
            default True.
        log_shape_factor (float): Logarithm of the shape factor for the increasing part:
            Whether the interpolation between the lower bound and the peak is linear (=0),
            convex (>1) or concave (<1) , by default 0.0.
        log_shape_factor_decreasing (float): Logarithm of the shape factor for the
            decreasing part. Whether the interpolation between the peak and the upper
            bound is linear (=0), convex (>0) or concave (<0), by default 0.0.
        peak_position (float): Position of the peak, by default 0.5.
        w (float): relative weight: desirability, when x=peak_position, by default = 1.
        bounds (tuple[float]): lower and upper bound of the desirability. Below
            bounds[0] the desirability is =0 (if clip=True) or <0 (if clip=False). Above
            bounds[1] the desirability is =0  (if clip=True) or <0 (if clip=False).
            Defaults to (0, 1).
    """

    type: Literal["PeakDesirabilityObjective"] = "PeakDesirabilityObjective"  # type: ignore
    log_shape_factor: float = 0.0
    log_shape_factor_decreasing: float = 0.0  # often named log_t
    peak_position: float = 0.5  # often named T

    def call_numpy(
        self,
        x: np.ndarray,
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> np.ndarray:
        y = np.zeros(x.shape)
        if self.clip:
            Incr = (x >= self.lower_bound) & (x <= self.peak_position)
            Decr = (x <= self.upper_bound) & (x > self.peak_position)
        else:
            Incr, Decr = x <= self.peak_position, x > self.peak_position

        s: float = np.exp(self.log_shape_factor)
        t: float = np.exp(self.log_shape_factor_decreasing)
        y[Incr] = np.power(
            np.divide(
                (x[Incr] - self.lower_bound), (self.peak_position - self.lower_bound)
            ),
            s,
        )
        y[Decr] = np.power(
            np.divide(
                (x[Decr] - self.upper_bound), (self.peak_position - self.upper_bound)
            ),
            t,
        )

        return y * self.w

    @pydantic.model_validator(mode="after")
    def validate_peak_position(self):
        bounds = self.bounds
        if self.peak_position < bounds[0] or self.peak_position > bounds[1]:
            raise ValueError(
                f"Peak position must be within bounds {bounds}, got {self.peak_position}"
            )
        return self

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`identity`

`IdentityObjective (Objective)`

An objective returning the identity as reward. The return can be scaled, when a lower and upper bound are provided.

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective
`bounds`	`Tuple[float]`	Bound for normalizing the objective between zero and one. Defaults to (0,1).

Source code in bofire/data_models/objectives/identity.py

class IdentityObjective(Objective):
    """An objective returning the identity as reward.
    The return can be scaled, when a lower and upper bound are provided.

    Attributes:
        w (float): float between zero and one for weighting the objective
        bounds (Tuple[float], optional): Bound for normalizing the objective between zero and one. Defaults to (0,1).

    """

    type: Literal["IdentityObjective"] = "IdentityObjective"  # type: ignore
    w: TWeight = 1
    bounds: Bounds = [0, 1]

    @property
    def lower_bound(self) -> float:
        return self.bounds[0]

    @property
    def upper_bound(self) -> float:
        return self.bounds[1]

    @field_validator("bounds")
    @classmethod
    def validate_lower_upper(cls, bounds):
        """Validation function to ensure that lower bound is always greater the upper bound

        Args:
            values (Dict): The attributes of the class

        Raises:
            ValueError: when a lower bound higher than the upper bound is passed

        Returns:
            Dict: The attributes of the class

        """
        if bounds[0] > bounds[1]:
            raise ValueError(
                f"lower bound must be <= upper bound, got {bounds[0]} > {bounds[1]}",
            )
        return bounds

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        """The call function returning a reward for passed x values

        Args:
            x (np.ndarray): An array of x values
            x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
                update the objective parameters on the fly. Defaults to None.

        Returns:
            np.ndarray: The identity as reward, might be normalized to the passed lower and upper bounds

        """
        return (x - self.lower_bound) / (self.upper_bound - self.lower_bound)

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

The call function returning a reward for passed x values

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values	required
`x_adapt`	`Optional[np.ndarray]`	An array of x values which are used to update the objective parameters on the fly. Defaults to None.	`None`

Returns:

Type	Description
`np.ndarray`	The identity as reward, might be normalized to the passed lower and upper bounds

Source code in bofire/data_models/objectives/identity.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray]:
    """The call function returning a reward for passed x values

    Args:
        x (np.ndarray): An array of x values
        x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
            update the objective parameters on the fly. Defaults to None.

    Returns:
        np.ndarray: The identity as reward, might be normalized to the passed lower and upper bounds

    """
    return (x - self.lower_bound) / (self.upper_bound - self.lower_bound)

`validate_lower_upper(bounds)` `classmethod`

Validation function to ensure that lower bound is always greater the upper bound

Parameters:

Name	Type	Description	Default
`values`	`Dict`	The attributes of the class	required

Exceptions:

Type	Description
`ValueError`	when a lower bound higher than the upper bound is passed

Returns:

Type	Description
`Dict`	The attributes of the class

Source code in bofire/data_models/objectives/identity.py

@field_validator("bounds")
@classmethod
def validate_lower_upper(cls, bounds):
    """Validation function to ensure that lower bound is always greater the upper bound

    Args:
        values (Dict): The attributes of the class

    Raises:
        ValueError: when a lower bound higher than the upper bound is passed

    Returns:
        Dict: The attributes of the class

    """
    if bounds[0] > bounds[1]:
        raise ValueError(
            f"lower bound must be <= upper bound, got {bounds[0]} > {bounds[1]}",
        )
    return bounds

`MaximizeObjective (IdentityObjective)`

Child class from the identity function without modifications, since the parent class is already defined as maximization

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective
`bounds`	`Tuple[float]`	Bound for normalizing the objective between zero and one. Defaults to (0,1).

Source code in bofire/data_models/objectives/identity.py

class MaximizeObjective(IdentityObjective):
    """Child class from the identity function without modifications, since the parent class is already defined as maximization

    Attributes:
        w (float): float between zero and one for weighting the objective
        bounds (Tuple[float], optional): Bound for normalizing the objective between zero and one. Defaults to (0,1).

    """

    type: Literal["MaximizeObjective"] = "MaximizeObjective"

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`MinimizeObjective (IdentityObjective)`

Class returning the negative identity as reward.

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective
`bounds`	`Tuple[float]`	Bound for normalizing the objective between zero and one. Defaults to (0,1).

Source code in bofire/data_models/objectives/identity.py

class MinimizeObjective(IdentityObjective):
    """Class returning the negative identity as reward.

    Attributes:
        w (float): float between zero and one for weighting the objective
        bounds (Tuple[float], optional): Bound for normalizing the objective between zero and one. Defaults to (0,1).

    """

    type: Literal["MinimizeObjective"] = "MinimizeObjective"

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        """The call function returning a reward for passed x values

        Args:
            x (np.ndarray): An array of x values
            x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
                update the objective parameters on the fly. Defaults to None.

        Returns:
            np.ndarray: The negative identity as reward, might be normalized to the passed lower and upper bounds

        """
        return -1.0 * (x - self.lower_bound) / (self.upper_bound - self.lower_bound)

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

The call function returning a reward for passed x values

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values	required
`x_adapt`	`Optional[np.ndarray]`	An array of x values which are used to update the objective parameters on the fly. Defaults to None.	`None`

Returns:

Type	Description
`np.ndarray`	The negative identity as reward, might be normalized to the passed lower and upper bounds

Source code in bofire/data_models/objectives/identity.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray]:
    """The call function returning a reward for passed x values

    Args:
        x (np.ndarray): An array of x values
        x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
            update the objective parameters on the fly. Defaults to None.

    Returns:
        np.ndarray: The negative identity as reward, might be normalized to the passed lower and upper bounds

    """
    return -1.0 * (x - self.lower_bound) / (self.upper_bound - self.lower_bound)

`objective`

`ConstrainedObjective`

This abstract class offers a convenience routine for transforming sigmoid based objectives to botorch output constraints.

Source code in bofire/data_models/objectives/objective.py

class ConstrainedObjective:
    """This abstract class offers a convenience routine for transforming sigmoid based objectives to botorch output constraints."""

`Objective (BaseModel)`

The base class for all objectives

Source code in bofire/data_models/objectives/objective.py

class Objective(BaseModel):
    """The base class for all objectives"""

    type: str

    @abstractmethod
    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        """Abstract method to define the call function for the class Objective

        Args:
            x (np.ndarray): An array of x values for which the objective should be evaluated.
            x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
                update the objective parameters on the fly. Defaults to None.

        Returns:
            np.ndarray: The desirability of the passed x values

        """

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

Abstract method to define the call function for the class Objective

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values for which the objective should be evaluated.	required
`x_adapt`	`Optional[np.ndarray]`	An array of x values which are used to update the objective parameters on the fly. Defaults to None.	`None`

Returns:

Type	Description
`np.ndarray`	The desirability of the passed x values

Source code in bofire/data_models/objectives/objective.py

@abstractmethod
def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray]:
    """Abstract method to define the call function for the class Objective

    Args:
        x (np.ndarray): An array of x values for which the objective should be evaluated.
        x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
            update the objective parameters on the fly. Defaults to None.

    Returns:
        np.ndarray: The desirability of the passed x values

    """

`sigmoid`

`MaximizeSigmoidObjective (SigmoidObjective)`

Class for a maximizing sigmoid objective

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective.
`steepness`	`float`	Steepness of the sigmoid function. Has to be greater than zero.
`tp`	`float`	Turning point of the sigmoid function.

Source code in bofire/data_models/objectives/sigmoid.py

class MaximizeSigmoidObjective(SigmoidObjective):
    """Class for a maximizing sigmoid objective

    Attributes:
        w (float): float between zero and one for weighting the objective.
        steepness (float): Steepness of the sigmoid function. Has to be greater than zero.
        tp (float): Turning point of the sigmoid function.

    """

    type: Literal["MaximizeSigmoidObjective"] = "MaximizeSigmoidObjective"

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        """The call function returning a sigmoid shaped reward for passed x values.

        Args:
            x (np.ndarray): An array of x values
            x_adapt (np.ndarray): An array of x values which are used to update the objective parameters on the fly.

        Returns:
            np.ndarray: A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

        """
        return 1 / (1 + np.exp(-1 * self.steepness * (x - self.tp)))

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

The call function returning a sigmoid shaped reward for passed x values.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values	required
`x_adapt`	`np.ndarray`	An array of x values which are used to update the objective parameters on the fly.	`None`

Returns:

Type	Description
`np.ndarray`	A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

Source code in bofire/data_models/objectives/sigmoid.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray]:
    """The call function returning a sigmoid shaped reward for passed x values.

    Args:
        x (np.ndarray): An array of x values
        x_adapt (np.ndarray): An array of x values which are used to update the objective parameters on the fly.

    Returns:
        np.ndarray: A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

    """
    return 1 / (1 + np.exp(-1 * self.steepness * (x - self.tp)))

`MinimizeSigmoidObjective (SigmoidObjective)`

Class for a minimizing a sigmoid objective

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective.
`steepness`	`float`	Steepness of the sigmoid function. Has to be greater than zero.
`tp`	`float`	Turning point of the sigmoid function.

Source code in bofire/data_models/objectives/sigmoid.py

class MinimizeSigmoidObjective(SigmoidObjective):
    """Class for a minimizing a sigmoid objective

    Attributes:
        w (float): float between zero and one for weighting the objective.
        steepness (float): Steepness of the sigmoid function. Has to be greater than zero.
        tp (float): Turning point of the sigmoid function.

    """

    type: Literal["MinimizeSigmoidObjective"] = "MinimizeSigmoidObjective"

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        """The call function returning a sigmoid shaped reward for passed x values.

        Args:
            x (np.ndarray): An array of x values
            x_adapt (np.ndarray): An array of x values which are used to update the objective parameters on the fly.

        Returns:
            np.ndarray: A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

        """
        return 1 - 1 / (1 + np.exp(-1 * self.steepness * (x - self.tp)))

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

The call function returning a sigmoid shaped reward for passed x values.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values	required
`x_adapt`	`np.ndarray`	An array of x values which are used to update the objective parameters on the fly.	`None`

Returns:

Type	Description
`np.ndarray`	A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

Source code in bofire/data_models/objectives/sigmoid.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray]:
    """The call function returning a sigmoid shaped reward for passed x values.

    Args:
        x (np.ndarray): An array of x values
        x_adapt (np.ndarray): An array of x values which are used to update the objective parameters on the fly.

    Returns:
        np.ndarray: A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

    """
    return 1 - 1 / (1 + np.exp(-1 * self.steepness * (x - self.tp)))

`MovingMaximizeSigmoidObjective (SigmoidObjective)`

Class for a maximizing sigmoid objective with a moving turning point that depends on so far observed x values.

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective when used in a weighting based strategy.
`steepness`	`float`	Steepness of the sigmoid function. Has to be greater than zero.
`tp`	`float`	Relative turning point of the sigmoid function. The actual turning point is calculated by adding the maximum of the observed x values to the relative turning point.

Source code in bofire/data_models/objectives/sigmoid.py

class MovingMaximizeSigmoidObjective(SigmoidObjective):
    """Class for a maximizing sigmoid objective with a moving turning point that depends on so far observed x values.

    Attributes:
        w (float): float between zero and one for weighting the objective when used in a weighting based strategy.
        steepness (float): Steepness of the sigmoid function. Has to be greater than zero.
        tp (float): Relative turning point of the sigmoid function. The actual turning point is calculated by adding
            the maximum of the observed x values to the relative turning point.

    """

    type: Literal["MovingMaximizeSigmoidObjective"] = "MovingMaximizeSigmoidObjective"

    def get_adjusted_tp(self, x: Union[pd.Series, np.ndarray]) -> float:
        """Get the adjusted turning point for the sigmoid function.

        Args:
            x (np.ndarray): An array of x values

        Returns:
            float: The adjusted turning point for the sigmoid function.

        """
        return x.max() + self.tp

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Union[pd.Series, np.ndarray],
    ) -> Union[pd.Series, np.ndarray]:
        """The call function returning a sigmoid shaped reward for passed x values.

        Args:
            x (np.ndarray): An array of x values
            x_adapt (np.ndarray): An array of x values which are used to update the objective parameters on the fly.

        Returns:
            np.ndarray: A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

        """
        return 1 / (
            1 + np.exp(-1 * self.steepness * (x - self.get_adjusted_tp(x_adapt)))
        )

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt)` `special`

The call function returning a sigmoid shaped reward for passed x values.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values	required
`x_adapt`	`np.ndarray`	An array of x values which are used to update the objective parameters on the fly.	required

Returns:

Type	Description
`np.ndarray`	A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

Source code in bofire/data_models/objectives/sigmoid.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Union[pd.Series, np.ndarray],
) -> Union[pd.Series, np.ndarray]:
    """The call function returning a sigmoid shaped reward for passed x values.

    Args:
        x (np.ndarray): An array of x values
        x_adapt (np.ndarray): An array of x values which are used to update the objective parameters on the fly.

    Returns:
        np.ndarray: A reward calculated with a sigmoid function. The stepness and the tipping point can be modified via passed arguments.

    """
    return 1 / (
        1 + np.exp(-1 * self.steepness * (x - self.get_adjusted_tp(x_adapt)))
    )

`get_adjusted_tp(self, x)`

Get the adjusted turning point for the sigmoid function.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	An array of x values	required

Returns:

Type	Description
`float`	The adjusted turning point for the sigmoid function.

Source code in bofire/data_models/objectives/sigmoid.py

def get_adjusted_tp(self, x: Union[pd.Series, np.ndarray]) -> float:
    """Get the adjusted turning point for the sigmoid function.

    Args:
        x (np.ndarray): An array of x values

    Returns:
        float: The adjusted turning point for the sigmoid function.

    """
    return x.max() + self.tp

`SigmoidObjective (Objective, ConstrainedObjective)`

Base class for all sigmoid shaped objectives

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective.
`steepness`	`float`	Steepness of the sigmoid function. Has to be greater than zero.
`tp`	`float`	Turning point of the sigmoid function.

Source code in bofire/data_models/objectives/sigmoid.py

class SigmoidObjective(Objective, ConstrainedObjective):
    """Base class for all sigmoid shaped objectives

    Attributes:
        w (float): float between zero and one for weighting the objective.
        steepness (float): Steepness of the sigmoid function. Has to be greater than zero.
        tp (float): Turning point of the sigmoid function.

    """

    steepness: TGt0
    tp: float
    w: TWeight = 1

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`target`

`CloseToTargetObjective (Objective)`

Optimize towards a target value. It can be used as objective in multiobjective scenarios.

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective.
`target_value`	`float`	target value that should be reached.
`exponent`	`float`	the exponent of the expression.

Source code in bofire/data_models/objectives/target.py

class CloseToTargetObjective(Objective):
    """Optimize towards a target value. It can be used as objective
    in multiobjective scenarios.

    Attributes:
        w (float): float between zero and one for weighting the objective.
        target_value (float): target value that should be reached.
        exponent (float): the exponent of the expression.

    """

    type: Literal["CloseToTargetObjective"] = "CloseToTargetObjective"
    w: TWeight = 1
    target_value: float
    exponent: float

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        return -1 * (np.abs(x - self.target_value) ** self.exponent)

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`TargetObjective (Objective, ConstrainedObjective)`

Class for objectives for optimizing towards a target value

Attributes:

Name	Type	Description
`w`	`float`	float between zero and one for weighting the objective.
`target_value`	`float`	target value that should be reached.
`tolerance`	`float`	Tolerance for reaching the target. Has to be greater than zero.
`steepness`	`float`	Steepness of the sigmoid function. Has to be greater than zero.

Source code in bofire/data_models/objectives/target.py

class TargetObjective(Objective, ConstrainedObjective):
    """Class for objectives for optimizing towards a target value

    Attributes:
        w (float): float between zero and one for weighting the objective.
        target_value (float): target value that should be reached.
        tolerance (float): Tolerance for reaching the target. Has to be greater than zero.
        steepness (float): Steepness of the sigmoid function. Has to be greater than zero.

    """

    type: Literal["TargetObjective"] = "TargetObjective"
    w: TWeight = 1
    target_value: float
    tolerance: TGe0
    steepness: TGt0

    def __call__(
        self,
        x: Union[pd.Series, np.ndarray],
        x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
    ) -> Union[pd.Series, np.ndarray]:
        """The call function returning a reward for passed x values.

        Args:
            x (np.array): An array of x values
            x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
                update the objective parameters on the fly. Defaults to None.

        Returns:
            np.array: An array of reward values calculated by the product of two sigmoidal shaped functions resulting in a maximum at the target value.

        """
        return (
            1
            / (
                1
                + np.exp(
                    -1 * self.steepness * (x - (self.target_value - self.tolerance)),
                )
            )
            * (
                1
                - 1
                / (
                    1.0
                    + np.exp(
                        -1
                        * self.steepness
                        * (x - (self.target_value + self.tolerance)),
                    )
                )
            )
        )

`__class_vars__` `special`

The names of the class variables defined on the model.

`__private_attributes__` `special`

Metadata about the private attributes of the model.

`__pydantic_complete__` `special`

Whether model building is completed, or if there are still undefined fields.

`__pydantic_computed_fields__` `special`

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

`__pydantic_custom_init__` `special`

Whether the model has a custom __init__ method.

`__pydantic_decorators__` `special`

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

`__pydantic_fields__` `special`

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

`__pydantic_generic_metadata__` `special`

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

`__pydantic_parent_namespace__` `special`

Parent namespace of the model, used for automatic rebuilding of models.

`__pydantic_post_init__` `special`

The name of the post-init method for the model, if defined.

`signature` `special`

The synthesized __init__ [Signature][inspect.Signature] of the model.

`model_config`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`call(self, x, x_adapt=None)` `special`

The call function returning a reward for passed x values.

Parameters:

Name	Type	Description	Default
`x`	`np.array`	An array of x values	required
`x_adapt`	`Optional[np.ndarray]`	An array of x values which are used to update the objective parameters on the fly. Defaults to None.	`None`

Returns:

Type	Description
`np.array`	An array of reward values calculated by the product of two sigmoidal shaped functions resulting in a maximum at the target value.

Source code in bofire/data_models/objectives/target.py

def __call__(
    self,
    x: Union[pd.Series, np.ndarray],
    x_adapt: Optional[Union[pd.Series, np.ndarray]] = None,
) -> Union[pd.Series, np.ndarray]:
    """The call function returning a reward for passed x values.

    Args:
        x (np.array): An array of x values
        x_adapt (Optional[np.ndarray], optional): An array of x values which are used to
            update the objective parameters on the fly. Defaults to None.

    Returns:
        np.array: An array of reward values calculated by the product of two sigmoidal shaped functions resulting in a maximum at the target value.

    """
    return (
        1
        / (
            1
            + np.exp(
                -1 * self.steepness * (x - (self.target_value - self.tolerance)),
            )
        )
        * (
            1
            - 1
            / (
                1.0
                + np.exp(
                    -1
                    * self.steepness
                    * (x - (self.target_value + self.tolerance)),
                )
            )
        )
    )

Domain

categorical

ConstrainedCategoricalObjective (ConstrainedObjective, Objective)

__class_vars__ special

__private_attributes__ special

__pydantic_complete__ special

__pydantic_computed_fields__ special

__pydantic_custom_init__ special

__pydantic_decorators__ special

__pydantic_fields__ special

__pydantic_generic_metadata__ special

__pydantic_parent_namespace__ special

__pydantic_post_init__ special

__signature__ special

model_config

__call__(self, x, x_adapt=None) special

from_dict_label(self)

to_dict(self)

to_dict_label(self)

validate_desireability(self)

desirabilities

DecreasingDesirabilityObjective (DesirabilityObjective)

__class_vars__ special

__private_attributes__ special

__pydantic_complete__ special

__pydantic_computed_fields__ special

__pydantic_custom_init__ special

__pydantic_decorators__ special

__pydantic_fields__ special

__pydantic_generic_metadata__ special

__pydantic_parent_namespace__ special

__pydantic_post_init__ special

__signature__ special

model_config

DesirabilityObjective (IdentityObjective)

__class_vars__ special

__private_attributes__ special

__pydantic_complete__ special

__pydantic_computed_fields__ special

__pydantic_custom_init__ special

__pydantic_decorators__ special

__pydantic_fields__ special

__pydantic_generic_metadata__ special

__pydantic_parent_namespace__ special

__pydantic_post_init__ special

__signature__ special

model_config

__call__(self, x, x_adapt) special

IncreasingDesirabilityObjective (DesirabilityObjective)

__class_vars__ special

__private_attributes__ special

__pydantic_complete__ special

__pydantic_computed_fields__ special

__pydantic_custom_init__ special

__pydantic_decorators__ special

__pydantic_fields__ special

__pydantic_generic_metadata__ special

__pydantic_parent_namespace__ special

__pydantic_post_init__ special

__signature__ special

model_config

PeakDesirabilityObjective (DesirabilityObjective)

__class_vars__ special

__private_attributes__ special

__pydantic_complete__ special

__pydantic_computed_fields__ special

__pydantic_custom_init__ special

__pydantic_decorators__ special

__pydantic_fields__ special

__pydantic_generic_metadata__ special

__pydantic_parent_namespace__ special

__pydantic_post_init__ special

__signature__ special

model_config

identity

IdentityObjective (Objective)

__class_vars__ special

__private_attributes__ special

__pydantic_complete__ special

__pydantic_computed_fields__ special

`categorical`

`ConstrainedCategoricalObjective (ConstrainedObjective, Objective)`

`__class_vars__` `special`

`__private_attributes__` `special`

`__pydantic_complete__` `special`

`__pydantic_computed_fields__` `special`

`__pydantic_custom_init__` `special`

`__pydantic_decorators__` `special`

`__pydantic_fields__` `special`

`__pydantic_generic_metadata__` `special`

`__pydantic_parent_namespace__` `special`

`__pydantic_post_init__` `special`

`signature` `special`

`model_config`

`call(self, x, x_adapt=None)` `special`

`from_dict_label(self)`

`to_dict(self)`

`to_dict_label(self)`

`validate_desireability(self)`

`desirabilities`

`DecreasingDesirabilityObjective (DesirabilityObjective)`

`__class_vars__` `special`

`__private_attributes__` `special`

`__pydantic_complete__` `special`

`__pydantic_computed_fields__` `special`

`__pydantic_custom_init__` `special`

`__pydantic_decorators__` `special`

`__pydantic_fields__` `special`

`__pydantic_generic_metadata__` `special`

`__pydantic_parent_namespace__` `special`

`__pydantic_post_init__` `special`

`signature` `special`

`model_config`

`DesirabilityObjective (IdentityObjective)`

`__class_vars__` `special`

`__private_attributes__` `special`

`__pydantic_complete__` `special`

`__pydantic_computed_fields__` `special`

`__pydantic_custom_init__` `special`

`__pydantic_decorators__` `special`

`__pydantic_fields__` `special`

`__pydantic_generic_metadata__` `special`

`__pydantic_parent_namespace__` `special`

`__pydantic_post_init__` `special`

`signature` `special`

`model_config`

`call(self, x, x_adapt)` `special`

`IncreasingDesirabilityObjective (DesirabilityObjective)`

`__class_vars__` `special`

`__private_attributes__` `special`

`__pydantic_complete__` `special`

`__pydantic_computed_fields__` `special`

`__pydantic_custom_init__` `special`

`__pydantic_decorators__` `special`

`__pydantic_fields__` `special`

`__pydantic_generic_metadata__` `special`

`__pydantic_parent_namespace__` `special`

`__pydantic_post_init__` `special`

`signature` `special`

`model_config`

`PeakDesirabilityObjective (DesirabilityObjective)`

`__class_vars__` `special`

`__private_attributes__` `special`

`__pydantic_complete__` `special`

`__pydantic_computed_fields__` `special`

`__pydantic_custom_init__` `special`

`__pydantic_decorators__` `special`

`__pydantic_fields__` `special`

`__pydantic_generic_metadata__` `special`

`__pydantic_parent_namespace__` `special`

`__pydantic_post_init__` `special`

`signature` `special`

`model_config`

`identity`

`IdentityObjective (Objective)`

`__class_vars__` `special`

`__private_attributes__` `special`

`__pydantic_complete__` `special`

`__pydantic_computed_fields__` `special`

`__pydantic_custom_init__` `special`