data_models.surrogates.api

data_models.surrogates.api

Classes

Name	Description
Surrogate
SingleTaskGPSurrogate
MixedSingleTaskGPSurrogate
MultiTaskGPSurrogate
RobustSingleTaskGPSurrogate	Robust Relevance Pursuit Single Task Gaussian Process Surrogate.
TanimotoGPSurrogate
LinearSurrogate
PolynomialSurrogate
RandomForestSurrogate
MLPEnsemble
BotorchSurrogates	“List of botorch surrogates.

Surrogate

data_models.surrogates.api.Surrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.Surrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: outputs: objective functions for the surrogate my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

SingleTaskGPSurrogate

data_models.surrogates.api.SingleTaskGPSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.SingleTaskGPSurrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

MixedSingleTaskGPSurrogate

data_models.surrogates.api.MixedSingleTaskGPSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.MixedSingleTaskGPSurrogate.is_output_implemented(
    my_type,
)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

MultiTaskGPSurrogate

data_models.surrogates.api.MultiTaskGPSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.MultiTaskGPSurrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

RobustSingleTaskGPSurrogate

data_models.surrogates.api.RobustSingleTaskGPSurrogate()

Robust Relevance Pursuit Single Task Gaussian Process Surrogate.

A robust single-task GP that learns a data-point specific noise level and is therefore more robust to outliers. See: https://botorch.org/docs/tutorials/relevance_pursuit_robust_regression/ Paper: https://arxiv.org/pdf/2410.24222

Attributes

Name	Type	Description
prior_mean_of_support	Optional[int]	The prior mean of the support.
convex_parametrization	bool	Whether to use convex parametrization of the sparse noise model.
cache_model_trace	bool	Whether to cache the model trace. This needs no be set to True if you want to view the model trace after optimization.

Note

The definition of “outliers” depends on the model capacity, so what is an outlier with respect to a simple model might not be an outlier with respect to a complex model. For this reason, it is necessary to bound the lengthscale of the GP kernel from below.

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.RobustSingleTaskGPSurrogate.is_output_implemented(
    my_type,
)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

TanimotoGPSurrogate

data_models.surrogates.api.TanimotoGPSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models
validate_moleculars	Checks that at least one of fingerprints, fragments, or fingerprintsfragments features are present.

is_output_implemented

data_models.surrogates.api.TanimotoGPSurrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

validate_moleculars

data_models.surrogates.api.TanimotoGPSurrogate.validate_moleculars()

Checks that at least one of fingerprints, fragments, or fingerprintsfragments features are present.

LinearSurrogate

data_models.surrogates.api.LinearSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.LinearSurrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

PolynomialSurrogate

data_models.surrogates.api.PolynomialSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.PolynomialSurrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

RandomForestSurrogate

data_models.surrogates.api.RandomForestSurrogate()

Methods

Name	Description
is_output_implemented	Abstract method to check output type for surrogate models

is_output_implemented

data_models.surrogates.api.RandomForestSurrogate.is_output_implemented(my_type)

Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise

MLPEnsemble

data_models.surrogates.api.MLPEnsemble()

BotorchSurrogates

data_models.surrogates.api.BotorchSurrogates()

“List of botorch surrogates.

Behaves similar to a Surrogate.

# data_models.surrogates.api { #bofire.data_models.surrogates.api } `data_models.surrogates.api` ## Classes | Name | Description | | --- | --- | | [Surrogate](#bofire.data_models.surrogates.api.Surrogate) | | | [SingleTaskGPSurrogate](#bofire.data_models.surrogates.api.SingleTaskGPSurrogate) | | | [MixedSingleTaskGPSurrogate](#bofire.data_models.surrogates.api.MixedSingleTaskGPSurrogate) | | | [MultiTaskGPSurrogate](#bofire.data_models.surrogates.api.MultiTaskGPSurrogate) | | | [RobustSingleTaskGPSurrogate](#bofire.data_models.surrogates.api.RobustSingleTaskGPSurrogate) | Robust Relevance Pursuit Single Task Gaussian Process Surrogate. | | [TanimotoGPSurrogate](#bofire.data_models.surrogates.api.TanimotoGPSurrogate) | | | [LinearSurrogate](#bofire.data_models.surrogates.api.LinearSurrogate) | | | [PolynomialSurrogate](#bofire.data_models.surrogates.api.PolynomialSurrogate) | | | [RandomForestSurrogate](#bofire.data_models.surrogates.api.RandomForestSurrogate) | | | [MLPEnsemble](#bofire.data_models.surrogates.api.MLPEnsemble) | | | [BotorchSurrogates](#bofire.data_models.surrogates.api.BotorchSurrogates) | "List of botorch surrogates. | ### Surrogate { #bofire.data_models.surrogates.api.Surrogate } ```python data_models.surrogates.api.Surrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.Surrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.Surrogate.is_output_implemented } ```python data_models.surrogates.api.Surrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: outputs: objective functions for the surrogate my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### SingleTaskGPSurrogate { #bofire.data_models.surrogates.api.SingleTaskGPSurrogate } ```python data_models.surrogates.api.SingleTaskGPSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.SingleTaskGPSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.SingleTaskGPSurrogate.is_output_implemented } ```python data_models.surrogates.api.SingleTaskGPSurrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### MixedSingleTaskGPSurrogate { #bofire.data_models.surrogates.api.MixedSingleTaskGPSurrogate } ```python data_models.surrogates.api.MixedSingleTaskGPSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.MixedSingleTaskGPSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.MixedSingleTaskGPSurrogate.is_output_implemented } ```python data_models.surrogates.api.MixedSingleTaskGPSurrogate.is_output_implemented( my_type, ) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### MultiTaskGPSurrogate { #bofire.data_models.surrogates.api.MultiTaskGPSurrogate } ```python data_models.surrogates.api.MultiTaskGPSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.MultiTaskGPSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.MultiTaskGPSurrogate.is_output_implemented } ```python data_models.surrogates.api.MultiTaskGPSurrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### RobustSingleTaskGPSurrogate { #bofire.data_models.surrogates.api.RobustSingleTaskGPSurrogate } ```python data_models.surrogates.api.RobustSingleTaskGPSurrogate() ``` Robust Relevance Pursuit Single Task Gaussian Process Surrogate. A robust single-task GP that learns a data-point specific noise level and is therefore more robust to outliers. See: https://botorch.org/docs/tutorials/relevance_pursuit_robust_regression/ Paper: https://arxiv.org/pdf/2410.24222 #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |------------------------|-----------------|------------------------------------------------------------------------------------------------------------------------| | prior_mean_of_support | Optional\[int\] | The prior mean of the support. | | convex_parametrization | bool | Whether to use convex parametrization of the sparse noise model. | | cache_model_trace | bool | Whether to cache the model trace. This needs no be set to True if you want to view the model trace after optimization. | #### Note {.doc-section .doc-section-note} The definition of "outliers" depends on the model capacity, so what is an outlier with respect to a simple model might not be an outlier with respect to a complex model. For this reason, it is necessary to bound the lengthscale of the GP kernel from below. #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.RobustSingleTaskGPSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.RobustSingleTaskGPSurrogate.is_output_implemented } ```python data_models.surrogates.api.RobustSingleTaskGPSurrogate.is_output_implemented( my_type, ) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### TanimotoGPSurrogate { #bofire.data_models.surrogates.api.TanimotoGPSurrogate } ```python data_models.surrogates.api.TanimotoGPSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.TanimotoGPSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | | [validate_moleculars](#bofire.data_models.surrogates.api.TanimotoGPSurrogate.validate_moleculars) | Checks that at least one of fingerprints, fragments, or fingerprintsfragments features are present. | ##### is_output_implemented { #bofire.data_models.surrogates.api.TanimotoGPSurrogate.is_output_implemented } ```python data_models.surrogates.api.TanimotoGPSurrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ##### validate_moleculars { #bofire.data_models.surrogates.api.TanimotoGPSurrogate.validate_moleculars } ```python data_models.surrogates.api.TanimotoGPSurrogate.validate_moleculars() ``` Checks that at least one of fingerprints, fragments, or fingerprintsfragments features are present. ### LinearSurrogate { #bofire.data_models.surrogates.api.LinearSurrogate } ```python data_models.surrogates.api.LinearSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.LinearSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.LinearSurrogate.is_output_implemented } ```python data_models.surrogates.api.LinearSurrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### PolynomialSurrogate { #bofire.data_models.surrogates.api.PolynomialSurrogate } ```python data_models.surrogates.api.PolynomialSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.PolynomialSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.PolynomialSurrogate.is_output_implemented } ```python data_models.surrogates.api.PolynomialSurrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### RandomForestSurrogate { #bofire.data_models.surrogates.api.RandomForestSurrogate } ```python data_models.surrogates.api.RandomForestSurrogate() ``` #### Methods | Name | Description | | --- | --- | | [is_output_implemented](#bofire.data_models.surrogates.api.RandomForestSurrogate.is_output_implemented) | Abstract method to check output type for surrogate models | ##### is_output_implemented { #bofire.data_models.surrogates.api.RandomForestSurrogate.is_output_implemented } ```python data_models.surrogates.api.RandomForestSurrogate.is_output_implemented(my_type) ``` Abstract method to check output type for surrogate models Args: my_type: continuous or categorical output Returns: bool: True if the output type is valid for the surrogate chosen, False otherwise ### MLPEnsemble { #bofire.data_models.surrogates.api.MLPEnsemble } ```python data_models.surrogates.api.MLPEnsemble() ``` ### BotorchSurrogates { #bofire.data_models.surrogates.api.BotorchSurrogates } ```python data_models.surrogates.api.BotorchSurrogates() ``` "List of botorch surrogates. Behaves similar to a Surrogate.