data_models.features.api

data_models.features.api

Classes

Name	Description
CategoricalInput	Base class for all categorical input features.
CategoricalOutput
ContinuousInput	Base class for all continuous input features.
ContinuousOutput	The base class for a continuous output feature
DiscreteInput	Feature with discretized ordinal values allowed in the optimization.
ContinuousDescriptorInput	Class for continuous input features with descriptors
CategoricalDescriptorInput	Class for categorical input features with descriptors
MolecularInput
CategoricalMolecularInput
TaskInput

CategoricalInput

data_models.features.api.CategoricalInput()

Base class for all categorical input features.

Attributes

Name	Type	Description
categories	List[str]	Names of the categories.
allowed	List[bool]	List of bools indicating if a category is allowed within the optimization.

Methods

Name	Description
fixed_value	Returns the categories to which the feature is fixed, None if the feature is not fixed
from_dummy_encoding	Convert points back from dummy encoding.
from_onehot_encoding	Converts values back from one-hot encoding.
from_ordinal_encoding	Convertes values back from ordinal encoding.
generate_allowed	Generates the list of allowed categories if not provided.
get_allowed_categories	Returns the allowed categories.
get_forbidden_categories	Returns the non-allowed categories
get_possible_categories	Return the superset of categories that have been used in the experimental dataset and
is_fixed	Returns True if there is only one allowed category.
is_fulfilled	Method to check if the values are all allowed categories.
sample	Draw random samples from the feature.
to_dummy_encoding	Converts values to a dummy-hot encoding, dropping the first categorical level.
to_onehot_encoding	Converts values to a one-hot encoding.
to_ordinal_encoding	Converts values to an ordinal integer based encoding.
validate_candidental	Method to validate the suggested candidates
validate_experimental	Method to validate the experimental dataFrame

fixed_value

data_models.features.api.CategoricalInput.fixed_value(transform_type=None)

Returns the categories to which the feature is fixed, None if the feature is not fixed

Returns

Name	Type	Description
	Union[List[str], List[float], None]	List[str]: List of categories or None

from_dummy_encoding

data_models.features.api.CategoricalInput.from_dummy_encoding(values)

Convert points back from dummy encoding.

Parameters

Name	Type	Description	Default
values	pd.DataFrame	Dummy-hot encoded values.	required

Raises

Name	Type	Description
	ValueError	If one-hot columns not present in `values`.

Returns

Name	Type	Description
	pd.Series	pd.Series: Series with categorical values.

from_onehot_encoding

data_models.features.api.CategoricalInput.from_onehot_encoding(values)

Converts values back from one-hot encoding.

Parameters

Name	Type	Description	Default
values	pd.DataFrame	One-hot encoded values.	required

Raises

Name	Type	Description
	ValueError	If one-hot columns not present in `values`.

Returns

Name	Type	Description
	pd.Series	pd.Series: Series with categorical values.

from_ordinal_encoding

data_models.features.api.CategoricalInput.from_ordinal_encoding(values)

Convertes values back from ordinal encoding.

Parameters

Name	Type	Description	Default
values	pd.Series	Ordinal encoded series.	required

Returns

Name	Type	Description
	pd.Series	pd.Series: Series with categorical values.

generate_allowed

data_models.features.api.CategoricalInput.generate_allowed(allowed, info)

Generates the list of allowed categories if not provided.

get_allowed_categories

data_models.features.api.CategoricalInput.get_allowed_categories()

Returns the allowed categories.

Returns

Name	Type	Description
	list[str]	list of str: The allowed categories

get_forbidden_categories

data_models.features.api.CategoricalInput.get_forbidden_categories()

Returns the non-allowed categories

Returns

Name	Type	Description
		List[str]: List of the non-allowed categories

get_possible_categories

data_models.features.api.CategoricalInput.get_possible_categories(values)

Return the superset of categories that have been used in the experimental dataset and that can be used in the optimization

Parameters

Name	Type	Description	Default
values	pd.Series	Series with the values for this feature	required

Returns

Name	Type	Description
list	list	list of possible categories

is_fixed

data_models.features.api.CategoricalInput.is_fixed()

Returns True if there is only one allowed category.

Returns

Name	Type	Description
	bool	[bool]: True if there is only one allowed category

is_fulfilled

data_models.features.api.CategoricalInput.is_fulfilled(values)

Method to check if the values are all allowed categories.

Parameters

Name	Type	Description	Default
values	pd.Series	A series with values for the input feature.	required

Returns

Name	Type	Description
	pd.Series	A series with boolean values indicating if the input feature is fulfilled.

sample

data_models.features.api.CategoricalInput.sample(n, seed=None)

Draw random samples from the feature.

Parameters

Name	Type	Description	Default
n	int	number of samples.	required
seed	int	random seed. Defaults to None.	`None`

Returns

Name	Type	Description
	pd.Series	pd.Series: drawn samples.

to_dummy_encoding

data_models.features.api.CategoricalInput.to_dummy_encoding(values)

Converts values to a dummy-hot encoding, dropping the first categorical level.

Parameters

Name	Type	Description	Default
values	pd.Series	Series to be transformed.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Dummy-hot transformed data frame.

to_onehot_encoding

data_models.features.api.CategoricalInput.to_onehot_encoding(values)

Converts values to a one-hot encoding.

Parameters

Name	Type	Description	Default
values	pd.Series	Series to be transformed.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: One-hot transformed data frame.

to_ordinal_encoding

data_models.features.api.CategoricalInput.to_ordinal_encoding(values)

Converts values to an ordinal integer based encoding.

Parameters

Name	Type	Description	Default
values	pd.Series	Series to be transformed.	required

Returns

Name	Type	Description
	pd.Series	pd.Series: Ordinal encoded values.

validate_candidental

data_models.features.api.CategoricalInput.validate_candidental(values)

Method to validate the suggested candidates

Parameters

Name	Type	Description	Default
values	pd.Series	A dataFrame with candidates	required

Raises

Name	Type	Description
	ValueError	when not all values for a feature are one of the allowed categories

Returns

Name	Type	Description
	pd.Series	pd.Series: The passed dataFrame with candidates

validate_experimental

data_models.features.api.CategoricalInput.validate_experimental(
    values,
    strict=False,
)

Method to validate the experimental dataFrame

Parameters

Name	Type	Description	Default
values	pd.Series	A dataFrame with experiments	required
strict	bool	Boolean to distinguish if the occurrence of fixed features in the dataset should be considered or not. Defaults to False.	`False`

Raises

Name	Type	Description
	ValueError	when an entry is not in the list of allowed categories
	ValueError	when there is no variation in a feature provided by the experimental data

Returns

Name	Type	Description
	pd.Series	pd.Series: A dataFrame with experiments

CategoricalOutput

data_models.features.api.CategoricalOutput()

Methods

Name	Description
validate_objective_categories	Validates that objective categories match the output categories

validate_objective_categories

data_models.features.api.CategoricalOutput.validate_objective_categories()

Validates that objective categories match the output categories

Raises

Name	Type	Description
	ValueError	when categories do not match objective categories

Returns

Name	Type	Description
		self

ContinuousInput

data_models.features.api.ContinuousInput()

Base class for all continuous input features.

Attributes

Name	Type	Description
bounds	Tuple[float, float]	A tuple that stores the lower and upper bound of the feature.
stepsize	PositiveFloat	Float indicating the allowed stepsize between lower and upper. Defaults to None.
local_relative_bounds	Tuple[float, float]	A tuple that stores the lower and upper bounds relative to a reference value. Defaults to None.
allow_zero	bool	A boolean indicating if the input feature can take inactive values. Useful for features that take values between `bounds`, but can also take a value of 0. One may choose to use a conditional kernel for this, if taking a value of 0 represents a distinct behaviour from non-zero values.

Methods

Name	Description
is_fulfilled	Method to check if the values are within the bounds of the feature.
round	Round values to the stepsize of the feature. If no stepsize is provided return the
sample	Draw random samples from the feature.
validate_candidental	Method to validate the suggested candidates

is_fulfilled

data_models.features.api.ContinuousInput.is_fulfilled(values, noise=1e-05)

Method to check if the values are within the bounds of the feature.

Parameters

Name	Type	Description	Default
values	pd.Series	A series with values for the input feature.	required
noise	float	A small value to allow for numerical errors. Defaults to 10e-6.	`1e-05`

Returns

Name	Type	Description
	pd.Series	A series with boolean values indicating if the input feature is fulfilled.

round

data_models.features.api.ContinuousInput.round(values)

Round values to the stepsize of the feature. If no stepsize is provided return the provided values.

Parameters

Name	Type	Description	Default
values	pd.Series	The values that should be rounded.	required

Returns

Name	Type	Description
	pd.Series	pd.Series: The rounded values

sample

data_models.features.api.ContinuousInput.sample(n, seed=None)

Draw random samples from the feature.

Parameters

Name	Type	Description	Default
n	int	number of samples.	required
seed	int	random seed. Defaults to None.	`None`

Returns

Name	Type	Description
	pd.Series	pd.Series: drawn samples.

validate_candidental

data_models.features.api.ContinuousInput.validate_candidental(values)

Method to validate the suggested candidates

Parameters

Name	Type	Description	Default
values	pd.Series	A dataFrame with candidates	required

Raises

Name	Type	Description
	ValueError	when non numerical values are passed
	ValueError	when values are larger than the upper bound of the feature
	ValueError	when values are lower than the lower bound of the feature

Returns

Name	Type	Description
	pd.Series	pd.Series: The passed dataFrame with candidates

ContinuousOutput

data_models.features.api.ContinuousOutput()

The base class for a continuous output feature

Attributes

Name	Type	Description
objective	objective	objective of the feature indicating in which direction it should be optimized. Defaults to `MaximizeObjective`.

DiscreteInput

data_models.features.api.DiscreteInput()

Feature with discretized ordinal values allowed in the optimization.

Attributes

Name	Type	Description
key(str)		key of the feature.
values(List[float])		the discretized allowed values during the optimization.

Methods

Name	Description
from_continuous	Rounds continuous values to the closest discrete ones.
is_fulfilled	Method to check if the values are close to the discrete values.
sample	Draw random samples from the feature.
validate_candidental	Method to validate the provided candidates.
validate_values_unique	Validates that provided values are unique.

from_continuous

data_models.features.api.DiscreteInput.from_continuous(values)

Rounds continuous values to the closest discrete ones.

Parameters

Name	Type	Description	Default
values	pd.DataFrame	Dataframe with continuous entries.	required

Returns

Name	Type	Description
	pd.Series	pd.Series: Series with discrete values.

is_fulfilled

data_models.features.api.DiscreteInput.is_fulfilled(values)

Method to check if the values are close to the discrete values.

Parameters

Name	Type	Description	Default
values	pd.Series	A series with values for the input feature.	required

Returns

Name	Type	Description
	pd.Series	A series with boolean values indicating if the input feature is fulfilled.

sample

data_models.features.api.DiscreteInput.sample(n, seed=None)

Draw random samples from the feature.

Parameters

Name	Type	Description	Default
n	int	number of samples.	required
seed	int	random seed. Defaults to None.	`None`

Returns

Name	Type	Description
	pd.Series	pd.Series: drawn samples.

validate_candidental

data_models.features.api.DiscreteInput.validate_candidental(values)

Method to validate the provided candidates.

Parameters

Name	Type	Description	Default
values	pd.Series	suggested candidates for the feature	required

Raises

Name	Type	Description
	ValueError	Raises error when one of the provided values is not contained in the list of allowed values.

Returns

Name	Type	Description
	pd.Series	pd.Series: suggested candidates for the feature

validate_values_unique

data_models.features.api.DiscreteInput.validate_values_unique(values)

Validates that provided values are unique.

Parameters

Name	Type	Description	Default
values	List[float]	List of values	required

Raises

Name	Type	Description
	ValueError	when values are non-unique.
	ValueError	when values contains only one entry.
	ValueError	when values is empty.

Returns

Name	Type	Description
		List[values]: Sorted list of values

ContinuousDescriptorInput

data_models.features.api.ContinuousDescriptorInput()

Class for continuous input features with descriptors

Attributes

Name	Type	Description
lower_bound	float	Lower bound of the feature in the optimization.
upper_bound	float	Upper bound of the feature in the optimization.
descriptors	List[str]	Names of the descriptors.
values	List[float]	Values of the descriptors.

Methods

Name	Description
to_df	Tabular overview of the feature as DataFrame
validate_list_lengths	Compares the length of the defined descriptors list with the provided values

to_df

data_models.features.api.ContinuousDescriptorInput.to_df()

Tabular overview of the feature as DataFrame

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: tabular overview of the feature as DataFrame

validate_list_lengths

data_models.features.api.ContinuousDescriptorInput.validate_list_lengths()

Compares the length of the defined descriptors list with the provided values

Parameters

Name	Type	Description	Default
values	Dict	Dictionary with all attributes	required

Raises

Name	Type	Description
	ValueError	when the number of descriptors does not math the number of provided values

Returns

Name	Type	Description
Dict		Dict with the attributes

CategoricalDescriptorInput

data_models.features.api.CategoricalDescriptorInput()

Class for categorical input features with descriptors

Attributes

Name	Type	Description
categories	List[str]	Names of the categories.
allowed	List[bool]	List of bools indicating if a category is allowed within the optimization.
descriptors	List[str]	List of strings representing the names of the descriptors.
values	List[List[float]]	List of lists representing the descriptor values.

Methods

Name	Description
fixed_value	Returns the categories to which the feature is fixed, None if the feature is not fixed
from_descriptor_encoding	Converts values back from descriptor encoding.
from_df	Creates a feature from a dataframe
to_descriptor_encoding	Converts values to descriptor encoding.
to_df	Tabular overview of the feature as DataFrame
validate_experimental	Method to validate the experimental dataFrame
validate_values	Validates the compatibility of passed values for the descriptors and the defined categories

fixed_value

data_models.features.api.CategoricalDescriptorInput.fixed_value(
    transform_type=None,
)

Returns the categories to which the feature is fixed, None if the feature is not fixed

Returns

Name	Type	Description
	Union[List[str], List[float], None]	List[str]: List of categories or None

from_descriptor_encoding

data_models.features.api.CategoricalDescriptorInput.from_descriptor_encoding(
    values,
)

Converts values back from descriptor encoding.

Parameters

Name	Type	Description	Default
values	pd.DataFrame	Descriptor encoded dataframe.	required

Raises

Name	Type	Description
	ValueError	If descriptor columns not found in the dataframe.

Returns

Name	Type	Description
	pd.Series	pd.Series: Series with categorical values.

from_df

data_models.features.api.CategoricalDescriptorInput.from_df(key, df)

Creates a feature from a dataframe

Parameters

Name	Type	Description	Default
key	str	The name of the feature	required
df	pd.DataFrame	Categories as rows and descriptors as columns	required

Returns

Name	Type	Description
type		description

to_descriptor_encoding

data_models.features.api.CategoricalDescriptorInput.to_descriptor_encoding(
    values,
)

Converts values to descriptor encoding.

Parameters

Name	Type	Description	Default
values	pd.Series	Values to transform.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Descriptor encoded dataframe.

to_df

data_models.features.api.CategoricalDescriptorInput.to_df()

Tabular overview of the feature as DataFrame

Returns

Name	Type	Description
		pd.DataFrame: tabular overview of the feature as DataFrame

validate_experimental

data_models.features.api.CategoricalDescriptorInput.validate_experimental(
    values,
    strict=False,
)

Method to validate the experimental dataFrame

Parameters

Name	Type	Description	Default
values	pd.Series	A dataFrame with experiments	required
strict	bool	Boolean to distinguish if the occurrence of fixed features in the dataset should be considered or not. Defaults to False.	`False`

Raises

Name	Type	Description
	ValueError	when an entry is not in the list of allowed categories
	ValueError	when there is no variation in a feature provided by the experimental data
	ValueError	when no variation is present or planned for a given descriptor

Returns

Name	Type	Description
	pd.Series	pd.Series: A dataFrame with experiments

validate_values

data_models.features.api.CategoricalDescriptorInput.validate_values(v, info)

Validates the compatibility of passed values for the descriptors and the defined categories

Parameters

Name	Type	Description	Default
v	List[List[float]]	Nested list with descriptor values	required
values	Dict	Dictionary with attributes	required

Raises

Name	Type	Description
	ValueError	when values have different length than categories
	ValueError	when rows in values have different length than descriptors
	ValueError	when a descriptor shows no variance in the data

Returns

Name	Type	Description
		List[List[float]]: Nested list with descriptor values

MolecularInput

data_models.features.api.MolecularInput()

Methods

Name	Description
get_bounds	Calculates the lower and upper bounds for the feature based on the given transform type and values.
to_descriptor_encoding	Converts values to descriptor encoding.

get_bounds

data_models.features.api.MolecularInput.get_bounds(
    transform_type,
    values,
    reference_value=None,
)

Calculates the lower and upper bounds for the feature based on the given transform type and values.

Parameters

Name	Type	Description	Default
transform_type	AnyMolFeatures	The type of transformation to apply to the data.	required
values	pd.Series	The actual data over which the lower and upper bounds are calculated.	required
reference_value	Optional[str]	The reference value for the transformation. Not used here. Defaults to None.	`None`

Returns

Name	Type	Description
	Tuple[List[float], List[float]]	Tuple[List[float], List[float]]: A tuple containing the lower and upper bounds of the transformed data.

Raises

Name	Type	Description
	NotImplementedError	Raised when `values` is None, as it is currently required for `MolecularInput`.

to_descriptor_encoding

data_models.features.api.MolecularInput.to_descriptor_encoding(
    transform_type,
    values,
)

Converts values to descriptor encoding.

Parameters

Name	Type	Description	Default
values	pd.Series	Values to transform.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Descriptor encoded dataframe.

CategoricalMolecularInput

data_models.features.api.CategoricalMolecularInput()

Methods

Name	Description
from_descriptor_encoding	Converts values back from descriptor encoding.
validate_smiles	Validates that categories are valid smiles. Note that this check can only

from_descriptor_encoding

data_models.features.api.CategoricalMolecularInput.from_descriptor_encoding(
    transform_type,
    values,
)

Converts values back from descriptor encoding.

Parameters

Name	Type	Description	Default
values	pd.DataFrame	Descriptor encoded dataframe.	required

Raises

Name	Type	Description
	ValueError	If descriptor columns not found in the dataframe.

Returns

Name	Type	Description
	pd.Series	pd.Series: Series with categorical values.

validate_smiles

data_models.features.api.CategoricalMolecularInput.validate_smiles(categories)

Validates that categories are valid smiles. Note that this check can only be executed when rdkit is available.

Parameters

Name	Type	Description	Default
categories	List[str]	List of smiles	required

Raises

Name	Type	Description
	ValueError	when string is not a smiles

Returns

Name	Type	Description
		List[str]: List of the smiles

TaskInput

data_models.features.api.TaskInput()

# data_models.features.api { #bofire.data_models.features.api } `data_models.features.api` ## Classes | Name | Description | | --- | --- | | [CategoricalInput](#bofire.data_models.features.api.CategoricalInput) | Base class for all categorical input features. | | [CategoricalOutput](#bofire.data_models.features.api.CategoricalOutput) | | | [ContinuousInput](#bofire.data_models.features.api.ContinuousInput) | Base class for all continuous input features. | | [ContinuousOutput](#bofire.data_models.features.api.ContinuousOutput) | The base class for a continuous output feature | | [DiscreteInput](#bofire.data_models.features.api.DiscreteInput) | Feature with discretized ordinal values allowed in the optimization. | | [ContinuousDescriptorInput](#bofire.data_models.features.api.ContinuousDescriptorInput) | Class for continuous input features with descriptors | | [CategoricalDescriptorInput](#bofire.data_models.features.api.CategoricalDescriptorInput) | Class for categorical input features with descriptors | | [MolecularInput](#bofire.data_models.features.api.MolecularInput) | | | [CategoricalMolecularInput](#bofire.data_models.features.api.CategoricalMolecularInput) | | | [TaskInput](#bofire.data_models.features.api.TaskInput) | | ### CategoricalInput { #bofire.data_models.features.api.CategoricalInput } ```python data_models.features.api.CategoricalInput() ``` Base class for all categorical input features. #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |------------|--------------|----------------------------------------------------------------------------| | categories | List\[str\] | Names of the categories. | | allowed | List\[bool\] | List of bools indicating if a category is allowed within the optimization. | #### Methods | Name | Description | | --- | --- | | [fixed_value](#bofire.data_models.features.api.CategoricalInput.fixed_value) | Returns the categories to which the feature is fixed, None if the feature is not fixed | | [from_dummy_encoding](#bofire.data_models.features.api.CategoricalInput.from_dummy_encoding) | Convert points back from dummy encoding. | | [from_onehot_encoding](#bofire.data_models.features.api.CategoricalInput.from_onehot_encoding) | Converts values back from one-hot encoding. | | [from_ordinal_encoding](#bofire.data_models.features.api.CategoricalInput.from_ordinal_encoding) | Convertes values back from ordinal encoding. | | [generate_allowed](#bofire.data_models.features.api.CategoricalInput.generate_allowed) | Generates the list of allowed categories if not provided. | | [get_allowed_categories](#bofire.data_models.features.api.CategoricalInput.get_allowed_categories) | Returns the allowed categories. | | [get_forbidden_categories](#bofire.data_models.features.api.CategoricalInput.get_forbidden_categories) | Returns the non-allowed categories | | [get_possible_categories](#bofire.data_models.features.api.CategoricalInput.get_possible_categories) | Return the superset of categories that have been used in the experimental dataset and | | [is_fixed](#bofire.data_models.features.api.CategoricalInput.is_fixed) | Returns True if there is only one allowed category. | | [is_fulfilled](#bofire.data_models.features.api.CategoricalInput.is_fulfilled) | Method to check if the values are all allowed categories. | | [sample](#bofire.data_models.features.api.CategoricalInput.sample) | Draw random samples from the feature. | | [to_dummy_encoding](#bofire.data_models.features.api.CategoricalInput.to_dummy_encoding) | Converts values to a dummy-hot encoding, dropping the first categorical level. | | [to_onehot_encoding](#bofire.data_models.features.api.CategoricalInput.to_onehot_encoding) | Converts values to a one-hot encoding. | | [to_ordinal_encoding](#bofire.data_models.features.api.CategoricalInput.to_ordinal_encoding) | Converts values to an ordinal integer based encoding. | | [validate_candidental](#bofire.data_models.features.api.CategoricalInput.validate_candidental) | Method to validate the suggested candidates | | [validate_experimental](#bofire.data_models.features.api.CategoricalInput.validate_experimental) | Method to validate the experimental dataFrame | ##### fixed_value { #bofire.data_models.features.api.CategoricalInput.fixed_value } ```python data_models.features.api.CategoricalInput.fixed_value(transform_type=None) ``` Returns the categories to which the feature is fixed, None if the feature is not fixed ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-------------------------------------------|---------------------------------------| | | Union\[List\[str\], List\[float\], None\] | List[str]: List of categories or None | ##### from_dummy_encoding { #bofire.data_models.features.api.CategoricalInput.from_dummy_encoding } ```python data_models.features.api.CategoricalInput.from_dummy_encoding(values) ``` Convert points back from dummy encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------------|---------------------------|------------| | values | pd.DataFrame | Dummy-hot encoded values. | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------| | | ValueError | If one-hot columns not present in `values`. | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|--------------------------------------------| | | pd.Series | pd.Series: Series with categorical values. | ##### from_onehot_encoding { #bofire.data_models.features.api.CategoricalInput.from_onehot_encoding } ```python data_models.features.api.CategoricalInput.from_onehot_encoding(values) ``` Converts values back from one-hot encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------------|-------------------------|------------| | values | pd.DataFrame | One-hot encoded values. | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------| | | ValueError | If one-hot columns not present in `values`. | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|--------------------------------------------| | | pd.Series | pd.Series: Series with categorical values. | ##### from_ordinal_encoding { #bofire.data_models.features.api.CategoricalInput.from_ordinal_encoding } ```python data_models.features.api.CategoricalInput.from_ordinal_encoding(values) ``` Convertes values back from ordinal encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|-------------------------|------------| | values | pd.Series | Ordinal encoded series. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|--------------------------------------------| | | pd.Series | pd.Series: Series with categorical values. | ##### generate_allowed { #bofire.data_models.features.api.CategoricalInput.generate_allowed } ```python data_models.features.api.CategoricalInput.generate_allowed(allowed, info) ``` Generates the list of allowed categories if not provided. ##### get_allowed_categories { #bofire.data_models.features.api.CategoricalInput.get_allowed_categories } ```python data_models.features.api.CategoricalInput.get_allowed_categories() ``` Returns the allowed categories. ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-------------|-------------------------------------| | | list\[str\] | list of str: The allowed categories | ##### get_forbidden_categories { #bofire.data_models.features.api.CategoricalInput.get_forbidden_categories } ```python data_models.features.api.CategoricalInput.get_forbidden_categories() ``` Returns the non-allowed categories ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|-----------------------------------------------| | | | List[str]: List of the non-allowed categories | ##### get_possible_categories { #bofire.data_models.features.api.CategoricalInput.get_possible_categories } ```python data_models.features.api.CategoricalInput.get_possible_categories(values) ``` Return the superset of categories that have been used in the experimental dataset and that can be used in the optimization ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|-----------------------------------------|------------| | values | pd.Series | Series with the values for this feature | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|-----------------------------| | list | list | list of possible categories | ##### is_fixed { #bofire.data_models.features.api.CategoricalInput.is_fixed } ```python data_models.features.api.CategoricalInput.is_fixed() ``` Returns True if there is only one allowed category. ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|----------------------------------------------------| | | bool | [bool]: True if there is only one allowed category | ##### is_fulfilled { #bofire.data_models.features.api.CategoricalInput.is_fulfilled } ```python data_models.features.api.CategoricalInput.is_fulfilled(values) ``` Method to check if the values are all allowed categories. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------------------------|------------| | values | pd.Series | A series with values for the input feature. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|----------------------------------------------------------------------------| | | pd.Series | A series with boolean values indicating if the input feature is fulfilled. | ##### sample { #bofire.data_models.features.api.CategoricalInput.sample } ```python data_models.features.api.CategoricalInput.sample(n, seed=None) ``` Draw random samples from the feature. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------|--------------------------------|------------| | n | int | number of samples. | _required_ | | seed | int | random seed. Defaults to None. | `None` | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|---------------------------| | | pd.Series | pd.Series: drawn samples. | ##### to_dummy_encoding { #bofire.data_models.features.api.CategoricalInput.to_dummy_encoding } ```python data_models.features.api.CategoricalInput.to_dummy_encoding(values) ``` Converts values to a dummy-hot encoding, dropping the first categorical level. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------|------------| | values | pd.Series | Series to be transformed. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------------|-------------------------------------------------| | | pd.DataFrame | pd.DataFrame: Dummy-hot transformed data frame. | ##### to_onehot_encoding { #bofire.data_models.features.api.CategoricalInput.to_onehot_encoding } ```python data_models.features.api.CategoricalInput.to_onehot_encoding(values) ``` Converts values to a one-hot encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------|------------| | values | pd.Series | Series to be transformed. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------------|-----------------------------------------------| | | pd.DataFrame | pd.DataFrame: One-hot transformed data frame. | ##### to_ordinal_encoding { #bofire.data_models.features.api.CategoricalInput.to_ordinal_encoding } ```python data_models.features.api.CategoricalInput.to_ordinal_encoding(values) ``` Converts values to an ordinal integer based encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------|------------| | values | pd.Series | Series to be transformed. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|------------------------------------| | | pd.Series | pd.Series: Ordinal encoded values. | ##### validate_candidental { #bofire.data_models.features.api.CategoricalInput.validate_candidental } ```python data_models.features.api.CategoricalInput.validate_candidental(values) ``` Method to validate the suggested candidates ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|-----------------------------|------------| | values | pd.Series | A dataFrame with candidates | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------------------------------| | | ValueError | when not all values for a feature are one of the allowed categories | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-------------------------------------------------| | | pd.Series | pd.Series: The passed dataFrame with candidates | ##### validate_experimental { #bofire.data_models.features.api.CategoricalInput.validate_experimental } ```python data_models.features.api.CategoricalInput.validate_experimental( values, strict=False, ) ``` Method to validate the experimental dataFrame ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------------------------------------------------------------------------------------------------------|------------| | values | pd.Series | A dataFrame with experiments | _required_ | | strict | bool | Boolean to distinguish if the occurrence of fixed features in the dataset should be considered or not. Defaults to False. | `False` | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------------------------------------| | | ValueError | when an entry is not in the list of allowed categories | | | ValueError | when there is no variation in a feature provided by the experimental data | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-----------------------------------------| | | pd.Series | pd.Series: A dataFrame with experiments | ### CategoricalOutput { #bofire.data_models.features.api.CategoricalOutput } ```python data_models.features.api.CategoricalOutput() ``` #### Methods | Name | Description | | --- | --- | | [validate_objective_categories](#bofire.data_models.features.api.CategoricalOutput.validate_objective_categories) | Validates that objective categories match the output categories | ##### validate_objective_categories { #bofire.data_models.features.api.CategoricalOutput.validate_objective_categories } ```python data_models.features.api.CategoricalOutput.validate_objective_categories() ``` Validates that objective categories match the output categories ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------------| | | ValueError | when categories do not match objective categories | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|---------------| | | | self | ### ContinuousInput { #bofire.data_models.features.api.ContinuousInput } ```python data_models.features.api.ContinuousInput() ``` Base class for all continuous input features. #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |-----------------------|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | bounds | Tuple\[float, float\] | A tuple that stores the lower and upper bound of the feature. | | stepsize | PositiveFloat | Float indicating the allowed stepsize between lower and upper. Defaults to None. | | local_relative_bounds | Tuple\[float, float\] | A tuple that stores the lower and upper bounds relative to a reference value. Defaults to None. | | allow_zero | bool | A boolean indicating if the input feature can take inactive values. Useful for features that take values between `bounds`, but can also take a value of 0. One may choose to use a conditional kernel for this, if taking a value of 0 represents a distinct behaviour from non-zero values. | #### Methods | Name | Description | | --- | --- | | [is_fulfilled](#bofire.data_models.features.api.ContinuousInput.is_fulfilled) | Method to check if the values are within the bounds of the feature. | | [round](#bofire.data_models.features.api.ContinuousInput.round) | Round values to the stepsize of the feature. If no stepsize is provided return the | | [sample](#bofire.data_models.features.api.ContinuousInput.sample) | Draw random samples from the feature. | | [validate_candidental](#bofire.data_models.features.api.ContinuousInput.validate_candidental) | Method to validate the suggested candidates | ##### is_fulfilled { #bofire.data_models.features.api.ContinuousInput.is_fulfilled } ```python data_models.features.api.ContinuousInput.is_fulfilled(values, noise=1e-05) ``` Method to check if the values are within the bounds of the feature. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|-----------------------------------------------------------------|------------| | values | pd.Series | A series with values for the input feature. | _required_ | | noise | float | A small value to allow for numerical errors. Defaults to 10e-6. | `1e-05` | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|----------------------------------------------------------------------------| | | pd.Series | A series with boolean values indicating if the input feature is fulfilled. | ##### round { #bofire.data_models.features.api.ContinuousInput.round } ```python data_models.features.api.ContinuousInput.round(values) ``` Round values to the stepsize of the feature. If no stepsize is provided return the provided values. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|------------------------------------|------------| | values | pd.Series | The values that should be rounded. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-------------------------------| | | pd.Series | pd.Series: The rounded values | ##### sample { #bofire.data_models.features.api.ContinuousInput.sample } ```python data_models.features.api.ContinuousInput.sample(n, seed=None) ``` Draw random samples from the feature. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------|--------------------------------|------------| | n | int | number of samples. | _required_ | | seed | int | random seed. Defaults to None. | `None` | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|---------------------------| | | pd.Series | pd.Series: drawn samples. | ##### validate_candidental { #bofire.data_models.features.api.ContinuousInput.validate_candidental } ```python data_models.features.api.ContinuousInput.validate_candidental(values) ``` Method to validate the suggested candidates ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|-----------------------------|------------| | values | pd.Series | A dataFrame with candidates | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|------------------------------------------------------------| | | ValueError | when non numerical values are passed | | | ValueError | when values are larger than the upper bound of the feature | | | ValueError | when values are lower than the lower bound of the feature | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-------------------------------------------------| | | pd.Series | pd.Series: The passed dataFrame with candidates | ### ContinuousOutput { #bofire.data_models.features.api.ContinuousOutput } ```python data_models.features.api.ContinuousOutput() ``` The base class for a continuous output feature #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |-----------|-----------|-----------------------------------------------------------------------------------------------------------------| | objective | objective | objective of the feature indicating in which direction it should be optimized. Defaults to `MaximizeObjective`. | ### DiscreteInput { #bofire.data_models.features.api.DiscreteInput } ```python data_models.features.api.DiscreteInput() ``` Feature with discretized ordinal values allowed in the optimization. #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |---------------------|--------|---------------------------------------------------------| | key(str) | | key of the feature. | | values(List[float]) | | the discretized allowed values during the optimization. | #### Methods | Name | Description | | --- | --- | | [from_continuous](#bofire.data_models.features.api.DiscreteInput.from_continuous) | Rounds continuous values to the closest discrete ones. | | [is_fulfilled](#bofire.data_models.features.api.DiscreteInput.is_fulfilled) | Method to check if the values are close to the discrete values. | | [sample](#bofire.data_models.features.api.DiscreteInput.sample) | Draw random samples from the feature. | | [validate_candidental](#bofire.data_models.features.api.DiscreteInput.validate_candidental) | Method to validate the provided candidates. | | [validate_values_unique](#bofire.data_models.features.api.DiscreteInput.validate_values_unique) | Validates that provided values are unique. | ##### from_continuous { #bofire.data_models.features.api.DiscreteInput.from_continuous } ```python data_models.features.api.DiscreteInput.from_continuous(values) ``` Rounds continuous values to the closest discrete ones. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------------|------------------------------------|------------| | values | pd.DataFrame | Dataframe with continuous entries. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-----------------------------------------| | | pd.Series | pd.Series: Series with discrete values. | ##### is_fulfilled { #bofire.data_models.features.api.DiscreteInput.is_fulfilled } ```python data_models.features.api.DiscreteInput.is_fulfilled(values) ``` Method to check if the values are close to the discrete values. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------------------------|------------| | values | pd.Series | A series with values for the input feature. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|----------------------------------------------------------------------------| | | pd.Series | A series with boolean values indicating if the input feature is fulfilled. | ##### sample { #bofire.data_models.features.api.DiscreteInput.sample } ```python data_models.features.api.DiscreteInput.sample(n, seed=None) ``` Draw random samples from the feature. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------|--------------------------------|------------| | n | int | number of samples. | _required_ | | seed | int | random seed. Defaults to None. | `None` | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|---------------------------| | | pd.Series | pd.Series: drawn samples. | ##### validate_candidental { #bofire.data_models.features.api.DiscreteInput.validate_candidental } ```python data_models.features.api.DiscreteInput.validate_candidental(values) ``` Method to validate the provided candidates. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|--------------------------------------|------------| | values | pd.Series | suggested candidates for the feature | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|----------------------------------------------------------------------------------------------| | | ValueError | Raises error when one of the provided values is not contained in the list of allowed values. | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-------------------------------------------------| | | pd.Series | pd.Series: suggested candidates for the feature | ##### validate_values_unique { #bofire.data_models.features.api.DiscreteInput.validate_values_unique } ```python data_models.features.api.DiscreteInput.validate_values_unique(values) ``` Validates that provided values are unique. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|---------------|----------------|------------| | values | List\[float\] | List of values | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|--------------------------------------| | | ValueError | when values are non-unique. | | | ValueError | when values contains only one entry. | | | ValueError | when values is empty. | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|-------------------------------------| | | | List[values]: Sorted list of values | ### ContinuousDescriptorInput { #bofire.data_models.features.api.ContinuousDescriptorInput } ```python data_models.features.api.ContinuousDescriptorInput() ``` Class for continuous input features with descriptors #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |-------------|---------------|-------------------------------------------------| | lower_bound | float | Lower bound of the feature in the optimization. | | upper_bound | float | Upper bound of the feature in the optimization. | | descriptors | List\[str\] | Names of the descriptors. | | values | List\[float\] | Values of the descriptors. | #### Methods | Name | Description | | --- | --- | | [to_df](#bofire.data_models.features.api.ContinuousDescriptorInput.to_df) | Tabular overview of the feature as DataFrame | | [validate_list_lengths](#bofire.data_models.features.api.ContinuousDescriptorInput.validate_list_lengths) | Compares the length of the defined descriptors list with the provided values | ##### to_df { #bofire.data_models.features.api.ContinuousDescriptorInput.to_df } ```python data_models.features.api.ContinuousDescriptorInput.to_df() ``` Tabular overview of the feature as DataFrame ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------------|------------------------------------------------------------| | | pd.DataFrame | pd.DataFrame: tabular overview of the feature as DataFrame | ##### validate_list_lengths { #bofire.data_models.features.api.ContinuousDescriptorInput.validate_list_lengths } ```python data_models.features.api.ContinuousDescriptorInput.validate_list_lengths() ``` Compares the length of the defined descriptors list with the provided values ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------|--------------------------------|------------| | values | Dict | Dictionary with all attributes | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|----------------------------------------------------------------------------| | | ValueError | when the number of descriptors does not math the number of provided values | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|--------------------------| | Dict | | Dict with the attributes | ### CategoricalDescriptorInput { #bofire.data_models.features.api.CategoricalDescriptorInput } ```python data_models.features.api.CategoricalDescriptorInput() ``` Class for categorical input features with descriptors #### Attributes {.doc-section .doc-section-attributes} | Name | Type | Description | |-------------|-----------------------|----------------------------------------------------------------------------| | categories | List\[str\] | Names of the categories. | | allowed | List\[bool\] | List of bools indicating if a category is allowed within the optimization. | | descriptors | List\[str\] | List of strings representing the names of the descriptors. | | values | List\[List\[float\]\] | List of lists representing the descriptor values. | #### Methods | Name | Description | | --- | --- | | [fixed_value](#bofire.data_models.features.api.CategoricalDescriptorInput.fixed_value) | Returns the categories to which the feature is fixed, None if the feature is not fixed | | [from_descriptor_encoding](#bofire.data_models.features.api.CategoricalDescriptorInput.from_descriptor_encoding) | Converts values back from descriptor encoding. | | [from_df](#bofire.data_models.features.api.CategoricalDescriptorInput.from_df) | Creates a feature from a dataframe | | [to_descriptor_encoding](#bofire.data_models.features.api.CategoricalDescriptorInput.to_descriptor_encoding) | Converts values to descriptor encoding. | | [to_df](#bofire.data_models.features.api.CategoricalDescriptorInput.to_df) | Tabular overview of the feature as DataFrame | | [validate_experimental](#bofire.data_models.features.api.CategoricalDescriptorInput.validate_experimental) | Method to validate the experimental dataFrame | | [validate_values](#bofire.data_models.features.api.CategoricalDescriptorInput.validate_values) | Validates the compatibility of passed values for the descriptors and the defined categories | ##### fixed_value { #bofire.data_models.features.api.CategoricalDescriptorInput.fixed_value } ```python data_models.features.api.CategoricalDescriptorInput.fixed_value( transform_type=None, ) ``` Returns the categories to which the feature is fixed, None if the feature is not fixed ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-------------------------------------------|---------------------------------------| | | Union\[List\[str\], List\[float\], None\] | List[str]: List of categories or None | ##### from_descriptor_encoding { #bofire.data_models.features.api.CategoricalDescriptorInput.from_descriptor_encoding } ```python data_models.features.api.CategoricalDescriptorInput.from_descriptor_encoding( values, ) ``` Converts values back from descriptor encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------------|-------------------------------|------------| | values | pd.DataFrame | Descriptor encoded dataframe. | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------------| | | ValueError | If descriptor columns not found in the dataframe. | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|--------------------------------------------| | | pd.Series | pd.Series: Series with categorical values. | ##### from_df { #bofire.data_models.features.api.CategoricalDescriptorInput.from_df } ```python data_models.features.api.CategoricalDescriptorInput.from_df(key, df) ``` Creates a feature from a dataframe ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------------|-----------------------------------------------|------------| | key | str | The name of the feature | _required_ | | df | pd.DataFrame | Categories as rows and descriptors as columns | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|---------------| | _type_ | | _description_ | ##### to_descriptor_encoding { #bofire.data_models.features.api.CategoricalDescriptorInput.to_descriptor_encoding } ```python data_models.features.api.CategoricalDescriptorInput.to_descriptor_encoding( values, ) ``` Converts values to descriptor encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|----------------------|------------| | values | pd.Series | Values to transform. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------------|---------------------------------------------| | | pd.DataFrame | pd.DataFrame: Descriptor encoded dataframe. | ##### to_df { #bofire.data_models.features.api.CategoricalDescriptorInput.to_df } ```python data_models.features.api.CategoricalDescriptorInput.to_df() ``` Tabular overview of the feature as DataFrame ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|------------------------------------------------------------| | | | pd.DataFrame: tabular overview of the feature as DataFrame | ##### validate_experimental { #bofire.data_models.features.api.CategoricalDescriptorInput.validate_experimental } ```python data_models.features.api.CategoricalDescriptorInput.validate_experimental( values, strict=False, ) ``` Method to validate the experimental dataFrame ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|---------------------------------------------------------------------------------------------------------------------------|------------| | values | pd.Series | A dataFrame with experiments | _required_ | | strict | bool | Boolean to distinguish if the occurrence of fixed features in the dataset should be considered or not. Defaults to False. | `False` | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------------------------------------| | | ValueError | when an entry is not in the list of allowed categories | | | ValueError | when there is no variation in a feature provided by the experimental data | | | ValueError | when no variation is present or planned for a given descriptor | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|-----------------------------------------| | | pd.Series | pd.Series: A dataFrame with experiments | ##### validate_values { #bofire.data_models.features.api.CategoricalDescriptorInput.validate_values } ```python data_models.features.api.CategoricalDescriptorInput.validate_values(v, info) ``` Validates the compatibility of passed values for the descriptors and the defined categories ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------------------|------------------------------------|------------| | v | List\[List\[float\]\] | Nested list with descriptor values | _required_ | | values | Dict | Dictionary with attributes | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|------------------------------------------------------------| | | ValueError | when values have different length than categories | | | ValueError | when rows in values have different length than descriptors | | | ValueError | when a descriptor shows no variance in the data | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|-------------------------------------------------------| | | | List[List[float]]: Nested list with descriptor values | ### MolecularInput { #bofire.data_models.features.api.MolecularInput } ```python data_models.features.api.MolecularInput() ``` #### Methods | Name | Description | | --- | --- | | [get_bounds](#bofire.data_models.features.api.MolecularInput.get_bounds) | Calculates the lower and upper bounds for the feature based on the given transform type and values. | | [to_descriptor_encoding](#bofire.data_models.features.api.MolecularInput.to_descriptor_encoding) | Converts values to descriptor encoding. | ##### get_bounds { #bofire.data_models.features.api.MolecularInput.get_bounds } ```python data_models.features.api.MolecularInput.get_bounds( transform_type, values, reference_value=None, ) ``` Calculates the lower and upper bounds for the feature based on the given transform type and values. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |-----------------|-----------------|------------------------------------------------------------------------------|------------| | transform_type | AnyMolFeatures | The type of transformation to apply to the data. | _required_ | | values | pd.Series | The actual data over which the lower and upper bounds are calculated. | _required_ | | reference_value | Optional\[str\] | The reference value for the transformation. Not used here. Defaults to None. | `None` | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|---------------------------------------|---------------------------------------------------------------------------------------------------------| | | Tuple\[List\[float\], List\[float\]\] | Tuple[List[float], List[float]]: A tuple containing the lower and upper bounds of the transformed data. | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|---------------------|---------------------------------------------------------------------------------| | | NotImplementedError | Raised when `values` is None, as it is currently required for `MolecularInput`. | ##### to_descriptor_encoding { #bofire.data_models.features.api.MolecularInput.to_descriptor_encoding } ```python data_models.features.api.MolecularInput.to_descriptor_encoding( transform_type, values, ) ``` Converts values to descriptor encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|-----------|----------------------|------------| | values | pd.Series | Values to transform. | _required_ | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------------|---------------------------------------------| | | pd.DataFrame | pd.DataFrame: Descriptor encoded dataframe. | ### CategoricalMolecularInput { #bofire.data_models.features.api.CategoricalMolecularInput } ```python data_models.features.api.CategoricalMolecularInput() ``` #### Methods | Name | Description | | --- | --- | | [from_descriptor_encoding](#bofire.data_models.features.api.CategoricalMolecularInput.from_descriptor_encoding) | Converts values back from descriptor encoding. | | [validate_smiles](#bofire.data_models.features.api.CategoricalMolecularInput.validate_smiles) | Validates that categories are valid smiles. Note that this check can only | ##### from_descriptor_encoding { #bofire.data_models.features.api.CategoricalMolecularInput.from_descriptor_encoding } ```python data_models.features.api.CategoricalMolecularInput.from_descriptor_encoding( transform_type, values, ) ``` Converts values back from descriptor encoding. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|--------------|-------------------------------|------------| | values | pd.DataFrame | Descriptor encoded dataframe. | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|---------------------------------------------------| | | ValueError | If descriptor columns not found in the dataframe. | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------|--------------------------------------------| | | pd.Series | pd.Series: Series with categorical values. | ##### validate_smiles { #bofire.data_models.features.api.CategoricalMolecularInput.validate_smiles } ```python data_models.features.api.CategoricalMolecularInput.validate_smiles(categories) ``` Validates that categories are valid smiles. Note that this check can only be executed when rdkit is available. ###### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |------------|-------------|----------------|------------| | categories | List\[str\] | List of smiles | _required_ | ###### Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|------------|-----------------------------| | | ValueError | when string is not a smiles | ###### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|--------|-------------------------------| | | | List[str]: List of the smiles | ### TaskInput { #bofire.data_models.features.api.TaskInput } ```python data_models.features.api.TaskInput() ```