Vocs
xopt.vocs.VOCS ¶
Bases: XoptBaseModel
Variables, Objectives, Constraints, and other Settings (VOCS) data structure to describe optimization problems.
Attributes:
variables : Dict[str, conlist(float, min_length=2, max_length=2)] Input variable names with a list of minimum and maximum values. constraints : Dict[str, conlist(Union[float, ConstraintEnum], min_length=2, max_length=2)] Constraint names with a list of constraint type and value. objectives : Dict[str, ObjectiveEnum] Objective names with type of objective. constants : Dict[str, Any] Constant names and values passed to evaluate function. observables : List[str] Observation names tracked alongside objectives and constraints.
Methods:
from_yaml(cls, yaml_text: str) -> 'VOCS'
Create a VOCS object from a YAML string.
as_yaml(self) -> str
Convert the VOCS object to a YAML string.
random_inputs(self, n: int = None, custom_bounds: dict = None, include_constants: bool = True, seed: int = None) -> list[dict]
Uniform sampling of the variables.
convert_dataframe_to_inputs(self, data: pd.DataFrame, include_constants: bool = True) -> pd.DataFrame
Extracts only inputs from a dataframe.
convert_numpy_to_inputs(self, inputs: np.ndarray, include_constants: bool = True) -> pd.DataFrame
Convert 2D numpy array to list of dicts (inputs) for evaluation.
variable_data(self, data: Union[pd.DataFrame, List[Dict], List[Dict]], prefix: str = "variable_") -> pd.DataFrame
Returns a dataframe containing variables according to vocs.variables
in sorted order.
objective_data(self, data: Union[pd.DataFrame, List[Dict], List[Dict]], prefix: str = "objective_", return_raw: bool = False) -> pd.DataFrame
Returns a dataframe containing objective data transformed according to vocs.objectives
.
constraint_data(self, data: Union[pd.DataFrame, List[Dict], List[Dict]], prefix: str = "constraint_") -> pd.DataFrame
Returns a dataframe containing constraint data transformed according to vocs.constraints
.
observable_data(self, data: Union[pd.DataFrame, List[Dict], List[Dict]], prefix: str = "observable_") -> pd.DataFrame
Returns a dataframe containing observable data.
feasibility_data(self, data: Union[pd.DataFrame, List[Dict], List[Dict]], prefix: str = "feasible_") -> pd.DataFrame
Returns a dataframe containing booleans denoting if a constraint is satisfied or not.
normalize_inputs(self, input_points: pd.DataFrame) -> pd.DataFrame
Normalize input data (transform data into the range [0,1]) based on the variable ranges defined in the VOCS.
denormalize_inputs(self, input_points: pd.DataFrame) -> pd.DataFrame
Denormalize input data (transform data from the range [0,1]) based on the variable ranges defined in the VOCS.
validate_input_data(self, input_points: pd.DataFrame) -> None
Validates input data. Raises an error if the input data does not satisfy requirements given by vocs.
extract_data(self, data: pd.DataFrame, return_raw: bool = False, return_valid: bool = False) -> tuple
Split dataframe into separate dataframes for variables, objectives and constraints based on vocs.
select_best(self, data: pd.DataFrame, n: int = 1) -> tuple
Get the best value and point for a given data set based on vocs.
cumulative_optimum(self, data: pd.DataFrame) -> pd.DataFrame
Returns the cumulative optimum for the given DataFrame.
Attributes¶
xopt.vocs.VOCS.all_names
property
¶
all_names
Returns all vocs names (variables, constants, objectives, constraints)
xopt.vocs.VOCS.bounds
property
¶
bounds
Returns a bounds array (mins, maxs) of shape (2, n_variables). Arrays of lower and upper bounds can be extracted by: mins, maxs = vocs.bounds
Returns:
Type | Description |
---|---|
ndarray
|
The bounds array. |
xopt.vocs.VOCS.constraint_names
property
¶
constraint_names
Returns a sorted list of constraint names
xopt.vocs.VOCS.n_outputs
property
¶
n_outputs
Returns the number of outputs len(objectives + constraints + observables)
Returns:
Type | Description |
---|---|
int
|
The number of outputs. |
xopt.vocs.VOCS.observable_names
property
¶
observable_names
Returns a sorted list of observable names
xopt.vocs.VOCS.output_names
property
¶
output_names
Returns a list of expected output keys: (objectives + constraints + observables) Each sub-list is sorted.
Returns:
Type | Description |
---|---|
List[str]
|
The list of expected output keys. |
Functions¶
xopt.vocs.VOCS.as_yaml ¶
as_yaml()
Convert the VOCS object to a YAML string.
Returns:
Type | Description |
---|---|
str
|
The YAML string representation of the VOCS object. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.constraint_data ¶
constraint_data(data, prefix='constraint_')
Returns a dataframe containing constraint data transformed according to
vocs.constraints
such that values that satisfy each constraint are negative.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[DataFrame, List[Dict]]
|
The data to be processed. |
required |
prefix
|
str
|
Prefix added to column names. Defaults to "constraint_". |
'constraint_'
|
Returns:
Type | Description |
---|---|
DataFrame
|
The processed dataframe. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.convert_dataframe_to_inputs ¶
convert_dataframe_to_inputs(data, include_constants=True)
Extracts only inputs from a dataframe.
This will add constants if include_constants
is true.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
DataFrame
|
The dataframe to extract inputs from. |
required |
include_constants
|
bool
|
Whether to include constants in the inputs. Defaults to True. |
True
|
Returns:
Type | Description |
---|---|
DataFrame
|
A dataframe containing the extracted inputs. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.convert_numpy_to_inputs ¶
convert_numpy_to_inputs(inputs, include_constants=True)
Convert 2D numpy array to list of dicts (inputs) for evaluation.
Assumes that the columns of the array match correspond to
sorted(self.vocs.variables.keys())
Parameters:
Name | Type | Description | Default |
---|---|---|---|
inputs
|
ndarray
|
The 2D numpy array to convert. |
required |
include_constants
|
bool
|
Whether to include constants in the inputs. Defaults to True. |
True
|
Returns:
Type | Description |
---|---|
DataFrame
|
A dataframe containing the converted inputs. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.correct_list_types ¶
correct_list_types(v)
make sure that constraint list types are correct
Source code in xopt/vocs.py
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xopt.vocs.VOCS.cumulative_optimum ¶
cumulative_optimum(data)
Returns the cumulative optimum for the given DataFrame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
DataFrame
|
Data for which the cumulative optimum shall be calculated. |
required |
Returns:
Type | Description |
---|---|
DataFrame
|
Cumulative optimum for the given DataFrame. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.denormalize_inputs ¶
denormalize_inputs(input_points)
Denormalize input data (transform data from the range [0,1]) based on the variable ranges defined in the VOCS.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input_points
|
DataFrame
|
A DataFrame containing normalized input data in the range [0,1]. |
required |
Returns:
Type | Description |
---|---|
DataFrame
|
A DataFrame with denormalized input data corresponding to the
specified variable ranges. Contains columns equal to the intersection
between |
Notes
If the input DataFrame is empty or no variable information is available in the VOCS, an empty DataFrame is returned.
Source code in xopt/vocs.py
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xopt.vocs.VOCS.extract_data ¶
extract_data(data, return_raw=False, return_valid=False)
split dataframe into seperate dataframes for variables, objectives and
constraints based on vocs - objective data is transformed based on
vocs.objectives
properties
Returns:
Type | Description |
---|---|
variable_data : DataFrame
|
objective_data : DataFrame Dataframe containing objective data constraint_data : DataFrame Dataframe containing constraint data observable_data : DataFrame Dataframe containing observable data |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.feasibility_data ¶
feasibility_data(data, prefix='feasible_')
Returns a dataframe containing booleans denoting if a constraint is satisfied or
not. Returned dataframe also contains a column feasible
which denotes if
all constraints are satisfied.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[DataFrame, List[Dict]]
|
The data to be processed. |
required |
prefix
|
str
|
Prefix added to column names. Defaults to "feasible_". |
'feasible_'
|
Returns:
Type | Description |
---|---|
DataFrame
|
The processed dataframe. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.from_yaml
classmethod
¶
from_yaml(yaml_text)
Create a VOCS object from a YAML string.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
yaml_text
|
str
|
The YAML string to create the VOCS object from. |
required |
Returns:
Type | Description |
---|---|
VOCS
|
The created VOCS object. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.grid_inputs ¶
grid_inputs(n, custom_bounds=None, include_constants=True)
Generate a meshgrid of inputs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n
|
Union[int, Dict[str, int]]
|
Number of points to generate along each axis. If an integer is provided, the same number of points is used for all variables. If a dictionary is provided, it should have variable names as keys and the number of points as values. |
required |
custom_bounds
|
dict
|
Custom bounds for the variables. If None, the default bounds from |
None
|
include_constants
|
bool
|
If True, include constant values from |
True
|
Returns:
Type | Description |
---|---|
DataFrame
|
A DataFrame containing the generated meshgrid of inputs. Each column corresponds to a variable, and each row represents a point in the grid. |
Raises:
Type | Description |
---|---|
TypeError
|
If |
ValueError
|
If |
Warns:
Type | Description |
---|---|
RuntimeWarning
|
If |
Notes
The function generates a meshgrid of inputs based on the specified bounds. If custom_bounds
are provided,
they are validated and clipped to ensure they lie within the domain of self.variables
. The resulting meshgrid
is flattened and returned as a DataFrame. If include_constants
is True, constant values from self.constants
are added to the DataFrame.
Source code in xopt/vocs.py
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xopt.vocs.VOCS.normalize_inputs ¶
normalize_inputs(input_points)
Normalize input data (transform data into the range [0,1]) based on the variable ranges defined in the VOCS.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input_points
|
DataFrame
|
A DataFrame containing input data to be normalized. |
required |
Returns:
Type | Description |
---|---|
DataFrame
|
A DataFrame with input data in the range [0,1] corresponding to the
specified variable ranges. Contains columns equal to the intersection
between |
Notes
If the input DataFrame is empty or no variable information is available in the VOCS, an empty DataFrame is returned.
Source code in xopt/vocs.py
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xopt.vocs.VOCS.objective_data ¶
objective_data(data, prefix='objective_', return_raw=False)
Returns a dataframe containing objective data transformed according to
vocs.objectives
such that we always assume minimization.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[DataFrame, List[Dict]]
|
The data to be processed. |
required |
prefix
|
str
|
Prefix added to column names. Defaults to "objective_". |
'objective_'
|
return_raw
|
bool
|
Whether to return raw objective data. Defaults to False. |
False
|
Returns:
Type | Description |
---|---|
DataFrame
|
The processed dataframe. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.observable_data ¶
observable_data(data, prefix='observable_')
Returns a dataframe containing observable data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[DataFrame, List[Dict]]
|
The data to be processed. |
required |
prefix
|
str
|
Prefix added to column names. Defaults to "observable_". |
'observable_'
|
Returns:
Type | Description |
---|---|
DataFrame
|
The processed dataframe. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.random_inputs ¶
random_inputs(n=None, custom_bounds=None, include_constants=True, seed=None)
Uniform sampling of the variables.
Returns a dict of inputs.
If include_constants, the vocs.constants are added to the dict.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n
|
int
|
Number of samples to generate. Defaults to None. |
None
|
custom_bounds
|
dict
|
Custom bounds for the variables. Defaults to None. |
None
|
include_constants
|
bool
|
Whether to include constants in the inputs. Defaults to True. |
True
|
seed
|
int
|
Seed for the random number generator. Defaults to None. |
None
|
Returns:
Type | Description |
---|---|
list[dict]
|
A list of dictionaries containing the sampled inputs. |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.select_best ¶
select_best(data, n=1)
get the best value and point for a given data set based on vocs - does not work for multi-objective problems - data that violates any constraints is ignored
Returns:
Type | Description |
---|---|
index: index of best point
|
value: value of best point params: input parameters that give the best point |
Source code in xopt/vocs.py
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xopt.vocs.VOCS.validate_input_data ¶
validate_input_data(input_points)
Validates input data. Raises an error if the input data does not satisfy requirements given by vocs.
Returns:
Type | Description |
---|---|
None
|
|
Raises:
Type | Description |
---|---|
ValueError: if input data does not satisfy requirements.
|
|
Source code in xopt/vocs.py
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xopt.vocs.VOCS.variable_data ¶
variable_data(data, prefix='variable_')
Returns a dataframe containing variables according to vocs.variables
in sorted order.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[DataFrame, List[Dict]]
|
The data to be processed. |
required |
prefix
|
str
|
Prefix added to column names. Defaults to "variable_". |
'variable_'
|
Returns:
Type | Description |
---|---|
DataFrame
|
The processed dataframe. |
Source code in xopt/vocs.py
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|