criterion module¶
Acquisition criterion for which the optimum would improve the regression model.
An acquisition criterion (also called infill criterion) is a function taking a model input value and returning a value of interest to maximize (default option) or minimize according to the meaning of the acquisition criterion.
Then, the input value optimizing this criterion can be used to enrich the dataset used by a machine learning algorithm in its training stage. This is the purpose of adaptive learning.
This notion of acquisition criterion is implemented through the
MLDataAcquisitionCriterion
class which is built from a
MLSupervisedAlgo
and inherits from MDOFunction
.
- class gemseo_mlearning.adaptive.criterion.MLDataAcquisitionCriterion(algo_distribution, **options)[source]¶
Bases:
MDOFunction
Acquisition criterion.
# noqa: D205 D212 D415
- Parameters:
algo_distribution (MLRegressorDistribution) – The distribution of a machine learning algorithm.
**options (MLDataAcquisitionCriterionOptionType) – The acquisition criterion options.
- class ApproximationMode(value)¶
Bases:
StrEnum
The approximation derivation modes.
- CENTERED_DIFFERENCES = 'centered_differences'¶
The centered differences method used to approximate the Jacobians by perturbing each variable with a small real number.
- COMPLEX_STEP = 'complex_step'¶
The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.
- FINITE_DIFFERENCES = 'finite_differences'¶
The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.
- class ConstraintType(value)¶
Bases:
StrEnum
The type of constraint.
- EQ = 'eq'¶
The type of function for equality constraint.
- INEQ = 'ineq'¶
The type of function for inequality constraint.
- class FunctionType(value)¶
Bases:
StrEnum
An enumeration.
- EQ = 'eq'¶
- INEQ = 'ineq'¶
- NONE = ''¶
- OBJ = 'obj'¶
- OBS = 'obs'¶
- check_grad(x_vect, approximation_mode=ApproximationMode.FINITE_DIFFERENCES, step=1e-06, error_max=1e-08)¶
Check the gradients of the function.
- Parameters:
x_vect (ndarray[Any, dtype[Number]]) – The vector at which the function is checked.
approximation_mode (ApproximationMode) –
The approximation mode.
By default it is set to “finite_differences”.
step (float) –
The step for the approximation of the gradients.
By default it is set to 1e-06.
error_max (float) –
The maximum value of the error.
By default it is set to 1e-08.
- Raises:
ValueError – Either if the approximation method is unknown, if the shapes of the analytical and approximated Jacobian matrices are inconsistent or if the analytical gradients are wrong.
- Return type:
None
- evaluate(x_vect)¶
Evaluate the function and store the dimension of the output space.
- static filt_0(arr, floor_value=1e-06)¶
Set the non-significant components of a vector to zero.
The component of a vector is non-significant if its absolute value is lower than a threshold.
- static from_pickle(file_path)¶
Deserialize a function from a file.
- Parameters:
file_path (str | Path) – The path to the file containing the function.
- Returns:
The function instance.
- Return type:
- classmethod generate_input_names(input_dim, input_names=None)¶
Generate the names of the inputs of the function.
- Parameters:
input_dim (int) – The dimension of the input space of the function.
input_names (Sequence[str] | None) – The initial names of the inputs of the function. If there is only one name, e.g.
["var"]
, use this name as a base name and generate the names of the inputs, e.g.["var!0", "var!1", "var!2"]
if the dimension of the input space is equal to 3. IfNone
, use"x"
as a base name and generate the names of the inputs, i.e.["x!0", "x!1", "x!2"]
.
- Returns:
The names of the inputs of the function.
- Return type:
Sequence[str]
- get_indexed_name(index)¶
Return the name of function component.
- static init_from_dict_repr(**attributes)¶
Initialize a new function.
This is typically used for deserialization.
- Parameters:
**attributes (Any) – The values of the serializable attributes listed in
MDOFunction.DICT_REPR_ATTR
.- Returns:
A function initialized from the provided data.
- Raises:
ValueError – If the name of an argument is not in
MDOFunction.DICT_REPR_ATTR
.- Return type:
- is_constraint()¶
Check if the function is a constraint.
The type of a constraint function is either ‘eq’ or ‘ineq’.
- Returns:
Whether the function is a constraint.
- Return type:
- offset(value)¶
Add an offset value to the function.
- static rel_err(a_vect, b_vect, error_max)¶
Compute the 2-norm of the difference between two vectors.
Normalize it with the 2-norm of the reference vector if the latter is greater than the maximal error.
- set_pt_from_database(database, design_space, normalize=False, jac=True, x_tolerance=1e-10)¶
Set the original function and Jacobian function from a database.
For a given input vector, the method
MDOFunction.func()
will return either the output vector stored in the database if the input vector is present orNone
. The same for the methodMDOFunction.jac()
.- Parameters:
database (Database) – The database to read.
design_space (DesignSpace) – The design space used for normalization.
normalize (bool) –
If
True
, the values of the inputs are unnormalized before call.By default it is set to False.
jac (bool) –
If
True
, a Jacobian pointer is also generated.By default it is set to True.
x_tolerance (float) –
The tolerance on the distance between inputs.
By default it is set to 1e-10.
- Return type:
None
- to_dict()¶
Create a dictionary representation of the function.
This is used for serialization. The pointers to the functions are removed.
- to_pickle(file_path)¶
Serialize the function and store it in a file.
- Parameters:
file_path (str | Path) – The path to the file to store the function.
- Return type:
None
- COEFF_FORMAT_ND: str = '{: .2e}'¶
The format to be applied to a number when represented in a matrix.
- DICT_REPR_ATTR: ClassVar[list[str]] = ['name', 'f_type', 'expr', 'input_names', 'dim', 'special_repr', 'output_names']¶
The names of the attributes to be serialized.
- algo_distribution: MLRegressorDistribution¶
The distribution of a machine learning algorithm assessor.
- property func: Callable[[ndarray[Any, dtype[Number]]], ndarray[Any, dtype[Number]] | Number]¶
The function to be evaluated from a given input vector.
- property has_jac: bool¶
Check if the function has an implemented Jacobian function.
- Returns:
Whether the function has an implemented Jacobian function.
- property input_names: list[str]¶
The names of the inputs of the function.
Use a copy of the original names.
- property jac: Callable[[ndarray[Any, dtype[Number]]], ndarray[Any, dtype[Number]]]¶
The Jacobian function to be evaluated from a given input vector.
- last_eval: OutputType | None¶
The value of the function output at the last evaluation.
None
if it has not yet been evaluated.
- property n_calls: int¶
The number of times the function has been evaluated.
This count is both multiprocess- and multithread-safe, thanks to the locking process used by
MDOFunction.evaluate()
.
- class gemseo_mlearning.adaptive.criterion.MLDataAcquisitionCriterionFactory[source]¶
Bases:
BaseFactory
A factory of
MLDataAcquisitionCriterion
.- Return type:
Any
- create(class_name, *args, **kwargs)¶
Return an instance of a class.
- Parameters:
- Returns:
The instance of the class.
- Raises:
TypeError – If the class cannot be instantiated.
- Return type:
- get_class(name)¶
Return a class from its name.
- Parameters:
name (str) – The name of the class.
- Returns:
The class.
- Raises:
ImportError – If the class is not available.
- Return type:
- get_default_option_values(name)¶
Return the constructor kwargs default values of a class.
- get_default_sub_option_values(name, **options)¶
Return the default values of the sub options of a class.
- Parameters:
- Returns:
The JSON grammar.
- Return type:
- get_library_name(name)¶
Return the name of the library related to the name of a class.
- get_options_doc(name)¶
Return the constructor documentation of a class.
- get_options_grammar(name, write_schema=False, schema_path=None)¶
Return the options JSON grammar for a class.
Attempt to generate a JSONGrammar from the arguments of the __init__ method of the class.
- Parameters:
- Returns:
The JSON grammar.
- Return type:
- get_sub_options_grammar(name, **options)¶
Return the JSONGrammar of the sub options of a class.
- Parameters:
- Returns:
The JSON grammar.
- Return type:
- is_available(name)¶
Return whether a class can be instantiated.
- update()¶
Search for the classes that can be instantiated.
- The search is done in the following order:
The fully qualified module names
The plugin packages
The packages from the environment variables
- Return type:
None