Regression algorithms#
Warning
Some capabilities may require the installation of GEMSEO with all its features and some others may depend on plugins.
Warning
All the features of the wrapped libraries may not be exposed through GEMSEO.
Note
The algorithm settings can be passed to a function of the form
function(..., settings_model: AlgorithmSettings | None = None, **settings: Any)
either one by one:
function(..., setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)
or using the argument name "settings_model"
and the Pydantic model associated with the algorithm:
settings_model = AlgorithmSettings(setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)
function(..., settings_model=settings_model)
GaussianProcessRegressor#
Module: gemseo.mlearning.regression.algos.gpr
from gemseo.settings.mlearning import GaussianProcessRegressor_Settings
- Optional settings
alpha : float | gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]], optional
The nugget effect to regularize the model.
By default it is set to 1e-10.
bounds : tuple | tuple[float, float] | collections.abc.Mapping[str, tuple[float, float]], optional
The lower and upper bounds of the length scales.
Either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of them. When
bounds
is empty or when an input has no pair, the lower bound is 0.01 and the upper bound is 100.This argument is ignored when
kernel
isNone
.By default it is set to ().
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
kernel : typing.Optional[typing.Annotated[sklearn.gaussian_process.kernels.Kernel, WithJsonSchema(json_schema={}, mode=None)]], optional
The kernel specifying the covariance model.
If
None
, use a Matérn(2.5).By default it is set to None.
n_restarts_optimizer : <class 'int'>, optional
The number of restarts of the optimizer.
By default it is set to 10.
optimizer : typing.Union[str, typing.Annotated[typing.Callable, WithJsonSchema(json_schema={}, mode=None)]], optional
The optimization algorithm to find the parameter length scales.
By default it is set to fmin_l_bfgs_b.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional
The random state parameter.
If
None
, use the global random state instance fromnumpy.random
. Creating the model multiple times will produce different results. Ifint
, use a new random number generator seeded by this integer. This will produce the same results.By default it is set to 0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
GradientBoostingRegressor#
Module: gemseo.mlearning.regression.algos.gradient_boosting
from gemseo.settings.mlearning import GradientBoostingRegressor_Settings
- Optional settings
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
n_estimators : <class 'int'>, optional
The number of boosting stages to perform.
By default it is set to 100.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
LinearRegressor#
Module: gemseo.mlearning.regression.algos.linreg
from gemseo.settings.mlearning import LinearRegressor_Settings
- Optional settings
fit_intercept : <class 'bool'>, optional
Whether to fit the intercept.
By default it is set to True.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
l2_penalty_ratio : <class 'float'>, optional
The penalty ratio related to the l2 regularization.
If 1, use the Ridge penalty. If 0, use the Lasso penalty. Between 0 and 1, use the ElasticNet penalty.
By default it is set to 1.0.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
penalty_level : <class 'float'>, optional
The penalty level greater or equal to 0.
If zero, there is no penalty.
By default it is set to 0.0.
random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional
The random state parameter in the case of a penalty.
If
None
, use the global random state instance fromnumpy.random
. Creating the model multiple times will produce different results. Ifint
, use a new random number generator seeded by this integer. This will produce the same results.By default it is set to 0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
MLPRegressor#
Module: gemseo.mlearning.regression.algos.mlp
from gemseo.settings.mlearning import MLPRegressor_Settings
- Optional settings
hidden_layer_sizes : tuple[typing.Annotated[int, Gt(gt=0)], ...], optional
The number of neurons per hidden layer.
By default it is set to (100,).
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional
The random state parameter.
If
None
, use the global random state instance fromnumpy.random
. Creating the model multiple times will produce different results. Ifint
, use a new random number generator seeded by this integer. This will produce the same results.By default it is set to 0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
MOERegressor#
Module: gemseo.mlearning.regression.algos.moe
from gemseo.settings.mlearning import MOE_Settings
- Optional settings
hard : <class 'bool'>, optional
Whether to use a hard classification.
By default it is set to True.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
OTGaussianProcessRegressor#
Module: gemseo.mlearning.regression.algos.ot_gpr
from gemseo.settings.mlearning import OTGaussianProcessRegressor_Settings
- Optional settings
covariance_model : typing.Union[collections.abc.Sequence[typing.Union[openturns.statistics.CovarianceModelImplementation, type[openturns.statistics.CovarianceModelImplementation], gemseo.mlearning.regression.algos.ot_gpr_settings.CovarianceModel]], openturns.statistics.CovarianceModelImplementation, type[openturns.statistics.CovarianceModelImplementation], gemseo.mlearning.regression.algos.ot_gpr_settings.CovarianceModel], optional
The covariance model of the Gaussian process.
Either an OpenTURNS covariance model class, an OpenTURNS covariance model class instance, a name of covariance model, or a list of OpenTURNS covariance model classes, OpenTURNS class instances and covariance model names, whose size is equal to the output dimension.
By default it is set to Matern52.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
multi_start_algo_name : <enum 'DOEAlgorithmName'>, optional
The name of the DOE algorithm.
This DOE is used for the multi-start optimization of the covariance model parameters.
By default it is set to OT_OPT_LHS.
multi_start_algo_settings : collections.abc.Mapping[str, typing.Any], optional
The settings of the DOE algorithm.
By default it is set to {}.
multi_start_n_samples : <class 'int'>, optional
The number of starting points of the multi-start optimizer.
This optimizer is used for the covariance model parameters.
By default it is set to 10.
optimization_space : gemseo.algos.design_space.DesignSpace | None, optional
The covariance model parameter space.
The size of a variable must take into account the size of the output space. If
None
, the algorithm will use a design space with bounds defined by OpenTURNS.By default it is set to None.
optimizer : <class 'openturns.optim.OptimizationAlgorithmImplementation'>, optional
The solver used to optimize the covariance model parameters.
By default it is set to class=TNC class=OptimizationAlgorithmImplementation problem=class=OptimizationProblem implementation=class=OptimizationProblemImplementation objective=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[] evaluationImplementation=class=NoEvaluation name=Unnamed gradientImplementation=class=NoGradient name=Unnamed hessianImplementation=class=NoHessian name=Unnamed equality constraint=none inequality constraint=none bounds=none minimization=true dimension=0 startingPoint=class=Point name=Unnamed dimension=0 values=[] maximumIterationNumber=100 maximumCallsNumber=1000 maximumAbsoluteError=1e-05 maximumRelativeError=1e-05 maximumResidualError=1e-05 maximumConstraintError=1e-05 scale=class=Point name=Unnamed dimension=0 values=[] offset=class=Point name=Unnamed dimension=0 values=[] maxCGit=50 eta=0.25 stepmx=10 accuracy=0.0001 fmin=1 rescale=1.3.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
trend : <enum 'Trend'>, optional
The name of the trend.
By default it is set to constant.
use_hmat : bool | None, optional
Whether to use the HMAT or LAPACK as linear algebra method.
If
None
, use HMAT when the learning size is greater thanMAX_SIZE_FOR_LAPACK
.By default it is set to None.
PCERegressor#
Module: gemseo.mlearning.regression.algos.pce
from gemseo.settings.mlearning import PCERegressor_Settings
- Optional settings
cleaning_options : gemseo.mlearning.regression.algos.pce_settings.CleaningOptions | None, optional
The options of the `CleaningStrategy`_.
If
None
, useDEFAULT_CLEANING_OPTIONS
.By default it is set to None.
degree : <class 'int'>, optional
The polynomial degree of the PCE.
By default it is set to 2.
discipline : gemseo.core.discipline.discipline.Discipline | None, optional
The discipline to be sampled.
Used only when
use_quadrature
isTrue
anddata
isNone
.By default it is set to None.
hyperbolic_parameter : <class 'float'>, optional
The \(q\)-quasi norm parameter of the `hyperbolic and anisotropic enumerate function`_, defined over the interval:math:]0,1].
By default it is set to 1.0.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
n_quadrature_points : <class 'int'>, optional
The total number of quadrature points.
These points are used to compute the marginal number of points by input dimension when
discipline
is notNone
. If0
, use \((1+P)^d\) points, where \(d\) is the dimension of the input space and \(P\) is the polynomial degree of the PCE.By default it is set to 0.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
probability_space : gemseo.algos.parameter_space.ParameterSpace | None, optional
The random input variables using
OTDistribution
.If
None
,PCERegressor
usesdata.misc["input_space"]
wheredata
is theIODataset
passed at instantiation.By default it is set to None.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
use_cleaning : <class 'bool'>, optional
Whether to use the `CleaningStrategy`_ algorithm.
Otherwise, use a fixed truncation strategy (`FixedStrategy`_).
By default it is set to False.
use_lars : <class 'bool'>, optional
Whether to use the `LARS`_ algorithm.
This argument is ignored when
use_quadrature
isTrue
.By default it is set to False.
use_quadrature : <class 'bool'>, optional
Whether to estimate the coefficients of the PCE by quadrature.
If so, use the quadrature points stored in
data
or samplediscipline
. Otherwise, estimate the coefficients by least-squares regression.By default it is set to False.
PolynomialRegressor#
Module: gemseo.mlearning.regression.algos.polyreg
from gemseo.settings.mlearning import PolynomialRegressor_Settings
- Optional settings
degree : <class 'int'>, optional
The polynomial degree.
By default it is set to 2.
fit_intercept : <class 'bool'>, optional
Whether to fit the intercept.
By default it is set to True.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
l2_penalty_ratio : <class 'float'>, optional
The penalty ratio related to the l2 regularization.
If 1, use the Ridge penalty. If 0, use the Lasso penalty. Between 0 and 1, use the ElasticNet penalty.
By default it is set to 1.0.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
penalty_level : <class 'float'>, optional
The penalty level greater or equal to 0.
If zero, there is no penalty.
By default it is set to 0.0.
random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional
The random state parameter in the case of a penalty.
If
None
, use the global random state instance fromnumpy.random
. Creating the model multiple times will produce different results. Ifint
, use a new random number generator seeded by this integer. This will produce the same results.By default it is set to 0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
RBFRegressor#
Module: gemseo.mlearning.regression.algos.rbf
from gemseo.settings.mlearning import RBFRegressor_Settings
- Optional settings
der_function : typing.Optional[typing.Annotated[typing.Callable[[gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]], gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]], WithJsonSchema(json_schema={}, mode=None)]], optional
The derivative of the radial basis function.
Only to be provided if
function
is a callable and if the use of the model with its derivative is required. IfNone
and iffunction
is a callable, an error will be raised. IfNone
and iffunction
is a string, the class will look for its internal implementation and will raise an error if it is missing. Theder_function
shall take three arguments (input_data
,norm_input_data
,eps
). For an RBF of the form function(\(r\)), der_function(\(x\), \(|x|\), \(\epsilon\)) shall return \(\epsilon^{-1} x/|x| f'(|x|/\epsilon)\).By default it is set to None.
epsilon : float | None, optional
An adjustable constant for Gaussian or multiquadric functions.
If
None
, use the average distance between input data.By default it is set to None.
function : typing.Union[gemseo.mlearning.regression.algos.rbf_settings.RBF, typing.Annotated[typing.Callable[[float, float], float], WithJsonSchema(json_schema={}, mode=None)]], optional
The radial basis function.
This function takes a radius \(r\) as input, representing a distance between two points. If it is a string, then it must be one of the following:
"multiquadric"
for \(\sqrt{(r/\epsilon)^2 + 1}\),"inverse"
for \(1/\sqrt{(r/\epsilon)^2 + 1}\),"gaussian"
for \(\exp(-(r/\epsilon)^2)\),"linear"
for \(r\),"cubic"
for \(r^3\),"quintic"
for \(r^5\),"thin_plate"
for \(r^2\log(r)\).
If it is a callable, then it must take the two arguments
self
andr
as inputs, e.g.lambda self, r: sqrt((r/self.epsilon)**2 + 1)
for the multiquadric function. The epsilon parameter will be available asself.epsilon
. Other keyword arguments passed in will be available as well.By default it is set to multiquadric.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
norm : typing.Union[str, typing.Annotated[typing.Callable[[gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]], gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]], float], WithJsonSchema(json_schema={}, mode=None)]], optional
The distance metric.
Either a distance function name known by SciPy or a function that computes the distance between two points.
By default it is set to euclidean.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
smooth : <class 'float'>, optional
The degree of smoothness.
0
involves an interpolation of the learning points.By default it is set to 0.0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
RandomForestRegressor#
Module: gemseo.mlearning.regression.algos.random_forest
from gemseo.settings.mlearning import RandomForestRegressor_Settings
- Optional settings
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
n_estimators : <class 'int'>, optional
The number of trees in the forest.
By default it is set to 100.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional
The random state parameter.
If
None
, use the global random state instance fromnumpy.random
. Creating the model multiple times will produce different results. Ifint
, use a new random number generator seeded by this integer. This will produce the same results.By default it is set to 0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
RegressorChain#
Module: gemseo.mlearning.regression.algos.regressor_chain
from gemseo.settings.mlearning import RegressorChain_Settings
- Optional settings
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
SMTRegressor#
Note
The plugin gemseo_mlearning is required.
Module: gemseo_mlearning.regression.smt_regressor
from gemseo.settings.mlearning import SMTRegressorSettings
- Required settings
model_class_name : <enum 'SurrogateModel'>
The class name of a surrogate model available in SMT,i.e. a subclass of`smt.surrogate_models.surrogate_model.SurrogateModel`.
- Optional settings
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
SVMRegressor#
Module: gemseo.mlearning.regression.algos.svm
from gemseo.settings.mlearning import SVMRegressor_Settings
- Optional settings
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
kernel : typing.Union[str, typing.Annotated[typing.Callable, WithJsonSchema(json_schema={}, mode=None)]], optional
The name of the kernel or a callable for the SVM.
Examples of names: "linear", "poly", "rbf", "sigmoid", "precomputed".
By default it is set to rbf.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
TPSRegressor#
Module: gemseo.mlearning.regression.algos.thin_plate_spline
from gemseo.settings.mlearning import TPSRegressor_Settings
- Optional settings
der_function : typing.Optional[typing.Annotated[typing.Callable[[gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]], gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]], WithJsonSchema(json_schema={}, mode=None)]], optional
The derivative of the radial basis function.
Only to be provided if
function
is a callable and if the use of the model with its derivative is required. IfNone
and iffunction
is a callable, an error will be raised. IfNone
and iffunction
is a string, the class will look for its internal implementation and will raise an error if it is missing. Theder_function
shall take three arguments (input_data
,norm_input_data
,eps
). For an RBF of the form function(\(r\)), der_function(\(x\), \(|x|\), \(\epsilon\)) shall return \(\epsilon^{-1} x/|x| f'(|x|/\epsilon)\).By default it is set to None.
epsilon : float | None, optional
An adjustable constant for Gaussian or multiquadric functions.
If
None
, use the average distance between input data.By default it is set to None.
function : typing.Literal[<RBF.THIN_PLATE: 'thin_plate'>], optional
The thin plate radial basis function for \(r^2\log(r)\).
By default it is set to thin_plate.
input_names : collections.abc.Sequence[str], optional
The names of the input variables
By default it is set to ().
norm : typing.Union[str, typing.Annotated[typing.Callable[[gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]], gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]], float], WithJsonSchema(json_schema={}, mode=None)]], optional
The distance metric.
Either a distance function name known by SciPy or a function that computes the distance between two points.
By default it is set to euclidean.
output_names : collections.abc.Sequence[str], optional
The names of the output variables
By default it is set to ().
parameters : collections.abc.Mapping[str, typing.Any], optional
Other parameters.
By default it is set to {}.
smooth : <class 'float'>, optional
The degree of smoothness.
0
involves an interpolation of the learning points.By default it is set to 0.0.
transformer : collections.abc.Mapping[str, typing.Any], optional
The strategies to transform the variables.
The values are instances of
BaseTransformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theBaseTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.