Regression models options¶
GaussianProcessRegression¶
-
class
gemseo.mlearning.regression.gpr.
GaussianProcessRegression
(data, transformer=None, input_names=None, output_names=None, kernel=None, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=10, random_state=None)[source] Gaussian process regression
Constructor.
- Parameters
data (Dataset) – learning dataset
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
kernel (openturns.Kernel) – kernel function. If None, use a Matern(2.5). Default: None.
alpha (float or array) – nugget effect. Default: 1e-10.
optimizer (str or callable) – optimization algorithm. Default: ‘fmin_l_bfgs_b’.
n_restarts_optimizer (int) – number of restarts of the optimizer. Default: 10.
random_state (int) – the seed used to initialize the centers. If None, the random number generator is the RandomState instance used by np.random Default: None.
LinearRegression¶
-
class
gemseo.mlearning.regression.linreg.
LinearRegression
(data, transformer=None, input_names=None, output_names=None, fit_intercept=True, penalty_level=0.0, l2_penalty_ratio=1.0, **parameters)[source] Linear regression
Constructor.
- Parameters
data (Dataset) – learning dataset.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
fit_intercept (bool) – if True, fit intercept. Default: True.
penalty_level (float) – penalty level greater or equal to 0. If 0, there is no penalty. Default: 0.
l2_penalty_ratio (float) – penalty ratio related to the l2 regularization. If 1, the penalty is the Ridge penalty. If 0, this is the Lasso penalty. Between 0 and 1, the penalty is the ElasticNet penalty. Default: None.
MixtureOfExperts¶
-
class
gemseo.mlearning.regression.moe.
MixtureOfExperts
(data, transformer=None, input_names=None, output_names=None, hard=True)[source] Mixture of experts regression.
Constructor.
- Parameters
data (Dataset) – learning dataset.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
hard (bool) – Indicator for hard or soft clustering/classification. Hard clustering/classification if True. Default: True.
PCERegression¶
-
class
gemseo.mlearning.regression.pce.
PCERegression
(data, probability_space, discipline=None, transformer=None, input_names=None, output_names=None, strategy='LS', degree=2, n_quad=None, stieltjes=True, sparse_param=None)[source] Polynomial chaos expansion.
Constructor.
- Parameters
data (Dataset) – learning dataset
probability_space (ParameterSpace) – probability space.
discipline (MDODiscipline) – discipline to evaluate if strategy=’Quad’ and data is empty.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
strategy (str) – strategy to compute the parameters of the PCE, either ‘LS’, ‘Quad’ or ‘SparseLS’. Default: ‘LS’.
degree (int) – polynomial degree of the PCE
n_quad (int) – number of quadrature points
stieltjes (bool) – stieltjes
sparse_param –
Parameters for the Sparse Cleaning Truncation Strategy and/or hyperbolic truncation of the initial basis:
max_considered_terms (int) – Maximum Considered Terms,
most_significant (int), Most Significant number to retain,
significance_factor (float), Significance Factor,
hyper_factor (float), factor for hyperbolic truncation strategy.
PolynomialRegression¶
-
class
gemseo.mlearning.regression.polyreg.
PolynomialRegression
(data, degree, transformer=None, input_names=None, output_names=None, fit_intercept=True, penalty_level=0.0, l2_penalty_ratio=1.0, **parameters)[source] Polynomial regression.
Constructor.
- Parameters
data (Dataset) – learning dataset.
degree (int) – Degree of polynomial. Default: 2.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
fit_intercept (bool) – if True, fit intercept. Default: True.
penalty_level – penalty level greater or equal to 0. If 0, there is no penalty. Default: 0.
l2_penalty_ratio (float) – penalty ratio related to the l2 regularization. If 1, the penalty is the Ridge penalty. If 0, this is the Lasso penalty. Between 0 and 1, the penalty is the ElasticNet penalty. Default: None.
RBFRegression¶
-
class
gemseo.mlearning.regression.rbf.
RBFRegression
(data, transformer=None, input_names=None, output_names=None, function='multiquadric', der_function=None, epsilon=None, **parameters)[source] Regression based on radial basis functions.
Constructor.
- Parameters
data (Dataset) – learning dataset
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables. Default: None.
output_names (list(str)) – names of the output variables. Default: None.
function (str or callable) – radial basis function. Default: ‘multiquadric’.
der_function (callable) – derivative of radial basis function, only to be provided if function is callable and not str. The der_function should take three arguments (input_data, norm_input_data, eps). For a RBF of the form function(\(r\)), der_function(\(x\), \(|x|\), \(\epsilon\)) should return \(\epsilon^{-1} x/|x| f'(|x|/\epsilon)\). Default: None.
epsilon (float) – Adjustable constant for Gaussian or multiquadrics functions. Default: None.
parameters – other RBF parameters (sklearn).
RandomForestRegressor¶
-
class
gemseo.mlearning.regression.random_forest.
RandomForestRegressor
(data, transformer=None, input_names=None, output_names=None, n_estimators=100, **parameters)[source] Random forest regression
Constructor.
- Parameters
data (Dataset) – learning dataset.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
n_estimators (int) – number of trees in the forest.
parameters – other keyword arguments for the sklearn algo.