genericROM.Containers.OperatorCompressionData.OperatorCompressionDataRegression module

class OperatorCompressionDataRegression(solutionName)[source]

Bases: OperatorCompressionDataBase

Class containing an OperatorCompressionDataRegression

regressor

custom machine learning regressor

Type:

custom class

scalerParameters

scaler for the input of the regression (the parameters of the problem)

Type:

sklearn.preprocessing._data.StandardScaler

scalerCoefficients

scaler for the output of the regression (the coefficients on the reduced solution on the reducedOrderBasis - reducedCoordinates)

Type:

sklearn.preprocessing._data.StandardScaler

GetModel()[source]

Returns the regressor

Returns:

regressor whose hyperparameters have been optimized by GridSearchCV

Return type:

sklearn.model_selection._search.GridSearchCV

GetScalerCoefficients()[source]

Returns the scaler for the coefficients

Returns:

scaler for the output of the regression (the coefficients on the reduced solution on the reducedOrderBasis - reducedCoordinates)

Return type:

sklearn.preprocessing._data.StandardScaler

GetScalerParameters()[source]

Returns the scaler for the parameters

Returns:

scaler for the input of the regression (the parameters of the problem)

Return type:

sklearn.preprocessing._data.StandardScaler

SetModel(model)[source]

Sets the regressor

Parameters:

model (sklearn.model_selection._search.GridSearchCV) – regressor whose hyperparameters have been optimized by GridSearchCV

SetScalerCoefficients(scalerCoefficients)[source]

Sets the scaler for the coefficients

Parameters:

scalerCoefficients (sklearn.preprocessing._data.StandardScaler) – scaler for the output of the regression (the coefficients on the reduced solution on the reducedOrderBasis - reducedCoordinates)

SetScalerParameters(scalerParameters)[source]

Sets the scaler for the parameters

Parameters:

scalerParameters (sklearn.preprocessing._data.StandardScaler) – scaler for the input of the regression (the parameters of the problem)