btb.tuning.metamodels.gaussian_process module¶

class btb.tuning.metamodels.gaussian_process.GaussianCopulaProcessMetaModel[source]¶

Bases: btb.tuning.metamodels.gaussian_process.GaussianProcessMetaModel

GaussianCopulaProcessMetaModel class.

This class represents a meta-model using an underlying GaussianProcessRegressor from sklearn.gaussian_process applying copulas.univariate.Univariate transformations to the input data and afterwards reverts it for the predictions.

During the fit process, this metamodel trains a univariate copula for each hyperparameter to then compute the cumulative distribution of these. Once the cumulative distribution has been calculated, we calculate the inverse of the normal cumulative distribution using scipy.stats.norm and use these transformations to train the GaussianProcessRegressor model.

When predicting the output value, an inverse of the normal cumulative distribution is computed to the normal cumulative distribution, using the previously trained univariate copula with the input data of the score.

_MODEL_KWARGS¶

Dictionary with the default kwargs for the GaussianProcessRegressor instantiation.

Type: dict

_MODEL_CLASS¶

Class to be instantiated and used for the self._model instantiation. In this case sklearn.gaussian_process.GaussainProcessRegressor

Type: type

class btb.tuning.metamodels.gaussian_process.GaussianProcessMetaModel[source]¶

Bases: btb.tuning.metamodels.base.BaseMetaModel

GaussianProcessMetaModel class.

This class represents a meta-model using an underlying GaussianProcessRegressor from sklearn.gaussian_process.

_MODEL_KWARGS¶

Dictionary with the default kwargs for the GaussianProcessRegressor instantiation.

Type: dict

_MODEL_CLASS¶

Class to be instantiated and used for the self._model instantiation. In this case sklearn.gaussian_process.GaussainProcessRegressor

Type: type