adelie.sklearn.GroupElasticNet#

class adelie.sklearn.GroupElasticNet(solver: str = 'grpnet', family: str = 'gaussian')[source]#

Group Elastic Net estimator with scikit-learn compatible API.

Parameters:
solverstr, optional

The solver to use. It must be one of the following:

  • "grpnet"

  • "cv_grpnet"

Default is "grpnet".

familystr, optional

The family of the response variable. It must be one of the following:

  • "gaussian"

  • "binomial"

  • "poisson"

  • "multigaussian"

  • "multinomial"

Default is "gaussian".

Methods

__init__([solver, family])

Initialize the GroupElasticNet estimator.

fit(X, y, **kwargs)

Fit the Group Elastic Net model.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict using the fitted Group Elastic Net model.

predict_proba(X)

Predict class probabilities.

score(X, y)

Compute the R-squared score of the model.

set_params(**params)

Set the parameters of this estimator.
__init__(solver: str = 'grpnet', family: str = 'gaussian')[source]#

Initialize the GroupElasticNet estimator.

fit(X: ndarray | MatrixNaiveBase32 | MatrixNaiveBase64, y: ndarray, **kwargs: Dict[str, Any])[source]#

Fit the Group Elastic Net model.

Parameters:
X(n, p) Union[ndarray, MatrixNaiveBase32, MatrixNaiveBase64]

Feature matrix. It is typically one of the matrices defined in adelie.matrix submodule or numpy.ndarray.

y(n,) ndarray

Response vector.

**kwargsDict[str, Any], optional

Additional arguments to pass to the solver.

Returns:
self

Returns an instance of self.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X: ndarray) → ndarray[source]#

Predict using the fitted Group Elastic Net model.

If self.family is either "binomial" or "multinomial", the output is class label predictions based on the largest probability predictions. Otherwise, the output is linear predictions.

Parameters:
X(n, p) Union[ndarray, MatrixNaiveBase32, MatrixNaiveBase64]

Feature matrix. It is typically one of the matrices defined in adelie.matrix submodule or numpy.ndarray.

Returns:
predsndarray

The class or linear predictions at X.

predict_proba(X: ndarray | MatrixNaiveBase32 | MatrixNaiveBase64) → ndarray[source]#

Predict class probabilities.

This method is only available for "binomial" and "multinomial" families.

Parameters:
X(n, p) Union[ndarray, MatrixNaiveBase32, MatrixNaiveBase64]

Feature matrix. It is typically one of the matrices defined in adelie.matrix submodule or numpy.ndarray.

Returns:
probandarray

The class probabilities at X.

score(X: ndarray, y: ndarray) → float[source]#

Compute the R-squared score of the model.

Parameters:
X(n, p) Union[ndarray, MatrixNaiveBase32, MatrixNaiveBase64]

Feature matrix. It is typically one of the matrices defined in adelie.matrix submodule or numpy.ndarray.

y(n,) ndarray

Response vector.

Returns:
R2float

The R-squared score.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.