spe.relaxed_lasso.RelaxedLasso#

class spe.relaxed_lasso.RelaxedLasso(lambd=1.0, fit_intercept=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')#

Relaxed lasso linear regression model.

Fits the usual lasso, then refits an unpenalized linear regression on features selected by the lasso. Additionally is a subclass of AdaptiveLinearSmoother.

Documentation is heavily lifted from Lasso and LinearRegression classes, both of which are utilized by this class.

Parameters:

lambdfloat, optional: Constant that multiplies the L1 term, controlling regularization strength. lambd must be a non-negative float i.e. in [0, inf).
fit_interceptbool, optional: Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). Default is True.
precomputebool or array-like of shape (n_features, n_features), optional: Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always False to preserve sparsity. Default is False.
copy_Xbool, optional: If True, X will be copied; else, it may be overwritten. Default is True.
max_iterint, optional: The maximum number of iterations. Default is 1000.
tolfloat, optional: The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol. Default is 1e-4.
warm_startbool, optional: When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Default is False.
positivebool, optional: When set to True, forces the coefficients to be positive. Default is False.
random_stateint, optional: The seed of the pseudo random number generator that selects a random feature to update. Used when selection is random. Pass an int for reproducible output across multiple function calls. Default is None.
selection{‘cyclic’, ‘random’}, optional: If set to random, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to random) often leads to significantly faster convergence especially when tol is higher than 1e-4. Default is ‘cyclic’.

Methods

`__init__`([lambd, fit_intercept, precompute, ...])
`fit`(X, lasso_y[, lin_y, sample_weight, ...])
`fit_linear`(X, y[, sample_weight])
`get_linear_smoother`(X, tr_idx, ts_idx[, ...])	Get fitted adaptive linear smoother matrix $S(W)$.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_selected_X`(X)
`predict`(X[, tr_idx, ts_idx, y_refit])	Compute $S(W)y_{refit}$ as predictions.
`set_fit_request`(*[, check_input, lasso_y, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, tr_idx, ts_idx, y_refit])	Request metadata passed to the `predict` method.

__init__(lambd=1.0, fit_intercept=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')#

fit(X, lasso_y, lin_y=None, sample_weight=None, check_input=True)#

fit_linear(X, y, sample_weight=None)#

get_linear_smoother(X, tr_idx, ts_idx, ret_full_P=False)#: Get fitted adaptive linear smoother matrix $S(W)$.

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.

get_selected_X(X)#

predict(X, tr_idx=None, ts_idx=None, y_refit=None)#

Compute $S(W)y_{refit}$ as predictions.

Computes predictions as adaptive linear smoothing where $S(W)$ is the output of instance’s AdaptiveLinearSmoother().

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for check_input parameter in fit.
lasso_ystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for lasso_y parameter in fit.
lin_ystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for lin_y parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

tr_idxstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for tr_idx parameter in predict.
ts_idxstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for ts_idx parameter in predict.
y_refitstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for y_refit parameter in predict.

Returns:

selfobject: The updated object.