Warning
DEPRECATED
class sklearn.linear_model.RandomizedLasso(*args, **kwargs)
[source]
Randomized Lasso.
Randomized Lasso works by subsampling the training data and computing a Lasso estimate where the penalty of a random subset of coefficients has been scaled. By performing this double randomization several times, the method assigns high scores to features that are repeatedly selected across randomizations. This is known as stability selection. In short, features selected more often are considered good features.
Parameters: 
alpha : float, ‘aic’, or ‘bic’, optional The regularization parameter alpha parameter in the Lasso. Warning: this is not the alpha parameter in the stability selection article which is scaling. scaling : float, optional The s parameter used to randomly scale the penalty of different features. Should be between 0 and 1. sample_fraction : float, optional The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. n_resampling : int, optional Number of randomized models. selection_threshold : float, optional The score above which features should be selected. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default True If True, the regressors X will be normalized before regression. This parameter is ignored when precompute : True  False  ‘auto’  arraylike Whether to use a precomputed Gram matrix to speed up calculations. If set to ‘auto’ let us decide. The Gram matrix can also be passed as argument, but it will be used only for the selection of parameter alpha, if alpha is ‘aic’ or ‘bic’. max_iter : integer, optional Maximum number of iterations to perform in the Lars algorithm. eps : float, optional The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems. Unlike the ‘tol’ parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by n_jobs : integer, optional Number of CPUs to use during the resampling. If ‘1’, use all the CPUs pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
memory : None, str or object with the joblib.Memory interface, optional (default=None) Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory. 

Attributes: 
scores_ : array, shape = [n_features] Feature scores between 0 and 1. all_scores_ : array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests 
See also
Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417473, September 2010 DOI: 10.1111/j.14679868.2010.00740.x
>>> from sklearn.linear_model import RandomizedLasso >>> randomized_lasso = RandomizedLasso()
fit (X, y)  Fit the model using X, y as training data. 
fit_transform (X[, y])  Fit to data, then transform it. 
get_params ([deep])  Get parameters for this estimator. 
get_support ([indices])  Get a mask, or integer index, of the features selected 
inverse_transform (X)  Reverse the transformation operation 
set_params (**params)  Set the parameters of this estimator. 
transform (X)  Reduce X to the selected features. 
__init__(*args, **kwargs)
[source]
DEPRECATED: The class RandomizedLasso is deprecated in 0.19 and will be removed in 0.21.
fit(X, y)
[source]
Fit the model using X, y as training data.
Parameters: 
X : arraylike, shape = [n_samples, n_features] Training data. y : arraylike, shape = [n_samples] Target values. Will be cast to X’s dtype if necessary 

Returns: 
self : object Returns an instance of self. 
fit_transform(X, y=None, **fit_params)
[source]
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: 
X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. 

Returns: 
X_new : numpy array of shape [n_samples, n_features_new] Transformed array. 
get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 
deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. 

Returns: 
params : mapping of string to any Parameter names mapped to their values. 
get_support(indices=False)
[source]
Get a mask, or integer index, of the features selected
Parameters: 
indices : boolean (default False) If True, the return value will be an array of integers, rather than a boolean mask. 

Returns: 
support : array An index that selects the retained features from a feature vector. If 
inverse_transform(X)
[source]
Reverse the transformation operation
Parameters: 
X : array of shape [n_samples, n_selected_features] The input samples. 

Returns: 
X_r : array of shape [n_samples, n_original_features]

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns:  self : 

transform(X)
[source]
Reduce X to the selected features.
Parameters: 
X : array of shape [n_samples, n_features] The input samples. 

Returns: 
X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features. 
© 2007–2017 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.linear_model.RandomizedLasso.html