- class sklearn.linear_model.RandomizedLasso(alpha='aic', scaling=0.5, sample_fraction=0.75, n_resampling=200, selection_threshold=0.25, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, random_state=None, n_jobs=1, pre_dispatch='3*n_jobs', memory=Memory(cachedir=None))¶
Randomized Lasso works by resampling the train data and computing a Lasso on each resampling. In short, the features selected more often are good features. It is also known as stability selection.
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 alpha parameter in the stability selection article used to randomly scale the 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.
precompute : True | False | ‘auto’
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.
max_iter : integer, optional
Maximum number of iterations to perform in the Lars algorithm.
eps : float, optional
The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the ‘tol’ parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.
n_jobs : integer, optional
Number of CPUs to use during the resampling. If ‘-1’, use all the CPUs
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 np.random.
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:
- None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
- An int, giving the exact number of total jobs that are spawned
- A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
memory : Instance of joblib.Memory or string
Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory.
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 scores_ is the max of all_scores_.
See examples/linear_model/plot_sparse_recovery.py for an example.
Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417-473, September 2010 DOI: 10.1111/j.1467-9868.2010.00740.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]) Return a mask, or list, of the features/indices selected. inverse_transform(X) Transform a new matrix using the selected features set_params(**params) Set the parameters of this estimator. transform(X) Transform a new matrix using the selected features
- __init__(alpha='aic', scaling=0.5, sample_fraction=0.75, n_resampling=200, selection_threshold=0.25, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, random_state=None, n_jobs=1, pre_dispatch='3*n_jobs', memory=Memory(cachedir=None))¶
- fit(X, y)¶
Fit the model using X, y as training data.
X : array-like, sparse matrix shape = [n_samples, n_features]
y : array-like, shape = [n_samples]
self : object
Returns an instance of self.
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
X : numpy array of shape [n_samples, n_features]
y : numpy array of shape [n_samples]
X_new : numpy array of shape [n_samples, n_features_new]
Get parameters for this estimator.
deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
params : mapping of string to any
Parameter names mapped to their values.
Return a mask, or list, of the features/indices selected.
Transform a new matrix using the selected features
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
Transform a new matrix using the selected features