dislib.regression.Lasso¶
ADMM Lasso
@Authors: Aleksandar Armacki and Lidija Fodor @Affiliation: Faculty of Sciences, University of Novi Sad, Serbia
This work is supported by the I-BiDaaS project, funded by the European Commission under Grant Agreement No. 780787.
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class
dislib.regression.lasso.base.
Lasso
(lmbd=0.001, rho=1, max_iter=100, atol=0.0001, rtol=0.01, verbose=False)[source]¶ Bases:
sklearn.base.BaseEstimator
Lasso represents the Least Absolute Shrinkage and Selection Operator (Lasso) for regression analysis, solved in a distributed manner with ADMM.
Parameters: - lmbd (float, optional (default=1e-3)) – The regularization parameter for Lasso regression.
- rho (float, optional (default=1)) – The penalty parameter for constraint violation.
- max_iter (int, optional (default=100)) – The maximum number of iterations of ADMM.
- atol (float, optional (default=1e-4)) – The absolute tolerance used to calculate the early stop criterion for ADMM.
- rtol (float, optional (default=1e-2)) – The relative tolerance used to calculate the early stop criterion for ADMM.
- verbose (boolean, optional (default=False)) – Whether to print information about the optimization process.
Variables: - coef (ds-array, shape=(1, n_features)) – Parameter vector.
- n_iter (int) – Number of iterations run by ADMM.
- converged (boolean) – Whether ADMM converged.
See also
ADMM
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fit
(x, y)[source]¶ Fits the model with training data. Optimization is carried out using ADMM.
Parameters: - x (ds-array, shape=(n_samples, n_features)) – Training samples.
- y (ds-array, shape=(n_samples, 1)) – Class labels of x.
Returns: self
Return type: