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https://github.com/gjjvdburg/sparsestep
SparseStep: Approximating the Counting Norm for Sparse Regularization
https://github.com/gjjvdburg/sparsestep
feature-selection lasso-variants r regularized-linear-regression sparse-regression sparse-regularization
Last synced: 9 days ago
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SparseStep: Approximating the Counting Norm for Sparse Regularization
- Host: GitHub
- URL: https://github.com/gjjvdburg/sparsestep
- Owner: GjjvdBurg
- License: gpl-3.0
- Created: 2017-01-25T13:08:02.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2021-01-12T16:31:17.000Z (almost 4 years ago)
- Last Synced: 2024-06-11T17:08:26.959Z (5 months ago)
- Topics: feature-selection, lasso-variants, r, regularized-linear-regression, sparse-regression, sparse-regularization
- Language: R
- Homepage: https://arxiv.org/abs/1701.06967
- Size: 85.9 KB
- Stars: 1
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SparseStep R Package
SparseStep is an R package for sparse regularized regression and provides an
alternative to methods such as best subset selection, elastic net, lasso, and
lars. The SparseStep method is introduced in the following paper:
[SparseStep: Approximating the Counting Norm for Sparse
Regularization](https://arxiv.org/abs/1701.06967) by G.J.J. van den Burg,
P.J.F. Groenen, and A. Alfons (*Arxiv preprint arXiv:1701.06967 [stat.ME]*,
2017).
This R package can be easily installed by running
``install.packages('sparsestep')`` in R. If you use the package in your work,
please cite the above reference using, for instance, the following BibTeX
entry:
```bibtex
@article{vandenburg2017sparsestep,
title = {{SparseStep}: Approximating the Counting Norm for Sparse Regularization},
author = {{Van den Burg}, G. J. J. and Groenen, P. J. F. and Alfons, A.},
journal = {arXiv preprint arXiv:1701.06967},
year = {2017}
}
```
## Introduction
The SparseStep method solves the regression problem regularized with the
[`l_0` norm](https://en.wikipedia.org/wiki/Lp_space#When_p_=_0). Since the
`l_0` term is highly non-convex and therefore difficult to optimize, this
non-convexity is introduced gradually in SparseStep during optimization. As in
other regularized regression methods such as ridge regression and lasso, a
regularization parameter ``lambda`` can be specified to control the amount of
regularization. The choice of regularization parameter affects how many
non-zero variables remain in the final model.
We will give a quick guide to SparseStep using the Prostate dataset from the
book [Elements of Statistical
Learning](https://web.stanford.edu/~hastie/ElemStatLearn/).
We will show a few examples of running SparseStep on the Prostate dataset from
the [lasso2](https://cran.r-project.org/web/packages/lasso2/index.html)
package. First we load the data and create a data matrix and outcome vector:
```r
> prostate <- read.table("http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/prostate.data")
> X <- prostate[prostate$train == T, c(-1, -10)]
> X <- as.matrix(X)
> y <- prostate[prostate$train == T, 1]
> y <- as.vector(y)
```
The easiest way to fit a SparseStep model is to use the ``path.sparsestep``
function. This estimates the entire path of solutions for the SparseStep model
for different values of the regularization parameter using a [golden section
search](https://en.wikipedia.org/wiki/Golden-section_search) algorithm.
```r
> path <- path.sparsestep(X, y)
Found maximum value of lambda: 2^( 7 )
Found minimum value of lambda: 2^( -3 )
Running search in interval [ -3 , 7 ] ...
Running search in interval [ -3 , 2 ] ...
Running search in interval [ -3 , -0.5 ] ...
Running search in interval [ -3 , -1.75 ] ...
Running search in interval [ -0.5 , 2 ] ...
Running search in interval [ -0.5 , 0.75 ] ...
Running search in interval [ 0.125 , 0.75 ] ...
Running search in interval [ 2 , 7 ] ...
> plot(path, col=1:nrow(path$beta)) # col specifies colors to matplot
> legend('topleft', legend=rownames(path$beta), lty=1, col=1:nrow(path$beta))
```
In the resulting plot we can see the coefficients of the features that are
included in the model at different values of ``lambda``:
The coefficients of the model can be obtained using ``coef(path)``, which
returns a sparse matrix:
```r
> coef(path)
9 x 9 sparse Matrix of class "dgCMatrix"
s0 s1 s2 s3 s4 s5 s6 s7
Intercept 1.31349155 1.313491553 1.313491553 1.31349155 1.313491553 1.31349155 1.3134916 1.313492
lweight -0.11336968 -0.113485291 . . . . . .
age 0.02010188 0.020182049 0.018605327 0.01491472 0.018704172 0.01623212 . .
lbph -0.05698125 -0.059026246 -0.069116923 . . . . .
svi 0.03511645 . . . . . . .
lcp 0.41845469 0.423398063 0.420516410 0.43806447 0.433449263 0.38174743 0.3887863 .
gleason 0.22438690 0.222333394 0.236944796 0.23503609 . . . .
pgg45 -0.00911273 -0.009084031 -0.008949463 -0.00853420 -0.004328518 . . .
lpsa 0.57545508 0.580111724 0.561063637 0.53017309 0.528953966 0.51473225 0.5336907 0.754266
s8
Intercept 1.313492
lweight .
age .
lbph .
svi .
lcp .
gleason .
pgg45 .
lpsa .
```
Note that the final model included in ``coef(beta)`` is a intercept-only
model, which is generally not very useful. Predicting out-of-sample data can
be done easily using the ``predict`` function.
By default SparseStep centers the regressors and outcome variable ``y`` and
normalizes the regressors ``X`` to ensure that the regularization is applied
evenly among them and the intercept is not penalized. If you prefer to use a
constant term in the regression and penalize this as well, you'll have to
transform the input data and disable the intercept:
```r
> Z <- cbind(constant=1, X)
> path <- path.sparsestep(Z, y, intercept=F)
...
> plot(path, col=1:nrow(path$beta))
> legend('bottomright', legend=rownames(path$beta), lty=1, col=1:nrow(path$beta))
```
Note that since we add the constant through the data matrix it is subject to
regularization and therefore sparsity:
For more information and examples, please see the documentation included with
the package. In particular, the following pages are good places to start:
```r
> ?'sparsestep-package'
> ?sparsestep
> ?path.sparsestep
```
## Reference
If you use SparseStep in any of your projects, please cite the paper using the
information available through the R command:
citation('sparsestep')
or use the following BibTeX code:
@article{van2017sparsestep,
title = {{SparseStep}: Approximating the Counting Norm for Sparse Regularization},
author = {Gerrit J.J. {van den Burg} and Patrick J.F. Groenen and Andreas Alfons},
journal = {arXiv preprint arXiv:1701.06967},
archiveprefix = {arXiv},
year = {2017},
eprint = {1701.06967},
url = {https://arxiv.org/abs/1701.06967},
primaryclass = {stat.ME},
keywords = {Statistics - Methodology, 62J05, 62J07},
}
## Notes
This package is licensed under GPLv3. Please see the LICENSE file for more
information. If you have any questions or comments about this package, please
open an issue [on GitHub](https://github.com/GjjvdBurg/sparsestep) (don't
hesitate, you're helping to make this project better for everyone!). If you
prefer to use email, please write to ``gertjanvandenburg at gmail dot com``.