s2netRcpp-package: The Generalized Semi-Supervised Elastic-Net

In s2net: The Generalized Semi-Supervised Elastic-Net

The Generalized Semi-Supervised Elastic-Net

Description

Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.

Details

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This package includes a very easy-to-use interface for handling data, with the s2Data function. The main function of the package is the s2netR function, which is a wrapper for the Rcpp_s2net (s2net) class.

Author(s)

Juan C. Laria [aut, cre] (<https://orcid.org/0000-0001-7734-9647>), Line H. Clemmensen [aut]

References

Laria, J.C., L. Clemmensen (2019). A generalized elastic-net for semi-supervised learning of sparse features.

Sogaard Larsen, J. et. al. (2019). Semi-supervised covariate shift modelling of spectroscopic data.

Ryan, K. J., & Culp, M. V. (2015). On semi-supervised linear regression in covariate shift problems. The Journal of Machine Learning Research, 16(1), 3183-3217.

See Also

s2Data, s2netR, Rcpp_s2net

Examples

data("auto_mpg")
train = s2Data(xL = auto_mpg$P1$xL, yL = auto_mpg$P1$yL,  xU = auto_mpg$P1$xU)

model = s2netR(train, 
                s2Params(lambda1 = 0.1, 
                           lambda2 = 0,
                           gamma1 = 0.1,
                           gamma2 = 100,
                           gamma3 = 0.1))

# here we tell it to transform the valid data as we did with train.
valid = s2Data(auto_mpg$P1$xU, auto_mpg$P1$yU, preprocess = train) 
ypred = predict(model, valid$xL)

## Not run: 
if(require(ggplot2)){
  ggplot() + 
    aes(x = ypred, y = valid$yL) + geom_point() + 
    geom_abline(intercept = 0, slope = 1, linetype = 2)
}

## End(Not run)

s2net documentation built on July 1, 2022, 1:06 a.m.