s2net (version 1.0)

s2net-package: s2net

Description

s2net

Details

The DESCRIPTION file: s2net s2net

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.

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

# NOT RUN {
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)
}
# }