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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
## s2net <img src="man/figures/s2net.png" float="right" width="150" />
[](https://github.com/jlaria/s2net/actions/workflows/R-CMD-check.yaml)
`r badger::badge_code_size("jlaria/s2net")`
`r badger::badge_last_commit("jlaria/s2net")`
`r badger::badge_cran_release("s2net", "orange")`
`r badger::badge_github_version("jlaria/s2net", "blue")`
### Overview
R package `s2net`
+ Our method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts.
+ Its mathematical formulation is presented from a general perspective, covering a wide range of models.
+ We develop a flexible and fast implementation for `s2net` in `R`, written in `C++` using `RcppArmadillo` and integrated into `R` via `Rcpp` modules.
### Installation
You can install the released version of `s2net` from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("s2net")
```
The development version can be installed with:
``` r
devtools::install_github("jlaria/s2net", build_vignettes = TRUE)
```
### Features
[](https://slides.com/juancarloslaria/deck#/)
### Example
This is a basic example which shows you how to use the package. Detailed examples can be found in the documentation and vignettes.
```{r}
library(s2net)
# Auto-MPG dataset is included for benchmark
data("auto_mpg")
```
Semi-supervised data is made of a labeled dataset `xL`, the labels `yL`, and unlabeled data `xU`. Package `s2net` includes the function `s2Data` to process semi-supervised datasets.
```{r}
head(auto_mpg$P2$xL, 2) # labeled data
head(auto_mpg$P2$yL, 2) # labels
head(auto_mpg$P2$xU, 2) # unlabeled data
train = s2Data(auto_mpg$P2$xL, auto_mpg$P2$yL, auto_mpg$P2$xU, preprocess = TRUE)
head(train$xL, 2)
```
The data is centered and scaled, and factor variables are automatically converted to numerical dummies. Constant columns are also removed. If we wanted to use validation/test data, we must pre-process it according to the training data, with:
```{r}
valid = s2Data(auto_mpg$P2$xU, auto_mpg$P2$yU, preprocess = train)
```
There are two ways to fit a semi-supervised elastic-net using `s2net`. The easiest way is using the function `s2netR`, that returns an object of S3 class `s2netR`.
```{r}
model = s2netR(train, params = s2Params(lambda1 = 0.01, lambda2 = 0.01, gamma1 = 0.01, gamma2 = 100, gamma3 = 0.1))
class(model)
model$beta
ypred = predict(model, valid$xL)
```
If we are fitting the semi-supervised elastic-net many times, using the same `train` data (for example, searching for the best hyperparameters), then it is faster to use the `C++` class `s2net` instead.
```{r}
obj = new(s2net, train, 0) # 0 = linear
# We fit the model with
obj$fit(s2Params(0.01, 0.01, 0.01, 100, 0.1), 0, 2) # frame = 0 (ExtJT), proj = 2 (auto)
obj$beta
ypred = obj$predict(valid$xL, 0) #0=default predictions
# or
ypred = predict(obj, valid$xL)
```
#### Further examples
More examples can be found in the package documentation and vignettes.
```{r, eval=FALSE}
vignette(package="s2net")
```
<!-- ## Example -->
<!-- This is a basic example which shows you how to solve a common problem: -->
<!-- ```{r example} -->
<!-- ## basic example code -->
<!-- ``` -->
<!-- What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so: -->
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