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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, echo=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "Readme_files/"
)
library(compboost)
```
# compboost: Fast and Flexible Component-Wise Boosting Framework <a href='https://danielschalk.com/compboost/'><img src='man/figures/logo.png' align="right" height="139" /></a>
[](https://travis-ci.org/schalkdaniel/compboost) [](https://ci.appveyor.com/project/schalkdaniel/compboost) [](https://coveralls.io/github/schalkdaniel/compboost?branch=master) [](#license) [](https://cran.r-project.org/package=compboost) [](http://joss.theoj.org/papers/94cfdbbfdfc8796c5bdb1a74ee59fcda)
[Documentation](https://danielschalk.com/compboost/) |
[Contributors](CONTRIBUTORS.md) |
[Release Notes](NEWS.md)
## Overview
Component-wise boosting applies the boosting framework to
statistical models, e.g., general additive models using component-wise smoothing
splines. Boosting these kinds of models maintains interpretability and enables
unbiased model selection in high dimensional feature spaces.
The `R` package `compboost` is an alternative implementation of component-wise
boosting written in `C++` to obtain high runtime
performance and full memory control. The main idea is to provide a modular
class system which can be extended without editing the
source code. Therefore, it is possible to use `R` functions as well as
`C++` functions for custom base-learners, losses, logging mechanisms or
stopping criteria.
For an introduction and overview about the functionality visit the [project page](https://schalkdaniel.github.io/compboost/).
## Installation
#### CRAN version:
```r
install.packages("compboost")
```
#### Developer version:
```r
devtools::install_github("schalkdaniel/compboost")
```
## Examples
The examples are rendered using <code>compboost `r packageVersion("compboost")`</code>.
The fastest way to train a `Compboost` model is to use the wrapper functions `boostLinear()` or `boostSplines()`:
```{r, results="hide", warnings=FALSE}
cboost = boostSplines(data = iris, target = "Sepal.Length", loss = LossQuadratic$new())
```
For more extensive examples and how to use the `R6` interface visit the [project page](https://danielschalk.com/compboost/articles/getting_started/use_case.html).
<!--
To be as flexible as possible one should use the `R6` API do define base-learner, losses, stopping criteria, or optimizer as desired. Another option is to use wrapper functions as described on the [project page](https://schalkdaniel.github.io/compboost/).
```{r cboost, fig.show="hide", out.width="70%"}
library(compboost)
# Check installed version:
packageVersion("compboost")
# Load data set with binary classification task:
data(PimaIndiansDiabetes, package = "mlbench")
# Create categorical feature:
PimaIndiansDiabetes$pregnant_cat = ifelse(PimaIndiansDiabetes$pregnant == 0, "no", "yes")
# Define Compboost object:
cboost = Compboost$new(data = PimaIndiansDiabetes, target = "diabetes", loss = LossBinomial$new())
cboost
# Add p-spline base-learner with default parameter:
cboost$addBaselearner(feature = "pressure", id = "spline", bl_factory = BaselearnerPSpline)
# Add another p-spline learner with custom parameters:
cboost$addBaselearner(feature = "age", id = "spline", bl_factory = BaselearnerPSpline, degree = 3,
n_knots = 10, penalty = 4, differences = 2)
# Add categorical feature (as single linear base-learner):
cboost$addBaselearner(feature = "pregnant_cat", id = "category", bl_factory = BaselearnerPolynomial,
degree = 1, intercept = FALSE)
# Check all registered base-learner:
cboost$getBaselearnerNames()
# Train model:
cboost$train(1000L, trace = 200L)
cboost
cboost$getBaselearnerNames()
selected_features = cboost$getSelectedBaselearner()
table(selected_features)
params = cboost$getEstimatedCoef()
str(params)
cboost$train(3000)
cboost$plot("age_spline", iters = c(100, 500, 1000, 2000, 3000)) +
ggthemes::theme_tufte() +
ggplot2::scale_color_brewer(palette = "Spectral")
```
<p align="center">
<img src="Readme_files/cboost-1.png" width="70%" />
</p>
-->
## Benchmark
To get an idea of the performance of compboost, we have conduct a small benchmark in which compboost is compared with mboost. For this purpose, the runtime behavior and memory consumption of the two packages were compared. The results of the benchmark can be read [here](https://github.com/schalkdaniel/compboost/tree/master/benchmark).
## Citing
To cite `compboost` in publications, please use:
> Schalk et al., (2018). compboost: Modular Framework for Component-Wise Boosting. Journal of Open Source Software, 3(30), 967, https://doi.org/10.21105/joss.00967
```
@article{schalk2018compboost,
author = {Daniel Schalk, Janek Thomas, Bernd Bischl},
title = {compboost: Modular Framework for Component-Wise Boosting},
URL = {https://doi.org/10.21105/joss.00967},
year = {2018},
publisher = {Journal of Open Source Software},
volume = {3},
number = {30},
pages = {967},
journal = {JOSS}
}
```
## Testing
### On your local machine
In order to test the pacakge functionality you can use devtools to test the pacakge on your local machine:
```{r, eval=FALSE}
devtools::test()
```
### Using docker
You can test the package locally using docker and the [`compboost-test` repository](https://hub.docker.com/r/schalkdaniel/compboost-test/):
- Latest `R` release:
```
docker run schalkdaniel/compboost-test
```
- Latest `R` devel build:
```
docker run schalkdaniel/compboost-test:devel
```