`ext_losses.Rmd`

- Read the use-case site to get to know how to define a
`Compboost`

object using the`R6`

interface

`compboost`

was designed to provide a component-wise boosting framework with maximal flexibility. This document gives an overview about how to define custom losses in `R`

as well as in `C++`

without recompiling the whole package. These custom losses then can be used for training the model or logging mechanisms.

In boosting the loss function has one huge restriction, it must be differentiable. Therefore, we need not just the definition of the loss function, but also the gradient of it. Even worse, in boosting the first iteration is initialized as loss optimal constant dependent on the response. To capture this, we have also to define a loss optimal constant as function of the response. Having this three components, it is quite easy to define custom losses.

For this topic we are rebuilding two different loss functions:

- The quadratic loss as easy example for
`C++`

- The Poisson loss for counting data as more sophisticated loss example for
`R`

`R`

For this example we are using the `VonBort`

dataset provided by the package `vcd`

:

“Data from von Bortkiewicz (1898), given by Andrews & Herzberg (1985), on number of deaths by horse or mule kicks in 14 corps of the Prussian army.”

`data(VonBort, package = "vcd")`

We like to model the deaths using the poisson regression context in boosting. That means we have to define the proper loss function, the gradient, and the constant initializer.

The scheme for the loss and the gradient is to specify a function of the following form:

- loss:
`function (truth, response)`

- gradient:
`function (truth, response)`

- constant initializer:
`function (truth)`

\[\frac{\partial}{\partial f} L(y,f) = \exp(f) - y\]

```
gradPoisson = function (truth, response) {
return(exp(response) - truth)
}
```

\[\mathsf{arg min}_{c\in\mathbb{R}} \sum_{i = 1}^n L\left(y^{(i)}, c\right) = \log(\bar{y})\]

Finally, having the three components defined, we can pass them to the `LossCustom`

object which sets them for the specific task:

`C++`

For this example we are using the `iris`

dataset with `Sepal.Length`

as target to keep it simple. We now want to replicate the quadratic loss. This is done by exposing external pointer to `R`

that holds the function definition which then passed to the `LossCustomCpp`

class.

A general advise is to write a `.cpp`

file that contains the whole definition. This file then just needs to be sourced by `Rcpp::sourceCpp()`

.

To be able to expose that functions we have to declare the head first:

```
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
typedef arma::mat (*lossFunPtr) (const arma::mat& true_value, const arma::mat& prediction);
typedef arma::mat (*gradFunPtr) (const arma::mat& true_value, const arma::mat& prediction);
typedef double (*constInitFunPtr) (const arma::mat& true_value);
```

As the type definition already indicates, the `C++`

function must have the following signatures:

- loss:
`arma::mat lossFun (const arma::mat& truth, const arma::mat& response)`

- gradient:
`arma::mat gradFun (const arma::mat& truth, const arma::mat& response)`

- constant initializer:
`constInitFun (const arma::mat& true_value)`

\[\mathsf{arg min}_{c\in\mathbb{R}} \sum_{i = 1}^n L\left(y^{(i)}, c\right) = \bar{y}\]

Now wrap the function to an XPtr. This one stores the pointer to the function and can be used as parameter for the `LossCustomCpp`

.

Note that it isn’t necessary to export the upper functions since we are interested in exporting the pointer not the function.

```
// [[Rcpp::export]]
Rcpp::XPtr<lossFunPtr> lossFunSetter ()
{
return Rcpp::XPtr<lossFunPtr> (new lossFunPtr (&lossFun));
}
// [[Rcpp::export]]
Rcpp::XPtr<gradFunPtr> gradFunSetter ()
{
return Rcpp::XPtr<gradFunPtr> (new gradFunPtr (&gradFun));
}
// [[Rcpp::export]]
Rcpp::XPtr<constInitFunPtr> constInitFunSetter ()
{
return Rcpp::XPtr<constInitFunPtr> (new constInitFunPtr (&constInitFun));
}
```

After exposing the function we can get the pointer to the function by calling it:

```
lossFunSetter()
#> <pointer: 0x365d760>
gradFunSetter()
#> <pointer: 0x6511340>
constInitFunSetter()
#> <pointer: 0x2084b60>
```

This can now passed to the `LossCustomCpp`

class: