XGBoost is designed to be an extensible library. One way to extend it is by providing our own objective function for training and corresponding metric for performance monitoring. This document introduces implementing a customized elementwise evaluation metric and objective for XGBoost. Although the introduction uses Python for demonstration, the concepts should be readily applicable to other language bindings.

Note

The ranking task does not support customized functions.

The customized functions defined here are only applicable to single node training. Distributed environment requires syncing with

`xgboost.rabit`

, the interface is subject to change hence beyond the scope of this tutorial.We also plan to improve the interface for multi-classes objective in the future.

In the following sections, we will provide a step by step walk through of implementing
`Squared Log Error(SLE)`

objective function:

\[\frac{1}{2}[log(pred + 1) - log(label + 1)]^2\]

and its default metric `Root Mean Squared Log Error(RMSLE)`

:

\[\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}\]

Although XGBoost has native support for said functions, using it for demonstration provides us the opportunity of comparing the result from our own implementation and the one from XGBoost internal for learning purposes. After finishing this tutorial, we should be able to provide our own functions for rapid experiments.

During model training, the objective function plays an important role: provide gradient
information, both first and second order gradient, based on model predictions and observed
data labels (or targets). Therefore, a valid objective function should accept two inputs,
namely prediction and labels. For implementing `SLE`

, we define:

```
import numpy as np
import xgboost as xgb
from typing import Tuple
def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:
'''Compute the gradient squared log error.'''
y = dtrain.get_label()
return (np.log1p(predt) - np.log1p(y)) / (predt + 1)
def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:
'''Compute the hessian for squared log error.'''
y = dtrain.get_label()
return ((-np.log1p(predt) + np.log1p(y) + 1) /
np.power(predt + 1, 2))
def squared_log(predt: np.ndarray,
dtrain: xgb.DMatrix) -> Tuple[np.ndarray, np.ndarray]:
'''Squared Log Error objective. A simplified version for RMSLE used as
objective function.
'''
predt[predt < -1] = -1 + 1e-6
grad = gradient(predt, dtrain)
hess = hessian(predt, dtrain)
return grad, hess
```

In the above code snippet, `squared_log`

is the objective function we want. It accepts a
numpy array `predt`

as model prediction, and the training DMatrix for obtaining required
information, including labels and weights (not used here). This objective is then used as
a callback function for XGBoost during training by passing it as an argument to
`xgb.train`

:

```
xgb.train({'tree_method': 'hist', 'seed': 1994}, # any other tree method is fine.
dtrain=dtrain,
num_boost_round=10,
obj=squared_log)
```

Notice that in our definition of the objective, whether we subtract the labels from the prediction or the other way around is important. If you find the training error goes up instead of down, this might be the reason.

So after having a customized objective, we might also need a corresponding metric to
monitor our model’s performance. As mentioned above, the default metric for `SLE`

is
`RMSLE`

. Similarly we define another callback like function as the new metric:

```
def rmsle(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
''' Root mean squared log error metric.'''
y = dtrain.get_label()
predt[predt < -1] = -1 + 1e-6
elements = np.power(np.log1p(y) - np.log1p(predt), 2)
return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y)))
```

Since we are demonstrating in Python, the metric or objective needs not be a function,
any callable object should suffice. Similarly to the objective function, our metric also
accepts `predt`

and `dtrain`

as inputs, but returns the name of metric itself and a
floating point value as result. After passing it into XGBoost as argument of `feval`

parameter:

```
xgb.train({'tree_method': 'hist', 'seed': 1994,
'disable_default_eval_metric': 1},
dtrain=dtrain,
num_boost_round=10,
obj=squared_log,
feval=rmsle,
evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
evals_result=results)
```

We will be able to see XGBoost printing something like:

```
[0] dtrain-PyRMSLE:1.37153 dtest-PyRMSLE:1.31487
[1] dtrain-PyRMSLE:1.26619 dtest-PyRMSLE:1.20899
[2] dtrain-PyRMSLE:1.17508 dtest-PyRMSLE:1.11629
[3] dtrain-PyRMSLE:1.09836 dtest-PyRMSLE:1.03871
[4] dtrain-PyRMSLE:1.03557 dtest-PyRMSLE:0.977186
[5] dtrain-PyRMSLE:0.985783 dtest-PyRMSLE:0.93057
...
```

Notice that the parameter `disable_default_eval_metric`

is used to suppress the default metric
in XGBoost.

For fully reproducible source code and comparison plots, see custom_rmsle.py.

A similar demo for multi-class objective function is also available, see demo/guide-python/custom_softmax.py for details.