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Customized loss function for quantile regression with XGBoost
import numpy as np
def xgb_quantile_eval(preds, dmatrix, quantile=0.2):
Customized evaluational metric that equals
to quantile regression loss (also known as
pinball loss).
Quantile regression is regression that
estimates a specified quantile of target's
distribution conditional on given features.
@type preds: numpy.ndarray
@type dmatrix: xgboost.DMatrix
@type quantile: float
@rtype: float
labels = dmatrix.get_label()
return ('q{}_loss'.format(quantile),
np.nanmean((preds >= labels) * (1 - quantile) * (preds - labels) +
(preds < labels) * quantile * (labels - preds)))
def xgb_quantile_obj(preds, dmatrix, quantile=0.2):
Computes first-order derivative of quantile
regression loss and a non-degenerate
substitute for second-order derivative.
Substitute is returned instead of zeros,
because XGBoost requires non-zero
second-order derivatives. See this page:
to see why it is possible to use this trick.
However, be sure that hyperparameter named
`max_delta_step` is small enough to satisfy:
```0.5 * max_delta_step <=
min(quantile, 1 - quantile)```.
@type preds: numpy.ndarray
@type dmatrix: xgboost.DMatrix
@type quantile: float
@rtype: tuple(numpy.ndarray)
assert 0 <= quantile <= 1
except AssertionError:
raise ValueError("Quantile value must be float between 0 and 1.")
labels = dmatrix.get_label()
errors = preds - labels
left_mask = errors < 0
right_mask = errors > 0
grad = -quantile * left_mask + (1 - quantile) * right_mask
hess = np.ones_like(preds)
return grad, hess
# Example of usage:
# bst = xgb.train(hyperparams, train, num_rounds,
# obj=xgb_quantile_obj, feval=xgb_quantile_eval)
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