Skip to content

Instantly share code, notes, and snippets.

What would you like to do?
custom objective and evaluation metric
import numpy as np
import xgboost as xgb
# advanced: customized loss function
print ('start running example to used customized objective function')
dtrain = xgb.DMatrix('agaricus.txt.train')
dtest = xgb.DMatrix('agaricus.txt.test')
# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param = {'max_depth': 2, 'eta': 1, 'silent': 1}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
# user define objective function, given prediction, return gradient and second order gradient
# this is log likelihood loss
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
return grad, hess
# user defined evaluation function, return a pair metric_name, result
# NOTE: when you do customized loss function, the default prediction value is margin
# this may make builtin evaluation metric not function properly
# for example, we are doing logistic loss, the prediction is score before logistic transformation
# the builtin evaluation error assumes input is after logistic transformation
# Take this in mind when you use the customization, and maybe you need write customized evaluation function
def evalerror(preds, dtrain):
labels = dtrain.get_label()
# return a pair metric_name, result
# since preds are margin(before logistic transformation, cutoff at 0)
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# training with customized objective, we can also do step by step training
# simply look at's implementation of train
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)

This comment has been minimized.

Copy link

@Cheung66 Cheung66 commented May 12, 2019


Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment