2c1a439869
* Remove GPU memory usage demo. * Add tests for demos. * Remove `silent`. * Remove shebang as it's not portable.
25 lines
1.0 KiB
Python
25 lines
1.0 KiB
Python
import xgboost as xgb
|
|
import numpy as np
|
|
|
|
# this script demonstrates how to fit gamma regression model (with log link function)
|
|
# in xgboost, before running the demo you need to generate the autoclaims dataset
|
|
# by running gen_autoclaims.R located in xgboost/demo/data.
|
|
|
|
data = np.genfromtxt('../data/autoclaims.csv', delimiter=',')
|
|
dtrain = xgb.DMatrix(data[0:4741, 0:34], data[0:4741, 34])
|
|
dtest = xgb.DMatrix(data[4741:6773, 0:34], data[4741:6773, 34])
|
|
|
|
# for gamma regression, we need to set the objective to 'reg:gamma', it also suggests
|
|
# to set the base_score to a value between 1 to 5 if the number of iteration is small
|
|
param = {'objective':'reg:gamma', 'booster':'gbtree', 'base_score':3}
|
|
|
|
# the rest of settings are the same
|
|
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
|
|
num_round = 30
|
|
|
|
# training and evaluation
|
|
bst = xgb.train(param, dtrain, num_round, watchlist)
|
|
preds = bst.predict(dtest)
|
|
labels = dtest.get_label()
|
|
print('test deviance=%f' % (2 * np.sum((labels - preds) / preds - np.log(labels) + np.log(preds))))
|