#!/usr/bin/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 = {'silent':1, '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))))
