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6f16f0e Nov 30, 2016
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@tqchen @jseabold @AbdealiJK
executable file 55 lines (42 sloc) 1.69 KB
#!/usr/bin/python
# this is the example script to use xgboost to train
import numpy as np
import xgboost as xgb
test_size = 550000
# path to where the data lies
dpath = 'data'
# load in training data, directly use numpy
dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
print ('finish loading from csv ')
label = dtrain[:,32]
data = dtrain[:,1:31]
# rescale weight to make it same as test set
weight = dtrain[:,31] * float(test_size) / len(label)
sum_wpos = sum( weight[i] for i in range(len(label)) if label[i] == 1.0 )
sum_wneg = sum( weight[i] for i in range(len(label)) if label[i] == 0.0 )
# print weight statistics
print ('weight statistics: wpos=%g, wneg=%g, ratio=%g' % ( sum_wpos, sum_wneg, sum_wneg/sum_wpos ))
# construct xgboost.DMatrix from numpy array, treat -999.0 as missing value
xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
# setup parameters for xgboost
param = {}
# use logistic regression loss, use raw prediction before logistic transformation
# since we only need the rank
param['objective'] = 'binary:logitraw'
# scale weight of positive examples
param['scale_pos_weight'] = sum_wneg/sum_wpos
param['eta'] = 0.1
param['max_depth'] = 6
param['eval_metric'] = 'auc'
param['silent'] = 1
param['nthread'] = 16
# you can directly throw param in, though we want to watch multiple metrics here
plst = list(param.items())+[('eval_metric', 'ams@0.15')]
watchlist = [ (xgmat,'train') ]
# boost 120 trees
num_round = 120
print ('loading data end, start to boost trees')
bst = xgb.train( plst, xgmat, num_round, watchlist );
# save out model
bst.save_model('higgs.model')
print ('finish training')