63 lines
1.9 KiB
Python
Executable File
63 lines
1.9 KiB
Python
Executable File
#!/usr/bin/python
|
|
# this is the example script to use xgboost to train
|
|
import inspect
|
|
import os
|
|
import sys
|
|
import numpy as np
|
|
# add path of xgboost python module
|
|
code_path = os.path.join(
|
|
os.path.split(inspect.getfile(inspect.currentframe()))[0], "../../wrapper")
|
|
|
|
sys.path.append(code_path)
|
|
|
|
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['bst:eta'] = 0.1
|
|
param['bst: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 tres
|
|
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')
|