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Compatibility with both Python 2(.7) and 3

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This commit is contained in:
Joerg Rings
2014-05-19 11:23:53 -05:00
parent 991634a58e
commit 93d83ca077
12 changed files with 93 additions and 67 deletions
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24
demo/kaggle-higgs/higgs-numpy.py
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@@ -1,9 +1,15 @@
#!/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
sys.path.append('../../python/')
code_path = os.path.join(
os.path.split(inspect.getfile(inspect.currentframe()))[0], "../../python")
sys.path.append(code_path)
import xgboost as xgb
test_size = 550000
@@ -12,19 +18,19 @@ test_size = 550000
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') } )
print 'finish loading from csv '
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 xrange(len(label)) if label[i] == 1.0 )
sum_wneg = sum( weight[i] for i in xrange(len(label)) if label[i] == 0.0 )
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 )
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 )
@@ -43,14 +49,14 @@ param['silent'] = 1
param['nthread'] = 16
# you can directly throw param in, though we want to watch multiple metrics here
plst = param.items()+[('eval_metric', 'ams@0.15')]
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'
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'
print ('finish training')