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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 96667b8bad
commit a958fe8d52
12 changed files with 93 additions and 67 deletions
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10
python/example/demo.py
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@@ -22,7 +22,7 @@ bst = xgb.train( param, dtrain, num_round, evallist )
# this is prediction
preds = bst.predict( dtest )
labels = dtest.get_label()
print 'error=%f' % ( sum(1 for i in xrange(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds)))
print ('error=%f' % ( sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
bst.save_model('0001.model')
# dump model
bst.dump_model('dump.raw.txt')
@@ -32,7 +32,7 @@ bst.dump_model('dump.raw.txt','featmap.txt')
###
# build dmatrix in python iteratively
#
print 'start running example of build DMatrix in python'
print ('start running example of build DMatrix in python')
dtrain = xgb.DMatrix()
labels = []
for l in open('agaricus.txt.train'):
@@ -50,7 +50,7 @@ bst = xgb.train( param, dtrain, num_round, evallist )
###
# build dmatrix from scipy.sparse
print 'start running example of build DMatrix from scipy.sparse'
print ('start running example of build DMatrix from scipy.sparse')
labels = []
row = []; col = []; dat = []
i = 0
@@ -68,7 +68,7 @@ dtrain.set_label(labels)
evallist = [(dtest,'eval'), (dtrain,'train')]
bst = xgb.train( param, dtrain, num_round, evallist )
print 'start running example of build DMatrix from numpy array'
print ('start running example of build DMatrix from numpy array')
# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation,then convert to DMatrix
npymat = csr.todense()
dtrain = xgb.DMatrix( npymat )
@@ -79,7 +79,7 @@ bst = xgb.train( param, dtrain, num_round, evallist )
###
# advanced: cutomsized loss function, set loss_type to 0, so that predict get untransformed score
#
print 'start running example to used cutomized objective function'
print ('start running example to used cutomized objective function')
# note: set loss_type properly, loss_type=2 means the prediction will get logistic transformed
# in most case, we may want to set loss_type = 0, to get untransformed score to compute gradient