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a correct version

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tqchen
2014-05-15 21:11:46 -07:00
parent 2be3f6ece0
commit 6af6d64f0b
5 changed files with 24 additions and 15 deletions
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17
demo/kaggle-higgs/higgs-numpy.py
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@@ -13,6 +13,8 @@ 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 '
label = dtrain[:,32]
data = dtrain[:,1:31]
# rescale weight to make it same as test set
@@ -25,25 +27,28 @@ sum_wneg = sum( weight[i] for i in xrange(len(label)) if label[i] == 0.0 )
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
xtrain = xgb.DMatrix( data, label=label, missing = -999.0 )
xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
# setup parameters for xgboost
params = {}
param = {}
# use logistic regression loss
param['loss_type'] = 3
# scale weight of positive examples
param['scale_pos_weight'] = sum_wpos/sum_wpos
param['scale_pos_weight'] = sum_wneg/sum_wpos
param['bst:eta'] = 0.1
param['bst:max_depth'] = 6
param['eval_metric'] = 'ams@0.15'
param['eval_metric'] = 'auc'
param['silent'] = 1
param['eval_train'] = 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')]
watchlist = [ (xgmat,'train') ]
# boost 120 tres
num_round = 120
print 'loading data end, start to boost trees'
bst = xgb.train( xtrain, param, num_round );
bst = xgb.train( plst, xgmat, num_round, watchlist );
# save out model
bst.save_model('higgs.model')

15
demo/kaggle-higgs/higgs-pred.py
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@@ -1,5 +1,5 @@
#!/usr/bin/python
# this is the example script to use xgboost to train
# make prediction
import sys
import numpy as np
# add path of xgboost python module
@@ -17,13 +17,14 @@ threshold_ratio = 0.15
# load in training data, directly use numpy
dtest = np.loadtxt( dpath+'/test.csv', delimiter=',', skiprows=1 )
data = dtest[:,1:31]
idx = dtest[:,1]
idx = dtest[:,0]
xtest = xgb.DMatrix( data, missing = -999.0 )
bst = xgb.Booster()
print 'finish loading from csv '
xgmat = xgb.DMatrix( data, missing = -999.0 )
bst = xgb.Booster({'nthread':16})
bst.load_model( modelfile )
ypred = bst.predict( xgmat )
ypred = bst.predict( dtest )
res = [ ( int(idx[i]), ypred[i] ) for i in xrange(len(ypred)) ]
rorder = {}
@@ -31,7 +32,7 @@ for k, v in sorted( res, key = lambda x:-x[1] ):
rorder[ k ] = len(rorder) + 1
# write out predictions
ntop = int( ratio * len(rorder ) )
ntop = int( threshold_ratio * len(rorder ) )
fo = open(outfile, 'w')
nhit = 0
ntot = 0
@@ -46,7 +47,7 @@ for k, v in res:
ntot += 1
fo.close()
print 'finished writing into model file'
print 'finished writing into prediction file'