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demo/kaggle-higgs/higgs-numpy.py

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+#!/usr/bin/python
+# this is the example script to use xgboost to train 
+import sys
+import numpy as np
+# add path of xgboost python module
+sys.path.append('../../python/')
+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') } )
+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  )
+
+# 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
+xtrain = xgb.DMatrix( data, label=label, missing = -999.0 )
+
+# setup parameters for xgboost
+params = {}
+# use logistic regression loss
+param['loss_type'] = 3
+# scale weight of positive examples
+param['scale_pos_weight'] = sum_wpos/sum_wpos
+param['bst:eta'] = 0.1 
+param['bst:max_depth'] = 6
+param['eval_metric'] = 'ams@0.15'
+param['silent'] = 1
+param['eval_train'] = 1
+param['nthread'] = 16
+
+# boost 120 tres
+num_round = 120
+print 'loading data end, start to boost trees'
+bst = xgb.train( xtrain, param, num_round );
+# save out model
+bst.save_model('higgs.model')
+
+print 'finish training'