51 lines
1.5 KiB
Python
Executable File
51 lines
1.5 KiB
Python
Executable File
#!/usr/bin/python
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# this is the example script to use xgboost to train
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import sys
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import numpy as np
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# add path of xgboost python module
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sys.path.append('../../python/')
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import xgboost as xgb
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test_size = 550000
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# path to where the data lies
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dpath = 'data'
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# load in training data, directly use numpy
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dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s') } )
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label = dtrain[:,32]
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data = dtrain[:,1:31]
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# rescale weight to make it same as test set
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weight = dtrain[:,31] * float(test_size) / len(label)
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sum_wpos = sum( weight[i] for i in xrange(len(label)) if label[i] == 1.0 )
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sum_wneg = sum( weight[i] for i in xrange(len(label)) if label[i] == 0.0 )
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# print weight statistics
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print 'weight statistics: wpos=%g, wneg=%g, ratio=%g' % ( sum_wpos, sum_wneg, sum_wneg/sum_wpos )
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# construct xgboost.DMatrix from numpy array, treat -999.0 as missing value
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xtrain = xgb.DMatrix( data, label=label, missing = -999.0 )
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# setup parameters for xgboost
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params = {}
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# use logistic regression loss
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param['loss_type'] = 3
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# scale weight of positive examples
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param['scale_pos_weight'] = sum_wpos/sum_wpos
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param['bst:eta'] = 0.1
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param['bst:max_depth'] = 6
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param['eval_metric'] = 'ams@0.15'
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param['silent'] = 1
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param['eval_train'] = 1
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param['nthread'] = 16
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# boost 120 tres
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num_round = 120
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print 'loading data end, start to boost trees'
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bst = xgb.train( xtrain, param, num_round );
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# save out model
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bst.save_model('higgs.model')
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print 'finish training'
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