53 lines
1.1 KiB
Python
Executable File
53 lines
1.1 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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# path to where the data lies
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dpath = 'data'
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modelfile = 'higgs.model'
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outfile = 'higgs.pred.csv'
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# make top 15% as positive
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threshold_ratio = 0.15
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# load in training data, directly use numpy
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dtest = np.loadtxt( dpath+'/test.csv', delimiter=',', skiprows=1 )
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data = dtest[:,1:31]
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idx = dtest[:,1]
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xtest = xgb.DMatrix( data, missing = -999.0 )
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bst = xgb.Booster()
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bst.load_model( modelfile )
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ypred = bst.predict( dtest )
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res = [ ( int(idx[i]), ypred[i] ) for i in xrange(len(ypred)) ]
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rorder = {}
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for k, v in sorted( res, key = lambda x:-x[1] ):
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rorder[ k ] = len(rorder) + 1
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# write out predictions
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ntop = int( ratio * len(rorder ) )
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fo = open(outfile, 'w')
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nhit = 0
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ntot = 0
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fo.write('EventId,RankOrder,Class\n')
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for k, v in res:
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if rorder[k] <= ntop:
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lb = 's'
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nhit += 1
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else:
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lb = 'b'
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fo.write('%s,%d,%s\n' % ( k, rorder[k], lb ) )
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ntot += 1
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fo.close()
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print 'finished writing into model file'
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