6f16f0ef58
* Fix various typos * Add override to functions that are overridden gcc gives warnings about functions that are being overridden by not being marked as oveirridden. This fixes it. * Use bst_float consistently Use bst_float for all the variables that involve weight, leaf value, gradient, hessian, gain, loss_chg, predictions, base_margin, feature values. In some cases, when due to additions and so on the value can take a larger value, double is used. This ensures that type conversions are minimal and reduces loss of precision.
55 lines
1.7 KiB
Python
Executable File
55 lines
1.7 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 numpy as np
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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'.encode('utf-8')) } )
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print ('finish loading from csv ')
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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 range(len(label)) if label[i] == 1.0 )
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sum_wneg = sum( weight[i] for i in range(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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xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
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# setup parameters for xgboost
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param = {}
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# use logistic regression loss, use raw prediction before logistic transformation
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# since we only need the rank
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param['objective'] = 'binary:logitraw'
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# scale weight of positive examples
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param['scale_pos_weight'] = sum_wneg/sum_wpos
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param['eta'] = 0.1
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param['max_depth'] = 6
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param['eval_metric'] = 'auc'
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param['silent'] = 1
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param['nthread'] = 16
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# you can directly throw param in, though we want to watch multiple metrics here
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plst = list(param.items())+[('eval_metric', 'ams@0.15')]
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watchlist = [ (xgmat,'train') ]
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# boost 120 trees
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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( plst, xgmat, num_round, watchlist );
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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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