66 lines
2.0 KiB
Python
Executable File
66 lines
2.0 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 time
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import numpy as np
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from sklearn.ensemble import GradientBoostingClassifier
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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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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
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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['bst:eta'] = 0.1
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param['bst:max_depth'] = 6
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param['eval_metric'] = 'auc'
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param['nthread'] = 4
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plst = param.items()+[('eval_metric', 'ams@0.15')]
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watchlist = [ (xgmat,'train') ]
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# boost 10 trees
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num_round = 10
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print ('loading data end, start to boost trees')
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print ("training GBM from sklearn")
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tmp = time.time()
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gbm = GradientBoostingClassifier(n_estimators=num_round, max_depth=6, verbose=2)
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gbm.fit(data, label)
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print ("sklearn.GBM costs: %s seconds" % str(time.time() - tmp))
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#raw_input()
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print ("training xgboost")
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threads = [1, 2, 4, 16]
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for i in threads:
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param['nthread'] = i
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tmp = time.time()
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plst = param.items()+[('eval_metric', 'ams@0.15')]
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bst = xgb.train( plst, xgmat, num_round, watchlist );
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print ("XGBoost with %d thread costs: %s seconds" % (i, str(time.time() - tmp)))
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print ('finish training')
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