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| #!/usr/bin/python | |
| # this is the example script to use xgboost to train | |
| import inspect | |
| import os | |
| import sys | |
| import numpy as np | |
| # add path of xgboost python module | |
| code_path = os.path.join( | |
| os.path.split(inspect.getfile(inspect.currentframe()))[0], "../../wrapper") | |
| sys.path.append(code_path) | |
| 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'.encode('utf-8')) } ) | |
| print ('finish loading from csv ') | |
| 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 range(len(label)) if label[i] == 1.0 ) | |
| sum_wneg = sum( weight[i] for i in range(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 | |
| xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight ) | |
| # setup parameters for xgboost | |
| param = {} | |
| # use logistic regression loss, use raw prediction before logistic transformation | |
| # since we only need the rank | |
| param['objective'] = 'binary:logitraw' | |
| # scale weight of positive examples | |
| param['scale_pos_weight'] = sum_wneg/sum_wpos | |
| param['eta'] = 0.1 | |
| param['max_depth'] = 6 | |
| param['eval_metric'] = 'auc' | |
| param['silent'] = 1 | |
| param['nthread'] = 16 | |
| # you can directly throw param in, though we want to watch multiple metrics here | |
| plst = list(param.items())+[('eval_metric', 'ams@0.15')] | |
| watchlist = [ (xgmat,'train') ] | |
| # boost 120 tres | |
| num_round = 120 | |
| print ('loading data end, start to boost trees') | |
| bst = xgb.train( plst, xgmat, num_round, watchlist ); | |
| # save out model | |
| bst.save_model('higgs.model') | |
| print ('finish training') |