Merge branch 'master' of ssh://github.com/tqchen/xgboost

demo/kaggle-higgs/higgs-cv.py

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+#!/usr/bin/python
+import sys
+import numpy as np
+sys.path.append('../../wrapper')
+import xgboost as xgb
+
+### load data in do training
+train = np.loadtxt('./data/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
+label  = train[:,32]
+data   = train[:,1:31]
+weight = train[:,31]
+dtrain = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
+param = {'max_depth':6, 'eta':0.1, 'silent':1, 'objective':'binary:logitraw', 'nthread':4}
+num_round = 120
+
+print ('running cross validation, with preprocessing function')
+# define the preprocessing function
+# used to return the preprocessed training, test data, and parameter
+# we can use this to do weight rescale, etc.
+# as a example, we try to set scale_pos_weight
+def fpreproc(dtrain, dtest, param):
+    label = dtrain.get_label()
+    ratio = float(np.sum(label == 0)) / np.sum(label==1)
+    param['scale_pos_weight'] = ratio
+    wtrain = dtrain.get_weight()
+    wtest = dtest.get_weight()
+    sum_weight = sum(wtrain) + sum(wtest)
+    wtrain *= sum_weight / sum(wtrain)
+    wtest *= sum_weight / sum(wtest)
+    dtrain.set_weight(wtrain)
+    dtest.set_weight(wtest)
+    return (dtrain, dtest, param)
+
+# do cross validation, for each fold
+# the dtrain, dtest, param will be passed into fpreproc
+# then the return value of fpreproc will be used to generate
+# results of that fold
+xgb.cv(param, dtrain, num_round, nfold=5,
+       metrics={'ams@0.15', 'auc'}, seed = 0, fpreproc = fpreproc)