a correct version

demo/kaggle-higgs/higgs-numpy.py

@@ -13,6 +13,8 @@ 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') } )
+print 'finish loading from csv '
+
 label  = dtrain[:,32]
 data   = dtrain[:,1:31]
 # rescale weight to make it same as test set
@@ -25,25 +27,28 @@ sum_wneg = sum( weight[i] for i in xrange(len(label)) if label[i] == 0.0  )
 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
-xtrain = xgb.DMatrix( data, label=label, missing = -999.0 )
+xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
 
 # setup parameters for xgboost
-params = {}
+param = {}
 # use logistic regression loss
 param['loss_type'] = 3
 # scale weight of positive examples
-param['scale_pos_weight'] = sum_wpos/sum_wpos
+param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['bst:eta'] = 0.1 
 param['bst:max_depth'] = 6
-param['eval_metric'] = 'ams@0.15'
+param['eval_metric'] = 'auc'
 param['silent'] = 1
-param['eval_train'] = 1
 param['nthread'] = 16
 
+# you can directly throw param in, though we want to watch multiple metrics here 
+plst = 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( xtrain, param, num_round );
+bst = xgb.train( plst, xgmat, num_round, watchlist );
 # save out model
 bst.save_model('higgs.model')