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')
 

demo/kaggle-higgs/higgs-pred.py

@@ -1,5 +1,5 @@
 #!/usr/bin/python
-# this is the example script to use xgboost to train 
+# make prediction 
 import sys
 import numpy as np
 # add path of xgboost python module
@@ -17,13 +17,14 @@ threshold_ratio = 0.15
 # load in training data, directly use numpy
 dtest = np.loadtxt( dpath+'/test.csv', delimiter=',', skiprows=1 )
 data   = dtest[:,1:31]
-idx = dtest[:,1]
+idx = dtest[:,0]
 
-xtest = xgb.DMatrix( data, missing = -999.0 )
-bst = xgb.Booster()
+print 'finish loading from csv '
+xgmat = xgb.DMatrix( data, missing = -999.0 )
+bst = xgb.Booster({'nthread':16})
 bst.load_model( modelfile )
+ypred = bst.predict( xgmat )
 
-ypred = bst.predict( dtest )
 res  = [ ( int(idx[i]), ypred[i] ) for i in xrange(len(ypred)) ] 
 
 rorder = {}
@@ -31,7 +32,7 @@ for k, v in sorted( res, key = lambda x:-x[1] ):
     rorder[ k ] = len(rorder) + 1
 
 # write out predictions
-ntop = int( ratio * len(rorder ) )
+ntop = int( threshold_ratio * len(rorder ) )
 fo = open(outfile, 'w')
 nhit = 0
 ntot = 0
@@ -46,7 +47,7 @@ for k, v in res:
     ntot += 1
 fo.close()
 
-print 'finished writing into model file'
+print 'finished writing into prediction file'
 
 
 

python/xgboost.py

@@ -33,7 +33,7 @@ def ctypes2numpy( cptr, length ):
 # data matrix used in xgboost
 class DMatrix:
     # constructor
-    def __init__(self, data=None, label=None, missing=0.0):
+    def __init__(self, data=None, label=None, missing=0.0, weight = None):
         self.handle = xglib.XGDMatrixCreate()
         if data == None:
             return
@@ -51,6 +51,8 @@ class DMatrix:
                 raise Exception, "can not intialize DMatrix from"+str(type(data))
         if label != None:
             self.set_label(label)
+        if weight !=None:
+            self.set_weight(weight)
 
     # convert data from csr matrix
     def __init_from_csr(self,csr):

regrank/xgboost_regrank_data.h

@@ -57,7 +57,7 @@ namespace xgboost{
             DMatrix(void){}
             /*! \brief get the number of instances */
             inline size_t Size() const{
-                return info.labels.size();
+                return data.NumRow();
             }           
             /*!
              * \brief load from text file

regrank/xgboost_regrank_eval.h

@@ -110,6 +110,7 @@ namespace xgboost{
             virtual float Eval(const std::vector<float> &preds,
                                const DMatrix::Info &info) const {
                 const unsigned ndata = static_cast<unsigned>(preds.size());
+                utils::Assert( info.weights.size() == ndata, "we need weight to evaluate ams");
                 std::vector< std::pair<float, unsigned> > rec(ndata);
                 
                 #pragma omp parallel for schedule( static )