Update demo scripts to use installed python library

demo/kaggle-higgs/higgs-pred.py

@@ -1,9 +1,6 @@
 #!/usr/bin/python
-# make prediction 
-import sys
+# make prediction
 import numpy as np
-# add path of xgboost python module
-sys.path.append('../../wrapper/')
 import xgboost as xgb
 
 # path to where the data lies
@@ -11,7 +8,7 @@ dpath = 'data'
 
 modelfile = 'higgs.model'
 outfile = 'higgs.pred.csv'
-# make top 15% as positive 
+# make top 15% as positive
 threshold_ratio = 0.15
 
 # load in training data, directly use numpy
@@ -24,7 +21,7 @@ xgmat = xgb.DMatrix( data, missing = -999.0 )
 bst = xgb.Booster({'nthread':16}, model_file = modelfile)
 ypred = bst.predict( xgmat )
 
-res  = [ ( int(idx[i]), ypred[i] ) for i in range(len(ypred)) ] 
+res  = [ ( int(idx[i]), ypred[i] ) for i in range(len(ypred)) ]
 
 rorder = {}
 for k, v in sorted( res, key = lambda x:-x[1] ):
@@ -36,12 +33,12 @@ fo = open(outfile, 'w')
 nhit = 0
 ntot = 0
 fo.write('EventId,RankOrder,Class\n')
-for k, v in res:        
+for k, v in res:
     if rorder[k] <= ntop:
         lb = 's'
         nhit += 1
     else:
-        lb = 'b'        
+        lb = 'b'
     # change output rank order to follow Kaggle convention
     fo.write('%s,%d,%s\n' % ( k,  len(rorder)+1-rorder[k], lb ) )
     ntot += 1