Merge pull request #7 from jrings/master

Compatibility with both Python 2(.7) and 3

demo/binary_classification/mapfeat.py

@@ -24,7 +24,7 @@ def loadfmap( fname ):
     return fmap, nmap
 
 def write_nmap( fo, nmap ):    
-    for i in xrange( len(nmap) ):
+    for i in range( len(nmap) ):
         fo.write('%d\t%s\ti\n' % (i, nmap[i]) )
 
 # start here
@@ -41,7 +41,7 @@ def write_nmap( fo, nmap ):
     else:
         assert arr[0] == 'e'
         fo.write('0')
-    for i in xrange( 1,len(arr) ):
+    for i in range( 1,len(arr) ):
         fo.write( ' %d:1' % fmap[i][arr[i].strip()] )
     fo.write('\n')
 

demo/binary_classification/mknfold.py

@@ -3,7 +3,7 @@
 import random
 
 if len(sys.argv) < 2:
-    print 'Usage:<filename> <k> [nfold = 5]'
+    print ('Usage:<filename> <k> [nfold = 5]')
     exit(0)
 
 random.seed( 10 )

demo/kaggle-higgs/higgs-numpy.py

@@ -1,9 +1,15 @@
 #!/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
-sys.path.append('../../python/')
+code_path = os.path.join(
+    os.path.split(inspect.getfile(inspect.currentframe()))[0], "../../python")
+
+sys.path.append(code_path)
+
 import xgboost as xgb
 
 test_size = 550000
@@ -12,19 +18,19 @@
 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 '
+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 xrange(len(label)) if label[i] == 1.0  )
-sum_wneg = sum( weight[i] for i in xrange(len(label)) if label[i] == 0.0  )
+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 )
+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 )
@@ -43,14 +49,14 @@
 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')]
+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'
+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'
+print ('finish training')

demo/kaggle-higgs/higgs-pred.py

@@ -19,13 +19,13 @@
 data   = dtest[:,1:31]
 idx = dtest[:,0]
 
-print 'finish loading from csv '
+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 )
 
-res  = [ ( int(idx[i]), ypred[i] ) for i in xrange(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] ):
@@ -47,7 +47,7 @@
     ntot += 1
 fo.close()
 
-print 'finished writing into prediction file'
+print ('finished writing into prediction file')
 
 
 

demo/kaggle-higgs/run.sh

@@ -1,4 +1,14 @@
 #!/bin/bash
 
-python higgs-numpy.py
-python higgs-pred.py
+python -u higgs-numpy.py
+ret=$?
+if [[ $ret != 0 ]]; then
+    echo "ERROR in higgs-numpy.py"
+    exit $ret
+fi
+python -u higgs-pred.py
+ret=$?
+if [[ $ret != 0 ]]; then
+    echo "ERROR in higgs-pred.py"
+    exit $ret
+fi

demo/kaggle-higgs/speedtest.py

@@ -14,18 +14,18 @@
 
 # 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 '
+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 xrange(len(label)) if label[i] == 1.0  )
-sum_wneg = sum( weight[i] for i in xrange(len(label)) if label[i] == 0.0  )
+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 )
+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 )
@@ -47,20 +47,20 @@
 watchlist = [ (xgmat,'train') ]
 # boost 10 tres
 num_round = 10
-print 'loading data end, start to boost trees'
-print "training GBM from sklearn"
+print ('loading data end, start to boost trees')
+print ("training GBM from sklearn")
 tmp = time.time()
 gbm = GradientBoostingClassifier(n_estimators=num_round, max_depth=6, verbose=2)
 gbm.fit(data, label)
-print "sklearn.GBM costs: %s seconds" % str(time.time() - tmp)
+print ("sklearn.GBM costs: %s seconds" % str(time.time() - tmp))
 #raw_input()
-print "training xgboost"
+print ("training xgboost")
 threads = [1, 2, 4, 16]
 for i in threads:
     param['nthread'] = i
     tmp = time.time()
     plst = param.items()+[('eval_metric', 'ams@0.15')]
     bst = xgb.train( plst, xgmat, num_round, watchlist );
-    print "XGBoost with %d thread costs: %s seconds" % (i, str(time.time() - tmp))
+    print ("XGBoost with %d thread costs: %s seconds" % (i, str(time.time() - tmp)))
 
-print 'finish training'
+print ('finish training')

demo/multiclass_classification/train.py

@@ -37,6 +37,6 @@
 # get prediction
 pred = bst.predict( xg_test );
 
-print 'predicting, classification error=%f' % (sum( int(pred[i]) != test_Y[i] for i in xrange(len(test_Y))) / float(len(test_Y)) )
+print ('predicting, classification error=%f' % (sum( int(pred[i]) != test_Y[i] for i in range(len(test_Y))) / float(len(test_Y)) ))
 
 

demo/rank/trans_data.py

@@ -2,18 +2,18 @@
 
 def save_data(group_data,output_feature,output_group):
     if len(group_data) == 0:
-	return
+        return
 
     output_group.write(str(len(group_data))+"\n")
     for data in group_data:
         # only include nonzero features
         feats = [ p for p in data[2:] if float(p.split(':')[1]) != 0.0 ]        
-	output_feature.write(data[0] + " " + " ".join(feats) + "\n")
+        output_feature.write(data[0] + " " + " ".join(feats) + "\n")
 
 if __name__ == "__main__":
     if len(sys.argv) != 4:
-        print "Usage: python trans_data.py [Ranksvm Format Input] [Output Feature File] [Output Group File]"
-	sys.exit(0)
+        print ("Usage: python trans_data.py [Ranksvm Format Input] [Output Feature File] [Output Group File]")
+        sys.exit(0)
 
     fi = open(sys.argv[1])
     output_feature = open(sys.argv[2],"w")
@@ -22,16 +22,16 @@ def save_data(group_data,output_feature,output_group):
     group_data = []
     group = ""
     for line in fi:
-	if not line:
-	    break
-	if "#" in line:
-	    line = line[:line.index("#")]
+        if not line:
+            break
+        if "#" in line:
+            line = line[:line.index("#")]
         splits = line.strip().split(" ")
-	if splits[1] != group:
-	    save_data(group_data,output_feature,output_group)
-	    group_data = []
-	group = splits[1]
-	group_data.append(splits)
+        if splits[1] != group:
+            save_data(group_data,output_feature,output_group)
+            group_data = []
+        group = splits[1]
+        group_data.append(splits)
 
     save_data(group_data,output_feature,output_group)
 

demo/regression/mapfeat.py

@@ -7,7 +7,7 @@
 for l in open( 'machine.data' ):
     arr = l.split(',')
     fo.write(arr[8])
-    for i in xrange( 0,6 ):
+    for i in range( 0,6 ):
         fo.write( ' %d:%s' %(i,arr[i+2]) )
 
     if arr[0] not in fmap:
@@ -24,9 +24,9 @@
 # list from machine.names
 names = ['vendor','MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX', 'PRP', 'ERP' ]; 
 
-for i in xrange(0,6):
+for i in range(0,6):
     fo.write( '%d\t%s\tint\n' % (i, names[i+1]))
 
-for v, k in sorted( fmap.iteritems(), key = lambda x:x[1] ):
+for v, k in sorted( fmap.items(), key = lambda x:x[1] ):
     fo.write( '%d\tvendor=%s\ti\n' % (k, v))
 fo.close()

demo/regression/mknfold.py

@@ -3,7 +3,7 @@
 import random
 
 if len(sys.argv) < 2:
-    print 'Usage:<filename> <k> [nfold = 5]'
+    print ('Usage:<filename> <k> [nfold = 5]')
     exit(0)
 
 random.seed( 10 )

python/example/demo.py

@@ -22,7 +22,7 @@
 # this is prediction
 preds = bst.predict( dtest )
 labels = dtest.get_label()
-print 'error=%f' % (  sum(1 for i in xrange(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds)))
+print ('error=%f' % (  sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
 bst.save_model('0001.model')
 # dump model
 bst.dump_model('dump.raw.txt')
@@ -32,7 +32,7 @@
 ###
 # build dmatrix in python iteratively
 #
-print 'start running example of build DMatrix in python'
+print ('start running example of build DMatrix in python')
 dtrain = xgb.DMatrix()
 labels = []
 for l in open('agaricus.txt.train'):
@@ -50,7 +50,7 @@
 
 ###
 # build dmatrix from scipy.sparse
-print 'start running example of build DMatrix from scipy.sparse'
+print ('start running example of build DMatrix from scipy.sparse')
 labels = []
 row = []; col = []; dat = []
 i = 0
@@ -68,7 +68,7 @@
 evallist  = [(dtest,'eval'), (dtrain,'train')]
 bst = xgb.train( param, dtrain, num_round, evallist )
 
-print 'start running example of build DMatrix from numpy array'
+print ('start running example of build DMatrix from numpy array')
 # NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation,then convert to DMatrix
 npymat = csr.todense()
 dtrain = xgb.DMatrix( npymat )
@@ -79,7 +79,7 @@
 ###
 # advanced: cutomsized loss function, set loss_type to 0, so that predict get untransformed score
 # 
-print 'start running example to used cutomized objective function'
+print ('start running example to used cutomized objective function')
 
 # note: set loss_type properly, loss_type=2 means the prediction will get logistic transformed
 #       in most case, we may want to set loss_type = 0, to get untransformed score to compute gradient