Update demo scripts to use installed python library

demo/guide-python/basic_walkthrough.py

@@ -1,10 +1,6 @@
 #!/usr/bin/python
-import sys
 import numpy as np
 import scipy.sparse
-# append the path to xgboost, you may need to change the following line
-# alternatively, you can add the path to PYTHONPATH environment variable
-sys.path.append('../../wrapper')
 import xgboost as xgb
 
 ### simple example
@@ -33,7 +29,7 @@ bst.dump_model('dump.nice.txt','../data/featmap.txt')
 # save dmatrix into binary buffer
 dtest.save_binary('dtest.buffer')
 bst.save_model('xgb.model')
-# load model and data in 
+# load model and data in
 bst2 = xgb.Booster(model_file='xgb.model')
 dtest2 = xgb.DMatrix('dtest.buffer')
 preds2 = bst2.predict(dtest2)

demo/guide-python/boost_from_prediction.py

@@ -1,7 +1,5 @@
 #!/usr/bin/python
-import sys
 import numpy as np
-sys.path.append('../../wrapper')
 import xgboost as xgb
 
 dtrain = xgb.DMatrix('../data/agaricus.txt.train')

demo/guide-python/cross_validation.py

@@ -1,7 +1,5 @@
 #!/usr/bin/python
-import sys
 import numpy as np
-sys.path.append('../../wrapper')
 import xgboost as xgb
 
 ### load data in do training
@@ -56,7 +54,7 @@ def evalerror(preds, dtrain):
     labels = dtrain.get_label()
     return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
 
-param = {'max_depth':2, 'eta':1, 'silent':1} 
+param = {'max_depth':2, 'eta':1, 'silent':1}
 # train with customized objective
 xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
        obj = logregobj, feval=evalerror)

demo/guide-python/custom_objective.py

@@ -1,11 +1,9 @@
 #!/usr/bin/python
-import sys
 import numpy as np
-sys.path.append('../../wrapper')
 import xgboost as xgb
 ###
 # advanced: cutomsized loss function
-# 
+#
 print ('start running example to used cutomized objective function')
 
 dtrain = xgb.DMatrix('../data/agaricus.txt.train')

demo/guide-python/generalized_linear_model.py

@@ -1,6 +1,4 @@
 #!/usr/bin/python
-import sys
-sys.path.append('../../wrapper')
 import xgboost as xgb
 ##
 #  this script demonstrate how to fit generalized linear model in xgboost
@@ -9,17 +7,17 @@ import xgboost as xgb
 dtrain = xgb.DMatrix('../data/agaricus.txt.train')
 dtest = xgb.DMatrix('../data/agaricus.txt.test')
 # change booster to gblinear, so that we are fitting a linear model
-# alpha is the L1 regularizer 
+# alpha is the L1 regularizer
 # lambda is the L2 regularizer
 # you can also set lambda_bias which is L2 regularizer on the bias term
 param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
          'alpha': 0.0001, 'lambda': 1 }
 
 # normally, you do not need to set eta (step_size)
-# XGBoost uses a parallel coordinate descent algorithm (shotgun), 
+# XGBoost uses a parallel coordinate descent algorithm (shotgun),
 # there could be affection on convergence with parallelization on certain cases
 # setting eta to be smaller value, e.g 0.5 can make the optimization more stable
-# param['eta'] = 1 
+# param['eta'] = 1
 
 ##
 # the rest of settings are the same

demo/guide-python/predict_first_ntree.py

@@ -1,7 +1,5 @@
 #!/usr/bin/python
-import sys
 import numpy as np
-sys.path.append('../../wrapper')
 import xgboost as xgb
 
 ### load data in do training

demo/guide-python/predict_leaf_indices.py

@@ -1,7 +1,5 @@
 #!/usr/bin/python
-import sys
 import numpy as np
-sys.path.append('../../wrapper')
 import xgboost as xgb
 
 ### load data in do training

demo/guide-python/sklearn_examples.py

@@ -4,8 +4,6 @@ Created on 1 Apr 2015
 @author: Jamie Hall
 '''
 
-import sys
-sys.path.append('../../wrapper')
 import xgboost as xgb
 
 import numpy as np