move python example

demo/guide-python/basic_walkthrough.py

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+#!/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
+# load file from text file, also binary buffer generated by xgboost
+dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+dtest = xgb.DMatrix('../data/agaricus.txt.test')
+
+# specify parameters via map, definition are same as c++ version
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+
+# specify validations set to watch performance
+watchlist  = [(dtest,'eval'), (dtrain,'train')]
+num_round = 2
+bst = xgb.train(param, dtrain, num_round, watchlist)
+
+# this is prediction
+preds = bst.predict(dtest)
+labels = dtest.get_label()
+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')
+# dump model with feature map
+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 
+bst2 = xgb.Booster(model_file='xgb.model')
+dtest2 = xgb.DMatrix('dtest.buffer')
+preds2 = bst2.predict(dtest2)
+# assert they are the same
+assert np.sum(np.abs(preds2-preds)) == 0
+
+###
+# build dmatrix from scipy.sparse
+print ('start running example of build DMatrix from scipy.sparse')
+labels = []
+row = []; col = []; dat = []
+i = 0
+for l in open('../data/agaricus.txt.train'):
+    arr = l.split()
+    labels.append( int(arr[0]))
+    for it in arr[1:]:
+        k,v = it.split(':')
+        row.append(i); col.append(int(k)); dat.append(float(v))
+    i += 1
+csr = scipy.sparse.csr_matrix( (dat, (row,col)) )
+dtrain = xgb.DMatrix( csr )
+dtrain.set_label(labels)
+watchlist  = [(dtest,'eval'), (dtrain,'train')]
+bst = xgb.train( param, dtrain, num_round, watchlist )
+
+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)
+dtrain.set_label(labels)
+watchlist  = [(dtest,'eval'), (dtrain,'train')]
+bst = xgb.train( param, dtrain, num_round, watchlist )
+
+