move python example

demo/guide-python/boost_from_prediction.py

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+#!/usr/bin/python
+import sys
+import numpy as np
+sys.path.append('../../wrapper')
+import xgboost as xgb
+
+dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+dtest = xgb.DMatrix('../data/agaricus.txt.test')
+watchlist  = [(dtest,'eval'), (dtrain,'train')]
+###
+# advanced: start from a initial base prediction
+#
+print ('start running example to start from a initial prediction')
+# specify parameters via map, definition are same as c++ version
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+# train xgboost for 1 round
+bst = xgb.train( param, dtrain, 1, watchlist )
+# Note: we need the margin value instead of transformed prediction in set_base_margin
+# do predict with output_margin=True, will always give you margin values before logistic transformation
+ptrain = bst.predict(dtrain, output_margin=True)
+ptest  = bst.predict(dtest, output_margin=True)
+dtrain.set_base_margin(ptrain)
+dtest.set_base_margin(ptest)
+
+print ('this is result of running from initial prediction')
+bst = xgb.train( param, dtrain, 1, watchlist )