add base_margin

python/example/demo.py

@@ -90,3 +90,22 @@ def evalerror(preds, dtrain):
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train
 bst = xgb.train(param, dtrain, num_round, evallist, logregobj, evalerror)
+
+
+###
+# 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 = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
+# train xgboost for 1 round
+bst = xgb.train( param, dtrain, 1, evallist )
+# 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, evallist )