Demo for boosting from prediction

import os

import xgboost as xgb

CURRENT_DIR = os.path.dirname(__file__)
dtrain = xgb.DMatrix(
    os.path.join(CURRENT_DIR, "../data/agaricus.txt.train?format=libsvm")
)
dtest = xgb.DMatrix(
    os.path.join(CURRENT_DIR, "../data/agaricus.txt.test?format=libsvm")
)
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, "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)