import os
import numpy as np
import xgboost as xgb
###
# advanced: customized loss function
#
print('start running example to used customized objective function')

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

# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param = {'max_depth': 2, 'eta': 1}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2


# user define objective function, given prediction, return gradient and second
# order gradient this is log likelihood loss
def logregobj(preds, dtrain):
    labels = dtrain.get_label()
    preds = 1.0 / (1.0 + np.exp(-preds))
    grad = preds - labels
    hess = preds * (1.0 - preds)
    return grad, hess


# user defined evaluation function, return a pair metric_name, result

# NOTE: when you do customized loss function, the default prediction value is
# margin. this may make builtin evaluation metric not function properly for
# example, we are doing logistic loss, the prediction is score before logistic
# transformation the builtin evaluation error assumes input is after logistic
# transformation Take this in mind when you use the customization, and maybe
# you need write customized evaluation function
def evalerror(preds, dtrain):
    labels = dtrain.get_label()
    # return a pair metric_name, result. The metric name must not contain a
    # colon (:) or a space since preds are margin(before logistic
    # transformation, cutoff at 0)
    return 'my-error', float(sum(labels != (preds > 0.0))) / len(labels)


# 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, watchlist, obj=logregobj,
                feval=evalerror)