Demo for using cross validation

import os

import numpy as np

import xgboost as xgb

# load data in do training
CURRENT_DIR = os.path.dirname(__file__)
dtrain = xgb.DMatrix(
    os.path.join(CURRENT_DIR, "../data/agaricus.txt.train?format=libsvm")
)
param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
num_round = 2

print("running cross validation")
# do cross validation, this will print result out as
# [iteration]  metric_name:mean_value+std_value
# std_value is standard deviation of the metric
xgb.cv(
    param,
    dtrain,
    num_round,
    nfold=5,
    metrics={"error"},
    seed=0,
    callbacks=[xgb.callback.EvaluationMonitor(show_stdv=True)],
)

print("running cross validation, disable standard deviation display")
# do cross validation, this will print result out as
# [iteration]  metric_name:mean_value
res = xgb.cv(
    param,
    dtrain,
    num_boost_round=10,
    nfold=5,
    metrics={"error"},
    seed=0,
    callbacks=[
        xgb.callback.EvaluationMonitor(show_stdv=False),
        xgb.callback.EarlyStopping(3),
    ],
)
print(res)
print("running cross validation, with preprocessing function")


# define the preprocessing function
# used to return the preprocessed training, test data, and parameter
# we can use this to do weight rescale, etc.
# as a example, we try to set scale_pos_weight
def fpreproc(dtrain, dtest, param):
    label = dtrain.get_label()
    ratio = float(np.sum(label == 0)) / np.sum(label == 1)
    param["scale_pos_weight"] = ratio
    return (dtrain, dtest, param)


# do cross validation, for each fold
# the dtrain, dtest, param will be passed into fpreproc
# then the return value of fpreproc will be used to generate
# results of that fold
xgb.cv(param, dtrain, num_round, nfold=5, metrics={"auc"}, seed=0, fpreproc=fpreproc)

###
# you can also do cross validation with customized loss function
# See custom_objective.py
##
print("running cross validation, with customized loss function")


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


def evalerror(preds, dtrain):
    labels = dtrain.get_label()
    return "error", float(sum(labels != (preds > 0.0))) / len(labels)


param = {"max_depth": 2, "eta": 1}
# train with customized objective
xgb.cv(param, dtrain, num_round, nfold=5, seed=0, obj=logregobj, feval=evalerror)