xgboost visualization with tensorboard

xgb_tb.py

from sklearn.datasets import load_boston
import pandas as pd

import xgboost as xgb
from tensorboard_logger import configure, log_value
from sklearn.cross_validation import train_test_split


def logspy(env):
    log_value("train", env.evaluation_result_list[0][1], step=env.iteration)
    log_value("val", env.evaluation_result_list[1][1], step=env.iteration)


configure("logs/sample-1234")

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)

x1, x2, y1, y2 = train_test_split(
    df, boston.target, test_size=0.1, random_state=18)

dtrain = xgb.DMatrix(x1, y1)
dvalid = xgb.DMatrix(x2, y2)
watchlist = [(dtrain, 'train'), (dvalid, 'valid')]

model = xgb.train(
    params={},
    num_boost_round=100,
    dtrain=dtrain,
    evals=watchlist,
    callbacks=[logspy])