basic_walkthrough.py

"""
Getting started with XGBoost
============================

This is a simple example of using the native XGBoost interface, there are other
interfaces in the Python package like scikit-learn interface and Dask interface.


See :doc:`/python/python_intro` and :doc:`/tutorials/index` for other references.

"""
import os
import pickle

import numpy as np
from sklearn.datasets import load_svmlight_file

import xgboost as xgb

# Make sure the demo knows where to load the data.
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, "demo")

# X is a scipy csr matrix, XGBoost supports many other input types,
X, y = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.train"))
dtrain = xgb.DMatrix(X, y)
# validation set
X_test, y_test = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.test"))
dtest = xgb.DMatrix(X_test, y_test)

# specify parameters via map, definition are same as c++ version
param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}

# specify validations set to watch performance
watchlist = [(dtest, "eval"), (dtrain, "train")]
# number of boosting rounds
num_round = 2
bst = xgb.train(param, dtrain, num_boost_round=num_round, evals=watchlist)

# run prediction
preds = bst.predict(dtest)
labels = dtest.get_label()
print(
    "error=%f"
    % (
        sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i])
        / float(len(preds))
    )
)
bst.save_model("model-0.json")
# dump model
bst.dump_model("dump.raw.txt")
# dump model with feature map
bst.dump_model("dump.nice.txt", os.path.join(DEMO_DIR, "data/featmap.txt"))

# save dmatrix into binary buffer
dtest.save_binary("dtest.dmatrix")
# save model
bst.save_model("model-1.json")
# load model and data in
bst2 = xgb.Booster(model_file="model-1.json")
dtest2 = xgb.DMatrix("dtest.dmatrix")
preds2 = bst2.predict(dtest2)
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0

# alternatively, you can pickle the booster
pks = pickle.dumps(bst2)
# load model and data in
bst3 = pickle.loads(pks)
preds3 = bst3.predict(dtest2)
# assert they are the same
assert np.sum(np.abs(preds3 - preds)) == 0