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import json
import os
import platform
import subprocess
import tempfile
import numpy
import pytest
import xgboost
from xgboost import testing as tm
class TestCLI:
template = '''
booster = gbtree
objective = reg:squarederror
eta = 1.0
gamma = 1.0
seed = {seed}
min_child_weight = 0
max_depth = 3
task = {task}
model_in = {model_in}
model_out = {model_out}
test_path = {test_path}
name_pred = {name_pred}
model_dir = {model_dir}
num_round = 10
data = {data_path}
eval[test] = {data_path}
'''
PROJECT_ROOT = tm.project_root(__file__)
def get_exe(self):
if platform.system() == 'Windows':
exe = 'xgboost.exe'
else:
exe = 'xgboost'
exe = os.path.join(self.PROJECT_ROOT, exe)
if not os.path.exists(exe):
pytest.skip("CLI executable not found.")
return exe
def test_cli_model(self):
data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
root=self.PROJECT_ROOT)
exe = self.get_exe()
seed = 1994
with tempfile.TemporaryDirectory() as tmpdir:
model_out_cli = os.path.join(
tmpdir, 'test_load_cli_model-cli.json')
model_out_py = os.path.join(
tmpdir, 'test_cli_model-py.json')
config_path = os.path.join(
tmpdir, 'test_load_cli_model.conf')
train_conf = self.template.format(data_path=data_path,
seed=seed,
task='train',
model_in='NULL',
model_out=model_out_cli,
test_path='NULL',
name_pred='NULL',
model_dir='NULL')
with open(config_path, 'w') as fd:
fd.write(train_conf)
subprocess.run([exe, config_path])
predict_out = os.path.join(tmpdir,
'test_load_cli_model-prediction')
predict_conf = self.template.format(task='pred',
seed=seed,
data_path=data_path,
model_in=model_out_cli,
model_out='NULL',
test_path=data_path,
name_pred=predict_out,
model_dir='NULL')
with open(config_path, 'w') as fd:
fd.write(predict_conf)
subprocess.run([exe, config_path])
cli_predt = numpy.loadtxt(predict_out)
parameters = {
'booster': 'gbtree',
'objective': 'reg:squarederror',
'eta': 1.0,
'gamma': 1.0,
'seed': seed,
'min_child_weight': 0,
'max_depth': 3
}
data = xgboost.DMatrix(data_path)
booster = xgboost.train(parameters, data, num_boost_round=10)
# CLI model doesn't contain feature info.
booster.feature_names = None
booster.feature_types = None
booster.set_attr(best_iteration=None)
booster.save_model(model_out_py)
py_predt = booster.predict(data)
numpy.testing.assert_allclose(cli_predt, py_predt)
cli_model = xgboost.Booster(model_file=model_out_cli)
cli_predt = cli_model.predict(data)
numpy.testing.assert_allclose(cli_predt, py_predt)
with open(model_out_cli, 'rb') as fd:
cli_model_bin = fd.read()
with open(model_out_py, 'rb') as fd:
py_model_bin = fd.read()
assert hash(cli_model_bin) == hash(py_model_bin)
def test_cli_help(self):
exe = self.get_exe()
completed = subprocess.run([exe], stdout=subprocess.PIPE)
error_msg = completed.stdout.decode('utf-8')
ret = completed.returncode
assert ret == 1
assert error_msg.find('Usage') != -1
assert error_msg.find('eval[NAME]') != -1
completed = subprocess.run([exe, '-V'], stdout=subprocess.PIPE)
msg = completed.stdout.decode('utf-8')
assert msg.find('XGBoost') != -1
v = xgboost.__version__
if v.find('dev') != -1:
assert msg.split(':')[1].strip() == v.split('-')[0]
elif v.find('rc') != -1:
assert msg.split(':')[1].strip() == v.split('rc')[0]
else:
assert msg.split(':')[1].strip() == v
def test_cli_model_json(self):
exe = self.get_exe()
data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
root=self.PROJECT_ROOT)
seed = 1994
with tempfile.TemporaryDirectory() as tmpdir:
model_out_cli = os.path.join(
tmpdir, 'test_load_cli_model-cli.json')
config_path = os.path.join(tmpdir, 'test_load_cli_model.conf')
train_conf = self.template.format(data_path=data_path,
seed=seed,
task='train',
model_in='NULL',
model_out=model_out_cli,
test_path='NULL',
name_pred='NULL',
model_dir='NULL')
with open(config_path, 'w') as fd:
fd.write(train_conf)
subprocess.run([exe, config_path])
with open(model_out_cli, 'r') as fd:
model = json.load(fd)
assert model['learner']['gradient_booster']['name'] == 'gbtree'
def test_cli_save_model(self):
'''Test save on final round'''
exe = self.get_exe()
data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
root=self.PROJECT_ROOT)
seed = 1994
with tempfile.TemporaryDirectory() as tmpdir:
model_out_cli = os.path.join(tmpdir, '0010.model')
config_path = os.path.join(tmpdir, 'test_load_cli_model.conf')
train_conf = self.template.format(data_path=data_path,
seed=seed,
task='train',
model_in='NULL',
model_out='NULL',
test_path='NULL',
name_pred='NULL',
model_dir=tmpdir)
with open(config_path, 'w') as fd:
fd.write(train_conf)
subprocess.run([exe, config_path])
assert os.path.exists(model_out_cli)
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