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Don't set seed on CLI interface. (#5563)

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Jiaming Yuan 2020-04-20 12:17:03 +08:00 committed by GitHub
parent ccd30e4491
commit b809f5d8b8
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3 changed files with 20 additions and 7 deletions
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1
python-package/xgboost/training.py
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@ -17,6 +17,7 @@ def _train_internal(params, dtrain,
"""internal training function"""
callbacks = [] if callbacks is None else callbacks
evals = list(evals)
params = params.copy()
if isinstance(params, dict) \
and 'eval_metric' in params \
and isinstance(params['eval_metric'], list):

1
src/cli_main.cc
View File

@ -340,7 +340,6 @@ int CLIRunTask(int argc, char *argv[]) {
common::ConfigParser cp(argv[1]);
auto cfg = cp.Parse();
cfg.emplace_back("seed", "0");
for (int i = 2; i < argc; ++i) {
char name[256], val[256];

25
tests/python/test_cli.py
View File

@ -13,7 +13,7 @@ booster = gbtree
objective = reg:squarederror
eta = 1.0
gamma = 1.0
seed = 0
seed = {seed}
min_child_weight = 0
max_depth = 3
task = {task}
@ -41,14 +41,18 @@ eval[test] = {data_path}
exe = os.path.join(project_root, exe)
assert os.path.exists(exe)
seed = 1994
with tempfile.TemporaryDirectory() as tmpdir:
model_out = os.path.join(tmpdir, 'test_load_cli_model')
model_out_cli = os.path.join(tmpdir, 'test_load_cli_model-cli.bin')
model_out_py = os.path.join(tmpdir, 'test_cli_model-py.bin')
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,
model_out=model_out_cli,
test_path='NULL',
name_pred='NULL')
with open(config_path, 'w') as fd:
@ -59,8 +63,9 @@ eval[test] = {data_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,
model_in=model_out_cli,
model_out='NULL',
test_path=data_path,
name_pred=predict_out)
@ -76,16 +81,24 @@ eval[test] = {data_path}
'objective': 'reg:squarederror',
'eta': 1.0,
'gamma': 1.0,
'seed': 0,
'seed': seed,
'min_child_weight': 0,
'max_depth': 3
}
data = xgboost.DMatrix(data_path)
booster = xgboost.train(parameters, data, num_boost_round=10)
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_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)