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Save model in ubj as the default. (#9947)

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This commit is contained in:
Jiaming Yuan
2024-01-05 17:53:36 +08:00
committed by GitHub
parent c03a4d5088
commit 38dd91f491
23 changed files with 598 additions and 550 deletions
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194
tests/python/test_basic_models.py
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@@ -15,33 +15,9 @@ dpath = tm.data_dir(__file__)
rng = np.random.RandomState(1994)
def json_model(model_path: str, parameters: dict) -> dict:
datasets = pytest.importorskip("sklearn.datasets")
X, y = datasets.make_classification(64, n_features=8, n_classes=3, n_informative=6)
if parameters.get("objective", None) == "multi:softmax":
parameters["num_class"] = 3
dm1 = xgb.DMatrix(X, y)
bst = xgb.train(parameters, dm1)
bst.save_model(model_path)
if model_path.endswith("ubj"):
import ubjson
with open(model_path, "rb") as ubjfd:
model = ubjson.load(ubjfd)
else:
with open(model_path, "r") as fd:
model = json.load(fd)
return model
class TestModels:
def test_glm(self):
param = {'verbosity': 0, 'objective': 'binary:logistic',
param = {'objective': 'binary:logistic',
'booster': 'gblinear', 'alpha': 0.0001, 'lambda': 1,
'nthread': 1}
dtrain, dtest = tm.load_agaricus(__file__)
@@ -73,7 +49,7 @@ class TestModels:
with tempfile.TemporaryDirectory() as tmpdir:
dtest_path = os.path.join(tmpdir, 'dtest.dmatrix')
model_path = os.path.join(tmpdir, 'xgboost.model.dart')
model_path = os.path.join(tmpdir, "xgboost.model.dart.ubj")
# save dmatrix into binary buffer
dtest.save_binary(dtest_path)
model_path = model_path
@@ -101,7 +77,6 @@ class TestModels:
# check whether sample_type and normalize_type work
num_round = 50
param['verbosity'] = 0
param['learning_rate'] = 0.1
param['rate_drop'] = 0.1
preds_list = []
@@ -214,8 +189,7 @@ class TestModels:
assert set(evals_result['eval'].keys()) == {'auc', 'error', 'logloss'}
def test_fpreproc(self):
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
num_round = 2
def fpreproc(dtrain, dtest, param):
@@ -229,8 +203,7 @@ class TestModels:
metrics={'auc'}, seed=0, fpreproc=fpreproc)
def test_show_stdv(self):
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
num_round = 2
dtrain, _ = tm.load_agaricus(__file__)
xgb.cv(param, dtrain, num_round, nfold=5,
@@ -273,142 +246,6 @@ class TestModels:
bst = xgb.train([], dm2)
bst.predict(dm2) # success
def test_model_binary_io(self):
model_path = 'test_model_binary_io.bin'
parameters = {'tree_method': 'hist', 'booster': 'gbtree',
'scale_pos_weight': '0.5'}
X = np.random.random((10, 3))
y = np.random.random((10,))
dtrain = xgb.DMatrix(X, y)
bst = xgb.train(parameters, dtrain, num_boost_round=2)
bst.save_model(model_path)
bst = xgb.Booster(model_file=model_path)
os.remove(model_path)
config = json.loads(bst.save_config())
assert float(config['learner']['objective'][
'reg_loss_param']['scale_pos_weight']) == 0.5
buf = bst.save_raw()
from_raw = xgb.Booster()
from_raw.load_model(buf)
buf_from_raw = from_raw.save_raw()
assert buf == buf_from_raw
def run_model_json_io(self, parameters: dict, ext: str) -> None:
if ext == "ubj" and tm.no_ubjson()["condition"]:
pytest.skip(tm.no_ubjson()["reason"])
loc = locale.getpreferredencoding(False)
model_path = 'test_model_json_io.' + ext
j_model = json_model(model_path, parameters)
assert isinstance(j_model['learner'], dict)
bst = xgb.Booster(model_file=model_path)
bst.save_model(fname=model_path)
if ext == "ubj":
import ubjson
with open(model_path, "rb") as ubjfd:
j_model = ubjson.load(ubjfd)
else:
with open(model_path, 'r') as fd:
j_model = json.load(fd)
assert isinstance(j_model['learner'], dict)
os.remove(model_path)
assert locale.getpreferredencoding(False) == loc
json_raw = bst.save_raw(raw_format="json")
from_jraw = xgb.Booster()
from_jraw.load_model(json_raw)
ubj_raw = bst.save_raw(raw_format="ubj")
from_ubjraw = xgb.Booster()
from_ubjraw.load_model(ubj_raw)
if parameters.get("multi_strategy", None) != "multi_output_tree":
# old binary model is not supported.
old_from_json = from_jraw.save_raw(raw_format="deprecated")
old_from_ubj = from_ubjraw.save_raw(raw_format="deprecated")
assert old_from_json == old_from_ubj
raw_json = bst.save_raw(raw_format="json")
pretty = json.dumps(json.loads(raw_json), indent=2) + "\n\n"
bst.load_model(bytearray(pretty, encoding="ascii"))
if parameters.get("multi_strategy", None) != "multi_output_tree":
# old binary model is not supported.
old_from_json = from_jraw.save_raw(raw_format="deprecated")
old_from_ubj = from_ubjraw.save_raw(raw_format="deprecated")
assert old_from_json == old_from_ubj
rng = np.random.default_rng()
X = rng.random(size=from_jraw.num_features() * 10).reshape(
(10, from_jraw.num_features())
)
predt_from_jraw = from_jraw.predict(xgb.DMatrix(X))
predt_from_bst = bst.predict(xgb.DMatrix(X))
np.testing.assert_allclose(predt_from_jraw, predt_from_bst)
@pytest.mark.parametrize("ext", ["json", "ubj"])
def test_model_json_io(self, ext: str) -> None:
parameters = {"booster": "gbtree", "tree_method": "hist"}
self.run_model_json_io(parameters, ext)
parameters = {
"booster": "gbtree",
"tree_method": "hist",
"multi_strategy": "multi_output_tree",
"objective": "multi:softmax",
}
self.run_model_json_io(parameters, ext)
parameters = {"booster": "gblinear"}
self.run_model_json_io(parameters, ext)
parameters = {"booster": "dart", "tree_method": "hist"}
self.run_model_json_io(parameters, ext)
@pytest.mark.skipif(**tm.no_json_schema())
def test_json_io_schema(self):
import jsonschema
model_path = 'test_json_schema.json'
path = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
doc = os.path.join(path, 'doc', 'model.schema')
with open(doc, 'r') as fd:
schema = json.load(fd)
parameters = {'tree_method': 'hist', 'booster': 'gbtree'}
jsonschema.validate(instance=json_model(model_path, parameters),
schema=schema)
os.remove(model_path)
parameters = {'tree_method': 'hist', 'booster': 'dart'}
jsonschema.validate(instance=json_model(model_path, parameters),
schema=schema)
os.remove(model_path)
try:
dtrain, _ = tm.load_agaricus(__file__)
xgb.train({'objective': 'foo'}, dtrain, num_boost_round=1)
except ValueError as e:
e_str = str(e)
beg = e_str.find('Objective candidate')
end = e_str.find('Stack trace')
e_str = e_str[beg: end]
e_str = e_str.strip()
splited = e_str.splitlines()
objectives = [s.split(': ')[1] for s in splited]
j_objectives = schema['properties']['learner']['properties'][
'objective']['oneOf']
objectives_from_schema = set()
for j_obj in j_objectives:
objectives_from_schema.add(
j_obj['properties']['name']['const'])
objectives = set(objectives)
assert objectives == objectives_from_schema
@pytest.mark.skipif(**tm.no_json_schema())
def test_json_dump_schema(self):
import jsonschema
@@ -470,29 +307,6 @@ class TestModels:
for d in text_dump:
assert d.find(r"feature \"2\"") != -1
def test_categorical_model_io(self):
X, y = tm.make_categorical(256, 16, 71, False)
Xy = xgb.DMatrix(X, y, enable_categorical=True)
booster = xgb.train({"tree_method": "approx"}, Xy, num_boost_round=16)
predt_0 = booster.predict(Xy)
with tempfile.TemporaryDirectory() as tempdir:
path = os.path.join(tempdir, "model.binary")
with pytest.raises(ValueError, match=r".*JSON/UBJSON.*"):
booster.save_model(path)
path = os.path.join(tempdir, "model.json")
booster.save_model(path)
booster = xgb.Booster(model_file=path)
predt_1 = booster.predict(Xy)
np.testing.assert_allclose(predt_0, predt_1)
path = os.path.join(tempdir, "model.ubj")
booster.save_model(path)
booster = xgb.Booster(model_file=path)
predt_1 = booster.predict(Xy)
np.testing.assert_allclose(predt_0, predt_1)
@pytest.mark.skipif(**tm.no_sklearn())
def test_attributes(self):
from sklearn.datasets import load_iris