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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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182
tests/python/test_basic.py
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@@ -10,46 +10,48 @@ import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = 'demo/data/'
dpath = "demo/data/"
rng = np.random.RandomState(1994)
class TestBasic:
def test_compat(self):
from xgboost.compat import lazy_isinstance
a = np.array([1, 2, 3])
assert lazy_isinstance(a, 'numpy', 'ndarray')
assert not lazy_isinstance(a, 'numpy', 'dataframe')
assert lazy_isinstance(a, "numpy", "ndarray")
assert not lazy_isinstance(a, "numpy", "dataframe")
def test_basic(self):
dtrain, dtest = tm.load_agaricus(__file__)
param = {'max_depth': 2, 'eta': 1,
'objective': 'binary:logistic'}
param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
# specify validations set to watch performance
watchlist = [(dtrain, 'train')]
watchlist = [(dtrain, "train")]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist, verbose_eval=True)
bst = xgb.train(param, dtrain, num_round, evals=watchlist, verbose_eval=True)
preds = bst.predict(dtrain)
labels = dtrain.get_label()
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
err = sum(
1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]
) / float(len(preds))
# error must be smaller than 10%
assert err < 0.1
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
err = sum(
1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]
) / float(len(preds))
# error must be smaller than 10%
assert err < 0.1
with tempfile.TemporaryDirectory() as tmpdir:
dtest_path = os.path.join(tmpdir, 'dtest.dmatrix')
dtest_path = os.path.join(tmpdir, "dtest.dmatrix")
# save dmatrix into binary buffer
dtest.save_binary(dtest_path)
# save model
model_path = os.path.join(tmpdir, 'model.booster')
model_path = os.path.join(tmpdir, "model.ubj")
bst.save_model(model_path)
# load model and data in
bst2 = xgb.Booster(model_file=model_path)
@@ -59,17 +61,21 @@ class TestBasic:
assert np.sum(np.abs(preds2 - preds)) == 0
def test_metric_config(self):
# Make sure that the metric configuration happens in booster so the
# string `['error', 'auc']` doesn't get passed down to core.
# Make sure that the metric configuration happens in booster so the string
# `['error', 'auc']` doesn't get passed down to core.
dtrain, dtest = tm.load_agaricus(__file__)
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic', 'eval_metric': ['error', 'auc']}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
param = {
"max_depth": 2,
"eta": 1,
"objective": "binary:logistic",
"eval_metric": ["error", "auc"],
}
watchlist = [(dtest, "eval"), (dtrain, "train")]
num_round = 2
booster = xgb.train(param, dtrain, num_round, watchlist)
booster = xgb.train(param, dtrain, num_round, evals=watchlist)
predt_0 = booster.predict(dtrain)
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, 'model.json')
path = os.path.join(tmpdir, "model.json")
booster.save_model(path)
booster = xgb.Booster(params=param, model_file=path)
@@ -78,22 +84,23 @@ class TestBasic:
def test_multiclass(self):
dtrain, dtest = tm.load_agaricus(__file__)
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'num_class': 2}
param = {"max_depth": 2, "eta": 1, "num_class": 2}
# specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
watchlist = [(dtest, "eval"), (dtrain, "train")]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)
bst = xgb.train(param, dtrain, num_round, evals=watchlist)
# this is prediction
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds))
if preds[i] != labels[i]) / float(len(preds))
err = sum(1 for i in range(len(preds)) if preds[i] != labels[i]) / float(
len(preds)
)
# error must be smaller than 10%
assert err < 0.1
with tempfile.TemporaryDirectory() as tmpdir:
dtest_path = os.path.join(tmpdir, 'dtest.buffer')
model_path = os.path.join(tmpdir, 'xgb.model')
dtest_path = os.path.join(tmpdir, "dtest.buffer")
model_path = os.path.join(tmpdir, "model.ubj")
# save dmatrix into binary buffer
dtest.save_binary(dtest_path)
# save model
@@ -108,33 +115,39 @@ class TestBasic:
def test_dump(self):
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
features = ['Feature1', 'Feature2']
features = ["Feature1", "Feature2"]
dm = xgb.DMatrix(data, label=target, feature_names=features)
params = {'objective': 'binary:logistic',
'eval_metric': 'logloss',
'eta': 0.3,
'max_depth': 1}
params = {
"objective": "binary:logistic",
"eval_metric": "logloss",
"eta": 0.3,
"max_depth": 1,
}
bst = xgb.train(params, dm, num_boost_round=1)
# number of feature importances should == number of features
dump1 = bst.get_dump()
assert len(dump1) == 1, 'Expected only 1 tree to be dumped.'
len(dump1[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
assert len(dump1) == 1, "Expected only 1 tree to be dumped."
len(
dump1[0].splitlines()
) == 3, "Expected 1 root and 2 leaves - 3 lines in dump."
dump2 = bst.get_dump(with_stats=True)
assert dump2[0].count('\n') == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
msg = 'Expected more info when with_stats=True is given.'
assert dump2[0].find('\n') > dump1[0].find('\n'), msg
assert (
dump2[0].count("\n") == 3
), "Expected 1 root and 2 leaves - 3 lines in dump."
msg = "Expected more info when with_stats=True is given."
assert dump2[0].find("\n") > dump1[0].find("\n"), msg
dump3 = bst.get_dump(dump_format="json")
dump3j = json.loads(dump3[0])
assert dump3j['nodeid'] == 0, 'Expected the root node on top.'
assert dump3j["nodeid"] == 0, "Expected the root node on top."
dump4 = bst.get_dump(dump_format="json", with_stats=True)
dump4j = json.loads(dump4[0])
assert 'gain' in dump4j, "Expected 'gain' to be dumped in JSON."
assert "gain" in dump4j, "Expected 'gain' to be dumped in JSON."
with pytest.raises(ValueError):
bst.get_dump(fmap="foo")
@@ -163,12 +176,14 @@ class TestBasic:
def test_load_file_invalid(self):
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file='incorrect_path')
xgb.Booster(model_file="incorrect_path")
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file=u'不正なパス')
xgb.Booster(model_file="不正なパス")
@pytest.mark.parametrize("path", ["모델.ubj", "がうる・ぐら.json"], ids=["path-0", "path-1"])
@pytest.mark.parametrize(
"path", ["모델.ubj", "がうる・ぐら.json"], ids=["path-0", "path-1"]
)
def test_unicode_path(self, tmpdir, path):
model_path = pathlib.Path(tmpdir) / path
dtrain, _ = tm.load_agaricus(__file__)
@@ -180,12 +195,11 @@ class TestBasic:
assert bst.get_dump(dump_format="text") == bst2.get_dump(dump_format="text")
def test_dmatrix_numpy_init_omp(self):
rows = [1000, 11326, 15000]
cols = 50
for row in rows:
X = np.random.randn(row, cols)
y = np.random.randn(row).astype('f')
y = np.random.randn(row).astype("f")
dm = xgb.DMatrix(X, y, nthread=0)
np.testing.assert_array_equal(dm.get_label(), y)
assert dm.num_row() == row
@@ -198,8 +212,7 @@ class TestBasic:
def test_cv(self):
dm, _ = tm.load_agaricus(__file__)
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
params = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
@@ -208,19 +221,18 @@ class TestBasic:
def test_cv_no_shuffle(self):
dm, _ = tm.load_agaricus(__file__)
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
params = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, shuffle=False, nfold=10,
as_pandas=False)
cv = xgb.cv(
params, dm, num_boost_round=10, shuffle=False, nfold=10, as_pandas=False
)
assert isinstance(cv, dict)
assert len(cv) == (4)
def test_cv_explicit_fold_indices(self):
dm, _ = tm.load_agaricus(__file__)
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
'binary:logistic'}
params = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
folds = [
# Train Test
([1, 3], [5, 8]),
@@ -228,15 +240,13 @@ class TestBasic:
]
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, folds=folds,
as_pandas=False)
cv = xgb.cv(params, dm, num_boost_round=10, folds=folds, as_pandas=False)
assert isinstance(cv, dict)
assert len(cv) == (4)
@pytest.mark.skipif(**tm.skip_s390x())
def test_cv_explicit_fold_indices_labels(self):
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
'reg:squarederror'}
params = {"max_depth": 2, "eta": 1, "objective": "reg:squarederror"}
N = 100
F = 3
dm = xgb.DMatrix(data=np.random.randn(N, F), label=np.arange(N))
@@ -252,9 +262,10 @@ class TestBasic:
super().__init__()
def after_iteration(
self, model,
self,
model,
epoch: int,
evals_log: xgb.callback.TrainingCallback.EvalsLog
evals_log: xgb.callback.TrainingCallback.EvalsLog,
):
print([fold.dtest.get_label() for fold in model.cvfolds])
@@ -263,12 +274,18 @@ class TestBasic:
# Run cross validation and capture standard out to test callback result
with tm.captured_output() as (out, err):
xgb.cv(
params, dm, num_boost_round=1, folds=folds, callbacks=[cb],
as_pandas=False
params,
dm,
num_boost_round=1,
folds=folds,
callbacks=[cb],
as_pandas=False,
)
output = out.getvalue().strip()
solution = ('[array([5., 8.], dtype=float32), array([23., 43., 11.],' +
' dtype=float32)]')
solution = (
"[array([5., 8.], dtype=float32), array([23., 43., 11.],"
+ " dtype=float32)]"
)
assert output == solution
@@ -285,7 +302,7 @@ class TestBasicPathLike:
"""Saving to a binary file using pathlib from a DMatrix."""
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
features = ['Feature1', 'Feature2']
features = ["Feature1", "Feature2"]
dm = xgb.DMatrix(data, label=target, feature_names=features)
@@ -299,42 +316,3 @@ class TestBasicPathLike:
"""An invalid model_file path should raise XGBoostError."""
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file=Path("invalidpath"))
def test_Booster_save_and_load(self):
"""Saving and loading model files from paths."""
save_path = Path("saveload.model")
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
features = ['Feature1', 'Feature2']
dm = xgb.DMatrix(data, label=target, feature_names=features)
params = {'objective': 'binary:logistic',
'eval_metric': 'logloss',
'eta': 0.3,
'max_depth': 1}
bst = xgb.train(params, dm, num_boost_round=1)
# save, assert exists
bst.save_model(save_path)
assert save_path.exists()
def dump_assertions(dump):
"""Assertions for the expected dump from Booster"""
assert len(dump) == 1, 'Exepcted only 1 tree to be dumped.'
assert len(dump[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines.'
# load the model again using Path
bst2 = xgb.Booster(model_file=save_path)
dump2 = bst2.get_dump()
dump_assertions(dump2)
# load again using load_model
bst3 = xgb.Booster()
bst3.load_model(save_path)
dump3 = bst3.get_dump()
dump_assertions(dump3)
# remove file
Path.unlink(save_path)