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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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123
tests/python/test_with_sklearn.py
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@@ -678,7 +678,6 @@ def test_split_value_histograms():
params = {
"max_depth": 6,
"eta": 0.01,
"verbosity": 0,
"objective": "binary:logistic",
"base_score": 0.5,
}
@@ -897,128 +896,6 @@ def test_validation_weights():
run_validation_weights(xgb.XGBClassifier)
def save_load_model(model_path):
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
digits = load_digits(n_class=2)
y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X, y):
xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
xgb_model.save_model(model_path)
xgb_model = xgb.XGBClassifier()
xgb_model.load_model(model_path)
assert isinstance(xgb_model.classes_, np.ndarray)
np.testing.assert_equal(xgb_model.classes_, np.array([0, 1]))
assert isinstance(xgb_model._Booster, xgb.Booster)
preds = xgb_model.predict(X[test_index])
labels = y[test_index]
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
assert xgb_model.get_booster().attr('scikit_learn') is None
# test native booster
preds = xgb_model.predict(X[test_index], output_margin=True)
booster = xgb.Booster(model_file=model_path)
predt_1 = booster.predict(xgb.DMatrix(X[test_index]),
output_margin=True)
assert np.allclose(preds, predt_1)
with pytest.raises(TypeError):
xgb_model = xgb.XGBModel()
xgb_model.load_model(model_path)
clf = xgb.XGBClassifier(booster="gblinear", early_stopping_rounds=1)
clf.fit(X, y, eval_set=[(X, y)])
best_iteration = clf.best_iteration
best_score = clf.best_score
predt_0 = clf.predict(X)
clf.save_model(model_path)
clf.load_model(model_path)
assert clf.booster == "gblinear"
predt_1 = clf.predict(X)
np.testing.assert_allclose(predt_0, predt_1)
assert clf.best_iteration == best_iteration
assert clf.best_score == best_score
clfpkl = pickle.dumps(clf)
clf = pickle.loads(clfpkl)
predt_2 = clf.predict(X)
np.testing.assert_allclose(predt_0, predt_2)
assert clf.best_iteration == best_iteration
assert clf.best_score == best_score
def test_save_load_model():
with tempfile.TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, "digits.model")
save_load_model(model_path)
with tempfile.TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, "digits.model.json")
save_load_model(model_path)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
with tempfile.TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, "digits.model.ubj")
digits = load_digits(n_class=2)
y = digits["target"]
X = digits["data"]
booster = xgb.train(
{"tree_method": "hist", "objective": "binary:logistic"},
dtrain=xgb.DMatrix(X, y),
num_boost_round=4,
)
predt_0 = booster.predict(xgb.DMatrix(X))
booster.save_model(model_path)
cls = xgb.XGBClassifier()
cls.load_model(model_path)
proba = cls.predict_proba(X)
assert proba.shape[0] == X.shape[0]
assert proba.shape[1] == 2 # binary
predt_1 = cls.predict_proba(X)[:, 1]
assert np.allclose(predt_0, predt_1)
cls = xgb.XGBModel()
cls.load_model(model_path)
predt_1 = cls.predict(X)
assert np.allclose(predt_0, predt_1)
# mclass
X, y = load_digits(n_class=10, return_X_y=True)
# small test_size to force early stop
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.01, random_state=1
)
clf = xgb.XGBClassifier(
n_estimators=64, tree_method="hist", early_stopping_rounds=2
)
clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
score = clf.best_score
clf.save_model(model_path)
clf = xgb.XGBClassifier()
clf.load_model(model_path)
assert clf.classes_.size == 10
assert clf.objective == "multi:softprob"
np.testing.assert_equal(clf.classes_, np.arange(10))
assert clf.n_classes_ == 10
assert clf.best_iteration == 27
assert clf.best_score == score
def test_RFECV():
from sklearn.datasets import load_breast_cancer, load_diabetes, load_iris
from sklearn.feature_selection import RFECV