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Remove ntree limit in python package. (#8345)

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- Remove `ntree_limit`. The parameter has been deprecated since 1.4.0.
- The SHAP package compatibility is broken.
This commit is contained in:
Jiaming Yuan
2023-03-31 19:01:55 +08:00
committed by GitHub
parent b647403baa
commit bac22734fb
17 changed files with 284 additions and 357 deletions
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16
tests/python/test_basic_models.py
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@@ -64,7 +64,7 @@ class TestModels:
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)
# this is prediction
preds = bst.predict(dtest, ntree_limit=num_round)
preds = bst.predict(dtest, iteration_range=(0, num_round))
labels = dtest.get_label()
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
@@ -83,7 +83,7 @@ class TestModels:
bst2 = xgb.Booster(params=param, model_file=model_path)
dtest2 = xgb.DMatrix(dtest_path)
preds2 = bst2.predict(dtest2, ntree_limit=num_round)
preds2 = bst2.predict(dtest2, iteration_range=(0, num_round))
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
@@ -96,7 +96,7 @@ class TestModels:
# check whether custom evaluation metrics work
bst = xgb.train(param, dtrain, num_round, watchlist,
feval=my_logloss)
preds3 = bst.predict(dtest, ntree_limit=num_round)
preds3 = bst.predict(dtest, iteration_range=(0, num_round))
assert all(preds3 == preds)
# check whether sample_type and normalize_type work
@@ -110,7 +110,7 @@ class TestModels:
param['sample_type'] = p[0]
param['normalize_type'] = p[1]
bst = xgb.train(param, dtrain, num_round, watchlist)
preds = bst.predict(dtest, ntree_limit=num_round)
preds = bst.predict(dtest, iteration_range=(0, num_round))
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
@@ -472,8 +472,8 @@ class TestModels:
X, y = load_iris(return_X_y=True)
cls = xgb.XGBClassifier(n_estimators=2)
cls.fit(X, y, early_stopping_rounds=1, eval_set=[(X, y)])
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
assert cls.get_booster().best_iteration == cls.n_estimators - 1
assert cls.best_iteration == cls.get_booster().best_iteration
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, "cls.json")
@@ -481,8 +481,8 @@ class TestModels:
cls = xgb.XGBClassifier(n_estimators=2)
cls.load_model(path)
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
assert cls.get_booster().best_iteration == cls.n_estimators - 1
assert cls.best_iteration == cls.get_booster().best_iteration
def run_slice(
self,