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Revert ntree limit fix (#6616)

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The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
This commit is contained in:
Jiaming Yuan
2021-01-19 23:51:16 +08:00
committed by GitHub
parent d132933550
commit d6d72de339
6 changed files with 32 additions and 21 deletions
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4
tests/python/test_basic_models.py
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@@ -347,7 +347,7 @@ 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 * cls.n_classes_
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
with tempfile.TemporaryDirectory() as tmpdir:
@@ -356,7 +356,7 @@ class TestModels:
cls = xgb.XGBClassifier(n_estimators=2)
cls.load_model(path)
assert cls.get_booster().best_ntree_limit == 2 * cls.n_classes_
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
@pytest.mark.skipif(**tm.no_sklearn())