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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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39
tests/python/test_with_sklearn.py
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@@ -63,9 +63,15 @@ def test_multiclass_classification(objective):
assert xgb_model.get_booster().num_boosted_rounds() == 100
preds = xgb_model.predict(X[test_index])
# test other params in XGBClassifier().fit
preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3)
preds2 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=(0, 1)
)
preds3 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=None
)
preds4 = xgb_model.predict(
X[test_index], output_margin=False, iteration_range=(0, 1)
)
labels = y[test_index]
check_pred(preds, labels, output_margin=False)
@@ -86,25 +92,21 @@ def test_multiclass_classification(objective):
assert proba.shape[1] == cls.n_classes_
def test_best_ntree_limit():
def test_best_iteration():
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
def train(booster, forest):
def train(booster: str, forest: Optional[int]) -> None:
rounds = 4
cls = xgb.XGBClassifier(
n_estimators=rounds, num_parallel_tree=forest, booster=booster
).fit(
X, y, eval_set=[(X, y)], early_stopping_rounds=3
)
assert cls.best_iteration == rounds - 1
if forest:
assert cls.best_ntree_limit == rounds * forest
else:
assert cls.best_ntree_limit == 0
# best_ntree_limit is used by default, assert that under gblinear it's
# best_iteration is used by default, assert that under gblinear it's
# automatically ignored due to being 0.
cls.predict(X)
@@ -430,12 +432,15 @@ def test_regression():
preds = xgb_model.predict(X[test_index])
# test other params in XGBRegressor().fit
preds2 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False,
ntree_limit=3)
preds2 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=(0, 3)
)
preds3 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=None
)
preds4 = xgb_model.predict(
X[test_index], output_margin=False, iteration_range=(0, 3)
)
labels = y[test_index]
assert mean_squared_error(preds, labels) < 25