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Support slicing tree model (#6302)

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This PR is meant the end the confusion around best_ntree_limit and unify model slicing. We have multi-class and random forests, asking users to understand how to set ntree_limit is difficult and error prone.

* Implement the save_best option in early stopping.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2020-11-03 02:27:39 -05:00
committed by GitHub
parent 29745c6df2
commit 2cc9662005
19 changed files with 550 additions and 37 deletions
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29
tests/python/test_callback.py
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@@ -113,6 +113,35 @@ class TestCallbacks(unittest.TestCase):
dump = booster.get_dump(dump_format='json')
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
def test_early_stopping_save_best_model(self):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
n_estimators = 100
cls = xgb.XGBClassifier(n_estimators=n_estimators)
early_stopping_rounds = 5
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
save_best=True)
cls.fit(X, y, eval_set=[(X, y)],
eval_metric=tm.eval_error_metric, callbacks=[early_stop])
booster = cls.get_booster()
dump = booster.get_dump(dump_format='json')
assert len(dump) == booster.best_iteration
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
save_best=True)
cls = xgb.XGBClassifier(booster='gblinear', n_estimators=10)
self.assertRaises(ValueError, lambda: cls.fit(X, y, eval_set=[(X, y)],
eval_metric=tm.eval_error_metric,
callbacks=[early_stop]))
# No error
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
save_best=False)
xgb.XGBClassifier(booster='gblinear', n_estimators=10).fit(
X, y, eval_set=[(X, y)],
eval_metric=tm.eval_error_metric,
callbacks=[early_stop])
def run_eta_decay(self, tree_method, deprecated_callback):
if deprecated_callback:
scheduler = xgb.callback.reset_learning_rate