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[Breaking] Remove Scikit-Learn default parameters (#5130)

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* Simplify Scikit-Learn parameter management.

* Copy base class for removing duplicated parameter signatures.
* Set all parameters to None.
* Handle None in set_param.
* Extract the doc.

Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
OrdoAbChao
2020-01-23 13:25:20 +01:00
committed by Jiaming Yuan
parent aa9a68010b
commit b4f952bd22
6 changed files with 270 additions and 293 deletions
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6
tests/python/test_early_stopping.py
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@@ -22,17 +22,17 @@ class TestEarlyStopping(unittest.TestCase):
y = digits['target']
X_train, X_test, y_train, y_test = train_test_split(X, y,
random_state=0)
clf1 = xgb.XGBClassifier()
clf1 = xgb.XGBClassifier(learning_rate=0.1)
clf1.fit(X_train, y_train, early_stopping_rounds=5, eval_metric="auc",
eval_set=[(X_test, y_test)])
clf2 = xgb.XGBClassifier()
clf2 = xgb.XGBClassifier(learning_rate=0.1)
clf2.fit(X_train, y_train, early_stopping_rounds=4, eval_metric="auc",
eval_set=[(X_test, y_test)])
# should be the same
assert clf1.best_score == clf2.best_score
assert clf1.best_score != 1
# check overfit
clf3 = xgb.XGBClassifier()
clf3 = xgb.XGBClassifier(learning_rate=0.1)
clf3.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
eval_set=[(X_test, y_test)])
assert clf3.best_score == 1