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Re-implement PR-AUC. (#7297)

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* Support binary/multi-class classification, ranking.
* Add documents.
* Handle missing data.
This commit is contained in:
Jiaming Yuan
2021-10-26 13:07:50 +08:00
committed by GitHub
parent a6bcd54b47
commit d4349426d8
12 changed files with 1035 additions and 655 deletions
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4
tests/python/test_with_dask.py
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@@ -587,7 +587,7 @@ def run_empty_dmatrix_auc(client: "Client", tree_method: str, n_workers: int) ->
cls = xgb.dask.DaskXGBClassifier(
tree_method=tree_method, n_estimators=2, use_label_encoder=False
)
cls.fit(X, y, eval_metric="auc", eval_set=[(valid_X, valid_y)])
cls.fit(X, y, eval_metric=["auc", "aucpr"], eval_set=[(valid_X, valid_y)])
# multiclass
X_, y_ = make_classification(
@@ -618,7 +618,7 @@ def run_empty_dmatrix_auc(client: "Client", tree_method: str, n_workers: int) ->
cls = xgb.dask.DaskXGBClassifier(
tree_method=tree_method, n_estimators=2, use_label_encoder=False
)
cls.fit(X, y, eval_metric="auc", eval_set=[(valid_X, valid_y)])
cls.fit(X, y, eval_metric=["auc", "aucpr"], eval_set=[(valid_X, valid_y)])
def test_empty_dmatrix_auc() -> None: