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Deprecate use_label_encoder in XGBClassifier. (#7822)

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* Deprecate `use_label_encoder` in XGBClassifier.

* We have removed the encoder, now prepare to remove the indicator.
This commit is contained in:
Jiaming Yuan
2022-04-21 13:14:02 +08:00
committed by GitHub
parent 5815df4c46
commit 52d4eda786
8 changed files with 21 additions and 41 deletions
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18
tests/python/test_with_dask.py
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@@ -777,9 +777,7 @@ def run_empty_dmatrix_auc(client: "Client", tree_method: str, n_workers: int) ->
valid_X = dd.from_array(valid_X_, chunksize=n_samples)
valid_y = dd.from_array(valid_y_, chunksize=n_samples)
cls = xgb.dask.DaskXGBClassifier(
tree_method=tree_method, n_estimators=2, use_label_encoder=False
)
cls = xgb.dask.DaskXGBClassifier(tree_method=tree_method, n_estimators=2)
cls.fit(X, y, eval_metric=["auc", "aucpr"], eval_set=[(valid_X, valid_y)])
# multiclass
@@ -808,9 +806,7 @@ def run_empty_dmatrix_auc(client: "Client", tree_method: str, n_workers: int) ->
valid_X = dd.from_array(valid_X_, chunksize=n_samples)
valid_y = dd.from_array(valid_y_, chunksize=n_samples)
cls = xgb.dask.DaskXGBClassifier(
tree_method=tree_method, n_estimators=2, use_label_encoder=False
)
cls = xgb.dask.DaskXGBClassifier(tree_method=tree_method, n_estimators=2)
cls.fit(X, y, eval_metric=["auc", "aucpr"], eval_set=[(valid_X, valid_y)])
@@ -837,14 +833,10 @@ def run_auc(client: "Client", tree_method: str) -> None:
valid_X = dd.from_array(valid_X_, chunksize=10)
valid_y = dd.from_array(valid_y_, chunksize=10)
cls = xgb.XGBClassifier(
tree_method=tree_method, n_estimators=2, use_label_encoder=False
)
cls = xgb.XGBClassifier(tree_method=tree_method, n_estimators=2)
cls.fit(X_, y_, eval_metric="auc", eval_set=[(valid_X_, valid_y_)])
dcls = xgb.dask.DaskXGBClassifier(
tree_method=tree_method, n_estimators=2, use_label_encoder=False
)
dcls = xgb.dask.DaskXGBClassifier(tree_method=tree_method, n_estimators=2)
dcls.fit(X, y, eval_metric="auc", eval_set=[(valid_X, valid_y)])
approx = dcls.evals_result()["validation_0"]["auc"]
@@ -1693,7 +1685,6 @@ def test_parallel_submits(client: "Client") -> None:
verbosity=1,
n_estimators=i + 1,
eval_metric="merror",
use_label_encoder=False,
)
f = client.submit(cls.fit, X, y, pure=False)
futures.append(f)
@@ -1786,7 +1777,6 @@ def test_parallel_submit_multi_clients() -> None:
verbosity=1,
n_estimators=i + 1,
eval_metric="merror",
use_label_encoder=False,
)
f = client.submit(cls.fit, X, y, pure=False)
futures.append((client, f))