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Mitigate flaky tests. (#7749)

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* Skip non-increasing test with external memory when subsample is used.
* Increase bin numbers for boost from prediction test. This mitigates the effect of
  non-deterministic partitioning.
This commit is contained in:
Jiaming Yuan
2022-03-28 21:20:50 +08:00
committed by GitHub
parent 39c5616af2
commit 8b3ecfca25
9 changed files with 50 additions and 46 deletions
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40
tests/python/test_with_dask.py
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@@ -337,14 +337,14 @@ def test_dask_predict_shape_infer(client: "Client") -> None:
assert prediction.shape[1] == 3
def run_boost_from_prediction_multi_clasas(
def run_boost_from_prediction_multi_class(
X: xgb.dask._DaskCollection,
y: xgb.dask._DaskCollection,
tree_method: str,
client: "Client"
client: "Client",
) -> None:
model_0 = xgb.dask.DaskXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4, tree_method=tree_method
learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=768
)
model_0.fit(X=X, y=y)
margin = xgb.dask.inplace_predict(
@@ -352,18 +352,18 @@ def run_boost_from_prediction_multi_clasas(
)
model_1 = xgb.dask.DaskXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4, tree_method=tree_method
learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=768
)
model_1.fit(X=X, y=y, base_margin=margin)
predictions_1 = xgb.dask.predict(
client,
model_1.get_booster(),
xgb.dask.DaskDMatrix(client, X, base_margin=margin),
output_margin=True
output_margin=True,
)
model_2 = xgb.dask.DaskXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8, tree_method=tree_method
learning_rate=0.3, n_estimators=8, tree_method=tree_method, max_bin=768
)
model_2.fit(X=X, y=y)
predictions_2 = xgb.dask.inplace_predict(
@@ -382,26 +382,29 @@ def run_boost_from_prediction_multi_clasas(
def run_boost_from_prediction(
X: xgb.dask._DaskCollection, y: xgb.dask._DaskCollection, tree_method: str, client: "Client"
X: xgb.dask._DaskCollection,
y: xgb.dask._DaskCollection,
tree_method: str,
client: "Client",
) -> None:
X = client.persist(X)
y = client.persist(y)
model_0 = xgb.dask.DaskXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4,
tree_method=tree_method)
learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=512
)
model_0.fit(X=X, y=y)
margin = model_0.predict(X, output_margin=True)
model_1 = xgb.dask.DaskXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4,
tree_method=tree_method)
learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=512
)
model_1.fit(X=X, y=y, base_margin=margin)
predictions_1 = model_1.predict(X, base_margin=margin)
cls_2 = xgb.dask.DaskXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8,
tree_method=tree_method)
learning_rate=0.3, n_estimators=8, tree_method=tree_method, max_bin=512
)
cls_2.fit(X=X, y=y)
predictions_2 = cls_2.predict(X)
@@ -415,8 +418,8 @@ def run_boost_from_prediction(
unmargined = xgb.dask.DaskXGBClassifier(n_estimators=4)
unmargined.fit(X=X, y=y, eval_set=[(X, y)], base_margin=margin)
margined_res = margined.evals_result()['validation_0']['logloss']
unmargined_res = unmargined.evals_result()['validation_0']['logloss']
margined_res = margined.evals_result()["validation_0"]["logloss"]
unmargined_res = unmargined.evals_result()["validation_0"]["logloss"]
assert len(margined_res) == len(unmargined_res)
for i in range(len(margined_res)):
@@ -429,12 +432,11 @@ def test_boost_from_prediction(tree_method: str, client: "Client") -> None:
from sklearn.datasets import load_breast_cancer, load_digits
X_, y_ = load_breast_cancer(return_X_y=True)
X, y = dd.from_array(X_, chunksize=200), dd.from_array(y_, chunksize=200)
run_boost_from_prediction(X, y, tree_method, client)
X_, y_ = load_digits(return_X_y=True)
X, y = dd.from_array(X_, chunksize=100), dd.from_array(y_, chunksize=100)
run_boost_from_prediction_multi_clasas(X, y, tree_method, client)
run_boost_from_prediction_multi_class(X, y, tree_method, client)
def test_inplace_predict(client: "Client") -> None:
@@ -1292,7 +1294,7 @@ class TestWithDask:
@given(params=hist_parameter_strategy,
dataset=tm.dataset_strategy)
@settings(deadline=None, suppress_health_check=suppress)
@settings(deadline=None, suppress_health_check=suppress, print_blob=True)
def test_hist(
self, params: Dict, dataset: tm.TestDataset, client: "Client"
) -> None:
@@ -1301,7 +1303,7 @@ class TestWithDask:
@given(params=exact_parameter_strategy,
dataset=tm.dataset_strategy)
@settings(deadline=None, suppress_health_check=suppress)
@settings(deadline=None, suppress_health_check=suppress, print_blob=True)
def test_approx(
self, client: "Client", params: Dict, dataset: tm.TestDataset
) -> None: