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Small cleanup to tests. (#7585)

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* Use random port in dask tests to avoid warnings for occupied port.
* Increase the difficulty of AUC tests.
This commit is contained in:
Jiaming Yuan
2022-01-21 14:26:57 +08:00
committed by GitHub
parent 9fd510faa5
commit 5ddd4a9d06
2 changed files with 27 additions and 24 deletions
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43
tests/python/test_with_dask.py
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@@ -41,10 +41,10 @@ else:
suppress = hypothesis.utils.conventions.not_set # type:ignore
@pytest.fixture(scope='module')
@pytest.fixture(scope="module")
def cluster():
with LocalCluster(
n_workers=2, threads_per_worker=2, dashboard_address=None
n_workers=2, threads_per_worker=2, dashboard_address=":0"
) as dask_cluster:
yield dask_cluster
@@ -123,7 +123,7 @@ def generate_array(
def test_from_dask_dataframe() -> None:
with LocalCluster(n_workers=kWorkers) as cluster:
with LocalCluster(n_workers=kWorkers, dashboard_address=":0") as cluster:
with Client(cluster) as client:
X, y, _ = generate_array()
@@ -166,7 +166,9 @@ def test_from_dask_dataframe() -> None:
def test_from_dask_array() -> None:
with LocalCluster(n_workers=kWorkers, threads_per_worker=5) as cluster:
with LocalCluster(
n_workers=kWorkers, threads_per_worker=5, dashboard_address=":0"
) as cluster:
with Client(cluster) as client:
X, y, _ = generate_array()
dtrain = DaskDMatrix(client, X, y)
@@ -180,12 +182,12 @@ def test_from_dask_array() -> None:
# force prediction to be computed
prediction = prediction.compute()
booster: xgb.Booster = result['booster']
booster: xgb.Booster = result["booster"]
single_node_predt = booster.predict(xgb.DMatrix(X.compute()))
np.testing.assert_allclose(prediction, single_node_predt)
config = json.loads(booster.save_config())
assert int(config['learner']['generic_param']['nthread']) == 5
assert int(config["learner"]["generic_param"]["nthread"]) == 5
from_arr = xgb.dask.predict(client, model=booster, data=X)
@@ -793,7 +795,7 @@ def run_empty_dmatrix_auc(client: "Client", tree_method: str, n_workers: int) ->
def test_empty_dmatrix_auc() -> None:
with LocalCluster(n_workers=8) as cluster:
with LocalCluster(n_workers=8, dashboard_address=":0") as cluster:
with Client(cluster) as client:
run_empty_dmatrix_auc(client, "hist", 8)
@@ -835,11 +837,12 @@ def run_auc(client: "Client", tree_method: str) -> None:
def test_auc(client: "Client") -> None:
run_auc(client, "hist")
# No test for Exact, as empty DMatrix handling are mostly for distributed
# environment and Exact doesn't support it.
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
def test_empty_dmatrix(tree_method) -> None:
with LocalCluster(n_workers=kWorkers) as cluster:
with LocalCluster(n_workers=kWorkers, dashboard_address=":0") as cluster:
with Client(cluster) as client:
parameters = {'tree_method': tree_method}
run_empty_dmatrix_reg(client, parameters)
@@ -933,7 +936,7 @@ async def run_dask_classifier_asyncio(scheduler_address: str) -> None:
def test_with_asyncio() -> None:
with LocalCluster() as cluster:
with LocalCluster(dashboard_address=":0") as cluster:
with Client(cluster) as client:
address = client.scheduler.address
output = asyncio.run(run_from_dask_array_asyncio(address))
@@ -946,16 +949,16 @@ def test_with_asyncio() -> None:
async def generate_concurrent_trainings() -> None:
async def train() -> None:
async with LocalCluster(n_workers=2,
threads_per_worker=1,
asynchronous=True,
dashboard_address=0) as cluster:
async with LocalCluster(
n_workers=2, threads_per_worker=1, asynchronous=True, dashboard_address=":0"
) as cluster:
async with Client(cluster, asynchronous=True) as client:
X, y, w = generate_array(with_weights=True)
dtrain = await DaskDMatrix(client, X, y, weight=w)
dvalid = await DaskDMatrix(client, X, y, weight=w)
output = await xgb.dask.train(client, {}, dtrain=dtrain)
await xgb.dask.predict(client, output, data=dvalid)
await asyncio.gather(train(), train())
@@ -1050,7 +1053,7 @@ def run_aft_survival(client: "Client", dmatrix_t: Type) -> None:
def test_dask_aft_survival() -> None:
with LocalCluster(n_workers=kWorkers) as cluster:
with LocalCluster(n_workers=kWorkers, dashboard_address=":0") as cluster:
with Client(cluster) as client:
run_aft_survival(client, DaskDMatrix)
@@ -1311,7 +1314,7 @@ class TestWithDask:
env["DMLC_TRACKER_URI"] = uri[1]
return subprocess.run([str(exe), test], env=env, capture_output=True)
with LocalCluster(n_workers=4) as cluster:
with LocalCluster(n_workers=4, dashboard_address=":0") as cluster:
with Client(cluster) as client:
workers = _get_client_workers(client)
rabit_args = client.sync(
@@ -1346,7 +1349,7 @@ class TestWithDask:
self.run_quantile('SameOnAllWorkers')
def test_n_workers(self) -> None:
with LocalCluster(n_workers=2) as cluster:
with LocalCluster(n_workers=2, dashboard_address=":0") as cluster:
with Client(cluster) as client:
workers = _get_client_workers(client)
from sklearn.datasets import load_breast_cancer
@@ -1437,7 +1440,7 @@ class TestWithDask:
generate unnecessary copies of data.
'''
with LocalCluster(n_workers=2) as cluster:
with LocalCluster(n_workers=2, dashboard_address=":0") as cluster:
with Client(cluster) as client:
X, y, _ = generate_array()
n_partitions = X.npartitions
@@ -1715,10 +1718,10 @@ def run_tree_stats(client: Client, tree_method: str) -> str:
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
def test_tree_stats(tree_method: str) -> None:
with LocalCluster(n_workers=1) as cluster:
with LocalCluster(n_workers=1, dashboard_address=":0") as cluster:
with Client(cluster) as client:
local = run_tree_stats(client, tree_method)
with LocalCluster(n_workers=2) as cluster:
with LocalCluster(n_workers=2, dashboard_address=":0") as cluster:
with Client(cluster) as client:
distributed = run_tree_stats(client, tree_method)
@@ -1734,7 +1737,7 @@ def test_parallel_submit_multi_clients() -> None:
from sklearn.datasets import load_digits
with LocalCluster(n_workers=4) as cluster:
with LocalCluster(n_workers=4, dashboard_address=":0") as cluster:
with Client(cluster) as client:
workers = _get_client_workers(client)