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[dask] [ci] add support for scikit-learn 0.24+ in tests (fixes #4031) #4032

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Mar 2, 2021
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[dask] [ci] add support for scikit-learn 0.24+ in tests (fixes #4031) #4032

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1fa7e68
[dask] [ci] add support for scikit-learn 0.24+ in tests (fixes #4031)
jameslamb Mar 1, 2021
821bfd4
Update tests/python_package_test/test_dask.py
jameslamb Mar 1, 2021
491ad95
try upgrading mixtexsetup
jameslamb Mar 1, 2021
6339a45
they changed the executable name UGH
jameslamb Mar 1, 2021
cf28a1f
more changes for executable name
jameslamb Mar 1, 2021
e4c3570
another path change
jameslamb Mar 1, 2021
4845136
changing package mirrors
jameslamb Mar 1, 2021
cd28b80
undo experiments
jameslamb Mar 1, 2021
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10 changes: 9 additions & 1 deletion tests/python_package_test/test_dask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,12 +28,16 @@
from dask.array.utils import assert_eq
from dask.distributed import Client, LocalCluster, default_client, wait
from distributed.utils_test import client, cluster_fixture, gen_cluster, loop
from pkg_resources import parse_version
from scipy.sparse import csr_matrix
from scipy.stats import spearmanr
from sklearn import __version__ as sk_version
from sklearn.datasets import make_blobs, make_regression

from .utils import make_ranking

sk_version = parse_version(sk_version)

# time, in seconds, to wait for the Dask client to close. Used to avoid teardown errors
# see https://distributed.dask.org/en/latest/api.html#distributed.Client.close
CLIENT_CLOSE_TIMEOUT = 120
Expand Down Expand Up @@ -1253,5 +1257,9 @@ def test_sklearn_integration(estimator, check, client):
# this test is separate because it takes a not-yet-constructed estimator
@pytest.mark.parametrize("estimator", list(_tested_estimators()))
def test_parameters_default_constructible(estimator):
name, Estimator = estimator.__class__.__name__, estimator.__class__
name = estimator.__class__.__name__
if sk_version >= parse_version("0.24"):
Estimator = estimator
else:
Estimator = estimator.__class__
sklearn_checks.check_parameters_default_constructible(name, Estimator)
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I did try just passing an instance always, so we could avoid parsing the scikit-learn version, but it looks like scikit-learn 0.22.x only supported passing a class in this method 😆

Here's what I got changing the code to the following on scikit-learn==0.22.0

@pytest.mark.parametrize("estimator", list(_tested_estimators()))
def test_parameters_default_constructible(estimator):
    name = estimator.__class__.__name__
    sklearn_checks.check_parameters_default_constructible(name, estimator)

E TypeError: 'DaskLGBMRegressor' object is not callable