8000 TST Accelerating ensemble/tests/test_voting.py::test_gridsearch by mohaseeb · Pull Request #21422 · scikit-learn/scikit-learn · GitHub
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TST Accelerating ensemble/tests/test_voting.py::test_gridsearch #21422

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Oct 24, 2021
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TST Accelerating ensemble/tests/test_voting.py::test_gridsearch #21422

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4 changes: 2 additions & 2 deletions sklearn/ensemble/tests/test_voting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -263,7 +263,7 @@ def test_multilabel():
def test_gridsearch():
"""Check GridSearch support."""
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf2 = RandomForestClassifier(random_state=1, n_estimators=3)
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@ogrisel ogrisel Oct 23, 2021 •
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If it's still slow, we could set max_depth=3 to make it run even faster.

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If it's still slow, we could set max_depth=3 to make it run even faster.

@ogrisel the test case is not part of the 20 slowest anymore.

We tried max_depth=3 locally, but it didn't result in a visible speedup; not sure why.

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did you measure the time? I think this should be enough and we should merge :)

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Yes we did measure the time (for each test impl we ran the test 3 times); the average running time is reported below:

  • original impl: 5.3 seconds
  • ogrisel recommended changes: 0.57 seconds
  • after adding max_depth=3: 0.59 seconds

clf3 = GaussianNB()
eclf = VotingClassifier(
estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="soft"
Expand All @@ -275,7 +275,7 @@ def test_gridsearch():
"weights": [[0.5, 0.5, 0.5], [1.0, 0.5, 0.5]],
}

grid = GridSearchCV(estimator=eclf, param_grid=params)
grid = GridSearchCV(estimator=eclf, param_grid=params, cv=2)
grid.fit(iris.data, iris.target)


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