Skip to content

[SPARK-7432] [MLLIB] fix flaky CrossValidator doctest #6572

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

[SPARK-7432] [MLLIB] fix flaky CrossValidator doctest #6572

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 9 additions & 10 deletions python/pyspark/ml/tuning.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -91,20 +91,19 @@ class CrossValidator(Estimator):
>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
>>> from pyspark.mllib.linalg import Vectors
>>> dataset = sqlContext.createDataFrame(
... [(Vectors.dense([0.0, 1.0]), 0.0),
... (Vectors.dense([1.0, 2.0]), 1.0),
... (Vectors.dense([0.55, 3.0]), 0.0),
... (Vectors.dense([0.45, 4.0]), 1.0),
... (Vectors.dense([0.51, 5.0]), 1.0)] * 10,
... [(Vectors.dense([0.0]), 0.0),
... (Vectors.dense([0.4]), 1.0),
... (Vectors.dense([0.5]), 0.0),
... (Vectors.dense([0.6]), 1.0),
... (Vectors.dense([1.0]), 1.0)] * 10,
... ["features", "label"])
>>> lr = LogisticRegression()
>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build()
>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
>>> evaluator = BinaryClassificationEvaluator()
>>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
>>> # SPARK-7432: The following test is flaky.
>>> # cvModel = cv.fit(dataset)
>>> # expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset)
>>> # cvModel.transform(dataset).collect() == expected.collect()
>>> cvModel = cv.fit(dataset)
>>> evaluator.evaluate(cvModel.transform(dataset))
0.8333...
"""

# a placeholder to make it appear in the generated doc
Expand Down