Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/12943#discussion_r64532238
--- Diff: python/pyspark/ml/classification.py ---
@@ -1063,6 +1064,13 @@ class MultilayerPerceptronClassifier(JavaEstimator,
HasFeaturesCol, HasLabelCol,
True
>>> model.weights == model2.weights
True
+ >>> mlp2 = mlp2.setInitialWeights([2, 5, 1, -7, -5, -10, 0, 0.6, -1,
2, -2, 1, 2, -7, -1, -2,
+ ... 2, 1, -1, 9, -9, 3, -3, -3, 3.0, 0, -1])
--- End diff --
In MLlib, Scala unit tests focus on verifying the correctness, Java unit
tests focus on compatibility verification, and Python doc tests should focus on
providing examples to users. So I think we should keep Python doc tests
succinct just as you see in the code base.
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