Github user MrBago commented on a diff in the pull request: https://github.com/apache/spark/pull/17373#discussion_r130747996 --- Diff: mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala --- @@ -82,6 +83,23 @@ class MultilayerPerceptronClassifierSuite } } + test("test model probability") { + val layers = Array[Int](2, 5, 2) + val trainer = new MultilayerPerceptronClassifier() + .setLayers(layers) + .setBlockSize(1) + .setSeed(123L) + .setMaxIter(100) + .setSolver("l-bfgs") + val model = trainer.fit(dataset) + model.setProbabilityCol("probability") + val result = model.transform(dataset) + val features2prob = udf { features: Vector => model.mlpModel.predict(features) } + val cmpVec = udf { (v1: Vector, v2: Vector) => v1 ~== v2 relTol 1e-3 } + assert(result.select(cmpVec(features2prob(col("features")), col("probability"))) + .rdd.map(_.getBoolean(0)).reduce(_ && _)) + } + --- End diff -- I think we should include a stronger test for this. I did a quick search and couldn't find a strong test for `mlpModel.predict`, it might be good to add one. Also, I believe this xor dataset only produces probability predictions ~equal to 0 or 1.
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