Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17373#discussion_r131824713
--- 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 --
@MrBago
Which way of the strong test should be done ? Add a test to check the
probability vector equals given vectors ?
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