Github user sethah commented on the issue:
https://github.com/apache/spark/pull/15414
What do you think about adding a new suite `PredictorSuite` where we can
create a mock predictor, and call train on data of various types. The train
method can just require that the label column is `DoubleType`:
````scala
class MockPredictor(override val uid: String)
extends Predictor[Vector, MockPredictor, MockPredictionModel] {
override def train(dataset: Dataset[_]): MockPredictionModel = {
require(dataset.schema("label").dataType == DoubleType)
new MockPredictionModel(uid)
}
override def copy(extra: ParamMap): MockPredictor = defaultCopy(extra)
}
class MockPredictionModel(override val uid: String)
extends PredictionModel[Vector, MockPredictionModel] {
override def predict(features: Vector): Double = 1.0
override def copy(extra: ParamMap): MockPredictionModel =
defaultCopy(extra)
}
````
Then we just have a test that calls `fit` for each type of data.
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