Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/871#discussion_r34168710
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/pipeline/Predictor.scala
---
@@ -72,12 +74,36 @@ trait Predictor[Self] extends Estimator[Self] with
WithParameters {
*/
def evaluate[Testing, PredictionValue](
testing: DataSet[Testing],
- evaluateParameters: ParameterMap = ParameterMap.Empty)(implicit
- evaluator: EvaluateDataSetOperation[Self, Testing, PredictionValue])
+ evaluateParameters: ParameterMap = ParameterMap.Empty)
+ (implicit evaluator: EvaluateDataSetOperation[Self, Testing,
PredictionValue])
: DataSet[(PredictionValue, PredictionValue)] = {
FlinkMLTools.registerFlinkMLTypes(testing.getExecutionEnvironment)
evaluator.evaluateDataSet(this, evaluateParameters, testing)
}
+
+ /** Calculates a numerical score for the [[Predictor]]
+ *
+ * By convention, higher scores are considered better, so even if a
loss is used as a performance
+ * measure, it will be negated, so that that higher is better.
+ * @param testing The evaluation DataSet, that contains the features
and the true value
+ * @param evaluateOperation An EvaluateDataSetOperation that produces
Double results
+ * @tparam Testing The type of the features and true value, for example
[[LabeledVector]]
+ * @return A DataSet containing one Double that indicates the score of
the predictor
+ */
+ def score[Testing](testing: DataSet[Testing])
--- End diff --
Then I would vote for generalizing, since having the `Predictor` only is
very limiting, we plan to have ANNs which might predict `Vector`s or
multi-label class which do the same.
So here's what I suggest: I remove the score function from this PR and open
a new one (hopefully by Friday) with a `ScoreOperation`
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