Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/871#discussion_r34142293
--- 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 --
That is true, the assumption I'm making here is that Predictors are either
Classifiers or Regressors. For classifiers, strings used as classes would be
first translated to numerical representations (by the user or automatically),
as it is my assumption currently that the canonical way to use a classifier is
to train it with a `DataSet[LabeledVector]`, which has numerical class labels.
This can of course become problematic if in the future we decide to
implement multi-label classification algorithms.
The other option is to try generalize calculateScore to take
`DataSet[(PredictionT, PredictionT)]`, which I think would mean that we have to
generalize most of the Score implementations as well.
Personally I think the current approach covers a majority of our use cases,
and we can deal with its limitations as problems come along.
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