[
https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618805#comment-14618805
]
ASF GitHub Bot commented on FLINK-2157:
---------------------------------------
Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/871#discussion_r34164439
--- 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 --
sklearn defines the score function separately for the Classifier and
Regressor mixins. They assume Double values for features and class labels.
We can also try defining it in a trait WithScore but I'm not sure how that
would work for chaining.
> Create evaluation framework for ML library
> ------------------------------------------
>
> Key: FLINK-2157
> URL: https://issues.apache.org/jira/browse/FLINK-2157
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Theodore Vasiloudis
> Labels: ML
> Fix For: 0.10
>
>
> Currently, FlinkML lacks means to evaluate the performance of trained models.
> It would be great to add some {{Evaluators}} which can calculate some score
> based on the information about true and predicted labels. This could also be
> used for the cross validation to choose the right hyper parameters.
> Possible scores could be F score [1], zero-one-loss score, etc.
> Resources
> [1] [http://en.wikipedia.org/wiki/F1_score]
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
