Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/871#discussion_r34137657
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/evaluation/Score.scala
---
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.evaluation
+
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.scala._
+import org.apache.flink.ml._
+
+import scala.reflect.ClassTag
+
+/**
+ * Evaluation score
+ *
+ * Takes a whole data set and then computes the evaluation score on them
(obviously, again encoded
+ * in a DataSet)
+ *
+ * @tparam PredictionType output type
+ */
+trait Score[PredictionType] {
+ def evaluate(trueAndPredicted: DataSet[(PredictionType,
PredictionType)]): DataSet[Double]
+}
+
+/** Traits to allow us to determine at runtime if a Score is a loss (lower
is better) or a
+ * performance score (higher is better)
+ */
+trait Loss
+
+trait PerformanceScore
+
+/**
+ * Metrics expressible as a mean of a function taking output pairs as input
+ *
+ * @param scoringFct function to apply to all elements
+ * @tparam PredictionType output type
+ */
+abstract class MeanScore[PredictionType: TypeInformation: ClassTag](
+ scoringFct: (PredictionType, PredictionType) => Double)
+ (implicit yyt: TypeInformation[(PredictionType, PredictionType)])
+ extends Score[PredictionType] with Serializable {
+ def evaluate(trueAndPredicted: DataSet[(PredictionType,
PredictionType)]): DataSet[Double] = {
+ trueAndPredicted.map(yy => scoringFct(yy._1, yy._2)).mean()
+ }
+}
+
+object RegressionScores {
+ /**
+ * Squared loss function
+ *
+ * returns (y1 - y2)'
+ *
+ * @return a Loss object
+ */
+ def squaredLoss = new MeanScore[Double]((y1,y2) => (y1 - y2) * (y1 -
y2)) with Loss
+
+ /**
+ * Zero One Loss Function also usable for score information
+ *
+ * returns 1 if sign of outputs differ and 0 if the signs are equal
+ *
+ * @return a Loss object
+ */
+ def zeroOneSignumLoss = new MeanScore[Double]({ (y1, y2) =>
+ val sy1 = scala.math.signum(y1)
+ val sy2 = scala.math.signum(y2)
+ if (sy1 == sy2) 0 else 1
+ }) with Loss
+
+ /** Calculates the coefficient of determination, $R^2^$
+ *
+ * $R^2^$ indicates how well the data fit the a calculated model
+ * Reference:
[[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+ */
+ def r2Score = new Score[Double] with PerformanceScore {
+ override def evaluate(trueAndPredicted: DataSet[(Double, Double)]):
DataSet[Double] = {
+ val onlyTrue = trueAndPredicted.map(truthPrediction =>
truthPrediction._1)
+ val meanTruth = onlyTrue.mean()
+
+ val ssRes = trueAndPredicted
+ .map(tp => (tp._1 - tp._2) * (tp._1 - tp._2)).reduce(_ + _)
+ val ssTot = onlyTrue
+ .mapWithBcVariable(meanTruth) {
+ case (truth: Double, meanTruth: Double) => (truth - meanTruth) *
(truth - meanTruth)
+ }.reduce(_ + _)
+
+ val r2 = ssRes
+ .mapWithBcVariable(ssTot) {
+ case (ssRes: Double, ssTot: Double) =>
+ // We avoid dividing by 0 and just assign 0.0
+ if (ssTot == 0.0) {
+ 0.0
+ }
+ else {
+ 1 - (ssRes / ssTot)
+ }
+ }
+ r2
+ }
+ }
+}
+
+object ClassificationScores {
+ /** Calculates the fraction of correct predictions
+ *
+ */
+ def accuracyScore =
+ new MeanScore[Double]((y1, y2) => if (y1 == y2) 1 else 0) with
PerformanceScore
+
+ /**
+ * Zero One Loss Function
+ *
+ * returns 1 if outputs differ and 0 if they are equal
+ *
+ * @tparam T output type
+ * @return a Loss object
+ */
+ def zeroOneLoss[T: TypeInformation: ClassTag] = {
+ // TODO: If T == Double, == comparison could be problematic
--- End diff --
I'm thinking about floating point accuracy, do you think that might cause
trouble with equality checks?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
