[
https://issues.apache.org/jira/browse/FLINK-1707?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15883258#comment-15883258
]
ASF GitHub Bot commented on FLINK-1707:
---------------------------------------
Github user greghogan commented on a diff in the pull request:
https://github.com/apache/flink/pull/2885#discussion_r103001003
--- Diff:
flink-examples/flink-examples-batch/src/main/java/org/apache/flink/examples/java/ap/AffinityPropagationBulk.java
---
@@ -0,0 +1,449 @@
+/*
+ * 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.examples.java.ap;
+
+import org.apache.flink.api.common.aggregators.ConvergenceCriterion;
+import org.apache.flink.api.common.aggregators.LongSumAggregator;
+import org.apache.flink.api.common.functions.FilterFunction;
+import org.apache.flink.api.common.functions.GroupReduceFunction;
+import org.apache.flink.api.common.functions.JoinFunction;
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.common.functions.RichJoinFunction;
+import org.apache.flink.api.common.operators.Order;
+import org.apache.flink.api.java.DataSet;
+import org.apache.flink.api.java.ExecutionEnvironment;
+import org.apache.flink.api.java.operators.IterativeDataSet;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.api.java.tuple.Tuple4;
+import org.apache.flink.examples.java.ap.util.AffinityPropagationData;
+import org.apache.flink.types.DoubleValue;
+import org.apache.flink.types.LongValue;
+import org.apache.flink.util.Collector;
+import
org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFieldsFirst;
+import
org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFieldsSecond;
+import
org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields;
+
+/**
+ * Created by joseprubio on 9/22/16.
+ */
+
+public class AffinityPropagationBulk {
+
+ private static final double DAMPING_FACTOR = 0.9;
+ private static final double CONVERGENCE_THRESHOLD = 0.12;
+ private static final String CONVERGENCE_MESSAGES = "message
convergence";
+
+ public static void main(String[] args) throws Exception {
+
+ ExecutionEnvironment env =
ExecutionEnvironment.getExecutionEnvironment();
+ env.getConfig().enableObjectReuse();
+
+ // Get input similarities Tuple3<src, target, similarity>
+ DataSet<Tuple3<LongValue, LongValue, DoubleValue>> similarities
=
+ AffinityPropagationData.getTuplesFromFile(env);
+
+ // Init input to iteration
+ DataSet<Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>>
initMessages
+ = similarities.map(new InitMessage());
+
+ // Iterate
+ IterativeDataSet<Tuple4<LongValue, LongValue, DoubleValue,
DoubleValue>> messages
+ = initMessages.iterate(20);
+
+ // Create aggregator
+
messages.registerAggregationConvergenceCriterion(CONVERGENCE_MESSAGES, new
LongSumAggregator(),
+ new MessageConvergence(similarities.count() * 2));
+
+ // Start responsibility message calculation
+ // r(i,k) <- s(i,k) - max {a(i,K) + s(i,K)} st K != k
+ // Iterate over Tuple6 <Source, Target, Responsibility ,
Availability, IsExemplar, ConvergenceCounter>
+
+ DataSet<Tuple3<LongValue, LongValue, DoubleValue>>
responsibilities = similarities
+
+ // Get a list of a(i,K) + s(i,K) values joining
similarities with messages
+
.join(messages).where("f0","f1").equalTo("f0","f1").with(new
joinAvailabilitySimilarity())
+
+ // Get a dataset with 2 higher values
+ .groupBy("f1").sortGroup("f2",
Order.DESCENDING).first(2)
+
+ // Create a Tuple4<Trg, MaxValue, MaxNeighbour,
SecondMaxValue> reducing the 2 tuples with higher values
+ .groupBy("f1").reduceGroup(new
responsibilityReduceGroup())
+
+ // Calculate the R messages "r(i,k) <- s(i,k) - value"
getting "value" joining
+ // similarities with previous tuple
+
.leftOuterJoin(similarities).where("f0").equalTo("f1").with(new
responsibilityValue())
+
+ // Responsibility damping
+
.join(messages).where("f0","f1").equalTo("f1","f0").with(new
dampedRValue(DAMPING_FACTOR, CONVERGENCE_THRESHOLD));
+
+ // Start availability message calculation
+ // a(i,k) <- min {0, r(k,k) + sum{max{0,r(I,k)}} I st I not in
{i,k}
+ // a(k,k) <- sum{max{0,r(I,k)} I st I not in {i,k}
+
+ DataSet<Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>>
availabilities = responsibilities
+
+ // Get the sum of the positive responsibilities and the
self responsibility per target
+ .groupBy("f1").reduceGroup(new
availabilityReduceGroup())
+
+ // Calculate the availability
+
.leftOuterJoin(responsibilities).where("f0").equalTo("f1").with(new
availabilityValue())
+
+ // Availability damping
+
.join(messages).where("f0","f1").equalTo("f0","f1").with(new
dampedAValue(DAMPING_FACTOR, CONVERGENCE_THRESHOLD));
+
+ // End iteration
+ DataSet<Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>>
finalMessages =
+ messages.closeWith(availabilities);
+
+ // Get exemplars
+ DataSet<Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>>
+ exemplars = finalMessages.filter(new FilterExemplars());
+
+ // Get clusters
+ DataSet<Tuple3<LongValue, LongValue, DoubleValue>> clusters =
exemplars
+
.join(similarities).where("f0").equalTo("f1").projectSecond(0,1,2);
+
+ // Refine clusters assigning exemplars to themselves
+ DataSet<Tuple3<LongValue, LongValue, DoubleValue>>
refinedClusters = clusters
+ .groupBy("f0").maxBy(2)
+
.leftOuterJoin(exemplars).where("f0").equalTo("f0").with(new refineClusters());
+
+ }
+
+ // Init input messages
+ private static class InitMessage implements
MapFunction<Tuple3<LongValue, LongValue, DoubleValue>,
+ Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>> {
+
+ Tuple4<LongValue, LongValue, DoubleValue, DoubleValue> output =
+ new Tuple4<>(new LongValue(), new LongValue(), new
DoubleValue(), new DoubleValue());
+
+ @Override
+ public Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>
+ map(Tuple3<LongValue, LongValue, DoubleValue> in) {
+ output.f0.setValue(in.f0.getValue());
+ output.f1.setValue(in.f1.getValue());
+ return output;
+ }
+ }
+
+ // Create a list of a(i,K) + s(i,K) values joining similarities with
messages
+ @ForwardedFieldsFirst("f0; f1")
+ @ForwardedFieldsSecond("f0; f1")
+ private static class joinAvailabilitySimilarity
+ implements JoinFunction<Tuple3<LongValue, LongValue,
DoubleValue>,
+ Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>,
+ Tuple3<LongValue, LongValue, DoubleValue>> {
+
+ private Tuple3<LongValue, LongValue, DoubleValue> output =
+ new Tuple3<>(new LongValue(), new LongValue(), new
DoubleValue());
+
+ // Receives Tuple6<Trg, MaxValue, MaxNeighbour, SecondMaxValue,
is> and Tuple3<src, target, similarity>
+ // and returns a Tuple5<>
+ @Override
+ public Tuple3<LongValue, LongValue, DoubleValue>
+ join(Tuple3<LongValue, LongValue, DoubleValue> similarity,
+ Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>
message) {
+
+ output.f0.setValue(similarity.f0.getValue());
+ output.f1.setValue(similarity.f1.getValue());
+ output.f2.setValue(similarity.f2.getValue() +
message.f3.getValue());
+
+ return output;
+ }
+ }
+
+ // Create a Tuple4<Trg, MaxValue, MaxNeighbour, SecondMaxValue>
reducing the 2 tuples with the max values
+ @ForwardedFields("f1->f0")
+ private static class responsibilityReduceGroup
+ implements GroupReduceFunction<Tuple3<LongValue, LongValue,
DoubleValue>,
+ Tuple4<LongValue, DoubleValue, LongValue, DoubleValue>> {
+
+ Tuple4<LongValue, DoubleValue, LongValue, DoubleValue> output =
new Tuple4<>(new LongValue(), new DoubleValue(),
+ new LongValue(), new DoubleValue());
+
+ @Override
+ public void reduce(Iterable<Tuple3<LongValue, LongValue,
DoubleValue>> maxValues,
+ Collector<Tuple4<LongValue,
DoubleValue, LongValue, DoubleValue>> out) throws Exception {
+
+ Long maxNeighbour = Long.valueOf(0);
+ Long trg = Long.valueOf(0);
+ double maxValue = 0;
+ double secondMaxValue = 0;
+
+ for (Tuple3<LongValue, LongValue, DoubleValue> val :
maxValues) {
+
+ if(val.f2.getValue() > maxValue){
+ secondMaxValue = maxValue;
+ maxValue = val.f2.getValue();
+ maxNeighbour = val.f0.getValue();
+ trg = val.f1.getValue();
+ }else{
+ secondMaxValue = val.f2.getValue();
+ }
+ }
+
+ output.f0.setValue(trg);
+ output.f1.setValue(maxValue);
+ output.f2.setValue(maxNeighbour);
+ output.f3.setValue(secondMaxValue);
+
+ out.collect(output);
+
+ }
+ }
+
+ // Subtract each responsibility
+ @ForwardedFieldsFirst("f0")
+ @ForwardedFieldsSecond("f0->f1; f1->f0")
+ private static class responsibilityValue
+ implements JoinFunction<Tuple4<LongValue, DoubleValue,
LongValue, DoubleValue>,
+ Tuple3<LongValue, LongValue, DoubleValue>,
+ Tuple3<LongValue, LongValue, DoubleValue>> {
+
+ Tuple3<LongValue, LongValue, DoubleValue> output = new
Tuple3<>(new LongValue(), new LongValue(),
+ new DoubleValue());
+
+ //Receives Tuple4<Trg, MaxValue, MaxNeighbour, SecondMaxValue>
and Tuple3<src, target, similarity>
+ @Override
+ public Tuple3<LongValue, LongValue, DoubleValue>
+ join(Tuple4<LongValue, DoubleValue, LongValue, DoubleValue>
maxValues,
+ Tuple3<LongValue, LongValue, DoubleValue> similarity) {
+
+ double responsibility;
+
+ if(similarity.f0.getValue() == maxValues.f2.getValue()){
+ responsibility = similarity.f2.getValue() -
maxValues.f3.getValue();
+ }else{
+ responsibility = similarity.f2.getValue() -
maxValues.f1.getValue();
+ }
+
+ output.f0.setValue(similarity.f1);
--- End diff --
Why are these flipped? I'm not seeing this in the formulas from the paper.
> Add an Affinity Propagation Library Method
> ------------------------------------------
>
> Key: FLINK-1707
> URL: https://issues.apache.org/jira/browse/FLINK-1707
> Project: Flink
> Issue Type: New Feature
> Components: Gelly
> Reporter: Vasia Kalavri
> Assignee: Josep Rubió
> Priority: Minor
> Labels: requires-design-doc
> Attachments: Binary_Affinity_Propagation_in_Flink_design_doc.pdf
>
>
> This issue proposes adding the an implementation of the Affinity Propagation
> algorithm as a Gelly library method and a corresponding example.
> The algorithm is described in paper [1] and a description of a vertex-centric
> implementation can be found is [2].
> [1]: http://www.psi.toronto.edu/affinitypropagation/FreyDueckScience07.pdf
> [2]: http://event.cwi.nl/grades2014/00-ching-slides.pdf
> Design doc:
> https://docs.google.com/document/d/1QULalzPqMVICi8jRVs3S0n39pell2ZVc7RNemz_SGA4/edit?usp=sharing
> Example spreadsheet:
> https://docs.google.com/spreadsheets/d/1CurZCBP6dPb1IYQQIgUHVjQdyLxK0JDGZwlSXCzBcvA/edit?usp=sharing
> Graph:
> https://docs.google.com/drawings/d/1PC3S-6AEt2Gp_TGrSfiWzkTcL7vXhHSxvM6b9HglmtA/edit?usp=sharing
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
