Github user greghogan commented on a diff in the pull request:
https://github.com/apache/flink/pull/2885#discussion_r103000108
--- 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);
+ output.f1.setValue(similarity.f0);
+ output.f2.setValue(responsibility);
+
+ return output;
+ }
+ }
+
+ // Return a Tuple3<Trg, PositiveResponsibilitiesAccumulator,
SelfResponsibility>
+ @ForwardedFields("f1->f0")
+ private static class availabilityReduceGroup
+ implements GroupReduceFunction<Tuple3<LongValue, LongValue,
DoubleValue>,
+ Tuple3<LongValue, DoubleValue, DoubleValue>> {
+
+ Tuple3<LongValue, DoubleValue, DoubleValue> output = new
Tuple3<>(new LongValue(), new DoubleValue(),
+ new DoubleValue());
+
+ @Override
+ public void reduce(Iterable<Tuple3<LongValue, LongValue,
DoubleValue>> responsibilities,
+ Collector<Tuple3<LongValue,
DoubleValue, DoubleValue>> out) throws Exception {
+
+ double accum = 0;
+ double selfResponsibility = 0;
+ Long trg = Long.valueOf(0);
+
+ for (Tuple3<LongValue, LongValue, DoubleValue> m :
responsibilities) {
+ if(m.f0.getValue() == m.f1.getValue()){
+ selfResponsibility = m.f2.getValue();
+ trg = m.f1.getValue();
+ }else{
+ if(m.f2.getValue() > 0){
+ accum = accum + m.f2.getValue();
+ }
+ }
+ }
+
+ output.f0.setValue(trg);
+ output.f1.setValue(accum);
+ output.f2.setValue(selfResponsibility);
+
+ out.collect(output);
+
+ }
+ }
+
+ // Joins a Tuple3<Trg, PositiveResponsibilitiesAccumulator,
SelfResponsibility> from previous step
+ // and the responsibilities. For each responsibility will calculate the
availability to be sent to the
+ // responsibility source. In case of self availability will calculate
the convergence too.
+ @ForwardedFieldsFirst("f0")
+ @ForwardedFieldsSecond("f0->f1; f1->f0")
+ private static class availabilityValue
+ implements JoinFunction<Tuple3<LongValue, DoubleValue,
DoubleValue>,
+ 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>
+ join(Tuple3<LongValue, DoubleValue, DoubleValue> first,
+ Tuple3<LongValue, LongValue, DoubleValue>
responsibility) throws Exception {
+
+ output.f0 = responsibility.f1;
+ output.f1 = responsibility.f0;
+ output.f2 = responsibility.f2;
+
+ //For self availability calculate the convergence
+ if(responsibility.f1.getValue() ==
responsibility.f0.getValue()){
+ output.f3 = new
DoubleValue(first.f1.getValue());
+ }else{
+ //Take the responsibility value in case is
positive, it will be subtracted to the positive accumulator later
+ if(responsibility.f2.getValue() > 0) {
+ output.f3 = new DoubleValue(Math.min(0,
first.f1.getValue() - responsibility.f2.getValue() + first.f2.getValue()));
+ }else{
+ output.f3 = new DoubleValue(Math.min(0,
first.f1.getValue() + first.f2.getValue()));
+ }
+
+ }
+
+ return output;
+ }
+ }
+
+ @ForwardedFieldsFirst("f0; f1")
+ @ForwardedFieldsSecond("f0->f1; f1->f0")
+ private static class dampedRValue
+ extends RichJoinFunction<Tuple3<LongValue, LongValue,
DoubleValue>,
+ Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>,
Tuple3<LongValue, LongValue, DoubleValue>> {
+
+ private double damping;
+ private double threshold;
+
+ Tuple3<LongValue, LongValue, DoubleValue> output = new
Tuple3<>(new LongValue(), new LongValue(), new DoubleValue());
+
+ dampedRValue(double damping, double threshold){
+ this.damping = damping;
+ this.threshold = threshold;
+ }
+
+ @Override
+ public Tuple3<LongValue, LongValue, DoubleValue>
+ join(Tuple3<LongValue, LongValue, DoubleValue> newValue,
+ Tuple4<LongValue, LongValue, DoubleValue, DoubleValue>
oldValue) {
+
+ boolean converged;
+
+ output.f0.setValue(newValue.f0.getValue());
+ output.f1.setValue(newValue.f1.getValue());
+
+ output.f2.setValue((1 - damping) *
newValue.f2.getValue() + damping * oldValue.f2.getValue());
+ converged = Math.abs(newValue.f2.getValue() -
oldValue.f2.getValue()) < threshold;
+
+ LongSumAggregator agg =
getIterationRuntimeContext().getIterationAggregator(CONVERGENCE_MESSAGES);
+ if(converged){
+ agg.aggregate(1);
--- End diff --
Could store the value as a class member and only call `aggregate` in the
close method.
---
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.
---
