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DIMSpan.java
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DIMSpan.java
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/*
* Copyright © 2014 - 2019 Leipzig University (Database Research Group)
*
* Licensed 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.gradoop.flink.algorithms.fsm.dimspan;
import org.apache.flink.api.common.functions.GroupCombineFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.aggregation.AggregationFunction;
import org.apache.flink.api.java.aggregation.SumAggregationFunction;
import org.apache.flink.api.java.operators.IterativeDataSet;
import org.gradoop.flink.algorithms.fsm.dimspan.comparison.AlphabeticalLabelComparator;
import org.gradoop.flink.algorithms.fsm.dimspan.comparison.InverseProportionalLabelComparator;
import org.gradoop.flink.algorithms.fsm.dimspan.comparison.LabelComparator;
import org.gradoop.flink.algorithms.fsm.dimspan.comparison.ProportionalLabelComparator;
import org.gradoop.flink.algorithms.fsm.dimspan.config.DIMSpanConfig;
import org.gradoop.flink.algorithms.fsm.dimspan.config.DIMSpanConstants;
import org.gradoop.flink.algorithms.fsm.dimspan.config.DataflowStep;
import org.gradoop.flink.algorithms.fsm.dimspan.config.DictionaryType;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.conversion.DFSCodeToEPGMGraphTransaction;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.CreateCollector;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.ExpandFrequentPatterns;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.Frequent;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.GrowFrequentPatterns;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.InitSingleEdgePatternEmbeddingsMap;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.IsFrequentPatternCollector;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.NotObsolete;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.ReportSupportedPatterns;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.VerifyPattern;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.mining.CompressPattern;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.AggregateMultipleFunctions;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.CreateDictionary;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.EncodeAndPruneEdges;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.EncodeAndPruneVertices;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.MinFrequency;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.NotEmpty;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.ReportEdgeLabels;
import org.gradoop.flink.algorithms.fsm.dimspan.functions.preprocessing.ReportVertexLabels;
import org.gradoop.flink.algorithms.fsm.dimspan.gspan.DirectedGSpanLogic;
import org.gradoop.flink.algorithms.fsm.dimspan.gspan.GSpanLogic;
import org.gradoop.flink.algorithms.fsm.dimspan.gspan.UndirectedGSpanLogic;
import org.gradoop.flink.algorithms.fsm.dimspan.tuples.GraphWithPatternEmbeddingsMap;
import org.gradoop.flink.algorithms.fsm.dimspan.tuples.LabeledGraphIntString;
import org.gradoop.flink.algorithms.fsm.dimspan.tuples.LabeledGraphStringString;
import org.gradoop.flink.model.impl.layouts.transactional.tuples.GraphTransaction;
import org.gradoop.flink.model.impl.operators.count.Count;
import org.gradoop.flink.model.impl.tuples.WithCount;
/**
* Abstract superclass of different implementations of the gSpan frequent
* subgraph mining algorithm as Gradoop operator
*/
public class DIMSpan {
/**
* Maximum number of iterations if set of k-edge frequent patterns is not running empty before.
*/
private static final int MAX_ITERATIONS = 100;
/**
* FSM configuration
*/
protected final DIMSpanConfig fsmConfig;
/**
* input graph collection cardinality
*/
protected DataSet<Long> graphCount;
/**
* minimum frequency for patterns to be considered to be frequent
*/
protected DataSet<Long> minFrequency;
/**
* Pattern growth and verification logic derived from gSpan.
* See <a href="https://www.cs.ucsb.edu/~xyan/software/gSpan.htm">gSpan</a>
*/
protected final GSpanLogic gSpan;
/**
* Vertex label dictionary for dictionary coding.
*/
private DataSet<String[]> vertexDictionary;
/**
* Edge label dictionary for dictionary coding.
*/
private DataSet<String[]> edgeDictionary;
/**
* Label comparator used for dictionary coding.
*/
private final LabelComparator comparator;
/**
* Constructor.
*
* @param fsmConfig FSM configuration
*/
public DIMSpan(DIMSpanConfig fsmConfig) {
this.fsmConfig = fsmConfig;
// set gSpan implementation depending on direction mode
gSpan = fsmConfig.isDirected() ?
new DirectedGSpanLogic(fsmConfig) :
new UndirectedGSpanLogic(fsmConfig);
// set comparator based on dictionary type
if (fsmConfig.getDictionaryType() == DictionaryType.PROPORTIONAL) {
comparator = new ProportionalLabelComparator();
} else if (fsmConfig.getDictionaryType() == DictionaryType.INVERSE_PROPORTIONAL) {
comparator = new InverseProportionalLabelComparator();
} else {
comparator = new AlphabeticalLabelComparator();
}
}
/**
* Executes the DIMSpan algorithm.
* Orchestration of preprocessing, mining and postprocessing.
*
* @param input input graph collection
* @return frequent patterns
*/
public DataSet<GraphTransaction> execute(DataSet<LabeledGraphStringString> input) {
DataSet<int[]> encodedInput = preProcess(input);
DataSet<WithCount<int[]>> encodedOutput = mine(encodedInput);
return postProcess(encodedOutput);
}
/**
* Triggers the label-frequency base preprocessing
*
* @param graphs input
* @return preprocessed input
*/
private DataSet<int[]> preProcess(DataSet<LabeledGraphStringString> graphs) {
// Determine cardinality of input graph collection
this.graphCount = Count
.count(graphs);
// Calculate minimum frequency
this.minFrequency = graphCount
.map(new MinFrequency(fsmConfig));
// Execute vertex label pruning and dictionary coding
DataSet<LabeledGraphIntString> graphsWithEncodedVertices = encodeVertices(graphs);
// Execute edge label pruning and dictionary coding
DataSet<int[]> encodedGraphs = encodeEdges(graphsWithEncodedVertices);
// return all non-obsolete encoded graphs
return encodedGraphs
.filter(new NotEmpty());
}
/**
* Triggers the iterative mining process.
*
* @param graphs preprocessed input graph collection
* @return frequent patterns
*/
protected DataSet<WithCount<int[]>> mine(DataSet<int[]> graphs) {
DataSet<GraphWithPatternEmbeddingsMap> searchSpace = graphs
.map(new InitSingleEdgePatternEmbeddingsMap(gSpan, fsmConfig));
// Workaround to support multiple data sinks: create pseudo-graph (collector),
// which embedding map will be used to union all k-edge frequent patterns
DataSet<GraphWithPatternEmbeddingsMap> collector = graphs
.getExecutionEnvironment()
.fromElements(true)
.map(new CreateCollector());
searchSpace = searchSpace.union(collector);
// ITERATION HEAD
IterativeDataSet<GraphWithPatternEmbeddingsMap> iterative = searchSpace
.iterate(MAX_ITERATIONS);
// ITERATION BODY
DataSet<WithCount<int[]>> reports = iterative
.flatMap(new ReportSupportedPatterns());
DataSet<WithCount<int[]>> frequentPatterns = getFrequentPatterns(reports);
DataSet<GraphWithPatternEmbeddingsMap> grownEmbeddings = iterative
.map(new GrowFrequentPatterns(gSpan, fsmConfig))
.withBroadcastSet(frequentPatterns, DIMSpanConstants.FREQUENT_PATTERNS)
.filter(new NotObsolete());
// ITERATION FOOTER
return iterative
.closeWith(grownEmbeddings, frequentPatterns)
// keep only collector and expand embedding map keys
.filter(new IsFrequentPatternCollector())
.flatMap(new ExpandFrequentPatterns());
}
/**
* Triggers the postprocessing.
*
* @param encodedOutput frequent patterns represented by multiplexed int-arrays
* @return Gradoop graph transactions
*/
private DataSet<GraphTransaction> postProcess(DataSet<WithCount<int[]>> encodedOutput) {
return encodedOutput
.map(new DFSCodeToEPGMGraphTransaction(fsmConfig))
.withBroadcastSet(vertexDictionary, DIMSpanConstants.VERTEX_DICTIONARY)
.withBroadcastSet(edgeDictionary, DIMSpanConstants.EDGE_DICTIONARY)
.withBroadcastSet(graphCount, DIMSpanConstants.GRAPH_COUNT);
}
/**
* Executes pruning and dictionary coding of vertex labels.
*
* @param graphs graphs with string-labels
* @return graphs with dictionary-encoded vertex labels
*/
private DataSet<LabeledGraphIntString> encodeVertices(DataSet<LabeledGraphStringString> graphs) {
// LABEL PRUNING
DataSet<WithCount<String>> vertexLabels = graphs
.flatMap(new ReportVertexLabels());
vertexLabels = getFrequentLabels(vertexLabels);
// DICTIONARY ENCODING
vertexDictionary = vertexLabels
.reduceGroup(new CreateDictionary(comparator));
return graphs
.map(new EncodeAndPruneVertices())
.withBroadcastSet(vertexDictionary, DIMSpanConstants.VERTEX_DICTIONARY);
}
/**
* Executes pruning and dictionary coding of edge labels.
*
* @param graphs graphs with dictionary-encoded vertex labels
* @return graphs with dictionary-encoded vertex and edge labels
*/
private DataSet<int[]> encodeEdges(DataSet<LabeledGraphIntString> graphs) {
DataSet<WithCount<String>> edgeLabels = graphs
.flatMap(new ReportEdgeLabels());
edgeLabels = getFrequentLabels(edgeLabels);
edgeDictionary = edgeLabels
.reduceGroup(new CreateDictionary(comparator));
return graphs
.map(new EncodeAndPruneEdges(fsmConfig))
.withBroadcastSet(edgeDictionary, DIMSpanConstants.EDGE_DICTIONARY);
}
/**
* Determines frequent labels.
*
* @param labels dataset of labels
*
* @return dataset of frequent labels
*/
private DataSet<WithCount<String>> getFrequentLabels(DataSet<WithCount<String>> labels) {
// enabled
if (fsmConfig.getDictionaryType() != DictionaryType.RANDOM) {
labels = labels
.groupBy(0)
.sum(1)
.filter(new Frequent<>())
.withBroadcastSet(minFrequency, DIMSpanConstants.MIN_FREQUENCY);
// disabled
} else {
labels = labels
.distinct();
}
return labels;
}
/**
* Identifies valid frequent patterns from a dataset of reported patterns.
*
* @param patterns reported patterns
* @return valid frequent patterns
*/
private DataSet<WithCount<int[]>> getFrequentPatterns(DataSet<WithCount<int[]>> patterns) {
// COMBINE
patterns = patterns
.groupBy(0)
.combineGroup(sumPartition());
if (fsmConfig.getPatternVerificationInStep() == DataflowStep.COMBINE) {
patterns = patterns
.filter(new VerifyPattern(gSpan, fsmConfig));
}
if (fsmConfig.getPatternCompressionInStep() == DataflowStep.COMBINE) {
patterns = patterns
.map(new CompressPattern());
}
// REDUCE
patterns = patterns
.groupBy(0)
.sum(1);
// FILTER
patterns = patterns
.filter(new Frequent<>())
.withBroadcastSet(minFrequency, DIMSpanConstants.MIN_FREQUENCY);
if (fsmConfig.getPatternVerificationInStep() == DataflowStep.FILTER) {
patterns = patterns
.filter(new VerifyPattern(gSpan, fsmConfig));
}
if (fsmConfig.getPatternCompressionInStep() == DataflowStep.FILTER) {
patterns = patterns
.map(new CompressPattern());
}
return patterns;
}
/**
* Creates a Flink sum aggregate function that can be applied in group combine operations.
*
* @return sum group combine function
*/
private GroupCombineFunction<WithCount<int[]>, WithCount<int[]>> sumPartition() {
@SuppressWarnings("unchecked")
AggregationFunction<Long>[] sum = new AggregationFunction[] {
new SumAggregationFunction.SumAggregationFunctionFactory()
.createAggregationFunction(Long.class)
};
int[] fields = { 1 };
return new AggregateMultipleFunctions(sum, fields);
}
public String getName() {
return this.getClass().getSimpleName();
}
}