Merge branch 'master' into csv

.gitignore

@@ -2,6 +2,7 @@
 .classpath
 
 bin/
+lib/
 target/
 tmp/
 .settings/
@@ -30,3 +31,5 @@ replay_*.log
 test-output
 .idea/
 out/
+.Rproj.user
+*.tsv

README.md

@@ -6,10 +6,6 @@
 
 Java 8
 
-## Use the library
-
-For examples, please consult the test classes and visit [Model Analyzer Examples](https://github.com/ZsoltKovari/model-analyzer-examples).
-
 ## License
 
 This project is distributed under the Eclipse Public License 1.0. The project was supported by the MONDO EU FP7 project (EU ICT-611125) and is currently developed by the MTA-BME Lendület Research Group on Cyber-Physical Systems.

analyzer/README.md

@@ -0,0 +1,23 @@
+# Standalone application for CLI execution
+
+Build the project, and move the files to the root of the repository.
+
+```bash
+cd $(git rev-parse --show-toplevel) && \
+  rm -rf lib/ bin/ && \
+  ./gradlew clean build installDist -x test && \
+  cp -R analyzer/build/install/analyzer/. analyzer && \
+  cd analyzer
+```
+
+Ensure that the JVM will have sufficient memory by using:
+
+```bash
+export JAVA_OPTS="-Xms4G -Xmx12G"
+```
+
+Run the analyzer with:
+
+```bash
+bin/analyzer panama/panama
+```

csv-example/build.gradle → analyzer/build.gradle

@@ -1,3 +1,5 @@
+apply plugin: 'application'
+mainClassName = 'analyzer.Main'
 
 dependencies {
   compile project(':hu.bme.mit.mba.modeladapters.csv')

analyzer/edges.csv

@@ -0,0 +1,3 @@
+0	intermediary_of	1
+1	officer_of	2
+2	registered_address	3

analyzer/panama/.gitignore

@@ -0,0 +1 @@
+*.csv

analyzer/src/main/java/analyzer/Analyzer.java

@@ -0,0 +1,54 @@
+package analyzer;
+
+import hu.bme.mit.mba.modelmetrics.AbstractModelMetric;
+import hu.bme.mit.mba.modelmetrics.ModelMetric;
+import org.supercsv.cellprocessor.Optional;
+import org.supercsv.cellprocessor.constraint.NotNull;
+import org.supercsv.cellprocessor.ift.CellProcessor;
+import org.supercsv.io.CsvMapWriter;
+import org.supercsv.io.ICsvMapWriter;
+import org.supercsv.prefs.CsvPreference;
+
+import java.io.FileWriter;
+import java.io.IOException;
+import java.util.List;
+import java.util.Map;
+
+public class Analyzer {
+
+    public static final CellProcessor[] processors = new CellProcessor[]{
+        new NotNull(),
+        new Optional(),
+        new Optional(),
+        new NotNull()
+    };
+
+    public static void showResult(ModelMetric metric) {
+        System.out.println(String.format("Data of %s : %s", metric.getName(), metric.getData().toString()));
+    }
+
+    public static void writeToTsv(List<AbstractModelMetric> metrics, String[] header, String filename) throws IOException {
+        ICsvMapWriter mapWriter = null;
+
+        try {
+            mapWriter = new CsvMapWriter(new FileWriter(filename), CsvPreference.TAB_PREFERENCE);
+
+            // write the header
+            mapWriter.writeHeader(header);
+
+            for (AbstractModelMetric metric : metrics) {
+                List<Map<String, Object>> mapList = metric.getTsvMaps(header);
+                for (Map<String, Object> map : mapList) {
+                    mapWriter.write(map, header, processors);
+                }
+            }
+
+        } finally {
+            if (mapWriter != null) {
+                mapWriter.close();
+            }
+        }
+
+    }
+
+}

analyzer/src/main/java/analyzer/Main.java

@@ -0,0 +1,116 @@
+package analyzer;
+
+import hu.bme.mit.mba.modeladapters.csv.CsvModelAdapter;
+import hu.bme.mit.mba.modelmetrics.AbstractModelMetric;
+import hu.bme.mit.mba.modelmetrics.impl.simple.Density;
+import hu.bme.mit.mba.modelmetrics.impl.simple.NumberOfEdges;
+import hu.bme.mit.mba.modelmetrics.impl.simple.NumberOfNodes;
+import hu.bme.mit.mba.modelmetrics.impl.typed.DimensionActivity;
+import hu.bme.mit.mba.modelmetrics.impl.typed.DimensionalClusteringCoefficient;
+import hu.bme.mit.mba.modelmetrics.impl.typed.DimensionalDegree;
+import hu.bme.mit.mba.modelmetrics.impl.typed.DimensionalDegreeEntropy;
+import hu.bme.mit.mba.modelmetrics.impl.typed.EdgeDimensionConnectivity;
+import hu.bme.mit.mba.modelmetrics.impl.typed.EdgeOverlap;
+import hu.bme.mit.mba.modelmetrics.impl.typed.MultiplexParticipationCoefficient;
+import hu.bme.mit.mba.modelmetrics.impl.typed.NodeActivity;
+import hu.bme.mit.mba.modelmetrics.impl.typed.NumberOfTypedEdges;
+import hu.bme.mit.mba.modelmetrics.impl.typed.PairwiseMultiplexity;
+import org.supercsv.cellprocessor.constraint.NotNull;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+public class Main {
+
+    public static void main(String[] args) throws IOException {
+        final String graph = args[0];
+        final String metrics = graph + ".tsv";
+        final String nodes = graph + "-nodes.csv";
+        final String edges = graph + "-edges.csv";
+
+        final String[] header = new String[]{"Category", "Instance", "Index", "Value"};
+        List<AbstractModelMetric> modelMetrics = new ArrayList<>();
+
+        // the adapter represents a bridge between model and metrics
+        CsvModelAdapter adapter = new CsvModelAdapter(new NotNull());
+
+        // adapter must be initialized, this will create an index which is necessary during the evaluation
+        adapter.init(nodes, edges);
+        System.out.println("initialized");
+
+        // calculate metrics
+//        System.out.println("degree");
+//        Degrees dd = new Degrees();
+//        dd.evaluate(adapter);
+//        modelMetrics.add(dd);
+
+        System.out.println("density");
+        Density d = new Density();
+        d.evaluate(adapter);
+        modelMetrics.add(d);
+
+        System.out.println("dimension activity");
+        DimensionActivity dimensionActivity = new DimensionActivity();
+        dimensionActivity.evaluate(adapter);
+        modelMetrics.add(dimensionActivity);
+
+        System.out.println("dimensional degree");
+        DimensionalDegree dimensionalDegree = new DimensionalDegree();
+        dimensionalDegree.evaluate(adapter);
+        modelMetrics.add(dimensionalDegree);
+
+        System.out.println("dimensional clustering coefficient");
+        DimensionalClusteringCoefficient dcc = new DimensionalClusteringCoefficient();
+        dcc.evaluate(adapter);
+        modelMetrics.add(dcc);
+
+        System.out.println("dimensional degree entropy");
+        DimensionalDegreeEntropy dde = new DimensionalDegreeEntropy();
+        dde.evaluate(adapter);
+        modelMetrics.add(dde);
+
+        System.out.println("edge dimension connectivity");
+        EdgeDimensionConnectivity edgeDimensionConnectivity = new EdgeDimensionConnectivity();
+        edgeDimensionConnectivity.evaluate(adapter);
+        modelMetrics.add(edgeDimensionConnectivity);
+
+        System.out.println("edge overlap");
+        EdgeOverlap eo = new EdgeOverlap();
+        eo.evaluate(adapter);
+        modelMetrics.add(eo);
+
+        MultiplexParticipationCoefficient mpc = new MultiplexParticipationCoefficient();
+        System.out.println("mpc");
+        mpc.evaluate(adapter);
+        modelMetrics.add(mpc);
+
+        System.out.println("node activity");
+        NodeActivity nodeActivity = new NodeActivity();
+        nodeActivity.evaluate(adapter);
+        modelMetrics.add(nodeActivity);
+
+        System.out.println("number of edges");
+        NumberOfEdges numberOfEdges = new NumberOfEdges();
+        numberOfEdges.evaluate(adapter);
+        modelMetrics.add(numberOfEdges);
+
+        System.out.println("number of nodes");
+        NumberOfNodes numberOfNodes = new NumberOfNodes();
+        numberOfNodes.evaluate(adapter);
+        modelMetrics.add(numberOfNodes);
+
+        System.out.println("number of typed edges");
+        NumberOfTypedEdges numberOfTypedEdges = new NumberOfTypedEdges();
+        numberOfEdges.evaluate(adapter);
+        modelMetrics.add(numberOfTypedEdges);
+
+        System.out.println("pairwise multiplexity");
+        PairwiseMultiplexity pm = new PairwiseMultiplexity();
+        pm.evaluate(adapter);
+        modelMetrics.add(pm);
+
+        Analyzer.writeToTsv(modelMetrics, header, metrics);
+    }
+
+}

analyzer/src/test/java/analyzer/CsvTest.java

@@ -0,0 +1,142 @@
+package analyzer;
+
+import hu.bme.mit.mba.modeladapters.csv.CsvModelAdapter;
+import hu.bme.mit.mba.modelmetrics.AbstractModelMetric;
+import hu.bme.mit.mba.modelmetrics.ModelMetric;
+import hu.bme.mit.mba.modelmetrics.impl.simple.Degrees;
+import hu.bme.mit.mba.modelmetrics.impl.simple.Density;
+import hu.bme.mit.mba.modelmetrics.impl.simple.NumberOfEdges;
+import hu.bme.mit.mba.modelmetrics.impl.simple.NumberOfNodes;
+import hu.bme.mit.mba.modelmetrics.impl.typed.*;
+import org.supercsv.cellprocessor.Optional;
+import org.supercsv.cellprocessor.constraint.NotNull;
+import org.supercsv.cellprocessor.ift.CellProcessor;
+import org.supercsv.io.CsvMapWriter;
+import org.supercsv.io.ICsvMapWriter;
+import org.supercsv.prefs.CsvPreference;
+import org.testng.annotations.Test;
+
+import java.io.FileWriter;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+public class CsvTest {
+
+    @Test
+    public void test() throws IOException {
+        final String filename = "modelmetrics.tsv";
+        final String[] header = new String[]{"Category", "Instance", "Index", "Value"};
+        final List<AbstractModelMetric> modelMetrics = new ArrayList<>();
+
+        // the adapter represents a bridge between model and metrics
+        CsvModelAdapter adapter = new CsvModelAdapter(new NotNull());
+
+        // adapter must be initialized, this will create an index which is necessary during the evaluation
+        adapter.init("nodes.csv", "edges.csv");
+        System.out.println("initialized");
+
+        // calculate metrics
+        System.out.println("dd");
+        Degrees dd = new Degrees();
+        dd.evaluate(adapter);
+        modelMetrics.add(dd);
+//        showResult(dd);
+
+        System.out.println("pm");
+        PairwiseMultiplexity pm = new PairwiseMultiplexity();
+        pm.evaluate(adapter);
+        showResult(pm);
+        modelMetrics.add(pm);
+
+        MultiplexParticipationCoefficient mpc = new MultiplexParticipationCoefficient();
+        System.out.println("mpc");
+        mpc.evaluate(adapter);
+        modelMetrics.add(mpc);
+        showResult(mpc);
+
+        System.out.println("dimension activity");
+        DimensionActivity dimensionActivity = new DimensionActivity();
+        dimensionActivity.evaluate(adapter);
+        showResult(dimensionActivity);
+        modelMetrics.add(dimensionActivity);
+
+        EdgeDimensionConnectivity edgeDimensionConnectivity = new EdgeDimensionConnectivity();
+        edgeDimensionConnectivity.evaluate(adapter);
+        showResult(edgeDimensionConnectivity);
+        modelMetrics.add(edgeDimensionConnectivity);
+
+        Density d = new Density();
+        d.evaluate(adapter);
+        modelMetrics.add(d);
+
+        NumberOfNodes numberOfNodes = new NumberOfNodes();
+        numberOfNodes.evaluate(adapter);
+        modelMetrics.add(numberOfNodes);
+
+        NumberOfEdges numberOfEdges = new NumberOfEdges();
+        numberOfEdges.evaluate(adapter);
+        modelMetrics.add(numberOfEdges);
+
+        NodeActivity nodeActivity = new NodeActivity();
+        nodeActivity.evaluate(adapter);
+        modelMetrics.add(nodeActivity);
+
+        NumberOfTypedEdges numberOfTypedEdges = new NumberOfTypedEdges();
+        numberOfEdges.evaluate(adapter);
+        modelMetrics.add(numberOfTypedEdges);
+
+        DimensionalDegree dimensionalDegree = new DimensionalDegree();
+        dimensionalDegree.evaluate(adapter);
+        modelMetrics.add(dimensionalDegree);
+
+        writeToTsv(modelMetrics, header, filename);
+
+    }
+
+
+    private void showResult(ModelMetric metric) {
+        System.out.println(String.format("Data of %s : %s", metric.getName(), metric.getData().toString()));
+    }
+
+    private static CellProcessor[] getProcessors() {
+
+        final CellProcessor[] processors = new CellProcessor[]{
+            new NotNull(),
+            new Optional(),
+            new Optional(),
+            new NotNull()
+        };
+
+        return processors;
+    }
+
+    private void writeToTsv(List<AbstractModelMetric> metrics, String[] header, String filename) throws IOException {
+        ICsvMapWriter mapWriter = null;
+
+        try {
+            mapWriter = new CsvMapWriter(new FileWriter(filename),
+                CsvPreference.TAB_PREFERENCE);
+
+            final CellProcessor[] processors = getProcessors();
+
+            // write the header
+            mapWriter.writeHeader(header);
+
+            for (AbstractModelMetric metric : metrics) {
+                List<Map<String, Object>> mapList = metric.getTsvMaps(header);
+                for (Map<String, Object> map : mapList) {
+                    mapWriter.write(map, header, processors);
+                }
+            }
+
+        } finally {
+            if (mapWriter != null) {
+                mapWriter.close();
+            }
+        }
+
+    }
+
+}

build.gradle

@@ -10,6 +10,7 @@ subprojects {
 
   repositories {
     jcenter()
+    maven { url "https://docs.inf.mit.bme.hu/graph-analyzer" }
   }
 
   test {