@Override
public void notifyAfterMobsim(AfterMobsimEvent event) {
Network network = event.getServices().getScenario().getNetwork();
DescriptiveStatistics error = new DescriptiveStatistics();
DescriptiveStatistics errorAbs = new DescriptiveStatistics();
DescriptivePiStatistics errorWeighted = new WSMStatsFactory().newInstance();
TDoubleArrayList errorVals = new TDoubleArrayList();
TDoubleArrayList caps = new TDoubleArrayList();
TDoubleArrayList speeds = new TDoubleArrayList();
for (Count count : counts.getCounts().values()) {
if (!count.getId().toString().startsWith(ODCalibrator.VIRTUAL_ID_PREFIX)) {
double obsVal = 0;
for (int i = 1; i < 25; i++) {
obsVal += count.getVolume(i).getValue();
}
if (obsVal > 0) {
double simVal = calculator.getOccupancy(count.getId());
simVal *= factor;
double err = (simVal - obsVal) / obsVal;
error.addValue(err);
errorAbs.addValue(Math.abs(err));
errorWeighted.addValue(Math.abs(err), 1 / obsVal);
Link link = network.getLinks().get(count.getId());
errorVals.add(Math.abs(err));
caps.add(link.getCapacity());
speeds.add(link.getFreespeed());
}
}
}
logger.info(
String.format(
"Relative counts error: mean = %s, var = %s, stderr = %s, min = %s, max = %s",
error.getMean(),
error.getVariance(),
error.getStandardDeviation(),
error.getMin(),
error.getMax()));
logger.info(
String.format(
"Absolute relative counts error: mean = %s, var = %s, stderr = %s, min = %s, max = %s",
errorAbs.getMean(),
errorAbs.getVariance(),
errorAbs.getStandardDeviation(),
errorAbs.getMin(),
errorAbs.getMax()));
logger.info(
String.format(
"Absolute weigthed relative counts error: mean = %s, var = %s, stderr = %s, min = %s, max = %s",
errorWeighted.getMean(),
errorWeighted.getVariance(),
errorWeighted.getStandardDeviation(),
errorWeighted.getMin(),
errorWeighted.getMax()));
String outdir = event.getServices().getControlerIO().getIterationPath(event.getIteration());
try {
TDoubleDoubleHashMap map = Correlations.mean(caps.toArray(), errorVals.toArray());
StatsWriter.writeHistogram(
map, "capacity", "counts", String.format("%s/countsError.capacity.txt", outdir));
map = Correlations.mean(speeds.toArray(), errorVals.toArray());
StatsWriter.writeHistogram(
map, "speed", "counts", String.format("%s/countsError.speed.txt", outdir));
StatsWriter.writeHistogram(
Histogram.createHistogram(error, new LinearDiscretizer(0.1), false),
"Error",
"Frequency",
String.format("%s/countsError.hist.txt", outdir));
StatsWriter.writeHistogram(
Histogram.createHistogram(errorAbs, new LinearDiscretizer(0.1), false),
"Error (absolute)",
"Frequency",
String.format("%s/countsErrorAbs.hist.txt", outdir));
StatsWriter.writeHistogram(
Histogram.createHistogram(errorWeighted, new LinearDiscretizer(0.1), true),
"Error (weighted)",
"Frequency",
String.format("%s/countsErrorWeighted.hist.txt", outdir));
CountsCompare2GeoJSON.write(calculator, counts, factor, network, outdir);
NetworkLoad2GeoJSON.write(
event.getServices().getScenario().getNetwork(),
calculator,
factor,
outdir + "/network.json");
} catch (Exception e) {
e.printStackTrace();
}
String rootOutDir = event.getServices().getControlerIO().getOutputPath();
boolean append = false;
if (event.getIteration() > 0) {
append = true;
}
writeErrorFile(error, String.format("%s/countsError.txt", rootOutDir), append);
writeErrorFile(errorAbs, String.format("%s/countsAbsError.txt", rootOutDir), append);
}
private void loadSociogramData(Collection<VertexRecord> records, SQLDumpReader sqlData) {
logger.info("Loading sociogram data...");
Map<String, VertexRecord> map = sqlData.getFullAlterKeyMappping(records);
TObjectIntHashMap<Vertex> rawDegrees = new TObjectIntHashMap<Vertex>();
for (Vertex v : proj.getVertices()) {
rawDegrees.put(v, v.getNeighbours().size());
}
int edgecnt = 0;
int doublecnt = 0;
int egoEdge = 0;
Set<Vertex> notOkVertices = new HashSet<Vertex>();
Set<Vertex> okVertices = new HashSet<Vertex>();
DescriptiveStatistics notOkStats = new DescriptiveStatistics();
DescriptiveStatistics okStats = new DescriptiveStatistics();
DescriptiveStatistics numDistr = new DescriptiveStatistics();
DescriptiveStatistics numDistrNoZero = new DescriptiveStatistics();
DescriptiveStatistics sizeDistr = new DescriptiveStatistics();
TDoubleArrayList sizeValues = new TDoubleArrayList();
TDoubleArrayList kSizeValues = new TDoubleArrayList();
TDoubleArrayList numValues = new TDoubleArrayList();
TDoubleArrayList numValues2 = new TDoubleArrayList();
TDoubleArrayList kNumValues = new TDoubleArrayList();
for (VertexRecord record : records) {
if (record.isEgo) {
List<Set<String>> cliques = sqlData.getCliques(record);
numDistr.addValue(cliques.size());
Vertex v = idMap.get(record.id);
numValues.add(cliques.size());
kNumValues.add(v.getNeighbours().size());
if (!cliques.isEmpty()) numDistrNoZero.addValue(cliques.size());
for (Set<String> clique : cliques) {
sizeDistr.addValue(clique.size());
sizeValues.add(clique.size());
kSizeValues.add(rawDegrees.get(projMap.get(v)));
numValues2.add(cliques.size());
List<SocialSparseVertex> vertices = new ArrayList<SocialSparseVertex>(clique.size());
for (String alter : clique) {
VertexRecord r = map.get(record.egoSQLId + alter);
if (r != null) {
SocialSparseVertex vertex = idMap.get(r.id);
if (vertex != null) {
vertices.add(vertex);
} else {
logger.warn("Vertex not found.");
}
} else {
logger.warn("Record not found.");
}
}
for (int i = 0; i < vertices.size(); i++) {
for (int j = i + 1; j < vertices.size(); j++) {
SampledVertexDecorator<SocialSparseVertex> vProj1 = projMap.get(vertices.get(i));
SampledVertexDecorator<SocialSparseVertex> vProj2 = projMap.get(vertices.get(j));
if (!vProj1.isSampled() && !vProj2.isSampled()) {
if (Math.random() < 0.62) {
SocialSparseEdge socialEdge =
builder.addEdge(graph, vertices.get(i), vertices.get(j));
if (socialEdge != null) {
projBuilder.addEdge(proj, vProj1, vProj2, socialEdge);
edgecnt++;
if (vProj1.isSampled() || vProj2.isSampled()) {
egoEdge++;
if (vProj1.isSampled()) notOkVertices.add(vProj1);
else notOkVertices.add(vProj2);
}
} else {
doublecnt++;
if (vProj1.isSampled()) okVertices.add(vProj1);
else if (vProj2.isSampled()) okVertices.add(vProj2);
}
}
}
}
}
}
}
}
for (Vertex v : okVertices) okStats.addValue(rawDegrees.get(v));
for (Vertex v : notOkVertices) notOkStats.addValue(rawDegrees.get(v));
try {
TDoubleDoubleHashMap hist =
Histogram.createHistogram(okStats, new LinearDiscretizer(1), false);
StatsWriter.writeHistogram(
hist,
"k",
"n",
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/k_ok.txt");
TDoubleDoubleHashMap hist2 =
Histogram.createHistogram(notOkStats, new LinearDiscretizer(1), false);
StatsWriter.writeHistogram(
hist2,
"k",
"n",
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/k_notok.txt");
TDoubleDoubleHashMap ratio = new TDoubleDoubleHashMap();
double[] keys = hist.keys();
for (double k : keys) {
double val1 = hist2.get(k);
double val2 = hist.get(k);
ratio.put(k, val1 / (val2 + val1));
}
StatsWriter.writeHistogram(
ratio,
"k",
"p",
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/k_ratio.txt");
logger.info("Mean num of cliques: " + numDistrNoZero.getMean());
logger.info("Mean size: " + sizeDistr.getMean());
logger.info("Median num of cliques: " + StatUtils.percentile(numDistrNoZero.getValues(), 50));
logger.info("Median size: " + StatUtils.percentile(sizeDistr.getValues(), 50));
TDoubleDoubleHashMap histNum =
Histogram.createHistogram(
numDistrNoZero,
FixedSampleSizeDiscretizer.create(numDistrNoZero.getValues(), 2, 20),
true);
Histogram.normalize(histNum);
StatsWriter.writeHistogram(
histNum,
"num",
"freq",
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/numCliques.txt");
TDoubleDoubleHashMap histSize =
Histogram.createHistogram(
sizeDistr, FixedSampleSizeDiscretizer.create(sizeDistr.getValues(), 2, 20), true);
Histogram.normalize(histSize);
StatsWriter.writeHistogram(
histSize,
"size",
"freq",
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/numPersons.txt");
Discretizer discretizer =
FixedSampleSizeDiscretizer.create(kSizeValues.toNativeArray(), 20, 20);
TDoubleArrayList valuesX = new TDoubleArrayList();
for (int i = 0; i < kSizeValues.size(); i++) {
valuesX.add(discretizer.discretize(kSizeValues.get(i)));
}
Correlations.writeToFile(
Correlations.mean(valuesX.toNativeArray(), sizeValues.toNativeArray()),
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/size_k.txt",
"k",
"size");
discretizer = FixedSampleSizeDiscretizer.create(kNumValues.toNativeArray(), 20, 20);
valuesX = new TDoubleArrayList();
for (int i = 0; i < kNumValues.size(); i++) {
valuesX.add(discretizer.discretize(kNumValues.get(i)));
}
Correlations.writeToFile(
Correlations.mean(valuesX.toNativeArray(), numValues.toNativeArray()),
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/num_k.txt",
"k",
"n");
Correlations.writeToFile(
Correlations.mean(numValues2.toNativeArray(), sizeValues.toNativeArray()),
"/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/size_num.txt",
"num",
"size");
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
logger.info(
String.format("Inserted %1$s edges, %2$s edges already present.", edgecnt, doublecnt));
logger.info(String.format("Inserted %1$s edges between at least one ego.", egoEdge));
}