@operator(
value = {"percent_absolute_deviation"},
content_type = IType.FLOAT,
category = {IOperatorCategory.MAP_COMPARAISON},
concept = {IConcept.STATISTIC})
@doc(
value =
"percent absolute deviation indicator for 2 series of values: percent_absolute_deviation(list_vals_observe,list_vals_sim)",
examples = {
@example(
value = "percent_absolute_deviation([200,300,150,150,200],[250,250,100,200,200])",
isExecutable = false)
})
public static double percentAbsoluteDeviation(
final IScope scope, final IList<Double> vals1, final IList<Double> vals2) {
if (vals1 == null || vals2 == null) {
return 1;
}
int nb = vals1.size();
if (nb != vals2.size()) {
return 0;
}
double sum = 0;
double coeff = 0;
for (int i = 0; i < nb; i++) {
double val1 = Cast.asFloat(scope, vals1.get(i));
double val2 = Cast.asFloat(scope, vals2.get(i));
coeff += val1;
sum += FastMath.abs(val1 - val2) * 100.0;
}
if (coeff == 0) {
return 0;
}
return sum / coeff;
}
@Override
public void addWorker(String worker) {
heartbeat.put(worker, System.currentTimeMillis());
if (!workers.contains(worker)) {
log.info("Adding worker " + worker);
workers.add(worker);
log.info("Number of workers is now " + workers.size());
}
}
protected void handleListAddMany(String[] args) {
int count = 1;
if (args.length > 1) count = Integer.parseInt(args[1]);
int successCount = 0;
long t0 = Clock.currentTimeMillis();
for (int i = 0; i < count; i++) {
boolean success = getList().add("obj" + i);
if (success) successCount++;
}
long t1 = Clock.currentTimeMillis();
println("Added " + successCount + " objects.");
println("size = " + list.size() + ", " + successCount * 1000 / (t1 - t0) + " evt/s");
}
@operator(
value = {"kappa"},
content_type = IType.FLOAT,
category = {IOperatorCategory.MAP_COMPARAISON},
concept = {})
@doc(
value =
"kappa indicator for 2 map comparisons: kappa(list_vals1,list_vals2,categories, weights). Reference: Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20. ",
examples = {
@example(
value =
"kappa([cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2],[cat1,cat2,cat3], [1.0, 2.0, 3.0, 1.0, 5.0])",
isExecutable = false)
})
public static double kappa(
final IScope scope,
final IList<Object> vals1,
final IList<Object> vals2,
final List<Object> categories,
final IList<Object> weights) {
if (vals1 == null || vals2 == null) {
return 1;
}
int nb = vals1.size();
if (nb != vals2.size()) {
return 0;
}
int nbCat = categories.size();
double[] X = new double[nbCat];
double[] Y = new double[nbCat];
double[][] contigency = new double[nbCat][nbCat];
for (int j = 0; j < nbCat; j++) {
X[j] = 0;
Y[j] = 0;
for (int k = 0; k < nbCat; k++) {
contigency[j][k] = 0;
}
}
Map<Object, Integer> categoriesId = new TOrderedHashMap<Object, Integer>();
for (int i = 0; i < nbCat; i++) {
categoriesId.put(categories.get(i), i);
}
double total = 0;
for (int i = 0; i < nb; i++) {
double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
total += weight;
Object val1 = vals1.get(i);
Object val2 = vals2.get(i);
int indexVal1 = categoriesId.get(val1);
int indexVal2 = categoriesId.get(val2);
X[indexVal1] += weight;
Y[indexVal2] += weight;
contigency[indexVal1][indexVal2] += weight;
}
for (int j = 0; j < nbCat; j++) {
X[j] /= total;
Y[j] /= total;
for (int k = 0; k < nbCat; k++) {
contigency[j][k] /= total;
}
}
double po = 0;
double pe = 0;
for (int i = 0; i < nbCat; i++) {
po += contigency[i][i];
pe += X[i] * Y[i];
}
if (pe == 1) {
return 1;
}
return (po - pe) / (1 - pe);
}
@operator(
value = {"kappa_sim"},
content_type = IType.FLOAT,
category = {IOperatorCategory.MAP_COMPARAISON},
concept = {})
@doc(
value =
"kappa simulation indicator for 2 map comparisons: kappa(list_valsInits,list_valsObs,list_valsSim, categories, weights). Reference: van Vliet, J., Bregt, A.K. & Hagen-Zanker, A. (2011). Revisiting Kappa to account for change in the accuracy assessment of land-use change models, Ecological Modelling 222(8)",
examples = {
@example(
value =
"kappa([cat1,cat1,cat2,cat2,cat2],[cat2,cat1,cat2,cat1,cat3],[cat2,cat1,cat2,cat3,cat3], [cat1,cat2,cat3],[1.0, 2.0, 3.0, 1.0, 5.0])",
isExecutable = false)
})
public static double kappaSimulation(
final IScope scope,
final IList<Object> valsInit,
final IList<Object> valsObs,
final IList<Object> valsSim,
final List<Object> categories,
final IList<Object> weights) {
if (valsInit == null || valsObs == null || valsSim == null) {
return 1;
}
int nb = valsInit.size();
if (nb != valsObs.size() || nb != valsSim.size()) {
return 0;
}
int nbCat = categories.size();
double[] O = new double[nbCat];
double[][] contigency = new double[nbCat][nbCat];
double[][] contigencyOA = new double[nbCat][nbCat];
double[][] contigencyOS = new double[nbCat][nbCat];
for (int j = 0; j < nbCat; j++) {
O[j] = 0;
for (int k = 0; k < nbCat; k++) {
contigency[j][k] = 0;
contigencyOA[j][k] = 0;
contigencyOS[j][k] = 0;
}
}
Map<Object, Integer> categoriesId = new TOrderedHashMap<Object, Integer>();
for (int i = 0; i < nbCat; i++) {
categoriesId.put(categories.get(i), i);
}
double total = 0;
for (int i = 0; i < nb; i++) {
double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
total += weight;
Object val1 = valsObs.get(i);
Object val2 = valsSim.get(i);
Object valO = valsInit.get(i);
int indexVal1 = categoriesId.get(val1);
int indexVal2 = categoriesId.get(val2);
int indexValO = categoriesId.get(valO);
O[indexValO] += weight;
contigency[indexVal1][indexVal2] += weight;
contigencyOA[indexValO][indexVal1] += weight;
contigencyOS[indexValO][indexVal2] += weight;
}
for (int j = 0; j < nbCat; j++) {
for (int k = 0; k < nbCat; k++) {
contigency[j][k] /= total;
if (O[j] > 0) {
contigencyOA[j][k] /= O[j];
contigencyOS[j][k] /= O[j];
}
}
O[j] /= total;
}
double po = 0;
double pe = 0;
for (int j = 0; j < nbCat; j++) {
po += contigency[j][j];
double sum = 0;
for (int i = 0; i < nbCat; i++) {
sum += contigencyOA[j][i] * contigencyOS[j][i];
}
pe += O[j] * sum;
}
if (pe == 1) {
return 1;
}
return (po - pe) / (1 - pe);
}
@Override
public int numWorkers() {
int num = workers.size();
return num;
}
public BaseHazelCastStateTracker(String connectionString, String type, int stateTrackerPort)
throws Exception {
log.info(
"Setting up hazelcast with type "
+ type
+ " connection string "
+ connectionString
+ " and port "
+ stateTrackerPort);
if (type.equals("master") && !PortTaken.portTaken(stateTrackerPort)) {
// sets up a proper connection string for reference wrt external actors needing a reference
if (connectionString.equals("master")) {
String host = InetAddress.getLocalHost().getHostName();
this.connectionString = host + ":" + stateTrackerPort;
}
this.hazelCastPort = stateTrackerPort;
config = hazelcast();
h = Hazelcast.newHazelcastInstance(config);
h.getCluster()
.addMembershipListener(
new MembershipListener() {
@Override
public void memberAdded(MembershipEvent membershipEvent) {
log.info("Member added " + membershipEvent.toString());
}
@Override
public void memberRemoved(MembershipEvent membershipEvent) {
log.info("Member removed " + membershipEvent.toString());
}
@Override
public void memberAttributeChanged(MemberAttributeEvent memberAttributeEvent) {
log.info("Member changed " + memberAttributeEvent.toString());
}
});
} else if (type.equals("master") && PortTaken.portTaken(stateTrackerPort))
throw new IllegalStateException(
"Specified type was master and the port specified was taken, please specify a different port");
else {
setConnectionString(connectionString);
log.info("Connecting to hazelcast on " + connectionString);
ClientConfig client = new ClientConfig();
client.getNetworkConfig().addAddress(connectionString);
h = HazelcastClient.newHazelcastClient(client);
}
this.type = type;
jobs = h.getList(JOBS);
workers = h.getList(WORKERS);
// we can make the assumption workers isn't empty because
// the master node by default comes with a applyTransformToDestination of workers
if (!this.type.equals("master")) {
while (workers.isEmpty()) {
log.warn("Waiting for data sync...");
Thread.sleep(1000);
}
log.info("Workers is " + workers.size());
}
begunTraining = h.getAtomicReference(BEGUN);
miniBatchSize = h.getAtomicReference(INPUT_SPLIT);
workerEnabled = h.getMap(WORKER_ENABLED);
replicate = h.getList(REPLICATE_WEIGHTS);
topics = h.getList(TOPICS);
updates = h.getList(UPDATES);
heartbeat = h.getMap(HEART_BEAT);
master = h.getAtomicReference(RESULT);
isPretrain = h.getAtomicReference(IS_PRETRAIN);
numTimesPretrain = h.getAtomicReference(NUM_TIMES_RUN_PRETRAIN);
numTimesPretrainRan = h.getAtomicReference(NUM_TIMES_PRETRAIN_RAN);
done = h.getAtomicReference(DONE);
validationEpochs = h.getAtomicReference(VALIDATION_EPOCHS);
improvementThreshold = h.getAtomicReference(IMPROVEMENT_THRESHOLD);
bestLoss = h.getAtomicReference(BEST_LOSS);
earlyStop = h.getAtomicReference(EARLY_STOP);
patience = h.getAtomicReference(PATIENCE);
patienceIncrease = h.getAtomicReference(PATIENCE_INCREASE);
numBatches = h.getAtomicReference(NUM_BATCHES_SO_FAR_RAN);
// applyTransformToDestination defaults only when master, otherwise, overrides previous values
if (type.equals("master")) {
begunTraining.set(false);
saver = createUpdateSaver();
numTimesPretrainRan.set(0);
numTimesPretrain.set(1);
isPretrain.set(true);
done.set(false);
resource = new StateTrackerDropWizardResource(this);
bestLoss.set(Double.POSITIVE_INFINITY);
earlyStop.set(true);
patience.set(40.0);
patienceIncrease.set(2.0);
improvementThreshold.set(0.995);
validationEpochs.set((int) Math.min(10, patience() / 2));
numBatches.set(0);
}
}