org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner Java Examples

@Test
public void GeneticSearchCandidateGenerator_getCandidate_GeneticExceptionShouldMarkCandidateAsFailed() {

    ScoreFunction scoreFunction = new BraninFunction.BraninScoreFunction();

    //Define configuration:
    CandidateGenerator candidateGenerator =
                    new GeneticSearchCandidateGenerator.Builder(new BraninFunction.BraninSpace(), scoreFunction)
                                    .selectionOperator(new TestSelectionOperator()).build();

    TestTerminationCondition testTerminationCondition = new TestTerminationCondition();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator).scoreFunction(scoreFunction)
                    .terminationConditions(testTerminationCondition).build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator());

    runner.addListeners(new LoggingStatusListener());
    runner.execute();

    Assert.assertTrue(testTerminationCondition.hasAFailedCandidate);
}
 

@Test
public void GeneticSearchCandidateGenerator_getCandidate_ShouldGenerateCandidates() throws Exception {

    ScoreFunction scoreFunction = new BraninFunction.BraninScoreFunction();

    //Define configuration:
    CandidateGenerator candidateGenerator =
                    new GeneticSearchCandidateGenerator.Builder(new BraninFunction.BraninSpace(), scoreFunction)
                                    .build();

    TestTerminationCondition testTerminationCondition = new TestTerminationCondition();
    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator).scoreFunction(scoreFunction)
                    .terminationConditions(new MaxCandidatesCondition(50), testTerminationCondition).build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator());

    runner.addListeners(new LoggingStatusListener());
    runner.execute();

    Assert.assertFalse(testTerminationCondition.hasAFailedCandidate);
}
 

@Override
public boolean terminate(IOptimizationRunner optimizationRunner) {
    if (++evalCount == 50) {
        // Generator did not handle GeneticGenerationException
        return true;
    }

    for (CandidateInfo candidateInfo : optimizationRunner.getCandidateStatus()) {
        if (candidateInfo.getCandidateStatus() == CandidateStatus.Failed) {
            hasAFailedCandidate = true;
            return true;
        }
    }

    return false;
}
 

@Test(timeout = 20000L)
public void testAllInvalidDataConfigMismatchCG() throws Exception {
    //Valid config - but mismatched with provided data

    File f = temp.newFolder();
    ComputationGraphSpace mls = new ComputationGraphSpace.Builder()
            .addInputs("in")
            .layer("0", new DenseLayerSpace.Builder().nIn(4).nOut(10)
                    .activation(Activation.TANH).build(), "in")
            .addLayer("1", new OutputLayerSpace.Builder().nIn(10).nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "0")
            .setOutputs("1")
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(
                    new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());
    runner.execute();
}
 

@Test(timeout = 20000L)
public void testAllInvalidConfigCG() throws Exception {
    //Invalid config - basically check that this actually terminates

    File f = temp.newFolder();
    ComputationGraphSpace mls = new ComputationGraphSpace.Builder()
            .addInputs("in")
            .layer("0", new DenseLayerSpace.Builder().nIn(4).nOut(new FixedValue<>(0))    //INVALID: nOut of 0
                    .activation(Activation.TANH)
                    .build(), "in")
            .layer("1", new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "0")
            .setOutputs("1")
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration);
    runner.execute();
}
 

@Test(timeout = 20000L)
public void testAllInvalidDataConfigMismatch() throws Exception {
    //Valid config - but mismatched with provided data

    File f = temp.newFolder();
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
            .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(10)    //INVALID: nOut of 0
                    .activation(Activation.TANH)
                    .build())
            .addLayer(new OutputLayerSpace.Builder().nIn(10).nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(
                    new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration);
    runner.execute();
}
 

@Test(timeout = 20000L)
public void testAllInvalidConfig() throws Exception {
    //Invalid config - basically check that this actually terminates

    File f = temp.newFolder();
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
            .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(new FixedValue<>(0))    //INVALID: nOut of 0
                            .activation(Activation.TANH)
                            .build())
            .addLayer(new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(
                    new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration);
    runner.execute();
}
 

private GlobalConfigPersistable getNewStatusPersistable(IOptimizationRunner r){
//        if(ocJson == null || this.startTime == 0L){
//            //Want to update config once start time has been set
//            ocJson = JsonMapper.asJson(r.getConfiguration());
//            this.startTime = r.getConfiguration().getExecutionStartTime();
//        }
        //TODO: cache global config, but we don't want to have outdated info (like uninitialized termination conditions)

        try {
            ocJson = JsonMapper.getMapper().writeValueAsString(r.getConfiguration());
        } catch (IOException e){
            throw new RuntimeException(e);
        }

        GlobalConfigPersistable p = new GlobalConfigPersistable.Builder()
                .sessionId(sessionId)
                .timestamp(System.currentTimeMillis())
                .optimizationConfigJson(ocJson)
                .candidateCounts(r.numCandidatesQueued(), r.numCandidatesCompleted(),
                        r.numCandidatesFailed(), r.numCandidatesTotal())
                .optimizationRunner(r.getClass().getSimpleName())
                .build();

        return p;
    }
 

@Test
    public void test() throws Exception {
        Map<String, Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, new HashMap<>());

        //Define configuration:
        CandidateGenerator candidateGenerator = new RandomSearchGenerator(new BraninFunction.BraninSpace(), commands);
        OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                        .candidateGenerator(candidateGenerator).scoreFunction(new BraninFunction.BraninScoreFunction())
                        .terminationConditions(new MaxCandidatesCondition(50)).build();

        IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator());

        runner.addListeners(new LoggingStatusListener());
        runner.execute();


//        System.out.println("----- Complete -----");
    }
 

public GraphLearningTask(Candidate candidate, DataProvider dataProvider, ScoreFunction scoreFunction,
                         ModelEvaluator modelEvaluator, List<StatusListener> listeners,
                         TaskListener taskListener, IOptimizationRunner runner) {
    this.candidate = candidate;
    this.dataProvider = dataProvider;
    this.scoreFunction = scoreFunction;
    this.modelEvaluator = modelEvaluator;
    this.listeners = listeners;
    this.taskListener = taskListener;
    this.runner = runner;
}
 

public GraphLearningTask(Candidate candidate, Class<? extends DataSource> dataSource, Properties dataSourceProperties,
                         ScoreFunction scoreFunction, ModelEvaluator modelEvaluator, List<StatusListener> listeners,
                         TaskListener taskListener, IOptimizationRunner runner) {
    this.candidate = candidate;
    this.dataSource = dataSource;
    this.dataSourceProperties = dataSourceProperties;
    this.scoreFunction = scoreFunction;
    this.modelEvaluator = modelEvaluator;
    this.listeners = listeners;
    this.taskListener = taskListener;
    this.runner = runner;
}
 

@Override
public Callable<OptimizationResult> create(Candidate candidate, DataProvider dataProvider,
                                           ScoreFunction scoreFunction, List<StatusListener> statusListener,
                                           IOptimizationRunner runner) {

    return new GraphLearningTask(candidate, dataProvider, scoreFunction, modelEvaluator, statusListener,
            taskListener, runner);
}
 

public DL4JLearningTask(Candidate candidate, Class<? extends DataSource> dataSource, Properties dataSourceProperties,
                        ScoreFunction scoreFunction, ModelEvaluator modelEvaluator, List<StatusListener> listeners, TaskListener taskListener,
                        IOptimizationRunner runner) {
    this.candidate = candidate;
    this.dataSource = dataSource;
    this.dataSourceProperties = dataSourceProperties;
    this.scoreFunction = scoreFunction;
    this.modelEvaluator = modelEvaluator;
    this.listeners = listeners;
    this.taskListener = taskListener;
    this.runner = runner;
}
 

@Override
public Callable<OptimizationResult> create(Candidate candidate, DataProvider dataProvider,
                                           ScoreFunction scoreFunction, List<StatusListener> statusListeners,
                                           IOptimizationRunner runner) {

    return new DL4JLearningTask(candidate, dataProvider, scoreFunction, modelEvaluator, statusListeners, taskListener, runner);
}
 

@Override
public void onCandidateStatusChange(CandidateInfo candidateInfo, IOptimizationRunner runner, OptimizationResult result) {
    ModelInfoPersistable p = lastModelInfoPersistable.get(candidateInfo.getIndex());
    if(p == null){
        p = new ModelInfoPersistable.Builder()
                .timestamp(candidateInfo.getCreatedTime())
                .sessionId(sessionId)
                .workerId(String.valueOf(candidateInfo.getIndex()))
                .modelIdx(candidateInfo.getIndex())
                .score(candidateInfo.getScore())
                .status(candidateInfo.getCandidateStatus())
                .exceptionStackTrace(candidateInfo.getExceptionStackTrace())
                .build();

        lastModelInfoPersistable.put(candidateInfo.getIndex(), p);
    }

    if(p.getScore() == null){
        p.setScore(candidateInfo.getScore());
    }

    if(result != null && p.getExceptionStackTrace() == null && result.getCandidateInfo().getExceptionStackTrace() != null){
        //Update exceptions that may have occurred since earlier model info instance
        p.setExceptionStackTrace(result.getCandidateInfo().getExceptionStackTrace());
    }

    p.setStatus(candidateInfo.getCandidateStatus());

    statsStorage.putUpdate(p);
}
 

@Override
        public Callable<OptimizationResult> create(final Candidate c, DataProvider dataProvider,
                        final ScoreFunction scoreFunction, final List<StatusListener> statusListeners,
                        IOptimizationRunner runner) {

            return new Callable<OptimizationResult>() {
                @Override
                public OptimizationResult call() throws Exception {

                    BraninConfig candidate = (BraninConfig) c.getValue();

                    double score = scoreFunction.score(candidate, null, (Map) null);
//                    System.out.println(candidate.getX1() + "\t" + candidate.getX2() + "\t" + score);

                    Thread.sleep(20);

                    if (statusListeners != null) {
                        for (StatusListener sl : statusListeners) {
                            sl.onCandidateIteration(null, null, 0);
                        }
                    }

                    CandidateInfo ci = new CandidateInfo(-1, CandidateStatus.Complete, score,
                                    System.currentTimeMillis(), null, null, null, null);

                    return new OptimizationResult(c, score, c.getIndex(), null, ci, null);
                }
            };
        }
 

public DL4JLearningTask(Candidate candidate, DataProvider dataProvider, ScoreFunction scoreFunction,
                ModelEvaluator modelEvaluator, List<StatusListener> listeners, TaskListener taskListener,
                        IOptimizationRunner runner) {
    this.candidate = candidate;
    this.dataProvider = dataProvider;
    this.scoreFunction = scoreFunction;
    this.modelEvaluator = modelEvaluator;
    this.listeners = listeners;
    this.taskListener = taskListener;
    this.runner = runner;
}
 

@Override
public Callable<OptimizationResult> create(Candidate candidate, Class<? extends DataSource> dataSource,
                Properties dataSourceProperties, ScoreFunction scoreFunction,
                List<StatusListener> statusListeners, IOptimizationRunner runner) {
    throw new UnsupportedOperationException();
}
 

@Override
public void initialize(IOptimizationRunner optimizationRunner) {
    startTime = System.currentTimeMillis();
    this.endTime = startTime + timeUnit.toMillis(duration);
}
 

@Override
public boolean terminate(IOptimizationRunner optimizationRunner) {
    return System.currentTimeMillis() >= endTime;
}
 

@Override
public void initialize(IOptimizationRunner optimizationRunner) {
    //No op
}
 

@Override
public boolean terminate(IOptimizationRunner optimizationRunner) {
    return optimizationRunner.numCandidatesCompleted() + optimizationRunner.numCandidatesFailed() >= maxCandidates;
}
 

/** Called when optimization runner starts execution */
void onInitialization(IOptimizationRunner runner);
 

/** Called when optimization runner terminates */
void onShutdown(IOptimizationRunner runner);
 

@Override
public void onInitialization(IOptimizationRunner runner) {
    //No op
}
 

@Override
public void onShutdown(IOptimizationRunner runner) {
    //No op
}
 

@Override
public void onRunnerStatusChange(IOptimizationRunner runner) {
    //No op
}
 

@Override
public void initialize(IOptimizationRunner optimizationRunner) {}
 

@Override
public void onCandidateStatusChange(CandidateInfo candidateInfo, IOptimizationRunner runner, OptimizationResult result) {
    //No op
}
 

@Override
public void onInitialization(IOptimizationRunner runner) {
    log.info("Optimization runner: initialized");
}