/// <summary>
/// Sets the priors of CBCC.
/// </summary>
/// <param name="workerCount">The number of workers.</param>
/// <param name="priors">The priors.</param>
protected override void SetPriors(int workerCount, BCCPosteriors priors)
{
int communityCount = m.SizeAsInt;
int labelCount = c.SizeAsInt;
WorkerCount.ObservedValue = workerCount;
NoiseMatrix.ObservedValue = PositiveDefiniteMatrix.IdentityScaledBy(labelCount, NoisePrecision);
CBCCPosteriors cbccPriors = (CBCCPosteriors)priors;
if (cbccPriors == null || cbccPriors.BackgroundLabelProb == null)
{
BackgroundLabelProbPrior.ObservedValue = Dirichlet.Uniform(labelCount);
}
else
{
BackgroundLabelProbPrior.ObservedValue = cbccPriors.BackgroundLabelProb;
}
if (cbccPriors == null || cbccPriors.CommunityProb == null)
{
CommunityProbPrior.ObservedValue = CommunityProbPriorObserved;
}
else
{
CommunityProbPrior.ObservedValue = cbccPriors.CommunityProb;
}
if (cbccPriors == null || cbccPriors.CommunityScoreMatrix == null)
{
CommunityScoreMatrixPrior.ObservedValue = CommunityScoreMatrixPriorObserved;
}
else
{
CommunityScoreMatrixPrior.ObservedValue = cbccPriors.CommunityScoreMatrix;
}
if (cbccPriors == null || cbccPriors.TrueLabelConstraint == null)
{
TrueLabelConstraint.ObservedValue = Util.ArrayInit(TaskCount, t => Discrete.Uniform(labelCount));
}
else
{
TrueLabelConstraint.ObservedValue = cbccPriors.TrueLabelConstraint;
}
}
CBCCPosteriors DeserializeCommunityPosteriors(string modelName, int numCommunities)
{
CBCCPosteriors cbccPriors = new CBCCPosteriors();
using (FileStream stream = new FileStream(modelName + "CommunityPriors.xml", FileMode.Open))
{
var serializer = new System.Xml.Serialization.XmlSerializer(typeof(NonTaskWorkerParameters));
var ntwp = (NonTaskWorkerParameters)serializer.Deserialize(stream);
if (ntwp.BackgroundLabelProb.Dimension != Mapping.LabelCount)
{
throw new ApplicationException("Unexpected number of labels");
}
BackgroundLabelProb = ntwp.BackgroundLabelProb;
cbccPriors.BackgroundLabelProb = ntwp.BackgroundLabelProb;
if (ntwp.CommunityScoreMatrix.Length != numCommunities)
{
throw new ApplicationException("Unexpected number of communities");
}
if (ntwp.CommunityScoreMatrix[0][0].Dimension != Mapping.LabelCount)
{
throw new ApplicationException("Unexpected number of labels");
}
CommunityScoreMatrix = ntwp.CommunityScoreMatrix;
cbccPriors.CommunityScoreMatrix = ntwp.CommunityScoreMatrix;
if (ntwp.CommunityProb.Dimension != numCommunities)
{
throw new ApplicationException("Unexpected number of communities");
}
CommunityProb = ntwp.CommunityProb;
cbccPriors.CommunityProb = ntwp.CommunityProb;
}
return(cbccPriors);
}
/// <summary>
/// Infers the posteriors of CBCC using the attached data.
/// </summary>
/// <param name="taskIndices">The matrix of the task indices (columns) of each worker (rows).</param>
/// <param name="workerLabels">The matrix of the labels (columns) of each worker (rows).</param>
/// <param name="priors">The priors.</param>
/// <returns></returns>
public override BCCPosteriors Infer(int[][] taskIndices, int[][] workerLabels, BCCPosteriors priors)
{
var cbccPriors = (CBCCPosteriors)priors;
VectorGaussian[][] scoreConstraint = (cbccPriors == null ? null : cbccPriors.WorkerScoreMatrixConstraint);
Discrete[] communityConstraint = (cbccPriors == null ? null : cbccPriors.WorkerCommunityConstraint);
SetPriors(workerLabels.Length, priors);
AttachData(taskIndices, workerLabels, scoreConstraint, communityConstraint);
var result = new CBCCPosteriors();
Engine.NumberOfIterations = NumberOfIterations;
result.Evidence = Engine.Infer <Bernoulli>(Evidence);
result.BackgroundLabelProb = Engine.Infer <Dirichlet>(BackgroundLabelProb);
result.WorkerConfusionMatrix = Engine.Infer <Dirichlet[][]>(WorkerConfusionMatrix);
result.TrueLabel = Engine.Infer <Discrete[]>(TrueLabel);
result.TrueLabelConstraint = Engine.Infer <Discrete[]>(TrueLabel, QueryTypes.MarginalDividedByPrior);
result.CommunityScoreMatrix = Engine.Infer <VectorGaussian[][]>(CommunityScoreMatrix);
result.CommunityConfusionMatrix = Engine.Infer <Dirichlet[][]>(CommunityConfusionMatrix);
result.WorkerScoreMatrixConstraint = Engine.Infer <VectorGaussian[][]>(ScoreMatrix, QueryTypes.MarginalDividedByPrior);
result.CommunityProb = Engine.Infer <Dirichlet>(CommunityProb);
result.Community = Engine.Infer <Discrete[]>(Community);
result.WorkerCommunityConstraint = Engine.Infer <Discrete[]>(Community, QueryTypes.MarginalDividedByPrior);
return(result);
}
BCCPosteriors ToPriors()
{
int numClasses = Mapping.LabelCount;
int numTasks = Mapping.TaskCount;
int numWorkers = Mapping.WorkerCount;
CBCCPosteriors cbccPriors = new CBCCPosteriors();
BCCPosteriors priors = IsCommunityModel ? cbccPriors : new BCCPosteriors();
priors.BackgroundLabelProb = BackgroundLabelProb;
priors.WorkerConfusionMatrix = Util.ArrayInit(numWorkers,
w =>
{
string wid = Mapping.WorkerIndexToId[w];
if (WorkerConfusionMatrix.ContainsKey(wid))
return Util.ArrayInit(numClasses, c => WorkerConfusionMatrix[wid][c]);
else
return Util.ArrayInit(numClasses, c => Dirichlet.Uniform(numClasses));
});
priors.TrueLabelConstraint = Util.ArrayInit(numTasks,
t =>
{
string tid = Mapping.TaskIndexToId[t];
if (TrueLabelConstraint.ContainsKey(tid))
return TrueLabelConstraint[Mapping.TaskIndexToId[t]];
else
return Discrete.Uniform(numClasses);
});
if (IsCommunityModel)
{
cbccPriors.CommunityConfusionMatrix = CommunityConfusionMatrix;
cbccPriors.WorkerScoreMatrixConstraint = Util.ArrayInit(numWorkers,
w =>
{
string wid = Mapping.WorkerIndexToId[w];
if (WorkerScoreMatrixConstraint.ContainsKey(wid))
return Util.ArrayInit(numClasses, c => WorkerScoreMatrixConstraint[wid][c]);
else
return Util.ArrayInit(numClasses, c => VectorGaussian.Uniform(numClasses));
});
cbccPriors.CommunityProb = CommunityProb;
cbccPriors.CommunityScoreMatrix = CommunityScoreMatrix;
cbccPriors.WorkerCommunityConstraint = Util.ArrayInit(numWorkers,
w =>
{
string wid = Mapping.WorkerIndexToId[w];
if (CommunityConstraint.ContainsKey(wid))
return CommunityConstraint[wid];
else
return Discrete.Uniform(CommunityCount);
});
}
priors.Evidence = ModelEvidence;
return priors;
}
CBCCPosteriors DeserializeCommunityPosteriors(string modelName, int numCommunities)
{
CBCCPosteriors cbccPriors = new CBCCPosteriors();
using (FileStream stream = new FileStream(modelName + "CommunityPriors.xml", FileMode.Open))
{
var serializer = new System.Xml.Serialization.XmlSerializer(typeof(NonTaskWorkerParameters));
var ntwp = (NonTaskWorkerParameters)serializer.Deserialize(stream);
if (ntwp.BackgroundLabelProb.Dimension != Mapping.LabelCount)
{
throw new ApplicationException("Unexpected number of labels");
}
BackgroundLabelProb = ntwp.BackgroundLabelProb;
cbccPriors.BackgroundLabelProb = ntwp.BackgroundLabelProb;
if (ntwp.CommunityScoreMatrix.Length != numCommunities)
{
throw new ApplicationException("Unexpected number of communities");
}
if (ntwp.CommunityScoreMatrix[0][0].Dimension != Mapping.LabelCount)
{
throw new ApplicationException("Unexpected number of labels");
}
CommunityScoreMatrix = ntwp.CommunityScoreMatrix;
cbccPriors.CommunityScoreMatrix = ntwp.CommunityScoreMatrix;
if (ntwp.CommunityProb.Dimension != numCommunities)
{
throw new ApplicationException("Unexpected number of communities");
}
CommunityProb = ntwp.CommunityProb;
cbccPriors.CommunityProb = ntwp.CommunityProb;
}
return cbccPriors;
}
BCCPosteriors ToPriors()
{
int numClasses = Mapping.LabelCount;
int numTasks = Mapping.TaskCount;
int numWorkers = Mapping.WorkerCount;
CBCCPosteriors cbccPriors = new CBCCPosteriors();
BCCPosteriors priors = IsCommunityModel ? cbccPriors : new BCCPosteriors();
priors.BackgroundLabelProb = BackgroundLabelProb;
priors.WorkerConfusionMatrix = Util.ArrayInit(numWorkers,
w =>
{
string wid = Mapping.WorkerIndexToId[w];
if (WorkerConfusionMatrix.ContainsKey(wid))
{
return(Util.ArrayInit(numClasses, c => WorkerConfusionMatrix[wid][c]));
}
else
{
return(Util.ArrayInit(numClasses, c => Dirichlet.Uniform(numClasses)));
}
});
priors.TrueLabelConstraint = Util.ArrayInit(numTasks,
t =>
{
string tid = Mapping.TaskIndexToId[t];
if (TrueLabelConstraint.ContainsKey(tid))
{
return(TrueLabelConstraint[Mapping.TaskIndexToId[t]]);
}
else
{
return(Discrete.Uniform(numClasses));
}
});
if (IsCommunityModel)
{
cbccPriors.CommunityConfusionMatrix = CommunityConfusionMatrix;
cbccPriors.WorkerScoreMatrixConstraint = Util.ArrayInit(numWorkers,
w =>
{
string wid = Mapping.WorkerIndexToId[w];
if (WorkerScoreMatrixConstraint.ContainsKey(wid))
{
return(Util.ArrayInit(numClasses, c => WorkerScoreMatrixConstraint[wid][c]));
}
else
{
return(Util.ArrayInit(numClasses, c => VectorGaussian.Uniform(numClasses)));
}
});
cbccPriors.CommunityProb = CommunityProb;
cbccPriors.CommunityScoreMatrix = CommunityScoreMatrix;
cbccPriors.WorkerCommunityConstraint = Util.ArrayInit(numWorkers,
w =>
{
string wid = Mapping.WorkerIndexToId[w];
if (CommunityConstraint.ContainsKey(wid))
{
return(CommunityConstraint[wid]);
}
else
{
return(Discrete.Uniform(CommunityCount));
}
});
}
priors.Evidence = ModelEvidence;
return(priors);
}
/// <summary>
/// Updates the inference results
/// </summary>
/// <param name="posteriors">The inferred posteriors.</param>
/// <param name="mode">The run mode.</param>
protected virtual void UpdateResults(BCCPosteriors posteriors, RunMode mode)
{
if (mode == RunMode.LookAheadExperiment)
{
for (int t = 0; t < posteriors.TrueLabel.Length; t++)
{
LookAheadTrueLabel[Mapping.TaskIndexToId[t]] = posteriors.TrueLabel[t];
}
for (int w = 0; w < posteriors.WorkerConfusionMatrix.Length; w++)
{
LookAheadWorkerConfusionMatrix[Mapping.WorkerIndexToId[w]] = posteriors.WorkerConfusionMatrix[w];
}
}
else if (mode == RunMode.Prediction)
{
for (int w = 0; w < posteriors.WorkerConfusionMatrix.Length; w++)
{
WorkerPrediction[Mapping.WorkerIndexToId[w]] = new Dictionary <string, Discrete>();
for (int tw = 0; tw < posteriors.WorkerPrediction[w].Length; tw++)
{
WorkerPrediction[Mapping.WorkerIndexToId[w]][Mapping.TaskIndexToId[tw]] = posteriors.WorkerPrediction[w][tw];
}
}
}
else
{
// Update results for BCC
BackgroundLabelProb = posteriors.BackgroundLabelProb;
for (int w = 0; w < posteriors.WorkerConfusionMatrix.Length; w++)
{
WorkerConfusionMatrix[Mapping.WorkerIndexToId[w]] = posteriors.WorkerConfusionMatrix[w];
WorkerConfusionMatrixMean[Mapping.WorkerIndexToId[w]] = posteriors.WorkerConfusionMatrix[w].Select(cm => cm.GetMean()).ToArray();
}
for (int t = 0; t < posteriors.TrueLabel.Length; t++)
{
TrueLabel[Mapping.TaskIndexToId[t]] = posteriors.TrueLabel[t];
}
for (int t = 0; t < posteriors.TrueLabelConstraint.Length; t++)
{
TrueLabelConstraint[Mapping.TaskIndexToId[t]] = posteriors.TrueLabelConstraint[t];
}
// Update results for CBCC
if (IsCommunityModel)
{
CBCCPosteriors cbccPosteriors = posteriors as CBCCPosteriors;
CommunityConfusionMatrix = cbccPosteriors.CommunityConfusionMatrix;
for (int w = 0; w < cbccPosteriors.WorkerScoreMatrixConstraint.Length; w++)
{
WorkerScoreMatrixConstraint[Mapping.WorkerIndexToId[w]] = cbccPosteriors.WorkerScoreMatrixConstraint[w];
CommunityConstraint[Mapping.WorkerIndexToId[w]] = cbccPosteriors.WorkerCommunityConstraint[w];
WorkerCommunity[Mapping.WorkerIndexToId[w]] = cbccPosteriors.Community[w];
}
CommunityProb = cbccPosteriors.CommunityProb;
CommunityScoreMatrix = cbccPosteriors.CommunityScoreMatrix;
}
this.ModelEvidence = posteriors.Evidence;
}
}
/// <summary>
/// Infers the posteriors of CBCC using the attached data.
/// </summary>
/// <param name="taskIndices">The matrix of the task indices (columns) of each worker (rows).</param>
/// <param name="workerLabels">The matrix of the labels (columns) of each worker (rows).</param>
/// <param name="priors">The priors.</param>
/// <returns></returns>
public override BCCPosteriors Infer(int[][] taskIndices, int[][] workerLabels, BCCPosteriors priors)
{
var cbccPriors = (CBCCPosteriors)priors;
VectorGaussian[][] scoreConstraint = (cbccPriors == null ? null : cbccPriors.WorkerScoreMatrixConstraint);
Discrete[] communityConstraint = (cbccPriors == null ? null : cbccPriors.WorkerCommunityConstraint);
SetPriors(workerLabels.Length, priors);
AttachData(taskIndices, workerLabels, scoreConstraint, communityConstraint);
var result = new CBCCPosteriors();
Engine.NumberOfIterations = NumberOfIterations;
result.Evidence = Engine.Infer<Bernoulli>(Evidence);
result.BackgroundLabelProb = Engine.Infer<Dirichlet>(BackgroundLabelProb);
result.WorkerConfusionMatrix = Engine.Infer<Dirichlet[][]>(WorkerConfusionMatrix);
result.TrueLabel = Engine.Infer<Discrete[]>(TrueLabel);
result.TrueLabelConstraint = Engine.Infer<Discrete[]>(TrueLabel, QueryTypes.MarginalDividedByPrior);
result.CommunityScoreMatrix = Engine.Infer<VectorGaussian[][]>(CommunityScoreMatrix);
result.CommunityConfusionMatrix = Engine.Infer<Dirichlet[][]>(CommunityConfusionMatrix);
result.WorkerScoreMatrixConstraint = Engine.Infer<VectorGaussian[][]>(ScoreMatrix, QueryTypes.MarginalDividedByPrior);
result.CommunityProb = Engine.Infer<Dirichlet>(CommunityProb);
result.Community = Engine.Infer<Discrete[]>(Community);
result.WorkerCommunityConstraint = Engine.Infer<Discrete[]>(Community, QueryTypes.MarginalDividedByPrior);
return result;
}
