/* Inference */
public BCCWordsPosteriors InferPosteriors(
int[][] workerLabel, int[][] workerTaskIndex, int[][] words, int[] wordCounts, int[] trueLabels = null,
int numIterations = 35)
{
ObserveCrowdLabels(workerLabel, workerTaskIndex);
ObserveWords(words, wordCounts);
if (trueLabels != null)
{
ObserveTrueLabels(trueLabels);
}
BCCWordsPosteriors posteriors = new BCCWordsPosteriors();
Console.WriteLine("\n***** BCC Words *****\n");
for (int it = 1; it <= numIterations; it++)
{
Engine.NumberOfIterations = it;
posteriors.TrueLabel = Engine.Infer <Discrete[]>(TrueLabel);
posteriors.WorkerConfusionMatrix = Engine.Infer <Dirichlet[][]>(WorkerConfusionMatrix);
posteriors.BackgroundLabelProb = Engine.Infer <Dirichlet>(BackgroundLabelProb);
posteriors.ProbWordPosterior = Engine.Infer <Dirichlet[]>(ProbWord);
Console.WriteLine("Iteration {0}:\t{1:0.0000}", it, posteriors.TrueLabel[0]);
}
posteriors.Evidence = Engine.Infer <Bernoulli>(evidence);
return(posteriors);
}
/// <summary>
/// Runs the majority vote method on the data.
/// </summary>
/// <param name="modelName"></param>
/// <param name="data">The data</param>
/// <param name="mode"></param>
/// <param name="calculateAccuracy">Compute the accuracy (true).</param>
/// <param name="fullData"></param>
/// <param name="model"></param>
/// <param name="useMajorityVote"></param>
/// <param name="useRandomLabel"></param>
/// <returns>The updated results</returns>
public void RunBCCWords(string modelName,
IList <Datum> data,
IList <Datum> fullData,
BCCWords model,
RunMode mode,
bool calculateAccuracy,
bool useMajorityVote = false,
bool useRandomLabel = false)
{
DataMappingWords MappingWords = null;
if (FullMapping == null)
{
FullMapping = new DataMapping(fullData);
}
if (Mapping == null)
{
// Build vocabulary
Console.Write("Building vocabulary...");
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
string[] corpus = data.Select(d => d.BodyText).Distinct().ToArray();
Vocabulary = BuildVocabularyFromCorpus(corpus);
Console.WriteLine("done. Elapsed time: {0}", stopwatch.Elapsed);
// Build data mapping
this.Mapping = new DataMappingWords(data, MappingWords.Vocabulary);
MappingWords = Mapping as DataMappingWords;
this.GoldLabels = MappingWords.GetGoldLabelsPerTaskId();
}
MappingWords = Mapping as DataMappingWords;
int[] trueLabels = null;
if (useMajorityVote)
{
if (MappingWords != null)
{
var majorityLabel = MappingWords.GetMajorityVotesPerTaskId(data);
trueLabels = Util.ArrayInit(FullMapping.TaskCount, i => majorityLabel.ContainsKey(Mapping.TaskIndexToId[i]) ? (int)majorityLabel[Mapping.TaskIndexToId[i]] : Rand.Int(Mapping.LabelMin, Mapping.LabelMax + 1));
data = MappingWords.BuildDataFromAssignedLabels(majorityLabel, data);
}
}
if (useRandomLabel)
{
var randomLabels = MappingWords.GetRandomLabelPerTaskId(data);
data = MappingWords.BuildDataFromAssignedLabels(randomLabels, data);
}
var labelsPerWorkerIndex = MappingWords.GetLabelsPerWorkerIndex(data);
var taskIndicesPerWorkerIndex = MappingWords.GetTaskIndicesPerWorkerIndex(data);
// Create model
ClearResults();
model.CreateModel(MappingWords.TaskCount, MappingWords.LabelCount, MappingWords.WordCount);
// Run model inference
BCCWordsPosteriors posteriors = model.InferPosteriors(labelsPerWorkerIndex, taskIndicesPerWorkerIndex, MappingWords.WordIndicesPerTaskIndex, MappingWords.WordCountsPerTaskIndex, trueLabels);
// Update results
UpdateResults(posteriors, mode);
// Compute accuracy
if (calculateAccuracy)
{
UpdateAccuracy();
}
}