static private ClickModelMarginals Model2(int numLabels, bool allowNoExams)
{
// Inference engine must be EP because of the ConstrainBetween constraint
InferenceEngine engine = new InferenceEngine();
if (!(engine.Algorithm is ExpectationPropagation))
{
Console.WriteLine("This example only runs with Expectation Propagation");
return(null);
}
engine.NumberOfIterations = 10;
// Includes lower and upper bounds
int numThresholds = numLabels + 1;
// Partition the dat into chunks to improve the schedule
int chunkSize = 200;
// Maximum number of passes through the data
int maxPasses = 5;
// The marginals at any given stage.
ClickModelMarginals marginals = new ClickModelMarginals(numLabels);
// Compare the marginals with the previous marginals to create
// a convergence criterion
Gaussian prevMargScoreMean;
Gamma prevMargJudgePrec;
Gamma prevMargClickPrec;
double convergenceThresh = 0.01;
// Get the arrays of human judgement labels, clicks, and examinations
int[] labels;
int[] clicks;
int[] exams;
string fileName = @"data\ClickModel.txt";
if (!File.Exists(fileName))
{
fileName = @"..\Samples\C#\ExamplesBrowser\data\ClickModel.txt";
}
LoadData(fileName, allowNoExams, out labels, out clicks, out exams);
// Convert the raw click data into uncertain Gaussian observations chunk-by-chunk
Gaussian[][][] allObs = getClickObservations(numLabels, chunkSize, labels, clicks, exams);
int numChunks = allObs.Length;
//-------------------------------------------------------------
// Specify prior distributions
//-------------------------------------------------------------
Gaussian priorScoreMean = Gaussian.FromMeanAndVariance(0.5, 1.0);
Gamma priorScorePrec = Gamma.FromMeanAndVariance(2.0, 0.0);
Gamma priorJudgePrec = Gamma.FromMeanAndVariance(2.0, 1.0);
Gamma priorClickPrec = Gamma.FromMeanAndVariance(2.0, 1.0);
Gaussian[] priorThresholds;
CreateThresholdPriors(numLabels, out priorThresholds);
//-----------------------------------------------------
// Create shared variables - these are the variables
// which are shared between all chunks
//-----------------------------------------------------
Model model = new Model(numChunks);
SharedVariable <double> scoreMean = SharedVariable <double> .Random(priorScoreMean).Named("scoreMean");
SharedVariable <double> scorePrec = SharedVariable <double> .Random(priorScorePrec).Named("scorePrec");
SharedVariable <double> judgePrec = SharedVariable <double> .Random(priorJudgePrec).Named("judgePrec");
SharedVariable <double> clickPrec = SharedVariable <double> .Random(priorClickPrec).Named("clickPrec");
SharedVariable <double>[] thresholds = new SharedVariable <double> [numThresholds];
for (int t = 0; t < numThresholds; t++)
{
thresholds[t] = SharedVariable <double> .Random(priorThresholds[t]).Named("threshold" + t);
}
//----------------------------------------------------------------------------------
// The model
//----------------------------------------------------------------------------------
// Gaussian click observations are given to the model - one set of observations
// per label class. Also the number of observations per label class is given to the model
VariableArray <Gaussian>[] observationDistribs = new VariableArray <Gaussian> [numLabels];
Variable <int>[] numberOfObservations = new Variable <int> [numLabels];
// For each label, and each observation (consisting of a human judgement and
// a Gaussian click observation), there is a latent score variable, a judgement
// score variable, and a click score variable
for (int i = 0; i < numLabels; i++)
{
numberOfObservations[i] = Variable.New <int>().Named("NumObs" + i);
Range r = new Range(numberOfObservations[i]).Named("N" + i);
observationDistribs[i] = Variable.Array <Gaussian>(r).Named("Obs" + i);
VariableArray <double> scores = Variable.Array <double>(r).Named("Scores" + i);
VariableArray <double> scoresJ = Variable.Array <double>(r).Named("ScoresJ" + i);
VariableArray <double> scoresC = Variable.Array <double>(r).Named("ScoresC" + i);
scores[r] = Variable.GaussianFromMeanAndPrecision(scoreMean.GetCopyFor(model), scorePrec.GetCopyFor(model)).ForEach(r);
scoresJ[r] = Variable.GaussianFromMeanAndPrecision(scores[r], judgePrec.GetCopyFor(model));
scoresC[r] = Variable.GaussianFromMeanAndPrecision(scores[r], clickPrec.GetCopyFor(model));
Variable.ConstrainEqualRandom(scoresC[r], observationDistribs[i][r]);
Variable.ConstrainBetween(scoresJ[r], thresholds[i].GetCopyFor(model), thresholds[i + 1].GetCopyFor(model));
}
//----------------------------------------------------------
// Outer loop iterates over a number of passes
// Inner loop iterates over the unique labels
//----------------------------------------------------------
Console.WriteLine("Training: sample size: " + labels.Length + "\n");
for (int pass = 0; pass < maxPasses; pass++)
{
prevMargScoreMean = marginals.marginalScoreMean;
prevMargJudgePrec = marginals.marginalJudgePrec;
prevMargClickPrec = marginals.marginalClickPrec;
for (int c = 0; c < numChunks; c++)
{
for (int i = 0; i < numLabels; i++)
{
numberOfObservations[i].ObservedValue = allObs[c][i].Length;
observationDistribs[i].ObservedValue = allObs[c][i];
}
model.InferShared(engine, c);
// Retrieve marginals
marginals.marginalScoreMean = scoreMean.Marginal <Gaussian>();
marginals.marginalScorePrec = scorePrec.Marginal <Gamma>();
marginals.marginalJudgePrec = judgePrec.Marginal <Gamma>();
marginals.marginalClickPrec = clickPrec.Marginal <Gamma>();
for (int i = 0; i < numThresholds; i++)
{
marginals.marginalThresh[i] = thresholds[i].Marginal <Gaussian>();
}
Console.WriteLine("\n****** Pass {0}, chunk {1} ******", pass, c);
Console.WriteLine("----- Marginals -----");
Console.WriteLine("scoreMean = {0}", marginals.marginalScoreMean);
Console.WriteLine("scorePrec = {0}", marginals.marginalScorePrec);
Console.WriteLine("judgePrec = {0}", marginals.marginalJudgePrec);
Console.WriteLine("clickPrec = {0}", marginals.marginalClickPrec);
for (int t = 0; t < numThresholds; t++)
{
Console.WriteLine("threshMean {0} = {1}", t, marginals.marginalThresh[t]);
}
}
// Test for convergence
if (marginals.marginalScoreMean.MaxDiff(prevMargScoreMean) < convergenceThresh &&
marginals.marginalJudgePrec.MaxDiff(prevMargJudgePrec) < convergenceThresh &&
marginals.marginalClickPrec.MaxDiff(prevMargClickPrec) < convergenceThresh)
{
Console.WriteLine("\n****** Inference converged ******\n");
break;
}
}
return(marginals);
}
private void LearnAPIClick5LabelModel(
int numLabels,
bool learnScoreMean,
bool learnScorePrec,
bool learnJudgePrec,
bool learnClickPrec,
bool learnThresholds,
double nominalScoreMean,
double nominalScorePrec,
double nominalJudgePrec,
double nominalClickPrec,
int[] labels,
int[] clicks,
int[] exams,
int chunkSize,
int nPasses,
bool printToConsole,
out Gaussian margScoreMean,
out Gamma margScorePrec,
out Gamma margJudgePrec,
out Gamma margClickPrec,
out Gaussian[] margThresh)
{
//------------------------------------------------------
// Observations
//------------------------------------------------------
Gaussian[][][] allObs = getClickObservations(numLabels, chunkSize, labels, clicks, exams);
int numChunks = allObs.Length;
////-------------------------------------------------------------
//// Prior distributions
////-------------------------------------------------------------
Gaussian priorScoreMean = Gaussian.FromMeanAndVariance(nominalScoreMean, learnScoreMean ? 1 : 0);
Gamma priorScorePrec = Gamma.FromMeanAndVariance(nominalScorePrec, learnScorePrec ? 1 : 0);
Gamma priorJudgePrec = Gamma.FromMeanAndVariance(nominalJudgePrec, learnJudgePrec ? 1 : 0);
Gamma priorClickPrec = Gamma.FromMeanAndVariance(nominalClickPrec, learnClickPrec ? 1 : 0);
Gaussian[] priorThreshMean;
CalculatePriors(learnThresholds, numLabels, out priorThreshMean);
////-----------------------------------------------------
//// Creates shared variables
////-----------------------------------------------------
int numThresholds = numLabels + 1;
SharedVariable <double> scoreMean = SharedVariable <double> .Random(priorScoreMean).Named("scoreMean");
SharedVariable <double> scorePrec = SharedVariable <double> .Random(priorScorePrec).Named("scorePrec");
SharedVariable <double> judgePrec = SharedVariable <double> .Random(priorJudgePrec).Named("judgePrec");
SharedVariable <double> clickPrec = SharedVariable <double> .Random(priorClickPrec).Named("clickPrec");
SharedVariable <double>[] thresholds = new SharedVariable <double> [numThresholds];
for (int t = 0; t < numThresholds; t++)
{
thresholds[t] = SharedVariable <double> .Random(priorThreshMean[t]).Named("threshMeans" + t);
}
//----------------------------------------------------------------------------------
// The model
//----------------------------------------------------------------------------------
Model model = new Model(numChunks);
VariableArray <Gaussian>[] clickObs = new VariableArray <Gaussian> [numLabels];
Variable <int>[] clickObsLength = new Variable <int> [numLabels];
for (int i = 0; i < numLabels; i++)
{
clickObsLength[i] = Variable.New <int>().Named("clickObsLength" + i);
Range r = new Range(clickObsLength[i]).Named("dataCount" + i);
clickObs[i] = Variable.Array <Gaussian>(r).Named("Obs" + i);
VariableArray <double> scores = Variable.Array <double>(r).Named("scores" + i);
VariableArray <double> scoresJ = Variable.Array <double>(r).Named("scoresJ" + i);
VariableArray <double> scoresC = Variable.Array <double>(r).Named("scoresC" + i);
scores[r] = Variable <double> .GaussianFromMeanAndPrecision(scoreMean.GetCopyFor(model), scorePrec.GetCopyFor(model)).ForEach(r);
scoresJ[r] = Variable <double> .GaussianFromMeanAndPrecision(scores[r], judgePrec.GetCopyFor(model));
scoresC[r] = Variable <double> .GaussianFromMeanAndPrecision(scores[r], clickPrec.GetCopyFor(model));
Variable.ConstrainBetween(scoresJ[r], thresholds[i].GetCopyFor(model), thresholds[i + 1].GetCopyFor(model));
Variable.ConstrainEqualRandom <double, Gaussian>(scoresC[r], clickObs[i][r]);
r.AddAttribute(new Sequential());
}
InferenceEngine engine = new InferenceEngine();
//----------------------------------------------------------
// Outer loop iterates over a number of passes
// Inner loop iterates over the unique labels
//----------------------------------------------------------
for (int pass = 0; pass < nPasses; pass++)
{
for (int c = 0; c < numChunks; c++)
{
for (int i = 0; i < numLabels; i++)
{
clickObsLength[i].ObservedValue = allObs[c][i].Length;
clickObs[i].ObservedValue = allObs[c][i];
}
// Infer the output messages
model.InferShared(engine, c);
if (printToConsole)
{
margScoreMean = scoreMean.Marginal <Gaussian>();
margScorePrec = scorePrec.Marginal <Gamma>();
margJudgePrec = judgePrec.Marginal <Gamma>();
margClickPrec = clickPrec.Marginal <Gamma>();
margThresh = new Gaussian[numThresholds];
for (int i = 0; i < numThresholds; i++)
{
margThresh[i] = thresholds[i].Marginal <Gaussian>();
}
Console.WriteLine("****** Pass {0}, chunk {1} ******", pass, c);
Console.WriteLine("----- Marginals -----");
Console.WriteLine("scoreMean = {0}", margScoreMean);
Console.WriteLine("scorePrec = {0}", margScorePrec);
Console.WriteLine("judgePrec = {0}", margJudgePrec);
Console.WriteLine("clickPrec = {0}", margClickPrec);
for (int t = 0; t < numThresholds; t++)
{
Console.WriteLine("threshMean {0} = {1}", t, margThresh[t]);
}
}
}
}
margScoreMean = scoreMean.Marginal <Gaussian>();
margScorePrec = scorePrec.Marginal <Gamma>();
margJudgePrec = judgePrec.Marginal <Gamma>();
margClickPrec = clickPrec.Marginal <Gamma>();
margThresh = new Gaussian[numThresholds];
for (int i = 0; i < numThresholds; i++)
{
margThresh[i] = thresholds[i].Marginal <Gaussian>();
}
}
/// <summary>
/// Constructs an LDA model
/// </summary>
/// <param name="sizeVocab">Size of vocabulary</param>
/// <param name="numTopics">Number of topics</param>
public LDAShared(int numBatches, int sizeVocab, int numTopics)
{
SizeVocab = sizeVocab;
NumTopics = numTopics;
ThetaSparsity = Sparsity.Dense;
PhiSparsity = Sparsity.ApproximateWithTolerance(0.00000000001); // Allow for round-off error
NumDocuments = Variable.New <int>().Named("NumDocuments");
NumBatches = numBatches;
IterationsPerPass = new int[] { 1, 3, 5, 7, 9 };
//---------------------------------------------
// The model
//---------------------------------------------
Range D = new Range(NumDocuments).Named("D");
Range W = new Range(SizeVocab).Named("W");
Range T = new Range(NumTopics).Named("T");
NumWordsInDoc = Variable.Array <int>(D).Named("NumWordsInDoc");
Range WInD = new Range(NumWordsInDoc[D]).Named("WInD");
Evidence = SharedVariable <bool> .Random(new Bernoulli(0.5)).Named("Evidence");
Evidence.IsEvidenceVariable = true;
Phi = SharedVariable <Vector> .Random(T, CreateUniformDirichletArray(numTopics, sizeVocab, PhiSparsity)).Named("Phi");
// Phi definition sub-model - just one copy
PhiDefModel = new Model(1).Named("PhiDefModel");
IfBlock evidencePhiDefBlock = null;
EvidencePhiDef = Evidence.GetCopyFor(PhiDefModel).Named("EvidencePhiDef");
evidencePhiDefBlock = Variable.If(EvidencePhiDef);
PhiDef = Variable.Array <Vector>(T).Named("PhiDef");
PhiDef.SetSparsity(PhiSparsity);
PhiDef.SetValueRange(W);
PhiPrior = Variable.Array <Dirichlet>(T).Named("PhiPrior");
PhiDef[T] = Variable <Vector> .Random(PhiPrior[T]);
Phi.SetDefinitionTo(PhiDefModel, PhiDef);
evidencePhiDefBlock.CloseBlock();
// Document sub-model - many copies
DocModel = new Model(numBatches).Named("DocModel");
IfBlock evidenceDocBlock = null;
EvidenceDoc = Evidence.GetCopyFor(DocModel).Named("EvidenceDoc");
evidenceDocBlock = Variable.If(EvidenceDoc);
Theta = Variable.Array <Vector>(D).Named("Theta");
Theta.SetSparsity(ThetaSparsity);
Theta.SetValueRange(T);
ThetaPrior = Variable.Array <Dirichlet>(D).Named("ThetaPrior");
Theta[D] = Variable <Vector> .Random(ThetaPrior[D]);
PhiDoc = Phi.GetCopyFor(DocModel);
PhiDoc.AddAttribute(new MarginalPrototype(Dirichlet.Uniform(sizeVocab, PhiSparsity)));
Words = Variable.Array(Variable.Array <int>(WInD), D).Named("Words");
WordCounts = Variable.Array(Variable.Array <double>(WInD), D).Named("WordCounts");
using (Variable.ForEach(D))
{
using (Variable.ForEach(WInD))
{
using (Variable.Repeat(WordCounts[D][WInD]))
{
Variable <int> topic = Variable.Discrete(Theta[D]).Named("topic");
using (Variable.Switch(topic))
{
Words[D][WInD] = Variable.Discrete(PhiDoc[topic]);
}
}
}
}
evidenceDocBlock.CloseBlock();
// Initialization to break symmetry
ThetaInit = Variable.Array <Dirichlet>(D).Named("ThetaInit");
Theta[D].InitialiseTo(ThetaInit[D]);
EnginePhiDef = new InferenceEngine(new VariationalMessagePassing());
EnginePhiDef.Compiler.ShowWarnings = false;
EnginePhiDef.ModelName = "LDASharedPhiDef";
Engine = new InferenceEngine(new VariationalMessagePassing());
Engine.OptimiseForVariables = new IVariable[] { Theta, PhiDoc, EvidenceDoc };
Engine.Compiler.ShowWarnings = false;
Engine.ModelName = "LDAShared";
Engine.Compiler.ReturnCopies = false;
Engine.Compiler.FreeMemory = true;
}
/// <summary>
/// Constructs an LDA model
/// </summary>
/// <param name="sizeVocab">Size of vocabulary</param>
/// <param name="numTopics">Number of topics</param>
public LDAShared(int numBatches, int sizeVocab, int numTopics)
{
SizeVocab = sizeVocab;
NumTopics = numTopics;
ThetaSparsity = Sparsity.Dense;
PhiSparsity = Sparsity.ApproximateWithTolerance(0.00000000001); // Allow for round-off error
NumDocuments = Variable.New<int>().Named("NumDocuments");
NumBatches = numBatches;
IterationsPerPass = new int[] { 1, 3, 5, 7, 9 };
//---------------------------------------------
// The model
//---------------------------------------------
Range D = new Range(NumDocuments).Named("D");
Range W = new Range(SizeVocab).Named("W");
Range T = new Range(NumTopics).Named("T");
NumWordsInDoc = Variable.Array<int>(D).Named("NumWordsInDoc");
Range WInD = new Range(NumWordsInDoc[D]).Named("WInD");
Evidence = SharedVariable<bool>.Random(new Bernoulli(0.5)).Named("Evidence");
Evidence.IsEvidenceVariable = true;
Phi = SharedVariable<Vector>.Random(T, CreateUniformDirichletArray(numTopics, sizeVocab, PhiSparsity)).Named("Phi");
// Phi definition sub-model - just one copy
PhiDefModel = new Model(1).Named("PhiDefModel");
IfBlock evidencePhiDefBlock = null;
EvidencePhiDef = Evidence.GetCopyFor(PhiDefModel).Named("EvidencePhiDef");
evidencePhiDefBlock = Variable.If(EvidencePhiDef);
PhiDef = Variable.Array<Vector>(T).Named("PhiDef");
PhiDef.SetSparsity(PhiSparsity);
PhiDef.SetValueRange(W);
PhiPrior = Variable.Array<Dirichlet>(T).Named("PhiPrior");
PhiDef[T] = Variable<Vector>.Random(PhiPrior[T]);
Phi.SetDefinitionTo(PhiDefModel, PhiDef);
evidencePhiDefBlock.CloseBlock();
// Document sub-model - many copies
DocModel = new Model(numBatches).Named("DocModel");
IfBlock evidenceDocBlock = null;
EvidenceDoc = Evidence.GetCopyFor(DocModel).Named("EvidenceDoc");
evidenceDocBlock = Variable.If(EvidenceDoc);
Theta = Variable.Array<Vector>(D).Named("Theta");
Theta.SetSparsity(ThetaSparsity);
Theta.SetValueRange(T);
ThetaPrior = Variable.Array<Dirichlet>(D).Named("ThetaPrior");
Theta[D] = Variable<Vector>.Random(ThetaPrior[D]);
PhiDoc = Phi.GetCopyFor(DocModel);
PhiDoc.AddAttribute(new MarginalPrototype(Dirichlet.Uniform(sizeVocab, PhiSparsity)));
Words = Variable.Array(Variable.Array<int>(WInD), D).Named("Words");
WordCounts = Variable.Array(Variable.Array<double>(WInD), D).Named("WordCounts");
using (Variable.ForEach(D))
{
using (Variable.ForEach(WInD))
{
using (Variable.Repeat(WordCounts[D][WInD]))
{
Variable<int> topic = Variable.Discrete(Theta[D]).Named("topic");
using (Variable.Switch(topic))
Words[D][WInD] = Variable.Discrete(PhiDoc[topic]);
}
}
}
evidenceDocBlock.CloseBlock();
// Initialization to break symmetry
ThetaInit = Variable.New<IDistribution<Vector[]>>().Named("ThetaInit");
Theta.InitialiseTo(ThetaInit);
EnginePhiDef = new InferenceEngine(new VariationalMessagePassing());
EnginePhiDef.Compiler.ShowWarnings = false;
EnginePhiDef.ModelName = "LDASharedPhiDef";
Engine = new InferenceEngine(new VariationalMessagePassing());
Engine.OptimiseForVariables = new IVariable[] { Theta, PhiDoc, EvidenceDoc };
Engine.Compiler.ShowWarnings = false;
Engine.ModelName = "LDAShared";
Engine.Compiler.ReturnCopies = false;
Engine.Compiler.FreeMemory = true;
}