public static void LogisticIrt(int numParams, PriorType priorType, AlgorithmType algType, string conditionPrefix = "")
{
// timing on Intel Core 2 Duo P9500 with 4GB RAM running Windows Vista
// 10_250 trial 1:
// Bayesian/Hierarchical 2000 = 5.4s inference only
// Variational/Hierarchical 50 iter = 4.2s inference only
// Variational/Hierarchical 10 iter = 0.85s inference only
// Variational_JJ/Hierarchical 50 iter = 0.1s inference only
// Variational_JJ/Hierarchical 10 iter = 0.04s inference only
// time on desktop:
// Variational/Hierarchical 10 iter = 0.75s inference only (including test)
// Variational_JJ/Hierarchical 10 iter = 0.07s inference only (including test)
LogisticIrtModel train = new LogisticIrtModel(numParams, priorType);
//train.engine.NumberOfIterations = 100;
//train.engine.ShowTimings = true;
string logistic_type = "";
//logistic_type = "JJ";
if (logistic_type == "JJ")
{
train.engine.Compiler.GivePriorityTo(typeof(LogisticOp_JJ96));
}
bool specialInitialization = false;
if (specialInitialization)
{
// change initialization
train.abilityMean.InitialiseTo(new Gaussian(5, 10));
train.abilityPrecision.InitialiseTo(new Gamma(1, 10));
train.difficultyMean.InitialiseTo(new Gaussian(5, 10));
train.difficultyPrecision.InitialiseTo(new Gamma(1, 10));
}
LogisticIrtTestModel test = new LogisticIrtTestModel(numParams);
train.engine.ShowProgress = false;
test.engine.ShowProgress = false;
if (algType == AlgorithmType.Variational)
{
train.engine.Algorithm = new VariationalMessagePassing();
test.engine.Algorithm = new VariationalMessagePassing();
}
bool showTiming = false;
string baseFolder = @"..\..\";
string modelName = numParams + "-PL";
//modelName = "Mild_skew";
//modelName = "Extreme_skew";
//modelName = "Lsat";
//modelName = "Wide_b";
string modelFolder = baseFolder + @"Data_mat\" + modelName;
DirectoryInfo modelDir = new DirectoryInfo(modelFolder);
foreach (DirectoryInfo conditionDir in modelDir.GetDirectories())
{
string condition = conditionDir.Name;
if (!condition.StartsWith(conditionPrefix))
{
continue;
}
int trimStart = condition.Length - 1;
string inputFolder = baseFolder + @"Data_mat\" + modelName + @"\" + condition;
string alg;
if (algType == AlgorithmType.Variational)
{
alg = "Variational" + logistic_type + @"\" + priorType;
}
else
{
alg = algType + @"\" + priorType;
}
string outputFolder = baseFolder + @"Estimates_mat\" + modelName + @"\" + alg + @"\" + condition;
Console.WriteLine(outputFolder);
DirectoryInfo outputDir = Directory.CreateDirectory(outputFolder);
DirectoryInfo inputDir = new DirectoryInfo(inputFolder);
foreach (FileInfo file in inputDir.GetFiles("*.mat"))
{
string name = file.Name;
string number = name; //.Substring(trimStart);
string outputFileName = outputFolder + @"\" + number;
if (File.Exists(outputFileName))
{
continue;
}
Console.WriteLine(file.FullName);
Dictionary <string, object> dict = MatlabReader.Read(file.FullName);
Matrix m = (Matrix)dict["Y"];
Gaussian[] abilityPost, difficultyPost;
Gamma[] discriminationPost = null;
Beta[] guessProbPost = null;
Matrix responseProbMean;
if (algType != AlgorithmType.MCMC)
{
// VMP
Stopwatch watch = new Stopwatch();
watch.Start();
train.ObserveResponses(m);
train.RunToConvergence();
abilityPost = train.engine.Infer <Gaussian[]>(train.ability);
difficultyPost = train.engine.Infer <Gaussian[]>(train.difficulty);
if (numParams >= 2)
{
discriminationPost = train.engine.Infer <Gamma[]>(train.discrimination);
}
if (numParams >= 3)
{
guessProbPost = train.engine.Infer <Beta[]>(train.guessProb);
}
responseProbMean = test.GetResponseProbs(abilityPost, difficultyPost, discriminationPost, guessProbPost);
watch.Stop();
if (showTiming)
{
Console.WriteLine(algType + " elapsed time = {0}ms", watch.ElapsedMilliseconds);
}
}
else
{
// sampler
LogisticIrtSampler sampler = new LogisticIrtSampler();
sampler.abilityMeanPrior = Gaussian.FromMeanAndVariance(0, 1e6);
sampler.abilityPrecPrior = Gamma.FromShapeAndRate(1, 1);
sampler.difficultyMeanPrior = Gaussian.FromMeanAndVariance(0, 1e6);
sampler.difficultyPrecPrior = Gamma.FromShapeAndRate(1, 1);
sampler.discriminationMeanPrior = Gaussian.FromMeanAndVariance(0, 1e6);
sampler.discriminationPrecPrior = Gamma.FromShapeAndRate(1, 1);
// for debugging
//sampler.abilityObserved = ((Matrix)dict["ability"]).ToArray<double>();
//sampler.difficultyObserved = ((Matrix)dict["difficulty"]).ToArray<double>();
//sampler.discriminationObserved = ((Matrix)dict["discrimination"]).ToArray<double>();
if (train.abilityMean.IsObserved)
{
sampler.abilityMeanPrior = Gaussian.PointMass(train.abilityMean.ObservedValue);
}
if (train.abilityPrecision.IsObserved)
{
sampler.abilityPrecPrior = Gamma.PointMass(train.abilityPrecision.ObservedValue);
}
if (train.difficultyMean.IsObserved)
{
sampler.difficultyMeanPrior = Gaussian.PointMass(train.difficultyMean.ObservedValue);
}
if (train.difficultyPrecision.IsObserved)
{
sampler.difficultyPrecPrior = Gamma.PointMass(train.difficultyPrecision.ObservedValue);
}
if (train.discriminationMean.IsObserved)
{
sampler.discriminationMeanPrior = Gaussian.PointMass(train.discriminationMean.ObservedValue);
}
if (train.discriminationPrecision.IsObserved)
{
sampler.discriminationPrecPrior = Gamma.PointMass(train.discriminationPrecision.ObservedValue);
}
Stopwatch watch = new Stopwatch();
watch.Start();
sampler.Sample(new Options(), m);
abilityPost = sampler.abilityPost;
difficultyPost = sampler.difficultyPost;
responseProbMean = sampler.responseProbMean;
discriminationPost = sampler.discriminationPost;
watch.Stop();
if (showTiming)
{
Console.WriteLine("MCMC elapsed time = {0}ms", watch.ElapsedMilliseconds);
}
}
bool showEstimates = false;
if (showEstimates)
{
Console.WriteLine("abilityMean = {0}", train.engine.Infer(train.abilityMean));
Console.WriteLine("abilityPrecision = {0}", train.engine.Infer(train.abilityPrecision));
//Console.WriteLine("abilityMean2 = {0}", train.engine.Infer(train.abilityMean2));
//Console.WriteLine("abilityPrecision2 = {0}", train.engine.Infer(train.abilityPrecision2));
Console.WriteLine("difficultyMean = {0}", train.engine.Infer(train.difficultyMean));
Console.WriteLine("difficultyPrecision = {0}", train.engine.Infer(train.difficultyPrecision));
}
if (showEstimates)
{
for (int i = 0; i < 10; i++)
{
Console.WriteLine(responseProbMean[i]);
}
//Console.WriteLine(ToMeanMatrix(difficultyPost));
}
using (MatlabWriter writer = new MatlabWriter(outputFileName))
{
writer.Write("ability", ToMeanMatrix(abilityPost));
writer.Write("ability_se", ToStddevMatrix(abilityPost));
writer.Write("difficulty", ToMeanMatrix(difficultyPost));
writer.Write("difficulty_se", ToStddevMatrix(difficultyPost));
if (discriminationPost != null)
{
writer.Write("discrimination", ToMeanMatrix(discriminationPost));
writer.Write("discrimination_se", ToStddevMatrix(discriminationPost));
}
if (guessProbPost != null)
{
writer.Write("guessing", ToMeanAndStddevMatrix(guessProbPost));
}
writer.Write("p", responseProbMean);
}
//break;
}
//break;
}
}
public static void OneShot(PriorType priorType, AlgorithmType algType, Matrix responses, string outputFolder, Options options)
{
Directory.CreateDirectory(outputFolder);
LogisticIrtModel train = new LogisticIrtModel(options.numParams, priorType);
LogisticIrtTestModel test = new LogisticIrtTestModel(options.numParams);
train.engine.Compiler.WriteSourceFiles = false;
test.engine.Compiler.WriteSourceFiles = false;
train.engine.ShowProgress = false;
test.engine.ShowProgress = false;
if (algType == AlgorithmType.Variational)
{
train.engine.Algorithm = new VariationalMessagePassing();
test.engine.Algorithm = new VariationalMessagePassing();
}
Gaussian[] abilityPost, difficultyPost;
Gamma[] discriminationPost = null;
Beta[] guessProbPost = null;
Matrix responseProbMean;
Matrix abilityCred, difficultyCred;
if (algType != AlgorithmType.MCMC)
{
train.ObserveResponses(responses);
train.RunToConvergence();
abilityPost = train.engine.Infer <Gaussian[]>(train.ability);
difficultyPost = train.engine.Infer <Gaussian[]>(train.difficulty);
if (options.numParams >= 2)
{
discriminationPost = train.engine.Infer <Gamma[]>(train.discrimination);
}
if (options.numParams >= 3)
{
guessProbPost = train.engine.Infer <Beta[]>(train.guessProb);
}
responseProbMean = test.GetResponseProbs(abilityPost, difficultyPost, discriminationPost, guessProbPost);
}
else
{ // MCMC
LogisticIrtSampler sampler = new LogisticIrtSampler();
sampler.abilityMeanPrior = Gaussian.FromMeanAndVariance(0, 1e6);
sampler.abilityPrecPrior = Gamma.FromShapeAndRate(1, 1);
sampler.difficultyMeanPrior = Gaussian.FromMeanAndVariance(0, 1e6);
sampler.difficultyPrecPrior = Gamma.FromShapeAndRate(1, 1);
sampler.discriminationMeanPrior = Gaussian.FromMeanAndVariance(0, 1e6);
sampler.discriminationPrecPrior = Gamma.FromShapeAndRate(1, 1);
if (train.abilityMean.IsObserved)
{
sampler.abilityMeanPrior = Gaussian.PointMass(train.abilityMean.ObservedValue);
}
if (train.abilityPrecision.IsObserved)
{
sampler.abilityPrecPrior = Gamma.PointMass(train.abilityPrecision.ObservedValue);
}
if (train.difficultyMean.IsObserved)
{
sampler.difficultyMeanPrior = Gaussian.PointMass(train.difficultyMean.ObservedValue);
}
if (train.difficultyPrecision.IsObserved)
{
sampler.difficultyPrecPrior = Gamma.PointMass(train.difficultyPrecision.ObservedValue);
}
if (train.discriminationMean.IsObserved)
{
sampler.discriminationMeanPrior = Gaussian.PointMass(train.discriminationMean.ObservedValue);
}
if (train.discriminationPrecision.IsObserved)
{
sampler.discriminationPrecPrior = Gamma.PointMass(train.discriminationPrecision.ObservedValue);
}
sampler.Sample(options, responses);
abilityPost = sampler.abilityPost;
difficultyPost = sampler.difficultyPost;
responseProbMean = sampler.responseProbMean;
discriminationPost = sampler.discriminationPost;
abilityCred = sampler.abilityCred;
difficultyCred = sampler.difficultyCred;
WriteMatrix(abilityCred, outputFolder + @"\ability_ci.txt");
WriteMatrix(difficultyCred, outputFolder + @"\difficulty_ci.txt");
}
bool showEstimates = false;
if (showEstimates)
{
Console.WriteLine("abilityMean = {0}", train.engine.Infer(train.abilityMean));
Console.WriteLine("abilityPrecision = {0}", train.engine.Infer(train.abilityPrecision));
//Console.WriteLine("abilityMean2 = {0}", train.engine.Infer(train.abilityMean2));
//Console.WriteLine("abilityPrecision2 = {0}", train.engine.Infer(train.abilityPrecision2));
Console.WriteLine("difficultyMean = {0}", train.engine.Infer(train.difficultyMean));
Console.WriteLine("difficultyPrecision = {0}", train.engine.Infer(train.difficultyPrecision));
}
WriteMatrix(ToMeanMatrix(abilityPost), outputFolder + @"\ability.txt");
WriteMatrix(ToStddevMatrix(abilityPost), outputFolder + @"\ability_se.txt");
WriteMatrix(ToMeanMatrix(difficultyPost), outputFolder + @"\difficulty.txt");
WriteMatrix(ToStddevMatrix(difficultyPost), outputFolder + @"\difficulty_se.txt");
WriteMatrix(responseProbMean, outputFolder + @"\p.txt");
if (discriminationPost != null)
{
WriteMatrix(ToMeanMatrix(discriminationPost), outputFolder + @"\discrimination.txt");
WriteMatrix(ToStddevMatrix(discriminationPost), outputFolder + @"\discrimination_se.txt");
}
if (guessProbPost != null)
{
WriteMatrix(ToMeanMatrix(guessProbPost), outputFolder + @"\guess.txt");
WriteMatrix(ToStddevMatrix(guessProbPost), outputFolder + @"\guess_se.txt");
}
}
