/// <summary>
/// Initializes a new instance of the <see cref="ToyDataRunner"/> class.
/// </summary>
/// <param name="trainModel">Train model.</param>
/// <param name="testModel">Test model.</param>
public static void Run(BinaryModel trainModel, BinaryModel testModel, bool testTransfer, bool testActive, bool testActiveTransfer)
{
var phase1PriorMean = new Gaussian(4, 1);
var phase1PriorPrecision = new Gamma(1, 1);
var phase2PriorMean = new Gaussian(4, 1);
var phase2PriorPrecision = new Gamma(1, 1);
// Generate data for 5 individuals
var data = new List<ToyData>();
for (int i = 0; i < 3; i++)
{
var toy = new ToyData
{
// NumberOfInstances = 200,
// NumberOfHoldoutInstances = i == 0 ? 0 : 1000,
NumberOfResidents = 5,
NumberOfFeatures = NumberOfFeatures,
NumberOfActivities = 2,
UseBias = false,
TruePriorMean = i == 0 ? phase1PriorMean : phase2PriorMean,
TruePriorPrecision = i == 0 ? phase1PriorPrecision : phase2PriorPrecision
};
toy.Generate(i == 2 ? NoisyExampleProportion : 0.0, 200);
if (i != 0)
{
// no need for holdout data in training set
toy.Generate(0.0, 1000, true);
}
data.Add(toy);
}
var priors = new Marginals
{
WeightMeans = DistributionArrayHelpers.CreateGaussianArray(NumberOfFeatures, 0, 1).ToArray(),
WeightPrecisions = DistributionArrayHelpers.CreateGammaArray(NumberOfFeatures, 1, 1).ToArray()
};
Console.WriteLine("Data Generated");
// TODO: Create meta-features that allow us to do the first form of transfer learning
// Train the community model
Console.WriteLine("Training Community Model");
var communityExperiment = new Experiment { TrainModel = trainModel, TestModel = testModel, Name = "Community" };
communityExperiment.RunBatch(data[0].DataSet, priors);
// PrintWeightPriors(communityExperiment.Posteriors, trainData.CommunityWeights);
// Utils.PlotPosteriors(communityExperiment.Posteriors.Weights, data[0].Weights);
// Utils.PlotPosteriors(communityExperiment.Posteriors.WeightMeans, communityExperiment.Posteriors.WeightPrecisions, null, "Community weights", "Feature");
// return;
if (testTransfer)
{
// Do online learning
// Console.WriteLine("Testing Online Model");
var onlineExperiment = new Experiment { TrainModel = trainModel, TestModel = testModel, Name = "Online" };
onlineExperiment.RunOnline(data[1].DataSet, data[1].HoldoutSet, priors);
// Do transfer learning
// Console.WriteLine("Testing Community Model");
var personalisationExperiment = new Experiment { TrainModel = trainModel, TestModel = testModel, Name = "Community" };
personalisationExperiment.RunOnline(data[1].DataSet, data[1].HoldoutSet, communityExperiment.Posteriors);
// Plot cumulative metrics
Utils.PlotCumulativeMetrics(new[] { onlineExperiment, personalisationExperiment }, "Toy Transfer");
}
else
{
Console.WriteLine("Skipping Transfer Learning");
}
// ACTIVE MODEL
if (testActive)
{
ActiveTransfer(trainModel, testModel, data, "Toy Active", priors);
}
else
{
Console.WriteLine("Skipping Active Learning");
}
if (testActiveTransfer)
{
Console.WriteLine("Note that the transfer learning is very effective here, so the active learning doesn't add much");
ActiveTransfer(trainModel, testModel, data, "Toy Active Transfer", communityExperiment.Posteriors);
}
else
{
Console.WriteLine("Skipping Active Transfer Learning");
}
// Now create different costs for acquiring labels - want to demonstrate that we choose from all 3 possible labels
}
/// <summary>
/// Initializes a new instance of the <see cref="ToyDataRunner"/> class.
/// </summary>
/// <param name="trainModel">Train model.</param>
/// <param name="testModel">Test model.</param>
public static void Run(BinaryModel trainModel, BinaryModel testModel, bool testTransfer, bool testActive, bool testActiveTransfer)
{
var phase1PriorMean = new Gaussian(4, 1);
var phase1PriorPrecision = new Gamma(1, 1);
var phase2PriorMean = new Gaussian(4, 1);
var phase2PriorPrecision = new Gamma(1, 1);
// Generate data for 5 individuals
var data = new List <ToyData>();
for (int i = 0; i < 3; i++)
{
var toy = new ToyData
{
// NumberOfInstances = 200,
// NumberOfHoldoutInstances = i == 0 ? 0 : 1000,
NumberOfResidents = 5,
NumberOfFeatures = NumberOfFeatures,
NumberOfActivities = 2,
UseBias = false,
TruePriorMean = i == 0 ? phase1PriorMean : phase2PriorMean,
TruePriorPrecision = i == 0 ? phase1PriorPrecision : phase2PriorPrecision
};
toy.Generate(i == 2 ? NoisyExampleProportion : 0.0, 200);
if (i != 0)
{
// no need for holdout data in training set
toy.Generate(0.0, 1000, true);
}
data.Add(toy);
}
var priors = new Marginals
{
WeightMeans = DistributionArrayHelpers.CreateGaussianArray(NumberOfFeatures, 0, 1).ToArray(),
WeightPrecisions = DistributionArrayHelpers.CreateGammaArray(NumberOfFeatures, 1, 1).ToArray()
};
Console.WriteLine("Data Generated");
// TODO: Create meta-features that allow us to do the first form of transfer learning
// Train the community model
Console.WriteLine("Training Community Model");
var communityExperiment = new Experiment {
TrainModel = trainModel, TestModel = testModel, Name = "Community"
};
communityExperiment.RunBatch(data[0].DataSet, priors);
// PrintWeightPriors(communityExperiment.Posteriors, trainData.CommunityWeights);
// Utils.PlotPosteriors(communityExperiment.Posteriors.Weights, data[0].Weights);
// Utils.PlotPosteriors(communityExperiment.Posteriors.WeightMeans, communityExperiment.Posteriors.WeightPrecisions, null, "Community weights", "Feature");
// return;
if (testTransfer)
{
// Do online learning
// Console.WriteLine("Testing Online Model");
var onlineExperiment = new Experiment {
TrainModel = trainModel, TestModel = testModel, Name = "Online"
};
onlineExperiment.RunOnline(data[1].DataSet, data[1].HoldoutSet, priors);
// Do transfer learning
// Console.WriteLine("Testing Community Model");
var personalisationExperiment = new Experiment {
TrainModel = trainModel, TestModel = testModel, Name = "Community"
};
personalisationExperiment.RunOnline(data[1].DataSet, data[1].HoldoutSet, communityExperiment.Posteriors);
// Plot cumulative metrics
Utils.PlotCumulativeMetrics(new[] { onlineExperiment, personalisationExperiment }, "Toy Transfer");
}
else
{
Console.WriteLine("Skipping Transfer Learning");
}
// ACTIVE MODEL
if (testActive)
{
ActiveTransfer(trainModel, testModel, data, "Toy Active", priors);
}
else
{
Console.WriteLine("Skipping Active Learning");
}
if (testActiveTransfer)
{
Console.WriteLine("Note that the transfer learning is very effective here, so the active learning doesn't add much");
ActiveTransfer(trainModel, testModel, data, "Toy Active Transfer", communityExperiment.Posteriors);
}
else
{
Console.WriteLine("Skipping Active Transfer Learning");
}
// Now create different costs for acquiring labels - want to demonstrate that we choose from all 3 possible labels
}
