private static Dictionary <string, Dictionary <string, object> > BuildPlotsForAsthmaResults(AllergenData allData, AllergenData data, int[] numClasses, AsthmaModel.Beliefs[] trainingResults)
{
var allergenSensitizationPlots = trainingResults.Select(res => AsthmaPlotData.GetSensitizationPerAllergenPerClass(res, data.Allergens)).ToArray();
var yearSensitizationPlots = trainingResults.Select(res => AsthmaPlotData.GetSensitizationPerYearPerClass(res, data.Allergens)).ToArray();
var sensitizationCounts = trainingResults.Select(res => AsthmaPlotData.GetNumberOfChildrenWithInferredSensitization(res, data.Allergens)).ToArray();
var probGainingSensitivity = trainingResults.Select(res => AsthmaPlotData.GetTransitionProbabilities(res, false, data.Allergens)).ToArray();
var probRetainingSensitivity = trainingResults.Select(res => AsthmaPlotData.GetTransitionProbabilities(res, true, data.Allergens)).ToArray();
var conditionalProbs = trainingResults.Select(res => AsthmaPlotData.GetConditionalProbsOfPositiveTestAsStrings(res)).ToArray();
var outcomePercentagePlots = trainingResults.Select(res => AsthmaPlotData.GetPercentageChildrenWithOutcome(res, allData, new int[] { allData.OutcomeNameToOutcomeIndex["Asthma"] })).ToArray();
var outcomePlusMinusPlots = trainingResults.Select(res => AsthmaPlotData.GetPlusMinusStringChildrenWithOutcome(res, allData, new int[] { allData.OutcomeNameToOutcomeIndex["Asthma"] })).ToArray();
var results = Enumerable.Range(0, numClasses.Length).ToDictionary(
c => "AsthmaResults" + numClasses[c],
c =>
{
var result = new Dictionary <string, object>();
result["AllergenSensitizationPlots"] = (object)allergenSensitizationPlots[c];
result["YearSensitizationPlots"] = (object)yearSensitizationPlots[c];
result["SensitizationPlots"] = (object)sensitizationCounts[c];
result["ProbabilityGainingSensitivity"] = (object)probGainingSensitivity[c];
result["ProbabilityRetainingSensitivity"] = (object)probRetainingSensitivity[c];
result["ConditionalProbsOfPositiveTest"] = conditionalProbs[c];
result["PercentageChildrenWithOutcome"] = outcomePercentagePlots[c];
result["PlusMinusChildrenWithOutcome"] = outcomePlusMinusPlots[c];
return(result);
});
return(results);
}
/// </param>
/// <summary>
/// Runs experiments, takes results and shows them via outputter.
/// </summary>
/// <param name="outputter">A container for experiments output.</param>
/// <param name="numClassesForMultiClassRuns">Numbers of classes to use in experiments.</param>
public static void RunExperiments(Outputter outputter, int[] numClassesForMultipleClassRuns)
{
bool showFactorGraph = false;
Rand.Restart(2);
Console.WriteLine($"\n{Contents.S2TryingOutTheModel.NumberedName}.\n");
Console.WriteLine("Running asthma model on synthetic data.");
Console.WriteLine("Results will slightly differ from those in the book.");
// Synthetic dataset was created using the following call
//DatasetSynthesizer.Synthesize(
// DatasetSynthesizer.DefaultSensitizationClassCollection,
// DatasetSynthesizer.DefaultTests,
// DatasetSynthesizer.DefaultDataMissingProbabilities,
// Path.Combine("Data", "SyntheticDataset.tsv"),
// Rand.Int());
AllergenData allData = new AllergenData();
allData.LoadDataFromTabDelimitedFile(Path.Combine("Data", "SyntheticDataset.tsv"));
outputter.Out(allData, Contents.S2TryingOutTheModel.NumberedName, "Asthma model", "Inputs");
var dataCounts = AsthmaPlotData.GetDataCounts(allData);
outputter.Out(dataCounts, Contents.S2TryingOutTheModel.NumberedName, "Asthma model", "DataCounts");
// Remove mould and peanut allergens from following analysis.
AllergenData data = AllergenData.WithAllergensRemoved(allData, new List <string> {
"Mould", "Peanut"
});
int[] numClasses1Comp = new int[] { 1 };
AsthmaModel.Beliefs[] trainingResults1Comp = RunTraining(data, numClasses1Comp, showFactorGraph);
outputter.Out(trainingResults1Comp, Contents.S2TryingOutTheModel.NumberedName, "Asthma model", "TrainingResults");
// Data for plots
Dictionary <string, Dictionary <string, object> > results1Comp = BuildPlotsForAsthmaResults(allData, data, numClasses1Comp, trainingResults1Comp);
outputter.Out(results1Comp, Contents.S2TryingOutTheModel.NumberedName, "Asthma model", "Plots");
Console.WriteLine($"\n{Contents.S4ModellingWithGates.NumberedName}.\n");
Rand.Restart(1);
var trialResult = RunClinicalTrialExperiment(showFactorGraph);
outputter.Out(trialResult, Contents.S4ModellingWithGates.NumberedName, "ClinicalTrialPlots");
Console.WriteLine($"\n{Contents.S5DiscoveringSensitizationClasses.NumberedName}.\n");
Console.WriteLine("Running asthma model on synthetic data.");
Console.WriteLine("Results will slightly differ from those in the book.");
outputter.Out(allData, Contents.S5DiscoveringSensitizationClasses.NumberedName, "Asthma model", "Inputs");
outputter.Out(dataCounts, Contents.S5DiscoveringSensitizationClasses.NumberedName, "Asthma model", "DataCounts");
Rand.Restart(3); // With this random seed we get exactly 4 classes in models allowing 5 or 6
// provided numClassesForMultipleClassRuns == new int[] { 2, 3, 4, 5, 6 }
// With other seeds we may get additional classes containing very few people, which is normal
AsthmaModel.Beliefs[] trainingResults = RunTraining(data, numClassesForMultipleClassRuns, showFactorGraph);
outputter.Out(trainingResults, Contents.S5DiscoveringSensitizationClasses.NumberedName, "Asthma model", "TrainingResults");
// Data for plots
Dictionary <string, Dictionary <string, object> > results = BuildPlotsForAsthmaResults(allData, data, numClassesForMultipleClassRuns, trainingResults);
outputter.Out(results, Contents.S5DiscoveringSensitizationClasses.NumberedName, "Asthma model", "Plots");
Console.WriteLine("\nCompleted all experiments.");
}
