private void replaceEnvironment(ITerminator terminator, TestEnvironment environment)
{
Type typeInQuestion = typeof(Terminator);
FieldInfo field = typeInQuestion.GetField("_environment", BindingFlags.NonPublic | BindingFlags.Instance);
field.SetValue(terminator, environment);
}
public void UpdateTerminator(ITerminator terminator)
{
if (terminator != null)
{
_terminator = terminator;
}
}
public void Launch()
{
if (generations != null)
{
generations.Reset();
}
if (setup == null)
{
setup = GetComponent <SetupScript>();
}
ui = GetComponent <UIManager>();
cars = setup.Setup();
carStates = new Dictionary <GameObject, CarState>();
carExecutors = new Dictionary <GameObject, GeneExecutor>();
generations = GetComponent <GenerationDB>();
fitness = (IFitnessFunction)System.Activator.CreateInstance(setup.FitnessFunctions.Where(type => type.Name.Equals(selectedFitnessFunction)).First());
terminator = (ITerminator)System.Activator.CreateInstance(setup.Terminators.Where(type => type.Name.Equals(selectedTerminator)).First());
mutator = (IMutator)System.Activator.CreateInstance(setup.Mutators.Where(type => type.Name.Equals(selectedMutator)).First());
foreach (string genetype in selectedGenes)
{
mutator.AssignGene(((IGene)System.Activator.CreateInstance(setup.GeneTypes.Where(type => type.Name.Equals(genetype)).First())).ID);
}
selector = (ISelector)System.Activator.CreateInstance(setup.Selectors.Where(type => type.Name.Equals(selectedSelector)).First());
recombiner = (IRecombiner)System.Activator.CreateInstance(setup.Recombiners.Where(type => type.Name.Equals(selectedRecombiner)).First());
init = (IInitializer)System.Activator.CreateInstance(setup.Initializers.Where(type => type.Name.Equals(selectedInitializer)).First());
foreach (string genetype in selectedGenes)
{
init.AssignGene(((IGene)System.Activator.CreateInstance(setup.GeneTypes.Where(type => type.Name.Equals(genetype)).First())).ID);
}
SetupCars();
simulation = StartCoroutine(FullSimulation());
}
/// <summary>
/// Initialise a new instance of the GeneticEngine class with the supplied plug-ins and populate the initial generation.
/// </summary>
/// <param name="populator">The populator plug-in. Generates the initial population.</param>
/// <param name="evaluator">The evaluator plug-in. Provides the fitness function.</param>
/// <param name="geneticOperator">The genetic operator plug-in. Processes one generation to produce the individuals for the next.</param>
/// <param name="terminator">The terminator plug-in. Provides the termination condition.</param>
/// <param name="outputter">The outputter plug-in or null for no output. Outputs each generation.</param>
/// <param name="generationFactory">The generation factory plug-in or null to use the default. Creates the generation container.</param>
public GeneticEngine(IPopulator populator, IEvaluator evaluator, IGeneticOperator geneticOperator, ITerminator terminator, IOutputter outputter = null, IGenerationFactory generationFactory = null)
{
if (populator == null)
{
throw new GeneticEngineException("populator must not be null");
}
if (evaluator == null)
{
throw new GeneticEngineException("pvaluator must not be null");
}
if (geneticOperator == null)
{
throw new GeneticEngineException("geneticOperator must not be null");
}
if (terminator == null)
{
throw new GeneticEngineException("terminator must not be null");
}
this.populator = populator;
this.evaluator = evaluator;
this.geneticOperator = geneticOperator;
this.terminator = terminator;
this.outputter = outputter;
this.generationFactory = generationFactory == null ? new AATreeGenerationFactory() : generationFactory;
Setup();
}
public bool Ignore(ITerminator term)
{
if (_indexDict.ContainsKey(term.Name))
{
return(false);
}
term.Weight = -1;
_ignores.Add(term.Name, term);
_indexDict.Add(term.Name, -1);
return(true);
}
/// <summary>
/// The InferPipelines methods are just public portals to the internal function that handle different
/// types of data being passed in: training IDataView, path to training file, or train and test files.
/// </summary>
public static AutoMlMlState InferPipelines(IHostEnvironment env, PipelineOptimizerBase autoMlEngine,
IDataView trainData, IDataView testData, int numTransformLevels, int batchSize, SupportedMetric metric,
out PipelinePattern bestPipeline, ITerminator terminator, MacroUtils.TrainerKinds trainerKind)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(trainData, nameof(trainData));
env.CheckValue(testData, nameof(testData));
int numOfRows = (int)(trainData.GetRowCount(false) ?? 1000);
AutoMlMlState amls = new AutoMlMlState(env, metric, autoMlEngine, terminator, trainerKind, trainData, testData);
bestPipeline = amls.InferPipelines(numTransformLevels, batchSize, numOfRows);
return(amls);
}
public static AutoMlMlState InferPipelines(IHostEnvironment env, PipelineOptimizerBase autoMlEngine, IDataView data, int numTransformLevels,
int batchSize, SupportedMetric metric, out PipelinePattern bestPipeline, int numOfSampleRows,
ITerminator terminator, MacroUtils.TrainerKinds trainerKind)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(data, nameof(data));
var splitOutput = TrainTestSplit.Split(env, new TrainTestSplit.Input {
Data = data, Fraction = 0.8f
});
AutoMlMlState amls = new AutoMlMlState(env, metric, autoMlEngine, terminator, trainerKind,
splitOutput.TrainData.Take(numOfSampleRows), splitOutput.TestData.Take(numOfSampleRows));
bestPipeline = amls.InferPipelines(numTransformLevels, batchSize, numOfSampleRows);
return(amls);
}
public bool Regist(ITerminator term)
{
if (_indexDict.ContainsKey(term.Name))
{
return(false);
}
if (term.Weight < 0)
{
return(false);
}
if (!_terminators.ContainsKey(term.Weight))
{
_terminators.Add(term.Weight, new Dictionary <string, ITerminator>());
}
_terminators[term.Weight].Add(term.Name, term);
_indexDict.Add(term.Name, term.Weight);
return(true);
}
public AutoMlMlState(IHostEnvironment env, SupportedMetric metric, IPipelineOptimizer autoMlEngine,
ITerminator terminator, MacroUtils.TrainerKinds trainerKind, IDataView trainData = null, IDataView testData = null,
string[] requestedLearners = null)
{
Contracts.CheckValue(env, nameof(env));
_sortedSampledElements =
metric.IsMaximizing ? new SortedList <double, PipelinePattern>(new ReversedComparer <double>()) :
new SortedList <double, PipelinePattern>();
_history = new List <PipelinePattern>();
_env = env;
_host = _env.Register("AutoMlState");
_trainData = trainData;
_testData = testData;
_terminator = terminator;
_requestedLearners = requestedLearners;
AutoMlEngine = autoMlEngine;
BatchCandidates = new PipelinePattern[] { };
Metric = metric;
TrainerKind = trainerKind;
}
public static AutoMlMlState InferPipelines(IHostEnvironment env, PipelineOptimizerBase autoMlEngine, string trainDataPath,
string schemaDefinitionFile, out string schemaDefinition, int numTransformLevels, int batchSize, SupportedMetric metric,
out PipelinePattern bestPipeline, int numOfSampleRows, ITerminator terminator, MacroUtils.TrainerKinds trainerKind)
{
Contracts.CheckValue(env, nameof(env));
// REVIEW: Should be able to infer schema by itself, without having to
// infer recipes. Look into this.
// Set loader settings through inference
RecipeInference.InferRecipesFromData(env, trainDataPath, schemaDefinitionFile,
out var _, out schemaDefinition, out var _, true);
#pragma warning disable 0618
var data = ImportTextData.ImportText(env, new ImportTextData.Input
{
InputFile = new SimpleFileHandle(env, trainDataPath, false, false),
CustomSchema = schemaDefinition
}).Data;
#pragma warning restore 0618
var splitOutput = TrainTestSplit.Split(env, new TrainTestSplit.Input {
Data = data, Fraction = 0.8f
});
AutoMlMlState amls = new AutoMlMlState(env, metric, autoMlEngine, terminator, trainerKind,
splitOutput.TrainData.Take(numOfSampleRows), splitOutput.TestData.Take(numOfSampleRows));
bestPipeline = amls.InferPipelines(numTransformLevels, batchSize, numOfSampleRows);
return(amls);
}
/// <summary>
///Check if the plugins have been selected by the user, if not the revelant exceptions are thrown
///asking the user to make a choice.
///So, if the populator,evaluators, geneticOperator and terminator choices are not chosen, then
///error messages are displayed to alert the user, that the choices for these four plugins cannot be null.
///If the plugins are chosen, the engine object is created according to the user choices and it is initialised.
///Once the engine is initialised, the rest of the buttons on the interface are activated and the fitness values
///are displayed.
/// </summary>
private void initEngineButton_Click(object sender, EventArgs e)
{
if (!engineRunning)
{
SetEngineRunning(true);
populator = null;
evaluator = null;
geneticOperator = null;
terminator = null;
outputter = null;
generationFactory = null;
string errorMsg = "";
if (cbPopulator.SelectedItem == null) errorMsg += "Populator must not be null\n";
else
{
string choice = getChoice(populators, cbPopulator);
try
{
populator = (IPopulator)loader.GetInstance(choice, (object)tbMapFile.Text);
}
catch (GeneticEngineException exception)
{
MessageBox.Show(exception.Message);
}
}
if (cbEvaluator.SelectedItem == null) errorMsg += "Evaluator must not be null\n";
else
{
string choice = getChoice(evaluators, cbEvaluator);
try
{
evaluator = (IEvaluator)loader.GetInstance(choice, null);
}
catch (GeneticEngineException exception)
{
MessageBox.Show(exception.Message);
}
}
if (cbGeneticOperator.SelectedItem == null) errorMsg += "Genetic Operator must not be null\n";
else
{
string choice = getChoice(geneticOperators, cbGeneticOperator);
try
{
geneticOperator = (IGeneticOperator)loader.GetInstance(choice, null);
}
catch (GeneticEngineException exception)
{
MessageBox.Show(exception.Message);
}
}
if (cbTerminator.SelectedItem == null) errorMsg += "Terminator must not be null\n";
else
{
string choice = getChoice(terminators, cbTerminator);
if ((int)targetFitness.Value == 0) errorMsg += "Provide a target fitness value greater than 1 for the terminator plug-in\n";
else
{
try
{
terminator = (ITerminator)loader.GetInstance(choice, (object)(uint)targetFitness.Value);
}
catch (GeneticEngineException exception)
{
MessageBox.Show(exception.Message);
}
}
}
if (cbOutputter.SelectedItem != null)
{
string choice = getChoice(outputters, cbOutputter);
if (choice != noOutputterString)
{
if (tbOutputFile.Text == "")
{
MessageBox.Show("Select an output file for the outputter\n");
}
else
{
outputter = (IOutputter)loader.GetInstance(choice, (object)tbOutputFile.Text);
}
}
}
if (cbGenerationFactory.SelectedItem != null)
{
string choice = getChoice(generationFactories, cbGenerationFactory);
if (choice != defaultGenerationFactoryString)
{
generationFactory = (IGenerationFactory)loader.GetInstance(choice, null);
}
}
if (errorMsg != "") MessageBox.Show(errorMsg + "Please make sure you have selected a populator, evaluator, genetic operator and terminator and then try pressing the button again\n");
else
{
try
{
displayOutputter = new DisplayOutputter(this, outputter);
engine = new GeneticEngine(populator, evaluator, geneticOperator, terminator, displayOutputter, generationFactory);
hasInitialised = true;
}
catch (GeneticEngineException ex)
{
MessageBox.Show(ex.Message);
}
}
SetEngineRunning(false);
}
}
public bool IsRegist(ITerminator term)
{
return(IsRegist(term.Name));
}
public bool IsIgnore(ITerminator term)
{
return(IsIgnore(term.Name));
}
public MyDIApplication(ITerminator terminatorDependency)
{
this.terminator = terminatorDependency;
}
