public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new KMeansClusteringStrategy <NeatGenome>(distanceMetric);
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create black box evaluator
SimpleEvaluator evaluator = new SimpleEvaluator(_optimizer);
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
IGenomeListEvaluator <NeatGenome> innerEvaluator = new UnityParallelListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator, _optimizer);
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
//ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
//ea.Initialize(innerEvaluator, genomeFactory, genomeList);//this can be used if the currentGeneration does not need to be updated at the start of the training
ea.Initialize(innerEvaluator, genomeFactory, genomeList, true);
return(ea);
}
/// <summary>
/// Initialize the experiment with some optional XML configuration data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_eaParams.SelectionProportion = 0.5;
_eaParams.ElitismProportion = 0.5;
_eaParams.OffspringAsexualProportion = 0.95;
_eaParams.OffspringSexualProportion = 0.05;
_eaParams.InterspeciesMatingProportion = 0.00;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.AddConnectionMutationProbability = 0.1;
_neatGenomeParams.AddNodeMutationProbability = 0.01;
_neatGenomeParams.ConnectionWeightMutationProbability = 0.89;
_neatGenomeParams.InitialInterconnectionsProportion = 0.05;
}
public virtual void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig,
"DefaultComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
DefaultComplexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(
_complexityRegulationStr,
_complexityThreshold);
DefaultSpeciationStrategy = new KMeansClusteringStrategy <NeatGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0));
DefaultNeatEvolutionAlgorithm = new NeatEvolutionAlgorithm <NeatGenome>(
NeatEvolutionAlgorithmParameters,
DefaultSpeciationStrategy,
DefaultComplexityRegulationStrategy);
}
public void TestIsUserInExperimentReturnsFalseIfAnyAudienceInANDConditionDoesNotPass()
{
var experiment = Config.GetExperimentFromKey("audience_combinations_experiment");
var userAttributes = new UserAttributes
{
{ "house", "Gryffindor" },
{ "lasers", 50 }
};
Assert.False(ExperimentUtils.IsUserInExperiment(Config, experiment, userAttributes, Logger));
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Evaluating audiences for experiment ""audience_combinations_experiment"": [""and"",[""or"",""3468206642"",""3988293898""],[""or"",""3988293899"",""3468206646"",""3468206647"",""3468206644"",""3468206643""]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206642"" with conditions: [""and"", [""or"", [""or"", {""name"": ""house"", ""type"": ""custom_attribute"", ""value"": ""Gryffindor""}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206642"" evaluated to TRUE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3988293899"" with conditions: [""and"",[""or"",[""or"",{""name"":""favorite_ice_cream"",""type"":""custom_attribute"",""match"":""exists""}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3988293899"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206646"" with conditions: [""and"",[""or"",[""or"",{""name"":""lasers"",""type"":""custom_attribute"",""match"":""exact"",""value"":45.5}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206646"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206647"" with conditions: [""and"",[""or"",[""or"",{""name"":""lasers"",""type"":""custom_attribute"",""match"":""gt"",""value"":70}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206647"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206644"" with conditions: [""and"",[""or"",[""or"",{""name"":""lasers"",""type"":""custom_attribute"",""match"":""lt"",""value"":1.0}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206644"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206643"" with conditions: [""and"",[""or"",[""or"",{""name"":""should_do_it"",""type"":""custom_attribute"",""match"":""exact"",""value"":true}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Audience condition {""type"":""custom_attribute"",""match"":""exact"",""name"":""should_do_it"",""value"":true} evaluated to UNKNOWN because no value was passed for user attribute ""should_do_it"""), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206643"" evaluated to UNKNOWN"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audiences for experiment ""audience_combinations_experiment"" collectively evaluated to FALSE"), Times.Once);
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weight difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
PreyCaptureEvaluator evaluator = new PreyCaptureEvaluator(_trialsPerEvaluation, _gridSize, _preyInitMoves, _preySpeed, _sensorRange, _maxTimesteps);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// TODO: evaluation scheme that re-evaluates existing genomes and takes average over time.
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator <NeatGenome> genomeListEvaluator = new ParallelGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// Initialize the evolution algorithm.
ea.Initialize(genomeListEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
/// <summary>
/// Initialize the experiment with some optional XML configuration data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_trialsPerEvaluation = XmlUtils.GetValueAsInt(xmlConfig, "TrialsPerEvaluation");
_gridSize = XmlUtils.GetValueAsInt(xmlConfig, "GridSize");
_preyInitMoves = XmlUtils.GetValueAsInt(xmlConfig, "PreyInitMoves");
_preySpeed = XmlUtils.GetValueAsDouble(xmlConfig, "PreySpeed");
_sensorRange = XmlUtils.GetValueAsDouble(xmlConfig, "SensorRange");
_maxTimesteps = XmlUtils.GetValueAsInt(xmlConfig, "MaxTimesteps");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_eaParams.ElitismProportion = 0.66;
_eaParams.SelectionProportion = 0.66;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.ActivationFn = LeakyReLU.__DefaultInstance;
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome2 population and their associated/parent genome2 factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the phenome evaluator.
IGenomeListEvaluator <NeatGenome> genomeListEvaluator = new ParallelCoevolutionListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, PhenomeEvaluator);
// Wrap a hall of fame evaluator around the baseline evaluator.
genomeListEvaluator = new ParallelHallOfFameListEvaluator <NeatGenome, IBlackBox>(50, 0.5, ea, genomeListEvaluator, genomeDecoder, PhenomeEvaluator);
// Initialize the evolution algorithm.
ea.Initialize(genomeListEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
public void TestIsUserInExperimentReturnsFalseIfNoAudienceInORConditionPass()
{
var experiment = Config.GetExperimentFromKey("feat_with_var_test");
var userAttributes = new UserAttributes
{
{ "house", "Ravenclaw" },
{ "lasers", 50 },
{ "should_do_it", false }
};
Assert.False(ExperimentUtils.IsUserInExperiment(Config, experiment, userAttributes, Logger));
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Evaluating audiences for experiment ""feat_with_var_test"": [""3468206642"",""3988293898"",""3988293899"",""3468206646"",""3468206647"",""3468206644"",""3468206643""]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206642"" with conditions: [""and"", [""or"", [""or"", {""name"": ""house"", ""type"": ""custom_attribute"", ""value"": ""Gryffindor""}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206642"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3988293899"" with conditions: [""and"",[""or"",[""or"",{""name"":""favorite_ice_cream"",""type"":""custom_attribute"",""match"":""exists""}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3988293899"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206646"" with conditions: [""and"",[""or"",[""or"",{""name"":""lasers"",""type"":""custom_attribute"",""match"":""exact"",""value"":45.5}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206646"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206647"" with conditions: [""and"",[""or"",[""or"",{""name"":""lasers"",""type"":""custom_attribute"",""match"":""gt"",""value"":70}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206647"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206644"" with conditions: [""and"",[""or"",[""or"",{""name"":""lasers"",""type"":""custom_attribute"",""match"":""lt"",""value"":1.0}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206644"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Starting to evaluate audience ""3468206643"" with conditions: [""and"",[""or"",[""or"",{""name"":""should_do_it"",""type"":""custom_attribute"",""match"":""exact"",""value"":true}]]]"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audience ""3468206643"" evaluated to FALSE"), Times.Once);
LoggerMock.Verify(l => l.Log(LogLevel.INFO, @"Audiences for experiment ""feat_with_var_test"" collectively evaluated to FALSE"), Times.Once);
}
/// <summary>
/// Initialize the experiment with some optional XML configuration data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
// Determine what function to regress.
string fnIdStr = XmlUtils.GetValueAsString(xmlConfig, "Function");
FunctionId fnId = (FunctionId)Enum.Parse(typeof(FunctionId), fnIdStr);
_fn = FunctionUtils.GetFunction(fnId);
// Read parameter sampling scheme settings.
int sampleResolution = XmlUtils.GetValueAsInt(xmlConfig, "SampleResolution");
double sampleMin = XmlUtils.GetValueAsDouble(xmlConfig, "SampleMin");
double sampleMax = XmlUtils.GetValueAsDouble(xmlConfig, "SampleMax");
_paramSamplingInfo = new ParamSamplingInfo(sampleMin, sampleMax, sampleResolution);
}
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new KMeansClusteringStrategy <NeatGenome>(distanceMetric);
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create black box evaluator
AgarEvaluator evaluator = new AgarEvaluator(_optimizer);
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
IGenomeListEvaluator <NeatGenome> innerEvaluator = new UnitySingleGenomeEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator, _optimizer);
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
//ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
return(ea);
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
_evaluator = CreateEvaluator();
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator <NeatGenome> innerEvaluator = new SerialGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, _evaluator);
/*new ParallelGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, _evaluator, _parallelOptions);*/
// Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determined by examining each genome's evaluation info object.
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
/// <summary>
/// Initialize the experiment with some optional XML configutation data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_guesses = XmlUtils.GetValueAsInt(xmlConfig, "Guesses");
Hashed = XmlUtils.TryGetValueAsBool(xmlConfig, "Hashed").HasValue ? XmlUtils.GetValueAsBool(xmlConfig, "Hashed") : false;
ValidationGuesses = XmlUtils.GetValueAsInt(xmlConfig, "ValidationGuesses");
// Load the passwords from file
string pwdfile = XmlUtils.TryGetValueAsString(xmlConfig, "ValidationPasswordFile");
if (pwdfile != null)
{
Console.Write("Loading passwords from [{0}]...", pwdfile);
if (_passwords == null || _passwords.Count == 0)
{
int?pwLength = XmlUtils.TryGetValueAsInt(xmlConfig, "PasswordLength");
if (pwLength.HasValue)
{
Console.Write("Filtering to {0}-character passwords...", pwLength.Value);
}
_passwords = PasswordUtil.LoadPasswords(pwdfile, pwLength);
}
else
{
Console.WriteLine("WARNING: Not loading passwords for experiment (already set)");
}
}
else
{
Console.WriteLine("WARNING: Not loading passwords for experiment (not provided in config file)");
}
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.FeedforwardOnly = false;
_neatGenomeParams.AddNodeMutationProbability = 0.03;
_neatGenomeParams.AddConnectionMutationProbability = 0.05;
// TODO: Load states from XML config file
// Generates all the valid states in the MC using all viable ASCII characters
var stateList = new List <string>();
for (uint i = 32; i < 127; i++)
{
stateList.Add(((char)i).ToString());
}
stateList.Add(null);
_states = stateList.ToArray();
_activationFnLibrary = MarkovActivationFunctionLibrary.CreateLibraryMc(_states);
}
public void TestDoesUserMeetAudienceConditionsAudienceNoMatch()
{
var userAttributes = new UserAttributes
{
{ "device_type", "Android" },
{ "location", "San Francisco" }
};
Assert.IsFalse(ExperimentUtils.DoesUserMeetAudienceConditions(Config, Config.GetExperimentFromKey("test_experiment"), null, "experiment", "test_experiment", Logger).ResultObject);
}
public void TestUserInExperimentAudienceMatch()
{
var userAttributes = new UserAttributes
{
{ "device_type", "iPhone" },
{ "location", "San Francisco" }
};
Assert.IsTrue(ExperimentUtils.DoesUserMeetAudienceConditions(Config, Config.GetExperimentFromKey("test_experiment"), userAttributes, "experiment", "test_experiment", Logger).ResultObject);
}
public void TestIsUserInExperimentAudienceNoMatch()
{
var userAttributes = new UserAttributes
{
{ "device_type", "Android" },
{ "location", "San Francisco" }
};
Assert.IsFalse(ExperimentUtils.IsUserInExperiment(Config, Config.GetExperimentFromKey("test_experiment"), null));
}
public void TestUserInExperimentAudienceMatch()
{
var userAttributes = new UserAttributes
{
{ "device_type", "iPhone" },
{ "location", "San Francisco" }
};
Assert.IsTrue(ExperimentUtils.IsUserInExperiment(Config, Config.GetExperimentFromKey("test_experiment"), userAttributes));
}
/// <summary>
/// Initialize the experiment with some optional XML configutation data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.OffspringAsexualProportion = 1.0;
_eaParams.OffspringSexualProportion = 0.0;
_eaParams.SpecieCount = _specieCount;
_eaParams.InterspeciesMatingProportion = 0.0;
_neatGenomeParams = new NeatGenomeParameters();
if (name == "Small mutation")
{
// Small mutation parameters
_neatGenomeParams.ConnectionWeightMutationProbability = 0.43;
_neatGenomeParams.AddConnectionMutationProbability = 0.25;
_neatGenomeParams.AddNodeMutationProbability = 1.0;
_neatGenomeParams.DeleteConnectionMutationProbability = 0.003;
_neatGenomeParams.NodeAuxStateMutationProbability = 0.0;
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
//_neatGenomeParams.ConnectionMutationInfoList.Add(new ConnectionMutationInfo(1, ConnectionPerturbanceType.JiggleGaussian, ConnectionSelectionType.Proportional, 0.15, 0, 0.0, 0.4));
}
if (name == "Big mutation")
{
// Big mutation parameters
_neatGenomeParams.ConnectionWeightMutationProbability = 0.65;
_neatGenomeParams.AddConnectionMutationProbability = 0.48;
_neatGenomeParams.AddNodeMutationProbability = 1.0;
_neatGenomeParams.DeleteConnectionMutationProbability = 0.003;
_neatGenomeParams.NodeAuxStateMutationProbability = 0.0;
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
//_neatGenomeParams.ConnectionMutationInfoList.Add(new ConnectionMutationInfo(1, ConnectionPerturbanceType.JiggleGaussian, ConnectionSelectionType.Proportional, 0.9, 0, 0.0, 0.4));
}
if (name == "Novelty")
{
//Novelty parameters
_neatGenomeParams.ConnectionWeightMutationProbability = 0.87;
_neatGenomeParams.AddConnectionMutationProbability = 0.67;
_neatGenomeParams.AddNodeMutationProbability = 1.0;
_neatGenomeParams.DeleteConnectionMutationProbability = 0.003;
_neatGenomeParams.NodeAuxStateMutationProbability = 0.0;
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
//_neatGenomeParams.ConnectionMutationInfoList.Add(new ConnectionMutationInfo(1, ConnectionPerturbanceType.JiggleGaussian, ConnectionSelectionType.Proportional, 0.9, 0, 0.0, 0.4));
}
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm list ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// </summary>
public ModuleNeatEvolutionAlgorithm <NeatGenome>[] CreateEvolutionAlgorithms(int populationSize)
{
// Create the modules.
List <Module> modules = new List <Module>();
for (int i = 0; i < _moduleCount; i++)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetricPitch = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategyPitch = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetricPitch, _parallelOptions);
IDistanceMetric distanceMetricRhythm = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategyRhythm = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetricRhythm, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationPitch =
ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
IComplexityRegulationStrategy complexityRegulationRhythm =
ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create and add a new module with strategies and the evolution parameters.
Module module = new Module((i + 1), _eaParams,
speciationStrategyPitch, speciationStrategyRhythm,
complexityRegulationPitch, complexityRegulationRhythm);
modules.Add(module);
}
// Hook-up the modules with each other in circular order.
// TODO Right now, they are hooked-up in circular order. Check whether it should be done otherwise.
//for (int i = 0; i < modules.Count; i++)
//{
// modules[i].SetParasiteModule(i != modules.Count - 1 ? modules[i + 1] : modules[0], _parasiteCount,
// _championCount, CreateGenomeDecoder());
//}
foreach (var module in modules)
{
module.SetParasiteModules(modules.Except(new List <Module>()
{
module
}).ToList(), _parasiteCount, _championCount, CreateGenomeDecoder(), CreateGenomeDecoder());
}
// Initialize each module.
foreach (var module in modules)
{
var rhythmFactory = CreateGenomeFactory();
var pitchFactory = CreateGenomeFactory();
module.Initialize(rhythmFactory, pitchFactory, populationSize);
}
// Finished. Return the evolution algorithms
return
(modules.Select(m => m.RhythmEvolutionAlgorithm).Concat(modules.Select(m => m.PitchEvolutionAlgorithm)).ToArray());
}
public void TestDoesUserMeetAudienceConditionsReturnsTrueIfAllAudiencesInANDConditionPass()
{
var experiment = Config.GetExperimentFromKey("audience_combinations_experiment");
var userAttributes = new UserAttributes
{
{ "house", "Gryffindor" },
{ "favorite_ice_cream", "walls" }
};
Assert.True(ExperimentUtils.DoesUserMeetAudienceConditions(Config, experiment, userAttributes, "experiment", experiment.Key, Logger).ResultObject);
}
public void TestDoesUserMeetAudienceConditionsReturnsTrueWithNoAudience()
{
var experiment = Config.GetExperimentFromKey("feat_with_var_test");
experiment.AudienceIds = new string[] { };
experiment.AudienceConditions = null;
Assert.True(ExperimentUtils.DoesUserMeetAudienceConditions(Config, experiment, null, "experiment", experiment.Key, Logger).ResultObject);
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Evaluating audiences for experiment ""feat_with_var_test"": []."), Times.Once);
}
public void TestIsUserInExperimentReturnsTrueWithNoAudience()
{
var experiment = Config.GetExperimentFromKey("feat_with_var_test");
experiment.AudienceIds = new string[] { };
experiment.AudienceConditions = null;
Assert.True(ExperimentUtils.IsUserInExperiment(Config, experiment, null, Logger));
LoggerMock.Verify(l => l.Log(LogLevel.DEBUG, @"Evaluating audiences for experiment ""feat_with_var_test"": []"), Times.Once);
}
public void TestIsUserInExperimentReturnsTrueIfAllAudiencesInANDConditionPass()
{
var experiment = Config.GetExperimentFromKey("audience_combinations_experiment");
var userAttributes = new UserAttributes
{
{ "house", "Gryffindor" },
{ "favorite_ice_cream", "walls" }
};
Assert.True(ExperimentUtils.IsUserInExperiment(Config, experiment, userAttributes, Logger));
}
public void TestIsUserInExperimentReturnsFalseIfAudienceInNOTConditionGetsPassed()
{
var experiment = Config.GetExperimentFromKey("feat_with_var_test");
experiment.AudienceIds = new string[] { "3468206645" };
var userAttributes = new UserAttributes
{
{ "browser_type", "Chrome" }
};
Assert.False(ExperimentUtils.IsUserInExperiment(Config, experiment, userAttributes, Logger));
}
private NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm_private(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList, HyperNEAT_Args args = null)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1, 0, 10);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> retVal = new NeatEvolutionAlgorithm <NeatGenome>(NeatEvolutionAlgorithmParameters, speciationStrategy, complexityRegulationStrategy);
// Genome Decoder
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = null;
if (args == null)
{
genomeDecoder = CreateGenomeDecoder();
}
else
{
genomeDecoder = CreateGenomeDecoder(args);
}
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator <NeatGenome> genomeEvaluator = null;
if (_phenomeEvaluator != null)
{
IGenomeListEvaluator <NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, _phenomeEvaluator, _parallelOptions);
// Wrap the list evaluator in a 'selective' evaluator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determined by examining each genome's evaluation info object.
genomeEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
}
else if (_phenomeEvaluators != null)
{
// Use the multi tick evaluator
genomeEvaluator = new TickGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, _phenomeEvaluators, _phenometickeval_roundRobinManager, _phenometickeval_worldTick);
}
else
{
throw new ApplicationException("One of the phenome evaluators needs to be populated");
}
// Initialize the evolution algorithm.
retVal.Initialize(genomeEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(retVal);
}
public void TestDoesUserMeetAudienceConditionsReturnsTrueIfAudienceInNOTConditionDoesNotPass()
{
var experiment = Config.GetExperimentFromKey("feat_with_var_test");
experiment.AudienceIds = new string[] { "3468206645" };
var userAttributes = new UserAttributes
{
{ "browser_type", "Safari" }
};
Assert.True(ExperimentUtils.DoesUserMeetAudienceConditions(Config, experiment, userAttributes, "experiment", experiment.Key, Logger));
}
public void TestDoesUserMeetAudienceConditionsReturnsFalseIfAudienceInNOTConditionGetsPassed()
{
var experiment = Config.GetExperimentFromKey("feat_with_var_test");
experiment.AudienceIds = new string[] { "3468206645" };
var userAttributes = new UserAttributes
{
{ "browser_type", "Chrome" }
};
Assert.False(ExperimentUtils.DoesUserMeetAudienceConditions(Config, experiment, userAttributes, "experiment", experiment.Key, Logger).ResultObject);
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
DoublePoleBalancingEvaluator evaluator;
switch (_variantStr)
{
case "DoublePole":
evaluator = new DoublePoleBalancingEvaluator();
break;
case "DoublePoleNv":
evaluator = new DoublePoleBalancingEvaluatorNv();
break;
case "DoublePoleNvAntiWiggle":
evaluator = new DoublePoleBalancingEvaluatorNvAntiWiggle();
break;
default:
throw new SharpNeatException(string.Format("DoublePoleBalancing experiment config XML specifies unknown variant [{0}]", _variantStr));
}
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator <NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
/// <summary>
/// Initialize the experiment with some optional XML configutation data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
//snake specific params
_swparams = SnakeUtils.GetParamsFromXml(xmlConfig);
_trialsPerEvaluation = XmlUtils.TryGetValueAsInt(xmlConfig, "TrialsPerEvaluation") ?? _trialsPerEvaluation;
_maxTicksWithoutEating = XmlUtils.TryGetValueAsInt(xmlConfig, "MaxTicksWithoutEating") ?? (_swparams.Height * _swparams.Width + _swparams.TicksBetweenFood);
_inputMapperName = XmlUtils.TryGetValueAsString(xmlConfig, "InputMapping") ?? _inputMapperName;
_outputMapperName = XmlUtils.TryGetValueAsString(xmlConfig, "OutputMapping") ?? _outputMapperName;
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
//string nameoftype = (string ) typeof(GridInputMapper).GetProperty("Name", BindingFlags.Public | BindingFlags.Static | BindingFlags.FlattenHierarchy).GetValue(null, null);
Type inputType = Assembly.GetAssembly(typeof(IInputMapper)).GetTypes().Where(myType => myType.IsClass && !myType.IsAbstract && (typeof(IInputMapper).IsAssignableFrom(myType)) &&
_inputMapperName == (string)myType.GetProperty("Name", BindingFlags.Public | BindingFlags.Static | BindingFlags.FlattenHierarchy).GetValue(null, null)).First();
_inputMapper = (IInputMapper)Activator.CreateInstance(inputType, _swparams);
Type outputType = Assembly.GetAssembly(typeof(IOutputMapper)).GetTypes().Where(myType => myType.IsClass && !myType.IsAbstract && (typeof(IOutputMapper).IsAssignableFrom(myType)) &&
_outputMapperName == (string)myType.GetProperty("Name", BindingFlags.Public | BindingFlags.Static | BindingFlags.FlattenHierarchy).GetValue(null, null)).First();
_outputMapper = (IOutputMapper)Activator.CreateInstance(outputType);
//foreach (Type type in
//Assembly.GetAssembly(typeof(IInputMapper)).GetTypes().Where(myType => myType.IsClass && !myType.IsAbstract && myType.IsSubclassOf(typeof(IInputMapper) )))
//{
// SnakeRunnerFactory srf = (SnakeRunnerFactory) Activator.CreateInstance(type, swOriginal);
// if (srf.Name == _ioLayers)
// {
// _srf = srf;
// break;
// }
//}
}
public override void Initialize(string name, XmlElement xmlConfig)
{
base.Initialize(name, xmlConfig);
var substrateElements = xmlConfig.GetElementsByTagName("Substrate");
if (substrateElements.Count != 1)
{
throw new ArgumentException("Must be only one substrate element in the xml.");
}
_substrate =
ExperimentUtils.ReadSubstrateFromXml(xmlConfig.GetElementsByTagName("Substrate")[0] as XmlElement, xmlConfig.GetElementsByTagName("SubstrateSettings")[0] as XmlElement);
_cppnActivationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "CPPNActivation");
_cppnInputLength = XmlUtils.TryGetValueAsBool(xmlConfig, "CPPNDistanceInput") ?? false;
}
/// <summary>
/// Initialize the experiment with some optional XML configutation data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
}
