public List <List <BinaryOption> > getSample(Dictionary <string, string> parameter)
{
int numberConfigs;
int optionWeight = 1;
string numConfigsValue = null;
if (parameter.ContainsKey(NUMBER_SAMPLES))
{
numConfigsValue = parameter[NUMBER_SAMPLES];
}
else
{
numConfigsValue = "asOW";
}
if (numConfigsValue.Contains("asOW"))
{
FeatureWise fw = new FeatureWise();
numberConfigs = fw.generateFeatureWiseConfigsCSP(vm).Count;
}
else if (numConfigsValue.Contains("asTW"))
{
numConfigsValue.Replace("asTW", "");
numberConfigs = Int32.Parse(numConfigsValue);
TWise tw = new TWise();
numberConfigs = tw.generateT_WiseVariants_new(vm, numberConfigs).Count;
}
else
{
numberConfigs = Int32.Parse(numConfigsValue);
}
if (parameter.ContainsKey(OPTION_WEIGHT))
{
optionWeight = Int32.Parse(parameter[OPTION_WEIGHT]);
}
List <BinaryOption> minimalConfiguration = ConfigurationBuilder.vg.MinimizeConfig(new List <BinaryOption>(), vm, true, null);
return(ConfigurationBuilder.vg.DistanceMaximization(vm, minimalConfiguration, numberConfigs, optionWeight));
}
/// <summary>
/// Creates the t-wise sampling according to the given t-value.
/// </summary>
/// <param name="vm">The variability model containing the binary options for which we want to generate the pair-wise configurations.</param>
/// <param name="t"> The t of the t-wise</param>
/// <returns>A list of configurations in which each configuration is represented by a list of SELECTED binary options</returns>
public List <List <BinaryOption> > generateT_WiseVariants_new(VariabilityModel vm, int t)
{
// dirty fix for twise issue
if (t == 1)
{
FeatureWise fw = new FeatureWise();
return(fw.generateFeatureWiseConfigurations(vm));
}
List <BinaryOption> candidate = new List <BinaryOption>();
List <List <BinaryOption> > result = new List <List <BinaryOption> >();
generatePowerSet(vm, candidate, t, result, 0);
//remove double entries...
List <List <BinaryOption> > resultCleaned = new List <List <BinaryOption> >();
List <String> configs = new List <string>();
foreach (List <BinaryOption> options in result)
{
options.Sort(delegate(BinaryOption o1, BinaryOption o2) { return(o1.Name.CompareTo(o2.Name)); });
String currConfig = "";
foreach (BinaryOption binOpt in options)
{
currConfig = currConfig + " " + binOpt.Name;
}
if (!configs.Contains(currConfig))
{
resultCleaned.Add(options);
configs.Add(currConfig);
}
}
return(resultCleaned);
}
/// <summary>
/// Returns a set of random binary partial configurations.
/// </summary>
/// <param name="parameters">Parameters for this random sampling. The following paramters are supported:
/// seed = the seed for the random generator (int required)
/// numConfigs = the number of configurations that have to be selected.
/// To be able ot select a number of configurations equal to the number selected by the OW heuristic or
/// the TWise heuristics, two special values can be given for this paramter. To select a number equal to
/// the OW heuristics use "asOW" as value and to select a number equal to a TWise heuristics with a t of X
/// use "asTWX".
/// </param>
/// <returns>A list of random binary partial configuartions.</returns>
public List <List <BinaryOption> > getRandomConfigs(Dictionary <String, String> parameters)
{
configurations.Clear();
int seed = 0;
int numConfigs = varModel.BinaryOptions.Count;
// parse parameters
if (parameters.ContainsKey("numConfigs"))
{
String numConfigsValue = parameters["numConfigs"];
if (!int.TryParse(numConfigsValue, out numConfigs))
{
// special constants as parameter (numConfigs = asOW or asTWX
if (numConfigsValue.Contains("asOW"))
{
FeatureWise fw = new FeatureWise();
numConfigs = fw.generateFeatureWiseConfigsCSP(varModel).Count;
}
else if (numConfigsValue.Contains("asTW"))
{
numConfigsValue = numConfigsValue.Replace("asTW", "").Trim();
int.TryParse(numConfigsValue, out numConfigs);
TWise tw = new TWise();
numConfigs = tw.generateT_WiseVariants_new(varModel, numConfigs).Count;
}
}
}
if (parameters.ContainsKey("seed"))
{
int.TryParse(parameters["seed"], out seed);
}
// build set of all valid binary partial configurations
VariantGenerator vg = new VariantGenerator();
List <List <BinaryOption> > allConfigs = vg.generateAllVariantsFast(varModel);
//repair wrong parameters
if (numConfigs >= allConfigs.Count)
{
if (numConfigs > allConfigs.Count)
{
GlobalState.logError.logLine("Random Sampling: numConfigs to large for variability model. num set to " + allConfigs.Count);
}
configurations = allConfigs;
return(allConfigs);
}
// select random configurations
Random r = new Random(seed);
for (int i = 0; i < numConfigs; i++)
{
List <BinaryOption> selectedConfig = allConfigs[r.Next(allConfigs.Count + 1)];
if (configurations.Contains(selectedConfig))
{
i -= 1;
}
else
{
configurations.Add(selectedConfig);
}
}
return(configurations);
}
/// <summary>
/// Performs the functionality of one command. If no functionality is found for the command, the command is retuned by this method.
/// </summary>
/// <param name="line">One command with its parameters.</param>
/// <returns>Returns an empty string if the command could be performed by the method. If the command could not be performed by the method, the original command is returned.</returns>
public string performOneCommand(string line)
{
GlobalState.logInfo.log(COMMAND + line);
// remove comment part of the line (the comment starts with an #)
line = line.Split(new Char[] { '#' }, 2)[0];
if (line.Length == 0)
return "";
// split line in command and parameters of the command
string[] components = line.Split(new Char[] { ' ' }, 2);
string command = components[0];
string task = "";
if (components.Length > 1)
task = components[1];
string[] taskAsParameter = task.Split(new Char[] { ' ' });
switch (command.ToLower())
{
case COMMAND_TRUEMODEL:
StreamReader readModel = new StreamReader(task);
String model = readModel.ReadLine().Trim();
readModel.Close();
exp.TrueModel = new InfluenceFunction(model.Replace(',','.'), GlobalState.varModel);
NFProperty artificalProp = new NFProperty("artificial");
GlobalState.currentNFP = artificalProp;
computeEvaluationDataSetBasedOnTrueModel();
break;
case COMMAND_SUBSCRIPT:
{
FileInfo fi = new FileInfo(task);
StreamReader reader = null;
if (!fi.Exists)
throw new FileNotFoundException(@"Automation script not found. ", fi.ToString());
reader = fi.OpenText();
Commands co = new Commands();
co.exp = this.exp;
while (!reader.EndOfStream)
{
String oneLine = reader.ReadLine().Trim();
co.performOneCommand(oneLine);
}
}
break;
case COMMAND_EVALUATION_SET:
{
GlobalState.evalutionSet.Configurations = ConfigurationReader.readConfigurations(task, GlobalState.varModel);
GlobalState.logInfo.log("Evaluation set loaded.");
}
break;
case COMMAND_CLEAR_GLOBAL:
SPLConqueror_Core.GlobalState.clear();
break;
case COMMAND_CLEAR_SAMPLING:
exp.clearSampling();
break;
case COMMAND_CLEAR_LEARNING:
exp.clear();
break;
case COMMAND_LOAD_CONFIGURATIONS:
GlobalState.allMeasurements.Configurations = (GlobalState.allMeasurements.Configurations.Union(ConfigurationReader.readConfigurations(task, GlobalState.varModel))).ToList();
GlobalState.logInfo.log(GlobalState.allMeasurements.Configurations.Count + " configurations loaded.");
break;
case COMMAND_SAMPLE_ALLBINARY:
{
VariantGenerator vg = new VariantGenerator(null);
if (taskAsParameter.Contains(COMMAND_VALIDATION))
{
exp.addBinarySelection_Validation(vg.generateAllVariantsFast(GlobalState.varModel));
exp.addBinarySampling_Validation(COMMAND_SAMPLE_ALLBINARY);
}
else
{
exp.addBinarySelection_Learning(vg.generateAllVariantsFast(GlobalState.varModel));
exp.addBinarySampling_Learning(COMMAND_SAMPLE_ALLBINARY);
}
break;
}
case COMMAND_ANALYZE_LEARNING:
{
GlobalState.logInfo.log("Models:");
FeatureSubsetSelection learning = exp.learning;
if (learning == null)
{
GlobalState.logError.log("Error... learning was not performed!");
break;
}
foreach (LearningRound lr in learning.LearningHistory)
{
double relativeError = 0;
if (GlobalState.evalutionSet.Configurations.Count > 0)
{
double relativeErro2r = exp.learning.computeError(lr.FeatureSet, GlobalState.evalutionSet.Configurations, out relativeError);
}
else
{
double relativeErro2r = exp.learning.computeError(lr.FeatureSet, GlobalState.allMeasurements.Configurations, out relativeError);
}
GlobalState.logInfo.log(lr.ToString() + relativeError);
}
break;
}
case COMMAND_EXERIMENTALDESIGN:
performOneCommand_ExpDesign(task);
break;
case COMMAND_SAMPLING_OPTIONORDER:
parseOptionOrder(task);
break;
case COMMAND_VARIABILITYMODEL:
GlobalState.varModel = VariabilityModel.loadFromXML(task);
if (GlobalState.varModel == null)
GlobalState.logError.log("No variability model found at " + task);
break;
case COMMAND_SET_NFP:
GlobalState.currentNFP = GlobalState.getOrCreateProperty(task.Trim());
break;
case COMMAND_SAMPLE_OPTIONWISE:
FeatureWise fw = new FeatureWise();
if (taskAsParameter.Contains(COMMAND_VALIDATION))
{
exp.addBinarySelection_Validation(fw.generateFeatureWiseConfigsCSP(GlobalState.varModel));
exp.addBinarySampling_Validation("FW");
}
else
{
//exp.addBinarySelection_Learning(fw.generateFeatureWiseConfigsCSP(GlobalState.varModel));
exp.addBinarySelection_Learning(fw.generateFeatureWiseConfigurations(GlobalState.varModel));
exp.addBinarySampling_Learning("FW");
}
break;
case COMMAND_LOG:
string location = task.Trim();
GlobalState.logInfo.close();
GlobalState.logInfo = new InfoLogger(location);
GlobalState.logError.close();
GlobalState.logError = new ErrorLogger(location + "_error");
break;
case COMMAND_SET_MLSETTING:
exp.mlSettings = ML_Settings.readSettings(task);
break;
case COMMAND_LOAD_MLSETTINGS:
exp.mlSettings = ML_Settings.readSettingsFromFile(task);
break;
case COMMAND_SAMPLE_PAIRWISE:
PairWise pw = new PairWise();
if (taskAsParameter.Contains(COMMAND_VALIDATION))
{
exp.addBinarySelection_Validation(pw.generatePairWiseVariants(GlobalState.varModel));
exp.addBinarySampling_Validation("PW");
}
else
{
exp.addBinarySelection_Learning(pw.generatePairWiseVariants(GlobalState.varModel));
exp.addBinarySampling_Learning("PW");
}
break;
case COMMAND_PRINT_MLSETTINGS:
GlobalState.logInfo.log(exp.mlSettings.ToString());
break;
case COMMAND_PRINT_CONFIGURATIONS:
{
List<Dictionary<NumericOption, double>> numericSampling = exp.NumericSelection_Learning;
List<List<BinaryOption>> binarySampling = exp.BinarySelections_Learning;
List<Configuration> configurations = new List<Configuration>();
foreach (Dictionary<NumericOption, double> numeric in numericSampling)
{
foreach (List<BinaryOption> binary in binarySampling)
{
Configuration config = Configuration.getConfiguration(binary, numeric);
if (!configurations.Contains(config) && GlobalState.varModel.configurationIsValid(config))
{
configurations.Add(config);
}
}
}
string[] para = task.Split(new char[] { ' ' });
// TODO very error prune..
ConfigurationPrinter printer = new ConfigurationPrinter(para[0], para[1], para[2], GlobalState.optionOrder);
printer.print(configurations);
break;
}
case COMMAND_SAMPLE_BINARY_RANDOM:
{
string[] para = task.Split(new char[] { ' ' });
int treshold = Convert.ToInt32(para[0]);
int modulu = Convert.ToInt32(para[1]);
VariantGenerator vg = new VariantGenerator(null);
if (taskAsParameter.Contains(COMMAND_VALIDATION))
{
exp.addBinarySelection_Validation(vg.generateRandomVariants(GlobalState.varModel, treshold, modulu));
exp.addBinarySampling_Validation("random " + task);
}
else
{
exp.addBinarySelection_Learning(vg.generateRandomVariants(GlobalState.varModel, treshold, modulu));
exp.addBinarySampling_Learning("random " + task);
}
break;
}
case COMMAND_START_LEARNING:
{
InfluenceModel infMod = new InfluenceModel(GlobalState.varModel, GlobalState.currentNFP);
List<Configuration> configurations_Learning = new List<Configuration>();
List<Configuration> configurations_Validation = new List<Configuration>();
if (exp.TrueModel == null)
{
//List<List<BinaryOption>> availableBinary
//configurations_Learning = GlobalState.getMeasuredConfigs(exp.BinarySelections_Learning, exp.NumericSelection_Learning);
configurations_Learning = GlobalState.getMeasuredConfigs(Configuration.getConfigurations(exp.BinarySelections_Learning, exp.NumericSelection_Learning));
configurations_Learning = configurations_Learning.Distinct().ToList();
configurations_Validation = GlobalState.getMeasuredConfigs(Configuration.getConfigurations(exp.BinarySelections_Validation, exp.NumericSelection_Validation));
configurations_Validation = configurations_Validation.Distinct().ToList();
//break;//todo only to get the configurations that we haven't measured
} else
{
foreach (List<BinaryOption> binConfig in exp.BinarySelections_Learning)
{
if (exp.NumericSelection_Learning.Count == 0)
{
Configuration c = new Configuration(binConfig);
c.setMeasuredValue(GlobalState.currentNFP, exp.TrueModel.eval(c));
if (!configurations_Learning.Contains(c))
configurations_Learning.Add(c);
continue;
}
foreach (Dictionary<NumericOption, double> numConf in exp.NumericSelection_Learning)
{
Configuration c = new Configuration(binConfig, numConf);
c.setMeasuredValue(GlobalState.currentNFP, exp.TrueModel.eval(c));
if(GlobalState.varModel.configurationIsValid(c))
// if (!configurations_Learning.Contains(c))
configurations_Learning.Add(c);
}
}
}
if (configurations_Learning.Count == 0)
{
configurations_Learning = configurations_Validation;
}
if (configurations_Learning.Count == 0)
{
GlobalState.logInfo.log("The learning set is empty! Cannot start learning!");
break;
}
if (configurations_Validation.Count == 0)
{
configurations_Validation = configurations_Learning;
}
//break;
GlobalState.logInfo.log("Learning: " + "NumberOfConfigurationsLearning:" + configurations_Learning.Count + " NumberOfConfigurationsValidation:" + configurations_Validation.Count
+ " UnionNumberOfConfigurations:" + (configurations_Learning.Union(configurations_Validation)).Count());
// prepare the machine learning
exp.learning.init(infMod, exp.mlSettings);
exp.learning.setLearningSet(configurations_Learning);
exp.learning.setValidationSet(configurations_Validation);
exp.learning.learn();
}
break;
case COMMAND_SAMPLE_NEGATIVE_OPTIONWISE:
// TODO there are two different variants in generating NegFW configurations.
NegFeatureWise neg = new NegFeatureWise();
if (taskAsParameter.Contains(COMMAND_VALIDATION))
{
exp.addBinarySelection_Validation(neg.generateNegativeFW(GlobalState.varModel));
exp.addBinarySampling_Validation("newFW");
}
else
{
exp.addBinarySelection_Learning(neg.generateNegativeFW(GlobalState.varModel));//neg.generateNegativeFWAllCombinations(GlobalState.varModel));
exp.addBinarySampling_Learning("newFW");
}
break;
default:
return command;
}
return "";
}
public static List<Configuration> buildConfigs(VariabilityModel vm, List<SamplingStrategies> strategies)
{
List<Configuration> result = new List<Configuration>();
VariantGenerator vg = new VariantGenerator(null);
ExperimentalDesign design = null;
List<List<BinaryOption>> binaryConfigs = new List<List<BinaryOption>>();
List<Dictionary<NumericOption, Double>> numericConfigs = new List<Dictionary<NumericOption, double>>();
foreach (SamplingStrategies strat in strategies)
{
switch (strat)
{
//Binary sampling heuristics
case SamplingStrategies.ALLBINARY:
binaryConfigs.AddRange(vg.generateAllVariantsFast(vm));
break;
case SamplingStrategies.BINARY_RANDOM:
binaryConfigs.AddRange(vg.generateRandomVariants(GlobalState.varModel, binaryThreshold, binaryModulu));
break;
case SamplingStrategies.OPTIONWISE:
FeatureWise fw = new FeatureWise();
binaryConfigs.AddRange(fw.generateFeatureWiseConfigurations(GlobalState.varModel));
break;
case SamplingStrategies.PAIRWISE:
PairWise pw = new PairWise();
binaryConfigs.AddRange(pw.generatePairWiseVariants(GlobalState.varModel));
break;
case SamplingStrategies.NEGATIVE_OPTIONWISE:
NegFeatureWise neg = new NegFeatureWise();//2nd option: neg.generateNegativeFWAllCombinations(GlobalState.varModel));
binaryConfigs.AddRange(neg.generateNegativeFW(GlobalState.varModel));
break;
//Experimental designs for numeric options
case SamplingStrategies.BOXBEHNKEN:
if (optionsToConsider.ContainsKey(SamplingStrategies.BOXBEHNKEN))
design = new BoxBehnkenDesign(optionsToConsider[SamplingStrategies.BOXBEHNKEN]);
else
design = new BoxBehnkenDesign(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.BOXBEHNKEN])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.CENTRALCOMPOSITE:
if (optionsToConsider.ContainsKey(SamplingStrategies.CENTRALCOMPOSITE))
design = new CentralCompositeInscribedDesign(optionsToConsider[SamplingStrategies.CENTRALCOMPOSITE]);
else
design = new CentralCompositeInscribedDesign(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.CENTRALCOMPOSITE])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.FULLFACTORIAL:
if (optionsToConsider.ContainsKey(SamplingStrategies.FULLFACTORIAL))
design = new FullFactorialDesign(optionsToConsider[SamplingStrategies.FULLFACTORIAL]);
else
design = new FullFactorialDesign(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.FULLFACTORIAL])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.HYPERSAMPLING:
if (optionsToConsider.ContainsKey(SamplingStrategies.HYPERSAMPLING))
design = new HyperSampling(optionsToConsider[SamplingStrategies.HYPERSAMPLING]);
else
design = new HyperSampling(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.HYPERSAMPLING])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.ONEFACTORATATIME:
if (optionsToConsider.ContainsKey(SamplingStrategies.ONEFACTORATATIME))
design = new OneFactorAtATime(optionsToConsider[SamplingStrategies.ONEFACTORATATIME]);
else
design = new OneFactorAtATime(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.ONEFACTORATATIME])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.KEXCHANGE:
if (optionsToConsider.ContainsKey(SamplingStrategies.KEXCHANGE))
design = new KExchangeAlgorithm(optionsToConsider[SamplingStrategies.KEXCHANGE]);
else
design = new KExchangeAlgorithm(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.KEXCHANGE])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.PLACKETTBURMAN:
if (optionsToConsider.ContainsKey(SamplingStrategies.PLACKETTBURMAN))
design = new PlackettBurmanDesign(optionsToConsider[SamplingStrategies.PLACKETTBURMAN]);
else
design = new PlackettBurmanDesign(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.PLACKETTBURMAN])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
case SamplingStrategies.RANDOM:
if (optionsToConsider.ContainsKey(SamplingStrategies.RANDOM))
design = new RandomSampling(optionsToConsider[SamplingStrategies.RANDOM]);
else
design = new RandomSampling(vm.NumericOptions);
foreach (Dictionary<string, string> expDesignParamSet in parametersOfExpDesigns[SamplingStrategies.RANDOM])
{
design.computeDesign(expDesignParamSet);
numericConfigs.AddRange(design.SelectedConfigurations);
}
break;
}
}
foreach (List<BinaryOption> binConfig in binaryConfigs)
{
if (numericConfigs.Count == 0)
{
Configuration c = new Configuration(binConfig);
result.Add(c);
}
foreach (Dictionary<NumericOption, double> numConf in numericConfigs)
{
Configuration c = new Configuration(binConfig, numConf);
result.Add(c);
}
}
return result.Distinct().ToList();
}
