private static void SymbolicDataAnalysisCrossoverPerformanceTest(ISymbolicDataAnalysisExpressionCrossover <IRegressionProblemData> crossover)
{
var twister = new MersenneTwister(31415);
var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
var grammar = new FullFunctionalExpressionGrammar();
var stopwatch = new Stopwatch();
grammar.MaximumFunctionArguments = 0;
grammar.MaximumFunctionDefinitions = 0;
grammar.MinimumFunctionArguments = 0;
grammar.MinimumFunctionDefinitions = 0;
var trees = Util.CreateRandomTrees(twister, dataset, grammar, PopulationSize, 1, MaxTreeLength, 0, 0);
foreach (ISymbolicExpressionTree tree in trees)
{
Util.InitTree(tree, twister, new List <string>(dataset.VariableNames));
}
var problemData = new RegressionProblemData(dataset, dataset.VariableNames, dataset.VariableNames.Last());
var problem = new SymbolicRegressionSingleObjectiveProblem();
problem.ProblemData = problemData;
var globalScope = new Scope("Global Scope");
globalScope.Variables.Add(new Core.Variable("Random", twister));
var context = new ExecutionContext(null, problem, globalScope);
context = new ExecutionContext(context, crossover, globalScope);
stopwatch.Start();
for (int i = 0; i != PopulationSize; ++i)
{
var parent0 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
var scopeParent0 = new Scope();
scopeParent0.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent0));
context.Scope.SubScopes.Add(scopeParent0);
var parent1 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
var scopeParent1 = new Scope();
scopeParent1.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent1));
context.Scope.SubScopes.Add(scopeParent1);
crossover.Execute(context, new CancellationToken());
context.Scope.SubScopes.Remove(scopeParent0); // clean the scope in preparation for the next iteration
context.Scope.SubScopes.Remove(scopeParent1); // clean the scope in preparation for the next iteration
}
stopwatch.Stop();
double msPerCrossover = 2 * stopwatch.ElapsedMilliseconds / (double)PopulationSize;
Console.WriteLine(crossover.Name + ": " + Environment.NewLine +
msPerCrossover + " ms per crossover (~" + Math.Round(1000.0 / (msPerCrossover)) + " crossover operations / s)");
foreach (var tree in trees)
{
HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Tests.Util.IsValid(tree);
}
}
public override IOperation Execute(IExecutionContext context, CancellationToken cancellationToken) {
try {
//create new executionContext for the instrumented operation to account for parameter lookup and name translation
var executionContext = new ExecutionContext(context.Parent, instrumentedOperator, context.Scope);
instrumentedOperator.ExecutionContext = executionContext;
instrumentedOperator.cancellationToken = cancellationToken;
foreach (ILookupParameter param in instrumentedOperator.Parameters.OfType<ILookupParameter>())
param.ExecutionContext = executionContext;
IOperation next = instrumentedOperator.InstrumentedApply();
return next;
}
finally {
foreach (ILookupParameter param in instrumentedOperator.Parameters.OfType<ILookupParameter>())
param.ExecutionContext = null;
instrumentedOperator.ExecutionContext = null;
}
}
public override IOperation Execute(IExecutionContext context, CancellationToken cancellationToken)
{
try {
//create new executionContext for the instrumented operation to account for parameter lookup and name translation
var executionContext = new ExecutionContext(context.Parent, instrumentedOperator, context.Scope);
instrumentedOperator.ExecutionContext = executionContext;
instrumentedOperator.cancellationToken = cancellationToken;
foreach (ILookupParameter param in instrumentedOperator.Parameters.OfType <ILookupParameter>())
{
param.ExecutionContext = executionContext;
}
IOperation next = instrumentedOperator.InstrumentedApply();
return(next);
}
finally {
foreach (ILookupParameter param in instrumentedOperator.Parameters.OfType <ILookupParameter>())
{
param.ExecutionContext = null;
}
instrumentedOperator.ExecutionContext = null;
}
}
private ExecutionContext(ExecutionContext original, Cloner cloner)
: base(original, cloner) {
parent = cloner.Clone(original.parent);
parameterizedItem = cloner.Clone(original.parameterizedItem);
scope = cloner.Clone(original.scope);
}
public override void Prepare() {
base.Prepare();
globalScope.Clear();
globalScope.Variables.Add(new Variable("Results", new ResultCollection()));
if ((engine != null) && (operatorGraph.InitialOperator != null)) {
ExecutionContext context = null;
if (Problem != null) {
foreach (var item in Problem.ExecutionContextItems)
context = new ExecutionContext(context, item, globalScope);
}
context = new ExecutionContext(context, this, globalScope);
context = new ExecutionContext(context, operatorGraph.InitialOperator, globalScope);
engine.Prepare(context);
}
}
private static void SymbolicDataAnalysisCrossoverPerformanceTest(ISymbolicDataAnalysisExpressionCrossover<IRegressionProblemData> crossover) {
var twister = new MersenneTwister(31415);
var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
var grammar = new FullFunctionalExpressionGrammar();
var stopwatch = new Stopwatch();
grammar.MaximumFunctionArguments = 0;
grammar.MaximumFunctionDefinitions = 0;
grammar.MinimumFunctionArguments = 0;
grammar.MinimumFunctionDefinitions = 0;
var trees = Util.CreateRandomTrees(twister, dataset, grammar, PopulationSize, 1, MaxTreeLength, 0, 0);
foreach (ISymbolicExpressionTree tree in trees) {
Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
}
var problemData = new RegressionProblemData(dataset, dataset.VariableNames, dataset.VariableNames.Last());
var problem = new SymbolicRegressionSingleObjectiveProblem();
problem.ProblemData = problemData;
var globalScope = new Scope("Global Scope");
globalScope.Variables.Add(new Core.Variable("Random", twister));
var context = new ExecutionContext(null, problem, globalScope);
context = new ExecutionContext(context, crossover, globalScope);
stopwatch.Start();
for (int i = 0; i != PopulationSize; ++i) {
var parent0 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
var scopeParent0 = new Scope();
scopeParent0.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent0));
context.Scope.SubScopes.Add(scopeParent0);
var parent1 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
var scopeParent1 = new Scope();
scopeParent1.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent1));
context.Scope.SubScopes.Add(scopeParent1);
crossover.Execute(context, new CancellationToken());
context.Scope.SubScopes.Remove(scopeParent0); // clean the scope in preparation for the next iteration
context.Scope.SubScopes.Remove(scopeParent1); // clean the scope in preparation for the next iteration
}
stopwatch.Stop();
double msPerCrossover = 2 * stopwatch.ElapsedMilliseconds / (double)PopulationSize;
Console.WriteLine(crossover.Name + ": " + Environment.NewLine +
msPerCrossover + " ms per crossover (~" + Math.Round(1000.0 / (msPerCrossover)) + " crossover operations / s)");
foreach (var tree in trees)
HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Tests.Util.IsValid(tree);
}