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);
}
}
private void TestFullGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
var twister = new MersenneTwister(31415);
var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
var grammar = new FullFunctionalExpressionGrammar();
grammar.MaximumFunctionArguments = 0;
grammar.MaximumFunctionDefinitions = 0;
grammar.MinimumFunctionArguments = 0;
grammar.MinimumFunctionDefinitions = 0;
var randomTrees = Util.CreateRandomTrees(twister, dataset, grammar, N, 1, 100, 0, 0);
foreach (ISymbolicExpressionTree tree in randomTrees) {
Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
}
double nodesPerSec = Util.CalculateEvaluatedNodesPerSec(randomTrees, interpreter, dataset, 3);
//mkommend: commented due to performance issues on the builder
//Assert.IsTrue(nodesPerSec > nodesPerSecThreshold); // evaluated nodes per seconds must be larger than 15mNodes/sec
}
private void TestFullGrammarPerformance(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold)
{
var twister = new MersenneTwister(31415);
var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
var grammar = new FullFunctionalExpressionGrammar();
var randomTrees = Util.CreateRandomTrees(twister, dataset, grammar, N, 1, 100, 0, 0);
foreach (ISymbolicExpressionTree tree in randomTrees)
{
Util.InitTree(tree, twister, new List <string>(dataset.VariableNames));
}
double nodesPerSec = Util.CalculateEvaluatedNodesPerSec(randomTrees, interpreter, dataset, 3);
//mkommend: commented due to performance issues on the builder
//Assert.IsTrue(nodesPerSec > nodesPerSecThreshold); // evaluated nodes per seconds must be larger than 15mNodes/sec
}
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);
}
