source: branches/HeuristicLab.Problems.GrammaticalOptimization/Main/Program.cs @ 11730

using System;
using System.Collections.Generic;
using System.Data;
using System.Diagnostics;
using System.Globalization;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using HeuristicLab.Algorithms.Bandits;
using HeuristicLab.Algorithms.Bandits.Models;
using HeuristicLab.Algorithms.GrammaticalOptimization;
using HeuristicLab.Problems.GrammaticalOptimization;

namespace Main {
  class Program {
    static void Main(string[] args) {
      CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;

      RunDemo();
      //RunGridTest();
    }

    private static void RunGridTest() {
      int maxIterations = 100000; // for poly-10 with 50000 evaluations no successful try with hl yet
      // var globalRandom = new Random(31415);
      var localRandSeed = 31415;
      var reps = 20;

      var policyFactories = new Func<Random, int, IPolicy>[]
        {
          (rand, numActions) => new GaussianThompsonSamplingPolicy(rand, numActions), 
          (rand, numActions) => new BernoulliThompsonSamplingPolicy(rand, numActions),
          (rand, numActions) => new RandomPolicy(rand, numActions), 
          (rand, numActions) => new EpsGreedyPolicy(rand, numActions, 0.01), 
          (rand, numActions) => new EpsGreedyPolicy(rand, numActions, 0.05), 
          (rand, numActions) => new EpsGreedyPolicy(rand, numActions, 0.1), 
          (rand, numActions) => new EpsGreedyPolicy(rand, numActions, 0.2), 
          (rand, numActions) => new EpsGreedyPolicy(rand, numActions, 0.5), 
          (rand, numActions) => new UCTPolicy(numActions, 0.1),
          (rand, numActions) => new UCTPolicy(numActions, 0.5),
          (rand, numActions) => new UCTPolicy(numActions, 1),
          (rand, numActions) => new UCTPolicy(numActions, 2),
          (rand, numActions) => new UCTPolicy(numActions, 5),
          (rand, numActions) => new UCTPolicy(numActions, 10),
          (rand, numActions) => new UCB1Policy(numActions), 
          (rand, numActions) => new UCB1TunedPolicy(numActions), 
          (rand, numActions) => new UCBNormalPolicy(numActions), 
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 0.1), 
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 0.5),
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 1),
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 5),
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 10),
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 20),
          (rand, numActions) => new BoltzmannExplorationPolicy(rand, numActions, 100),
          (rand, numActions) => new ChernoffIntervalEstimationPolicy(numActions, 0.01), 
          (rand, numActions) => new ChernoffIntervalEstimationPolicy(numActions, 0.05),
          (rand, numActions) => new ChernoffIntervalEstimationPolicy(numActions, 0.1),
          (rand, numActions) => new ChernoffIntervalEstimationPolicy(numActions, 0.2),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 10, 0.01), 
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 10, 0.05),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 10, 0.1),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 10, 0.2),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 100, 0.01), 
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 100, 0.05),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 100, 0.1),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 100, 0.2),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 1000, 0.01), 
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 1000, 0.05),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 1000, 0.1),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 1000, 0.2),
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 5000, 0.01), 
          (rand, numActions) => new ThresholdAscentPolicy(numActions, 10000, 0.01), 
        };

      var tasks = new List<Task>();
      foreach (var randomTries in new int[] { 1, 10, /* 5, 100 /*, 500, 1000 */}) {
        foreach (var policyFactory in policyFactories) {
          var myPolicyFactory = policyFactory;
          var myRandomTries = randomTries;
          var localRand = new Random(localRandSeed);
          var options = new ParallelOptions();
          options.MaxDegreeOfParallelism = 1;
          Parallel.For(0, reps, options, (i) => {
            //var t = Task.Run(() => {
            Random myLocalRand;
            lock (localRand)
              myLocalRand = new Random(localRand.Next());

            //for (int i = 0; i < reps; i++) {

            int iterations = 0;
            var sw = new Stopwatch();
            var globalStatistics = new SentenceSetStatistics();

            var problem = new SymbolicRegressionPoly10Problem();
            //var problem = new SantaFeAntProblem();
            //var problem = new PalindromeProblem();
            //var problem = new HardPalindromeProblem();
            //var problem = new RoyalPairProblem();
            //var problem = new EvenParityProblem();
            var alg = new MctsSampler(problem, 25, myLocalRand, myRandomTries, myPolicyFactory);
            //var alg = new ExhaustiveBreadthFirstSearch(problem, 25);
            //var alg = new AlternativesContextSampler(problem, 25);

            alg.SolutionEvaluated += (sentence, quality) => {
              iterations++;
              globalStatistics.AddSentence(sentence, quality);
              if (iterations % 10000 == 0) {
                Console.WriteLine("{0,4} {1,7} {2,5} {3,25} {4}", alg.treeDepth, alg.treeSize, myRandomTries, myPolicyFactory(myLocalRand, 1), globalStatistics);
              }
            };

            sw.Start();

            alg.Run(maxIterations);

            sw.Stop();
            //Console.WriteLine("{0,5} {1} {2}", randomTries, policyFactory(1), globalStatistics);
            //}
            //});
            //tasks.Add(t);
          });
        }
      }
      //Task.WaitAll(tasks.ToArray());
    }

    private static void RunDemo() {
      // TODO: warum funktioniert die alte Implementierung von GaussianThompson besser für SantaFe als alte? Siehe Vergleich: alte vs. neue implementierung GaussianThompsonSampling
      // TODO: why does GaussianThompsonSampling work so well with MCTS for the artificial ant problem?
      // TODO: wie kann ich sampler noch vergleichen bzw. was kann man messen um die qualität des samplers abzuschätzen (bis auf qualität und iterationen bis zur besten lösung) => ziel schnellere iterationen zu gutem ergebnis
      // TODO: likelihood für R=1 bei Gaussian oder GaussianMixture einfach berechenbar?
      // TODO: research thompson sampling for max bandit?
      // TODO: ausführlicher test von strategien für k-armed max bandit
      // TODO: verify TA implementation using example from the original paper
      // TODO: reference HL.ProblemInstances and try on tower dataset
      // TODO: compare results for different policies also for the symb-reg problem
      // TODO: separate policy from MCTS tree data structure to allow sharing of information over disconnected parts of the tree (semantic equivalence)
      // TODO: implement thompson sampling for gaussian mixture models
      // TODO: implement inspection for MCTS (eventuell interactive command line für statistiken aus dem baum anzeigen)
      // TODO: implement ACO-style bandit policy
      // TODO: implement sequences that can be manipulated in-place (instead of strings), alternatives are also stored as sequences, for a sequence the index of the first NT-symb can be stored 
      // TODO: gleichzeitige modellierung von transformierter zielvariable (y, 1/y, log(y), exp(y), sqrt(y), ...)
      // TODO: vergleich bei complete-randomly möglichst kurze sätze generieren vs. einfach zufällig alternativen wählen
      // TODO: reward discounting (für veränderliche reward distributions über zeit). speziellen unit-test dafür erstellen


      int maxIterations = 10000000;
      int iterations = 0;
      var sw = new Stopwatch();
      double bestQuality = 0;
      string bestSentence = "";
      var globalStatistics = new SentenceSetStatistics();
      var random = new Random();

      //var problem = new SymbolicRegressionPoly10Problem();
      var problem = new SantaFeAntProblem();
      //var problem = new PalindromeProblem();
      //var problem = new HardPalindromeProblem();
      //var problem = new RoyalPairProblem();
      //var problem = new EvenParityProblem();
      //var alg = new MctsSampler(problem, 17, random, 1, (rand, numActions) => new GenericThompsonSamplingPolicy(rand, numActions, new GaussianModel(numActions, 0.5, 10)));
      //var alg = new ExhaustiveBreadthFirstSearch(problem, 17);
      //var alg = new AlternativesContextSampler(problem, random, 17, 4, (rand, numActions) => new RandomPolicy(rand, numActions));
      //var alg = new ExhaustiveDepthFirstSearch(problem, 17);
      // var alg = new AlternativesSampler(problem, 17);
      var alg = new RandomSearch(problem, random, 17);

      alg.FoundNewBestSolution += (sentence, quality) => {
        bestQuality = quality;
        bestSentence = sentence;
        Console.WriteLine("{0,10} {1,10:F5} {2,10:F5} {3}", iterations, bestQuality, quality, sentence);
      };
      alg.SolutionEvaluated += (sentence, quality) => {
        iterations++;
        globalStatistics.AddSentence(sentence, quality);
        if (iterations % 1000 == 0) {
          //alg.PrintStats();
        }
        if (iterations % 10000 == 0) {
          //Console.WriteLine("{0,10} {1,10:F5} {2,10:F5} {3}", iterations, bestQuality, quality, sentence);
          //Console.WriteLine("{0,4} {1,7} {2}", alg.treeDepth, alg.treeSize, globalStatistics);
          Console.WriteLine(globalStatistics);
        }
      };


      sw.Start();

      alg.Run(maxIterations);

      sw.Stop();

      Console.WriteLine("{0,10} Best soultion: {1,10:F5} {2}", iterations, bestQuality, bestSentence);
      Console.WriteLine("{0:F2} sec {1,10:F1} sols/sec {2,10:F1} ns/sol",
        sw.Elapsed.TotalSeconds,
        maxIterations / (double)sw.Elapsed.TotalSeconds,
        (double)sw.ElapsedMilliseconds * 1000 / maxIterations);
    }
  }
}