source: branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/Main/Program.cs @ 12700

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Globalization;
using System.Linq;
using System.Text.RegularExpressions;
using HeuristicLab.Algorithms.Bandits.BanditPolicies;
using HeuristicLab.Algorithms.Bandits.GrammarPolicies;
using HeuristicLab.Algorithms.GrammaticalOptimization;
using HeuristicLab.Algorithms.MonteCarloTreeSearch;
using HeuristicLab.Algorithms.MonteCarloTreeSearch.Simulation;
using HeuristicLab.Problems.GrammaticalOptimization;

// NOTES: gkronber
// TODO: feature extraction for full symbolic expressions and experiment for all benchmark problems
// TODO: why does GaussianThompsonSampling work so well with MCTS for the artificial ant problem?
// TODO: research thompson sampling for max bandit?
// TODO: verify TA implementation using example from the original paper      
// TODO: implement thompson sampling for gaussian mixture models
// TODO: gleichzeitige modellierung von transformierter zielvariable (y, 1/y, log(y), exp(y), sqrt(y), ...)
// TODO: vergleich bei complete-randomly möglichst kurze sÀtze generieren vs. einfach zufÀllig alternativen wÀhlen
// TODO: reward discounting (fÌr verÀnderliche reward distributions Ìber zeit). speziellen unit-test dafÌr erstellen
// TODO: constant optimization 
using HeuristicLab.Problems.GrammaticalOptimization.SymbReg;


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

      RunDemo();
    }


    private static void RunDemo() {

      for (int i = 0; i < 100; i++) {
        int maxIterations = 2000000;
        int iterations = 0;

        var globalStatistics = new SentenceSetStatistics();
        ResetAlleleStatistics();
        var random = new Random();

        var problem = new SymbolicRegressionPoly10Problem();
        //var problem = new SantaFeAntProblem();              
        //var problem = new RoyalPairProblem(25);
        //var problem = new FindPhrasesProblem(random, 10, 5, 3, 5, 5, 1.0, 0.9, true);
        //var problem = new PrimePolynomialProblem();
        //var problem = new SymbolicRegressionProblem(random,
        //  //@"C:\reps\HeuristicLab\branches\HeuristicLab.Problems.GrammaticalOptimization\HeuristicLab.Problems.GrammaticalOptimization.SymbReg\nht-train.csv",
        //  @"C:\reps\fhooe-new\research\Datasets\Benchmark\kommenda-1.csv",
        //  1.0,
        //  true);
        // //var problem = new PrimePolynomialProblem();
        // var alg = new SequentialSearch(problem, 25, random, 0,
        //   new HeuristicLab.Algorithms.Bandits.GrammarPolicies.GenericGrammarPolicy(problem, new UCB1TunedPolicy()));
        var policy = new GenericPolicy(problem);
        var alg = new SequentialSearch(problem, 23, random, 0,
          policy);
        //var alg = new MonteCarloTreeSearch(problem, 23, random, new UCB1Policy(), new RandomSimulation(problem, random, 30));


        alg.FoundNewBestSolution += (sentence, quality) => {
          //Console.WriteLine("{0}", globalStatistics);
        };

        alg.SolutionEvaluated += (sentence, quality) => {
          iterations++;
          globalStatistics.AddSentence(sentence, quality);
          UpdateAlleleStatistics(sentence);
          // comment this if you don't want to see solver statistics
          if (iterations % 100 == 0) {
            if (iterations % 1000 == 0) {
              Console.Clear();
            }
            Console.SetCursorPosition(0, 0);
            Console.WriteLine(iterations);
            WriteAlleleStatistics();
            Console.WriteLine(globalStatistics.BestSentenceQuality);
            Console.WriteLine(globalStatistics.BestSentence);
            Console.WriteLine(globalStatistics);
            //alg.PrintStats();
            policy.PrintStats();
            //ResetAlleleStatistics();
          }
          
          // uncomment this if you want to collect statistics of the generated sentences
          //if (iterations % 100 == 0) {
          //  Console.WriteLine("{0}", globalStatistics);
          //}
        };

        var sw = new Stopwatch();
        sw.Start();
        alg.Run(maxIterations);
        sw.Stop();

        Console.WriteLine(globalStatistics);

        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);
      }
    }

    private static void UpdateAlleleStatistics(string sentence) {
      for (int i = 0; i < sentence.Length; i++) {
        var allele = sentence.Substring(i, 1);
        if (alleleStatistics.ContainsKey(allele)) alleleStatistics[allele]++;
      }
      for (int i = 0; i < sentence.Length - 2; i+=2) {
        var allele = sentence.Substring(i, 3);
        if (alleleStatistics.ContainsKey(allele)) alleleStatistics[allele]++;
      }
      for (int i = 0; i < sentence.Length - 4; i+=2) {
        var allele = sentence.Substring(i, 5);
        if (alleleStatistics.ContainsKey(allele)) alleleStatistics[allele]++;
      }
    }


    private static Dictionary<string, int> alleleStatistics;

    private static void ResetAlleleStatistics() {
      alleleStatistics = new Dictionary<string, int>()
      {
        {"a", 0},
        {"b", 0},
        {"c", 0},
        {"d", 0},
        {"e", 0},
        {"f", 0},
        {"g", 0},
        {"h", 0},
        {"i", 0},
        {"j", 0},
        {"a*b", 0},
        {"b*a", 0},
        {"c*d", 0},
        {"d*c", 0},
        {"e*f", 0},
        {"f*e", 0},
        {"a*g*i", 0},
        {"a*i*g", 0},
        {"g*a*i", 0},
        {"g*i*a", 0},
        {"i*g*a", 0},
        {"i*a*g", 0},
        {"j*c*f", 0},
        {"j*f*c", 0},
        {"c*j*f", 0},
        {"c*f*j", 0},
        {"f*c*j", 0},
        {"f*j*c", 0}
      };
    }


    private static void WriteAlleleStatistics() {
      double count = alleleStatistics.Sum(e => e.Value);
      foreach (var entry in alleleStatistics.OrderByDescending(e=>e.Value)) {
        Console.WriteLine("{0,-10} {1,-10}", entry.Key, entry.Value);
      }
    }
  }
}