Esempio n. 1
0
  /**
   * This method is the access point to the planning procedure. Initially, it adds all variables
   * from axioms to the set of found vars, then does the linear planning. If lp does not solve the
   * problem and there are subtasks, goal-driven recursive planning with backtracking is invoked.
   * Planning is performed until no new variables are introduced into the algorithm.
   */
  public EvaluationAlgorithm invokePlaning(Problem problem, boolean _computeAll) {
    long startTime = System.currentTimeMillis();

    computeAll = _computeAll;
    EvaluationAlgorithm algorithm = new EvaluationAlgorithm();

    PlanningContext context = problem.getCurrentContext();

    // add all axioms at the beginning of an algorithm
    Collection<Var> flattened = new HashSet<Var>();
    for (Iterator<Rel> axiomIter = problem.getAxioms().iterator(); axiomIter.hasNext(); ) {
      Rel rel = axiomIter.next();

      unfoldVarsToSet(rel.getOutputs(), flattened);

      // do not overwrite values of variables that come via args of compute() or as inputs of
      // independent subtasks
      if (!problem.getAssumptions().containsAll(flattened)
      // do not overwrite values of already known variables.
      // typically this is the case when a value of a variable
      // is given in a scheme via a properties window
      //                    && !problem.getKnownVars().containsAll( flattened )
      ) {
        algorithm.addRel(rel);
      }
      axiomIter.remove();
      context.getKnownVars().addAll(flattened);
      flattened.clear();
    }

    context.getFoundVars().addAll(context.getKnownVars());

    // remove all known vars with no relations
    for (Iterator<Var> varIter = context.getKnownVars().iterator(); varIter.hasNext(); ) {
      if (varIter.next().getRels().isEmpty()) {
        varIter.remove();
      }
    }

    // start planning
    if (problem.getRelsWithSubtasks().isEmpty()
        && linearForwardSearch(context, algorithm, computeAll)) {
      if (isLinearLoggingOn()) logger.debug("Problem solved without subtasks");
    } else if (!problem.getRelsWithSubtasks().isEmpty() && subtaskPlanning(problem, algorithm)) {
      if (isLinearLoggingOn()) logger.debug("Problem solved with subtasks");
    } else if (!computeAll) {
      if (isLinearLoggingOn()) logger.debug("Problem not solved");
    }

    if (!nested) {
      logger.info("Planning time: " + (System.currentTimeMillis() - startTime) + "ms.");
    }
    return algorithm;
  }
Esempio n. 2
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  public static void MLalgo() {
    try {
      Problem problem = new Problem();
      problem.l = train_count; // number of training examples
      problem.n = max_feature_count; // number of features
      problem.x = train_matrix; // feature nodes
      problem.y = ylable; // target values;

      SolverType solver = SolverType.L2R_LR; // -s 0
      double C = 1.0; // cost of constraints violation
      double eps = 0.01; // stopping criteria

      Parameter parameter = new Parameter(solver, C, eps);
      model = Linear.train(problem, parameter);

      File modelFile = new File("model");
      model.save(modelFile);
      // load model or use it directly
      model = Model.load(modelFile);
    } catch (Exception e) {
      e.printStackTrace();
    }
  }
Esempio n. 3
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  void getInfo() {
    Scanner console = new Scanner(System.in);
    int maxCost = 0, maxSols = 0;
    Problem prob = null;
    Solver solve = null;
    ArrStack stk = new ArrStack();

    System.out.print("Enter problem type, solution type, " + "max cost and max # of solutions: ");
    try {
      prob = (Problem) Class.forName(console.next()).newInstance();
      solve = (Solver) Class.forName(console.next()).newInstance();
      maxCost = console.nextInt();
      maxSols = console.nextInt();
    } catch (Exception e) {
      System.out.println("" + e);
    }

    try {
      prob.read(console);
    } catch (Exception e) {
      System.out.println("Read error: " + e);
      return;
    }

    Solver.Solution[] sols;
    sols = solve.solveProblem(prob, maxCost, maxSols);
    if (sols == null) {
      System.out.println("No solutions ");
      return;
    }
    System.out.println("Answers are: ");
    for (int ans = 0; ans < sols.length && sols[ans] != null; ans++) {
      System.out.println("Answer " + ans + " with cost " + (sols[ans].mSteps.length - 1));
      for (int stepNdx = 1; stepNdx < sols[ans].mSteps.length; stepNdx++)
        System.out.println("   " + sols[ans].mSteps[stepNdx]);
    }
  }
  private void mainClassifierFunction(int option, String trainFile, String testFile, String ddgFile)
      throws IOException {
    // SentimentClassifierHindi this = new SentimentClassifierHindi();
    // int finalSize = this.SentimentClassifierHindi();
    int finalSize = this.generateFeature(option, trainFile, testFile, ddgFile);
    System.out.println("Hello aspectCategorizationSemEval2016!");

    // Create features
    Problem problem = new Problem();

    // Save X to problem
    double a[] = new double[this.trainingFeature.size()];
    File file = new File(rootDirectory + "\\dataset\\trainingLabels.txt");
    BufferedReader reader = new BufferedReader(new FileReader(file));
    String read;
    int count = 0;
    while ((read = reader.readLine()) != null) {
      // System.out.println(read);
      a[count++] = Double.parseDouble(read.toString());
    }

    // Feature[][] f = new Feature[][]{ {}, {}, {}, {}, {}, {} };

    // trainingFeature = trainingObject.getList();
    Feature[][] trainFeatureVector = new Feature[trainingFeature.size()][finalSize];

    System.out.println("Training Instances: " + trainingFeature.size());
    System.out.println("Feature Length: " + finalSize);
    System.out.println("Test Instances: " + testFeature.size());

    for (int i = 0; i < trainingFeature.size(); i++) {
      // System.out.println();
      // System.out.println(trainingFeature.get(i));
      System.out.println(i + " trained.");
      for (int j = 0; j < finalSize; j++) {
        // System.out.print(trainingFeature.get(i).get(j + 1)+" ");
        // trainingFeature.get(i).
        if (trainingFeature.get(i).containsKey(j + 1)) {
          // System.out.print(j + 1 + ", ");
          trainFeatureVector[i][j] = new FeatureNode(j + 1, trainingFeature.get(i).get(j + 1));
        } else {
          trainFeatureVector[i][j] = new FeatureNode(j + 1, 0.0);
        }
      }
      // System.out.println();
    }

    problem.l = trainingFeature.size(); // number of training examples
    problem.n = finalSize; // number of features
    problem.x = trainFeatureVector; // feature nodes
    problem.y = a; // target values ----

    BasicParser bp = new BasicParser();

    SolverType solver = SolverType.L2R_LR; // -s 7
    double C = 0.75; // cost of constraints violation
    double eps = 0.0001; // stopping criteria

    Parameter parameter = new Parameter(solver, C, eps);
    Model model = Linear.train(problem, parameter);
    File modelFile = new File("model");
    model.save(modelFile);

    // PrintWriter write = new PrintWriter(new BufferedWriter(new FileWriter(rootDirectory +
    // "\\dataset\\predictedLabels.txt")));
    PrintWriter write =
        new PrintWriter(
            new BufferedWriter(
                new FileWriter(
                    rootDirectory
                        + "\\dataset\\dataset_aspectCategorization\\predictedHotelsLabels.txt")));

    if (option == 1) {
      BufferedReader trainReader =
          new BufferedReader(
              new FileReader(
                  new File(
                      rootDirectory + "\\dataset\\dataset_aspectCategorization\\" + trainFile)));
      HashMap<String, Integer> id = new HashMap<String, Integer>();
      HashMap<String, String> review = new HashMap<String, String>();
      double[] val = new double[trainingFeature.size()];
      double[] tempVal = new double[trainingFeature.size()];
      LinearCopy.crossValidation(problem, parameter, 5, val, tempVal);
      for (int i = 0; i < trainingFeature.size(); i++) {
        int flag = 0;
        String tokens[] = trainReader.readLine().split("\\|");
        if (id.containsKey(tokens[1]) == true || tokens[2].compareToIgnoreCase("True") == 0) {
        } else {
          // System.out.println(tokens[1]);
          /*int max = -1;
          double probMax = -1.0;
          for(int j=0; j<13; j++){
              if(probMax<val[i][j]){
                  probMax = val[i][j];
                  max = j;
              }
          }*/
          // System.out.println(tempVal[i]);
          write.println((int) (val[i]));
          write.println("next");
          id.put(tokens[1], 1);
          System.out.println(tokens[1] + "\t" + (int) (val[i]));
          if (review.containsKey(tokens[1])) {
            System.out.println(tokens[3]);
            System.out.println(review.get(tokens[1]));
          } else {
            review.put(tokens[1], tokens[3]);
          }
        } /*else{
              for (int j = 0; j < 13; j++) {
                  //System.out.print(val[i][j]+", ");
                  if (val[i] >= 0.185) {
                      flag = 1;
                      //System.out.println("i");
                      write.println(j + 1);
                  }
              }
              if (flag == 1) {
                  write.println("next");
              } else {
                  write.println("-1");
                  write.println("next");
              }
              //write.println(prediction);
              id.put(tokens[1], 1);
              //System.out.println();
          }*/
      }
      write.close();
      return;
    }

    if (option == 3) {
      System.out.println(rootDirectory);
      BufferedReader testReader =
          new BufferedReader(
              new FileReader(
                  new File(
                      rootDirectory + "\\dataset\\dataset_aspectCategorization\\" + testFile)));
      HashMap<String, Integer> id = new HashMap<String, Integer>();
      model = Model.load(modelFile);
      int countNext = 0;
      for (int i = 0; i < testFeature.size(); i++) {
        // System.out.println(i+", "+testFeature.size()+", "+testFeature.get(i).size());
        Feature[] instance = new Feature[testFeature.get(i).size()];
        int j = 0;
        for (Map.Entry<Integer, Double> entry : testFeature.get(i).entrySet()) {
          // System.out.print(entry.getKey() + ": " + entry.getValue() + ";   ");
          // listOfMaps.get(i).put(start + entry.getKey(), entry.getValue());
          // do stuff
          instance[j++] = new FeatureNode(entry.getKey(), entry.getValue());
        }

        // double d = LinearCopy.predict(model, instance);

        double[] predict = new double[85];
        double prediction = LinearCopy.predictProbability(model, instance, predict);

        int labelMap[] = new int[13];
        labelMap = model.getLabels();

        for (int ar = 0; ar < labelMap.length; ar++) {
          System.out.println("********************** " + ar + ": " + labelMap[ar]);
        }

        // System.out.println(prediction);
        // Arrays.sort(predict, Collections.reverseOrder());
        // System.out.println();
        // double prediction = LinearCopy.predict(model, instance);
        String tokens[] = testReader.readLine().split("\\|");
        // System.out.println(tokens[1]);

        int flag = -1;
        if (id.containsKey(tokens[1]) == true || tokens[2].compareToIgnoreCase("True") == 0) {
          flag = 4;
          // System.out.println("OutofScope: "+tokens[1]);
        } else if (tokens[3].compareToIgnoreCase("abc") == 0) {
          flag = 2;
          System.out.println(tokens[1]);
          write.println("-1");
          write.println("next");
          countNext++;
          id.put(tokens[1], 1);
        } else {
          flag = 0;
          for (int p = 0; p < 85; p++) {
            if (predict[p] >= 0.128) {
              flag = 1;
              write.println(labelMap[p]);
            }
          }
          if (flag == 1) {
            countNext++;
            write.println("next");
          } else {
            countNext++;
            write.println("-1");
            write.println("next");
          }

          // write.println((int)d);
          // write.println("next");

          /*write.println(prediction);
          write.println("next");*/
          id.put(tokens[1], 1);
        }

        if (flag == -1) {
          System.out.println("-1,   " + tokens[1]);
        }
      }

      write.close();
      System.out.println("count " + countNext);
    }
    write.close();
  }
Esempio n. 5
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  /**
   * Goal-driven recursive (depth-first, exhaustive) search with backtracking
   *
   * @param problem
   * @param algorithm
   * @param subtaskRelsInPath
   * @param depth
   */
  private boolean subtaskPlanningImpl(
      PlanningContext context,
      Set<Rel> relsWithSubtasks,
      EvaluationAlgorithm algorithm,
      LinkedList<Rel> subtaskRelsInPath,
      int depth) {

    Set<Rel> relsWithSubtasksCopy = new LinkedHashSet<Rel>(relsWithSubtasks);

    Set<Rel> relsWithSubtasksToRemove = new LinkedHashSet<Rel>();

    boolean firstMLB = true;

    // start building Maximal Linear Branch (MLB)
    MLB:
    while (!relsWithSubtasksCopy.isEmpty()) {

      if (isSubtaskLoggingOn()) {
        String print = p(depth) + "Starting new MLB with: ";
        for (Rel rel : relsWithSubtasksCopy) {
          print +=
              "\n" + p(depth) + "  " + rel.getParent().getFullName() + " : " + rel.getDeclaration();
        }
        /*
        print += "\n" + p( depth ) + " All remaining rels in problem:";
        for ( Rel rel : problem.getAllRels() ) {
            print += "\n" + p( depth ) + " " + rel.getParentObjectName() + " : " + rel.getDeclaration();
        }
        print += "\n" + p( depth ) + "All found variables: ";
        for ( Var var : problem.getFoundVars() ) {
            print += "\n" + p( depth ) + " " + var.toString();
        }
        */
        logger.debug(print);
      }

      // if this is a first attempt to construct an MLB to solve a subtask(i.e. depth>0),
      // do not invoke linear planning because it has already been done
      if ((depth == 0) || !firstMLB) {

        boolean solvedIntermediately = linearForwardSearch(context, algorithm, true);

        // Having constructed some MLBs the (sub)problem may be solved
        // and there is no need in wasting precious time planning unnecessary branches
        if (solvedIntermediately
            && ( // on the top level optimize only if computing goals
            (depth == 0 && !computeAll)
                // otherwise (inside subtasks) always optimize
                || (depth != 0))) {
          // If the problem is solved, optimize and return
          if (!isOptDisabled) Optimizer.optimize(context, algorithm);
          return true;
        }
      } else {
        firstMLB = false;
      }

      // or children
      OR:
      for (Iterator<Rel> subtaskRelIterator = relsWithSubtasksCopy.iterator();
          subtaskRelIterator.hasNext(); ) {

        Rel subtaskRel = subtaskRelIterator.next();

        if (isSubtaskLoggingOn())
          logger.debug(
              p(depth)
                  + "OR: depth: "
                  + (depth + 1)
                  + " rel - "
                  + subtaskRel.getParent().getFullName()
                  + " : "
                  + subtaskRel.getDeclaration());

        if (subtaskRel.equals(subtaskRelsInPath.peekLast())
            || (!context.isRelReadyToUse(subtaskRel))
            || context.getFoundVars().containsAll(subtaskRel.getOutputs())
            || (!isSubtaskRepetitionAllowed && subtaskRelsInPath.contains(subtaskRel))) {

          if (isSubtaskLoggingOn()) {
            logger.debug(p(depth) + "skipped");
            if (!context.isRelReadyToUse(subtaskRel)) {
              logger.debug(p(depth) + "because it has unknown inputs"); // TODO print unknown
            } else if (context.getFoundVars().containsAll(subtaskRel.getOutputs())) {
              logger.debug(p(depth) + "because all outputs in FoundVars");
            } else if (subtaskRel.equals(subtaskRelsInPath.peekLast())) {
              logger.debug(p(depth) + "because it is nested in itself");
            } else if (!isSubtaskRepetitionAllowed && subtaskRelsInPath.contains(subtaskRel)) {
              logger.debug(
                  p(depth)
                      + "This rel with subtasks is already in use, path: "
                      + subtaskRelsInPath);
            }
          }
          continue OR;
        }

        LinkedList<Rel> newPath = new LinkedList<Rel>(subtaskRelsInPath);
        newPath.add(subtaskRel);

        PlanningResult result = new PlanningResult(subtaskRel, true);

        // this is true if all subtasks are solvable
        boolean allSolved = true;
        // and children
        AND:
        for (SubtaskRel subtask : subtaskRel.getSubtasks()) {
          if (isSubtaskLoggingOn()) logger.debug(p(depth) + "AND: subtask - " + subtask);

          EvaluationAlgorithm sbtAlgorithm = null;

          ////////////////////// INDEPENDENT SUBTASK////////////////////////////////////////
          if (subtask.isIndependent()) {
            if (isSubtaskLoggingOn()) logger.debug("Independent!!!");

            if (subtask.isSolvable() == null) {
              if (isSubtaskLoggingOn())
                logger.debug("Start solving independent subtask " + subtask.getDeclaration());
              // independent subtask is solved only once
              Problem problemContext = subtask.getContext();
              DepthFirstPlanner planner = new DepthFirstPlanner();
              planner.indSubtasks = indSubtasks;
              planner.nested = true;
              sbtAlgorithm = planner.invokePlaning(problemContext, isOptDisabled);
              PlanningContext indCntx = problemContext.getCurrentContext();
              boolean solved = indCntx.getFoundVars().containsAll(indCntx.getAllGoals());
              if (solved) {
                subtask.setSolvable(Boolean.TRUE);
                indSubtasks.put(subtask, sbtAlgorithm);
                if (isSubtaskLoggingOn()) logger.debug("Solved " + subtask.getDeclaration());
              } else {
                subtask.setSolvable(Boolean.FALSE);
                if (RuntimeProperties.isLogInfoEnabled()) {
                  logger.debug("Unable to solve " + subtask.getDeclaration());
                }
              }
              allSolved &= solved;
            } else if (subtask.isSolvable() == Boolean.TRUE) {
              if (isSubtaskLoggingOn()) logger.debug("Already solved");
              allSolved &= true;
              sbtAlgorithm = indSubtasks.get(subtask);
            } else {
              if (isSubtaskLoggingOn()) logger.debug("Not solvable");
              allSolved &= false;
            }
            if (isSubtaskLoggingOn()) logger.debug("End of independent subtask " + subtask);

            if (!allSolved) {
              continue OR;
            }

            assert sbtAlgorithm != null;

            result.addSubtaskAlgorithm(subtask, sbtAlgorithm);
          }
          ////////////////////// DEPENDENT SUBTASK//////////////////////////////////////
          else {
            // lets clone the environment
            PlanningContext newContext = prepareNewContext(context, subtask);

            sbtAlgorithm = new EvaluationAlgorithm();

            // during linear planning, if some goals are found, they are removed from the set
            // "goals"
            boolean solved =
                linearForwardSearch(
                    newContext,
                    sbtAlgorithm,
                    // do not optimize here, because the solution may require additional rels with
                    // subtasks
                    true);

            if (solved) {
              if (isSubtaskLoggingOn()) logger.debug(p(depth) + "SOLVED subtask: " + subtask);

              if (!isOptDisabled) {
                // if a subtask has been solved, optimize its algorithm
                Optimizer.optimize(newContext, sbtAlgorithm);
              }

              result.addSubtaskAlgorithm(subtask, sbtAlgorithm);
              allSolved &= solved;
              continue AND;
            } else if (!solved && (depth == maxDepth)) {
              if (isSubtaskLoggingOn())
                logger.debug(p(depth) + "NOT SOLVED and cannot go any deeper, subtask: " + subtask);
              continue OR;
            }

            if (isSubtaskLoggingOn()) logger.debug(p(depth) + "Recursing deeper");

            solved =
                subtaskPlanningImpl(newContext, relsWithSubtasks, sbtAlgorithm, newPath, depth + 1);

            if (isSubtaskLoggingOn()) logger.debug(p(depth) + "Back to depth " + (depth + 1));

            // the linear planning has been performed at the end of MLB on the depth+1,
            // if the problem was solved, there is no need to run linear planning again
            if ((solved || (solved = linearForwardSearch(newContext, sbtAlgorithm, true)))
                && !isOptDisabled) {
              // if solved, optimize here with full list of goals in order to get rid of
              // unnecessary subtask instances and other relations
              Optimizer.optimize(newContext, sbtAlgorithm);
            }

            if (isSubtaskLoggingOn())
              logger.debug(p(depth) + (solved ? "" : "NOT") + " SOLVED subtask: " + subtask);

            allSolved &= solved;

            // if at least one subtask is not solvable, try another
            // branch
            if (!allSolved) {
              continue OR;
            }

            result.addSubtaskAlgorithm(subtask, sbtAlgorithm);
          }
        } // AND

        if (allSolved) {
          algorithm.add(result);

          Set<Var> newVars = new LinkedHashSet<Var>();

          unfoldVarsToSet(subtaskRel.getOutputs(), newVars);

          context.getKnownVars().addAll(newVars);
          context.getFoundVars().addAll(newVars);

          subtaskRelIterator.remove();

          if (isSubtaskLoggingOn()) {
            logger.debug(
                p(depth)
                    + "SOLVED ALL SUBTASKS for "
                    + subtaskRel.getParent().getFullName()
                    + " : "
                    + subtaskRel.getDeclaration());
            logger.debug(p(depth) + "Updating the problem graph and continuing building new MLB");
          }

          // this is used for incremental dfs
          if (depth == 0) {
            relsWithSubtasksToRemove.add(subtaskRel);
          }

          continue MLB;
        }
        if (isSubtaskLoggingOn())
          logger.debug(
              p(depth)
                  + "NOT SOLVED ALL subtasks, removing from path "
                  + subtaskRel.getParent().getFullName()
                  + " : "
                  + subtaskRel.getDeclaration());
        newPath.remove(subtaskRel);
      } // end OR

      // exit loop because there are no more rels with subtasks to be
      // applied
      // (i.e. no more rels can introduce new variables into the
      // algorithm)
      if (isSubtaskLoggingOn()) logger.debug(p(depth) + "No more MLB can be constructed");
      break MLB;
    }

    // incremental dfs, remove solved subtasks
    if (depth == 0) {
      relsWithSubtasks.removeAll(relsWithSubtasksToRemove);
    }

    return false;
  }
Esempio n. 6
0
  private boolean subtaskPlanning(Problem problem, EvaluationAlgorithm algorithm) {

    if (isSubtaskLoggingOn())
      logger.debug("!!!--------- Starting Planning With Subtasks ---------!!!");

    final int maxDepthBackup = maxDepth;
    if (isSubtaskLoggingOn())
      logger.debug(
          "maxDepthBackup:" + maxDepthBackup + " sbt: " + problem.getRelsWithSubtasks().size());
    PlanningContext context = problem.getCurrentContext();

    try {

      Set<Rel> relsWithSubtasks = new LinkedHashSet<Rel>(problem.getRelsWithSubtasks());

      if (isIncremental) {
        int incrementalDepth = 0;

        while (incrementalDepth
            <= (isSubtaskRepetitionAllowed
                ? maxDepthBackup
                : problem.getRelsWithSubtasks().size() - 1)) {
          if (isSubtaskLoggingOn())
            logger.debug(
                "Incremental dfs, with max depth "
                    + (incrementalDepth + 1)
                    + " and "
                    + problem.getRelsWithSubtasks().size()
                    + " subtasks to solve");

          maxDepth = incrementalDepth++;

          // if we need to compute some specific goals, after reaching a certain depth, but not the
          // maximal depth,
          // the problem may be solved and there is no need to go any deeper.
          if (subtaskPlanningImpl(context, relsWithSubtasks, algorithm, new LinkedList<Rel>(), 0)) {
            if (isSubtaskLoggingOn())
              logger.debug("The problem was solved during idfs after some intermediate MLB");
            return true;
          }

          if (isSubtaskLoggingOn())
            logger.debug("Unsolved subtask left: " + problem.getRelsWithSubtasks().size());
        }

        if (isSubtaskLoggingOn()) logger.debug("Fininshed incremental dfs");

      } else {
        if (!isSubtaskRepetitionAllowed) {
          maxDepth = problem.getRelsWithSubtasks().size() - 1;
        }

        if (isSubtaskLoggingOn())
          logger.debug("Starting subtask dfs with maxDepth: " + (maxDepth + 1));

        if (subtaskPlanningImpl(context, relsWithSubtasks, algorithm, new LinkedList<Rel>(), 0)) {
          if (isSubtaskLoggingOn())
            logger.debug("The problem was solved during dfs after some intermediate MLB");
          return true;
        }
      }

    } finally {
      if (isSubtaskLoggingOn()) logger.debug("Fininshed dfs");

      maxDepth = maxDepthBackup;
      indSubtasks.clear();
    }

    if (isSubtaskLoggingOn()) logger.debug("Invoking final linear planning");

    return linearForwardSearch(context, algorithm, computeAll);
  }