Examples of cc.mallet.util.Timing

A class for timing things. Originally inspired by the Timing class in the Stanford NLP cade, but completely rewritten.

Created: Dec 30, 2004 @author

    return avgEnergy - entropy;
  }


  public void computeMarginals (FactorGraph mdl)
  {
    Timing timing = new Timing ();


    this.mdl = mdl;
    rg = regioner.constructRegionGraph (mdl);
    RegionEdge[] pairs = chooseMessageSendingOrder ();


    newMessages = new MessageArray (rg);


    timing.tick ("GBP Region Graph construction");
    
    int iter = 0;
    do {


      oldMessages = newMessages;
      newMessages = oldMessages.duplicate ();
      sender.setMessageArray (oldMessages, newMessages);


      for (int i = 0; i < pairs.length; i++) {
        RegionEdge edge = pairs[i];
        sender.sendMessage (edge);
      }


      if (logger.isLoggable (Level.FINER)) {
        timing.tick ("GBP iteration "+iter);
      }


      iter++;


      if (useInertia)

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//    initForGraph (mdl);
    Assignment assn = initialAssignment (mdl);
    if (assn == null)
  throw new IllegalArgumentException ("GibbsSampler: Could not find feasible assignment for model "+mdl);


    Timing timing = new Timing ();
    for (int i = 0; i < burnin; i++) {
      assn = doOnePass (mdl, assn);
    }
    timing.tick ("Burnin");


    Assignment ret = new Assignment ();
    for (int i = 0; i < N; i++) {
      assn = doOnePass (mdl, assn);
      ret.addRow (assn);
    }
    timing.tick ("Sampling");


    return ret;
  }

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    // don't cache test set
    boolean wasCached = acrf.isCacheUnrolledGraphs ();
    acrf.setCacheUnrolledGraphs (false);


    Timing timing = new Timing ();


    if (!eval.evaluate (acrf, iter+1, trainingList, validationList, testSet)) {
      logger.info ("ACRF trainer: evaluator returned false. Quitting.");
      timing.tick ("Evaluation time (iteration "+iter+")");
      return true;
    }


    timing.tick ("Evaluation time (iteration "+iter+")");


    // set test set caching back to normal
    acrf.setCacheUnrolledGraphs (wasCached);
    return false;
  }

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  {
    FactorGraph baseFg = RandomGraphs.randomFrustratedGrid (25, 1.0, new java.util.Random (3324879));
    Assignment assn = new Assignment (baseFg, new int[baseFg.numVariables ()]);
    double val = baseFg.logValue (assn);


    Timing timing = new Timing ();


    int numReps = 100;
    for (int rep = 0; rep < numReps; rep++) {
      FactorGraph fg = new FactorGraph (baseFg.numVariables ());
      for (int fi = 0; fi < baseFg.factors().size(); fi++) {
        fg.multiplyBy (baseFg.getFactor (fi));
      }
      assertEquals (val, fg.logValue (assn), 1e-5);
    }
    long time1 = timing.elapsedTime ();
    timing.tick ("No-expansion time");


    for (int rep = 0; rep < numReps; rep++) {
      FactorGraph fg = new FactorGraph ();
      for (int fi = 0; fi < baseFg.factors().size(); fi++) {
        fg.multiplyBy (baseFg.getFactor (fi));
      }
      assertEquals (val, fg.logValue (assn), 1e-5);
    }
    long time2 = timing.elapsedTime ();
    timing.tick ("With-expansion time");


    assertTrue (time1 < time2);
  }

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    return extor;
  }


  private ACRF perTemplateTrain ()
  {
    Timing timing = new Timing ();
    boolean hasConverged = false;


    ACRF miniAcrf = null;
    if (training == null) setupData ();
    for (int ti = 0; ti < tmpls.length; ti++) {
      ACRF.Template[] theseTmpls = new ACRF.Template[ti+1];
      System.arraycopy (tmpls, 0, theseTmpls, 0, theseTmpls.length);
      logger.info ("***PerTemplateTrain: Round "+ti+"\n  Templates: "+
              CollectionUtils.dumpToString (Arrays.asList (theseTmpls), " "));
      miniAcrf = new ACRF (featurePipe, theseTmpls);
      setupAcrf (miniAcrf);
      ACRFEvaluator eval = setupEvaluator ("tmpl"+ti);
      hasConverged = trainer.train (miniAcrf, training, null, testing, eval, perTemplateIterations);
      timing.tick ("PerTemplateTrain round "+ti);
    }


    // finish by training to convergence
    ACRFEvaluator eval = setupEvaluator ("full");
    if (!hasConverged)

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    return eval;
  }


  protected void setupData ()
  {
    Timing timing = new Timing ();
    training = new InstanceList (featurePipe);
    training.addThruPipe (new PipedIterator (trainIterator, tokPipe));
    if (trainingPct > 0) training = subsetData (training, trainingPct);


    if (testIterator != null) {
      testing = new InstanceList (featurePipe);
      testing.addThruPipe (new PipedIterator (testIterator, tokPipe));
      if (testingPct > 0) testing = subsetData (testing, trainingPct);
    }


    timing.tick ("Data loading");
  }

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    }


    private double computeValueGradientForAssn (Assignment observations, ACRF.UnrolledVarSet clique, Variable target)
    {
      numCvgaCalls++;
      Timing timing = new Timing ();


      ACRF.Template tmpl = clique.getTemplate ();
      int tidx = tmpl.index;
      Assignment cliqueAssn = Assignment.restriction (observations, clique);
      int M = target.getNumOutcomes ();
      double[] vals = new double [M];
      int[] singles = new int [M];
      for (int assnIdx = 0; assnIdx < M; assnIdx++) {
        cliqueAssn.setValue (target, assnIdx);
        vals[assnIdx] = computeLogFactorValue (cliqueAssn, tmpl, clique.getFv ());
        singles[assnIdx] = cliqueAssn.singleIndex ();
      }
      double logZ = Maths.sumLogProb (vals);


      for (int assnIdx = 0; assnIdx < M; assnIdx++) {
        double marginal = Math.exp (vals[assnIdx] - logZ);
        int expIdx = singles[assnIdx];
        expectations[tidx][expIdx].plusEqualsSparse (clique.getFv (), marginal);
        if (defaultExpectations[tidx].location (expIdx) != -1) {
          defaultExpectations[tidx].incrementValue (expIdx, marginal);
        }
      }


      int observedVal = observations.get (target);


      timePerCvgaCall += timing.elapsedTime ();


      return vals[observedVal] - logZ;
    }

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//    initForGraph (mdl);
    Assignment assn = initialAssignment (mdl);
    if (assn == null)
  throw new IllegalArgumentException ("GibbsSampler: Could not find feasible assignment for model "+mdl);


    Timing timing = new Timing ();
    for (int i = 0; i < burnin; i++) {
      assn = doOnePass (mdl, assn);
    }
    timing.tick ("Burnin");


    Assignment ret = new Assignment ();
    for (int i = 0; i < N; i++) {
      assn = doOnePass (mdl, assn);
      ret.addRow (assn);
    }
    timing.tick ("Sampling");


    return ret;
  }

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    // don't cache test set
    boolean wasCached = acrf.isCacheUnrolledGraphs ();
    acrf.setCacheUnrolledGraphs (false);


    Timing timing = new Timing ();


    if (!eval.evaluate (acrf, iter+1, trainingList, validationList, testSet)) {
      logger.info ("ACRF trainer: evaluator returned false. Quitting.");
      timing.tick ("Evaluation time (iteration "+iter+")");
      return true;
    }


    timing.tick ("Evaluation time (iteration "+iter+")");


    // set test set caching back to normal
    acrf.setCacheUnrolledGraphs (wasCached);
    return false;
  }

View Full Code Here

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