private void TrainingStats(MutableContext[] paramStats) {
var numCorrect = 0;
var oei = 0;
sequenceStream.Reset();
Sequence sequence;
while ((sequence = sequenceStream.Read()) != null) {
var taggerEvents = sequenceStream.UpdateContext(sequence,
new PerceptronModel(
Array.ConvertAll(paramStats, input => (Context) input),
pMap,
outcomeLabels));
for (var ei = 0; ei < taggerEvents.Length; ei++,oei++) {
var max = oMap[taggerEvents[ei].Outcome];
if (max == outcomeList[oei]) {
numCorrect ++;
}
}
}
Display(string.Format(". ({0}/{1}) {2}", numCorrect, numEvents, (double) numCorrect/numEvents));
}
public AbstractModel TrainModel(int trainIterations, ISequenceStream trainStream, int cutoff,
bool trainUseAverage) {
iterations = trainIterations;
useAverage = trainUseAverage;
sequenceStream = trainStream;
info.Append("Trained using Perceptron Sequence algorithm.\n\n");
var di = new OnePassDataIndexer(new SequenceStreamEventStream(trainStream), cutoff, false);
trainStream.Reset();
numSequences = 0;
while (trainStream.Read() != null) {
numSequences++;
}
outcomeList = di.GetOutcomeList();
predLabels = di.GetPredLabels();
pMap = new IndexHashTable<string>(predLabels, 0.7d);
// Incorporation indexed data for training...
numEvents = di.GetNumEvents();
outcomeLabels = di.GetOutcomeLabels();
oMap = new Dictionary<string, int>();
for (var i = 0; i < outcomeLabels.Length; i++) {
oMap.Add(outcomeLabels[i], i);
}
outcomeList = di.GetOutcomeList();
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
if (trainUseAverage) {
updates = new int[numPreds][][];
for (var i = 0; i < numPreds; i++) {
updates[i] = new int[numOutcomes][];
for (var j = 0; j < numOutcomes; j++) {
updates[i][j] = new int[3];
}
}
}
// done.
Display("done.\n");
info.Append("Number of Event Tokens: {0}\n" +
" Number of Outcomes: {1}\n" +
" Number of Predicates: {2}\n", numEvents, numOutcomes, numPreds);
Display("\tNumber of Event Tokens: " + numEvents);
Display("\t Number of Outcomes: " + numOutcomes);
Display("\t Number of Predicates: " + numPreds);
param = new MutableContext[numPreds];
if (trainUseAverage)
averageParams = new MutableContext[numPreds];
allOutcomesPattern = new int[numOutcomes];
for (var i = 0; i < numOutcomes; i++) {
allOutcomesPattern[i] = i;
}
for (var pi = 0; pi < numPreds; pi++) {
param[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
if (trainUseAverage)
averageParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
for (var aoi = 0; aoi < numOutcomes; aoi++) {
param[pi].SetParameter(aoi, 0.0d);
if (trainUseAverage)
averageParams[pi].SetParameter(aoi, 0.0d);
}
}
Display("Computing model parameters...");
FindParameters();
Display("...done.");
/*************** Create and return the model ******************/
// ReSharper disable CoVariantArrayConversion
if (trainUseAverage)
return new PerceptronModel(averageParams, predLabels, outcomeLabels) {
info = info
};
return new PerceptronModel(param, predLabels, outcomeLabels) {
info = info
};
// ReSharper restore CoVariantArrayConversion
}
private MutableContext[] FindParameters(int iterations, bool useAverage) {
info.Append(" Number of Iterations: {0}\n", iterations);
Display("\nPerforming " + iterations + " iterations.\n");
var allOutcomesPattern = new int[numOutcomes];
for (var oi = 0; oi < numOutcomes; oi++)
allOutcomesPattern[oi] = oi;
/** Stores the estimated parameter value of each predicate during iteration. */
var param = new MutableContext[numPreds];
for (var pi = 0; pi < numPreds; pi++) {
param[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
for (var aoi = 0; aoi < numOutcomes; aoi++)
param[pi].SetParameter(aoi, 0.0);
}
// ReSharper disable once CoVariantArrayConversion
var evalParams = new EvalParameters(param, numOutcomes);
// Stores the sum of parameter values of each predicate over many iterations.
var summedParams = new MutableContext[numPreds];
if (useAverage) {
for (var pi = 0; pi < numPreds; pi++) {
summedParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
for (var aoi = 0; aoi < numOutcomes; aoi++)
summedParams[pi].SetParameter(aoi, 0.0);
}
}
// Keep track of the previous three accuracies. The difference of
// the mean of these and the current training set accuracy is used
// with tolerance to decide whether to stop.
var prevAccuracy1 = 0.0;
var prevAccuracy2 = 0.0;
var prevAccuracy3 = 0.0;
// A counter for the denominator for averaging.
var numTimesSummed = 0;
double stepSize = 1;
for (var i = 1; i <= iterations; i++) {
// Decrease the step size by a small amount.
if (stepSizeDecrease > 0)
stepSize *= 1 - stepSizeDecrease;
if (Monitor != null && Monitor.Token.CanBeCanceled)
Monitor.Token.ThrowIfCancellationRequested();
var numCorrect = 0;
for (var ei = 0; ei < numUniqueEvents; ei++) {
var targetOutcome = outcomeList[ei];
for (var ni = 0; ni < numTimesEventsSeen[ei]; ni++) {
// Compute the model's prediction according to the current parameters.
var modelDistribution = new double[numOutcomes];
PerceptronModel.Eval(
contexts[ei],
values != null ? values[ei] : null,
modelDistribution,
evalParams, false);
var maxOutcome = MaxIndex(modelDistribution);
// If the predicted outcome is different from the target
// outcome, do the standard update: boost the parameters
// associated with the target and reduce those associated
// with the incorrect predicted outcome.
if (maxOutcome != targetOutcome) {
for (var ci = 0; ci < contexts[ei].Length; ci++) {
var pi = contexts[ei][ci];
if (values == null) {
param[pi].UpdateParameter(targetOutcome, stepSize);
param[pi].UpdateParameter(maxOutcome, -stepSize);
} else {
param[pi].UpdateParameter(targetOutcome, stepSize*values[ei][ci]);
param[pi].UpdateParameter(maxOutcome, -stepSize*values[ei][ci]);
}
}
}
// Update the counts for accuracy.
if (maxOutcome == targetOutcome)
numCorrect++;
}
}
// Calculate the training accuracy and display.
var trainingAccuracy = (double) numCorrect/numEvents;
Display(string.Format("{0,-4} {1} of {2} - {3}",
i,
numCorrect,
numEvents,
trainingAccuracy));
// TODO: Make averaging configurable !!!
bool doAveraging;
if (useAverage && UseSkippedAveraging && (i < 20 || IsPerfectSquare(i))) {
doAveraging = true;
} else if (useAverage) {
doAveraging = true;
} else {
doAveraging = false;
}
if (doAveraging) {
numTimesSummed++;
for (var pi = 0; pi < numPreds; pi++)
for (var aoi = 0; aoi < numOutcomes; aoi++)
summedParams[pi].UpdateParameter(aoi, param[pi].Parameters[aoi]);
}
// If the tolerance is greater than the difference between the
// current training accuracy and all of the previous three
// training accuracies, stop training.
if (Math.Abs(prevAccuracy1 - trainingAccuracy) < tolerance &&
Math.Abs(prevAccuracy2 - trainingAccuracy) < tolerance &&
Math.Abs(prevAccuracy3 - trainingAccuracy) < tolerance) {
Display("Stopping: change in training set accuracy less than " + tolerance + "\n");
break;
}
// Update the previous training accuracies.
prevAccuracy1 = prevAccuracy2;
prevAccuracy2 = prevAccuracy3;
prevAccuracy3 = trainingAccuracy;
}
// Output the final training stats.
TrainingStats(evalParams);
if (!useAverage)
return param;
if (numTimesSummed == 0) // Improbable but possible according to the Coverity.
numTimesSummed = 1;
// Create averaged parameters
for (var pi = 0; pi < numPreds; pi++)
for (var aoi = 0; aoi < numOutcomes; aoi++)
summedParams[pi].SetParameter(aoi, summedParams[pi].Parameters[aoi]/numTimesSummed);
return summedParams;
}