public virtual void TestEndToEndSingleUnaries()
{
IList <Tree> binarizedTrees = BuildTestTreebank();
Oracle oracle = new Oracle(binarizedTrees, false, Java.Util.Collections.Singleton("ROOT"));
RunEndToEndTest(binarizedTrees, oracle);
}
public virtual void TestBuildParentMap()
{
Tree tree = Tree.ValueOf("(A (B foo) (C bar))");
IDictionary <Tree, Tree> parents = Oracle.BuildParentMap(tree);
int total = RecursiveTestBuildParentMap(tree, parents);
NUnit.Framework.Assert.AreEqual(total, parents.Count);
}
public TrainTreeProcessor(PerceptronModel _enclosing, IList <Tree> binarizedTrees, IList <IList <ITransition> > transitionLists, IList <PerceptronModel.Update> updates, Oracle oracle)
{
this._enclosing = _enclosing;
// this needs to be a synchronized list
this.binarizedTrees = binarizedTrees;
this.transitionLists = transitionLists;
this.updates = updates;
this.oracle = oracle;
}
public static void RunEndToEndTest(IList <Tree> binarizedTrees, Oracle oracle)
{
for (int index = 0; index < binarizedTrees.Count; ++index)
{
State state = ShiftReduceParser.InitialStateFromGoldTagTree(binarizedTrees[index]);
while (!state.IsFinished())
{
OracleTransition gold = oracle.GoldTransition(index, state);
NUnit.Framework.Assert.IsTrue(gold.transition != null);
state = gold.transition.Apply(state);
}
NUnit.Framework.Assert.AreEqual(binarizedTrees[index], state.stack.Peek());
}
}
private void TrainModel(string serializedPath, Edu.Stanford.Nlp.Tagger.Common.Tagger tagger, Random random, IList <Tree> binarizedTrees, IList <IList <ITransition> > transitionLists, Treebank devTreebank, int nThreads, ICollection <string> allowedFeatures
)
{
double bestScore = 0.0;
int bestIteration = 0;
PriorityQueue <ScoredObject <PerceptronModel> > bestModels = null;
if (op.TrainOptions().averagedModels > 0)
{
bestModels = new PriorityQueue <ScoredObject <PerceptronModel> >(op.TrainOptions().averagedModels + 1, ScoredComparator.AscendingComparator);
}
IList <int> indices = Generics.NewArrayList();
for (int i = 0; i < binarizedTrees.Count; ++i)
{
indices.Add(i);
}
Oracle oracle = null;
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.Oracle)
{
oracle = new Oracle(binarizedTrees, op.compoundUnaries, rootStates);
}
IList <PerceptronModel.Update> updates = Generics.NewArrayList();
MulticoreWrapper <int, Pair <int, int> > wrapper = null;
if (nThreads != 1)
{
updates = Java.Util.Collections.SynchronizedList(updates);
wrapper = new MulticoreWrapper <int, Pair <int, int> >(op.trainOptions.trainingThreads, new PerceptronModel.TrainTreeProcessor(this, binarizedTrees, transitionLists, updates, oracle));
}
IntCounter <string> featureFrequencies = null;
if (op.TrainOptions().featureFrequencyCutoff > 1)
{
featureFrequencies = new IntCounter <string>();
}
for (int iteration = 1; iteration <= op.trainOptions.trainingIterations; ++iteration)
{
Timing trainingTimer = new Timing();
int numCorrect = 0;
int numWrong = 0;
Java.Util.Collections.Shuffle(indices, random);
for (int start = 0; start < indices.Count; start += op.trainOptions.batchSize)
{
int end = Math.Min(start + op.trainOptions.batchSize, indices.Count);
Triple <IList <PerceptronModel.Update>, int, int> result = TrainBatch(indices.SubList(start, end), binarizedTrees, transitionLists, updates, oracle, wrapper);
numCorrect += result.second;
numWrong += result.third;
foreach (PerceptronModel.Update update in result.first)
{
foreach (string feature in update.features)
{
if (allowedFeatures != null && !allowedFeatures.Contains(feature))
{
continue;
}
Weight weights = featureWeights[feature];
if (weights == null)
{
weights = new Weight();
featureWeights[feature] = weights;
}
weights.UpdateWeight(update.goldTransition, update.delta);
weights.UpdateWeight(update.predictedTransition, -update.delta);
if (featureFrequencies != null)
{
featureFrequencies.IncrementCount(feature, (update.goldTransition >= 0 && update.predictedTransition >= 0) ? 2 : 1);
}
}
}
updates.Clear();
}
trainingTimer.Done("Iteration " + iteration);
log.Info("While training, got " + numCorrect + " transitions correct and " + numWrong + " transitions wrong");
OutputStats();
double labelF1 = 0.0;
if (devTreebank != null)
{
EvaluateTreebank evaluator = new EvaluateTreebank(op, null, new ShiftReduceParser(op, this), tagger);
evaluator.TestOnTreebank(devTreebank);
labelF1 = evaluator.GetLBScore();
log.Info("Label F1 after " + iteration + " iterations: " + labelF1);
if (labelF1 > bestScore)
{
log.Info("New best dev score (previous best " + bestScore + ")");
bestScore = labelF1;
bestIteration = iteration;
}
else
{
log.Info("Failed to improve for " + (iteration - bestIteration) + " iteration(s) on previous best score of " + bestScore);
if (op.trainOptions.stalledIterationLimit > 0 && (iteration - bestIteration >= op.trainOptions.stalledIterationLimit))
{
log.Info("Failed to improve for too long, stopping training");
break;
}
}
log.Info();
if (bestModels != null)
{
bestModels.Add(new ScoredObject <PerceptronModel>(new PerceptronModel(this), labelF1));
if (bestModels.Count > op.TrainOptions().averagedModels)
{
bestModels.Poll();
}
}
}
if (op.TrainOptions().saveIntermediateModels&& serializedPath != null && op.trainOptions.debugOutputFrequency > 0)
{
string tempName = Sharpen.Runtime.Substring(serializedPath, 0, serializedPath.Length - 7) + "-" + Filename.Format(iteration) + "-" + Nf.Format(labelF1) + ".ser.gz";
ShiftReduceParser temp = new ShiftReduceParser(op, this);
temp.SaveModel(tempName);
}
// TODO: we could save a cutoff version of the model,
// especially if we also get a dev set number for it, but that
// might be overkill
if (iteration % 10 == 0 && op.TrainOptions().decayLearningRate > 0.0)
{
learningRate *= op.TrainOptions().decayLearningRate;
}
}
// end for iterations
if (wrapper != null)
{
wrapper.Join();
}
if (bestModels != null)
{
if (op.TrainOptions().cvAveragedModels&& devTreebank != null)
{
IList <ScoredObject <PerceptronModel> > models = Generics.NewArrayList();
while (bestModels.Count > 0)
{
models.Add(bestModels.Poll());
}
Java.Util.Collections.Reverse(models);
double bestF1 = 0.0;
int bestSize = 0;
for (int i_1 = 1; i_1 <= models.Count; ++i_1)
{
log.Info("Testing with " + i_1 + " models averaged together");
// TODO: this is kind of ugly, would prefer a separate object
AverageScoredModels(models.SubList(0, i_1));
ShiftReduceParser temp = new ShiftReduceParser(op, this);
EvaluateTreebank evaluator = new EvaluateTreebank(temp.GetOp(), null, temp, tagger);
evaluator.TestOnTreebank(devTreebank);
double labelF1 = evaluator.GetLBScore();
log.Info("Label F1 for " + i_1 + " models: " + labelF1);
if (labelF1 > bestF1)
{
bestF1 = labelF1;
bestSize = i_1;
}
}
AverageScoredModels(models.SubList(0, bestSize));
}
else
{
AverageScoredModels(bestModels);
}
}
// TODO: perhaps we should filter the features and then get dev
// set scores. That way we can merge the models which are best
// after filtering.
if (featureFrequencies != null)
{
FilterFeatures(featureFrequencies.KeysAbove(op.TrainOptions().featureFrequencyCutoff));
}
CondenseFeatures();
}
/// <summary>
/// Trains a batch of trees and returns the following: a list of
/// Update objects, the number of transitions correct, and the number
/// of transitions wrong.
/// </summary>
/// <remarks>
/// Trains a batch of trees and returns the following: a list of
/// Update objects, the number of transitions correct, and the number
/// of transitions wrong.
/// If the model is trained with multiple threads, it is expected
/// that a valid MulticoreWrapper is passed in which does the
/// processing. In that case, the processing is done on all of the
/// trees without updating any weights, which allows the results for
/// multithreaded training to be reproduced.
/// </remarks>
private Triple <IList <PerceptronModel.Update>, int, int> TrainBatch(IList <int> indices, IList <Tree> binarizedTrees, IList <IList <ITransition> > transitionLists, IList <PerceptronModel.Update> updates, Oracle oracle, MulticoreWrapper <int, Pair <int
, int> > wrapper)
{
int numCorrect = 0;
int numWrong = 0;
if (op.trainOptions.trainingThreads == 1)
{
foreach (int index in indices)
{
Pair <int, int> count = TrainTree(index, binarizedTrees, transitionLists, updates, oracle);
numCorrect += count.first;
numWrong += count.second;
}
}
else
{
foreach (int index in indices)
{
wrapper.Put(index);
}
wrapper.Join(false);
while (wrapper.Peek())
{
Pair <int, int> result = wrapper.Poll();
numCorrect += result.first;
numWrong += result.second;
}
}
return(new Triple <IList <PerceptronModel.Update>, int, int>(updates, numCorrect, numWrong));
}
private Pair <int, int> TrainTree(int index, IList <Tree> binarizedTrees, IList <IList <ITransition> > transitionLists, IList <PerceptronModel.Update> updates, Oracle oracle)
{
int numCorrect = 0;
int numWrong = 0;
Tree tree = binarizedTrees[index];
ReorderingOracle reorderer = null;
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderOracle || op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderBeam)
{
reorderer = new ReorderingOracle(op);
}
// TODO. This training method seems to be working in that it
// trains models just like the gold and early termination methods do.
// However, it causes the feature space to go crazy. Presumably
// leaving out features with low weights or low frequencies would
// significantly help with that. Otherwise, not sure how to keep
// it under control.
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.Oracle)
{
State state = ShiftReduceParser.InitialStateFromGoldTagTree(tree);
while (!state.IsFinished())
{
IList <string> features = featureFactory.Featurize(state);
ScoredObject <int> prediction = FindHighestScoringTransition(state, features, true);
if (prediction == null)
{
throw new AssertionError("Did not find a legal transition");
}
int predictedNum = prediction.Object();
ITransition predicted = transitionIndex.Get(predictedNum);
OracleTransition gold = oracle.GoldTransition(index, state);
if (gold.IsCorrect(predicted))
{
numCorrect++;
if (gold.transition != null && !gold.transition.Equals(predicted))
{
int transitionNum = transitionIndex.IndexOf(gold.transition);
if (transitionNum < 0)
{
// TODO: do we want to add unary transitions which are
// only possible when the parser has gone off the rails?
continue;
}
updates.Add(new PerceptronModel.Update(features, transitionNum, -1, learningRate));
}
}
else
{
numWrong++;
int transitionNum = -1;
if (gold.transition != null)
{
transitionNum = transitionIndex.IndexOf(gold.transition);
}
// TODO: this can theoretically result in a -1 gold
// transition if the transition exists, but is a
// CompoundUnaryTransition which only exists because the
// parser is wrong. Do we want to add those transitions?
updates.Add(new PerceptronModel.Update(features, transitionNum, predictedNum, learningRate));
}
state = predicted.Apply(state);
}
}
else
{
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.Beam || op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderBeam)
{
if (op.TrainOptions().beamSize <= 0)
{
throw new ArgumentException("Illegal beam size " + op.TrainOptions().beamSize);
}
IList <ITransition> transitions = Generics.NewLinkedList(transitionLists[index]);
PriorityQueue <State> agenda = new PriorityQueue <State>(op.TrainOptions().beamSize + 1, ScoredComparator.AscendingComparator);
State goldState = ShiftReduceParser.InitialStateFromGoldTagTree(tree);
agenda.Add(goldState);
// int transitionCount = 0;
while (transitions.Count > 0)
{
ITransition goldTransition = transitions[0];
ITransition highestScoringTransitionFromGoldState = null;
double highestScoreFromGoldState = 0.0;
PriorityQueue <State> newAgenda = new PriorityQueue <State>(op.TrainOptions().beamSize + 1, ScoredComparator.AscendingComparator);
State highestScoringState = null;
State highestCurrentState = null;
foreach (State currentState in agenda)
{
bool isGoldState = (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderBeam && goldState.AreTransitionsEqual(currentState));
IList <string> features = featureFactory.Featurize(currentState);
ICollection <ScoredObject <int> > stateTransitions = FindHighestScoringTransitions(currentState, features, true, op.TrainOptions().beamSize, null);
foreach (ScoredObject <int> transition in stateTransitions)
{
State newState = transitionIndex.Get(transition.Object()).Apply(currentState, transition.Score());
newAgenda.Add(newState);
if (newAgenda.Count > op.TrainOptions().beamSize)
{
newAgenda.Poll();
}
if (highestScoringState == null || highestScoringState.Score() < newState.Score())
{
highestScoringState = newState;
highestCurrentState = currentState;
}
if (isGoldState && (highestScoringTransitionFromGoldState == null || transition.Score() > highestScoreFromGoldState))
{
highestScoringTransitionFromGoldState = transitionIndex.Get(transition.Object());
highestScoreFromGoldState = transition.Score();
}
}
}
// This can happen if the REORDER_BEAM method backs itself
// into a corner, such as transitioning to something that
// can't have a FinalizeTransition applied. This doesn't
// happen for the BEAM method because in that case the correct
// state (eg one with ROOT) isn't on the agenda so it stops.
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderBeam && highestScoringTransitionFromGoldState == null)
{
break;
}
State newGoldState = goldTransition.Apply(goldState, 0.0);
// if highest scoring state used the correct transition, no training
// otherwise, down the last transition, up the correct
if (!newGoldState.AreTransitionsEqual(highestScoringState))
{
++numWrong;
IList <string> goldFeatures = featureFactory.Featurize(goldState);
int lastTransition = transitionIndex.IndexOf(highestScoringState.transitions.Peek());
updates.Add(new PerceptronModel.Update(featureFactory.Featurize(highestCurrentState), -1, lastTransition, learningRate));
updates.Add(new PerceptronModel.Update(goldFeatures, transitionIndex.IndexOf(goldTransition), -1, learningRate));
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.Beam)
{
// If the correct state has fallen off the agenda, break
if (!ShiftReduceUtils.FindStateOnAgenda(newAgenda, newGoldState))
{
break;
}
else
{
transitions.Remove(0);
}
}
else
{
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderBeam)
{
if (!ShiftReduceUtils.FindStateOnAgenda(newAgenda, newGoldState))
{
if (!reorderer.Reorder(goldState, highestScoringTransitionFromGoldState, transitions))
{
break;
}
newGoldState = highestScoringTransitionFromGoldState.Apply(goldState);
if (!ShiftReduceUtils.FindStateOnAgenda(newAgenda, newGoldState))
{
break;
}
}
else
{
transitions.Remove(0);
}
}
}
}
else
{
++numCorrect;
transitions.Remove(0);
}
goldState = newGoldState;
agenda = newAgenda;
}
}
else
{
if (op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.ReorderOracle || op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod.EarlyTermination || op.TrainOptions().trainingMethod == ShiftReduceTrainOptions.TrainingMethod
.Gold)
{
State state = ShiftReduceParser.InitialStateFromGoldTagTree(tree);
IList <ITransition> transitions = transitionLists[index];
transitions = Generics.NewLinkedList(transitions);
bool keepGoing = true;
while (transitions.Count > 0 && keepGoing)
{
ITransition transition = transitions[0];
int transitionNum = transitionIndex.IndexOf(transition);
IList <string> features = featureFactory.Featurize(state);
int predictedNum = FindHighestScoringTransition(state, features, false).Object();
ITransition predicted = transitionIndex.Get(predictedNum);
if (transitionNum == predictedNum)
{
transitions.Remove(0);
state = transition.Apply(state);
numCorrect++;
}
else
{
numWrong++;
// TODO: allow weighted features, weighted training, etc
updates.Add(new PerceptronModel.Update(features, transitionNum, predictedNum, learningRate));
switch (op.TrainOptions().trainingMethod)
{
case ShiftReduceTrainOptions.TrainingMethod.EarlyTermination:
{
keepGoing = false;
break;
}
case ShiftReduceTrainOptions.TrainingMethod.Gold:
{
transitions.Remove(0);
state = transition.Apply(state);
break;
}
case ShiftReduceTrainOptions.TrainingMethod.ReorderOracle:
{
keepGoing = reorderer.Reorder(state, predicted, transitions);
if (keepGoing)
{
state = predicted.Apply(state);
}
break;
}
default:
{
throw new ArgumentException("Unexpected method " + op.TrainOptions().trainingMethod);
}
}
}
}
}
}
}
return(Pair.MakePair(numCorrect, numWrong));
}