// fill value & derivative
protected internal override void Calculate(double[] theta)
{
dvModel.VectorToParams(theta);
double localValue = 0.0;
double[] localDerivative = new double[theta.Length];
TwoDimensionalMap <string, string, SimpleMatrix> binaryW_dfsG;
TwoDimensionalMap <string, string, SimpleMatrix> binaryW_dfsB;
binaryW_dfsG = TwoDimensionalMap.TreeMap();
binaryW_dfsB = TwoDimensionalMap.TreeMap();
TwoDimensionalMap <string, string, SimpleMatrix> binaryScoreDerivativesG;
TwoDimensionalMap <string, string, SimpleMatrix> binaryScoreDerivativesB;
binaryScoreDerivativesG = TwoDimensionalMap.TreeMap();
binaryScoreDerivativesB = TwoDimensionalMap.TreeMap();
IDictionary <string, SimpleMatrix> unaryW_dfsG;
IDictionary <string, SimpleMatrix> unaryW_dfsB;
unaryW_dfsG = new SortedDictionary <string, SimpleMatrix>();
unaryW_dfsB = new SortedDictionary <string, SimpleMatrix>();
IDictionary <string, SimpleMatrix> unaryScoreDerivativesG;
IDictionary <string, SimpleMatrix> unaryScoreDerivativesB;
unaryScoreDerivativesG = new SortedDictionary <string, SimpleMatrix>();
unaryScoreDerivativesB = new SortedDictionary <string, SimpleMatrix>();
IDictionary <string, SimpleMatrix> wordVectorDerivativesG = new SortedDictionary <string, SimpleMatrix>();
IDictionary <string, SimpleMatrix> wordVectorDerivativesB = new SortedDictionary <string, SimpleMatrix>();
foreach (TwoDimensionalMap.Entry <string, string, SimpleMatrix> entry in dvModel.binaryTransform)
{
int numRows = entry.GetValue().NumRows();
int numCols = entry.GetValue().NumCols();
binaryW_dfsG.Put(entry.GetFirstKey(), entry.GetSecondKey(), new SimpleMatrix(numRows, numCols));
binaryW_dfsB.Put(entry.GetFirstKey(), entry.GetSecondKey(), new SimpleMatrix(numRows, numCols));
binaryScoreDerivativesG.Put(entry.GetFirstKey(), entry.GetSecondKey(), new SimpleMatrix(1, numRows));
binaryScoreDerivativesB.Put(entry.GetFirstKey(), entry.GetSecondKey(), new SimpleMatrix(1, numRows));
}
foreach (KeyValuePair <string, SimpleMatrix> entry_1 in dvModel.unaryTransform)
{
int numRows = entry_1.Value.NumRows();
int numCols = entry_1.Value.NumCols();
unaryW_dfsG[entry_1.Key] = new SimpleMatrix(numRows, numCols);
unaryW_dfsB[entry_1.Key] = new SimpleMatrix(numRows, numCols);
unaryScoreDerivativesG[entry_1.Key] = new SimpleMatrix(1, numRows);
unaryScoreDerivativesB[entry_1.Key] = new SimpleMatrix(1, numRows);
}
if (op.trainOptions.trainWordVectors)
{
foreach (KeyValuePair <string, SimpleMatrix> entry_2 in dvModel.wordVectors)
{
int numRows = entry_2.Value.NumRows();
int numCols = entry_2.Value.NumCols();
wordVectorDerivativesG[entry_2.Key] = new SimpleMatrix(numRows, numCols);
wordVectorDerivativesB[entry_2.Key] = new SimpleMatrix(numRows, numCols);
}
}
// Some optimization methods prints out a line without an end, so our
// debugging statements are misaligned
Timing scoreTiming = new Timing();
scoreTiming.Doing("Scoring trees");
int treeNum = 0;
MulticoreWrapper <Tree, Pair <DeepTree, DeepTree> > wrapper = new MulticoreWrapper <Tree, Pair <DeepTree, DeepTree> >(op.trainOptions.trainingThreads, new DVParserCostAndGradient.ScoringProcessor(this));
foreach (Tree tree in trainingBatch)
{
wrapper.Put(tree);
}
wrapper.Join();
scoreTiming.Done();
while (wrapper.Peek())
{
Pair <DeepTree, DeepTree> result = wrapper.Poll();
DeepTree goldTree = result.first;
DeepTree bestTree = result.second;
StringBuilder treeDebugLine = new StringBuilder();
Formatter formatter = new Formatter(treeDebugLine);
bool isDone = (Math.Abs(bestTree.GetScore() - goldTree.GetScore()) <= 0.00001 || goldTree.GetScore() > bestTree.GetScore());
string done = isDone ? "done" : string.Empty;
formatter.Format("Tree %6d Highest tree: %12.4f Correct tree: %12.4f %s", treeNum, bestTree.GetScore(), goldTree.GetScore(), done);
log.Info(treeDebugLine.ToString());
if (!isDone)
{
// if the gold tree is better than the best hypothesis tree by
// a large enough margin, then the score difference will be 0
// and we ignore the tree
double valueDelta = bestTree.GetScore() - goldTree.GetScore();
//double valueDelta = Math.max(0.0, - scoreGold + bestScore);
localValue += valueDelta;
// get the context words for this tree - should be the same
// for either goldTree or bestTree
IList <string> words = GetContextWords(goldTree.GetTree());
// The derivatives affected by this tree are only based on the
// nodes present in this tree, eg not all matrix derivatives
// will be affected by this tree
BackpropDerivative(goldTree.GetTree(), words, goldTree.GetVectors(), binaryW_dfsG, unaryW_dfsG, binaryScoreDerivativesG, unaryScoreDerivativesG, wordVectorDerivativesG);
BackpropDerivative(bestTree.GetTree(), words, bestTree.GetVectors(), binaryW_dfsB, unaryW_dfsB, binaryScoreDerivativesB, unaryScoreDerivativesB, wordVectorDerivativesB);
}
++treeNum;
}
double[] localDerivativeGood;
double[] localDerivativeB;
if (op.trainOptions.trainWordVectors)
{
localDerivativeGood = NeuralUtils.ParamsToVector(theta.Length, binaryW_dfsG.ValueIterator(), unaryW_dfsG.Values.GetEnumerator(), binaryScoreDerivativesG.ValueIterator(), unaryScoreDerivativesG.Values.GetEnumerator(), wordVectorDerivativesG.Values
.GetEnumerator());
localDerivativeB = NeuralUtils.ParamsToVector(theta.Length, binaryW_dfsB.ValueIterator(), unaryW_dfsB.Values.GetEnumerator(), binaryScoreDerivativesB.ValueIterator(), unaryScoreDerivativesB.Values.GetEnumerator(), wordVectorDerivativesB.Values
.GetEnumerator());
}
else
{
localDerivativeGood = NeuralUtils.ParamsToVector(theta.Length, binaryW_dfsG.ValueIterator(), unaryW_dfsG.Values.GetEnumerator(), binaryScoreDerivativesG.ValueIterator(), unaryScoreDerivativesG.Values.GetEnumerator());
localDerivativeB = NeuralUtils.ParamsToVector(theta.Length, binaryW_dfsB.ValueIterator(), unaryW_dfsB.Values.GetEnumerator(), binaryScoreDerivativesB.ValueIterator(), unaryScoreDerivativesB.Values.GetEnumerator());
}
// correct - highest
for (int i = 0; i < localDerivativeGood.Length; i++)
{
localDerivative[i] = localDerivativeB[i] - localDerivativeGood[i];
}
// TODO: this is where we would combine multiple costs if we had parallelized the calculation
value = localValue;
derivative = localDerivative;
// normalizing by training batch size
value = (1.0 / trainingBatch.Count) * value;
ArrayMath.MultiplyInPlace(derivative, (1.0 / trainingBatch.Count));
// add regularization to cost:
double[] currentParams = dvModel.ParamsToVector();
double regCost = 0;
foreach (double currentParam in currentParams)
{
regCost += currentParam * currentParam;
}
regCost = op.trainOptions.regCost * 0.5 * regCost;
value += regCost;
// add regularization to gradient
ArrayMath.MultiplyInPlace(currentParams, op.trainOptions.regCost);
ArrayMath.PairwiseAddInPlace(derivative, currentParams);
}
/// <exception cref="System.IO.IOException"/>
public static void Main(string[] args)
{
string modelPath = null;
string outputPath = null;
string inputPath = null;
string testTreebankPath = null;
IFileFilter testTreebankFilter = null;
IList <string> unusedArgs = Generics.NewArrayList();
for (int argIndex = 0; argIndex < args.Length;)
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-model"))
{
modelPath = args[argIndex + 1];
argIndex += 2;
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-output"))
{
outputPath = args[argIndex + 1];
argIndex += 2;
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-input"))
{
inputPath = args[argIndex + 1];
argIndex += 2;
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-testTreebank"))
{
Pair <string, IFileFilter> treebankDescription = ArgUtils.GetTreebankDescription(args, argIndex, "-testTreebank");
argIndex = argIndex + ArgUtils.NumSubArgs(args, argIndex) + 1;
testTreebankPath = treebankDescription.First();
testTreebankFilter = treebankDescription.Second();
}
else
{
unusedArgs.Add(args[argIndex++]);
}
}
}
}
}
string[] newArgs = Sharpen.Collections.ToArray(unusedArgs, new string[unusedArgs.Count]);
LexicalizedParser parser = ((LexicalizedParser)LexicalizedParser.LoadModel(modelPath, newArgs));
DVModel model = DVParser.GetModelFromLexicalizedParser(parser);
File outputFile = new File(outputPath);
FileSystem.CheckNotExistsOrFail(outputFile);
FileSystem.MkdirOrFail(outputFile);
int count = 0;
if (inputPath != null)
{
Reader input = new BufferedReader(new FileReader(inputPath));
DocumentPreprocessor processor = new DocumentPreprocessor(input);
foreach (IList <IHasWord> sentence in processor)
{
count++;
// index from 1
IParserQuery pq = parser.ParserQuery();
if (!(pq is RerankingParserQuery))
{
throw new ArgumentException("Expected a RerankingParserQuery");
}
RerankingParserQuery rpq = (RerankingParserQuery)pq;
if (!rpq.Parse(sentence))
{
throw new Exception("Unparsable sentence: " + sentence);
}
IRerankerQuery reranker = rpq.RerankerQuery();
if (!(reranker is DVModelReranker.Query))
{
throw new ArgumentException("Expected a DVModelReranker");
}
DeepTree deepTree = ((DVModelReranker.Query)reranker).GetDeepTrees()[0];
IdentityHashMap <Tree, SimpleMatrix> vectors = deepTree.GetVectors();
foreach (KeyValuePair <Tree, SimpleMatrix> entry in vectors)
{
log.Info(entry.Key + " " + entry.Value);
}
FileWriter fout = new FileWriter(outputPath + File.separator + "sentence" + count + ".txt");
BufferedWriter bout = new BufferedWriter(fout);
bout.Write(SentenceUtils.ListToString(sentence));
bout.NewLine();
bout.Write(deepTree.GetTree().ToString());
bout.NewLine();
foreach (IHasWord word in sentence)
{
OutputMatrix(bout, model.GetWordVector(word.Word()));
}
Tree rootTree = FindRootTree(vectors);
OutputTreeMatrices(bout, rootTree, vectors);
bout.Flush();
fout.Close();
}
}
}
/// <exception cref="System.Exception"/>
public static void Main(string[] args)
{
string modelPath = null;
string outputPath = null;
string testTreebankPath = null;
IFileFilter testTreebankFilter = null;
IList <string> unusedArgs = new List <string>();
for (int argIndex = 0; argIndex < args.Length;)
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-model"))
{
modelPath = args[argIndex + 1];
argIndex += 2;
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-testTreebank"))
{
Pair <string, IFileFilter> treebankDescription = ArgUtils.GetTreebankDescription(args, argIndex, "-testTreebank");
argIndex = argIndex + ArgUtils.NumSubArgs(args, argIndex) + 1;
testTreebankPath = treebankDescription.First();
testTreebankFilter = treebankDescription.Second();
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-output"))
{
outputPath = args[argIndex + 1];
argIndex += 2;
}
else
{
unusedArgs.Add(args[argIndex++]);
}
}
}
}
if (modelPath == null)
{
throw new ArgumentException("Need to specify -model");
}
if (testTreebankPath == null)
{
throw new ArgumentException("Need to specify -testTreebank");
}
if (outputPath == null)
{
throw new ArgumentException("Need to specify -output");
}
string[] newArgs = Sharpen.Collections.ToArray(unusedArgs, new string[unusedArgs.Count]);
LexicalizedParser lexparser = ((LexicalizedParser)LexicalizedParser.LoadModel(modelPath, newArgs));
Treebank testTreebank = null;
if (testTreebankPath != null)
{
log.Info("Reading in trees from " + testTreebankPath);
if (testTreebankFilter != null)
{
log.Info("Filtering on " + testTreebankFilter);
}
testTreebank = lexparser.GetOp().tlpParams.MemoryTreebank();
testTreebank.LoadPath(testTreebankPath, testTreebankFilter);
log.Info("Read in " + testTreebank.Count + " trees for testing");
}
FileWriter @out = new FileWriter(outputPath);
BufferedWriter bout = new BufferedWriter(@out);
log.Info("Parsing " + testTreebank.Count + " trees");
int count = 0;
IList <FindNearestNeighbors.ParseRecord> records = Generics.NewArrayList();
foreach (Tree goldTree in testTreebank)
{
IList <Word> tokens = goldTree.YieldWords();
IParserQuery parserQuery = lexparser.ParserQuery();
if (!parserQuery.Parse(tokens))
{
throw new AssertionError("Could not parse: " + tokens);
}
if (!(parserQuery is RerankingParserQuery))
{
throw new ArgumentException("Expected a LexicalizedParser with a Reranker attached");
}
RerankingParserQuery rpq = (RerankingParserQuery)parserQuery;
if (!(rpq.RerankerQuery() is DVModelReranker.Query))
{
throw new ArgumentException("Expected a LexicalizedParser with a DVModel attached");
}
DeepTree tree = ((DVModelReranker.Query)rpq.RerankerQuery()).GetDeepTrees()[0];
SimpleMatrix rootVector = null;
foreach (KeyValuePair <Tree, SimpleMatrix> entry in tree.GetVectors())
{
if (entry.Key.Label().Value().Equals("ROOT"))
{
rootVector = entry.Value;
break;
}
}
if (rootVector == null)
{
throw new AssertionError("Could not find root nodevector");
}
@out.Write(tokens + "\n");
@out.Write(tree.GetTree() + "\n");
for (int i = 0; i < rootVector.GetNumElements(); ++i)
{
@out.Write(" " + rootVector.Get(i));
}
@out.Write("\n\n\n");
count++;
if (count % 10 == 0)
{
log.Info(" " + count);
}
records.Add(new FindNearestNeighbors.ParseRecord(tokens, goldTree, tree.GetTree(), rootVector, tree.GetVectors()));
}
log.Info(" done parsing");
IList <Pair <Tree, SimpleMatrix> > subtrees = Generics.NewArrayList();
foreach (FindNearestNeighbors.ParseRecord record in records)
{
foreach (KeyValuePair <Tree, SimpleMatrix> entry in record.nodeVectors)
{
if (entry.Key.GetLeaves().Count <= maxLength)
{
subtrees.Add(Pair.MakePair(entry.Key, entry.Value));
}
}
}
log.Info("There are " + subtrees.Count + " subtrees in the set of trees");
PriorityQueue <ScoredObject <Pair <Tree, Tree> > > bestmatches = new PriorityQueue <ScoredObject <Pair <Tree, Tree> > >(101, ScoredComparator.DescendingComparator);
for (int i_1 = 0; i_1 < subtrees.Count; ++i_1)
{
log.Info(subtrees[i_1].First().YieldWords());
log.Info(subtrees[i_1].First());
for (int j = 0; j < subtrees.Count; ++j)
{
if (i_1 == j)
{
continue;
}
// TODO: look at basic category?
double normF = subtrees[i_1].Second().Minus(subtrees[j].Second()).NormF();
bestmatches.Add(new ScoredObject <Pair <Tree, Tree> >(Pair.MakePair(subtrees[i_1].First(), subtrees[j].First()), normF));
if (bestmatches.Count > 100)
{
bestmatches.Poll();
}
}
IList <ScoredObject <Pair <Tree, Tree> > > ordered = Generics.NewArrayList();
while (bestmatches.Count > 0)
{
ordered.Add(bestmatches.Poll());
}
Java.Util.Collections.Reverse(ordered);
foreach (ScoredObject <Pair <Tree, Tree> > pair in ordered)
{
log.Info(" MATCHED " + pair.Object().second.YieldWords() + " ... " + pair.Object().Second() + " with a score of " + pair.Score());
}
log.Info();
log.Info();
bestmatches.Clear();
}
/*
* for (int i = 0; i < records.size(); ++i) {
* if (i % 10 == 0) {
* log.info(" " + i);
* }
* List<ScoredObject<ParseRecord>> scored = Generics.newArrayList();
* for (int j = 0; j < records.size(); ++j) {
* if (i == j) continue;
*
* double score = 0.0;
* int matches = 0;
* for (Map.Entry<Tree, SimpleMatrix> first : records.get(i).nodeVectors.entrySet()) {
* for (Map.Entry<Tree, SimpleMatrix> second : records.get(j).nodeVectors.entrySet()) {
* String firstBasic = dvparser.dvModel.basicCategory(first.getKey().label().value());
* String secondBasic = dvparser.dvModel.basicCategory(second.getKey().label().value());
* if (firstBasic.equals(secondBasic)) {
++matches;
* double normF = first.getValue().minus(second.getValue()).normF();
* score += normF * normF;
* }
* }
* }
* if (matches == 0) {
* score = Double.POSITIVE_INFINITY;
* } else {
* score = score / matches;
* }
* //double score = records.get(i).vector.minus(records.get(j).vector).normF();
* scored.add(new ScoredObject<ParseRecord>(records.get(j), score));
* }
* Collections.sort(scored, ScoredComparator.ASCENDING_COMPARATOR);
*
* out.write(records.get(i).sentence.toString() + "\n");
* for (int j = 0; j < numNeighbors; ++j) {
* out.write(" " + scored.get(j).score() + ": " + scored.get(j).object().sentence + "\n");
* }
* out.write("\n\n");
* }
* log.info();
*/
bout.Flush();
@out.Flush();
@out.Close();
}