/// <summary>
/// Returns the index with the highest value in the
/// <paramref name="predictions"/>
/// matrix.
/// Indexed from 0.
/// </summary>
private static int GetPredictedClass(SimpleMatrix predictions)
{
int argmax = 0;
for (int i = 1; i < predictions.GetNumElements(); ++i)
{
if (predictions.Get(i) > predictions.Get(argmax))
{
argmax = i;
}
}
return(argmax);
}
public static double[] ParamsToVector(int totalSize, params IEnumerator <SimpleMatrix>[] matrices)
{
double[] theta = new double[totalSize];
int index = 0;
foreach (IEnumerator <SimpleMatrix> matrixIterator in matrices)
{
while (matrixIterator.MoveNext())
{
SimpleMatrix matrix = matrixIterator.Current;
int numElements = matrix.GetNumElements();
//System.out.println(Integer.toString(numElements)); // to know what matrices are
for (int i = 0; i < numElements; ++i)
{
theta[index] = matrix.Get(i);
++index;
}
}
}
if (index != totalSize)
{
throw new AssertionError("Did not entirely fill the theta vector: expected " + totalSize + " used " + index);
}
return(theta);
}
/// <exception cref="System.IO.IOException"/>
public static void OutputMatrix(BufferedWriter bout, SimpleMatrix matrix)
{
for (int i = 0; i < matrix.GetNumElements(); ++i)
{
bout.Write(" " + matrix.Get(i));
}
bout.NewLine();
}
public static IList <double> GetPredictionsAsStringList(Tree tree)
{
SimpleMatrix predictions = GetPredictions(tree);
IList <double> listOfPredictions = new List <double>();
for (int i = 0; i < predictions.NumRows(); i++)
{
listOfPredictions.Add(predictions.Get(i));
}
return(listOfPredictions);
}
private static SimpleTensor GetTensorGradient(SimpleMatrix deltaFull, SimpleMatrix leftVector, SimpleMatrix rightVector)
{
int size = deltaFull.GetNumElements();
SimpleTensor Wt_df = new SimpleTensor(size * 2, size * 2, size);
// TODO: combine this concatenation with computeTensorDeltaDown?
SimpleMatrix fullVector = NeuralUtils.Concatenate(leftVector, rightVector);
for (int slice = 0; slice < size; ++slice)
{
Wt_df.SetSlice(slice, fullVector.Scale(deltaFull.Get(slice)).Mult(fullVector.Transpose()));
}
return(Wt_df);
}
/// <summary>Returns true iff every element of matrix is 0</summary>
public static bool IsZero(SimpleMatrix matrix)
{
int size = matrix.GetNumElements();
for (int i = 0; i < size; ++i)
{
if (matrix.Get(i) != 0.0)
{
return(false);
}
}
return(true);
}
/// <summary>Applies tanh to each of the entries in the matrix.</summary>
/// <remarks>Applies tanh to each of the entries in the matrix. Returns a new matrix.</remarks>
public static SimpleMatrix ElementwiseApplyTanh(SimpleMatrix input)
{
SimpleMatrix output = new SimpleMatrix(input);
for (int i = 0; i < output.NumRows(); ++i)
{
for (int j = 0; j < output.NumCols(); ++j)
{
output.Set(i, j, Math.Tanh(output.Get(i, j)));
}
}
return(output);
}
private static SimpleMatrix ComputeTensorDeltaDown(SimpleMatrix deltaFull, SimpleMatrix leftVector, SimpleMatrix rightVector, SimpleMatrix W, SimpleTensor Wt)
{
SimpleMatrix WTDelta = W.Transpose().Mult(deltaFull);
SimpleMatrix WTDeltaNoBias = WTDelta.ExtractMatrix(0, deltaFull.NumRows() * 2, 0, 1);
int size = deltaFull.GetNumElements();
SimpleMatrix deltaTensor = new SimpleMatrix(size * 2, 1);
SimpleMatrix fullVector = NeuralUtils.Concatenate(leftVector, rightVector);
for (int slice = 0; slice < size; ++slice)
{
SimpleMatrix scaledFullVector = fullVector.Scale(deltaFull.Get(slice));
deltaTensor = deltaTensor.Plus(Wt.GetSlice(slice).Plus(Wt.GetSlice(slice).Transpose()).Mult(scaledFullVector));
}
return(deltaTensor.Plus(WTDeltaNoBias));
}
/// <summary>Applies softmax to all of the elements of the matrix.</summary>
/// <remarks>
/// Applies softmax to all of the elements of the matrix. The return
/// matrix will have all of its elements sum to 1. If your matrix is
/// not already a vector, be sure this is what you actually want.
/// </remarks>
public static SimpleMatrix Softmax(SimpleMatrix input)
{
SimpleMatrix output = new SimpleMatrix(input);
for (int i = 0; i < output.NumRows(); ++i)
{
for (int j = 0; j < output.NumCols(); ++j)
{
output.Set(i, j, Math.Exp(output.Get(i, j)));
}
}
double sum = output.ElementSum();
// will be safe, since exp should never return 0
return(output.Scale(1.0 / sum));
}
/// <summary>Return as a double the probability of the predicted class.</summary>
/// <remarks>
/// Return as a double the probability of the predicted class. If it is not defined for a node,
/// it will return -1
/// </remarks>
/// <returns>Either the label probability or -1.0 if none</returns>
public static double GetPredictedClassProb(ILabel label)
{
if (!(label is CoreLabel))
{
throw new ArgumentException("CoreLabels required to get the attached predicted class probability");
}
int val = ((CoreLabel)label).Get(typeof(RNNCoreAnnotations.PredictedClass));
SimpleMatrix predictions = ((CoreLabel)label).Get(typeof(RNNCoreAnnotations.Predictions));
if (val != null)
{
return(predictions.Get(val));
}
else
{
return(-1.0);
}
}
/// <summary>Outputs the scores from the tree.</summary>
/// <remarks>
/// Outputs the scores from the tree. Counts the tree nodes the
/// same as setIndexLabels.
/// </remarks>
private static int OutputTreeScores(TextWriter @out, Tree tree, int index)
{
if (tree.IsLeaf())
{
return(index);
}
@out.Write(" " + index + ':');
SimpleMatrix vector = RNNCoreAnnotations.GetPredictions(tree);
for (int i = 0; i < vector.GetNumElements(); ++i)
{
@out.Write(" " + Nf.Format(vector.Get(i)));
}
@out.WriteLine();
index++;
foreach (Tree child in tree.Children())
{
index = OutputTreeScores(@out, child, index);
}
return(index);
}
/// <exception cref="System.IO.IOException"/>
public static void Main(string[] args)
{
string modelPath = null;
string outputDir = null;
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"))
{
outputDir = args[argIndex + 1];
argIndex += 2;
}
else
{
log.Info("Unknown argument " + args[argIndex]);
Help();
}
}
}
if (outputDir == null || modelPath == null)
{
Help();
}
File outputFile = new File(outputDir);
FileSystem.CheckNotExistsOrFail(outputFile);
FileSystem.MkdirOrFail(outputFile);
LexicalizedParser parser = ((LexicalizedParser)LexicalizedParser.LoadModel(modelPath));
DVModel model = DVParser.GetModelFromLexicalizedParser(parser);
string binaryWDir = outputDir + File.separator + "binaryW";
FileSystem.MkdirOrFail(binaryWDir);
foreach (TwoDimensionalMap.Entry <string, string, SimpleMatrix> entry in model.binaryTransform)
{
string filename = binaryWDir + File.separator + entry.GetFirstKey() + "_" + entry.GetSecondKey() + ".txt";
DumpMatrix(filename, entry.GetValue());
}
string binaryScoreDir = outputDir + File.separator + "binaryScore";
FileSystem.MkdirOrFail(binaryScoreDir);
foreach (TwoDimensionalMap.Entry <string, string, SimpleMatrix> entry_1 in model.binaryScore)
{
string filename = binaryScoreDir + File.separator + entry_1.GetFirstKey() + "_" + entry_1.GetSecondKey() + ".txt";
DumpMatrix(filename, entry_1.GetValue());
}
string unaryWDir = outputDir + File.separator + "unaryW";
FileSystem.MkdirOrFail(unaryWDir);
foreach (KeyValuePair <string, SimpleMatrix> entry_2 in model.unaryTransform)
{
string filename = unaryWDir + File.separator + entry_2.Key + ".txt";
DumpMatrix(filename, entry_2.Value);
}
string unaryScoreDir = outputDir + File.separator + "unaryScore";
FileSystem.MkdirOrFail(unaryScoreDir);
foreach (KeyValuePair <string, SimpleMatrix> entry_3 in model.unaryScore)
{
string filename = unaryScoreDir + File.separator + entry_3.Key + ".txt";
DumpMatrix(filename, entry_3.Value);
}
string embeddingFile = outputDir + File.separator + "embeddings.txt";
FileWriter fout = new FileWriter(embeddingFile);
BufferedWriter bout = new BufferedWriter(fout);
foreach (KeyValuePair <string, SimpleMatrix> entry_4 in model.wordVectors)
{
bout.Write(entry_4.Key);
SimpleMatrix vector = entry_4.Value;
for (int i = 0; i < vector.NumRows(); ++i)
{
bout.Write(" " + vector.Get(i, 0));
}
bout.Write("\n");
}
bout.Close();
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();
}