/** Outputs the scores from the tree. Counts the tree nodes the same as setIndexLabels. */
  static int outputTreeScores(PrintStream out, Tree tree, int index) {
    if (tree.isLeaf()) {
      return index;
    }

    out.print("  " + index + ":");
    SimpleMatrix vector = RNNCoreAnnotations.getPredictions(tree);
    for (int i = 0; i < vector.getNumElements(); ++i) {
      out.print("  " + NF.format(vector.get(i)));
    }
    out.println();
    index++;
    for (Tree child : tree.children()) {
      index = outputTreeScores(out, child, index);
    }
    return index;
  }
Beispiel #2
0
  private SentimentModel(
      TwoDimensionalMap<String, String, SimpleMatrix> binaryTransform,
      TwoDimensionalMap<String, String, SimpleTensor> binaryTensors,
      TwoDimensionalMap<String, String, SimpleMatrix> binaryClassification,
      Map<String, SimpleMatrix> unaryClassification,
      Map<String, SimpleMatrix> wordVectors,
      RNNOptions op) {
    this.op = op;

    this.binaryTransform = binaryTransform;
    this.binaryTensors = binaryTensors;
    this.binaryClassification = binaryClassification;
    this.unaryClassification = unaryClassification;
    this.wordVectors = wordVectors;
    this.numClasses = op.numClasses;
    if (op.numHid <= 0) {
      int nh = 0;
      for (SimpleMatrix wv : wordVectors.values()) {
        nh = wv.getNumElements();
      }
      this.numHid = nh;
    } else {
      this.numHid = op.numHid;
    }
    this.numBinaryMatrices = binaryTransform.size();
    binaryTransformSize = numHid * (2 * numHid + 1);
    if (op.useTensors) {
      binaryTensorSize = numHid * numHid * numHid * 4;
    } else {
      binaryTensorSize = 0;
    }
    binaryClassificationSize = (op.combineClassification) ? 0 : numClasses * (numHid + 1);

    numUnaryMatrices = unaryClassification.size();
    unaryClassificationSize = numClasses * (numHid + 1);

    rand = new Random(op.randomSeed);

    identity = SimpleMatrix.identity(numHid);
  }
Beispiel #3
0
  /** The traditional way of initializing an empty model suitable for training. */
  public SentimentModel(RNNOptions op, List<Tree> trainingTrees) {
    this.op = op;
    rand = new Random(op.randomSeed);

    if (op.randomWordVectors) {
      initRandomWordVectors(trainingTrees);
    } else {
      readWordVectors();
    }
    if (op.numHid > 0) {
      this.numHid = op.numHid;
    } else {
      int size = 0;
      for (SimpleMatrix vector : wordVectors.values()) {
        size = vector.getNumElements();
        break;
      }
      this.numHid = size;
    }

    TwoDimensionalSet<String, String> binaryProductions = TwoDimensionalSet.hashSet();
    if (op.simplifiedModel) {
      binaryProductions.add("", "");
    } else {
      // TODO
      // figure out what binary productions we have in these trees
      // Note: the current sentiment training data does not actually
      // have any constituent labels
      throw new UnsupportedOperationException("Not yet implemented");
    }

    Set<String> unaryProductions = Generics.newHashSet();
    if (op.simplifiedModel) {
      unaryProductions.add("");
    } else {
      // TODO
      // figure out what unary productions we have in these trees (preterminals only, after the
      // collapsing)
      throw new UnsupportedOperationException("Not yet implemented");
    }

    this.numClasses = op.numClasses;

    identity = SimpleMatrix.identity(numHid);

    binaryTransform = TwoDimensionalMap.treeMap();
    binaryTensors = TwoDimensionalMap.treeMap();
    binaryClassification = TwoDimensionalMap.treeMap();

    // When making a flat model (no symantic untying) the
    // basicCategory function will return the same basic category for
    // all labels, so all entries will map to the same matrix
    for (Pair<String, String> binary : binaryProductions) {
      String left = basicCategory(binary.first);
      String right = basicCategory(binary.second);
      if (binaryTransform.contains(left, right)) {
        continue;
      }
      binaryTransform.put(left, right, randomTransformMatrix());
      if (op.useTensors) {
        binaryTensors.put(left, right, randomBinaryTensor());
      }
      if (!op.combineClassification) {
        binaryClassification.put(left, right, randomClassificationMatrix());
      }
    }
    numBinaryMatrices = binaryTransform.size();
    binaryTransformSize = numHid * (2 * numHid + 1);
    if (op.useTensors) {
      binaryTensorSize = numHid * numHid * numHid * 4;
    } else {
      binaryTensorSize = 0;
    }
    binaryClassificationSize = (op.combineClassification) ? 0 : numClasses * (numHid + 1);

    unaryClassification = Generics.newTreeMap();

    // When making a flat model (no symantic untying) the
    // basicCategory function will return the same basic category for
    // all labels, so all entries will map to the same matrix
    for (String unary : unaryProductions) {
      unary = basicCategory(unary);
      if (unaryClassification.containsKey(unary)) {
        continue;
      }
      unaryClassification.put(unary, randomClassificationMatrix());
    }
    numUnaryMatrices = unaryClassification.size();
    unaryClassificationSize = numClasses * (numHid + 1);

    // System.err.println("Binary transform matrices:");
    // System.err.println(binaryTransform);
    // System.err.println("Binary classification matrices:");
    // System.err.println(binaryClassification);
    // System.err.println("Unary classification matrices:");
    // System.err.println(unaryClassification);
  }