/**
* Load the directed graph from the given file
*
* @param fileName the file to load the cart from
* @param featDefinition the feature definition
* @param dummy unused, just here for compatibility with the FeatureFileIndexer.
* @throws IOException , {@link MaryConfigurationException} if a problem occurs while loading
*/
public DirectedGraph load(InputStream inStream) throws IOException, MaryConfigurationException {
BufferedInputStream buffInStream = new BufferedInputStream(inStream);
assert buffInStream.markSupported();
buffInStream.mark(10000);
// open the CART-File and read the header
DataInput raf = new DataInputStream(buffInStream);
MaryHeader maryHeader = new MaryHeader(raf);
if (!maryHeader.hasCurrentVersion()) {
throw new IOException("Wrong version of database file");
}
if (maryHeader.getType() != MaryHeader.DIRECTED_GRAPH) {
if (maryHeader.getType() == MaryHeader.CARTS) {
buffInStream.reset();
return new MaryCARTReader().loadFromStream(buffInStream);
} else {
throw new IOException("Not a directed graph file");
}
}
// Read properties
short propDataLength = raf.readShort();
Properties props;
if (propDataLength == 0) {
props = null;
} else {
byte[] propsData = new byte[propDataLength];
raf.readFully(propsData);
ByteArrayInputStream bais = new ByteArrayInputStream(propsData);
props = new Properties();
props.load(bais);
bais.close();
}
// Read the feature definition
FeatureDefinition featureDefinition = new FeatureDefinition(raf);
// read the decision nodes
int numDecNodes = raf.readInt(); // number of decision nodes
// First we need to read all nodes into memory, then we can link them properly
// in terms of parent/child.
DecisionNode[] dns = new DecisionNode[numDecNodes];
int[][] childIndexes = new int[numDecNodes][];
for (int i = 0; i < numDecNodes; i++) {
// read one decision node
int featureNameIndex = raf.readInt();
int nodeTypeNr = raf.readInt();
DecisionNode.Type nodeType = DecisionNode.Type.values()[nodeTypeNr];
int numChildren = 2; // for binary nodes
switch (nodeType) {
case BinaryByteDecisionNode:
int criterion = raf.readInt();
dns[i] =
new DecisionNode.BinaryByteDecisionNode(
featureNameIndex, (byte) criterion, featureDefinition);
break;
case BinaryShortDecisionNode:
criterion = raf.readInt();
dns[i] =
new DecisionNode.BinaryShortDecisionNode(
featureNameIndex, (short) criterion, featureDefinition);
break;
case BinaryFloatDecisionNode:
float floatCriterion = raf.readFloat();
dns[i] =
new DecisionNode.BinaryFloatDecisionNode(
featureNameIndex, floatCriterion, featureDefinition);
break;
case ByteDecisionNode:
numChildren = raf.readInt();
if (featureDefinition.getNumberOfValues(featureNameIndex) != numChildren) {
throw new IOException(
"Inconsistent cart file: feature "
+ featureDefinition.getFeatureName(featureNameIndex)
+ " should have "
+ featureDefinition.getNumberOfValues(featureNameIndex)
+ " values, but decision node "
+ i
+ " has only "
+ numChildren
+ " child nodes");
}
dns[i] =
new DecisionNode.ByteDecisionNode(featureNameIndex, numChildren, featureDefinition);
break;
case ShortDecisionNode:
numChildren = raf.readInt();
if (featureDefinition.getNumberOfValues(featureNameIndex) != numChildren) {
throw new IOException(
"Inconsistent cart file: feature "
+ featureDefinition.getFeatureName(featureNameIndex)
+ " should have "
+ featureDefinition.getNumberOfValues(featureNameIndex)
+ " values, but decision node "
+ i
+ " has only "
+ numChildren
+ " child nodes");
}
dns[i] =
new DecisionNode.ShortDecisionNode(featureNameIndex, numChildren, featureDefinition);
}
dns[i].setUniqueDecisionNodeId(i + 1);
// now read the children, indexes only:
childIndexes[i] = new int[numChildren];
for (int k = 0; k < numChildren; k++) {
childIndexes[i][k] = raf.readInt();
}
}
// read the leaves
int numLeafNodes = raf.readInt(); // number of leaves, it does not include empty leaves
LeafNode[] lns = new LeafNode[numLeafNodes];
for (int j = 0; j < numLeafNodes; j++) {
// read one leaf node
int leafTypeNr = raf.readInt();
LeafNode.LeafType leafNodeType = LeafNode.LeafType.values()[leafTypeNr];
switch (leafNodeType) {
case IntArrayLeafNode:
int numData = raf.readInt();
int[] data = new int[numData];
for (int d = 0; d < numData; d++) {
data[d] = raf.readInt();
}
lns[j] = new LeafNode.IntArrayLeafNode(data);
break;
case FloatLeafNode:
float stddev = raf.readFloat();
float mean = raf.readFloat();
lns[j] = new LeafNode.FloatLeafNode(new float[] {stddev, mean});
break;
case IntAndFloatArrayLeafNode:
case StringAndFloatLeafNode:
int numPairs = raf.readInt();
int[] ints = new int[numPairs];
float[] floats = new float[numPairs];
for (int d = 0; d < numPairs; d++) {
ints[d] = raf.readInt();
floats[d] = raf.readFloat();
}
if (leafNodeType == LeafNode.LeafType.IntAndFloatArrayLeafNode)
lns[j] = new LeafNode.IntAndFloatArrayLeafNode(ints, floats);
else lns[j] = new LeafNode.StringAndFloatLeafNode(ints, floats);
break;
case FeatureVectorLeafNode:
throw new IllegalArgumentException(
"Reading feature vector leaf nodes is not yet implemented");
case PdfLeafNode:
throw new IllegalArgumentException("Reading pdf leaf nodes is not yet implemented");
}
lns[j].setUniqueLeafId(j + 1);
}
// Graph nodes
int numDirectedGraphNodes = raf.readInt();
DirectedGraphNode[] graphNodes = new DirectedGraphNode[numDirectedGraphNodes];
int[] dgnLeafIndices = new int[numDirectedGraphNodes];
int[] dgnDecIndices = new int[numDirectedGraphNodes];
for (int g = 0; g < numDirectedGraphNodes; g++) {
graphNodes[g] = new DirectedGraphNode(null, null);
graphNodes[g].setUniqueGraphNodeID(g + 1);
dgnLeafIndices[g] = raf.readInt();
dgnDecIndices[g] = raf.readInt();
}
// Now, link up the decision nodes with their daughters
for (int i = 0; i < numDecNodes; i++) {
// System.out.print(dns[i]+" "+dns[i].getFeatureName()+" ");
for (int k = 0; k < childIndexes[i].length; k++) {
Node child = childIndexToNode(childIndexes[i][k], dns, lns, graphNodes);
dns[i].addDaughter(child);
// System.out.print(" "+dns[i].getDaughter(k));
}
// System.out.println();
}
// And link up directed graph nodes
for (int g = 0; g < numDirectedGraphNodes; g++) {
Node leaf = childIndexToNode(dgnLeafIndices[g], dns, lns, graphNodes);
graphNodes[g].setLeafNode(leaf);
Node dec = childIndexToNode(dgnDecIndices[g], dns, lns, graphNodes);
if (dec != null && !dec.isDecisionNode())
throw new IllegalArgumentException("Only decision nodes allowed, read " + dec.getClass());
graphNodes[g].setDecisionNode((DecisionNode) dec);
// System.out.println("Graph node "+(g+1)+", leaf: "+Integer.toHexString(dgnLeafIndices[g])+",
// "+leaf+" -- dec: "+Integer.toHexString(dgnDecIndices[g])+", "+dec);
}
Node rootNode;
if (graphNodes.length > 0) {
rootNode = graphNodes[0];
} else if (dns.length > 0) {
rootNode = dns[0];
// CART behaviour, not sure if this is needed:
// Now count all data once, so that getNumberOfData()
// will return the correct figure.
((DecisionNode) rootNode).countData();
} else if (lns.length > 0) {
rootNode = lns[0]; // single-leaf tree...
} else {
rootNode = null;
}
// set the rootNode as the rootNode of cart
return new DirectedGraph(rootNode, featureDefinition, props);
}
/**
* Get the theoretical number of leaves, given a feature sequence.
*
* @return The number of leaves, or -1 if the capacity of the long integer was blown.
*/
public long getTheoreticalNumberOfLeaves(int[] feaSeq) {
long ret = 1;
for (int i = 0; i < feaSeq.length; i++) {
// System.out.println( "Feature [" + i + "] has [" +
// featureDefinition.getNumberOfValues( featureSequence[i] ) + "] values."
// + "(Number of leaves = [" + ret + "].)" );
ret *= featureDefinition.getNumberOfValues(feaSeq[i]);
if (ret < 0) return (-1);
}
return (ret);
}
/**
* A local sort at a particular node along the deep sorting operation. This is a recursive
* function.
*
* @param currentFeatureIdx The currently tested feature.
* @param currentNode The current node, holding the currently processed zone in the array of
* feature vectors.
*/
private void sortNode(int currentFeatureIdx, MaryNode currentNode) {
/* If we have reached a leaf, do a final sort according to the unit index and return: */
if (currentFeatureIdx == featureSequence.length) {
Arrays.sort(featureVectors, currentNode.from, currentNode.to, cui);
numberOfLeaves++;
/*System.out.print( "LEAF ! (" + (currentNode.to-currentNode.from) + " units)" );
for ( int i = currentNode.from; i < currentNode.to; i++ ) {
System.out.print( " (" + featureVectors[i].getUnitIndex() + " 0)" );
}
System.out.println( "" );*/
return;
}
/* Else: */
int currentFeature = featureSequence[currentFeatureIdx];
FeatureVector.FeatureType featureType = featureVectors[0].getFeatureType(currentFeature);
/* Register the feature currently used for the splitting */
currentNode.setFeatureIndex(currentFeature);
/* Perform the sorting according to the currently considered feature: */
/* 1) position the comparator onto the right feature */
c.setFeatureIdx(currentFeature, featureType);
/* 2) do the sorting */
Arrays.sort(featureVectors, currentNode.from, currentNode.to, c);
/* Then, seek for the zones where the feature value is the same,
* and launch the next sort level on these. */
int nVal = featureDefinition.getNumberOfValues(currentFeature);
currentNode.split(nVal);
int nextFrom = currentNode.from;
int nextTo = currentNode.from;
for (int i = 0; i < nVal; i++) {
nextFrom = nextTo;
// System.out.print( "Next node begins at " + nextFrom );
while ((nextTo < currentNode.to)
&& (featureVectors[nextTo].getFeatureAsInt(currentFeature) == i)) {
// System.out.print( " " + featureVectors[nextTo].getFeatureAsInt( currentFeature ) );
nextTo++;
}
// System.out.println( " and ends at " + nextTo + " for a total of " + (nextTo-nextFrom) + "
// units." );
if ((nextTo - nextFrom) != 0) {
MaryNode nod = new MaryNode(nextFrom, nextTo);
currentNode.setChild(i, nod);
// System.out.print("(" + i + " isByteOf " + currentFeature + ")" );
sortNode(currentFeatureIdx + 1, nod);
} else currentNode.setChild(i, null);
}
}
/**
* Export this feature definition in the "all.desc" format which can be read by wagon.
*
* @param out the destination of the data
* @param featuresToIgnore a set of Strings containing the names of features that wagon should
* ignore. Can be null.
*/
private void createDescFile() throws IOException {
PrintWriter out = new PrintWriter(new FileOutputStream(descFile));
Set<String> featuresToIgnore = new HashSet<String>();
featuresToIgnore.add("unit_logf0");
featuresToIgnore.add("unit_duration");
int numDiscreteFeatures =
featureDefinition.getNumberOfByteFeatures() + featureDefinition.getNumberOfShortFeatures();
out.println("(");
out.println("(occurid cluster)");
for (int i = 0, n = featureDefinition.getNumberOfFeatures(); i < n; i++) {
out.print("( ");
String featureName = featureDefinition.getFeatureName(i);
out.print(featureName);
if (featuresToIgnore != null && featuresToIgnore.contains(featureName)) {
out.print(" ignore");
}
if (i < numDiscreteFeatures) { // list values
for (int v = 0, vmax = featureDefinition.getNumberOfValues(i); v < vmax; v++) {
out.print(" ");
// Print values surrounded by double quotes, and make sure any
// double quotes in the value are preceded by a backslash --
// otherwise, we get problems e.g. for sentence_punc
String val = featureDefinition.getFeatureValueAsString(i, v);
if (val.indexOf('"') != -1) {
StringBuilder buf = new StringBuilder();
for (int c = 0; c < val.length(); c++) {
char ch = val.charAt(c);
if (ch == '"') buf.append("\\\"");
else buf.append(ch);
}
val = buf.toString();
}
out.print("\"" + val + "\"");
}
out.println(" )");
} else { // float feature
out.println(" float )");
}
}
out.println(")");
out.close();
}
