@Override
public void process(final DAVOptions options) {
final String[] args = getOriginalArgs();
final UseModality<DAVOptions> executed;
final int maxSplitIndex = splitPlan.getMaxSplitIndex();
final ProgressLogger logger = new ProgressLogger(LOGGER);
logger.expectedUpdates = maxSplitIndex;
logger.itemsName = "splits";
logger.priority = Level.INFO;
logger.start("Parallel split processing");
final SplitParallelRegion region = new SplitParallelRegion(maxSplitIndex, args, logger);
try {
getParallelTeam().execute(region);
} catch (Exception e) {
LOGGER.error("An exception occurred.", e);
}
logger.stop();
/** Time the duration of the sequence: */
timeService.setModelId(modelId);
timeService.stop();
executed = region.getExecuted();
if (executed != null && executed instanceof SequenceMode) {
// if we executed SequenceMode
final SequenceMode sequenceMode = (SequenceMode) executed;
if (evaluateStatistics) {
final String label = sequenceMode.getValue("label");
final String statsFilename = sequenceMode.getValue("predictions-filename");
if (statsFilename != null && label != null) {
// and the sequence defined the variables "predictions-filename" and "label"
try {
final List<String> statsModeArgs =
new ObjectArrayList<String>(
new String[] {
"--mode",
"stats",
"--predictions",
statsFilename,
"--submission-file",
labelPrefix(label) + "-maqcii-submission.txt",
"--label",
label,
"--model-id",
modelId,
"--dataset-name",
options.datasetName,
"--other-measures",
"prec,rec,F-1,MCC,binary-auc"
});
if (options.adjustSignalToFloorValue) {
statsModeArgs.add("--floor");
statsModeArgs.add(Double.toString(options.signalFloorValue));
}
// extract survival options if any
// TODO: clean this up - we should not be checking for "%survival%"
final String survivalFileName = sequenceMode.getValue("survival");
if (StringUtils.isNotBlank(survivalFileName)
&& !"%survival%".equals(survivalFileName)) {
statsModeArgs.add("--survival");
statsModeArgs.add(survivalFileName);
}
LOGGER.debug("Estimating statistics: " + statsModeArgs);
// we create a new DAVMode here since we want to use the old StatsMode code
// which is no longer exposed by DiscoverAndValidate (BDVal main method)
final DAVMode statsMode = new DAVMode();
statsMode.registerMode("stats", StatsMode.class);
final DAVOptions statsModeOptions = new DAVOptions();
statsMode.process(
statsModeArgs.toArray(new String[statsModeArgs.size()]), statsModeOptions);
} catch (Exception e) {
LOGGER.error("Error executing --mode stats for all splits", e);
}
}
}
}
}
/**
* Perform the split transcripts mode.
*
* @throws IOException error reading / writing
*/
@Override
public void execute() throws IOException {
// Load the gene to transcripts file
if (!config.validate()) {
throw new IOException("Invalid SplitTranscripts configuration");
}
final GeneTranscriptRelationships gtr = new GeneTranscriptRelationships();
final IndexedIdentifier transcriptIdents = new IndexedIdentifier();
final Int2ObjectMap<MutableString> transcriptIndexToIdMap =
new Int2ObjectOpenHashMap<MutableString>();
final List<FastXEntry> fastxEntries = new LinkedList<FastXEntry>();
//
// Pass through the file once to collect the transcript - gene relationships
//
int entryCount = 0;
try {
for (final FastXEntry entry : new FastXReader(config.getInputFile())) {
entryCount++;
parseHeader(entry.getEntryHeader());
final MutableString transcriptId = transcriptHeader.get("transcriptId");
final MutableString geneId = transcriptHeader.get("geneId");
final int transcriptIndex = transcriptIdents.registerIdentifier(transcriptId);
gtr.addRelationship(geneId, transcriptIndex);
transcriptIndexToIdMap.put(transcriptIndex, transcriptId);
fastxEntries.add(entry.clone());
}
} catch (CloneNotSupportedException e) {
LOG.error("Couldn't clone for some reason", e);
throw new GobyRuntimeException("Couldn't clone for some reason", e);
}
LOG.info("Loading map of genes-transcripts complete.");
//
// Scan through the transcript-gene relationships to determine which
// transcript id goes into which file
//
final Int2IntMap transcriptIndex2FileIndex = new Int2IntOpenHashMap();
final String configOutputFilename = config.getOutputBase() + ".config";
final String configOutputPath = FilenameUtils.getFullPath(configOutputFilename);
if (StringUtils.isNotBlank(configOutputPath)) {
LOG.info("Creating output directory: " + configOutputPath);
FileUtils.forceMkdir(new File(configOutputPath));
}
PrintWriter configOutput = null;
try {
configOutput = new PrintWriter(configOutputFilename);
configOutput.println("Ensembl Gene ID\tEnsembl Transcript ID");
final Int2IntMap fileIndex2NumberOfEntries = new Int2IntOpenHashMap();
fileIndex2NumberOfEntries.defaultReturnValue(0);
transcriptIndex2FileIndex.defaultReturnValue(-1);
final int initialNumberOfFiles = getNumberOfFiles(gtr, transcriptIndex2FileIndex);
for (int geneIndex = 0; geneIndex < gtr.getNumberOfGenes(); geneIndex++) {
final MutableString geneId = gtr.getGeneId(geneIndex);
final IntSet transcriptIndices = gtr.getTranscriptSet(geneIndex);
int fileNum = 0;
for (final int transcriptIndex : transcriptIndices) {
if (transcriptIndex2FileIndex.get(transcriptIndex) != -1) {
LOG.warn("Skipping repeated transcriptIndex: " + transcriptIndex);
continue;
}
final int maxEntriesPerFile = config.getMaxEntriesPerFile();
final int numberOfEntriesInOriginalBucket = fileIndex2NumberOfEntries.get(fileNum);
final int adjustedFileIndex =
fileNum
+ initialNumberOfFiles * (numberOfEntriesInOriginalBucket / maxEntriesPerFile);
transcriptIndex2FileIndex.put(transcriptIndex, adjustedFileIndex);
fileIndex2NumberOfEntries.put(fileNum, fileIndex2NumberOfEntries.get(fileNum) + 1);
final MutableString transcriptId = transcriptIndexToIdMap.get(transcriptIndex);
configOutput.printf("%s\t%s%n", geneId, transcriptId);
fileNum++;
}
}
} finally {
IOUtils.closeQuietly(configOutput);
}
final int numFiles = getFileIndices(transcriptIndex2FileIndex).size();
if (LOG.isInfoEnabled()) {
LOG.info(
NumberFormat.getInstance().format(entryCount)
+ " entries will be written to "
+ numFiles
+ " files");
final int maxEntriesPerFile = config.getMaxEntriesPerFile();
if (maxEntriesPerFile < Integer.MAX_VALUE) {
LOG.info("Each file will contain at most " + maxEntriesPerFile + " entries");
}
}
// formatter for uniquely numbering files each with the same number of digits
final NumberFormat fileNumberFormatter = getNumberFormatter(numFiles - 1);
final ProgressLogger progressLogger = new ProgressLogger();
progressLogger.expectedUpdates = entryCount;
progressLogger.itemsName = "entries";
progressLogger.start();
// Write each file one at a time rather than in the order they appear in the input file
// to avoid the issue of having too many streams open at the same or continually opening
// and closing streams which is quite costly. We could store the gene/transcripts in
// memory and then just write the files at the end but that could be worse.
for (final int fileIndex : getFileIndices(transcriptIndex2FileIndex)) {
final String filename =
config.getOutputBase() + "." + fileNumberFormatter.format(fileIndex) + ".fa.gz";
PrintStream printStream = null;
try {
// each file is compressed
printStream = new PrintStream(new GZIPOutputStream(new FileOutputStream(filename)));
//
// Read through the input file get the actual sequence information
//
final Iterator<FastXEntry> entries = fastxEntries.iterator();
while (entries.hasNext()) {
final FastXEntry entry = entries.next();
parseHeader(entry.getEntryHeader());
final MutableString transcriptId = transcriptHeader.get("transcriptId");
final MutableString geneId = transcriptHeader.get("geneId");
final int transcriptIndex = transcriptIdents.getInt(transcriptId);
final int transcriptFileIndex = transcriptIndex2FileIndex.get(transcriptIndex);
if (transcriptFileIndex == fileIndex) {
printStream.print(entry.getHeaderSymbol());
printStream.print(transcriptId);
printStream.print(" gene:");
printStream.println(geneId);
printStream.println(entry.getEntrySansHeader());
entries.remove();
progressLogger.lightUpdate();
}
}
} finally {
IOUtils.closeQuietly(printStream);
}
}
assert progressLogger.count == entryCount : "Some entries were not processed!";
progressLogger.done();
}
/** Computes the next step of the Power Method. */
public void step() throws IOException {
double[] oldRank = rank, newRank = previousRank;
DoubleArrays.fill(newRank, 0.0);
// for each node, calculate its outdegree and redistribute its rank among pointed nodes
double accum = 0.0;
progressLogger.expectedUpdates = numNodes;
progressLogger.start("Iteration " + (++iterationNumber) + "...");
final ArcLabelledNodeIterator nodeIterator = g.nodeIterator();
int i, outdegree, j, n = numNodes;
int[] succ;
Label[] lab;
while (n-- != 0) {
i = nodeIterator.nextInt();
outdegree = nodeIterator.outdegree();
if (outdegree == 0 || buckets != null && buckets.get(i)) accum += oldRank[i];
else {
j = outdegree;
succ = nodeIterator.successorArray();
lab = nodeIterator.labelArray();
while (j-- != 0) {
newRank[succ[j]] += (oldRank[i] * lab[j].getFloat()) / sumoutweight[i];
}
}
progressLogger.update();
}
progressLogger.done();
final double accumOverNumNodes = accum / numNodes;
final double oneOverNumNodes = 1.0 / numNodes;
if (preference != null)
if (preferentialAdjustment == null)
for (i = numNodes; i-- != 0; )
newRank[i] =
alpha * newRank[i]
+ (1 - alpha) * preference.getDouble(i)
+ alpha * accumOverNumNodes;
else
for (i = numNodes; i-- != 0; )
newRank[i] =
alpha * newRank[i]
+ (1 - alpha) * preference.getDouble(i)
+ alpha * accum * preferentialAdjustment.getDouble(i);
else if (preferentialAdjustment == null)
for (i = numNodes; i-- != 0; )
newRank[i] = alpha * newRank[i] + (1 - alpha) * oneOverNumNodes + alpha * accumOverNumNodes;
else
for (i = numNodes; i-- != 0; )
newRank[i] =
alpha * newRank[i]
+ (1 - alpha) * oneOverNumNodes
+ alpha * accum * preferentialAdjustment.getDouble(i);
// make the rank just computed the new rank
rank = newRank;
previousRank = oldRank;
// Compute derivatives.
n = iterationNumber;
if (subset == null) {
for (i = 0; i < order.length; i++) {
final int k = order[i];
final double alphak = Math.pow(alpha, k);
final double nFallingK = Util.falling(n, k);
for (j = 0; j < numNodes; j++)
derivative[i][j] += nFallingK * (rank[j] - previousRank[j]) / alphak;
}
} else {
for (i = 0; i < order.length; i++) {
final int k = order[i];
final double alphak = Math.pow(alpha, k);
final double nFallingK = Util.falling(n, k);
for (int t : subset) derivative[i][t] += nFallingK * (rank[t] - previousRank[t]) / alphak;
}
}
// Compute coefficients, if required.
if (coeffBasename != null) {
final DataOutputStream coefficients =
new DataOutputStream(
new FastBufferedOutputStream(
new FileOutputStream(coeffBasename + "-" + (iterationNumber))));
final double alphaN = Math.pow(alpha, n);
for (i = 0; i < numNodes; i++) coefficients.writeDouble((rank[i] - previousRank[i]) / alphaN);
coefficients.close();
}
}
/**
* For a specific sub-set of blocks (child nodes), find a 'base' subset of parents for which the
* block's logLikelihood is not -Infinity
*
* @param candidateParentsPerNode
* @param chosenArcsPerNode
* @param setOfBlocks
* @return
*/
protected double getOutOfMinusInfinity(
Int2ObjectOpenHashMap<IntOpenHashSet> candidateParentsPerNode,
Int2ObjectOpenHashMap<ObjectOpenHashSet<Arc>> chosenArcsPerNode,
IntOpenHashSet setOfBlocks,
TIntDoubleHashMap logLPerNode) {
double totalLogL = 0;
ProgressLogger pl = new ProgressLogger(LOGGER, ProgressLogger.TEN_SECONDS, "blocks");
pl.start("Begin initializing, to avoid zero likelihood, using set-cover heuristic");
pl.expectedUpdates = setOfBlocks.size();
int nArcs = 0;
for (int v : setOfBlocks) {
pl.update();
IntOpenHashSet vParents = candidateParentsPerNode.get(v);
Int2ObjectOpenHashMap<IntOpenHashSet> parentActions =
new Int2ObjectOpenHashMap<IntOpenHashSet>();
Int2ObjectOpenHashMap<IntArrayList> cPlusV = auxiliary.getCplusOnline(v);
Int2ObjectOpenHashMap<IntArrayList> cMinusV = auxiliary.getCminusOnline(v);
if (cPlusV != null) {
IntSet actions = cPlusV.keySet();
// Heuristic: first add the parents that participate in A+ for
// most actions
for (int action : actions) {
for (int u : cPlusV.get(action)) {
if (!parentActions.containsKey(u)) {
parentActions.put(u, new IntOpenHashSet());
}
parentActions.get(u).add(action);
}
}
}
KeepMaximum km = new KeepMaximum();
km.addAllKey2Listsize(parentActions);
IntOpenHashSet baseSetOfParents = new IntOpenHashSet();
double logL = Double.NEGATIVE_INFINITY;
while (logL == Double.NEGATIVE_INFINITY && (km.getMaximumKey() != -1)) {
int u = km.getMaximumKey();
if (baseSetOfParents.contains(u)) {
throw new IllegalStateException("Attempted to add twice the same parent");
}
baseSetOfParents.add(u);
logL = blockLogLikelihood(v, cPlusV, cMinusV, baseSetOfParents);
IntOpenHashSet uActions = parentActions.get(u);
for (int parent : vParents) {
parentActions.get(parent).removeAll(uActions);
}
vParents.remove(u);
parentActions.remove(u);
km.reset();
km.addAllKey2Listsize(parentActions);
}
// keep track of the likelihood
totalLogL += logL;
if (logLPerNode != null) {
logLPerNode.put(v, logL);
}
chosenArcsPerNode.put(v, new ObjectOpenHashSet<Arc>());
for (int u : baseSetOfParents) {
nArcs++;
chosenArcsPerNode.get(v).add(new Arc(u, v));
}
}
pl.stop("Done initialization. Added " + nArcs + " arcs, logLikelihood=" + totalLogL);
return totalLogL;
}