public void stocGradTrain(Parser parser, boolean testEachRound) {
int numUpdates = 0;
List<LexEntry> fixedEntries = new LinkedList<LexEntry>();
fixedEntries.addAll(parser.returnLex().getLexicon());
// add all sentential lexical entries.
for (int l = 0; l < trainData.size(); l++) {
parser.addLexEntries(trainData.getDataSet(l).makeSentEntries());
}
parser.setGlobals();
DataSet data = null;
// for each pass over the data
for (int j = 0; j < EPOCHS; j++) {
System.out.println("Training, iteration " + j);
int total = 0, correct = 0, wrong = 0, looCorrect = 0, looWrong = 0;
for (int l = 0; l < trainData.size(); l++) {
// the variables to hold the current training example
String words = null;
Exp sem = null;
data = trainData.getDataSet(l);
if (verbose) System.out.println("---------------------");
String filename = trainData.getFilename(l);
if (verbose) System.out.println("DataSet: " + filename);
if (verbose) System.out.println("---------------------");
// loop through the training examples
// try to create lexical entries for each training example
for (int i = 0; i < data.size(); i++) {
// print running stats
if (verbose) {
if (total != 0) {
double r = (double) correct / total;
double p = (double) correct / (correct + wrong);
System.out.print(i + ": =========== r:" + r + " p:" + p);
System.out.println(" (epoch:" + j + " file:" + l + " " + filename + ")");
} else System.out.println(i + ": ===========");
}
// get the training example
words = data.sent(i);
sem = data.sem(i);
if (verbose) {
System.out.println(words);
System.out.println(sem);
}
List<String> tokens = Parser.tokenize(words);
if (tokens.size() > maxSentLen) continue;
total++;
String mes = null;
boolean hasCorrect = false;
// first, get all possible lexical entries from
// a manipulation of the best parse.
List<LexEntry> lex = makeLexEntriesChart(words, sem, parser);
if (verbose) {
System.out.println("Adding:");
for (LexEntry le : lex) {
System.out.println(le + " : " + LexiconFeatSet.initialWeight(le));
}
}
parser.addLexEntries(lex);
if (verbose) System.out.println("Lex Size: " + parser.returnLex().size());
// first parse to see if we are currently correct
if (verbose) mes = "First";
parser.parseTimed(words, null, mes);
Chart firstChart = parser.getChart();
Exp best = parser.bestSem();
// this just collates and outputs the training
// accuracy.
if (sem.equals(best)) {
// System.out.println(parser.bestParses().get(0));
if (verbose) {
System.out.println("CORRECT:" + best);
lex = parser.getMaxLexEntriesFor(sem);
System.out.println("Using:");
printLex(lex);
if (lex.size() == 0) {
System.out.println("ERROR: empty lex");
}
}
correct++;
} else {
if (verbose) {
System.out.println("WRONG: " + best);
lex = parser.getMaxLexEntriesFor(best);
System.out.println("Using:");
printLex(lex);
if (best != null && lex.size() == 0) {
System.out.println("ERROR: empty lex");
}
}
wrong++;
}
// compute first half of parameter update:
// subtract the expectation of parameters
// under the distribution that is conditioned
// on the sentence alone.
double norm = firstChart.computeNorm();
HashVector update = new HashVector();
HashVector firstfeats = null, secondfeats = null;
if (norm != 0.0) {
firstfeats = firstChart.computeExpFeatVals();
firstfeats.divideBy(norm);
firstfeats.dropSmallEntries();
firstfeats.addTimesInto(-1.0, update);
} else continue;
firstChart = null;
if (verbose) mes = "Second";
parser.parseTimed(words, sem, mes);
hasCorrect = parser.hasParseFor(sem);
// compute second half of parameter update:
// add the expectation of parameters
// under the distribution that is conditioned
// on the sentence and correct logical form.
if (!hasCorrect) continue;
Chart secondChart = parser.getChart();
double secnorm = secondChart.computeNorm(sem);
if (norm != 0.0) {
secondfeats = secondChart.computeExpFeatVals(sem);
secondfeats.divideBy(secnorm);
secondfeats.dropSmallEntries();
secondfeats.addTimesInto(1.0, update);
lex = parser.getMaxLexEntriesFor(sem);
data.setBestLex(i, lex);
if (verbose) {
System.out.println("Best LexEntries:");
printLex(lex);
if (lex.size() == 0) {
System.out.println("ERROR: empty lex");
}
}
} else continue;
// now do the update
double scale = alpha_0 / (1.0 + c * numUpdates);
if (verbose) System.out.println("Scale: " + scale);
update.multiplyBy(scale);
update.dropSmallEntries();
numUpdates++;
if (verbose) {
System.out.println("Update:");
System.out.println(update);
}
if (!update.isBad()) {
if (!update.valuesInRange(-100, 100)) {
System.out.println("WARNING: large update");
System.out.println("first feats: " + firstfeats);
System.out.println("second feats: " + secondfeats);
}
parser.updateParams(update);
} else {
System.out.println(
"ERROR: Bad Update: " + update + " -- norm: " + norm + " -- feats: ");
parser.getParams().printValues(update);
System.out.println();
}
} // end for each training example
} // end for each data set
double r = (double) correct / total;
// we can prune the lexical items that were not used
// in a max scoring parse.
if (pruneLex) {
Lexicon cur = new Lexicon();
cur.addLexEntries(fixedEntries);
cur.addLexEntries(data.getBestLex());
parser.setLexicon(cur);
}
if (testEachRound) {
System.out.println("Testing");
test(parser, false);
}
} // end epochs loop
}