private void dumpMetadata(String group, List<Example> examples) {
LispTree tree = LispTree.proto.newList();
tree.addChild("metadata");
tree.addChild(LispTree.proto.newList("group", group));
tree.addChild(LispTree.proto.newList("size", "" + examples.size()));
tree.print(out);
out.println();
}
private void dumpExample(LispTree tree) {
out.println("(example");
for (LispTree subtree : tree.children.subList(1, tree.children.size())) {
if (!subtree.isLeaf() && "derivations".equals(subtree.children.get(0).value)) {
if (subtree.children.size() == 1) {
out.println(" (derivations)");
} else {
out.println(" (derivations");
for (LispTree derivation : subtree.children.subList(1, subtree.children.size())) {
out.write(" ");
derivation.print(Integer.MAX_VALUE, Integer.MAX_VALUE, out);
out.write("\n");
}
out.println(" )");
}
} else {
out.write(" ");
subtree.print(Integer.MAX_VALUE, Integer.MAX_VALUE, out);
out.write("\n");
}
}
out.println(")");
}
public static Evaluation eval(InferState state) {
// Print out information about how well we're doing.
Evaluation evaluation = new Evaluation();
Candidate trueCandidate = state.getTrueCandidate();
Candidate predCandidate = state.getCandidates().get(0);
PredictionContext context = state.getContext();
NgramContext ngramContext = NgramContext.get(context);
Statistics statistics = state.getStatistics();
Corpus corpus = statistics.getProjectLangCorpus(context.getPath());
DataSummary summary = statistics.getStatistic(NgramKNCounts.class, corpus).getSummary();
Params params = state.getParams();
boolean oracle = state.isOracle();
int rank = state.getRank();
double entropy = state.getEntropy();
double reciprocalRank = state.getReciprocalRank();
boolean isIdent = state.isIdent();
boolean correct = state.isCorrect();
String path = context.getPath();
String trueTokenStr = trueCandidate.token;
String predToken = predCandidate.token;
evaluation.add("accuracy", correct);
evaluation.add("oracle", oracle);
evaluation.add("rank", rank);
evaluation.add("reciprocalRank", reciprocalRank);
if (oracle) {
evaluation.add("entropy", entropy);
}
if (isIdent) {
evaluation.add("identAccuracy", correct);
evaluation.add("identOracle", oracle);
if (oracle) {
evaluation.add("identEntropy", entropy);
evaluation.add("identReciprocalRank", reciprocalRank);
for (int i = 0; i < Main.clusters; i++) {
evaluation.add("identEntropy" + i, -Math.log(trueCandidate.clusterProbs[i]));
}
}
}
String contextStr = ngramContext.contextStr();
if (Main.verbose >= 2) {
String entropyStr = oracle ? Fmt.D(entropy) : "N/A";
begin_track(
"Example %s [%s]: %s (%d candidates, rank %s, entropy %s)",
path,
correct ? "CORRECT" : "WRONG",
contextStr,
state.getCandidates().size(),
rank,
entropyStr);
logs("True (prob= %s): [%s]", Fmt.D(trueCandidate.prob), trueTokenStr);
logs("Pred (prob= %s): [%s]", Fmt.D(predCandidate.prob), predToken);
if (oracle) {
KneserNey.logKNIs(true);
KneserNey.computeProb(CandidateNgram.get(context, trueCandidate), summary);
KneserNey.logKNIs(false);
}
// begin_track("True");
FeatureVector.logFeatureWeights("True", trueCandidate.features.toMap(), params);
// for (int i = 0; i < Main.clusters; i++) {
// logs("cluster=%d, score %s, prob %s", i, Fmt.D(trueCandidate.clusterScores[i]),
// Fmt.D(trueCandidate.clusterProbs[i]));
// FeatureVector.logFeatureWeights("cluster=" + i,
// trueCandidate.clusterFeatures.toMap(),
// params, Main.clusterDecorators[i]);
// }
// end_track();
KneserNey.logKNIs(true);
KneserNey.computeProb(CandidateNgram.get(context, predCandidate), summary);
KneserNey.logKNIs(false);
FeatureVector.logFeatureWeights("Pred", predCandidate.features.toMap(), params);
// for (Candidate candidate : candidates) {
// begin_track("Candidate " + candidate.token);
// for (int i = 0; i < Main.clusters; i++) {
// logs("cluster=%d, score %s, prob %s", i, Fmt.D(candidate.clusterScores[i]),
// Fmt.D(candidate.clusterProbs[i]));
// FeatureVector.logFeatureWeights("cluster=" + i,
// candidate.clusterFeatures.toMap(),
// params, Main.clusterDecorators[i]);
// }
// end_track();
// }
FeatureVector.logFeatureDiff(
"True - Pred", trueCandidate.features, predCandidate.features, params);
end_track();
}
// Longest context that has been seen
int context_max_n = ngramContext.getMax_n() - 1;
while (context_max_n > 0
&& !summary.counts[context_max_n].containsKey(ngramContext.subContext(context_max_n + 1)))
context_max_n--;
evaluation.add("context_max_n", context_max_n);
predOut.println(
"path="
+ path
+ "\tident="
+ (isIdent ? 1 : 0)
+ "\tcontext="
+ contextStr
+ "\tcontext_max_n="
+ context_max_n
+ "\ttrue="
+ trueTokenStr
+ "\tpred="
+ predToken
+ "\trank="
+ rank
+ "\tentropy="
+ entropy);
predOut.flush();
entOut.println(
path
+ "\t"
+ state.getTrueToken().loc()
+ "\t"
+ (isIdent ? 1 : 0)
+ "\t"
+ (oracle ? entropy : (state.isOov() ? "oov" : "offBeam"))
+ "\t"
+ reciprocalRank);
entOut.flush();
return evaluation;
}