Esempio n. 1
0
 /**
  * Adding a new config means merging contexts with existing configs for {@code (s, i, pi, _)},
  * where {@code s} is the {@link ATNConfig#state}, {@code i} is the {@link ATNConfig#alt}, and
  * {@code pi} is the {@link ATNConfig#semanticContext}. We use {@code (s,i,pi)} as key.
  *
  * <p>This method updates {@link #dipsIntoOuterContext} and {@link #hasSemanticContext} when
  * necessary.
  */
 public boolean add(
     @NotNull ATNConfig config,
     @Nullable DoubleKeyMap<PredictionContext, PredictionContext, PredictionContext> mergeCache) {
   if (readonly) throw new IllegalStateException("This set is readonly");
   if (config.semanticContext != SemanticContext.NONE) {
     hasSemanticContext = true;
   }
   if (config.reachesIntoOuterContext > 0) {
     dipsIntoOuterContext = true;
   }
   ATNConfig existing = configLookup.getOrAdd(config);
   if (existing == config) { // we added this new one
     cachedHashCode = -1;
     configs.add(config); // track order here
     return true;
   }
   // a previous (s,i,pi,_), merge with it and save result
   boolean rootIsWildcard = !fullCtx;
   PredictionContext merged =
       PredictionContext.merge(existing.context, config.context, rootIsWildcard, mergeCache);
   // no need to check for existing.context, config.context in cache
   // since only way to create new graphs is "call rule" and here. We
   // cache at both places.
   existing.reachesIntoOuterContext =
       Math.max(existing.reachesIntoOuterContext, config.reachesIntoOuterContext);
   existing.context = merged; // replace context; no need to alt mapping
   return true;
 }
 @Override
 public String toString() {
   String up = parent != null ? parent.toString() : "";
   if (up.length() == 0) {
     if (returnState == EMPTY_RETURN_STATE) {
       return "$";
     }
     return String.valueOf(returnState);
   }
   return String.valueOf(returnState) + " " + up;
 }
  @Override
  public boolean equals(Object o) {
    if (this == o) {
      return true;
    } else if (!(o instanceof SingletonPredictionContext)) {
      return false;
    }

    if (this.hashCode() != o.hashCode()) {
      return false; // can't be same if hash is different
    }

    SingletonPredictionContext s = (SingletonPredictionContext) o;
    return returnState == s.returnState && (parent != null && parent.equals(s.parent));
  }
Esempio n. 4
0
  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;
  }
 SingletonPredictionContext(PredictionContext parent, int returnState) {
   super(calculateHashCode(parent != null ? 31 ^ parent.hashCode() : 1, 31 ^ returnState));
   assert returnState != ATNState.INVALID_STATE_NUMBER;
   this.parent = parent;
   this.returnState = returnState;
 }