private void updateTop(
CandidateSet[] candidates,
Candidate[] path,
PriorityQueue<Correction> corrections,
double cutoffScore,
double score)
throws IOException {
score = Math.exp(score);
assert Math.abs(score - score(path, candidates)) < 0.00001;
if (score > cutoffScore) {
if (corrections.size() < maxNumCorrections) {
Candidate[] c = new Candidate[candidates.length];
System.arraycopy(path, 0, c, 0, path.length);
corrections.add(new Correction(score, c));
} else if (corrections.top().compareTo(score, path) < 0) {
Correction top = corrections.top();
System.arraycopy(path, 0, top.candidates, 0, path.length);
top.score = score;
corrections.updateTop();
}
}
}
/*
* More Ideas:
* - add ability to find whitespace problems -> we can build a poor mans decompounder with our index based on a automaton?
* - add ability to build different error models maybe based on a confusion matrix?
* - try to combine a token with its subsequent token to find / detect word splits (optional)
* - for this to work we need some way to defined the position length of a candidate
* - phonetic filters could be interesting here too for candidate selection
*/
@Override
public Suggestion<? extends Entry<? extends Option>> innerExecute(
String name,
PhraseSuggestionContext suggestion,
IndexSearcher searcher,
CharsRefBuilder spare)
throws IOException {
double realWordErrorLikelihood = suggestion.realworldErrorLikelyhood();
final PhraseSuggestion response = new PhraseSuggestion(name, suggestion.getSize());
final IndexReader indexReader = searcher.getIndexReader();
List<PhraseSuggestionContext.DirectCandidateGenerator> generators = suggestion.generators();
final int numGenerators = generators.size();
final List<CandidateGenerator> gens = new ArrayList<>(generators.size());
for (int i = 0; i < numGenerators; i++) {
PhraseSuggestionContext.DirectCandidateGenerator generator = generators.get(i);
DirectSpellChecker directSpellChecker = SuggestUtils.getDirectSpellChecker(generator);
Terms terms = MultiFields.getTerms(indexReader, generator.field());
if (terms != null) {
gens.add(
new DirectCandidateGenerator(
directSpellChecker,
generator.field(),
generator.suggestMode(),
indexReader,
realWordErrorLikelihood,
generator.size(),
generator.preFilter(),
generator.postFilter(),
terms));
}
}
final String suggestField = suggestion.getField();
final Terms suggestTerms = MultiFields.getTerms(indexReader, suggestField);
if (gens.size() > 0 && suggestTerms != null) {
final NoisyChannelSpellChecker checker =
new NoisyChannelSpellChecker(
realWordErrorLikelihood, suggestion.getRequireUnigram(), suggestion.getTokenLimit());
final BytesRef separator = suggestion.separator();
TokenStream stream =
checker.tokenStream(
suggestion.getAnalyzer(), suggestion.getText(), spare, suggestion.getField());
WordScorer wordScorer =
suggestion
.model()
.newScorer(
indexReader, suggestTerms, suggestField, realWordErrorLikelihood, separator);
Result checkerResult =
checker.getCorrections(
stream,
new MultiCandidateGeneratorWrapper(
suggestion.getShardSize(), gens.toArray(new CandidateGenerator[gens.size()])),
suggestion.maxErrors(),
suggestion.getShardSize(),
wordScorer,
suggestion.confidence(),
suggestion.gramSize());
PhraseSuggestion.Entry resultEntry =
buildResultEntry(suggestion, spare, checkerResult.cutoffScore);
response.addTerm(resultEntry);
final BytesRefBuilder byteSpare = new BytesRefBuilder();
final EarlyTerminatingCollector collector = Lucene.createExistsCollector();
final CompiledScript collateScript;
if (suggestion.getCollateQueryScript() != null) {
collateScript = suggestion.getCollateQueryScript();
} else if (suggestion.getCollateFilterScript() != null) {
collateScript = suggestion.getCollateFilterScript();
} else {
collateScript = null;
}
final boolean collatePrune = (collateScript != null) && suggestion.collatePrune();
for (int i = 0; i < checkerResult.corrections.length; i++) {
Correction correction = checkerResult.corrections[i];
spare.copyUTF8Bytes(correction.join(SEPARATOR, byteSpare, null, null));
boolean collateMatch = true;
if (collateScript != null) {
// Checks if the template query collateScript yields any documents
// from the index for a correction, collateMatch is updated
final Map<String, Object> vars = suggestion.getCollateScriptParams();
vars.put(SUGGESTION_TEMPLATE_VAR_NAME, spare.toString());
final ExecutableScript executable = scriptService.executable(collateScript, vars);
final BytesReference querySource = (BytesReference) executable.run();
final ParsedQuery parsedQuery;
if (suggestion.getCollateFilterScript() != null) {
parsedQuery =
suggestion
.getQueryParserService()
.parse(
QueryBuilders.constantScoreQuery(QueryBuilders.wrapperQuery(querySource)));
} else {
parsedQuery = suggestion.getQueryParserService().parse(querySource);
}
collateMatch = Lucene.exists(searcher, parsedQuery.query(), collector);
}
if (!collateMatch && !collatePrune) {
continue;
}
Text phrase = new StringText(spare.toString());
Text highlighted = null;
if (suggestion.getPreTag() != null) {
spare.copyUTF8Bytes(
correction.join(
SEPARATOR, byteSpare, suggestion.getPreTag(), suggestion.getPostTag()));
highlighted = new StringText(spare.toString());
}
if (collatePrune) {
resultEntry.addOption(
new Suggestion.Entry.Option(
phrase, highlighted, (float) (correction.score), collateMatch));
} else {
resultEntry.addOption(
new Suggestion.Entry.Option(phrase, highlighted, (float) (correction.score)));
}
}
} else {
response.addTerm(buildResultEntry(suggestion, spare, Double.MIN_VALUE));
}
return response;
}
