/// <exception cref="System.IO.IOException"/> /// <exception cref="System.TypeLoadException"/> private ICounter <CandidatePhrase> LearnNewPhrasesPrivate(string label, PatternsForEachToken patternsForEachToken, ICounter <E> patternsLearnedThisIter, ICounter <E> allSelectedPatterns, ICollection <CandidatePhrase> alreadyIdentifiedWords, CollectionValuedMap <E, Triple <string, int, int> > matchedTokensByPat, ICounter <CandidatePhrase> scoreForAllWordsThisIteration, TwoDimensionalCounter <CandidatePhrase, E> terms, TwoDimensionalCounter <CandidatePhrase, E> wordsPatExtracted, TwoDimensionalCounter <E , CandidatePhrase> patternsAndWords4Label, string identifier, ICollection <CandidatePhrase> ignoreWords, bool computeProcDataFreq) { ICollection <CandidatePhrase> alreadyLabeledWords = new HashSet <CandidatePhrase>(); if (constVars.doNotApplyPatterns) { // if want to get the stats by the lossy way of just counting without // applying the patterns ConstantsAndVariables.DataSentsIterator sentsIter = new ConstantsAndVariables.DataSentsIterator(constVars.batchProcessSents); while (sentsIter.MoveNext()) { Pair <IDictionary <string, DataInstance>, File> sentsf = sentsIter.Current; this.StatsWithoutApplyingPatterns(sentsf.First(), patternsForEachToken, patternsLearnedThisIter, wordsPatExtracted); } } else { if (patternsLearnedThisIter.Size() > 0) { this.ApplyPats(patternsLearnedThisIter, label, wordsPatExtracted, matchedTokensByPat, alreadyLabeledWords); } } if (computeProcDataFreq) { if (!phraseScorer.wordFreqNorm.Equals(PhraseScorer.Normalization.None)) { Redwood.Log(Redwood.Dbg, "computing processed freq"); foreach (KeyValuePair <CandidatePhrase, double> fq in Data.rawFreq.EntrySet()) { double @in = fq.Value; if (phraseScorer.wordFreqNorm.Equals(PhraseScorer.Normalization.Sqrt)) { @in = Math.Sqrt(@in); } else { if (phraseScorer.wordFreqNorm.Equals(PhraseScorer.Normalization.Log)) { @in = 1 + Math.Log(@in); } else { throw new Exception("can't understand the normalization"); } } System.Diagnostics.Debug.Assert(!double.IsNaN(@in), "Why is processed freq nan when rawfreq is " + @in); Data.processedDataFreq.SetCount(fq.Key, @in); } } else { Data.processedDataFreq = Data.rawFreq; } } if (constVars.wordScoring.Equals(GetPatternsFromDataMultiClass.WordScoring.Weightednorm)) { foreach (CandidatePhrase en in wordsPatExtracted.FirstKeySet()) { if (!constVars.GetOtherSemanticClassesWords().Contains(en) && (en.GetPhraseLemma() == null || !constVars.GetOtherSemanticClassesWords().Contains(CandidatePhrase.CreateOrGet(en.GetPhraseLemma()))) && !alreadyLabeledWords.Contains(en)) { terms.AddAll(en, wordsPatExtracted.GetCounter(en)); } } RemoveKeys(terms, ConstantsAndVariables.GetStopWords()); ICounter <CandidatePhrase> phraseScores = phraseScorer.ScorePhrases(label, terms, wordsPatExtracted, allSelectedPatterns, alreadyIdentifiedWords, false); System.Console.Out.WriteLine("count for word U.S. is " + phraseScores.GetCount(CandidatePhrase.CreateOrGet("U.S."))); ICollection <CandidatePhrase> ignoreWordsAll; if (ignoreWords != null && !ignoreWords.IsEmpty()) { ignoreWordsAll = CollectionUtils.UnionAsSet(ignoreWords, constVars.GetOtherSemanticClassesWords()); } else { ignoreWordsAll = new HashSet <CandidatePhrase>(constVars.GetOtherSemanticClassesWords()); } Sharpen.Collections.AddAll(ignoreWordsAll, constVars.GetSeedLabelDictionary()[label]); Sharpen.Collections.AddAll(ignoreWordsAll, constVars.GetLearnedWords(label).KeySet()); System.Console.Out.WriteLine("ignoreWordsAll contains word U.S. is " + ignoreWordsAll.Contains(CandidatePhrase.CreateOrGet("U.S."))); ICounter <CandidatePhrase> finalwords = ChooseTopWords(phraseScores, terms, phraseScores, ignoreWordsAll, constVars.thresholdWordExtract); phraseScorer.PrintReasonForChoosing(finalwords); scoreForAllWordsThisIteration.Clear(); Counters.AddInPlace(scoreForAllWordsThisIteration, phraseScores); Redwood.Log(ConstantsAndVariables.minimaldebug, "\n\n## Selected Words for " + label + " : " + Counters.ToSortedString(finalwords, finalwords.Size(), "%1$s:%2$.2f", "\t")); if (constVars.goldEntities != null) { IDictionary <string, bool> goldEntities4Label = constVars.goldEntities[label]; if (goldEntities4Label != null) { StringBuilder s = new StringBuilder(); finalwords.KeySet().Stream().ForEach(null); Redwood.Log(ConstantsAndVariables.minimaldebug, "\n\n## Gold labels for selected words for label " + label + " : " + s.ToString()); } else { Redwood.Log(Redwood.Dbg, "No gold entities provided for label " + label); } } if (constVars.outDir != null && !constVars.outDir.IsEmpty()) { string outputdir = constVars.outDir + "/" + identifier + "/" + label; IOUtils.EnsureDir(new File(outputdir)); TwoDimensionalCounter <CandidatePhrase, CandidatePhrase> reasonForWords = new TwoDimensionalCounter <CandidatePhrase, CandidatePhrase>(); foreach (CandidatePhrase word in finalwords.KeySet()) { foreach (E l in wordsPatExtracted.GetCounter(word).KeySet()) { foreach (CandidatePhrase w2 in patternsAndWords4Label.GetCounter(l)) { reasonForWords.IncrementCount(word, w2); } } } Redwood.Log(ConstantsAndVariables.minimaldebug, "Saving output in " + outputdir); string filename = outputdir + "/words.json"; // the json object is an array corresponding to each iteration - of list // of objects, // each of which is a bean of entity and reasons IJsonArrayBuilder obj = Javax.Json.Json.CreateArrayBuilder(); if (writtenInJustification.Contains(label) && writtenInJustification[label]) { IJsonReader jsonReader = Javax.Json.Json.CreateReader(new BufferedInputStream(new FileInputStream(filename))); IJsonArray objarr = jsonReader.ReadArray(); foreach (IJsonValue o in objarr) { obj.Add(o); } jsonReader.Close(); } IJsonArrayBuilder objThisIter = Javax.Json.Json.CreateArrayBuilder(); foreach (CandidatePhrase w in reasonForWords.FirstKeySet()) { IJsonObjectBuilder objinner = Javax.Json.Json.CreateObjectBuilder(); IJsonArrayBuilder l = Javax.Json.Json.CreateArrayBuilder(); foreach (CandidatePhrase w2 in reasonForWords.GetCounter(w).KeySet()) { l.Add(w2.GetPhrase()); } IJsonArrayBuilder pats = Javax.Json.Json.CreateArrayBuilder(); foreach (E p in wordsPatExtracted.GetCounter(w)) { pats.Add(p.ToStringSimple()); } objinner.Add("reasonwords", l); objinner.Add("patterns", pats); objinner.Add("score", finalwords.GetCount(w)); objinner.Add("entity", w.GetPhrase()); objThisIter.Add(objinner.Build()); } obj.Add(objThisIter); // Redwood.log(ConstantsAndVariables.minimaldebug, channelNameLogger, // "Writing justification at " + filename); IOUtils.WriteStringToFile(StringUtils.Normalize(StringUtils.ToAscii(obj.Build().ToString())), filename, "ASCII"); writtenInJustification[label] = true; } if (constVars.justify) { Redwood.Log(Redwood.Dbg, "\nJustification for phrases:\n"); foreach (CandidatePhrase word in finalwords.KeySet()) { Redwood.Log(Redwood.Dbg, "Phrase " + word + " extracted because of patterns: \t" + Counters.ToSortedString(wordsPatExtracted.GetCounter(word), wordsPatExtracted.GetCounter(word).Size(), "%1$s:%2$f", "\n")); } } // if (usePatternResultAsLabel) // if (answerLabel != null) // labelWords(sents, commonEngWords, finalwords.keySet(), // patterns.keySet(), outFile); // else // throw new RuntimeException("why is the answer label null?"); return(finalwords); } else { if (constVars.wordScoring.Equals(GetPatternsFromDataMultiClass.WordScoring.Bpb)) { Counters.AddInPlace(terms, wordsPatExtracted); ICounter <CandidatePhrase> maxPatWeightTerms = new ClassicCounter <CandidatePhrase>(); IDictionary <CandidatePhrase, E> wordMaxPat = new Dictionary <CandidatePhrase, E>(); foreach (KeyValuePair <CandidatePhrase, ClassicCounter <E> > en in terms.EntrySet()) { ICounter <E> weights = new ClassicCounter <E>(); foreach (E k in en.Value.KeySet()) { weights.SetCount(k, patternsLearnedThisIter.GetCount(k)); } maxPatWeightTerms.SetCount(en.Key, Counters.Max(weights)); wordMaxPat[en.Key] = Counters.Argmax(weights); } Counters.RemoveKeys(maxPatWeightTerms, alreadyIdentifiedWords); double maxvalue = Counters.Max(maxPatWeightTerms); ICollection <CandidatePhrase> words = Counters.KeysAbove(maxPatWeightTerms, maxvalue - 1e-10); CandidatePhrase bestw = null; if (words.Count > 1) { double max = double.NegativeInfinity; foreach (CandidatePhrase w in words) { if (terms.GetCount(w, wordMaxPat[w]) > max) { max = terms.GetCount(w, wordMaxPat[w]); bestw = w; } } } else { if (words.Count == 1) { bestw = words.GetEnumerator().Current; } else { return(new ClassicCounter <CandidatePhrase>()); } } Redwood.Log(ConstantsAndVariables.minimaldebug, "Selected Words: " + bestw); return(Counters.AsCounter(Arrays.AsList(bestw))); } else { throw new Exception("wordscoring " + constVars.wordScoring + " not identified"); } } }
private void RunParallelApplyPats(IDictionary <string, DataInstance> sents, string label, E pattern, TwoDimensionalCounter <CandidatePhrase, E> wordsandLemmaPatExtracted, CollectionValuedMap <E, Triple <string, int, int> > matchedTokensByPat, ICollection <CandidatePhrase> alreadyLabeledWords) { Redwood.Log(Redwood.Dbg, "Applying pattern " + pattern + " to a total of " + sents.Count + " sentences "); IList <string> notAllowedClasses = new List <string>(); IList <string> sentids = CollectionUtils.ToList(sents.Keys); if (constVars.doNotExtractPhraseAnyWordLabeledOtherClass) { foreach (string l in constVars.GetAnswerClass().Keys) { if (!l.Equals(label)) { notAllowedClasses.Add(l); } } notAllowedClasses.Add("OTHERSEM"); } IDictionary <TokenSequencePattern, E> surfacePatternsLearnedThisIterConverted = null; IDictionary <SemgrexPattern, E> depPatternsLearnedThisIterConverted = null; if (constVars.patternType.Equals(PatternFactory.PatternType.Surface)) { surfacePatternsLearnedThisIterConverted = new Dictionary <TokenSequencePattern, E>(); string patternStr = null; try { patternStr = pattern.ToString(notAllowedClasses); TokenSequencePattern pat = ((TokenSequencePattern)TokenSequencePattern.Compile(constVars.env[label], patternStr)); surfacePatternsLearnedThisIterConverted[pat] = pattern; } catch (Exception e) { log.Info("Error applying pattern " + patternStr + ". Probably an ill formed pattern (can be because of special symbols in label names). Contact the software developer."); throw; } } else { if (constVars.patternType.Equals(PatternFactory.PatternType.Dep)) { depPatternsLearnedThisIterConverted = new Dictionary <SemgrexPattern, E>(); SemgrexPattern pat = SemgrexPattern.Compile(pattern.ToString(notAllowedClasses), new Env(constVars.env[label].GetVariables())); depPatternsLearnedThisIterConverted[pat] = pattern; } else { throw new NotSupportedException(); } } //Apply the patterns and extract candidate phrases int num; int numThreads = constVars.numThreads; //If number of sentences is less, do not create so many threads if (sents.Count < 50) { numThreads = 1; } if (numThreads == 1) { num = sents.Count; } else { num = sents.Count / (numThreads - 1); } IExecutorService executor = Executors.NewFixedThreadPool(constVars.numThreads); IList <IFuture <Triple <TwoDimensionalCounter <CandidatePhrase, E>, CollectionValuedMap <E, Triple <string, int, int> >, ICollection <CandidatePhrase> > > > list = new List <IFuture <Triple <TwoDimensionalCounter <CandidatePhrase, E>, CollectionValuedMap <E , Triple <string, int, int> >, ICollection <CandidatePhrase> > > >(); for (int i = 0; i < numThreads; i++) { ICallable <Triple <TwoDimensionalCounter <CandidatePhrase, E>, CollectionValuedMap <E, Triple <string, int, int> >, ICollection <CandidatePhrase> > > task = null; if (pattern.type.Equals(PatternFactory.PatternType.Surface)) { //Redwood.log(Redwood.DBG, "Applying pats: assigning sentences " + i*num + " to " +Math.min(sentids.size(), (i + 1) * num) + " to thread " + (i+1)); task = new ApplyPatterns(sents, num == sents.Count ? sentids : sentids.SubList(i * num, Math.Min(sentids.Count, (i + 1) * num)), surfacePatternsLearnedThisIterConverted, label, constVars.removeStopWordsFromSelectedPhrases, constVars.removePhrasesWithStopWords , constVars); } else { task = new ApplyDepPatterns(sents, num == sents.Count ? sentids : sentids.SubList(i * num, Math.Min(sentids.Count, (i + 1) * num)), depPatternsLearnedThisIterConverted, label, constVars.removeStopWordsFromSelectedPhrases, constVars.removePhrasesWithStopWords , constVars); } IFuture <Triple <TwoDimensionalCounter <CandidatePhrase, E>, CollectionValuedMap <E, Triple <string, int, int> >, ICollection <CandidatePhrase> > > submit = executor.Submit(task); list.Add(submit); } // Now retrieve the result foreach (IFuture <Triple <TwoDimensionalCounter <CandidatePhrase, E>, CollectionValuedMap <E, Triple <string, int, int> >, ICollection <CandidatePhrase> > > future in list) { try { Triple <TwoDimensionalCounter <CandidatePhrase, E>, CollectionValuedMap <E, Triple <string, int, int> >, ICollection <CandidatePhrase> > result = future.Get(); Redwood.Log(ConstantsAndVariables.extremedebug, "Pattern " + pattern + " extracted phrases " + result.First()); wordsandLemmaPatExtracted.AddAll(result.First()); matchedTokensByPat.AddAll(result.Second()); Sharpen.Collections.AddAll(alreadyLabeledWords, result.Third()); } catch (Exception e) { executor.ShutdownNow(); throw new Exception(e); } } executor.Shutdown(); }