public virtual void TestSetCount() { NUnit.Framework.Assert.AreEqual(c.TotalCount(), 21.0); c.SetCount("p", "q", 1.0); NUnit.Framework.Assert.AreEqual(c.TotalCount(), 22.0); NUnit.Framework.Assert.AreEqual(c.TotalCount("p"), 1.0); NUnit.Framework.Assert.AreEqual(c.GetCount("p", "q"), 1.0); c.Remove("p", "q"); }
public static void PrintCounter(TwoDimensionalCounter <string, string> cnt, string fname) { try { PrintWriter pw = new PrintWriter(new TextWriter(new FileOutputStream(new File(fname)), false, "UTF-8")); foreach (string key in cnt.FirstKeySet()) { foreach (string val in cnt.GetCounter(key).KeySet()) { pw.Printf("%s\t%s\t%d%n", key, val, (int)cnt.GetCount(key, val)); } } pw.Close(); } catch (UnsupportedEncodingException e) { Sharpen.Runtime.PrintStackTrace(e); } catch (FileNotFoundException e) { Sharpen.Runtime.PrintStackTrace(e); } }
/// <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"); } } }
public virtual double[] GetInformationGains() { // assert size > 0; // data = trimToSize(data); // Don't need to trim to size, and trimming is dangerous the dataset is empty (you can't add to it thereafter) labels = TrimToSize(labels); // counts the number of times word X is present ClassicCounter <F> featureCounter = new ClassicCounter <F>(); // counts the number of time a document has label Y ClassicCounter <L> labelCounter = new ClassicCounter <L>(); // counts the number of times the document has label Y given word X is present TwoDimensionalCounter <F, L> condCounter = new TwoDimensionalCounter <F, L>(); for (int i = 0; i < labels.Length; i++) { labelCounter.IncrementCount(labelIndex.Get(labels[i])); // convert the document to binary feature representation bool[] doc = new bool[featureIndex.Size()]; //logger.info(i); for (int j = 0; j < data[i].Length; j++) { doc[data[i][j]] = true; } for (int j_1 = 0; j_1 < doc.Length; j_1++) { if (doc[j_1]) { featureCounter.IncrementCount(featureIndex.Get(j_1)); condCounter.IncrementCount(featureIndex.Get(j_1), labelIndex.Get(labels[i]), 1.0); } } } double entropy = 0.0; for (int i_1 = 0; i_1 < labelIndex.Size(); i_1++) { double labelCount = labelCounter.GetCount(labelIndex.Get(i_1)); double p = labelCount / Size(); entropy -= p * (Math.Log(p) / Math.Log(2)); } double[] ig = new double[featureIndex.Size()]; Arrays.Fill(ig, entropy); for (int i_2 = 0; i_2 < featureIndex.Size(); i_2++) { F feature = featureIndex.Get(i_2); double featureCount = featureCounter.GetCount(feature); double notFeatureCount = Size() - featureCount; double pFeature = featureCount / Size(); double pNotFeature = (1.0 - pFeature); if (featureCount == 0) { ig[i_2] = 0; continue; } if (notFeatureCount == 0) { ig[i_2] = 0; continue; } double sumFeature = 0.0; double sumNotFeature = 0.0; for (int j = 0; j < labelIndex.Size(); j++) { L label = labelIndex.Get(j); double featureLabelCount = condCounter.GetCount(feature, label); double notFeatureLabelCount = Size() - featureLabelCount; // yes, these dont sum to 1. that is correct. // one is the prob of the label, given that the // feature is present, and the other is the prob // of the label given that the feature is absent double p = featureLabelCount / featureCount; double pNot = notFeatureLabelCount / notFeatureCount; if (featureLabelCount != 0) { sumFeature += p * (Math.Log(p) / Math.Log(2)); } if (notFeatureLabelCount != 0) { sumNotFeature += pNot * (Math.Log(pNot) / Math.Log(2)); } } //System.out.println(pNot+" "+(Math.log(pNot)/Math.log(2))); //logger.info(pFeature+" * "+sumFeature+" = +"+); //logger.info("^ "+pNotFeature+" "+sumNotFeature); ig[i_2] += pFeature * sumFeature + pNotFeature * sumNotFeature; } /* earlier the line above used to be: ig[i] = pFeature*sumFeature + pNotFeature*sumNotFeature; * This completely ignored the entropy term computed above. So added the "+=" to take that into account. * -Ramesh ([email protected]) */ return(ig); }