diff --git a/vipra-cmd/src/main/java/de/vipra/cmd/file/DTMSequenceIndex.java b/vipra-cmd/src/main/java/de/vipra/cmd/file/DTMSequenceIndex.java
index fabdb6e0b22d34a1e64c888aac6527d96189ed47..bd6b19eb5c8b75b99019fcc46df7c64d45dc8b3d 100644
--- a/vipra-cmd/src/main/java/de/vipra/cmd/file/DTMSequenceIndex.java
+++ b/vipra-cmd/src/main/java/de/vipra/cmd/file/DTMSequenceIndex.java
@@ -88,6 +88,16 @@ public class DTMSequenceIndex implements Closeable, Iterable<DTMSequenceIndex.DT
return windowSizes.size();
}
+ public Date getStartDate(int index) {
+ // TODO implement
+ return null;
+ }
+
+ public Date getEndDate(int index) {
+ // TODO implement
+ return null;
+ }
+
@Override
public Iterator<DTMDateIndexEntry> iterator() {
Collections.sort(entries);
diff --git a/vipra-cmd/src/main/java/de/vipra/cmd/lda/DTMAnalyzer.java b/vipra-cmd/src/main/java/de/vipra/cmd/lda/DTMAnalyzer.java
index 05e5a3a2b92d70474a2af0cddcf10b37f9f1ecac..188d49f3467374e49995b9742d4e382bdc992c4c 100644
--- a/vipra-cmd/src/main/java/de/vipra/cmd/lda/DTMAnalyzer.java
+++ b/vipra-cmd/src/main/java/de/vipra/cmd/lda/DTMAnalyzer.java
@@ -11,10 +11,7 @@ import java.util.Collections;
import java.util.Comparator;
import java.util.HashSet;
import java.util.List;
-import java.util.Map.Entry;
import java.util.Set;
-import java.util.regex.Matcher;
-import java.util.regex.Pattern;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
@@ -27,9 +24,7 @@ import de.vipra.cmd.file.FilebaseIndex;
import de.vipra.util.ArrayUtils;
import de.vipra.util.Config;
import de.vipra.util.Constants;
-import de.vipra.util.CountMap;
import de.vipra.util.StringUtils;
-import de.vipra.util.Tuple;
import de.vipra.util.ex.ConfigException;
import de.vipra.util.ex.DatabaseException;
import de.vipra.util.model.ArticleFull;
@@ -146,9 +141,6 @@ public class DTMAnalyzer extends Analyzer {
final List<TopicFull> newTopics = new ArrayList<>(Constants.K_TOPICS);
// collects created words
final Set<Word> newWords = new HashSet<>(wordCount);
- // collect mapping between words and topics
- @SuppressWarnings("unchecked")
- final Tuple<Double, Integer>[] wordTopicMapping = new Tuple[wordCount];
log.info("vocabulary size: " + wordCount);
log.info("sequences: " + sequencesCount);
@@ -162,7 +154,6 @@ public class DTMAnalyzer extends Analyzer {
// create new topic
final TopicFull newTopic = new TopicFull();
final List<Sequence> newSequences = new ArrayList<>(sequencesCount);
- final List<TopicWord> newTopicWords = new ArrayList<>(wordCount);
newTopic.setSequences(newSequences);
in = new BufferedReader(new InputStreamReader(new FileInputStream(seqFile)));
@@ -178,41 +169,9 @@ public class DTMAnalyzer extends Analyzer {
in.close();
- // find maximums
- final double[] maxWordLikelinesses = ArrayUtils.findRowMaximum(likelinesses);
+ // find maximum
final double[] maxSeqLikelinesses = ArrayUtils.findColMaximum(likelinesses);
- final double maxOverallLikeliness = ArrayUtils.findMaximum(maxSeqLikelinesses);
- final double minAcceptableLikeliness = (maxOverallLikeliness >= 0 ? 1
- : 2 - Constants.MINIMUM_RELATIVE_PROB) * maxOverallLikeliness;
- // static topic and word topic mapping
- // most likely words form the static topic over all sequences
- for (int idxWord = 0; idxWord < wordCount; idxWord++) {
- if (maxWordLikelinesses[idxWord] >= minAcceptableLikeliness) {
- // add word to static topic
- final Word word = vocab.getWord(idxWord);
- newWords.add(word);
- final TopicWord topicWord = new TopicWord(word, maxWordLikelinesses[idxWord]);
- newTopicWords.add(topicWord);
-
- // check if better word topic mapping than previous
- final Tuple<Double, Integer> tuple = wordTopicMapping[idxWord];
- if (tuple == null)
- wordTopicMapping[idxWord] = new Tuple<>(maxWordLikelinesses[idxWord], idxTopic);
- else if (maxWordLikelinesses[idxWord] > tuple.first())
- tuple.setSecond(idxTopic);
- }
- }
-
- if (newTopicWords.isEmpty())
- continue;
-
- newTopic.setWords(newTopicWords);
-
- String msg = "topic with " + newTopicWords.size() + " "
- + StringUtils.quantity(newTopicWords.size(), "word") + ", sequences: [";
-
- // dynamic topics
// go through each sequence and gather all words that are above
// the minimum relative word likeliness
for (int idxSeq = 0; idxSeq < sequencesCount; idxSeq++) {
@@ -234,13 +193,11 @@ public class DTMAnalyzer extends Analyzer {
final Sequence newSequence = new Sequence();
newSequence.setNumber(idxSeq);
newSequence.setWords(newSeqTopicWords);
+ newSequence.setStartDate(sequences.getStartDate(idxSeq));
+ newSequence.setEndDate(sequences.getEndDate(idxSeq));
newSequences.add(newSequence);
-
- msg += " " + newSeqTopicWords.size();
}
- log.info(msg + " ]");
-
newTopics.add(newTopic);
}
@@ -265,60 +222,36 @@ public class DTMAnalyzer extends Analyzer {
// create topic references
- final File multFile = new File(modelDir, "dtm-mult.dat");
- in = new BufferedReader(new InputStreamReader(new FileInputStream(multFile)));
- final Pattern wordCountPattern = Pattern.compile("(\\d+):(\\d+)");
- int articleIndex = 0;
-
- // for each article in the model file
- while ((line = in.readLine()) != null) {
- // get topic id from word id, count topics
- final CountMap<Integer> countMap = new CountMap<>();
- final Matcher matcher = wordCountPattern.matcher(line);
- double totalCount = 0;
- while (matcher.find()) {
- final Integer wordId = Integer.parseInt(matcher.group(1));
- final Tuple<Double, Integer> wordTopicTuple = wordTopicMapping[wordId];
- if (wordTopicTuple != null) {
- final int count = Integer.parseInt(matcher.group(2));
- countMap.count(wordTopicTuple.second(), count);
- totalCount += count;
- }
+ final File gamFile = new File(outDirSeq, "gam.dat");
+ in = new BufferedReader(new InputStreamReader(new FileInputStream(gamFile)));
+
+ for (String articleId : index) {
+ // normalize topic proportions
+ double totalTopicProportions = 0;
+ double[] topicProportions = new double[Constants.K_TOPICS];
+ for (int idxTopic = 0; idxTopic < Constants.K_TOPICS; idxTopic++) {
+ double topicProportion = Double.parseDouble(in.readLine());
+ topicProportions[idxTopic] = topicProportion;
+ totalTopicProportions += topicProportion;
}
- // create list of topics refs referencing topics with counted
- // occurrences, sum accepted topic word count
- long reducedCount = 0;
- final List<TopicRef> newTopicRefs = new ArrayList<>(countMap.size());
- for (final Entry<Integer, Integer> entry : countMap.entrySet()) {
- // check if topic above threshold
- if ((entry.getValue() / totalCount) >= Constants.TOPIC_THRESHOLD) {
- reducedCount += entry.getValue();
- final TopicFull topic = newTopics.get(entry.getKey());
- // TODO words with low relative likeliness are ignored.
- // topic references from this file are possibly wrong.
- // fix this by checking if the word is actually accepted
- // by the referenced topic.
- final TopicRef ref = new TopicRef();
- ref.setCount(entry.getValue());
- ref.setTopic(new Topic(topic.getId()));
- newTopicRefs.add(ref);
- }
+ // create topic references
+ final List<TopicRef> newTopicRefs = new ArrayList<>(Constants.K_TOPICS);
+ for (int idxTopic = 0; idxTopic < Constants.K_TOPICS; idxTopic++) {
+ TopicRef newTopicRef = new TopicRef();
+ TopicFull topicFull = newTopics.get(idxTopic);
+ newTopicRef.setTopic(new Topic(topicFull.getId()));
+ newTopicRef.setShare(topicProportions[idxTopic] / totalTopicProportions);
+ newTopicRefs.add(newTopicRef);
}
- log.info("article with " + totalCount + " topic refs and " + reducedCount + " reduced topic refs ("
- + (reducedCount - totalCount) + ")");
-
- // calculate each accepted topic share
- for (final TopicRef ref : newTopicRefs)
- ref.setShare((double) ref.getCount() / reducedCount);
-
+ // update article
if (!newTopicRefs.isEmpty()) {
Collections.sort(newTopicRefs, Comparator.reverseOrder());
// update article with topic references (partial update)
final ArticleFull article = new ArticleFull();
- article.setId(index.get(articleIndex++));
+ article.setId(articleId);
article.setTopics(newTopicRefs);
try {
diff --git a/vipra-util/src/main/java/de/vipra/util/model/Sequence.java b/vipra-util/src/main/java/de/vipra/util/model/Sequence.java
index 0a7a4f03c4ae0115a764b805416e6b3a1c8ff26a..36be515e2596f09e06f869df9ba0ffa593d9733d 100644
--- a/vipra-util/src/main/java/de/vipra/util/model/Sequence.java
+++ b/vipra-util/src/main/java/de/vipra/util/model/Sequence.java
@@ -10,19 +10,28 @@ import org.mongodb.morphia.annotations.Embedded;
@Embedded
public class Sequence implements Comparable<Sequence>, Serializable {
- private Date date;
+ private Date startDate;
+ private Date endDate;
private Integer number;
@Embedded
private List<TopicWord> words;
- public Date getDate() {
- return date;
+ public Date getStartDate() {
+ return startDate;
}
- public void setDate(final Date date) {
- this.date = date;
+ public void setStartDate(Date startDate) {
+ this.startDate = startDate;
+ }
+
+ public Date getEndDate() {
+ return endDate;
+ }
+
+ public void setEndDate(Date endDate) {
+ this.endDate = endDate;
}
public Integer getNumber() {
@@ -48,7 +57,8 @@ public class Sequence implements Comparable<Sequence>, Serializable {
@Override
public String toString() {
- return "Sequence [date=" + date + ", number=" + number + ", words=" + words + "]";
+ return "Sequence [startDate=" + startDate + ", endDate=" + endDate + ", number=" + number + ", words=" + words
+ + "]";
}
}