我正在使用槌球庫進行主題建模。我的數據集是在filePath路徑和csvIterator似乎可以讀取數據,因爲model.getData()有大約27000行等於我的數據集。 我寫了一個循環,打印10個第一個文檔的實例和主題序列,但記號的大小是0.我哪裏出錯了?獲取槌球中所有文檔的實例和主題序列
在下面,我想顯示前10個主題中的前10個詞的比例,但所有輸出都是相同的。在cosole出
例如:
----文檔0
0 0.200 COM(1723年)的twitter(1225)的http(871)CBR(688)堪培拉(626)
1個0.200 COM(981)的twitter(901)天(205)可以(159)星期三(156)
2 0.200的twitter(1068)的COM(947)動作(433)actvcc(317)堪培拉(302)
3 0.200 http(1039)can貝拉(841)職位(378)dlvr(313)的COM(228)
4 0.200 COM(1185)WWW(1074)HTTP(831)新聞(708)canberratimes(560)
----文獻1
0 0.200 COM(1723年)的twitter(1225)的http(871)CBR(688)堪培拉(626)
1 0.200 COM(981)的twitter(901)天(205)可以(159) (156)
2 0.200 twitter(1068)com(947)act(433)actvcc(317)canberra(302)
3 0.200 HTTP(1039)堪培拉(841)職位(378)dlvr(313)的COM(228)
4 0.200 COM(1185)WWW(1074)HTTP(831)新聞(708)canberratimes(560 )
據我所知,LDA模型生成每個文檔並將它們分配給主題的單詞。那麼爲什麼每個文件的結果都是一樣的?
ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
pipeList.add(new CharSequenceLowercase());
pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}[\\p{L}\\p{P}]+\\p{L}")));
//stoplists/en.txt
pipeList.add(new TokenSequenceRemoveStopwords(new File(pathStopWords), "UTF-8", false, false, false));
pipeList.add(new TokenSequence2FeatureSequence());
InstanceList instances = new InstanceList(new SerialPipes(pipeList));
Reader fileReader = new InputStreamReader(new FileInputStream(new File(filePath)), "UTF-8");
//header of my data set
// row,location,username,hashtaghs,text,retweets,date,favorites,numberOfComment
CsvIterator csvIterator = new CsvIterator(fileReader,
Pattern.compile("^(\\d+)[,]*[^,]*[,]*[^,]*[,]*[^,]*[,]*([^,]*)[,]*[^,]*[,]*[^,]*[,]*[^,]*[,]*[^,]*$"),
2, 0, 1);
instances.addThruPipe(csvIterator); // data, label, name fields
int numTopics = 5;
ParallelTopicModel model = new ParallelTopicModel(numTopics, 1.0, 0.01);
model.addInstances(instances);
model.setNumThreads(2);
model.setNumIterations(50);
model.estimate();
Alphabet dataAlphabet = instances.getDataAlphabet();
ArrayList<TopicAssignment> arrayTopics = model.getData();
for (int i = 0; i < 10; i++) {
System.out.println("---- document " + i);
FeatureSequence tokens = (FeatureSequence) model.getData().get(i).instance.getData();
LabelSequence topics = model.getData().get(i).topicSequence;
Formatter out = new Formatter(new StringBuilder(), Locale.US);
for (int position = 0; position < tokens.getLength(); position++) {
out.format("%s-%d ", dataAlphabet.lookupObject(tokens.getIndexAtPosition(position)),
topics.getIndexAtPosition(position));
}
System.out.println(out);
double[] topicDistribution = model.getTopicProbabilities(i);
ArrayList<TreeSet<IDSorter>> topicSortedWords = model.getSortedWords();
for (int topic = 0; topic < numTopics; topic++) {
Iterator<IDSorter> iterator = topicSortedWords.get(topic).iterator();
out = new Formatter(new StringBuilder(), Locale.US);
out.format("%d\t%.3f\t", topic, topicDistribution[topic]);
int rank = 0;
while (iterator.hasNext() && rank < 5) {
IDSorter idCountPair = iterator.next();
out.format("%s (%.0f) ", dataAlphabet.lookupObject(idCountPair.getID()), idCountPair.getWeight());
rank++;
}
System.out.println(out);
}
StringBuilder topicZeroText = new StringBuilder();
Iterator<IDSorter> iterator = topicSortedWords.get(0).iterator();
int rank = 0;
while (iterator.hasNext() && rank < 5) {
IDSorter idCountPair = iterator.next();
topicZeroText.append(dataAlphabet.lookupObject(idCountPair.getID()) + " ");
rank++;
}
}