public static void trainLibSVMs() throws Exception {
System.out.println("train LibSVMs.");
Indexer<String> featureIndexer = IOUtils.readIndexer(WORD_INDEXER_FILE);
Indexer<String> labelIndexer = new Indexer<String>();
labelIndexer.add("Non-Relevant");
labelIndexer.add("Relevant");
List<SparseVector> trainData = SparseVector.readList(TRAIN_DATA_FILE);
List<SparseVector> testData = SparseVector.readList(TEST_DATA_FILE);
Collections.shuffle(trainData);
Collections.shuffle(testData);
LibSvmTrainer trainer = new LibSvmTrainer();
LibSvmWrapper wrapper = trainer.train(labelIndexer, featureIndexer, trainData);
wrapper.evalute(testData);
}
public static void generateData2() throws Exception {
System.out.println("generate data.");
List<BaseQuery> bqs = QueryReader.readTrecCdsQueries(MIRPath.TREC_CDS_QUERY_2014_FILE);
Map<Integer, BaseQuery> queryMap = new HashMap<Integer, BaseQuery>();
for (BaseQuery bq : bqs) {
int qid = Integer.parseInt(bq.getId());
queryMap.put(qid, bq);
}
Indexer<String> wordIndexer = new Indexer<String>();
Indexer<String> typeIndexer = new Indexer<String>();
TextFileWriter writer = new TextFileWriter(DATA_FILE);
List<SparseVector> data = new ArrayList<SparseVector>();
TextFileReader reader = new TextFileReader(MIRPath.TREC_CDS_QUERY_DOC_FILE);
while (reader.hasNext()) {
List<String> lines = reader.getNextLines();
List<SparseVector> svs = new ArrayList<SparseVector>();
int qid = -1;
for (int i = 0; i < lines.size(); i++) {
String line = lines.get(i);
String[] parts = line.split("\t");
double relevance = -1;
if (i == 0) {
qid = Integer.parseInt(parts[1]);
} else {
relevance = Double.parseDouble(parts[1]);
}
StrCounter c = new StrCounter();
String[] toks = parts[2].split(" ");
for (int j = 0; j < toks.length; j++) {
String[] two = StrUtils.split2Two(":", toks[j]);
c.incrementCount(two[0], Double.parseDouble(two[1]));
}
SparseVector sv = VectorUtils.toSparseVector(c, wordIndexer, true);
if (i > 0) {
sv.setLabel((int) relevance);
}
svs.add(sv);
}
TrecCdsQuery tcq = (TrecCdsQuery) queryMap.get(qid);
String type = tcq.getType();
int typeId = typeIndexer.getIndex(type);
// SparseVector q = svs.get(0);
for (int i = 1; i < svs.size(); i++) {
SparseVector d = svs.get(i);
double relevance = d.label();
if (relevance > 0) {
d.setLabel(typeId);
} else {
d.setLabel(3);
}
// SparseVector qd = VectorMath.add(q, d);
// qd.setLabel(d.label());
data.add(d);
}
}
reader.close();
writer.close();
SparseVector.write(DATA_FILE, data);
IOUtils.write(WORD_INDEXER_FILE, wordIndexer);
}
public static void trainLibLinear() throws Exception {
System.out.println("train SVMs.");
Indexer<String> featureIndexer = IOUtils.readIndexer(WORD_INDEXER_FILE);
List<SparseVector> trainData = SparseVector.readList(TRAIN_DATA_FILE);
List<SparseVector> testData = SparseVector.readList(TEST_DATA_FILE);
Collections.shuffle(trainData);
Collections.shuffle(testData);
// List[] lists = new List[] { trainData, testData };
//
// for (int i = 0; i < lists.length; i++) {
// List<SparseVector> list = lists[i];
// for (int j = 0; j < list.size(); j++) {
// SparseVector sv = list.get(j);
// if (sv.label() > 0) {
// sv.setLabel(1);
// }
// }
// }
Problem prob = new Problem();
prob.l = trainData.size();
prob.n = featureIndexer.size() + 1;
prob.y = new double[prob.l];
prob.x = new Feature[prob.l][];
prob.bias = -1;
if (prob.bias >= 0) {
prob.n++;
}
for (int i = 0; i < trainData.size(); i++) {
SparseVector x = trainData.get(i);
Feature[] input = new Feature[prob.bias > 0 ? x.size() + 1 : x.size()];
for (int j = 0; j < x.size(); j++) {
int index = x.indexAtLoc(j) + 1;
double value = x.valueAtLoc(j);
assert index >= 0;
input[j] = new FeatureNode(index + 1, value);
}
if (prob.bias >= 0) {
input[input.length - 1] = new FeatureNode(prob.n, prob.bias);
}
prob.x[i] = input;
prob.y[i] = x.label();
}
Model model = Linear.train(prob, getSVMParamter());
CounterMap<Integer, Integer> cm = new CounterMap<Integer, Integer>();
for (int i = 0; i < testData.size(); i++) {
SparseVector sv = testData.get(i);
Feature[] input = new Feature[sv.size()];
for (int j = 0; j < sv.size(); j++) {
int index = sv.indexAtLoc(j) + 1;
double value = sv.valueAtLoc(j);
input[j] = new FeatureNode(index + 1, value);
}
double[] dec_values = new double[model.getNrClass()];
Linear.predictValues(model, input, dec_values);
int max_id = ArrayMath.argmax(dec_values);
int pred = model.getLabels()[max_id];
int answer = sv.label();
cm.incrementCount(answer, pred, 1);
}
System.out.println(cm);
model.save(new File(MODEL_FILE));
}
public void collect2() throws Exception {
String[] queryFileNames = MIRPath.QueryFileNames;
String[] indexDirNames = MIRPath.IndexDirNames;
String[] relDataFileNames = MIRPath.RelevanceFileNames;
String[] docMapFileNames = MIRPath.DocIdMapFileNames;
String[] queryDocFileNames = MIRPath.QueryDocFileNames;
IndexSearcher[] indexSearchers = SearcherUtils.getIndexSearchers(indexDirNames);
Analyzer analyzer = MedicalEnglishAnalyzer.getAnalyzer();
for (int i = 0; i < queryFileNames.length; i++) {
List<BaseQuery> bqs = new ArrayList<BaseQuery>();
CounterMap<String, String> queryRels = new CounterMap<String, String>();
File queryFile = new File(queryFileNames[i]);
File relvFile = new File(relDataFileNames[i]);
if (i == 0) {
bqs = QueryReader.readTrecCdsQueries(queryFileNames[i]);
queryRels = RelevanceReader.readTrecCdsRelevances(relDataFileNames[i]);
} else if (i == 1) {
bqs = QueryReader.readClefEHealthQueries(queryFileNames[i]);
queryRels = RelevanceReader.readClefEHealthRelevances(relDataFileNames[i]);
} else if (i == 2) {
bqs = QueryReader.readOhsumedQueries(queryFileNames[i]);
queryRels = RelevanceReader.readOhsumedRelevances(relDataFileNames[i]);
} else if (i == 3) {
bqs = QueryReader.readTrecGenomicsQueries(queryFileNames[i]);
queryRels = RelevanceReader.readTrecGenomicsRelevances(relDataFileNames[i]);
}
List<Counter<String>> qs = new ArrayList<Counter<String>>();
for (int j = 0; j < bqs.size(); j++) {
BaseQuery bq = bqs.get(j);
qs.add(AnalyzerUtils.getWordCounts(bq.getSearchText(), analyzer));
}
BidMap<String, String> docIdMap = DocumentIdMapper.readDocumentIdMap(docMapFileNames[i]);
// queryRelevances = RelevanceReader.filter(queryRelevances, docIdMap);
// baseQueries = QueryReader.filter(baseQueries, queryRelevances);
List<SparseVector> docRelData =
DocumentIdMapper.mapDocIdsToIndexIds(bqs, queryRels, docIdMap);
IndexReader indexReader = indexSearchers[i].getIndexReader();
if (bqs.size() != docRelData.size()) {
throw new Exception();
}
TextFileWriter writer = new TextFileWriter(queryDocFileNames[i]);
for (int j = 0; j < bqs.size(); j++) {
BaseQuery bq = bqs.get(j);
SparseVector docRels = docRelData.get(j);
Indexer<String> wordIndexer = new Indexer<String>();
Counter<String> qwcs = qs.get(j);
SparseVector q = VectorUtils.toSparseVector(qs.get(j), wordIndexer, true);
{
SparseVector docFreqs =
VectorUtils.toSparseVector(
WordCountBox.getDocFreqs(indexReader, IndexFieldName.CONTENT, qs.get(j).keySet()),
wordIndexer,
true);
computeTFIDFs(q, docFreqs, indexReader.maxDoc());
}
WordCountBox wcb = WordCountBox.getWordCountBox(indexReader, docRels, wordIndexer);
SparseMatrix sm = wcb.getDocWordCounts();
SparseVector docFreqs = wcb.getCollDocFreqs();
for (int k = 0; k < sm.rowSize(); k++) {
int docId = sm.indexAtRowLoc(k);
SparseVector sv = sm.vectorAtRowLoc(k);
computeTFIDFs(sv, docFreqs, wcb.getNumDocsInCollection());
}
writer.write(String.format("#Query\t%d\t%s\n", j + 1, bq.toString()));
writer.write(
String.format("#Query Words\t%s\n", toString(VectorUtils.toCounter(q, wordIndexer))));
docRels.sortByValue();
for (int k = 0; k < docRels.size(); k++) {
int docId = docRels.indexAtLoc(k);
double rel = docRels.valueAtLoc(k);
if (rel == 0) {
continue;
}
Document doc = indexReader.document(docId);
List<Integer> ws = wcb.getDocWords().get(docId);
StringBuffer sb = new StringBuffer();
for (int l = 0; l < ws.size(); l++) {
int w = ws.get(l);
boolean found = false;
if (q.location(w) > -1) {
found = true;
}
if (found) {
sb.append(
String.format(
"%d\t%s\t%s\n", l + 1, wordIndexer.getObject(w), found ? 1 + "" : ""));
}
}
String content = doc.get(IndexFieldName.CONTENT);
SparseVector sv = sm.rowAlways(docId);
if (sv.size() == 0) {
continue;
}
writer.write(String.format("DOC-ID\t%d\nRelevance\t%d\n", docId, (int) rel));
writer.write(String.format("Loc\tWord\tMark\n%s\n", sb.toString()));
}
writer.write("\n");
}
}
}