package ivory.lsh.bitext;
import ivory.core.RetrievalEnvironment;
import ivory.core.data.dictionary.DefaultFrequencySortedDictionary;
import ivory.core.data.stat.DfTableArray;
import ivory.core.tokenize.Tokenizer;
import ivory.core.tokenize.TokenizerFactory;
import ivory.core.util.CLIRUtils;
import ivory.pwsim.score.Bm25;
import ivory.pwsim.score.ScoringModel;
import java.io.IOException;
import java.io.InputStream;
import java.util.Map;
import opennlp.model.MaxentModel;
import ivory.lsh.bitext.MoreGenericModelReader;
import opennlp.tools.sentdetect.SentenceDetectorME;
import opennlp.tools.sentdetect.SentenceModel;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import com.google.common.collect.Maps;
import edu.umd.cloud9.io.array.ArrayListOfIntsWritable;
import edu.umd.cloud9.io.array.ArrayListWritable;
import edu.umd.cloud9.io.map.HMapIFW;
import edu.umd.cloud9.io.map.HMapSFW;
import edu.umd.cloud9.io.map.HMapSIW;
import edu.umd.cloud9.util.map.MapKI.Entry;
import edu.umd.hooka.Vocab;
import edu.umd.hooka.VocabularyWritable;
import edu.umd.hooka.alignment.HadoopAlign;
import edu.umd.hooka.ttables.TTable_monolithic_IFAs;
public class PreprocessHelper {
private String eLang, fLang, eDir;
private int MinVectorTerms, MinSentenceLength;
private SentenceDetectorME fModel, eModel;
private Tokenizer fTok, eTok;
private VocabularyWritable eVocabSrc, eVocabTrg, fVocabTrg, fVocabSrc;
private TTable_monolithic_IFAs f2e_Probs;
private TTable_monolithic_IFAs e2f_Probs;
private ScoringModel fScoreFn, eScoreFn;
private MaxentModel classifier;
private DfTableArray dfTable;
private DefaultFrequencySortedDictionary dict;
private final Logger sLogger = Logger.getLogger(PreprocessHelper.class);
private static final HMapSIW lang2AvgSentLen = new HMapSIW();
static {
// took average # of tokens per sentence in Wikipedia data
lang2AvgSentLen.put("en",21);
lang2AvgSentLen.put("de",16);
lang2AvgSentLen.put("zh",27);
lang2AvgSentLen.put("fr",18);
lang2AvgSentLen.put("tr",12);
lang2AvgSentLen.put("ar",22);
lang2AvgSentLen.put("es",19);
// set to same as fr for now (no data yet)
lang2AvgSentLen.put("cs",18);
};
/**
* Implemented for HDFS cluster mode, files read from local cache
*/
public PreprocessHelper(int minVectorTerms, int minSentenceLength, JobConf conf) throws Exception {
super();
sLogger.setLevel(Level.INFO);
fLang = conf.get("fLang");
eLang = conf.get("eLang");
eDir = conf.get("eDir");
MinVectorTerms = minVectorTerms;
MinSentenceLength = minSentenceLength;
loadModels(conf);
}
/**
* Implemented for non-cluster mode, files read directly from local FS
*/
public PreprocessHelper(int minVectorTerms, int minSentenceLength, Configuration conf) throws Exception {
super();
// sLogger.setLevel(Level.DEBUG);
fLang = conf.get("fLang");
eLang = conf.get("eLang");
eDir = conf.get("eDir");
MinVectorTerms = minVectorTerms;
MinSentenceLength = minSentenceLength;
loadFModels(conf);
loadEModels(conf);
}
public void loadModels(JobConf job) throws Exception{
loadFModels(job);
loadEModels(job);
}
@SuppressWarnings("deprecation")
private void loadFModels(JobConf conf) throws Exception {
sLogger.info("Loading models for " + fLang + " ...");
FileSystem fs = FileSystem.get(conf);
FileSystem localFs = FileSystem.getLocal(conf);
Path[] localFiles = DistributedCache.getLocalCacheFiles(conf);
String sentDetectorFile = getSentDetectorFile(fLang); //localFiles[6].toString();
String eVocabSrcFile = getSrcVocab(eLang, fLang); //localFiles[3].toString();
String eVocabTrgFile = getTrgVocab(fLang, eLang); //localFiles[4].toString();
String fVocabSrcFile = getSrcVocab(fLang, eLang); //localFiles[7].toString();
String fVocabTrgFile = getTrgVocab(eLang, fLang); //localFiles[8].toString();
String f2e_ttableFile = getTTable(fLang, eLang); //localFiles[9].toString();
String e2f_ttableFile = getTTable(eLang, fLang); //localFiles[10].toString();
String modelFileName = getClassifierFile(); //localFiles[12].toString();
Map<String, Path> pathMapping = Maps.newHashMap();
for (Path p : localFiles) {
sLogger.info("In DistributedCache: " + p);
if (p.toString().contains(sentDetectorFile)) {
pathMapping.put(sentDetectorFile, p);
sLogger.info("--> sentdetector");
} else if (p.toString().contains(eVocabSrcFile)) {
pathMapping.put(eVocabSrcFile, p);
sLogger.info("--> eVocabSrcFile");
} else if (p.toString().contains(eVocabTrgFile)) {
pathMapping.put(eVocabTrgFile, p);
sLogger.info("--> eVocabTrgFile");
} else if (p.toString().contains(fVocabSrcFile)) {
pathMapping.put(fVocabSrcFile, p);
sLogger.info("--> fVocabSrcFile");
} else if (p.toString().contains(fVocabTrgFile)) {
pathMapping.put(fVocabTrgFile, p);
sLogger.info("--> fVocabTrgFile");
} else if (p.toString().contains(f2e_ttableFile)) {
pathMapping.put(f2e_ttableFile, p);
sLogger.info("--> f2e_ttableFile");
} else if (p.toString().contains(e2f_ttableFile)) {
pathMapping.put(e2f_ttableFile, p);
sLogger.info("--> e2f_ttableFile");
} else if (p.toString().contains(modelFileName)) {
pathMapping.put(modelFileName, p);
sLogger.info("--> classifier model");
}
}
InputStream modelIn = localFs.open(pathMapping.get(sentDetectorFile));
SentenceModel model = new SentenceModel(modelIn);
fModel = new SentenceDetectorME(model);
sLogger.info("Sentence model created successfully from " + pathMapping.get(sentDetectorFile));
eVocabSrc = (VocabularyWritable) HadoopAlign.loadVocab(pathMapping.get(eVocabSrcFile), localFs);
eVocabTrg = (VocabularyWritable) HadoopAlign.loadVocab(pathMapping.get(eVocabTrgFile), localFs);
fVocabSrc = (VocabularyWritable) HadoopAlign.loadVocab(pathMapping.get(fVocabSrcFile), localFs);
fVocabTrg = (VocabularyWritable) HadoopAlign.loadVocab(pathMapping.get(fVocabTrgFile), localFs);
f2e_Probs = new TTable_monolithic_IFAs(localFs, pathMapping.get(f2e_ttableFile), true);
e2f_Probs = new TTable_monolithic_IFAs(localFs, pathMapping.get(e2f_ttableFile), true);
// tokenizer file not read from cache, since it might be a directory (e.g. Chinese segmenter)
String tokenizerFile = conf.get("fTokenizer");
fTok = TokenizerFactory.createTokenizer(fs, fLang, tokenizerFile, true, conf.get("fStopword"), conf.get("fStemmedStopword"), null);
sLogger.info("Tokenizer and vocabs created successfully from " + fLang + " " + tokenizerFile + "," + conf.get("fStopword") + "," + conf.get("fStemmedStopword"));
// average sentence length = just a heuristic derived from sample text
fScoreFn = (ScoringModel) new Bm25();
fScoreFn.setAvgDocLength(lang2AvgSentLen.get(fLang));
// we use df table of English side, so we should read collection doc count from English dir
RetrievalEnvironment eEnv = new RetrievalEnvironment(eDir, fs);
fScoreFn.setDocCount(eEnv.readCollectionDocumentCount());
if (pathMapping.containsKey(modelFileName)) {
classifier = new MoreGenericModelReader(pathMapping.get(modelFileName), localFs).constructModel();
sLogger.info("Bitext classifier created successfully from " + pathMapping.get(modelFileName));
}
}
private void loadEModels(JobConf conf) throws Exception {
sLogger.info("Loading models for " + eLang + " ...");
String sentDetectorFile = getSentDetectorFile(eLang); //localFiles[1].toString();
Path[] localFiles = DistributedCache.getLocalCacheFiles(conf);
Map<String, Path> pathMapping = Maps.newHashMap();
for (Path p : localFiles) {
sLogger.info("In DistributedCache: " + p);
if (p.toString().contains(sentDetectorFile)) {
pathMapping.put(sentDetectorFile, p);
sLogger.info("--> sentdetector");
}
}
FileSystem localFs = FileSystem.getLocal(conf);
InputStream modelIn = localFs.open(pathMapping.get(sentDetectorFile));
SentenceModel model = new SentenceModel(modelIn);
eModel = new SentenceDetectorME(model);
sLogger.info("Sentence model created successfully.");
FileSystem fs = FileSystem.get(conf);
RetrievalEnvironment env = new RetrievalEnvironment(eDir, fs);
sLogger.info("Environment created successfully at " + eDir);
String tokenizerFile = conf.get("eTokenizer");
eTok = TokenizerFactory.createTokenizer(fs, eLang, tokenizerFile, true, conf.get("eStopword"), conf.get("eStemmedStopword"), null);
sLogger.info("Tokenizer and vocabs created successfully from " + eLang + " " + tokenizerFile + "," + conf.get("eStopword") + "," + conf.get("eStemmedStopword"));
eScoreFn = (ScoringModel) new Bm25();
eScoreFn.setAvgDocLength(lang2AvgSentLen.get(eLang)); //average sentence length = heuristic based on De-En data
eScoreFn.setDocCount(env.readCollectionDocumentCount());
dict = new DefaultFrequencySortedDictionary(new Path(env.getIndexTermsData()), new Path(env.getIndexTermIdsData()), new Path(env.getIndexTermIdMappingData()), fs);
dfTable = new DfTableArray(new Path(env.getDfByTermData()), fs);
}
public HMapSFW createFDocVector(String sentence) {
return createFDocVector(sentence, new HMapSIW());
}
public HMapSFW createFDocVector(String sentence, HMapSIW term2Tf) {
String[] terms = fTok.processContent(sentence);
for(String term : terms){
term2Tf.increment(term);
}
//translated tf values
HMapIFW transTermTf = new HMapIFW();
for(Entry<String> entry : term2Tf.entrySet()){
String fTerm = entry.getKey();
int tf = entry.getValue();
// transTermTf won't be updated if fTerm not in vocab
transTermTf = CLIRUtils.updateTFsByTerm(fTerm, tf, transTermTf, eVocabSrc, eVocabTrg, fVocabSrc, fVocabTrg, e2f_Probs, f2e_Probs, eTok, sLogger);
}
HMapSFW weightedVector = CLIRUtils.createTermDocVector(terms.length, transTermTf, eVocabTrg, fScoreFn, dict, dfTable, true, sLogger);
// don't count numbers for the min #terms constraint since Wikipedia has "sentences" full of numbers that doesn't make any sense
int numNonNumbers = 0;
for(String term : weightedVector.keySet()){
if (!term.matches("\\d+")) {
numNonNumbers++;
}
}
if(numNonNumbers < MinVectorTerms){
return null;
}else {
return weightedVector;
}
}
public HMapSFW createEDocVector(String sentence) {
return createEDocVector(sentence, new HMapSIW());
}
public HMapSFW createEDocVector(String sentence, HMapSIW term2Tf) {
HMapSFW weightedVector = new HMapSFW();
String[] terms = eTok.processContent(sentence);
for(String term : terms){
term2Tf.increment(term);
}
weightedVector = CLIRUtils.createTermDocVector(terms.length, term2Tf, eScoreFn, dict, dfTable, true, sLogger);
// don't count numbers for the min #terms constraint since Wikipedia has "sentences" full of numbers that doesn't make any sense
int numNonNumbers = 0;
for(String term : weightedVector.keySet()){
if (!term.matches("\\d+")) {
numNonNumbers++;
}
}
if(numNonNumbers < MinVectorTerms){
return null;
}else {
return weightedVector;
}
}
public ArrayListWritable<Text> getESentences(String text, ArrayListWritable<HMapSFW> vectors, ArrayListOfIntsWritable sentLengths) throws ClassNotFoundException, InstantiationException, IllegalAccessException, IOException {
ArrayListWritable<Text> sentences = new ArrayListWritable<Text>();
String[] lines = text.split("\n");
for(String line : lines){
if(!line.matches("\\s+") && !line.isEmpty()){
String[] sents = eModel.sentDetect(line);
for(String sent : sents){
if(sent.contains("date:")||sent.contains("jpg")||sent.contains("png")||sent.contains("gif")||sent.contains("fontsize:")||sent.contains("category:")){
continue;
}
int length = eTok.getNumberTokens(sent);
if(length >= MinSentenceLength){
HMapSFW vector = createEDocVector(sent.toString());
if(vector != null){
vectors.add(vector);
sentences.add(new Text(sent));
if (sentLengths != null) sentLengths.add(length);
}
}
}
}
}
return sentences;
}
public ArrayListWritable<Text> getFSentences(String text, ArrayListWritable<HMapSFW> vectors, ArrayListOfIntsWritable sentLengths) throws ClassNotFoundException, InstantiationException, IllegalAccessException, IOException {
// sLogger.setLevel(Level.DEBUG);
sLogger.debug("text length="+text.length());
ArrayListWritable<Text> sentences = new ArrayListWritable<Text>();
String[] lines = text.split("\n");
sLogger.debug("num lines="+lines.length);
for(String line : lines){
// convert '。' to standard period character '.' for sentence detector to work
StringBuffer sb = new StringBuffer();
for (int i = 0; i < line.length(); i++) {
char c = line.charAt(i);
String unicode = String.format("%04x", (int) c);
if (unicode.equals("3002")) {
sb.append(". ");
}else {
sb.append(c);
}
}
line = sb.toString();
sLogger.debug("line="+line);
if (!line.matches("\\s+") && !line.isEmpty()) {
String[] sents = fModel.sentDetect(line);
for (String sent : sents) {
sLogger.debug("sent="+sent);
// discard some of the non-text content in Wikipedia
if (sent.contains("datei:") || sent.contains("jpg") || sent.contains("png") || sent.contains("fontsize:") || sent.contains("kategorie:")) {
continue;
}
int length = fTok.getNumberTokens(sent);
if (length >= MinSentenceLength) {
HMapSFW vector = createFDocVector(sent);
if (vector != null) {
vectors.add(vector);
sentences.add(new Text(sent));
sLogger.debug("added="+vector);
if (sentLengths != null) sentLengths.add(length);
}
}
}
}
}
sLogger.setLevel(Level.INFO);
return sentences;
}
private String getSentDetectorFile(String lang) {
return lang+"-sent.bin";
}
private String getClassifierFile() {
return "classifier-";
}
private String getTTable(String srcLang, String trgLang) {
return "ttable." + srcLang + "-" + trgLang;
}
private String getTrgVocab(String srcLang, String trgLang) {
return "vocab." + srcLang + "-" + trgLang + "." + trgLang;
}
private String getSrcVocab(String srcLang, String trgLang) {
return "vocab." + srcLang + "-" + trgLang + "." + srcLang;
}
public MaxentModel getClassifier() {
return classifier;
}
public Tokenizer getETokenizer() {
return eTok;
}
public Tokenizer getFTokenizer() {
return fTok;
}
public TTable_monolithic_IFAs getE2F() {
return e2f_Probs;
}
public TTable_monolithic_IFAs getF2E() {
return f2e_Probs;
}
public Vocab getFSrc() {
return fVocabSrc;
}
public Vocab getETrg() {
return eVocabTrg;
}
public Vocab getESrc() {
return eVocabSrc;
}
public Vocab getFTrg() {
return fVocabTrg;
}
public SentenceDetectorME getFSentenceModel() {
return fModel;
}
public SentenceDetectorME getESentenceModel() {
return eModel;
}
/**
* Load from local FS instead of HDFS
*/
private void loadFModels(Configuration conf) throws Exception {
sLogger.info("Loading models for " + fLang + " ...");
// FileSystem fs = FileSystem.get(conf);
FileSystem localFs = FileSystem.getLocal(conf);
InputStream modelIn = localFs.open(new Path(conf.get("eSentDetectorFile")));
SentenceModel model = new SentenceModel(modelIn);
fModel = new SentenceDetectorME(model);
sLogger.info("Sentence model created successfully.");
eVocabSrc = (VocabularyWritable) HadoopAlign.loadVocab(new Path(conf.get("eVocabSrcFile")), localFs);
eVocabTrg = (VocabularyWritable) HadoopAlign.loadVocab(new Path(conf.get("eVocabTrgFile")), localFs);
fVocabSrc = (VocabularyWritable) HadoopAlign.loadVocab(new Path(conf.get("fVocabSrcFile")), localFs);
fVocabTrg = (VocabularyWritable) HadoopAlign.loadVocab(new Path(conf.get("fVocabTrgFile")), localFs);
f2e_Probs = new TTable_monolithic_IFAs(localFs, new Path(conf.get("f2e_ttableFile")), true);
e2f_Probs = new TTable_monolithic_IFAs(localFs, new Path(conf.get("e2f_ttableFile")), true);
// tokenizer file not read from cache, since it might be a directory (e.g. Chinese segmenter)
String tokenizerFile = conf.get("fTokenizer");
fTok = TokenizerFactory.createTokenizer(localFs, fLang, tokenizerFile, true, conf.get("fStopword"), null, null);
sLogger.info("Tokenizer and vocabs created successfully.");
// average sentence length = just a heuristic derived from sample text
fScoreFn = (ScoringModel) new Bm25();
fScoreFn.setAvgDocLength(lang2AvgSentLen.get(fLang));
// we use df table of English side, so we should read collection doc count from English dir
RetrievalEnvironment eEnv = new RetrievalEnvironment(eDir, localFs);
fScoreFn.setDocCount(eEnv.readCollectionDocumentCount());
classifier = new MoreGenericModelReader(new Path(conf.get("modelFileName")), localFs).constructModel();
}
private void loadEModels(Configuration conf) throws Exception {
sLogger.info("Loading models for " + eLang + " ...");
FileSystem localFs = FileSystem.getLocal(conf);
InputStream modelIn = localFs.open(new Path(conf.get("fSentDetectorFile")));
SentenceModel model = new SentenceModel(modelIn);
eModel = new SentenceDetectorME(model);
sLogger.info("Sentence model created successfully.");
// FileSystem fs = FileSystem.get(conf);
RetrievalEnvironment env = new RetrievalEnvironment(eDir, localFs);
sLogger.info("Environment created successfully.");
String tokenizerFile = conf.get("eTokenizer");
eTok = TokenizerFactory.createTokenizer(localFs, eLang, tokenizerFile, true, conf.get("eStopword"), null, null);
sLogger.info("Tokenizer and vocabs created successfully.");
eScoreFn = (ScoringModel) new Bm25();
eScoreFn.setAvgDocLength(lang2AvgSentLen.get(eLang)); //average sentence length = heuristic based on De-En data
eScoreFn.setDocCount(env.readCollectionDocumentCount());
dict = new DefaultFrequencySortedDictionary(new Path(env.getIndexTermsData()), new Path(env.getIndexTermIdsData()), new Path(env.getIndexTermIdMappingData()), localFs);
dfTable = new DfTableArray(new Path(env.getDfByTermData()), localFs);
}
public float getFOOVRate(String fSent) {
return fTok.getOOVRate(fSent, fVocabSrc);
}
public float getEOOVRate(String eSent) {
return eTok.getOOVRate(eSent, eVocabSrc);
}
}