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Posted to commits@joshua.apache.org by mj...@apache.org on 2016/08/21 17:42:28 UTC
[19/22] incubator-joshua git commit: JOSHUA-291 - static analysis
based code improvements on adagrad package
JOSHUA-291 - static analysis based code improvements on adagrad package
Project: http://git-wip-us.apache.org/repos/asf/incubator-joshua/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-joshua/commit/233818d6
Tree: http://git-wip-us.apache.org/repos/asf/incubator-joshua/tree/233818d6
Diff: http://git-wip-us.apache.org/repos/asf/incubator-joshua/diff/233818d6
Branch: refs/heads/JOSHUA-284
Commit: 233818d6ce526f3a77b33110d67314b723371743
Parents: 44b1b7b
Author: Tommaso Teofili <to...@apache.org>
Authored: Sun Aug 7 19:21:59 2016 +0200
Committer: Tommaso Teofili <to...@apache.org>
Committed: Thu Aug 18 09:57:05 2016 +0200
----------------------------------------------------------------------
.../org/apache/joshua/adagrad/AdaGradCore.java | 79 +++--
.../org/apache/joshua/adagrad/Optimizer.java | 348 +++++++++----------
2 files changed, 207 insertions(+), 220 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-joshua/blob/233818d6/src/main/java/org/apache/joshua/adagrad/AdaGradCore.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/joshua/adagrad/AdaGradCore.java b/src/main/java/org/apache/joshua/adagrad/AdaGradCore.java
index e51e8a3..9dc81a4 100755
--- a/src/main/java/org/apache/joshua/adagrad/AdaGradCore.java
+++ b/src/main/java/org/apache/joshua/adagrad/AdaGradCore.java
@@ -33,6 +33,7 @@ import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import java.text.DecimalFormat;
import java.util.ArrayList;
+import java.util.Collections;
import java.util.Date;
import java.util.HashMap;
import java.util.HashSet;
@@ -132,9 +133,9 @@ public class AdaGradCore {
/* *********************************************************** */
// private double[] lambda;
- private ArrayList<Double> lambda = new ArrayList<Double>();
+ private ArrayList<Double> lambda = new ArrayList<>();
// the current weight vector. NOTE: indexing starts at 1.
- private ArrayList<Double> bestLambda = new ArrayList<Double>();
+ private final ArrayList<Double> bestLambda = new ArrayList<>();
// the best weight vector across all iterations
private boolean[] isOptimizable;
@@ -341,8 +342,8 @@ public class AdaGradCore {
// and one line for the normalization method
// indexing starts at 1 in these arrays
for (int p = 0; p <= numParams; ++p)
- lambda.add(new Double(0));
- bestLambda.add(new Double(0));
+ lambda.add(0d);
+ bestLambda.add(0d);
// why only lambda is a list? because the size of lambda
// may increase over time, but other arrays are specified in
// the param config file, only used for initialization
@@ -497,7 +498,7 @@ public class AdaGradCore {
indicesOfInterest_all = temp_TSA;
for (int i = 0; i < numSentences; ++i) {
- indicesOfInterest_all[i] = new TreeSet<Integer>();
+ indicesOfInterest_all[i] = new TreeSet<>();
}
} // void initialize(...)
@@ -522,9 +523,9 @@ public class AdaGradCore {
if (folder.exists()) {
File[] listOfFiles = folder.listFiles();
- for (int i = 0; i < listOfFiles.length; i++) {
- if (listOfFiles[i].isFile()) {
- files = listOfFiles[i].getName();
+ for (File listOfFile : listOfFiles) {
+ if (listOfFile.isFile()) {
+ files = listOfFile.getName();
if (files.startsWith("AdaGrad.temp")) {
deleteFile(files);
}
@@ -627,11 +628,11 @@ public class AdaGradCore {
// save feats and stats for all candidates(old & new)
HashMap<String, String>[] feat_hash = new HashMap[numSentences];
for (int i = 0; i < numSentences; i++)
- feat_hash[i] = new HashMap<String, String>();
+ feat_hash[i] = new HashMap<>();
HashMap<String, String>[] stats_hash = new HashMap[numSentences];
for (int i = 0; i < numSentences; i++)
- stats_hash[i] = new HashMap<String, String>();
+ stats_hash[i] = new HashMap<>();
while (!done) { // NOTE: this "loop" will only be carried out once
println("--- Starting AdaGrad iteration #" + iteration + " @ " + (new Date()) + " ---", 1);
@@ -848,7 +849,7 @@ public class AdaGradCore {
// (It's not actually a bug, but only because existingCandStats gets
// cleared before moving to the next source sentence.)
// FIX: should be made an array, indexed by i
- HashMap<String, String> existingCandStats = new HashMap<String, String>();
+ HashMap<String, String> existingCandStats = new HashMap<>();
// VERY IMPORTANT:
// A CANDIDATE X MAY APPEARED IN ITER 1, ITER 3
// BUT IF THE USER SPECIFIED TO CONSIDER ITERATIONS FROM ONLY ITER 2, THEN
@@ -943,7 +944,7 @@ public class AdaGradCore {
String[] sentsCurrIt_currSrcSent = new String[sizeOfNBest + 1];
- Vector<String> unknownCands_V = new Vector<String>();
+ Vector<String> unknownCands_V = new Vector<>();
// which candidates (of the i'th source sentence) have not been seen before
// this iteration?
@@ -1122,7 +1123,7 @@ public class AdaGradCore {
// initialized as zero anyway
if (featId > numParams) {
++numParams;
- lambda.add(new Double(0));
+ lambda.add(0d);
}
}
}
@@ -1236,7 +1237,7 @@ public class AdaGradCore {
lambda.set(p, bestLambda.get(p));
// and set the rest of lambda to be 0
for (int p = 0; p < lambda.size() - bestLambda.size(); ++p)
- lambda.set(p + bestLambda.size(), new Double(0));
+ lambda.set(p + bestLambda.size(), 0d);
}
return null; // this means that the old values should be kept by the caller
@@ -1284,7 +1285,7 @@ public class AdaGradCore {
}
}
- Vector<String> output = new Vector<String>();
+ Vector<String> output = new Vector<>();
// note: initialLambda[] has length = numParamsOld
// augmented with new feature weights, initial values are 0
@@ -1328,8 +1329,8 @@ public class AdaGradCore {
/************* end optimization **************/
- for (int i = 0; i < output.size(); i++)
- println(output.get(i));
+ for (String anOutput : output)
+ println(anOutput);
// check if any parameter has been updated
boolean anyParamChanged = false;
@@ -1407,7 +1408,7 @@ public class AdaGradCore {
// (interpolation with previous wt vector)
double interCoef = 1.0; // no interpolation for now
for (int i = 1; i <= numParams; i++)
- lambda.set(i, interCoef * finalLambda[i] + (1 - interCoef) * lambda.get(i).doubleValue());
+ lambda.set(i, interCoef * finalLambda[i] + (1 - interCoef) * lambda.get(i));
println("Next iteration will decode with lambda: " + lambdaToString(lambda), 1);
println("", 1);
@@ -1441,9 +1442,9 @@ public class AdaGradCore {
retStr += "(listing the first " + featToPrint + " lambdas)";
for (int c = 1; c <= featToPrint - 1; ++c) {
- retStr += "" + String.format("%.4f", lambdaA.get(c).doubleValue()) + ", ";
+ retStr += "" + String.format("%.4f", lambdaA.get(c)) + ", ";
}
- retStr += "" + String.format("%.4f", lambdaA.get(numParams).doubleValue()) + "}";
+ retStr += "" + String.format("%.4f", lambdaA.get(numParams)) + "}";
return retStr;
}
@@ -1476,7 +1477,7 @@ public class AdaGradCore {
println("Running external decoder...", 1);
try {
- ArrayList<String> cmd = new ArrayList<String>();
+ ArrayList<String> cmd = new ArrayList<>();
cmd.add(decoderCommandFileName);
if (passIterationToDecoder)
@@ -1627,7 +1628,7 @@ public class AdaGradCore {
if (c_match == -1) {
outFile.println(line);
} else {
- if (Math.abs(params.get(c_match).doubleValue()) > 1e-20)
+ if (Math.abs(params.get(c_match)) > 1e-20)
outFile.println(Vocabulary.word(c_match) + " " + params.get(c_match));
}
@@ -1636,7 +1637,7 @@ public class AdaGradCore {
// now append weights of new features
for (int c = origFeatNum + 1; c <= numParams; ++c) {
- if (Math.abs(params.get(c).doubleValue()) > 1e-20)
+ if (Math.abs(params.get(c)) > 1e-20)
outFile.println(Vocabulary.word(c) + " " + params.get(c));
}
@@ -1667,7 +1668,7 @@ public class AdaGradCore {
// read default value
lambda.set(c, inFile_init.nextDouble());
- defaultLambda[c] = lambda.get(c).doubleValue();
+ defaultLambda[c] = lambda.get(c);
// read isOptimizable
dummy = inFile_init.next();
@@ -1849,7 +1850,7 @@ public class AdaGradCore {
boolean format3 = false;
- HashSet<String> seenStrings = new HashSet<String>();
+ HashSet<String> seenStrings = new HashSet<>();
BufferedReader inFile = new BufferedReader(new FileReader(docInfoFileName));
for (int i = 0; i < numSentences; ++i) {
// set format3 = true if a duplicate is found
@@ -1861,8 +1862,8 @@ public class AdaGradCore {
inFile.close();
- HashSet<String> seenDocNames = new HashSet<String>();
- HashMap<String, Integer> docOrder = new HashMap<String, Integer>();
+ HashSet<String> seenDocNames = new HashSet<>();
+ HashMap<String, Integer> docOrder = new HashMap<>();
// maps a document name to the order (0-indexed) in which it was seen
inFile = new BufferedReader(new FileReader(docInfoFileName));
@@ -1989,7 +1990,7 @@ public class AdaGradCore {
try {
PrintWriter outFile_lambdas = new PrintWriter(finalLambdaFileName);
for (int c = 1; c <= numParams; ++c) {
- outFile_lambdas.println(Vocabulary.word(c) + " ||| " + lambda.get(c).doubleValue());
+ outFile_lambdas.println(Vocabulary.word(c) + " ||| " + lambda.get(c));
}
outFile_lambdas.close();
@@ -2003,7 +2004,7 @@ public class AdaGradCore {
private String[] cfgFileToArgsArray(String fileName) {
checkFile(fileName);
- Vector<String> argsVector = new Vector<String>();
+ Vector<String> argsVector = new Vector<>();
BufferedReader inFile = null;
try {
@@ -2015,7 +2016,7 @@ public class AdaGradCore {
if (line != null && line.length() > 0 && line.charAt(0) != '#') {
- if (line.indexOf("#") != -1) { // discard comment
+ if (line.contains("#")) { // discard comment
line = line.substring(0, line.indexOf("#"));
}
@@ -2038,7 +2039,7 @@ public class AdaGradCore {
// cmu modification(from meteor for zmert)
// Parse args
- ArrayList<String> argList = new ArrayList<String>();
+ ArrayList<String> argList = new ArrayList<>();
StringBuilder arg = new StringBuilder();
boolean quoted = false;
for (int i = 0; i < line.length(); i++) {
@@ -2071,9 +2072,7 @@ public class AdaGradCore {
argsVector.add(paramA[1]);
} else if (paramA.length > 2 && (paramA[0].equals("-m") || paramA[0].equals("-docSet"))) {
// -m (metricName), -docSet are allowed to have extra optinos
- for (int opt = 0; opt < paramA.length; ++opt) {
- argsVector.add(paramA[opt]);
- }
+ Collections.addAll(argsVector, paramA);
} else {
String msg = "Malformed line in config file:" + origLine;
throw new RuntimeException(msg);
@@ -2413,7 +2412,7 @@ public class AdaGradCore {
if (val < 0 || val > 1) {
throw new RuntimeException("passIterationToDecoder should be either 0 or 1");
}
- passIterationToDecoder = (val == 1) ? true : false;
+ passIterationToDecoder = (val == 1);
} else if (option.equals("-decOut")) {
decoderOutFileName = args[i + 1];
} else if (option.equals("-decExit")) {
@@ -2808,7 +2807,7 @@ public class AdaGradCore {
str = " " + str + " ";
str = str.replaceAll("\\s+", " ");
- TreeSet<Integer> splitIndices = new TreeSet<Integer>();
+ TreeSet<Integer> splitIndices = new TreeSet<>();
for (int i = 0; i < str.length(); ++i) {
char ch = str.charAt(i);
@@ -2855,7 +2854,7 @@ public class AdaGradCore {
// remove spaces around dashes
if (normMethod == 2 || normMethod == 4) {
- TreeSet<Integer> skipIndices = new TreeSet<Integer>();
+ TreeSet<Integer> skipIndices = new TreeSet<>();
str = " " + str + " ";
for (int i = 0; i < str.length(); ++i) {
@@ -3031,7 +3030,7 @@ public class AdaGradCore {
}
private ArrayList<Double> randomLambda() {
- ArrayList<Double> retLambda = new ArrayList<Double>(1 + numParams);
+ ArrayList<Double> retLambda = new ArrayList<>(1 + numParams);
for (int c = 1; c <= numParams; ++c) {
if (isOptimizable[c]) {
@@ -3092,8 +3091,8 @@ public class AdaGradCore {
// print("discarding: ",4);
int numCandidates = slope.length;
- HashSet<Integer> discardedIndices = new HashSet<Integer>();
- HashMap<Double, Integer> indicesOfSlopes = new HashMap<Double, Integer>();
+ HashSet<Integer> discardedIndices = new HashSet<>();
+ HashMap<Double, Integer> indicesOfSlopes = new HashMap<>();
// maps slope to index of best candidate that has that slope.
// ("best" as in the one with the highest offset)
http://git-wip-us.apache.org/repos/asf/incubator-joshua/blob/233818d6/src/main/java/org/apache/joshua/adagrad/Optimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/joshua/adagrad/Optimizer.java b/src/main/java/org/apache/joshua/adagrad/Optimizer.java
index 722c593..16c25cd 100755
--- a/src/main/java/org/apache/joshua/adagrad/Optimizer.java
+++ b/src/main/java/org/apache/joshua/adagrad/Optimizer.java
@@ -42,13 +42,12 @@ public class Optimizer {
feat_hash = _feat_hash; // feature hash table
stats_hash = _stats_hash; // suff. stats hash table
finalLambda = new double[initialLambda.length];
- for(int i = 0; i < finalLambda.length; i++)
- finalLambda[i] = initialLambda[i];
+ System.arraycopy(initialLambda, 0, finalLambda, 0, finalLambda.length);
}
//run AdaGrad for one epoch
public double[] runOptimizer() {
- List<Integer> sents = new ArrayList<Integer>();
+ List<Integer> sents = new ArrayList<>();
for( int i = 0; i < sentNum; ++i )
sents.add(i);
double[] avgLambda = new double[initialLambda.length]; //only needed if averaging is required
@@ -87,14 +86,14 @@ public class Optimizer {
double Hii = 0;
double gradiiSquare = 0;
int lastUpdateTime = 0;
- HashMap<Integer, Integer> lastUpdate = new HashMap<Integer, Integer>();
- HashMap<Integer, Double> lastVal = new HashMap<Integer, Double>();
- HashMap<Integer, Double> H = new HashMap<Integer, Double>();
+ HashMap<Integer, Integer> lastUpdate = new HashMap<>();
+ HashMap<Integer, Double> lastVal = new HashMap<>();
+ HashMap<Integer, Double> H = new HashMap<>();
while( sentCount < sentNum ) {
loss = 0;
thisBatchSize = batchSize;
++numBatch;
- HashMap<Integer, Double> featDiff = new HashMap<Integer, Double>();
+ HashMap<Integer, Double> featDiff = new HashMap<>();
for(int b = 0; b < batchSize; ++b ) {
//find out oracle and prediction
s = sents.get(sentCount);
@@ -124,51 +123,48 @@ public class Optimizer {
//accumulate difference feature vector
if ( b == 0 ) {
- for (int i = 0; i < vecOraFeat.length; i++) {
- featInfo = vecOraFeat[i].split("=");
- diffFeatId = Integer.parseInt(featInfo[0]);
- featDiff.put(diffFeatId, Double.parseDouble(featInfo[1]));
- }
- for (int i = 0; i < vecPredFeat.length; i++) {
- featInfo = vecPredFeat[i].split("=");
- diffFeatId = Integer.parseInt(featInfo[0]);
- if (featDiff.containsKey(diffFeatId)) { //overlapping features
- diff = featDiff.get(diffFeatId)-Double.parseDouble(featInfo[1]);
- if ( Math.abs(diff) > 1e-20 )
- featDiff.put(diffFeatId, diff);
- else
- featDiff.remove(diffFeatId);
+ for (String aVecOraFeat : vecOraFeat) {
+ featInfo = aVecOraFeat.split("=");
+ diffFeatId = Integer.parseInt(featInfo[0]);
+ featDiff.put(diffFeatId, Double.parseDouble(featInfo[1]));
+ }
+ for (String aVecPredFeat : vecPredFeat) {
+ featInfo = aVecPredFeat.split("=");
+ diffFeatId = Integer.parseInt(featInfo[0]);
+ if (featDiff.containsKey(diffFeatId)) { //overlapping features
+ diff = featDiff.get(diffFeatId) - Double.parseDouble(featInfo[1]);
+ if (Math.abs(diff) > 1e-20)
+ featDiff.put(diffFeatId, diff);
+ else
+ featDiff.remove(diffFeatId);
+ } else //features only firing in the 2nd feature vector
+ featDiff.put(diffFeatId, -1.0 * Double.parseDouble(featInfo[1]));
}
- else //features only firing in the 2nd feature vector
- featDiff.put(diffFeatId, -1.0*Double.parseDouble(featInfo[1]));
- }
} else {
- for (int i = 0; i < vecOraFeat.length; i++) {
- featInfo = vecOraFeat[i].split("=");
- diffFeatId = Integer.parseInt(featInfo[0]);
- if (featDiff.containsKey(diffFeatId)) { //overlapping features
- diff = featDiff.get(diffFeatId)+Double.parseDouble(featInfo[1]);
- if ( Math.abs(diff) > 1e-20 )
- featDiff.put(diffFeatId, diff);
- else
- featDiff.remove(diffFeatId);
+ for (String aVecOraFeat : vecOraFeat) {
+ featInfo = aVecOraFeat.split("=");
+ diffFeatId = Integer.parseInt(featInfo[0]);
+ if (featDiff.containsKey(diffFeatId)) { //overlapping features
+ diff = featDiff.get(diffFeatId) + Double.parseDouble(featInfo[1]);
+ if (Math.abs(diff) > 1e-20)
+ featDiff.put(diffFeatId, diff);
+ else
+ featDiff.remove(diffFeatId);
+ } else //features only firing in the new oracle feature vector
+ featDiff.put(diffFeatId, Double.parseDouble(featInfo[1]));
}
- else //features only firing in the new oracle feature vector
- featDiff.put(diffFeatId, Double.parseDouble(featInfo[1]));
- }
- for (int i = 0; i < vecPredFeat.length; i++) {
- featInfo = vecPredFeat[i].split("=");
- diffFeatId = Integer.parseInt(featInfo[0]);
- if (featDiff.containsKey(diffFeatId)) { //overlapping features
- diff = featDiff.get(diffFeatId)-Double.parseDouble(featInfo[1]);
- if ( Math.abs(diff) > 1e-20 )
- featDiff.put(diffFeatId, diff);
- else
- featDiff.remove(diffFeatId);
+ for (String aVecPredFeat : vecPredFeat) {
+ featInfo = aVecPredFeat.split("=");
+ diffFeatId = Integer.parseInt(featInfo[0]);
+ if (featDiff.containsKey(diffFeatId)) { //overlapping features
+ diff = featDiff.get(diffFeatId) - Double.parseDouble(featInfo[1]);
+ if (Math.abs(diff) > 1e-20)
+ featDiff.put(diffFeatId, diff);
+ else
+ featDiff.remove(diffFeatId);
+ } else //features only firing in the new prediction feature vector
+ featDiff.put(diffFeatId, -1.0 * Double.parseDouble(featInfo[1]));
}
- else //features only firing in the new prediction feature vector
- featDiff.put(diffFeatId, -1.0*Double.parseDouble(featInfo[1]));
- }
}
//remember the model scores here are already scaled
@@ -350,7 +346,7 @@ public class Optimizer {
} //for ( int iter = 0; iter < adagradIter; ++iter ) {
//non-optimizable weights should remain unchanged
- ArrayList<Double> cpFixWt = new ArrayList<Double>();
+ ArrayList<Double> cpFixWt = new ArrayList<>();
for ( int i = 1; i < isOptimizable.length; ++i ) {
if ( ! isOptimizable[i] )
cpFixWt.add(finalLambda[i]);
@@ -388,26 +384,25 @@ public class Optimizer {
// find out the 1-best candidate for each sentence
// this depends on the training mode
maxModelScore = NegInf;
- for (Iterator it = candSet.iterator(); it.hasNext();) {
- modelScore = 0.0;
- candStr = it.next().toString();
+ for (String aCandSet : candSet) {
+ modelScore = 0.0;
+ candStr = aCandSet.toString();
- feat_str = feat_hash[i].get(candStr).split("\\s+");
+ feat_str = feat_hash[i].get(candStr).split("\\s+");
- String[] feat_info;
+ String[] feat_info;
- for (int f = 0; f < feat_str.length; f++) {
- feat_info = feat_str[f].split("=");
- modelScore +=
- Double.parseDouble(feat_info[1]) * finalLambda[Vocabulary.id(feat_info[0])];
- }
+ for (String aFeat_str : feat_str) {
+ feat_info = aFeat_str.split("=");
+ modelScore += Double.parseDouble(feat_info[1]) * finalLambda[Vocabulary.id(feat_info[0])];
+ }
- if (maxModelScore < modelScore) {
- maxModelScore = modelScore;
- tmpStatsVal = stats_hash[i].get(candStr).split("\\s+"); // save the
- // suff stats
- }
- }
+ if (maxModelScore < modelScore) {
+ maxModelScore = modelScore;
+ tmpStatsVal = stats_hash[i].get(candStr).split("\\s+"); // save the
+ // suff stats
+ }
+ }
for (int j = 0; j < suffStatsCount; j++)
corpusStatsVal[j] += Integer.parseInt(tmpStatsVal[j]); // accumulate
@@ -451,115 +446,108 @@ public class Optimizer {
else
worstPredScore = PosInf;
}
-
- for (Iterator it = candSet.iterator(); it.hasNext();) {
- cand = it.next().toString();
- candMetric = computeSentMetric(sentId, cand); //compute metric score
-
- //start to compute model score
- candScore = 0;
- featStr = feat_hash[sentId].get(cand).split("\\s+");
- feats = "";
-
- for (int i = 0; i < featStr.length; i++) {
- featInfo = featStr[i].split("=");
- actualFeatId = Vocabulary.id(featInfo[0]);
- candScore += Double.parseDouble(featInfo[1]) * lambda[actualFeatId];
- if ( (actualFeatId < isOptimizable.length && isOptimizable[actualFeatId]) ||
- actualFeatId >= isOptimizable.length )
- feats += actualFeatId + "=" + Double.parseDouble(featInfo[1]) + " ";
- }
-
- candScore *= featScale; //scale the model score
-
- //is this cand oracle?
- if(oraSelectMode == 1) {//"hope", b=1, r=1
- if(evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
- if( bestOraScore<=(candScore-candMetric) ) {
- bestOraScore = candScore-candMetric;
- oraMetric = candMetric;
- oraScore = candScore;
- oraFeat = feats;
- oraCand = cand;
- }
- }
- else {
- if( bestOraScore<=(candScore+candMetric) ) {
- bestOraScore = candScore+candMetric;
- oraMetric = candMetric;
- oraScore = candScore;
- oraFeat = feats;
- oraCand = cand;
- }
- }
- }
- else {//best metric score(ex: max BLEU), b=1, r=0
- if(evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
- if( bestOraScore>=candMetric ) {
- bestOraScore = candMetric;
- oraMetric = candMetric;
- oraScore = candScore;
- oraFeat = feats;
- oraCand = cand;
- }
- }
- else {
- if( bestOraScore<=candMetric ) {
- bestOraScore = candMetric;
- oraMetric = candMetric;
- oraScore = candScore;
- oraFeat = feats;
- oraCand = cand;
- }
- }
- }
-
- //is this cand prediction?
- if(predSelectMode == 1) {//"fear"
- if(evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
- if( worstPredScore<=(candScore+candMetric) ) {
- worstPredScore = candScore+candMetric;
- predMetric = candMetric;
- predScore = candScore;
- predFeat = feats;
- }
- }
- else {
- if( worstPredScore<=(candScore-candMetric) ) {
- worstPredScore = candScore-candMetric;
- predMetric = candMetric;
- predScore = candScore;
- predFeat = feats;
- }
- }
- }
- else if(predSelectMode == 2) {//model prediction(max model score)
- if( worstPredScore<=candScore ) {
- worstPredScore = candScore;
- predMetric = candMetric;
- predScore = candScore;
- predFeat = feats;
- }
- }
- else {//worst metric score(ex: min BLEU)
- if(evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
- if( worstPredScore<=candMetric ) {
- worstPredScore = candMetric;
- predMetric = candMetric;
- predScore = candScore;
- predFeat = feats;
- }
- }
- else {
- if( worstPredScore>=candMetric ) {
- worstPredScore = candMetric;
- predMetric = candMetric;
- predScore = candScore;
- predFeat = feats;
- }
- }
- }
- }
+
+ for (String aCandSet : candSet) {
+ cand = aCandSet.toString();
+ candMetric = computeSentMetric(sentId, cand); //compute metric score
+
+ //start to compute model score
+ candScore = 0;
+ featStr = feat_hash[sentId].get(cand).split("\\s+");
+ feats = "";
+
+ for (String aFeatStr : featStr) {
+ featInfo = aFeatStr.split("=");
+ actualFeatId = Vocabulary.id(featInfo[0]);
+ candScore += Double.parseDouble(featInfo[1]) * lambda[actualFeatId];
+ if ((actualFeatId < isOptimizable.length && isOptimizable[actualFeatId])
+ || actualFeatId >= isOptimizable.length)
+ feats += actualFeatId + "=" + Double.parseDouble(featInfo[1]) + " ";
+ }
+
+ candScore *= featScale; //scale the model score
+
+ //is this cand oracle?
+ if (oraSelectMode == 1) {//"hope", b=1, r=1
+ if (evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
+ if (bestOraScore <= (candScore - candMetric)) {
+ bestOraScore = candScore - candMetric;
+ oraMetric = candMetric;
+ oraScore = candScore;
+ oraFeat = feats;
+ oraCand = cand;
+ }
+ } else {
+ if (bestOraScore <= (candScore + candMetric)) {
+ bestOraScore = candScore + candMetric;
+ oraMetric = candMetric;
+ oraScore = candScore;
+ oraFeat = feats;
+ oraCand = cand;
+ }
+ }
+ } else {//best metric score(ex: max BLEU), b=1, r=0
+ if (evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
+ if (bestOraScore >= candMetric) {
+ bestOraScore = candMetric;
+ oraMetric = candMetric;
+ oraScore = candScore;
+ oraFeat = feats;
+ oraCand = cand;
+ }
+ } else {
+ if (bestOraScore <= candMetric) {
+ bestOraScore = candMetric;
+ oraMetric = candMetric;
+ oraScore = candScore;
+ oraFeat = feats;
+ oraCand = cand;
+ }
+ }
+ }
+
+ //is this cand prediction?
+ if (predSelectMode == 1) {//"fear"
+ if (evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
+ if (worstPredScore <= (candScore + candMetric)) {
+ worstPredScore = candScore + candMetric;
+ predMetric = candMetric;
+ predScore = candScore;
+ predFeat = feats;
+ }
+ } else {
+ if (worstPredScore <= (candScore - candMetric)) {
+ worstPredScore = candScore - candMetric;
+ predMetric = candMetric;
+ predScore = candScore;
+ predFeat = feats;
+ }
+ }
+ } else if (predSelectMode == 2) {//model prediction(max model score)
+ if (worstPredScore <= candScore) {
+ worstPredScore = candScore;
+ predMetric = candMetric;
+ predScore = candScore;
+ predFeat = feats;
+ }
+ } else {//worst metric score(ex: min BLEU)
+ if (evalMetric.getToBeMinimized()) {//if the smaller the metric score, the better
+ if (worstPredScore <= candMetric) {
+ worstPredScore = candMetric;
+ predMetric = candMetric;
+ predScore = candScore;
+ predFeat = feats;
+ }
+ } else {
+ if (worstPredScore >= candMetric) {
+ worstPredScore = candMetric;
+ predMetric = candMetric;
+ predScore = candScore;
+ predFeat = feats;
+ }
+ }
+ }
+ }
oraPredScore[0] = oraMetric;
oraPredScore[1] = oraScore;
@@ -695,14 +683,14 @@ public class Optimizer {
return finalMetricScore;
}
- private Vector<String> output;
+ private final Vector<String> output;
private double[] initialLambda;
- private double[] finalLambda;
+ private final double[] finalLambda;
private double finalMetricScore;
- private HashMap<String, String>[] feat_hash;
- private HashMap<String, String>[] stats_hash;
- private int paramDim;
- private boolean[] isOptimizable;
+ private final HashMap<String, String>[] feat_hash;
+ private final HashMap<String, String>[] stats_hash;
+ private final int paramDim;
+ private final boolean[] isOptimizable;
public static int sentNum;
public static int adagradIter; //AdaGrad internal iterations
public static int oraSelectMode;