package edu.stanford.nlp.parser.metrics;
import edu.stanford.nlp.util.logging.Redwood;
import java.io.PrintWriter;
import java.util.Map;
import java.util.Random;
import java.util.Set;
import java.util.TreeMap;
import java.util.regex.Pattern;
import edu.stanford.nlp.ling.Label;
import edu.stanford.nlp.trees.Constituent;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.stats.ClassicCounter;
import edu.stanford.nlp.stats.Counter;
import edu.stanford.nlp.util.Generics;
/**
* Computes labeled precision and recall (evalb) at the constituent category level.
*
* @author Roger Levy
* @author Spence Green
*/
public class EvalbByCat extends AbstractEval {
/** A logger for this class */
private static Redwood.RedwoodChannels log = Redwood.channels(EvalbByCat.class);
private final Evalb evalb;
// Only evaluate categories that match this regular expression
private Pattern pLabelFilter = null;
private final Counter<Label> precisions;
private final Counter<Label> recalls;
private final Counter<Label> f1s;
private final Counter<Label> precisions2;
private final Counter<Label> recalls2;
private final Counter<Label> pnums2;
private final Counter<Label> rnums2;
public EvalbByCat(String str, boolean runningAverages) {
super(str, runningAverages);
evalb = new Evalb(str, false);
precisions = new ClassicCounter<>();
recalls = new ClassicCounter<>();
f1s = new ClassicCounter<>();
precisions2 = new ClassicCounter<>();
recalls2 = new ClassicCounter<>();
pnums2 = new ClassicCounter<>();
rnums2 = new ClassicCounter<>();
}
public EvalbByCat(String str, boolean runningAverages, String labelRegex) {
this(str, runningAverages);
if (labelRegex != null) {
pLabelFilter = Pattern.compile(labelRegex.trim());
}
}
@Override
protected Set<Constituent> makeObjects(Tree tree) {
return evalb.makeObjects(tree);
}
private Map<Label,Set<Constituent>> makeObjectsByCat(Tree t) {
Map<Label,Set<Constituent>> objMap = Generics.newHashMap();
Set<Constituent> objSet = makeObjects(t);
for (Constituent lc : objSet) {
Label l = lc.label();
if (!objMap.keySet().contains(l)) {
objMap.put(l, Generics.<Constituent>newHashSet());
}
objMap.get(l).add(lc);
}
return objMap;
}
@Override
public void evaluate(Tree guess, Tree gold, PrintWriter pw) {
if(gold == null || guess == null) {
System.err.printf("%s: Cannot compare against a null gold or guess tree!%n",this.getClass().getName());
return;
}
Map<Label,Set<Constituent>> guessDeps = makeObjectsByCat(guess);
Map<Label,Set<Constituent>> goldDeps = makeObjectsByCat(gold);
Set<Label> cats = Generics.newHashSet(guessDeps.keySet());
cats.addAll(goldDeps.keySet());
if (pw != null && runningAverages) {
pw.println("========================================");
pw.println("Labeled Bracketed Evaluation by Category");
pw.println("========================================");
}
++num;
for (Label cat : cats) {
Set<Constituent> thisGuessDeps = guessDeps.containsKey(cat) ? guessDeps.get(cat) : Generics.<Constituent>newHashSet();
Set<Constituent> thisGoldDeps = goldDeps.containsKey(cat) ? goldDeps.get(cat) : Generics.<Constituent>newHashSet();
double currentPrecision = precision(thisGuessDeps, thisGoldDeps);
double currentRecall = precision(thisGoldDeps, thisGuessDeps);
double currentF1 = (currentPrecision > 0.0 && currentRecall > 0.0 ? 2.0 / (1.0 / currentPrecision + 1.0 / currentRecall) : 0.0);
precisions.incrementCount(cat, currentPrecision);
recalls.incrementCount(cat, currentRecall);
f1s.incrementCount(cat, currentF1);
precisions2.incrementCount(cat, thisGuessDeps.size() * currentPrecision);
pnums2.incrementCount(cat, thisGuessDeps.size());
recalls2.incrementCount(cat, thisGoldDeps.size() * currentRecall);
rnums2.incrementCount(cat, thisGoldDeps.size());
if (pw != null && runningAverages) {
pw.println(cat + "\tP: " + ((int) (currentPrecision * 10000)) / 100.0 + " (sent ave " + ((int) (precisions.getCount(cat) * 10000 / num)) / 100.0 + ") (evalb " + ((int) (precisions2.getCount(cat) * 10000 / pnums2.getCount(cat))) / 100.0 + ")");
pw.println("\tR: " + ((int) (currentRecall * 10000)) / 100.0 + " (sent ave " + ((int) (recalls.getCount(cat) * 10000 / num)) / 100.0 + ") (evalb " + ((int) (recalls2.getCount(cat) * 10000 / rnums2.getCount(cat))) / 100.0 + ")");
double cF1 = 2.0 / (rnums2.getCount(cat) / recalls2.getCount(cat) + pnums2.getCount(cat) / precisions2.getCount(cat));
String emit = str + " F1: " + ((int) (currentF1 * 10000)) / 100.0 + " (sent ave " + ((int) (10000 * f1s.getCount(cat) / num)) / 100.0 + ", evalb " + ((int) (10000 * cF1)) / 100.0 + ")";
pw.println(emit);
}
}
if (pw != null && runningAverages) {
pw.println("========================================");
}
}
private Set<Label> getEvalLabelSet(Set<Label> labelSet) {
if (pLabelFilter == null) {
return Generics.newHashSet(precisions.keySet());
} else {
Set<Label> evalSet = Generics.newHashSet(precisions.keySet().size());
for (Label label : labelSet) {
if (pLabelFilter.matcher(label.value()).matches()) {
evalSet.add(label);
}
}
return evalSet;
}
}
@Override
public void display(boolean verbose, PrintWriter pw) {
if (precisions.keySet().size() != recalls.keySet().size()) {
log.error("Different counts for precisions and recalls!");
return;
}
final Set<Label> cats = getEvalLabelSet(precisions.keySet());
final Random rand = new Random();
Map<Double,Label> f1Map = new TreeMap<>();
for (Label cat : cats) {
double pnum2 = pnums2.getCount(cat);
double rnum2 = rnums2.getCount(cat);
double prec = precisions2.getCount(cat) / pnum2;
double rec = recalls2.getCount(cat) / rnum2;
double f1 = 2.0 / (1.0 / prec + 1.0 / rec);
if(new Double(f1).equals(Double.NaN)) f1 = -1.0;
if(f1Map.containsKey(f1)) {
f1Map.put(f1 + (rand.nextDouble()/1000.0), cat);
} else {
f1Map.put(f1, cat);
}
}
pw.println("============================================================");
pw.println("Labeled Bracketed Evaluation by Category -- final statistics");
pw.println("============================================================");
// Per category
double catPrecisions = 0.0;
double catPrecisionNums = 0.0;
double catRecalls = 0.0;
double catRecallNums = 0.0;
for (Label cat : f1Map.values()) {
double pnum2 = pnums2.getCount(cat);
double rnum2 = rnums2.getCount(cat);
double prec = precisions2.getCount(cat) / pnum2;
prec *= 100.0;
double rec = recalls2.getCount(cat) / rnum2;
rec *= 100.0;
double f1 = 2.0 / (1.0 / prec + 1.0 / rec);
catPrecisions += precisions2.getCount(cat);
catPrecisionNums += pnum2;
catRecalls += recalls2.getCount(cat);
catRecallNums += rnum2;
String LP = pnum2 == 0.0 ? "N/A" : String.format("%.2f", prec);
String LR = rnum2 == 0.0 ? "N/A" : String.format("%.2f", rec);
String F1 = (pnum2 == 0.0 || rnum2 == 0.0) ? "N/A": String.format("%.2f", f1);
pw.printf("%s\tLP: %s\tguessed: %d\tLR: %s\tgold: %d\t F1: %s%n",
cat.value(),
LP,
(int) pnum2,
LR,
(int) rnum2,
F1);
}
pw.println("============================================================");
// Totals
double prec = catPrecisions / catPrecisionNums;
double rec = catRecalls / catRecallNums;
double f1 = (2 * prec * rec) / (prec + rec);
pw.printf("Total\tLP: %.2f\tguessed: %d\tLR: %.2f\tgold: %d\t F1: %.2f%n",
prec*100.0,
(int) catPrecisionNums,
rec*100.0,
(int) catRecallNums,
f1*100.0);
pw.println("============================================================");
}
}