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edu.stanford.nlp.parser.metrics.AbstractEval Maven / Gradle / Ivy
package edu.stanford.nlp.parser.metrics;
import java.util.*;
import java.io.PrintWriter;
import java.text.NumberFormat;
import java.text.DecimalFormat;
import edu.stanford.nlp.parser.KBestViterbiParser;
import edu.stanford.nlp.stats.ClassicCounter;
import edu.stanford.nlp.stats.Counters;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.util.Generics;
/**
* A framework for Set-based precision/recall/F1 evaluation.
*
* @author Dan Klein
*/
public abstract class AbstractEval implements Eval {
private static final boolean DEBUG = false;
protected final String str;
protected final boolean runningAverages;
private double precision = 0.0;
private double recall = 0.0;
private double f1 = 0.0;
protected double num = 0.0;
private double exact = 0.0;
private double precision2 = 0.0;
private double recall2 = 0.0;
private double pnum2 = 0.0;
private double rnum2 = 0.0;
protected double curF1 = 0.0;
public AbstractEval() {
this(true);
}
public AbstractEval(boolean runningAverages) {
this("", runningAverages);
}
public AbstractEval(String str) {
this(str, true);
}
public AbstractEval(String str, boolean runningAverages) {
this.str = str;
this.runningAverages = runningAverages;
}
public double getSentAveF1() {
return f1 / num;
}
public double getEvalbF1() {
return 2.0 / (rnum2 / recall2 + pnum2 / precision2);
}
/**
* Return the evalb F1% from the last call to {@link #evaluate}.
*
* @return The F1 percentage
*/
public double getLastF1() {
return curF1 * 100.0;
}
/** @return The evalb (micro-averaged) F1 times 100 to make it
* a number between 0 and 100.
*/
public double getEvalbF1Percent() {
return getEvalbF1() * 100.0;
}
public double getExact() {
return exact / num;
}
public double getExactPercent() {
return getExact() * 100.0;
}
public int getNum() {
return (int) num;
}
// should be able to pass in a comparator!
protected static double precision(Set s1, Set s2) {
double n = 0.0;
double p = 0.0;
for (Object o1 : s1) {
if (s2.contains(o1)) {
p += 1.0;
}
if (DEBUG) {
if (s2.contains(o1)) {
System.err.println("Eval Found: "+o1);
} else {
System.err.println("Eval Failed to find: "+o1);
}
}
n += 1.0;
}
if (DEBUG) System.err.println("Matched " + p + " of " + n);
return (n > 0.0 ? p / n : 0.0);
}
protected abstract Set makeObjects(Tree tree);
public void evaluate(Tree guess, Tree gold) {
evaluate(guess, gold, new PrintWriter(System.out, true));
}
/* Evaluates precision and recall by calling makeObjects() to make a
* set of structures for guess Tree and gold Tree, and compares them
* with each other.
*/
public void evaluate(Tree guess, Tree gold, PrintWriter pw) {
evaluate(guess, gold, pw, 1.0);
}
public void evaluate(Tree guess, Tree gold, PrintWriter pw, double weight) {
if (DEBUG) {
System.err.println("Evaluating gold tree:");
gold.pennPrint(System.err);
System.err.println("and guess tree");
guess.pennPrint(System.err);
}
Set dep1 = makeObjects(guess);
Set dep2 = makeObjects(gold);
final double curPrecision = precision(dep1, dep2);
final double curRecall = precision(dep2, dep1);
curF1 = (curPrecision > 0.0 && curRecall > 0.0 ? 2.0 / (1.0 / curPrecision + 1.0 / curRecall) : 0.0);
precision += curPrecision * weight;
recall += curRecall * weight;
f1 += curF1 * weight;
num += weight;
precision2 += dep1.size() * curPrecision * weight;
pnum2 += dep1.size() * weight;
recall2 += dep2.size() * curRecall * weight;
rnum2 += dep2.size() * weight;
if (curF1 > 0.9999) {
exact += 1.0;
}
if (pw != null) {
pw.print(" P: " + ((int) (curPrecision * 10000)) / 100.0);
if (runningAverages) {
pw.println(" (sent ave " + ((int) (precision * 10000 / num)) / 100.0 + ") (evalb " + ((int) (precision2 * 10000 / pnum2)) / 100.0 + ")");
}
pw.print(" R: " + ((int) (curRecall * 10000)) / 100.0);
if (runningAverages) {
pw.print(" (sent ave " + ((int) (recall * 10000 / num)) / 100.0 + ") (evalb " + ((int) (recall2 * 10000 / rnum2)) / 100.0 + ")");
}
pw.println();
double cF1 = 2.0 / (rnum2 / recall2 + pnum2 / precision2);
pw.print(str + " F1: " + ((int) (curF1 * 10000)) / 100.0);
if (runningAverages) {
pw.print(" (sent ave " + ((int) (10000 * f1 / num)) / 100.0 + ", evalb " + ((int) (10000 * cF1)) / 100.0 + ") Exact: " + ((int) (10000 * exact / num)) / 100.0);
}
// pw.println(" N: " + getNum());
pw.println(" N: " + num);
}
/*
Sentence s = guess.yield();
for (Object obj : s) {
if (curF1 < 0.7) {
badwords.incrementCount(obj);
} else {
goodwords.incrementCount(obj);
}
}
*/
}
/*
private Counter goodwords = new Counter();
private Counter badwords = new Counter();
public void printGoodBad() {
System.out.println("Printing bad categories");
for (Object key : Counters.keysAbove(badwords, 5.0)) {
System.out.println("In badwords 5 times: " + key);
double numb = badwords.getCount(key);
double numg = goodwords.getCount(key);
if (numb / (numb + numg) > 0.1) {
System.out.println("Bad word! " + key + " (" +
(numb / (numb + numg)) + " bad)");
// EncodingPrintWriter.out.println("Bad word! " + key + " (" +
// (numb / (numb + numg)) + " bad)",
// "GB18030");
}
}
}
*/
public void display(boolean verbose) {
display(verbose, new PrintWriter(System.out, true));
}
public void display(boolean verbose, PrintWriter pw) {
double prec = precision2 / pnum2;//(num > 0.0 ? precision/num : 0.0);
double rec = recall2 / rnum2;//(num > 0.0 ? recall/num : 0.0);
double f = 2.0 / (1.0 / prec + 1.0 / rec);//(num > 0.0 ? f1/num : 0.0);
//System.out.println(" Precision: "+((int)(10000.0*prec))/100.0);
//System.out.println(" Recall: "+((int)(10000.0*rec))/100.0);
//System.out.println(" F1: "+((int)(10000.0*f))/100.0);
pw.println(str + " summary evalb: LP: " + ((int) (10000.0 * prec)) / 100.0 + " LR: " + ((int) (10000.0 * rec)) / 100.0 + " F1: " + ((int) (10000.0 * f)) / 100.0 + " Exact: " + ((int) (10000.0 * exact / num)) / 100.0 + " N: " + getNum());
/*
double prec = (num > 0.0 ? precision/num : 0.0);
double rec = (num > 0.0 ? recall/num : 0.0);
double f = (num > 0.0 ? f1/num : 0.0);
System.out.println(" Precision: "+prec);
System.out.println(" Recall: "+rec);
System.out.println(" F1: "+f);
*/
}
public static class RuleErrorEval extends AbstractEval {
//private boolean verbose = false;
private ClassicCounter over = new ClassicCounter<>();
private ClassicCounter under = new ClassicCounter<>();
protected static String localize(Tree tree) {
if (tree.isLeaf()) {
return "";
}
StringBuilder sb = new StringBuilder();
sb.append(tree.label());
sb.append(" ->");
for (int i = 0; i < tree.children().length; i++) {
sb.append(' ');
sb.append(tree.children()[i].label());
}
return sb.toString();
}
@Override
protected Set makeObjects(Tree tree) {
Set localTrees = Generics.newHashSet();
for (Tree st : tree.subTreeList()) {
localTrees.add(localize(st));
}
return localTrees;
}
@Override
public void evaluate(Tree t1, Tree t2, PrintWriter pw) {
Set s1 = makeObjects(t1);
Set s2 = makeObjects(t2);
for (String o1 : s1) {
if (!s2.contains(o1)) {
over.incrementCount(o1);
}
}
for (String o2 : s2) {
if (!s1.contains(o2)) {
under.incrementCount(o2);
}
}
}
private static void display(ClassicCounter c, int num, PrintWriter pw) {
List rules = new ArrayList<>(c.keySet());
Collections.sort(rules, Counters.toComparatorDescending(c));
int rSize = rules.size();
if (num > rSize) {
num = rSize;
}
for (int i = 0; i < num; i++) {
pw.println(rules.get(i) + " " + c.getCount(rules.get(i)));
}
}
@Override
public void display(boolean verbose, PrintWriter pw) {
//this.verbose = verbose;
pw.println("Most frequently underproposed rules:");
display(under, (verbose ? 100 : 10), pw);
pw.println("Most frequently overproposed rules:");
display(over, (verbose ? 100 : 10), pw);
}
public RuleErrorEval(String str) {
super(str);
}
} // end class RuleErrorEval
/** This class counts which categories are over and underproposed in trees.
*/
public static class CatErrorEval extends AbstractEval {
private ClassicCounter over = new ClassicCounter<>();
private ClassicCounter under = new ClassicCounter<>();
/** Unused. Fake satisfying the abstract class. */
@Override
protected Set makeObjects(Tree tree) {
return null;
}
private static List myMakeObjects(Tree tree) {
List cats = new LinkedList<>();
for (Tree st : tree.subTreeList()) {
cats.add(st.value());
}
return cats;
}
@Override
public void evaluate(Tree t1, Tree t2, PrintWriter pw) {
List s1 = myMakeObjects(t1);
List s2 = myMakeObjects(t2);
List del2 = new LinkedList<>(s2);
// we delete out as we find them so we can score correctly a cat with
// a certain cardinality in a tree.
for (String o1 : s1) {
if ( ! del2.remove(o1)) {
over.incrementCount(o1);
}
}
for (String o2 : s2) {
if (! s1.remove(o2)) {
under.incrementCount(o2);
}
}
}
private static void display(ClassicCounter c, PrintWriter pw) {
List cats = new ArrayList<>(c.keySet());
Collections.sort(cats, Counters.toComparatorDescending(c));
for (T ob : cats) {
pw.println(ob + " " + c.getCount(ob));
}
}
@Override
public void display(boolean verbose, PrintWriter pw) {
pw.println("Most frequently underproposed categories:");
display(under, pw);
pw.println("Most frequently overproposed categories:");
display(over, pw);
}
public CatErrorEval(String str) {
super(str);
}
} // end class CatErrorEval
/** This isn't really a kind of AbstractEval: we're sort of cheating here. */
public static class ScoreEval extends AbstractEval {
double totScore = 0.0;
double n = 0.0;
NumberFormat nf = new DecimalFormat("0.000");
@Override
protected Set makeObjects(Tree tree) {
return null;
}
public void recordScore(KBestViterbiParser parser, PrintWriter pw) {
double score = parser.getBestScore();
totScore += score;
n++;
if (pw != null) {
pw.print(str + " score: " + nf.format(score));
if (runningAverages) {
pw.print(" average score: " + nf.format(totScore / n));
}
pw.println();
}
}
@Override
public void display(boolean verbose, PrintWriter pw) {
if (pw != null) {
pw.println(str + " total score: " + nf.format(totScore) +
" average score: " + ((n == 0.0) ? "N/A": nf.format(totScore / n)));
}
}
public ScoreEval(String str, boolean runningAverages) {
super(str, runningAverages);
}
} // end class DependencyEval
} // end class AbstractEval
