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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.
package edu.stanford.nlp.sentiment;
import edu.stanford.nlp.util.logging.Redwood;
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations;
import edu.stanford.nlp.neural.rnn.TopNGramRecord;
import edu.stanford.nlp.stats.ClassicCounter;
import edu.stanford.nlp.stats.Counter;
import edu.stanford.nlp.stats.IntCounter;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.util.ConfusionMatrix;
import edu.stanford.nlp.util.Generics;
import edu.stanford.nlp.util.StringUtils;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.List;
import java.util.Set;
/**
*
* @author John Bauer
* @author Michael Haas (extracted this abstract class from Evaluate)
*/
public abstract class AbstractEvaluate {
/** A logger for this class */
private static Redwood.RedwoodChannels log = Redwood.channels(AbstractEvaluate.class);
String[] equivalenceClassNames;
int labelsCorrect;
int labelsIncorrect;
// the matrix will be [gold][predicted]
int[][] labelConfusion;
int rootLabelsCorrect;
int rootLabelsIncorrect;
int[][] rootLabelConfusion;
IntCounter lengthLabelsCorrect;
IntCounter lengthLabelsIncorrect;
TopNGramRecord ngrams;
// TODO: make this an option
static final int NUM_NGRAMS = 5;
int[][] equivalenceClasses;
protected static final NumberFormat NF = new DecimalFormat("0.000000");
private RNNOptions op = null;
public AbstractEvaluate(RNNOptions options) {
this.op = options;
this.reset();
}
protected static void printConfusionMatrix(String name, int[][] confusion) {
log.info(name + " confusion matrix");
ConfusionMatrix confusionMatrix = new ConfusionMatrix<>();
confusionMatrix.setUseRealLabels(true);
for (int i = 0; i < confusion.length; ++i) {
for (int j = 0; j < confusion[i].length; ++j) {
confusionMatrix.add(j, i, confusion[i][j]);
}
}
log.info(confusionMatrix);
}
protected static double[] approxAccuracy(int[][] confusion, int[][] classes) {
int[] correct = new int[classes.length];
int[] total = new int[classes.length];
double[] results = new double[classes.length];
for (int i = 0; i < classes.length; ++i) {
for (int j = 0; j < classes[i].length; ++j) {
for (int k = 0; k < classes[i].length; ++k) {
correct[i] += confusion[classes[i][j]][classes[i][k]];
}
for (int k = 0; k < confusion[classes[i][j]].length; ++k) {
total[i] += confusion[classes[i][j]][k];
}
}
results[i] = ((double) correct[i]) / ((double) (total[i]));
}
return results;
}
protected static double approxCombinedAccuracy(int[][] confusion, int[][] classes) {
int correct = 0;
int total = 0;
for (int[] aClass : classes) {
for (int j = 0; j < aClass.length; ++j) {
for (int k = 0; k < aClass.length; ++k) {
correct += confusion[aClass[j]][aClass[k]];
}
for (int k = 0; k < confusion[aClass[j]].length; ++k) {
total += confusion[aClass[j]][k];
}
}
}
return ((double) correct) / ((double) (total));
}
public void reset() {
labelsCorrect = 0;
labelsIncorrect = 0;
labelConfusion = new int[op.numClasses][op.numClasses];
rootLabelsCorrect = 0;
rootLabelsIncorrect = 0;
rootLabelConfusion = new int[op.numClasses][op.numClasses];
lengthLabelsCorrect = new IntCounter<>();
lengthLabelsIncorrect = new IntCounter<>();
equivalenceClasses = op.equivalenceClasses;
equivalenceClassNames = op.equivalenceClassNames;
if (op.testOptions.ngramRecordSize > 0) {
ngrams = new TopNGramRecord(op.numClasses, op.testOptions.ngramRecordSize,
op.testOptions.ngramRecordMaximumLength);
} else {
ngrams = null;
}
}
public void eval(List trees) {
this.populatePredictedLabels(trees);
for (Tree tree : trees) {
eval(tree);
}
}
public void eval(Tree tree) {
//cag.forwardPropagateTree(tree);
countTree(tree);
countRoot(tree);
countLengthAccuracy(tree);
if (ngrams != null) {
ngrams.countTree(tree);
}
}
protected int countLengthAccuracy(Tree tree) {
if (tree.isLeaf()) {
return 0;
}
Integer gold = RNNCoreAnnotations.getGoldClass(tree);
Integer predicted = RNNCoreAnnotations.getPredictedClass(tree);
int length;
if (tree.isPreTerminal()) {
length = 1;
} else {
length = 0;
for (Tree child : tree.children()) {
length += countLengthAccuracy(child);
}
}
if (gold >= 0) {
if (gold.equals(predicted)) {
lengthLabelsCorrect.incrementCount(length);
} else {
lengthLabelsIncorrect.incrementCount(length);
}
}
return length;
}
protected void countTree(Tree tree) {
if (tree.isLeaf()) {
return;
}
for (Tree child : tree.children()) {
countTree(child);
}
Integer gold = RNNCoreAnnotations.getGoldClass(tree);
Integer predicted = RNNCoreAnnotations.getPredictedClass(tree);
if (gold >= 0) {
if (gold.equals(predicted)) {
labelsCorrect++;
} else {
labelsIncorrect++;
}
labelConfusion[gold][predicted]++;
}
}
protected void countRoot(Tree tree) {
Integer gold = RNNCoreAnnotations.getGoldClass(tree);
Integer predicted = RNNCoreAnnotations.getPredictedClass(tree);
if (gold >= 0) {
if (gold.equals(predicted)) {
rootLabelsCorrect++;
} else {
rootLabelsIncorrect++;
}
rootLabelConfusion[gold][predicted]++;
}
}
public double exactNodeAccuracy() {
return (double) labelsCorrect / ((double) (labelsCorrect + labelsIncorrect));
}
public double exactRootAccuracy() {
return (double) rootLabelsCorrect / ((double) (rootLabelsCorrect + rootLabelsIncorrect));
}
public Counter lengthAccuracies() {
Set keys = Generics.newHashSet();
keys.addAll(lengthLabelsCorrect.keySet());
keys.addAll(lengthLabelsIncorrect.keySet());
Counter results = new ClassicCounter<>();
for (Integer key : keys) {
results.setCount(key, lengthLabelsCorrect.getCount(key) / (lengthLabelsCorrect.getCount(key) + lengthLabelsIncorrect.getCount(key)));
}
return results;
}
public void printLengthAccuracies() {
Counter accuracies = lengthAccuracies();
Set keys = Generics.newTreeSet();
keys.addAll(accuracies.keySet());
log.info("Label accuracy at various lengths:");
for (Integer key : keys) {
log.info(StringUtils.padLeft(Integer.toString(key), 4) + ": " + NF.format(accuracies.getCount(key)));
}
}
public void printSummary() {
log.info("EVALUATION SUMMARY");
log.info("Tested " + (labelsCorrect + labelsIncorrect) + " labels");
log.info(" " + labelsCorrect + " correct");
log.info(" " + labelsIncorrect + " incorrect");
log.info(" " + NF.format(exactNodeAccuracy()) + " accuracy");
log.info("Tested " + (rootLabelsCorrect + rootLabelsIncorrect) + " roots");
log.info(" " + rootLabelsCorrect + " correct");
log.info(" " + rootLabelsIncorrect + " incorrect");
log.info(" " + NF.format(exactRootAccuracy()) + " accuracy");
printConfusionMatrix("Label", labelConfusion);
printConfusionMatrix("Root label", rootLabelConfusion);
if (equivalenceClasses != null && equivalenceClassNames != null) {
double[] approxLabelAccuracy = approxAccuracy(labelConfusion, equivalenceClasses);
for (int i = 0; i < equivalenceClassNames.length; ++i) {
log.info("Approximate " + equivalenceClassNames[i] + " label accuracy: " + NF.format(approxLabelAccuracy[i]));
}
log.info("Combined approximate label accuracy: " + NF.format(approxCombinedAccuracy(labelConfusion, equivalenceClasses)));
double[] approxRootLabelAccuracy = approxAccuracy(rootLabelConfusion, equivalenceClasses);
for (int i = 0; i < equivalenceClassNames.length; ++i) {
log.info("Approximate " + equivalenceClassNames[i] + " root label accuracy: " + NF.format(approxRootLabelAccuracy[i]));
}
log.info("Combined approximate root label accuracy: " + NF.format(approxCombinedAccuracy(rootLabelConfusion, equivalenceClasses)));
log.info();
}
if (op.testOptions.ngramRecordSize > 0) {
log.info(ngrams);
}
if (op.testOptions.printLengthAccuracies) {
printLengthAccuracies();
}
}
/**
* Sets the predicted sentiment label for all trees given.
*
* This method sets the {@link RNNCoreAnnotations.PredictedClass} annotation
* for all nodes in all trees.
*
* @param trees List of Trees to be annotated
*/
public abstract void populatePredictedLabels(List trees);
}