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package edu.stanford.nlp.ie;
import edu.stanford.nlp.util.logging.Redwood;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
import java.util.Properties;
import java.util.Set;
import java.util.stream.Collectors;
import edu.stanford.nlp.io.IOUtils;
import edu.stanford.nlp.io.RuntimeIOException;
import edu.stanford.nlp.ie.crf.CRFClassifier;
import edu.stanford.nlp.ie.ner.CMMClassifier;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.ling.HasWord;
import edu.stanford.nlp.pipeline.DefaultPaths;
import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
import edu.stanford.nlp.sequences.SeqClassifierFlags;
import edu.stanford.nlp.util.CoreMap;
import edu.stanford.nlp.util.ErasureUtils;
import edu.stanford.nlp.util.Generics;
import edu.stanford.nlp.util.StringUtils;
import edu.stanford.nlp.util.PropertiesUtils;
/**
* Merges the outputs of two or more AbstractSequenceClassifiers according to
* a simple precedence scheme: any given base classifier contributes only
* classifications of labels that do not exist in the base classifiers specified
* before, and that do not have any token overlap with labels assigned by
* higher priority classifiers.
*
* This is a pure AbstractSequenceClassifier, i.e., it sets the AnswerAnnotation label.
* If you work with NER classifiers, you should use NERClassifierCombiner. This class
* inherits from ClassifierCombiner, and takes care that all AnswerAnnotations are also
* copied to NERAnnotation.
*
* You can specify up to 10 base classifiers using the -loadClassifier1 to -loadClassifier10
* properties. We also maintain the older usage when only two base classifiers were accepted,
* specified using -loadClassifier and -loadAuxClassifier.
*
* ms 2009: removed all NER functionality (see NERClassifierCombiner), changed code so it
* accepts an arbitrary number of base classifiers, removed dead code.
*
* @author Chris Cox
* @author Mihai Surdeanu
*/
public class ClassifierCombiner extends AbstractSequenceClassifier {
/** A logger for this class */
private static Redwood.RedwoodChannels log = Redwood.channels(ClassifierCombiner.class);
private static final boolean DEBUG = false;
private List> baseClassifiers;
/**
* NORMAL means that if one classifier uses PERSON, later classifiers can't also add PERSON, for example.
* HIGH_RECALL allows later models do set PERSON as long as it doesn't clobber existing annotations.
*/
enum CombinationMode {
NORMAL, HIGH_RECALL
}
static final CombinationMode DEFAULT_COMBINATION_MODE = CombinationMode.NORMAL;
static final String COMBINATION_MODE_PROPERTY = "ner.combinationMode";
final CombinationMode combinationMode;
// keep track of properties used to initialize
public Properties initProps;
// keep track of paths used to load CRFs
private List initLoadPaths = new ArrayList<>();
/**
* @param p Properties File that specifies loadClassifier
* and loadAuxClassifier properties or, alternatively, loadClassifier[1-10] properties.
* @throws FileNotFoundException If classifier files not found
*/
public ClassifierCombiner(Properties p) throws IOException {
super(p);
this.combinationMode = extractCombinationModeSafe(p);
String loadPath1, loadPath2;
List paths = new ArrayList<>();
//
// preferred configuration: specify up to 10 base classifiers using loadClassifier1 to loadClassifier10 properties
//
if((loadPath1 = p.getProperty("loadClassifier1")) != null && (loadPath2 = p.getProperty("loadClassifier2")) != null) {
paths.add(loadPath1);
paths.add(loadPath2);
for(int i = 3; i <= 10; i ++){
String path;
if ((path = p.getProperty("loadClassifier" + i)) != null) {
paths.add(path);
}
}
loadClassifiers(p, paths);
}
//
// second accepted setup (backward compatible): two classifier given in loadClassifier and loadAuxClassifier
//
else if((loadPath1 = p.getProperty("loadClassifier")) != null && (loadPath2 = p.getProperty("loadAuxClassifier")) != null){
paths.add(loadPath1);
paths.add(loadPath2);
loadClassifiers(p, paths);
}
//
// fall back strategy: use the two default paths on NLP machines
//
else {
paths.add(DefaultPaths.DEFAULT_NER_THREECLASS_MODEL);
paths.add(DefaultPaths.DEFAULT_NER_MUC_MODEL);
loadClassifiers(p, paths);
}
this.initLoadPaths = new ArrayList<>(paths);
this.initProps = p;
}
/** Loads a series of base classifiers from the paths specified using the
* Properties specified.
*
* @param props Properties for the classifier to use (encodings, output format, etc.)
* @param combinationMode How to handle multiple classifiers specifying the same entity type
* @param loadPaths Paths to the base classifiers
* @throws IOException If IO errors in loading classifier files
*/
public ClassifierCombiner(Properties props, CombinationMode combinationMode, String... loadPaths) throws IOException {
super(props);
this.combinationMode = combinationMode;
List paths = new ArrayList<>(Arrays.asList(loadPaths));
loadClassifiers(props, paths);
this.initLoadPaths = new ArrayList<>(paths);
this.initProps = props;
}
/** Loads a series of base classifiers from the paths specified using the
* Properties specified.
*
* @param combinationMode How to handle multiple classifiers specifying the same entity type
* @param loadPaths Paths to the base classifiers
* @throws IOException If IO errors in loading classifier files
*/
public ClassifierCombiner(CombinationMode combinationMode, String... loadPaths) throws IOException {
this(new Properties(), combinationMode, loadPaths);
}
/** Loads a series of base classifiers from the paths specified.
*
* @param loadPaths Paths to the base classifiers
* @throws FileNotFoundException If classifier files not found
*/
public ClassifierCombiner(String... loadPaths) throws IOException {
this(DEFAULT_COMBINATION_MODE, loadPaths);
}
/** Combines a series of base classifiers.
*
* @param classifiers The base classifiers
*/
@SafeVarargs
public ClassifierCombiner(AbstractSequenceClassifier... classifiers) {
super(new Properties());
this.combinationMode = DEFAULT_COMBINATION_MODE;
baseClassifiers = new ArrayList<>(Arrays.asList(classifiers));
flags.backgroundSymbol = baseClassifiers.get(0).flags.backgroundSymbol;
this.initProps = new Properties();
}
// constructor for building a ClassifierCombiner from an ObjectInputStream
public ClassifierCombiner(ObjectInputStream ois, Properties props) throws IOException, ClassNotFoundException, ClassCastException {
// read the initial Properties out of the ObjectInputStream so you can properly start the AbstractSequenceClassifier
// note now we load in props from command line and overwrite any that are given for command line
super(PropertiesUtils.overWriteProperties((Properties) ois.readObject(),props));
// read another copy of initProps that I have helpfully included
// TODO: probably set initProps in AbstractSequenceClassifier to avoid this writing twice thing, its hacky
this.initProps = PropertiesUtils.overWriteProperties((Properties) ois.readObject(),props);
// read the initLoadPaths
this.initLoadPaths = (ArrayList) ois.readObject();
// read the combinationMode from the serialized version
String cm = (String) ois.readObject();
// see if there is a commandline override for the combinationMode, else set newCM to the serialized version
CombinationMode newCM;
if (props.getProperty("ner.combinationMode") != null) {
// there is a possible commandline override, have to see if its valid
try {
// see if the commandline has a proper value
newCM = CombinationMode.valueOf(props.getProperty("ner.combinationMode"));
} catch (IllegalArgumentException e) {
// the commandline override did not have a proper value, so just use the serialized version
newCM = CombinationMode.valueOf(cm);
}
} else {
// there was no commandline override given, so just use the serialized version
newCM = CombinationMode.valueOf(cm);
}
this.combinationMode = newCM;
// read in the base classifiers
Integer numClassifiers = ois.readInt();
// set up the list of base classifiers
this.baseClassifiers = new ArrayList<>();
int i = 0;
while (i < numClassifiers) {
try {
log.info("loading CRF...");
CRFClassifier newCRF = ErasureUtils.uncheckedCast(CRFClassifier.getClassifier(ois, props));
baseClassifiers.add(newCRF);
i++;
} catch (Exception e) {
try {
log.info("loading CMM...");
CMMClassifier newCMM = ErasureUtils.uncheckedCast(CMMClassifier.getClassifier(ois, props));
baseClassifiers.add(newCMM);
i++;
} catch (Exception ex) {
ex.printStackTrace();
throw new IOException("Couldn't load classifier!");
}
}
}
}
/**
* Either finds COMBINATION_MODE_PROPERTY or returns a default value
*/
public static CombinationMode extractCombinationMode(Properties p) {
String mode = p.getProperty(COMBINATION_MODE_PROPERTY);
if (mode == null) {
return DEFAULT_COMBINATION_MODE;
} else {
return CombinationMode.valueOf(mode.toUpperCase());
}
}
/**
* Either finds COMBINATION_MODE_PROPERTY or returns a default
* value. If the value is not a legal value, a warning is printed.
*/
public static CombinationMode extractCombinationModeSafe(Properties p) {
try {
return extractCombinationMode(p);
} catch (IllegalArgumentException e) {
log.info("Illegal value of " + COMBINATION_MODE_PROPERTY + ": " + p.getProperty(COMBINATION_MODE_PROPERTY));
log.info(" Legal values:");
for (CombinationMode mode : CombinationMode.values()) {
log.info(" " + mode);
}
log.info();
return CombinationMode.NORMAL;
}
}
private void loadClassifiers(Properties props, List paths) throws IOException {
baseClassifiers = new ArrayList<>();
for(String path: paths){
AbstractSequenceClassifier cls = loadClassifierFromPath(props, path);
baseClassifiers.add(cls);
if(DEBUG){
System.err.printf("Successfully loaded classifier #%d from %s.%n", baseClassifiers.size(), path);
}
}
if (baseClassifiers.size() > 0) {
flags.backgroundSymbol = baseClassifiers.get(0).flags.backgroundSymbol;
}
}
public static AbstractSequenceClassifier loadClassifierFromPath(Properties props, String path)
throws IOException {
//try loading as a CRFClassifier
try {
return ErasureUtils.uncheckedCast(CRFClassifier.getClassifier(path, props));
} catch (Exception e) {
e.printStackTrace();
}
//try loading as a CMMClassifier
try {
return ErasureUtils.uncheckedCast(CMMClassifier.getClassifier(path));
} catch (Exception e) {
//fail
//log.info("Couldn't load classifier from path :"+path);
throw new IOException("Couldn't load classifier from " + path, e);
}
}
@Override
public Set labels() {
Set labs = Generics.newHashSet();
for(AbstractSequenceClassifier cls: baseClassifiers)
labs.addAll(cls.labels());
return labs;
}
/**
* Reads the Answer annotations in the given labellings (produced by the base models)
* and combines them using a priority ordering, i.e., for a given baseDocument all
* labellings seen before in the baseDocuments list have higher priority.
* Writes the answer to AnswerAnnotation in the labeling at position 0
* (considered to be the main document).
*
* @param baseDocuments Results of all base AbstractSequenceClassifier models
* @return A List of IN with the combined annotations. (This is an
* updating of baseDocuments.get(0), not a new List.)
*/
private List mergeDocuments(List> baseDocuments){
// we should only get here if there is something to merge
assert(! baseClassifiers.isEmpty() && ! baseDocuments.isEmpty());
// all base outputs MUST have the same length (we generated them internally!)
for(int i = 1; i < baseDocuments.size(); i ++)
assert(baseDocuments.get(0).size() == baseDocuments.get(i).size());
String background = baseClassifiers.get(0).flags.backgroundSymbol;
// baseLabels.get(i) points to the labels assigned by baseClassifiers.get(i)
List> baseLabels = new ArrayList<>();
Set seenLabels = Generics.newHashSet();
for (AbstractSequenceClassifier baseClassifier : baseClassifiers) {
Set labs = baseClassifier.labels();
if (combinationMode != CombinationMode.HIGH_RECALL) {
labs.removeAll(seenLabels);
} else {
labs.remove(baseClassifier.flags.backgroundSymbol);
labs.remove(background);
}
seenLabels.addAll(labs);
baseLabels.add(labs);
}
if (DEBUG) {
for(int i = 0; i < baseLabels.size(); i ++)
log.info("mergeDocuments: Using classifier #" + i + " for " + baseLabels.get(i));
log.info("mergeDocuments: Background symbol is " + background);
log.info("Base model outputs:");
for( int i = 0; i < baseDocuments.size(); i ++){
System.err.printf("Output of model #%d:", i);
for (IN l : baseDocuments.get(i)) {
log.info(' ');
log.info(l.get(CoreAnnotations.AnswerAnnotation.class));
}
log.info();
}
}
// incrementally merge each additional model with the main model (i.e., baseDocuments.get(0))
// this keeps adding labels from the additional models to mainDocument
// hence, when all is done, mainDocument contains the labels of all base models
List mainDocument = baseDocuments.get(0);
for (int i = 1; i < baseDocuments.size(); i ++) {
mergeTwoDocuments(mainDocument, baseDocuments.get(i), baseLabels.get(i), background);
}
if (DEBUG) {
log.info("Output of combined model:");
for (IN l: mainDocument) {
log.info(' ');
log.info(l.get(CoreAnnotations.AnswerAnnotation.class));
}
log.info();
log.info();
}
return mainDocument;
}
/** This merges in labels from the auxDocument into the mainDocument when
* tokens have one of the labels in auxLabels, and the subsequence
* labeled with this auxLabel does not conflict with any non-background
* labelling in the mainDocument.
*/
static void mergeTwoDocuments(List mainDocument, List auxDocument, Set auxLabels, String background) {
boolean insideAuxTag = false;
boolean auxTagValid = true;
String prevAnswer = background;
Collection constituents = new ArrayList<>();
Iterator auxIterator = auxDocument.listIterator();
for (INN wMain : mainDocument) {
String mainAnswer = wMain.get(CoreAnnotations.AnswerAnnotation.class);
INN wAux = auxIterator.next();
String auxAnswer = wAux.get(CoreAnnotations.AnswerAnnotation.class);
boolean insideMainTag = !mainAnswer.equals(background);
/* if the auxiliary classifier gave it one of the labels unique to
auxClassifier, we might set the mainLabel to that. */
if (auxLabels.contains(auxAnswer)) {
if ( ! prevAnswer.equals(auxAnswer) && ! prevAnswer.equals(background)) {
if (auxTagValid){
for (INN wi : constituents) {
wi.set(CoreAnnotations.AnswerAnnotation.class, prevAnswer);
}
}
auxTagValid = true;
constituents = new ArrayList<>();
}
insideAuxTag = true;
if (insideMainTag) { auxTagValid = false; }
prevAnswer = auxAnswer;
constituents.add(wMain);
} else {
if (insideAuxTag) {
if (auxTagValid){
for (INN wi : constituents) {
wi.set(CoreAnnotations.AnswerAnnotation.class, prevAnswer);
}
}
constituents = new ArrayList<>();
}
insideAuxTag=false;
auxTagValid = true;
prevAnswer = background;
}
}
// deal with a sequence final auxLabel
if (auxTagValid){
for (INN wi : constituents) {
wi.set(CoreAnnotations.AnswerAnnotation.class, prevAnswer);
}
}
}
/**
* Generates the AnswerAnnotation labels of the combined model for the given
* tokens, storing them in place in the tokens.
*
* @param tokens A List of IN
* @return The passed in parameters, which will have the AnswerAnnotation field added/overwritten
*/
@Override
public List classify(List tokens) {
if (baseClassifiers.isEmpty()) {
return tokens;
}
List> baseOutputs = new ArrayList<>();
// the first base model works in place, modifying the original tokens
List output = baseClassifiers.get(0).classifySentence(tokens);
// classify(List) is supposed to work in place, so add AnswerAnnotation to tokens!
for (int i = 0, sz = output.size(); i < sz; i++) {
tokens.get(i).set(CoreAnnotations.AnswerAnnotation.class, output.get(i).get(CoreAnnotations.AnswerAnnotation.class));
}
baseOutputs.add(tokens);
for (int i = 1, sz = baseClassifiers.size(); i < sz; i ++) {
//List copy = deepCopy(tokens);
// no need for deep copy: classifySentence creates a copy of the input anyway
// List copy = tokens;
output = baseClassifiers.get(i).classifySentence(tokens);
baseOutputs.add(output);
}
assert(baseOutputs.size() == baseClassifiers.size());
List finalAnswer = mergeDocuments(baseOutputs);
return finalAnswer;
}
@SuppressWarnings("unchecked")
@Override
public void train(Collection> docs,
DocumentReaderAndWriter readerAndWriter) {
throw new UnsupportedOperationException();
}
// write a ClassifierCombiner to disk, this is based on CRFClassifier code
@Override
public void serializeClassifier(String serializePath) {
log.info("Serializing classifier to " + serializePath + "...");
ObjectOutputStream oos = null;
try {
oos = IOUtils.writeStreamFromString(serializePath);
serializeClassifier(oos);
log.info("done.");
} catch (Exception e) {
throw new RuntimeIOException("Failed to save classifier", e);
} finally {
IOUtils.closeIgnoringExceptions(oos);
}
}
// method for writing a ClassifierCombiner to an ObjectOutputStream
public void serializeClassifier(ObjectOutputStream oos) {
try {
// record the properties used to initialize
oos.writeObject(initProps);
// this is a bit of a hack, but have to write this twice so you can get it again
// after you initialize AbstractSequenceClassifier
// basically when this is read from the ObjectInputStream, I read it once to call
// super(props) and then I read it again so I can set this.initProps
// TODO: probably should have AbstractSequenceClassifier store initProps to get rid of this double writing
oos.writeObject(initProps);
// record the initial loadPaths
oos.writeObject(initLoadPaths);
// record the combinationMode
String combinationModeString = combinationMode.name();
oos.writeObject(combinationModeString);
// get the number of classifiers to write to disk
Integer numClassifiers = baseClassifiers.size();
oos.writeInt(numClassifiers);
// go through baseClassifiers and write each one to disk with CRFClassifier's serialize method
log.info("");
for (AbstractSequenceClassifier asc : baseClassifiers) {
//CRFClassifier crfc = (CRFClassifier) asc;
//log.info("Serializing a base classifier...");
asc.serializeClassifier(oos);
}
} catch (IOException e) {
throw new RuntimeIOException(e);
}
}
@Override
public void loadClassifier(ObjectInputStream in, Properties props) throws IOException, ClassCastException, ClassNotFoundException {
throw new UnsupportedOperationException();
}
@Override
public List classifyWithGlobalInformation(List tokenSeq, CoreMap doc, CoreMap sent) {
return classify(tokenSeq);
}
// static method for getting a ClassifierCombiner from a string path
public static ClassifierCombiner getClassifier(String loadPath, Properties props) throws IOException,
ClassNotFoundException, ClassCastException {
ObjectInputStream ois = IOUtils.readStreamFromString(loadPath);
ClassifierCombiner returnCC = getClassifier(ois, props);
IOUtils.closeIgnoringExceptions(ois);
return returnCC;
}
// static method for getting a ClassifierCombiner from ObjectInputStream
public static ClassifierCombiner getClassifier(ObjectInputStream ois, Properties props) throws IOException,
ClassCastException, ClassNotFoundException {
return new ClassifierCombiner(ois, props);
}
// run a particular CRF of this ClassifierCombiner on a testFile
// user can say -crfToExamine 0 to get 1st element or -crfToExamine /edu/stanford/models/muc7.crf.ser.gz
// this does not currently support drill down on CMM's
public static void examineCRF(ClassifierCombiner cc, String crfNameOrIndex, SeqClassifierFlags flags,
String testFile, String testFiles,
DocumentReaderAndWriter readerAndWriter) throws Exception {
CRFClassifier crf;
// potential index into baseClassifiers
int ci;
// set ci with the following rules
// 1. first see if ci is an index into baseClassifiers
// 2. if its not an integer or wrong size, see if its a file name of a loadPath
try {
ci = Integer.parseInt(crfNameOrIndex);
if (ci < 0 || ci >= cc.baseClassifiers.size()) {
// ci is not an int corresponding to an element in baseClassifiers, see if name of a crf loadPath
ci = cc.initLoadPaths.indexOf(crfNameOrIndex);
}
} catch (NumberFormatException e) {
// cannot interpret crfNameOrIndex as an integer, see if name of a crf loadPath
ci = cc.initLoadPaths.indexOf(crfNameOrIndex);
}
// if ci corresponds to an index in baseClassifiers, get the crf at that index, otherwise set crf to null
if (ci >= 0 && ci < cc.baseClassifiers.size()) {
// TODO: this will break if baseClassifiers contains something that is not a CRF
crf = (CRFClassifier) cc.baseClassifiers.get(ci);
} else {
crf = null;
}
// if you can get a specific crf, generate the appropriate report, if null do nothing
if (crf != null) {
// if there is a crf and testFile was set , do the crf stuff for a single testFile
if (testFile != null) {
if (flags.searchGraphPrefix != null) {
crf.classifyAndWriteViterbiSearchGraph(testFile, flags.searchGraphPrefix, crf.makeReaderAndWriter());
} else if (flags.printFirstOrderProbs) {
crf.printFirstOrderProbs(testFile, readerAndWriter);
} else if (flags.printFactorTable) {
crf.printFactorTable(testFile, readerAndWriter);
} else if (flags.printProbs) {
crf.printProbs(testFile, readerAndWriter);
} else if (flags.useKBest) {
// TO DO: handle if user doesn't provide kBest
int k = flags.kBest;
crf.classifyAndWriteAnswersKBest(testFile, k, readerAndWriter);
} else if (flags.printLabelValue) {
crf.printLabelInformation(testFile, readerAndWriter);
} else {
// no crf test flag provided
log.info("Warning: no crf test flag was provided, running classify and write answers");
crf.classifyAndWriteAnswers(testFile,readerAndWriter,true);
}
} else if (testFiles != null) {
// if there is a crf and testFiles was set , do the crf stuff for testFiles
// if testFile was set as well, testFile overrides
List files = Arrays.asList(testFiles.split(",")).stream().map(File::new).collect(Collectors.toList());
if (flags.printProbs) {
// there is a crf and printProbs
crf.printProbs(files, crf.defaultReaderAndWriter());
} else {
log.info("Warning: no crf test flag was provided, running classify files and write answers");
crf.classifyFilesAndWriteAnswers(files, crf.defaultReaderAndWriter(), true);
}
}
}
}
// show some info about a ClassifierCombiner
public static void showCCInfo(ClassifierCombiner cc) {
log.info("");
log.info("classifiers used:");
log.info("");
if (cc.initLoadPaths.size() == cc.baseClassifiers.size()) {
for (int i = 0 ; i < cc.initLoadPaths.size() ; i++) {
log.info("baseClassifiers index "+i+" : "+cc.initLoadPaths.get(i));
}
} else {
for (int i = 0 ; i < cc.initLoadPaths.size() ; i++) {
log.info("baseClassifiers index "+i);
}
}
log.info("");
log.info("combinationMode: "+cc.combinationMode);
log.info("");
}
/**
* Some basic testing of the ClassifierCombiner.
*
* @param args Command-line arguments as properties: -loadClassifier1 serializedFile -loadClassifier2 serializedFile
* @throws Exception If IO or serialization error loading classifiers
*/
public static void main(String[] args) throws Exception {
Properties props = StringUtils.argsToProperties(args);
ClassifierCombiner ec = new ClassifierCombiner(props);
log.info(ec.classifyToString("Marketing : Sony Hopes to Win Much Bigger Market For Wide Range of Small-Video Products --- By Andrew B. Cohen Staff Reporter of The Wall Street Journal"));
}
}