// AbstractSequenceClassifier -- a framework for probabilistic sequence models.
// Copyright (c) 2002-2008 The Board of Trustees of
// The Leland Stanford Junior University. All Rights Reserved.
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
//
// For more information, bug reports, fixes, contact:
// Christopher Manning
// Dept of Computer Science, Gates 1A
// Stanford CA 94305-9010
// USA
// Support/Questions: java-nlp-user@lists.stanford.edu
// Licensing: java-nlp-support@lists.stanford.edu
// http://nlp.stanford.edu/downloads/crf-classifier.shtml
package edu.stanford.nlp.ie;
import edu.stanford.nlp.fsm.DFSA;
import edu.stanford.nlp.io.IOUtils;
import edu.stanford.nlp.io.RegExFileFilter;
import edu.stanford.nlp.io.RuntimeIOException;
import edu.stanford.nlp.ling.CoreAnnotation;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.ling.HasWord;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.objectbank.ObjectBank;
import edu.stanford.nlp.objectbank.ResettableReaderIteratorFactory;
import edu.stanford.nlp.process.CoreLabelTokenFactory;
import edu.stanford.nlp.process.CoreTokenFactory;
import edu.stanford.nlp.sequences.*;
import edu.stanford.nlp.stats.ClassicCounter;
import edu.stanford.nlp.stats.Counter;
import edu.stanford.nlp.stats.Counters;
import edu.stanford.nlp.stats.Sampler;
import edu.stanford.nlp.stats.TwoDimensionalCounter;
import edu.stanford.nlp.util.*;
import edu.stanford.nlp.util.concurrent.*;
import edu.stanford.nlp.util.logging.Redwood;
import java.io.*;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.function.Function;
import java.util.regex.Pattern;
import java.util.zip.GZIPInputStream;
/**
* This class provides common functionality for (probabilistic) sequence models.
* It is a superclass of our CMM and CRF sequence classifiers, and is even used
* in the (deterministic) NumberSequenceClassifier. See implementing classes for
* more information.
* <p>
* An implementation must implement these 5 abstract methods: <br>
* {@code List<IN> classify(List<IN> document); } <br>
* {@code List<IN> classifyWithGlobalInformation(List<IN> tokenSequence, final CoreMap document, final CoreMap sentence); } <br>
* {@code void train(Collection<List<IN>> docs, DocumentReaderAndWriter<IN> readerAndWriter); } <br>
* {@code void serializeClassifier(String serializePath); } <br>
* {@code void loadClassifier(ObjectInputStream in, Properties props) throws IOException,
* ClassCastException, ClassNotFoundException; } <br>
* but a runtime (or rule-based) implementation can usefully implement just the first,
* and throw UnsupportedOperationException for the rest. Additionally, this method throws
* UnsupportedOperationException by default, but is implemented for some classifiers: <br>
* {@code Pair<Counter<Integer>, TwoDimensionalCounter<Integer,String>> printProbsDocument(List<CoreLabel> document); } <br>
*
* @author Jenny Finkel
* @author Dan Klein
* @author Christopher Manning
* @author Dan Cer
* @author sonalg (made the class generic)
*/
public abstract class AbstractSequenceClassifier<IN extends CoreMap> implements Function<String, String> {
/** A logger for this class */
private static Redwood.RedwoodChannels log = Redwood.channels(AbstractSequenceClassifier.class);
public SeqClassifierFlags flags;
public Index<String> classIndex; // = null;
// Thang Sep13: multiple feature factories (NERFeatureFactory, EmbeddingFeatureFactory)
public List<FeatureFactory<IN>> featureFactories;
protected IN pad;
private CoreTokenFactory<IN> tokenFactory;
public int windowSize;
/** Different threads can add or query knownLCWords at the same time,
* so we need a concurrent data structure. Created in reinit().
*/
protected MaxSizeConcurrentHashSet<String> knownLCWords; // = null;
private DocumentReaderAndWriter<IN> defaultReaderAndWriter;
/** This is the DocumentReaderAndWriter used for reading training and testing files.
* It is the DocumentReaderAndWriter specified by the readerAndWriter flag and
* defaults to {@code edu.stanford.nlp.sequences.ColumnDocumentReaderAndWriter} which
* is suitable for reading CoNLL-style TSV files.
*
* @return The default DocumentReaderAndWriter
*/
public DocumentReaderAndWriter<IN> defaultReaderAndWriter() {
return defaultReaderAndWriter;
}
private DocumentReaderAndWriter<IN> plainTextReaderAndWriter;
/** This is the default DocumentReaderAndWriter used for reading text files for runtime
* classification. It is the DocumentReaderAndWriter specified by the plainTextDocumentReaderAndWriter
* flag and defaults to {@code edu.stanford.nlp.sequences.PlainTextDocumentReaderAndWriter} which
* is suitable for reading plain text files, in languages with a Tokenizer available.
*
* @return The default plain text DocumentReaderAndWriter
*/
public DocumentReaderAndWriter<IN> plainTextReaderAndWriter() {
return plainTextReaderAndWriter;
}
/**
* Construct a SeqClassifierFlags object based on the passed in properties,
* and then call the other constructor.
*
* @param props See SeqClassifierFlags for known properties.
*/
public AbstractSequenceClassifier(Properties props) {
this(new SeqClassifierFlags(props));
}
/**
* Initialize the featureFactory and other variables based on the passed in
* flags.
*
* @param flags A specification of the AbstractSequenceClassifier to construct.
*/
public AbstractSequenceClassifier(SeqClassifierFlags flags) {
this.flags = flags;
// Thang Sep13: allow for multiple feature factories.
this.featureFactories = Generics.newArrayList();
if (flags.featureFactory != null) {
FeatureFactory<IN> factory = new MetaClass(flags.featureFactory).createInstance(flags.featureFactoryArgs); // for compatibility
featureFactories.add(factory);
}
if (flags.featureFactories != null) {
for (int i = 0; i < flags.featureFactories.length; i++) {
FeatureFactory<IN> indFeatureFactory = new MetaClass(flags.featureFactories[i]).
createInstance(flags.featureFactoriesArgs.get(i));
this.featureFactories.add(indFeatureFactory);
}
}
if (flags.tokenFactory == null) {
tokenFactory = (CoreTokenFactory<IN>) new CoreLabelTokenFactory();
} else {
this.tokenFactory = new MetaClass(flags.tokenFactory).createInstance(flags.tokenFactoryArgs);
}
pad = tokenFactory.makeToken();
windowSize = flags.maxLeft + 1;
reinit();
}
/**
* This method should be called after there have been changes to the flags
* (SeqClassifierFlags) variable, such as after deserializing a classifier. It
* is called inside the loadClassifier methods. It assumes that the flags
* variable and the pad variable exist, but reinitializes things like the pad
* variable, featureFactory and readerAndWriter based on the flags.
* <p>
* <i>Implementation note:</i> At the moment this variable doesn't set
* windowSize or featureFactory, since they are being serialized separately in
* the file, but we should probably stop serializing them and just
* reinitialize them from the flags?
*/
protected final void reinit() {
pad.set(CoreAnnotations.AnswerAnnotation.class, flags.backgroundSymbol);
pad.set(CoreAnnotations.GoldAnswerAnnotation.class, flags.backgroundSymbol);
for (FeatureFactory featureFactory : featureFactories) {
featureFactory.init(flags);
}
defaultReaderAndWriter = makeReaderAndWriter();
if (flags.readerAndWriter != null &&
flags.readerAndWriter.equals(flags.plainTextDocumentReaderAndWriter)) {
plainTextReaderAndWriter = defaultReaderAndWriter;
} else {
plainTextReaderAndWriter = makePlainTextReaderAndWriter();
}
if (knownLCWords == null || knownLCWords.isEmpty()) {
// reinit limits max (additional) size. We temporarily loosen this during training
knownLCWords = new MaxSizeConcurrentHashSet<>(flags.maxAdditionalKnownLCWords);
} else {
knownLCWords.setMaxSize(knownLCWords.size() + flags.maxAdditionalKnownLCWords);
}
}
public Set<String> getKnownLCWords() {
return knownLCWords;
}
/**
* Makes a DocumentReaderAndWriter based on the flags the CRFClassifier
* was constructed with. Will create an instance of the class specified in
* the property flags.readerAndWriter and
* initialize it with the CRFClassifier's flags.
*
* @return The appropriate ReaderAndWriter for training/testing this classifier
*/
public DocumentReaderAndWriter<IN> makeReaderAndWriter() {
DocumentReaderAndWriter<IN> readerAndWriter;
try {
readerAndWriter = ReflectionLoading.loadByReflection(flags.readerAndWriter);
} catch (Exception e) {
throw new RuntimeException(String.format("Error loading flags.readerAndWriter: '%s'", flags.readerAndWriter), e);
}
readerAndWriter.init(flags);
return readerAndWriter;
}
/**
* Makes a DocumentReaderAndWriter based on
* flags.plainTextReaderAndWriter. Useful for reading in
* untokenized text documents or reading plain text from the command
* line. An example of a way to use this would be to return a
* edu.stanford.nlp.wordseg.Sighan2005DocumentReaderAndWriter for
* the Chinese Segmenter.
*/
public DocumentReaderAndWriter<IN> makePlainTextReaderAndWriter() {
String readerClassName = flags.plainTextDocumentReaderAndWriter;
// We set this default here if needed because there may be models
// which don't have the reader flag set
if (readerClassName == null) {
readerClassName = SeqClassifierFlags.DEFAULT_PLAIN_TEXT_READER;
}
DocumentReaderAndWriter<IN> readerAndWriter;
try {
readerAndWriter = ReflectionLoading.loadByReflection(readerClassName);
} catch (Exception e) {
throw new RuntimeException(String.format("Error loading flags.plainTextDocumentReaderAndWriter: '%s'", flags.plainTextDocumentReaderAndWriter), e);
}
readerAndWriter.init(flags);
return readerAndWriter;
}
/**
* Returns the background class for the classifier.
*
* @return The background class name
*/
public String backgroundSymbol() {
return flags.backgroundSymbol;
}
public Set<String> labels() {
return Generics.newHashSet(classIndex.objectsList());
}
/**
* Classify a List of IN. This method returns a new list of tokens, not
* the list of tokens passed in, and runs the new tokens through
* ObjectBankWrapper. (Both these behaviors are different from that of the
* classify(List) method.
*
* @param tokenSequence The List of IN to be classified.
* @return The classified List of IN, where the classifier output for
* each token is stored in its
* {@link edu.stanford.nlp.ling.CoreAnnotations.AnswerAnnotation}
* field.
*/
public List<IN> classifySentence(List<? extends HasWord> tokenSequence) {
List<IN> document = preprocessTokens(tokenSequence);
classify(document);
return document;
}
private List<IN> preprocessTokens(List<? extends HasWord> tokenSequence) {
// log.info("knownLCWords.size is " + knownLCWords.size() + "; knownLCWords.maxSize is " + knownLCWords.getMaxSize() +
// ", prior to NER for " + getClass().toString());
List<IN> document = new ArrayList<>();
int i = 0;
for (HasWord word : tokenSequence) {
IN wi; // initialized below
if (word instanceof CoreMap) {
// copy all annotations! some are required later in
// AbstractSequenceClassifier.classifyWithInlineXML
// wi = (IN) new ArrayCoreMap((ArrayCoreMap) word);
wi = tokenFactory.makeToken((IN) word);
} else {
wi = tokenFactory.makeToken();
wi.set(CoreAnnotations.TextAnnotation.class, word.word());
// wi.setWord(word.word());
}
wi.set(CoreAnnotations.PositionAnnotation.class, Integer.toString(i));
wi.set(CoreAnnotations.AnswerAnnotation.class, backgroundSymbol());
document.add(wi);
i++;
}
// TODO get rid of ObjectBankWrapper
ObjectBankWrapper<IN> wrapper = new ObjectBankWrapper<>(flags, null, knownLCWords);
wrapper.processDocument(document);
// log.info("Size of knownLCWords is " + knownLCWords.size() + ", after NER for " + getClass().toString());
return document;
}
/**
* Classify a List of IN using whatever additional information is passed in globalInfo.
* Used by SUTime (NumberSequenceClassifier), which requires the doc date to resolve relative dates.
*
* @param tokenSequence The List of IN to be classified.
* @return The classified List of IN, where the classifier output for
* each token is stored in its "answer" field.
*/
public List<IN> classifySentenceWithGlobalInformation(List<? extends HasWord> tokenSequence, final CoreMap doc, final CoreMap sentence) {
List<IN> document = preprocessTokens(tokenSequence);
classifyWithGlobalInformation(document, doc, sentence);
return document;
}
public SequenceModel getSequenceModel(List<IN> doc) {
throw new UnsupportedOperationException();
}
public Sampler<List<IN>> getSampler(final List<IN> input) {
return new Sampler<List<IN>>() {
SequenceModel model = getSequenceModel(input);
SequenceSampler sampler = new SequenceSampler();
@Override
public List<IN> drawSample() {
int[] sampleArray = sampler.bestSequence(model);
List<IN> sample = new ArrayList<>();
int i = 0;
for (IN word : input) {
IN newWord = tokenFactory.makeToken(word);
newWord.set(CoreAnnotations.AnswerAnnotation.class, classIndex.get(sampleArray[i++]));
sample.add(newWord);
}
return sample;
}
};
}
/** Takes a list of tokens and provides the K best sequence labelings of these tokens with their scores.
*
* @param doc The List of tokens
* @param answerField The key for each token into which the label for the token will be written
* @param k The number of best sequence labelings to generate
* @return A Counter where each key is a List of tokens with labels written in the answerField and its value
* is the score (conditional probability) assigned to this labeling of the sequence.
*/
public Counter<List<IN>> classifyKBest(List<IN> doc, Class<? extends CoreAnnotation<String>> answerField, int k) {
if (doc.isEmpty()) {
return new ClassicCounter<>();
}
// TODO get rid of ObjectBankWrapper
// i'm sorry that this is so hideous - JRF
ObjectBankWrapper<IN> obw = new ObjectBankWrapper<>(flags, null, knownLCWords);
doc = obw.processDocument(doc);
SequenceModel model = getSequenceModel(doc);
KBestSequenceFinder tagInference = new KBestSequenceFinder();
Counter<int[]> bestSequences = tagInference.kBestSequences(model, k);
Counter<List<IN>> kBest = new ClassicCounter<>();
for (int[] seq : bestSequences.keySet()) {
List<IN> kth = new ArrayList<>();
int pos = model.leftWindow();
for (IN fi : doc) {
IN newFL = tokenFactory.makeToken(fi);
String guess = classIndex.get(seq[pos]);
fi.remove(CoreAnnotations.AnswerAnnotation.class); // because fake answers will get
// added during testing
newFL.set(answerField, guess);
pos++;
kth.add(newFL);
}
kBest.setCount(kth, bestSequences.getCount(seq));
}
return kBest;
}
private DFSA<String, Integer> getViterbiSearchGraph(List<IN> doc, Class<? extends CoreAnnotation<String>> answerField) {
if (doc.isEmpty()) {
return new DFSA<>(null);
}
// TODO get rid of ObjectBankWrapper
ObjectBankWrapper<IN> obw = new ObjectBankWrapper<>(flags, null, knownLCWords);
doc = obw.processDocument(doc);
SequenceModel model = getSequenceModel(doc);
return ViterbiSearchGraphBuilder.getGraph(model, classIndex);
}
/**
* Classify the tokens in a String. Each sentence becomes a separate document.
*
* @param str A String with tokens in one or more sentences of text to be
* classified.
* @return {@link List} of classified sentences (each a List of something that
* extends {@link CoreMap}).
*/
public List<List<IN>> classify(String str) {
ObjectBank<List<IN>> documents =
makeObjectBankFromString(str, plainTextReaderAndWriter);
return classifyObjectBank(documents);
}
/**
* Classify the tokens in a String. Each sentence becomes a separate document.
* Doesn't override default readerAndWriter.
*
* @param str A String with tokens in one or more sentences of text to be classified.
* @return {@link List} of classified sentences (each a List of something that
* extends {@link CoreMap}).
*/
public List<List<IN>> classifyRaw(String str,
DocumentReaderAndWriter<IN> readerAndWriter) {
ObjectBank<List<IN>> documents =
makeObjectBankFromString(str, readerAndWriter);
return classifyObjectBank(documents);
}
/**
* Classify the contents of a file.
*
* @param filename Contains the sentence(s) to be classified.
* @return {@link List} of classified List of IN.
*/
public List<List<IN>> classifyFile(String filename) {
ObjectBank<List<IN>> documents =
makeObjectBankFromFile(filename, plainTextReaderAndWriter);
return classifyObjectBank(documents);
}
/**
* Classify the tokens in an ObjectBank.
*
* @param documents The documents in an ObjectBank to classify.
* @return {@link List} of classified sentences (each a List of something that
* extends {@link CoreMap}).
*/
private List<List<IN>> classifyObjectBank(ObjectBank<List<IN>> documents) {
List<List<IN>> result = new ArrayList<>();
for (List<IN> document : documents) {
classify(document);
List<IN> sentence = new ArrayList<>();
for (IN wi : document) {
// TaggedWord word = new TaggedWord(wi.word(), wi.answer());
// sentence.add(word);
sentence.add(wi);
}
result.add(sentence);
}
return result;
}
/**
* Maps a String input to an XML-formatted rendition of applying NER to the
* String. Implements the Function interface. Calls
* classifyWithInlineXML(String) [q.v.].
*/
@Override
public String apply(String in) {
return classifyWithInlineXML(in);
}
/**
* Classify the contents of a {@link String} to one of several String
* representations that shows the classes. Plain text or XML input is expected
* and the {@link PlainTextDocumentReaderAndWriter} is used. The classifier
* will tokenize the text and treat each sentence as a separate document. The
* output can be specified to be in a choice of three formats: slashTags
* (e.g., Bill/PERSON Smith/PERSON died/O ./O), inlineXML (e.g.,
* <PERSON>Bill Smith</PERSON> went to
* <LOCATION>Paris</LOCATION> .), or xml, for stand-off XML (e.g.,
* <wi num="0" entity="PERSON">Sue</wi> <wi num="1"
* entity="O">shouted</wi> ). There is also a binary choice as to
* whether the spacing between tokens of the original is preserved or whether
* the (tagged) tokens are printed with a single space (for inlineXML or
* slashTags) or a single newline (for xml) between each one.
* <p>
* <i>Fine points:</i> The slashTags and xml formats show tokens as
* transformed by any normalization processes inside the tokenizer, while
* inlineXML shows the tokens exactly as they appeared in the source text.
* When a period counts as both part of an abbreviation and as an end of
* sentence marker, it is included twice in the output String for slashTags or
* xml, but only once for inlineXML, where it is not counted as part of the
* abbreviation (or any named entity it is part of). For slashTags with
* preserveSpacing=true, there will be two successive periods such as "Jr.."
* The tokenized (preserveSpacing=false) output will have a space or a newline
* after the last token.
*
* @param sentences
* The String to be classified. It will be tokenized and
* divided into documents according to (heuristically
* determined) sentence boundaries.
* @param outputFormat
* The format to put the output in: one of "slashTags", "xml",
* "inlineXML", "tsv", or "tabbedEntities"
* @param preserveSpacing
* Whether to preserve the input spacing between tokens, which may
* sometimes be none (true) or whether to tokenize the text and print
* it with one space between each token (false)
* @return A {@link String} with annotated with classification information.
*/
public String classifyToString(String sentences, String outputFormat, boolean preserveSpacing) {
PlainTextDocumentReaderAndWriter.OutputStyle outFormat =
PlainTextDocumentReaderAndWriter.OutputStyle.fromShortName(outputFormat);
ObjectBank<List<IN>> documents =
makeObjectBankFromString(sentences, plainTextReaderAndWriter);
StringBuilder sb = new StringBuilder();
for (List<IN> doc : documents) {
List<IN> docOutput = classify(doc);
if (plainTextReaderAndWriter instanceof PlainTextDocumentReaderAndWriter) {
// TODO: implement this particular method and its options in
// the other documentReaderAndWriters
sb.append(((PlainTextDocumentReaderAndWriter<IN>) plainTextReaderAndWriter).getAnswers(docOutput, outFormat, preserveSpacing));
} else {
StringWriter sw = new StringWriter();
PrintWriter pw = new PrintWriter(sw);
plainTextReaderAndWriter.printAnswers(docOutput, pw);
pw.flush();
sb.append(sw);
sb.append('\n');
}
}
return sb.toString();
}
/**
* Classify the contents of a {@link String}. Plain text or XML is expected
* and the {@link PlainTextDocumentReaderAndWriter} is used by default.
* The classifier
* will treat each sentence as a separate document. The output can be
* specified to be in a choice of formats: Output is in inline XML format
* (e.g. <PERSON>Bill Smith</PERSON> went to
* <LOCATION>Paris</LOCATION> .)
*
* @param sentences
* The string to be classified
* @return A {@link String} with annotated with classification information.
*/
public String classifyWithInlineXML(String sentences) {
return classifyToString(sentences, "inlineXML", true);
}
/**
* Classify the contents of a String to a tagged word/class String. Plain text
* or XML input is expected and the {@link PlainTextDocumentReaderAndWriter}
* is used by default.
* Output looks like: My/O name/O is/O Bill/PERSON Smith/PERSON ./O
*
* @param sentences
* The String to be classified
* @return A String annotated with classification information.
*/
public String classifyToString(String sentences) {
return classifyToString(sentences, "slashTags", true);
}
/**
* Classify the contents of a {@link String} to classified character offset
* spans. Plain text or XML input text is expected and the
* {@link PlainTextDocumentReaderAndWriter} is used by default.
* Output is a (possibly
* empty, but not {@code null}) List of Triples. Each Triple is an entity
* name, followed by beginning and ending character offsets in the original
* String. Character offsets can be thought of as fenceposts between the
* characters, or, like certain methods in the Java String class, as character
* positions, numbered starting from 0, with the end index pointing to the
* position AFTER the entity ends. That is, end - start is the length of the
* entity in characters.
* <p>
* <i>Fine points:</i> Token offsets are true wrt the source text, even though
* the tokenizer may internally normalize certain tokens to String
* representations of different lengths (e.g., " becoming `` or ''). When a
* period counts as both part of an abbreviation and as an end of sentence
* marker, and that abbreviation is part of a named entity, the reported
* entity string excludes the period.
*
* @param sentences The string to be classified
* @return A {@link List} of {@link Triple}s, each of which gives an entity
* type and the beginning and ending character offsets.
*/
public List<Triple<String, Integer, Integer>> classifyToCharacterOffsets(String sentences) {
ObjectBank<List<IN>> documents =
makeObjectBankFromString(sentences, plainTextReaderAndWriter);
List<Triple<String, Integer, Integer>> entities = new ArrayList<>();
for (List<IN> doc : documents) {
String prevEntityType = flags.backgroundSymbol;
Triple<String, Integer, Integer> prevEntity = null;
classify(doc);
for (IN fl : doc) {
String guessedAnswer = fl.get(CoreAnnotations.AnswerAnnotation.class);
if (guessedAnswer.equals(flags.backgroundSymbol)) {
if (prevEntity != null) {
entities.add(prevEntity);
prevEntity = null;
}
} else {
if (!guessedAnswer.equals(prevEntityType)) {
if (prevEntity != null) {
entities.add(prevEntity);
}
prevEntity = new Triple<>(guessedAnswer,
fl.get(CoreAnnotations.CharacterOffsetBeginAnnotation.class),
fl.get(CoreAnnotations.CharacterOffsetEndAnnotation.class));
} else {
assert prevEntity != null; // if you read the code carefully, this
// should always be true!
prevEntity.setThird(fl.get(CoreAnnotations.CharacterOffsetEndAnnotation.class));
}
}
prevEntityType = guessedAnswer;
}
// include any entity at end of doc
if (prevEntity != null) {
entities.add(prevEntity);
}
}
return entities;
}
/**
* Have a word segmenter segment a String into a list of words.
* ONLY USE IF YOU LOADED A CHINESE WORD SEGMENTER!!!!!
*
* @param sentence The string to be classified
* @return List of words
*/
// todo: This method is currently [2016] only called in a very small number of places:
// the parser's jsp webapp, ChineseSegmenterAnnotator, and SegDemo.
// Maybe we could eliminate it?
// It also seems like it should be using the plainTextReaderAndWriter, not default?
public List<String> segmentString(String sentence) {
return segmentString(sentence, defaultReaderAndWriter);
}
public List<String> segmentString(String sentence, DocumentReaderAndWriter<IN> readerAndWriter) {
ObjectBank<List<IN>> docs = makeObjectBankFromString(sentence, readerAndWriter);
StringWriter stringWriter = new StringWriter();
PrintWriter stringPrintWriter = new PrintWriter(stringWriter);
for (List<IN> doc : docs) {
classify(doc);
readerAndWriter.printAnswers(doc, stringPrintWriter);
stringPrintWriter.println();
}
stringPrintWriter.close();
String segmented = stringWriter.toString();
return Arrays.asList(segmented.split("\\s"));
}
/*
* Classify the contents of {@link SeqClassifierFlags scf.testFile}. The file
* should be in the format expected based on {@link SeqClassifierFlags
* scf.documentReader}.
*
* @return A {@link List} of {@link List}s of classified something that
* extends {@link CoreMap} where each {@link List} refers to a
* document/sentence.
*/
// public ObjectBank<List<IN>> test() {
// return test(flags.testFile);
// }
/**
* Classify a {@link List} of something that extends{@link CoreMap}.
* The classifications are added in place to the items of the document,
* which is also returned by this method.
*
* <i>Warning:</i> In many circumstances, you should not call this method directly.
* In particular, if you call this method directly, your document will not be preprocessed
* to add things like word distributional similarity class or word shape features that your
* classifier may rely on to work correctly. In such cases, you should call
* {@link #classifySentence(List<? extends HasWord>) classifySentence} instead.
*
* @param document A {@link List} of something that extends {@link CoreMap}.
* @return The same {@link List}, but with the elements annotated with their
* answers (stored under the
* {@link edu.stanford.nlp.ling.CoreAnnotations.AnswerAnnotation}
* key). The answers will be the class labels defined by the CRF
* Classifier. They might be things like entity labels (in BIO
* notation or not) or something like "1" vs. "0" on whether to
* begin a new token here or not (in word segmentation).
*/
// todo [cdm 2017]: Check that our own NER code doesn't call this method wrongly anywhere.
public abstract List<IN> classify(List<IN> document);
/**
* Classify a {@link List} of something that extends {@link CoreMap} using as
* additional information whatever is stored in the document and sentence.
* This is needed for SUTime (NumberSequenceClassifier), which requires
* the document date to resolve relative dates.
*
* @param tokenSequence A {@link List} of something that extends {@link CoreMap}
* @param document
* @param sentence
* @return Classified version of the input tokenSequence
*/
public abstract List<IN> classifyWithGlobalInformation(List<IN> tokenSequence, final CoreMap document, final CoreMap sentence);
/**
* Classification is finished for the document.
* Do any cleanup (if information was stored as part of the document for global classification)
* @param document
*/
public void finalizeClassification(final CoreMap document) {
}
/**
* Train the classifier based on values in flags. It will use the first of
* these variables that is defined: trainFiles (and baseTrainDir),
* trainFileList, trainFile.
*/
public void train() {
if (flags.trainFiles != null) {
train(flags.baseTrainDir, flags.trainFiles, defaultReaderAndWriter);
} else if (flags.trainFileList != null) {
String[] files = flags.trainFileList.split(",");
train(files, defaultReaderAndWriter);
} else {
train(flags.trainFile, defaultReaderAndWriter);
}
}
public void train(String filename) {
train(filename, defaultReaderAndWriter);
}
public void train(String filename,
DocumentReaderAndWriter<IN> readerAndWriter) {
// only for the OCR data does this matter
flags.ocrTrain = true;
train(makeObjectBankFromFile(filename, readerAndWriter), readerAndWriter);
}
public void train(String baseTrainDir, String trainFiles,
DocumentReaderAndWriter<IN> readerAndWriter) {
// only for the OCR data does this matter
flags.ocrTrain = true;
train(makeObjectBankFromFiles(baseTrainDir, trainFiles, readerAndWriter),
readerAndWriter);
}
public void train(String[] trainFileList,
DocumentReaderAndWriter<IN> readerAndWriter) {
// only for the OCR data does this matter
flags.ocrTrain = true;
train(makeObjectBankFromFiles(trainFileList, readerAndWriter),
readerAndWriter);
}
/**
* Trains a classifier from a Collection of sequences.
* Note that the Collection can be (and usually is) an ObjectBank.
*
* @param docs An ObjectBank or a collection of sequences of IN
*/
public void train(Collection<List<IN>> docs) {
train(docs, defaultReaderAndWriter);
}
/**
* Trains a classifier from a Collection of sequences.
* Note that the Collection can be (and usually is) an ObjectBank.
*
* @param docs An ObjectBank or a collection of sequences of IN
* @param readerAndWriter A DocumentReaderAndWriter to use when loading test files
*/
public abstract void train(Collection<List<IN>> docs,
DocumentReaderAndWriter<IN> readerAndWriter);
/**
* Reads a String into an ObjectBank object. NOTE: that the current
* implementation of ReaderIteratorFactory will first try to interpret each
* string as a filename, so this method will yield unwanted results if it
* applies to a string that is at the same time a filename. It prints out a
* warning, at least.
*
* @param string The String which will be the content of the ObjectBank
* @return The ObjectBank
*/
public ObjectBank<List<IN>>
makeObjectBankFromString(String string,
DocumentReaderAndWriter<IN> readerAndWriter) {
if (flags.announceObjectBankEntries) {
log.info("Reading data using " + readerAndWriter.getClass());
if (flags.inputEncoding == null) {
log.info("Getting data from " + string + " (default encoding)");
} else {
log.info("Getting data from " + string + " (" + flags.inputEncoding + " encoding)");
}
}
// return new ObjectBank<List<IN>>(new
// ResettableReaderIteratorFactory(string), readerAndWriter);
// TODO
return new ObjectBankWrapper<>(flags, new ObjectBank<>(new ResettableReaderIteratorFactory(string),
readerAndWriter), knownLCWords);
}
public ObjectBank<List<IN>> makeObjectBankFromFile(String filename) {
return makeObjectBankFromFile(filename, defaultReaderAndWriter);
}
public ObjectBank<List<IN>> makeObjectBankFromFile(String filename,
DocumentReaderAndWriter<IN> readerAndWriter) {
String[] fileAsArray = { filename };
return makeObjectBankFromFiles(fileAsArray, readerAndWriter);
}
public ObjectBank<List<IN>> makeObjectBankFromFiles(String[] trainFileList,
DocumentReaderAndWriter<IN> readerAndWriter) {
// try{
Collection<File> files = new ArrayList<>();
for (String trainFile : trainFileList) {
File f = new File(trainFile);
files.add(f);
}
// System.err.printf("trainFileList contains %d file%s in encoding %s.%n", files.size(), files.size() == 1 ? "": "s", flags.inputEncoding);
// TODO get rid of ObjectBankWrapper
// return new ObjectBank<List<IN>>(new
// ResettableReaderIteratorFactory(files), readerAndWriter);
return new ObjectBankWrapper<>(flags, new ObjectBank<>(new ResettableReaderIteratorFactory(files, flags.inputEncoding),
readerAndWriter), knownLCWords);
// } catch (IOException e) {
// throw new RuntimeException(e);
// }
}
public ObjectBank<List<IN>> makeObjectBankFromFiles(String baseDir, String filePattern,
DocumentReaderAndWriter<IN> readerAndWriter) {
File path = new File(baseDir);
FileFilter filter = new RegExFileFilter(Pattern.compile(filePattern));
File[] origFiles = path.listFiles(filter);
Collection<File> files = new ArrayList<>();
for (File file : origFiles) {
if (file.isFile()) {
if (flags.announceObjectBankEntries) {
log.info("Getting data from " + file + " (" + flags.inputEncoding + " encoding)");
}
files.add(file);
}
}
if (files.isEmpty()) {
throw new RuntimeException("No matching files: " + baseDir + '\t' + filePattern);
}
// return new ObjectBank<List<IN>>(new
// ResettableReaderIteratorFactory(files, flags.inputEncoding),
// readerAndWriter);
// TODO get rid of ObjectBankWrapper
return new ObjectBankWrapper<>(flags, new ObjectBank<>(new ResettableReaderIteratorFactory(files,
flags.inputEncoding), readerAndWriter), knownLCWords);
}
public ObjectBank<List<IN>> makeObjectBankFromFiles(Collection<File> files,
DocumentReaderAndWriter<IN> readerAndWriter) {
if (files.isEmpty()) {
throw new RuntimeException("Attempt to make ObjectBank with empty file list");
}
// return new ObjectBank<List<IN>>(new
// ResettableReaderIteratorFactory(files, flags.inputEncoding),
// readerAndWriter);
// TODO get rid of ObjectBankWrapper
return new ObjectBankWrapper<>(flags, new ObjectBank<>(new ResettableReaderIteratorFactory(files,
flags.inputEncoding), readerAndWriter), knownLCWords);
}
/**
* Set up an ObjectBank that will allow one to iterate over a collection of
* documents obtained from the passed in Reader. Each document will be
* represented as a list of IN. If the ObjectBank iterator() is called until
* hasNext() returns false, then the Reader will be read till end of file, but
* no reading is done at the time of this call. Reading is done using the
* reading method specified in {@code flags.documentReader}, and for some
* reader choices, the column mapping given in {@code flags.map}.
*
* @param in
* Input data addNEWLCWords do we add new lowercase words from this
* data to the word shape classifier
* @return The list of documents
*/
public ObjectBank<List<IN>> makeObjectBankFromReader(BufferedReader in,
DocumentReaderAndWriter<IN> readerAndWriter) {
if (flags.announceObjectBankEntries) {
log.info("Reading data using " + readerAndWriter.getClass());
}
// TODO get rid of ObjectBankWrapper
// return new ObjectBank<List<IN>>(new ResettableReaderIteratorFactory(in),
// readerAndWriter);
return new ObjectBankWrapper<>(flags, new ObjectBank<>(new ResettableReaderIteratorFactory(in),
readerAndWriter), knownLCWords);
}
/**
* Takes the file, reads it in, and prints out the likelihood of each possible
* label at each point.
*
* @param filename The path to the specified file
*/
public void printProbs(String filename,
DocumentReaderAndWriter<IN> readerAndWriter) {
// only for the OCR data does this matter
flags.ocrTrain = false;
ObjectBank<List<IN>> docs =
makeObjectBankFromFile(filename, readerAndWriter);
printProbsDocuments(docs);
}
/**
* Takes the files, reads them in, and prints out the likelihood of each possible
* label at each point.
*
* @param testFiles A Collection of files
*/
public void printProbs(Collection<File> testFiles,
DocumentReaderAndWriter<IN> readerWriter) {
ObjectBank<List<IN>> documents = makeObjectBankFromFiles(testFiles, readerWriter);
printProbsDocuments(documents);
}
/**
* Takes a {@link List} of documents and prints the likelihood of each
* possible label at each point.
*
* @param documents A {@link List} of {@link List} of something that extends
* {@link CoreMap}.
*/
public void printProbsDocuments(ObjectBank<List<IN>> documents) {
Counter<Integer> calibration = new ClassicCounter<>();
Counter<Integer> correctByBin = new ClassicCounter<>();
TwoDimensionalCounter<Integer,String> calibratedTokens = new TwoDimensionalCounter<>();
for (List<IN> doc : documents) {
Triple<Counter<Integer>, Counter<Integer>, TwoDimensionalCounter<Integer,String>> triple = printProbsDocument(doc);
if (triple != null) {
Counters.addInPlace(calibration, triple.first());
Counters.addInPlace(correctByBin, triple.second());
calibratedTokens.addAll(triple.third());
}
System.out.println();
}
if (calibration.size() > 0) {
// we stored stuff, so print it out
PrintWriter pw = new PrintWriter(System.err);
outputCalibrationInfo(pw, calibration, correctByBin, calibratedTokens);
pw.flush();
}
}
private static void outputCalibrationInfo(PrintWriter pw,
Counter<Integer> calibration,
Counter<Integer> correctByBin,
TwoDimensionalCounter<Integer, String> calibratedTokens) {
final int numBins = 10;
pw.println(); // in practice may well be in middle of line when called
pw.println("----------------------------------------");
pw.println("Probability distribution given to tokens (Counts for all class-token pairs; accuracy for this bin; examples are gold entity tokens in bin)");
pw.println("----------------------------------------");
for (int i = 0; i < numBins; i++) {
pw.printf("[%.1f-%.1f%c: %.0f %.2f%n",
((double) i) / numBins,
((double) (i+1)) / numBins,
i == (numBins - 1) ? ']': ')',
calibration.getCount(i),
correctByBin.getCount(i) / calibration.getCount(i));
}
pw.println("----------------------------------------");
for (int i = 0; i < numBins; i++) {
pw.printf("[%.1f-%.1f%c: %s%n",
((double) i) / numBins,
((double) (i+1)) / numBins,
i == (numBins - 1) ? ']': ')',
Counters.toSortedString(calibratedTokens.getCounter(i), 20, "%s=%.0f", ", ", "[%s]"));
}
pw.println("----------------------------------------");
}
public void classifyStdin() throws IOException {
classifyStdin(plainTextReaderAndWriter);
}
public void classifyStdin(DocumentReaderAndWriter<IN> readerWriter) throws IOException {
BufferedReader is = IOUtils.readerFromStdin(flags.inputEncoding);
for (String line; (line = is.readLine()) != null; ) {
Collection<List<IN>> documents = makeObjectBankFromString(line, readerWriter);
if (flags.keepEmptySentences && documents.isEmpty()) {
documents = Collections.<List<IN>>singletonList(Collections.<IN>emptyList());
}
classifyAndWriteAnswers(documents, readerWriter, false);
}
}
public Triple<Counter<Integer>, Counter<Integer>, TwoDimensionalCounter<Integer,String>> printProbsDocument(List<IN> document) {
throw new UnsupportedOperationException("Not implemented for this class.");
}
/** Does nothing by default. Subclasses can override if necessary. */
public void dumpFeatures(Collection<List<IN>> documents) {}
/**
* Load a text file, run the classifier on it, and then print the answers to
* stdout (with timing to stderr). This uses the value of flags.plainTextDocumentReaderAndWriter
* to determine how to read the textFile format. By default this gives
* edu.stanford.nlp.sequences.PlainTextDocumentReaderAndWriter.
* <i>Note:</i> This means that it works right for
* a plain textFile (and not a tab-separated columns test file).
*
* @param textFile The file to test on.
*/
public void classifyAndWriteAnswers(String textFile)
throws IOException {
classifyAndWriteAnswers(textFile, plainTextReaderAndWriter(), false);
}
/**
* Load a test file, run the classifier on it, and then print the answers to
* stdout (with timing to stderr). This uses the value of flags.documentReader
* to determine testFile format. By default, this means that it is set up to
* read a tab-separated columns test file
*
* @param testFile The file to test on.
* @param outputScores Whether to calculate and then log performance scores (P/R/F1)
* @return A Triple of P/R/F1 if outputScores is true, else null
*/
public Triple<Double,Double,Double> classifyAndWriteAnswers(String testFile, boolean outputScores)
throws IOException {
return classifyAndWriteAnswers(testFile, defaultReaderAndWriter(), outputScores);
}
/**
* Load a test file, run the classifier on it, and then print the answers to
* stdout (with timing to stderr).
*
* @param testFile The file to test on.
* @param readerWriter A reader and writer to use for the output
* @param outputScores Whether to calculate and then log performance scores (P/R/F1)
* @return A Triple of P/R/F1 if outputScores is true, else null
*/
public Triple<Double,Double,Double> classifyAndWriteAnswers(String testFile,
DocumentReaderAndWriter<IN> readerWriter,
boolean outputScores)
throws IOException {
ObjectBank<List<IN>> documents =
makeObjectBankFromFile(testFile, readerWriter);
return classifyAndWriteAnswers(documents, readerWriter, outputScores);
}
/** If the flag
* {@code outputEncoding} is defined, the output is written in that
* character encoding, otherwise in the system default character encoding.
*/
public Triple<Double,Double,Double> classifyAndWriteAnswers(String testFile, OutputStream outStream,
DocumentReaderAndWriter<IN> readerWriter, boolean outputScores)
throws IOException {
ObjectBank<List<IN>> documents = makeObjectBankFromFile(testFile, readerWriter);
PrintWriter pw = IOUtils.encodedOutputStreamPrintWriter(outStream, flags.outputEncoding, true);
return classifyAndWriteAnswers(documents, pw, readerWriter, outputScores);
}
public Triple<Double,Double,Double> classifyAndWriteAnswers(String baseDir, String filePattern,
DocumentReaderAndWriter<IN> readerWriter,
boolean outputScores)
throws IOException {
ObjectBank<List<IN>> documents = makeObjectBankFromFiles(baseDir, filePattern, readerWriter);
return classifyAndWriteAnswers(documents, readerWriter, outputScores);
}
/** Run the classifier on a collection of text files.
* Uses the plainTextReaderAndWriter to process them.
*
* @param textFiles A File Collection to process.
* @throws IOException For any IO error
*/
public void classifyFilesAndWriteAnswers(Collection<File> textFiles)
throws IOException {
classifyFilesAndWriteAnswers(textFiles, plainTextReaderAndWriter, false);
}
public void classifyFilesAndWriteAnswers(Collection<File> testFiles,
DocumentReaderAndWriter<IN> readerWriter, boolean outputScores)
throws IOException {
ObjectBank<List<IN>> documents =
makeObjectBankFromFiles(testFiles, readerWriter);
classifyAndWriteAnswers(documents, readerWriter, outputScores);
}
public Triple<Double,Double,Double> classifyAndWriteAnswers(Collection<List<IN>> documents,
DocumentReaderAndWriter<IN> readerWriter,
boolean outputScores)
throws IOException {
return classifyAndWriteAnswers(documents,
IOUtils.encodedOutputStreamPrintWriter(System.out, flags.outputEncoding, true),
readerWriter, outputScores);
}
/**
*
* @param documents
* @param printWriter
* @param readerWriter
* @param outputScores Whether to calculate and output the performance scores (P/R/F1) of the classifier
* @return A Triple of overall P/R/F1, if outputScores is true, else {@code null}. The scores are done
* on a 0-100 scale like percentages.
* @throws IOException
*/
public Triple<Double,Double,Double> classifyAndWriteAnswers(Collection<List<IN>> documents,
PrintWriter printWriter,
DocumentReaderAndWriter<IN> readerWriter,
boolean outputScores)
throws IOException {
if (flags.exportFeatures != null) {
dumpFeatures(documents);
}
Timing timer = new Timing();
Counter<String> entityTP = new ClassicCounter<>();
Counter<String> entityFP = new ClassicCounter<>();
Counter<String> entityFN = new ClassicCounter<>();
boolean resultsCounted = outputScores;
int numWords = 0;
int numDocs = 0;
final AtomicInteger threadCompletionCounter = new AtomicInteger(0);
ThreadsafeProcessor<List<IN>, List<IN>> threadProcessor =
new ThreadsafeProcessor<List<IN>, List<IN>>() {
@Override
public List<IN> process(List<IN> doc) {
doc = classify(doc);
int completedNo = threadCompletionCounter.incrementAndGet();
if (flags.verboseMode) log.info(completedNo + " examples completed");
return doc;
}
@Override
public ThreadsafeProcessor<List<IN>, List<IN>> newInstance() {
return this;
}
};
MulticoreWrapper<List<IN>, List<IN>> wrapper = null;
if (flags.multiThreadClassifier != 0) {
wrapper = new MulticoreWrapper<>(flags.multiThreadClassifier, threadProcessor);
}
for (List<IN> doc: documents) {
numWords += doc.size();
numDocs++;
if (wrapper != null) {
wrapper.put(doc);
while (wrapper.peek()) {
List<IN> results = wrapper.poll();
writeAnswers(results, printWriter, readerWriter);
resultsCounted = resultsCounted && countResults(results, entityTP, entityFP, entityFN);
}
} else {
List<IN> results = threadProcessor.process(doc);
writeAnswers(results, printWriter, readerWriter);
resultsCounted = resultsCounted && countResults(results, entityTP, entityFP, entityFN);
}
}
if (wrapper != null) {
wrapper.join();
while (wrapper.peek()) {
List<IN> results = wrapper.poll();
writeAnswers(results, printWriter, readerWriter);
resultsCounted = resultsCounted && countResults(results, entityTP, entityFP, entityFN);
}
}
long millis = timer.stop();
double wordspersec = numWords / (((double) millis) / 1000);
NumberFormat nf = new DecimalFormat("0.00"); // easier way!
log.info(StringUtils.getShortClassName(this) +
" tagged " + numWords + " words in " + numDocs +
" documents at " + nf.format(wordspersec) +
" words per second.");
if (outputScores) {
return printResults(entityTP, entityFP, entityFN);
} else {
return null;
}
}
/**
* Load a test file, run the classifier on it, and then print the answers to
* stdout (with timing to stderr). This uses the value of flags.documentReader
* to determine testFile format.
*
* @param testFile The name of the file to test on.
* @param k How many best to print
* @param readerAndWriter Class to be used for printing answers
*/
public void classifyAndWriteAnswersKBest(String testFile, int k,
DocumentReaderAndWriter<IN> readerAndWriter)
throws IOException {
ObjectBank<List<IN>> documents = makeObjectBankFromFile(testFile, readerAndWriter);
PrintWriter pw = IOUtils.encodedOutputStreamPrintWriter(System.out, flags.outputEncoding, true);
classifyAndWriteAnswersKBest(documents, k, pw, readerAndWriter);
pw.flush();
}
/**
* Run the classifier on the documents in an ObjectBank, and print the
* answers to a given PrintWriter (with timing to stderr). The value of
* flags.documentReader is used to determine testFile format.
*
* @param documents The ObjectBank to test on.
*/
public void classifyAndWriteAnswersKBest(ObjectBank<List<IN>> documents, int k, PrintWriter printWriter,
DocumentReaderAndWriter<IN> readerAndWriter) throws IOException {
Timing timer = new Timing();
int numWords = 0;
int numSentences = 0;
for (List<IN> doc : documents) {
Counter<List<IN>> kBest = classifyKBest(doc, CoreAnnotations.AnswerAnnotation.class, k);
numWords += doc.size();
List<List<IN>> sorted = Counters.toSortedList(kBest);
int n = 1;
for (List<IN> l : sorted) {
printWriter.println("<sentence id=" + numSentences + " k=" + n + " logProb=" + kBest.getCount(l) + " prob="
+ Math.exp(kBest.getCount(l)) + '>');
writeAnswers(l, printWriter, readerAndWriter);
printWriter.println("</sentence>");
n++;
}
numSentences++;
}
long millis = timer.stop();
double wordspersec = numWords / (((double) millis) / 1000);
NumberFormat nf = new DecimalFormat("0.00"); // easier way!
log.info(this.getClass().getName() + " tagged " + numWords + " words in " + numSentences
+ " documents at " + nf.format(wordspersec) + " words per second.");
}
/**
* Load a test file, run the classifier on it, and then write a Viterbi search
* graph for each sequence.
*
* @param testFile The file to test on.
*/
public void classifyAndWriteViterbiSearchGraph(String testFile, String searchGraphPrefix, DocumentReaderAndWriter<IN> readerAndWriter) throws IOException {
Timing timer = new Timing();
ObjectBank<List<IN>> documents = makeObjectBankFromFile(testFile, readerAndWriter);
int numWords = 0;
int numSentences = 0;
for (List<IN> doc : documents) {
DFSA<String, Integer> tagLattice = getViterbiSearchGraph(doc, CoreAnnotations.AnswerAnnotation.class);
numWords += doc.size();
PrintWriter latticeWriter = new PrintWriter(new FileOutputStream(searchGraphPrefix + '.' + numSentences
+ ".wlattice"));
PrintWriter vsgWriter = new PrintWriter(new FileOutputStream(searchGraphPrefix + '.' + numSentences + ".lattice"));
if (readerAndWriter instanceof LatticeWriter) {
((LatticeWriter<IN, String, Integer>) readerAndWriter).printLattice(tagLattice, doc, latticeWriter);
}
tagLattice.printAttFsmFormat(vsgWriter);
latticeWriter.close();
vsgWriter.close();
numSentences++;
}
long millis = timer.stop();
double wordspersec = numWords / (((double) millis) / 1000);
NumberFormat nf = new DecimalFormat("0.00"); // easier way!
log.info(this.getClass().getName() + " tagged " + numWords + " words in " + numSentences
+ " documents at " + nf.format(wordspersec) + " words per second.");
}
/**
* Write the classifications of the Sequence classifier to a writer in a
* format determined by the DocumentReaderAndWriter used.
*
* @param doc Documents to write out
* @param printWriter Writer to use for output
* @throws IOException If an IO problem
*/
public void writeAnswers(List<IN> doc, PrintWriter printWriter,
DocumentReaderAndWriter<IN> readerAndWriter)
throws IOException {
if (flags.lowerNewgeneThreshold) {
return;
}
if (flags.numRuns <= 1) {
readerAndWriter.printAnswers(doc, printWriter);
// out.println();
printWriter.flush();
}
}
/**
* Count results using a method appropriate for the tag scheme being used.
*/
public boolean countResults(List<IN> doc,
Counter<String> entityTP,
Counter<String> entityFP,
Counter<String> entityFN) {
String bg = (flags.evaluateBackground ? null : flags.backgroundSymbol);
if (flags.sighanPostProcessing) {
// TODO: this is extremely indicative of being a Chinese Segmenter,
// but it would still be better to have something more concrete
return countResultsSegmenter(doc, entityTP, entityFP, entityFN);
}
return IOBUtils.countEntityResults(doc, entityTP, entityFP, entityFN, bg);
}
// TODO: could make this a parameter for the model
private static final String CUT_LABEL = "Cut";
public static boolean countResultsSegmenter(List<? extends CoreMap> doc,
Counter<String> entityTP,
Counter<String> entityFP,
Counter<String> entityFN) {
// count from 1 because each label represents cutting or
// not cutting at a word, so we don't count the first word
for (int i = 1; i < doc.size(); ++i) {
CoreMap word = doc.get(i);
String gold = word.get(CoreAnnotations.GoldAnswerAnnotation.class);
String guess = word.get(CoreAnnotations.AnswerAnnotation.class);
if (gold == null || guess == null) {
return false;
}
if (gold.equals("1") && guess.equals("1")) {
entityTP.incrementCount(CUT_LABEL, 1.0);
} else if (gold.equals("0") && guess.equals("1")) {
entityFP.incrementCount(CUT_LABEL, 1.0);
} else if (gold.equals("1") && guess.equals("0")) {
entityFN.incrementCount(CUT_LABEL, 1.0);
}
}
return true;
}
/**
* Given counters of true positives, false positives, and false
* negatives, prints out precision, recall, and f1 for each key.
*/
public static Triple<Double,Double,Double> printResults(Counter<String> entityTP, Counter<String> entityFP,
Counter<String> entityFN) {
Set<String> entities = new TreeSet<>();
entities.addAll(entityTP.keySet());
entities.addAll(entityFP.keySet());
entities.addAll(entityFN.keySet());
log.info(" Entity\tP\tR\tF1\tTP\tFP\tFN");
for (String entity : entities) {
double tp = entityTP.getCount(entity);
double fp = entityFP.getCount(entity);
double fn = entityFN.getCount(entity);
printPRLine(entity, tp, fp, fn);
}
double tp = entityTP.totalCount();
double fp = entityFP.totalCount();
double fn = entityFN.totalCount();
return printPRLine("Totals", tp, fp, fn);
}
/**
* Print a line of precision, recall, and f1 scores, titled by entity.
*
* @return A Triple of the P/R/F, done on a 0-100 scale like percentages
*/
private static Triple<Double,Double,Double> printPRLine(String entity, double tp, double fp, double fn) {
double precision = (tp == 0.0 && fp == 0.0) ? 0.0 : tp / (tp + fp);
double recall = (tp == 0.0 && fn == 0.0) ? 1.0 : tp / (tp + fn);
double f1 = ((precision == 0.0 || recall == 0.0) ?
0.0 : 2.0 / (1.0 / precision + 1.0 / recall));
log.info(String.format("%15s\t%.4f\t%.4f\t%.4f\t%.0f\t%.0f\t%.0f%n",
entity, precision, recall, f1, tp, fp, fn));
return new Triple<>(precision * 100, recall * 100, f1 * 100);
}
/**
* Serialize a sequence classifier to a file on the given path.
*
* @param serializePath The path/filename to write the classifier to.
*/
public abstract void serializeClassifier(String serializePath);
/** Serialize a sequence classifier to an object output stream **/
public abstract void serializeClassifier(ObjectOutputStream oos);
/**
* Loads a classifier from the given input stream.
* Any exceptions are rethrown as unchecked exceptions.
* This method does not close the InputStream.
*
* @param in The InputStream to read from
*/
public void loadClassifierNoExceptions(InputStream in, Properties props) {
// load the classifier
try {
loadClassifier(in, props);
} catch (IOException e) {
throw new RuntimeIOException(e);
} catch (ClassNotFoundException cnfe) {
throw new RuntimeException(cnfe);
}
}
/**
* Load a classifier from the specified InputStream. No extra properties are
* supplied. This does not close the InputStream.
*
* @param in The InputStream to load the serialized classifier from
* @throws IOException If there are problems accessing the input stream
* @throws ClassCastException If there are problems interpreting the serialized data
* @throws ClassNotFoundException If there are problems interpreting the serialized data
*/
public void loadClassifier(InputStream in) throws IOException, ClassCastException, ClassNotFoundException {
loadClassifier(in, null);
}
/**
* Load a classifier from the specified InputStream. The classifier is
* reinitialized from the flags serialized in the classifier. This does not
* close the InputStream.
*
* @param in The InputStream to load the serialized classifier from
* @param props This Properties object will be used to update the
* SeqClassifierFlags which are read from the serialized classifier
* @throws IOException If there are problems accessing the input stream
* @throws ClassCastException If there are problems interpreting the serialized data
* @throws ClassNotFoundException If there are problems interpreting the serialized data
*/
public void loadClassifier(InputStream in, Properties props) throws IOException, ClassCastException,
ClassNotFoundException {
loadClassifier(new ObjectInputStream(in), props);
}
/**
* Load a classifier from the specified input stream. The classifier is
* reinitialized from the flags serialized in the classifier.
*
* @param in The InputStream to load the serialized classifier from
* @param props This Properties object will be used to update the
* SeqClassifierFlags which are read from the serialized classifier
* @throws IOException If there are problems accessing the input stream
* @throws ClassCastException If there are problems interpreting the serialized data
* @throws ClassNotFoundException If there are problems interpreting the serialized data
*/
public abstract void loadClassifier(ObjectInputStream in, Properties props) throws IOException, ClassCastException,
ClassNotFoundException;
/**
* Loads a classifier from the file specified by loadPath. If loadPath ends in
* .gz, uses a GZIPInputStream, else uses a regular FileInputStream.
*/
public void loadClassifier(String loadPath) throws ClassCastException, IOException, ClassNotFoundException {
loadClassifier(loadPath, null);
}
/**
* Loads a classifier from the file, classpath resource, or URL specified by loadPath. If loadPath ends in
* .gz, uses a GZIPInputStream.
*/
public void loadClassifier(String loadPath, Properties props) throws ClassCastException, IOException, ClassNotFoundException {
InputStream is = IOUtils.getInputStreamFromURLOrClasspathOrFileSystem(loadPath);
Timing t = new Timing();
loadClassifier(is, props);
is.close();
t.done(log, "Loading classifier from " + loadPath);
}
public void loadClassifierNoExceptions(String loadPath) {
loadClassifierNoExceptions(loadPath, null);
}
public void loadClassifierNoExceptions(String loadPath, Properties props) {
try {
loadClassifier(loadPath, props);
} catch (IOException e) {
throw new RuntimeIOException(e);
} catch (ClassCastException|ClassNotFoundException e) {
throw new RuntimeException(e);
}
}
public void loadClassifier(File file) throws ClassCastException, IOException, ClassNotFoundException {
loadClassifier(file, null);
}
/**
* Loads a classifier from the file specified. If the file's name ends in .gz,
* uses a GZIPInputStream, else uses a regular FileInputStream. This method
* closes the File when done.
*
* @param file Loads a classifier from this file.
* @param props Properties in this object will be used to overwrite those
* specified in the serialized classifier
*
* @throws IOException If there are problems accessing the input stream
* @throws ClassCastException If there are problems interpreting the serialized data
* @throws ClassNotFoundException If there are problems interpreting the serialized data
*/
public void loadClassifier(File file, Properties props) throws ClassCastException, IOException,
ClassNotFoundException {
Timing t = new Timing();
BufferedInputStream bis;
if (file.getName().endsWith(".gz")) {
bis = new BufferedInputStream(new GZIPInputStream(new FileInputStream(file)));
} else {
bis = new BufferedInputStream(new FileInputStream(file));
}
loadClassifier(bis, props);
bis.close();
t.done(log, "Loading classifier from " + file.getAbsolutePath());
}
public void loadClassifierNoExceptions(File file) {
loadClassifierNoExceptions(file, null);
}
public void loadClassifierNoExceptions(File file, Properties props) {
try {
loadClassifier(file, props);
} catch (Exception e) {
log.info("Error deserializing " + file.getAbsolutePath());
throw new RuntimeException(e);
}
}
/**
* This function will load a classifier that is stored inside a jar file (if
* it is so stored). The classifier should be specified as its full path
* in a jar. If the classifier is not stored in the jar file or this is not run
* from inside a jar file, then this function will throw a RuntimeException.
*
* @param modelName
* The name of the model file. Iff it ends in .gz, then it is assumed
* to be gzip compressed.
* @param props
* A Properties object which can override certain properties in the
* serialized file, such as the DocumentReaderAndWriter. You can pass
* in {@code null} to override nothing.
*/
// todo [john bauer 2015]: This method may not be necessary. Perhaps use the IOUtils equivalents
public void loadJarClassifier(String modelName, Properties props) {
Timing t = new Timing();
try {
InputStream is = getClass().getResourceAsStream(modelName);
if (modelName.endsWith(".gz")) {
is = new GZIPInputStream(is);
}
is = new BufferedInputStream(is);
loadClassifier(is, props);
is.close();
t.done(log, "Loading CLASSPATH classifier " + modelName);
} catch (Exception e) {
String msg = "Error loading classifier from jar file (most likely you are not running this code from a jar file or the named classifier is not stored in the jar file)";
throw new RuntimeException(msg, e);
}
}
private transient PrintWriter cliqueWriter;
private transient int writtenNum; // = 0;
/** Print the String features generated from a IN */
protected void printFeatures(IN wi, Collection<String> features) {
if (flags.printFeatures == null || writtenNum >= flags.printFeaturesUpto) {
return;
}
if (cliqueWriter == null) {
cliqueWriter = IOUtils.getPrintWriterOrDie("features-" + flags.printFeatures + ".txt");
writtenNum = 0;
}
if (wi instanceof CoreLabel) {
cliqueWriter.print(wi.get(CoreAnnotations.TextAnnotation.class) + ' ' + wi.get(CoreAnnotations.PartOfSpeechAnnotation.class) + ' '
+ wi.get(CoreAnnotations.GoldAnswerAnnotation.class) + '\t');
} else {
cliqueWriter.print(wi.get(CoreAnnotations.TextAnnotation.class)
+ wi.get(CoreAnnotations.GoldAnswerAnnotation.class) + '\t');
}
boolean first = true;
List<String> featsList = new ArrayList<>(features);
Collections.sort(featsList);
for (String feat : featsList) {
if (first) {
first = false;
} else {
cliqueWriter.print(" ");
}
cliqueWriter.print(feat);
}
cliqueWriter.println();
writtenNum++;
}
/** Print the String features generated from a token. */
protected void printFeatureLists(IN wi, Collection<List<String>> features) {
if (flags.printFeatures == null || writtenNum >= flags.printFeaturesUpto) {
return;
}
printFeatureListsHelper(wi, features);
}
// Separating this method out lets printFeatureLists be inlined, which is good since it is usually a no-op.
private void printFeatureListsHelper(IN wi, Collection<List<String>> features) {
if (cliqueWriter == null) {
cliqueWriter = IOUtils.getPrintWriterOrDie("features-" + flags.printFeatures + ".txt");
writtenNum = 0;
}
if (wi instanceof CoreLabel) {
cliqueWriter.print(wi.get(CoreAnnotations.TextAnnotation.class) + ' ' + wi.get(CoreAnnotations.PartOfSpeechAnnotation.class) + ' '
+ wi.get(CoreAnnotations.GoldAnswerAnnotation.class) + '\t');
} else {
cliqueWriter.print(wi.get(CoreAnnotations.TextAnnotation.class)
+ wi.get(CoreAnnotations.GoldAnswerAnnotation.class) + '\t');
}
boolean first = true;
for (List<String> featList : features) {
List<String> sortedFeatList = new ArrayList<>(featList);
Collections.sort(sortedFeatList);
for (String feat : sortedFeatList) {
if (first) {
first = false;
} else {
cliqueWriter.print(" ");
}
cliqueWriter.print(feat);
}
cliqueWriter.print(" ");
}
cliqueWriter.println();
writtenNum++;
}
public int windowSize() {
return windowSize;
}
}