/**
* Build the associator on the filtered data.
*
* @param data the training data
* @throws Exception if the Associator could not be built successfully
*/
public void buildAssociations(Instances data) throws Exception {
if (m_Associator == null) throw new Exception("No base associator has been set!");
// create copy and set class-index
data = new Instances(data);
if (getClassIndex() == 0) {
data.setClassIndex(data.numAttributes() - 1);
} else {
data.setClassIndex(getClassIndex() - 1);
}
if (getClassIndex() != -1) {
// remove instances with missing class
data.deleteWithMissingClass();
}
m_Filter.setInputFormat(data); // filter capabilities are checked here
data = Filter.useFilter(data, m_Filter);
// can associator handle the data?
getAssociator().getCapabilities().testWithFail(data);
m_FilteredInstances = data.stringFreeStructure();
m_Associator.buildAssociations(data);
}
private List<Instance> myExtractKeyphrases(String document, int numOfPhrases) throws Exception {
// Check whether there is actually any data
//
if (document.length() == 0 || document.equals("")) {
throw new Exception("Couldn't find any data!");
}
FastVector atts = new FastVector(3);
atts.addElement(new Attribute("doc", (FastVector) null));
atts.addElement(new Attribute("keyphrases", (FastVector) null));
Instances data = new Instances("keyphrase_training_data", atts, 0);
List<Instance> myInstances = new ArrayList<Instance>();
double[] newInst = new double[2];
newInst[0] = (double) data.attribute(0).addStringValue(document);
newInst[1] = Instance.missingValue();
data.add(new Instance(1.0, newInst));
m_KEAFilter.input(data.instance(0));
data = data.stringFreeStructure();
ke.setNumPhrases(numOfPhrases);
int numPhrases = numOfPhrases; // ke.getNumPhrases();
Instance[] topRankedInstances = new Instance[numPhrases];
Instance inst;
// Iterating over all extracted keyphrases (inst)
while ((inst = m_KEAFilter.output()) != null) {
int index = (int) inst.value(m_KEAFilter.getRankIndex()) - 1;
if (index < numPhrases) {
topRankedInstances[index] = inst;
}
}
double numExtracted = 0, numCorrect = 0;
for (int i = 0; i < numPhrases; i++) {
if (topRankedInstances[i] != null) {
if (!topRankedInstances[i].isMissing(topRankedInstances[i].numAttributes() - 1)) {
numExtracted += 1.0;
}
if ((int) topRankedInstances[i].value(topRankedInstances[i].numAttributes() - 1) == 1) {
numCorrect += 1.0;
}
myInstances.add(topRankedInstances[i]);
}
}
return myInstances;
}
/** 分类过程 */
public double classifyMessage(String message) throws Exception {
filter.input(makeInstance(message, instances.stringFreeStructure()));
Instance filteredInstance = filter.output(); // 必须使用原来的filter
double predicted = classifier.classifyInstance(filteredInstance); // (int)predicted是类标索引
// System.out.println("Message classified as : "
// + instances.classAttribute().value((int) predicted));
return predicted;
}
/**
* Sets the format of the input instances. If the filter is able to determine the output format
* before seeing any input instances, it does so here. This default implementation clears the
* output format and output queue, and the new batch flag is set. Overriders should call <code>
* super.setInputFormat(Instances)</code>
*
* @param instanceInfo an Instances object containing the input instance structure (any instances
* contained in the object are ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @throws Exception if the inputFormat can't be set successfully
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
testInputFormat(instanceInfo);
m_InputFormat = instanceInfo.stringFreeStructure();
m_OutputFormat = null;
m_OutputQueue = new Queue();
m_NewBatch = true;
m_FirstBatchDone = false;
initInputLocators(m_InputFormat, null);
return false;
}
/**
* This will remove all buffered instances from the inputformat dataset. Use this method rather
* than getInputFormat().delete();
*/
protected void flushInput() {
if ((m_InputStringAtts.getAttributeIndices().length > 0)
|| (m_InputRelAtts.getAttributeIndices().length > 0)) {
m_InputFormat = m_InputFormat.stringFreeStructure();
m_InputStringAtts = new StringLocator(m_InputFormat, m_InputStringAtts.getAllowedIndices());
m_InputRelAtts = new RelationalLocator(m_InputFormat, m_InputRelAtts.getAllowedIndices());
} else {
// This more efficient than new Instances(m_InputFormat, 0);
m_InputFormat.delete();
}
}
/**
* Sets the format of output instances. The derived class should use this method once it has
* determined the outputformat. The output queue is cleared.
*
* @param outputFormat the new output format
*/
protected void setOutputFormat(Instances outputFormat) {
if (outputFormat != null) {
m_OutputFormat = outputFormat.stringFreeStructure();
initOutputLocators(m_OutputFormat, null);
// Rename the relation
String relationName = outputFormat.relationName() + "-" + this.getClass().getName();
if (this instanceof OptionHandler) {
String[] options = ((OptionHandler) this).getOptions();
for (int i = 0; i < options.length; i++) {
relationName += options[i].trim();
}
}
m_OutputFormat.setRelationName(relationName);
} else {
m_OutputFormat = null;
}
m_OutputQueue = new Queue();
}
/**
* Signify that this batch of input to the filter is finished. If the filter requires all
* instances prior to filtering, output() may now be called to retrieve the filtered instances.
* Any subsequent instances filtered should be filtered based on setting obtained from the first
* batch (unless the inputFormat has been re-assigned or new options have been set). This default
* implementation assumes all instance processing occurs during inputFormat() and input().
*
* @return true if there are instances pending output
* @throws NullPointerException if no input structure has been defined,
* @throws Exception if there was a problem finishing the batch.
*/
public boolean batchFinished() throws Exception {
if (m_InputFormat == null) {
throw new NullPointerException("No input instance format defined");
}
flushInput();
m_NewBatch = true;
m_FirstBatchDone = true;
if (m_OutputQueue.empty()) {
// Clear out references to old strings/relationals occasionally
if ((m_OutputStringAtts.getAttributeIndices().length > 0)
|| (m_OutputRelAtts.getAttributeIndices().length > 0)) {
m_OutputFormat = m_OutputFormat.stringFreeStructure();
m_OutputStringAtts =
new StringLocator(m_OutputFormat, m_OutputStringAtts.getAllowedIndices());
}
}
return (numPendingOutput() != 0);
}
/** Builds the model from the files */
public void extractKeyphrases(Hashtable stems) throws Exception {
Vector stats = new Vector();
// Check whether there is actually any data
if (stems.size() == 0) {
throw new Exception("Couldn't find any data!");
}
FastVector atts = new FastVector(2);
atts.addElement(new Attribute("doc", (FastVector) null));
atts.addElement(new Attribute("keyphrases", (FastVector) null));
Instances data = new Instances("keyphrase_training_data", atts, 0);
// Extract keyphrases
Enumeration elem = stems.keys();
while (elem.hasMoreElements()) {
String str = (String) elem.nextElement();
double[] newInst = new double[2];
try {
File txt = new File(m_dirName + "/" + str + ".txt");
Reader is;
if (!m_encoding.equals("default")) {
is = new BomStrippingInputStreamReader(new FileInputStream(txt), m_encoding);
} else {
is = new BomStrippingInputStreamReader(new FileInputStream(txt));
}
StringBuffer txtStr = new StringBuffer();
int c;
while ((c = is.read()) != -1) {
txtStr.append((char) c);
}
newInst[0] = (double) data.attribute(0).addStringValue(txtStr.toString());
} catch (Exception e) {
if (m_debug) {
System.err.println("Can't read document " + str + ".txt");
}
newInst[0] = Instance.missingValue();
}
try {
File key = new File(m_dirName + "/" + str + ".key");
Reader is;
if (!m_encoding.equals("default")) {
is = new BomStrippingInputStreamReader(new FileInputStream(key), m_encoding);
} else {
is = new BomStrippingInputStreamReader(new FileInputStream(key));
}
StringBuffer keyStr = new StringBuffer();
int c;
while ((c = is.read()) != -1) {
keyStr.append((char) c);
}
newInst[1] = (double) data.attribute(1).addStringValue(keyStr.toString());
} catch (Exception e) {
if (m_debug) {
System.err.println("No keyphrases for stem " + str + ".");
}
newInst[1] = Instance.missingValue();
}
data.add(new Instance(1.0, newInst));
m_KEAFilter.input(data.instance(0));
data = data.stringFreeStructure();
if (m_debug) {
System.err.println("-- Document: " + str);
}
Instance[] topRankedInstances = new Instance[m_numPhrases];
Instance inst;
while ((inst = m_KEAFilter.output()) != null) {
int index = (int) inst.value(m_KEAFilter.getRankIndex()) - 1;
if (index < m_numPhrases) {
topRankedInstances[index] = inst;
}
}
if (m_debug) {
System.err.println("-- Keyphrases and feature values:");
}
FileOutputStream out = null;
PrintWriter printer = null;
File key = new File(m_dirName + "/" + str + ".key");
if (!key.exists()) {
out = new FileOutputStream(m_dirName + "/" + str + ".key");
if (!m_encoding.equals("default")) {
printer = new PrintWriter(new OutputStreamWriter(out, m_encoding));
} else {
printer = new PrintWriter(out);
}
}
double numExtracted = 0, numCorrect = 0;
for (int i = 0; i < m_numPhrases; i++) {
if (topRankedInstances[i] != null) {
if (!topRankedInstances[i].isMissing(topRankedInstances[i].numAttributes() - 1)) {
numExtracted += 1.0;
}
if ((int) topRankedInstances[i].value(topRankedInstances[i].numAttributes() - 1)
== topRankedInstances[i]
.attribute(topRankedInstances[i].numAttributes() - 1)
.indexOfValue("True")) {
numCorrect += 1.0;
}
if (printer != null) {
printer.print(topRankedInstances[i].stringValue(m_KEAFilter.getUnstemmedPhraseIndex()));
if (m_AdditionalInfo) {
printer.print("\t");
printer.print(topRankedInstances[i].stringValue(m_KEAFilter.getStemmedPhraseIndex()));
printer.print("\t");
printer.print(
Utils.doubleToString(
topRankedInstances[i].value(m_KEAFilter.getProbabilityIndex()), 4));
}
printer.println();
}
if (m_debug) {
System.err.println(topRankedInstances[i]);
}
}
}
if (numExtracted > 0) {
if (m_debug) {
System.err.println("-- " + numCorrect + " correct");
}
stats.addElement(new Double(numCorrect));
}
if (printer != null) {
printer.flush();
printer.close();
out.close();
}
}
double[] st = new double[stats.size()];
for (int i = 0; i < stats.size(); i++) {
st[i] = ((Double) stats.elementAt(i)).doubleValue();
}
double avg = Utils.mean(st);
double stdDev = Math.sqrt(Utils.variance(st));
System.err.println(
"Avg. number of correct keyphrases: "
+ Utils.doubleToString(avg, 2)
+ " +/- "
+ Utils.doubleToString(stdDev, 2));
System.err.println("Based on " + stats.size() + " documents");
m_KEAFilter.batchFinished();
}
/** Builds the model from the training data */
public void buildModel(HashSet<String> fileNames) throws Exception {
// Check whether there is actually any data
if (fileNames.size() == 0) {
throw new Exception("Couldn't find any data in " + inputDirectoryName);
}
System.err.println("-- Building the model... ");
FastVector atts = new FastVector(3);
atts.addElement(new Attribute("filename", (FastVector) null));
atts.addElement(new Attribute("document", (FastVector) null));
atts.addElement(new Attribute("keyphrases", (FastVector) null));
Instances data = new Instances("keyphrase_training_data", atts, 0);
// Build model
mauiFilter = new MauiFilter();
mauiFilter.setDebug(getDebug());
mauiFilter.setMaxPhraseLength(getMaxPhraseLength());
mauiFilter.setMinPhraseLength(getMinPhraseLength());
mauiFilter.setMinNumOccur(getMinNumOccur());
mauiFilter.setStemmer(getStemmer());
mauiFilter.setDocumentLanguage(getDocumentLanguage());
mauiFilter.setVocabularyName(getVocabularyName());
mauiFilter.setVocabularyFormat(getVocabularyFormat());
mauiFilter.setStopwords(getStopwords());
if (wikipedia != null) {
mauiFilter.setWikipedia(wikipedia);
} else if (wikipediaServer.equals("localhost") && wikipediaDatabase.equals("database")) {
mauiFilter.setWikipedia(wikipedia);
} else {
mauiFilter.setWikipedia(
wikipediaServer, wikipediaDatabase, cacheWikipediaData, wikipediaDataDirectory);
}
if (classifier != null) {
mauiFilter.setClassifier(classifier);
}
mauiFilter.setInputFormat(data);
// set features configurations
mauiFilter.setBasicFeatures(useBasicFeatures);
mauiFilter.setKeyphrasenessFeature(useKeyphrasenessFeature);
mauiFilter.setFrequencyFeatures(useFrequencyFeatures);
mauiFilter.setPositionsFeatures(usePositionsFeatures);
mauiFilter.setLengthFeature(useLengthFeature);
mauiFilter.setThesaurusFeatures(useNodeDegreeFeature);
mauiFilter.setBasicWikipediaFeatures(useBasicWikipediaFeatures);
mauiFilter.setAllWikipediaFeatures(useAllWikipediaFeatures);
mauiFilter.setThesaurusFeatures(useNodeDegreeFeature);
mauiFilter.setClassifier(classifier);
mauiFilter.setContextSize(contextSize);
mauiFilter.setMinKeyphraseness(minKeyphraseness);
mauiFilter.setMinSenseProbability(minSenseProbability);
if (!vocabularyName.equals("none") && !vocabularyName.equals("wikipedia")) {
mauiFilter.loadThesaurus(getStemmer(), getStopwords());
}
System.err.println("-- Reading the input documents... ");
for (String fileName : fileNames) {
double[] newInst = new double[3];
newInst[0] = (double) data.attribute(0).addStringValue(fileName);
;
File documentTextFile = new File(inputDirectoryName + "/" + fileName + ".txt");
File documentTopicsFile = new File(inputDirectoryName + "/" + fileName + ".key");
try {
InputStreamReader is;
if (!documentEncoding.equals("default")) {
is = new InputStreamReader(new FileInputStream(documentTextFile), documentEncoding);
} else {
is = new InputStreamReader(new FileInputStream(documentTextFile));
}
// Reading the file content
StringBuffer txtStr = new StringBuffer();
int c;
while ((c = is.read()) != -1) {
txtStr.append((char) c);
}
is.close();
// Adding the text of the document to the instance
newInst[1] = (double) data.attribute(1).addStringValue(txtStr.toString());
} catch (Exception e) {
System.err.println("Problem with reading " + documentTextFile);
e.printStackTrace();
newInst[1] = Instance.missingValue();
}
try {
InputStreamReader is;
if (!documentEncoding.equals("default")) {
is = new InputStreamReader(new FileInputStream(documentTopicsFile), documentEncoding);
} else {
is = new InputStreamReader(new FileInputStream(documentTopicsFile));
}
// Reading the content of the keyphrase file
StringBuffer keyStr = new StringBuffer();
int c;
while ((c = is.read()) != -1) {
keyStr.append((char) c);
}
// Adding the topics to the file
newInst[2] = (double) data.attribute(2).addStringValue(keyStr.toString());
} catch (Exception e) {
System.err.println("Problem with reading " + documentTopicsFile);
e.printStackTrace();
newInst[2] = Instance.missingValue();
}
data.add(new Instance(1.0, newInst));
mauiFilter.input(data.instance(0));
data = data.stringFreeStructure();
}
mauiFilter.batchFinished();
while ((mauiFilter.output()) != null) {}
;
}