public weka.core.Instances toWekaInstances() {
// attributes
FastVector wattrs = new FastVector();
Iterator itr = attributes.iterator();
while (itr.hasNext()) {
Attribute attr = (Attribute) itr.next();
wattrs.addElement(attr.toWekaAttribute());
}
// data instances
weka.core.Instances winsts = new weka.core.Instances(name, wattrs, instances.size());
itr = instances.iterator();
while (itr.hasNext()) {
Instance inst = (Instance) itr.next();
Iterator itrval = inst.getValues().iterator();
Iterator itrmis = inst.getMissing().iterator();
double[] vals = new double[wattrs.size()];
for (int i = 0; i < wattrs.size(); i++) {
double val = (Double) itrval.next();
if ((Boolean) itrmis.next()) {
vals[i] = weka.core.Instance.missingValue();
} else {
vals[i] = val;
}
}
weka.core.Instance winst = new weka.core.Instance(1, vals);
winst.setDataset(winsts);
winsts.add(winst);
}
winsts.setClassIndex(this.class_index);
return winsts;
}
/**
* Input an instance for filtering. Ordinarily the instance is processed and made available for
* output immediately. Some filters require all instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be collected with output().
* @exception IllegalStateException if no input format has been defined.
* @exception Exception if there was a problem during the filtering.
*/
public boolean input(Instance instance) throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
double[] vals = new double[instance.numAttributes() + 1];
for (int i = 0; i < instance.numAttributes(); i++) {
if (instance.isMissing(i)) {
vals[i] = Instance.missingValue();
} else {
vals[i] = instance.value(i);
}
}
evaluateExpression(vals);
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new Instance(instance.weight(), vals);
}
copyStringValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
return true;
}
// calculate if an App fits to a pm
// TODO: gst: use WEKA to calc fit factor!!
private int calculateFit(App app2, VirtualMachine vm) {
int output = 0;
if (Action.isOnlyLearning() == false && CreateAppInsertIntoVm.evaluation != null) {
// is free space available in the VM
if (app2.getCpu() + vm.getCurrentCpuUsage() < vm.getCurrentCpuAllocation()
&& app2.getMemory() + vm.getCurrentMemoryUsage() < vm.getCurrentMemoryAllocation()
&& app2.getStorage() + vm.getCurrentStorageUsage() < vm.getCurrentCpuAllocation()) {
Instance instance = createInstance(Instance.missingValue(), vm);
instance.setDataset(CreateAppInsertIntoVm.getKnowledgeBase());
try {
output = (int) (evaluation.evaluateModelOnce(classifier, instance) * 100);
} catch (Exception e) {
e.printStackTrace();
}
}
} else {
if (app2.getCpu() + vm.getCurrentCpuUsage() < vm.getCurrentCpuAllocation()
&& app2.getMemory() + vm.getCurrentMemoryUsage() < vm.getCurrentMemoryAllocation()
&& app2.getStorage() + vm.getCurrentStorageUsage() < vm.getCurrentCpuAllocation()) {
output = randomData.nextInt(1, 100);
}
}
return output;
}
// use the TriTrainer Classifier to classify Instance;
public double classifyInstance(Instance instance) throws Exception {
double result;
double[] dist;
int index;
dist = distributionForInstance(instance); // 分类概率
if (instance.classAttribute().isNominal()) {
index = Utils.maxIndex(dist); // 返回概率最大的
if (dist[index] == 0) result = Instance.missingValue();
else result = dist[index];
} else if (instance.classAttribute().isNumeric()) {
result = dist[0];
} else {
result = Instance.missingValue();
}
return result;
}
/**
* Evaluate the expression using the supplied array of attribute values. The result is stored in
* the last element of the array. Assumes that the infix expression has been converted to postfix
* and stored in m_postFixExpVector
*
* @param vals the values to apply the expression to
* @exception Exception if something goes wrong
*/
private void evaluateExpression(double[] vals) throws Exception {
Stack operands = new Stack();
for (int i = 0; i < m_postFixExpVector.size(); i++) {
Object nextob = m_postFixExpVector.elementAt(i);
if (nextob instanceof NumericOperand) {
operands.push(new Double(((NumericOperand) nextob).m_numericConst));
} else if (nextob instanceof AttributeOperand) {
double value = vals[((AttributeOperand) nextob).m_attributeIndex];
if (value == Instance.missingValue()) {
vals[vals.length - 1] = Instance.missingValue();
break;
}
if (((AttributeOperand) nextob).m_negative) {
value = -value;
}
operands.push(new Double(value));
} else if (nextob instanceof Operator) {
char op = ((Operator) nextob).m_operator;
if (isUnaryFunction(op)) {
double operand = ((Double) operands.pop()).doubleValue();
double result = ((Operator) nextob).applyFunction(operand);
operands.push(new Double(result));
} else {
double second = ((Double) operands.pop()).doubleValue();
double first = ((Double) operands.pop()).doubleValue();
double result = ((Operator) nextob).applyOperator(first, second);
operands.push(new Double(result));
}
} else {
throw new Exception("Unknown object in postfix vector!");
}
}
if (operands.size() != 1) {
throw new Exception("Problem applying function");
}
Double result = ((Double) operands.pop());
if (result.isNaN() || result.isInfinite()) {
vals[vals.length - 1] = Instance.missingValue();
} else {
vals[vals.length - 1] = result.doubleValue();
}
}
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;
}
/**
* Classifies the given test instance. The instance has to belong to a dataset when it's being
* classified. Note that a classifier MUST implement either this or distributionForInstance().
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or Instance.missingValue() if no
* prediction is made
* @exception Exception if an error occurred during the prediction
*/
public double classifyInstance(Instance instance) throws Exception {
double[] dist = distributionForInstance(instance);
if (dist == null) {
throw new Exception("Null distribution predicted");
}
double max = 0;
int maxIndex = 0;
for (int i = 0; i < dist.length; i++) {
if (dist[i] > max) {
maxIndex = i;
max = dist[i];
}
}
if (max > 0) {
return maxIndex;
} else {
return Instance.missingValue();
}
}
/**
* Creates a new instance the same as one instance (the "destination") but with some attribute
* values copied from another instance (the "source")
*
* @param source the source instance
* @param dest the destination instance
* @return the new merged instance
*/
protected Instance mergeInstances(Instance source, Instance dest) {
Instances outputFormat = outputFormatPeek();
double[] vals = new double[outputFormat.numAttributes()];
for (int i = 0; i < vals.length; i++) {
if ((i != outputFormat.classIndex()) && (m_SelectedCols.isInRange(i))) {
if (source != null) {
vals[i] = source.value(i);
} else {
vals[i] = Instance.missingValue();
}
} else {
vals[i] = dest.value(i);
}
}
Instance inst = null;
if (dest instanceof SparseInstance) {
inst = new SparseInstance(dest.weight(), vals);
} else {
inst = new Instance(dest.weight(), vals);
}
inst.setDataset(dest.dataset());
return inst;
}
/**
* Makes a database query to convert a table into a set of instances
*
* @param query the query to convert to instances
* @return the instances contained in the result of the query, NULL if the SQL query doesn't
* return a ResultSet, e.g., DELETE/INSERT/UPDATE
* @throws Exception if an error occurs
*/
public Instances retrieveInstances(String query) throws Exception {
if (m_Debug) System.err.println("Executing query: " + query);
connectToDatabase();
if (execute(query) == false) {
if (m_PreparedStatement.getUpdateCount() == -1) {
throw new Exception("Query didn't produce results");
} else {
if (m_Debug) System.err.println(m_PreparedStatement.getUpdateCount() + " rows affected.");
close();
return null;
}
}
ResultSet rs = getResultSet();
if (m_Debug) System.err.println("Getting metadata...");
ResultSetMetaData md = rs.getMetaData();
if (m_Debug) System.err.println("Completed getting metadata...");
// Determine structure of the instances
int numAttributes = md.getColumnCount();
int[] attributeTypes = new int[numAttributes];
Hashtable[] nominalIndexes = new Hashtable[numAttributes];
FastVector[] nominalStrings = new FastVector[numAttributes];
for (int i = 1; i <= numAttributes; i++) {
/* switch (md.getColumnType(i)) {
case Types.CHAR:
case Types.VARCHAR:
case Types.LONGVARCHAR:
case Types.BINARY:
case Types.VARBINARY:
case Types.LONGVARBINARY:*/
switch (translateDBColumnType(md.getColumnTypeName(i))) {
case STRING:
// System.err.println("String --> nominal");
attributeTypes[i - 1] = Attribute.NOMINAL;
nominalIndexes[i - 1] = new Hashtable();
nominalStrings[i - 1] = new FastVector();
break;
case TEXT:
// System.err.println("Text --> string");
attributeTypes[i - 1] = Attribute.STRING;
nominalIndexes[i - 1] = new Hashtable();
nominalStrings[i - 1] = new FastVector();
break;
case BOOL:
// System.err.println("boolean --> nominal");
attributeTypes[i - 1] = Attribute.NOMINAL;
nominalIndexes[i - 1] = new Hashtable();
nominalIndexes[i - 1].put("false", new Double(0));
nominalIndexes[i - 1].put("true", new Double(1));
nominalStrings[i - 1] = new FastVector();
nominalStrings[i - 1].addElement("false");
nominalStrings[i - 1].addElement("true");
break;
case DOUBLE:
// System.err.println("BigDecimal --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case BYTE:
// System.err.println("byte --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case SHORT:
// System.err.println("short --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case INTEGER:
// System.err.println("int --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case LONG:
// System.err.println("long --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case FLOAT:
// System.err.println("float --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case DATE:
attributeTypes[i - 1] = Attribute.DATE;
break;
case TIME:
attributeTypes[i - 1] = Attribute.DATE;
break;
default:
// System.err.println("Unknown column type");
attributeTypes[i - 1] = Attribute.STRING;
}
}
// For sqlite
// cache column names because the last while(rs.next()) { iteration for
// the tuples below will close the md object:
Vector<String> columnNames = new Vector<String>();
for (int i = 0; i < numAttributes; i++) {
columnNames.add(md.getColumnName(i + 1));
}
// Step through the tuples
if (m_Debug) System.err.println("Creating instances...");
FastVector instances = new FastVector();
int rowCount = 0;
while (rs.next()) {
if (rowCount % 100 == 0) {
if (m_Debug) {
System.err.print("read " + rowCount + " instances \r");
System.err.flush();
}
}
double[] vals = new double[numAttributes];
for (int i = 1; i <= numAttributes; i++) {
/*switch (md.getColumnType(i)) {
case Types.CHAR:
case Types.VARCHAR:
case Types.LONGVARCHAR:
case Types.BINARY:
case Types.VARBINARY:
case Types.LONGVARBINARY:*/
switch (translateDBColumnType(md.getColumnTypeName(i))) {
case STRING:
String str = rs.getString(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
Double index = (Double) nominalIndexes[i - 1].get(str);
if (index == null) {
index = new Double(nominalStrings[i - 1].size());
nominalIndexes[i - 1].put(str, index);
nominalStrings[i - 1].addElement(str);
}
vals[i - 1] = index.doubleValue();
}
break;
case TEXT:
String txt = rs.getString(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
Double index = (Double) nominalIndexes[i - 1].get(txt);
if (index == null) {
index = new Double(nominalStrings[i - 1].size());
nominalIndexes[i - 1].put(txt, index);
nominalStrings[i - 1].addElement(txt);
}
vals[i - 1] = index.doubleValue();
}
break;
case BOOL:
boolean boo = rs.getBoolean(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
vals[i - 1] = (boo ? 1.0 : 0.0);
}
break;
case DOUBLE:
// BigDecimal bd = rs.getBigDecimal(i, 4);
double dd = rs.getDouble(i);
// Use the column precision instead of 4?
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
// newInst.setValue(i - 1, bd.doubleValue());
vals[i - 1] = dd;
}
break;
case BYTE:
byte by = rs.getByte(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
vals[i - 1] = (double) by;
}
break;
case SHORT:
short sh = rs.getShort(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
vals[i - 1] = (double) sh;
}
break;
case INTEGER:
int in = rs.getInt(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
vals[i - 1] = (double) in;
}
break;
case LONG:
long lo = rs.getLong(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
vals[i - 1] = (double) lo;
}
break;
case FLOAT:
float fl = rs.getFloat(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
vals[i - 1] = (double) fl;
}
break;
case DATE:
Date date = rs.getDate(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
// TODO: Do a value check here.
vals[i - 1] = (double) date.getTime();
}
break;
case TIME:
Time time = rs.getTime(i);
if (rs.wasNull()) {
vals[i - 1] = Instance.missingValue();
} else {
// TODO: Do a value check here.
vals[i - 1] = (double) time.getTime();
}
break;
default:
vals[i - 1] = Instance.missingValue();
}
}
Instance newInst;
if (m_CreateSparseData) {
newInst = new SparseInstance(1.0, vals);
} else {
newInst = new Instance(1.0, vals);
}
instances.addElement(newInst);
rowCount++;
}
// disconnectFromDatabase(); (perhaps other queries might be made)
// Create the header and add the instances to the dataset
if (m_Debug) System.err.println("Creating header...");
FastVector attribInfo = new FastVector();
for (int i = 0; i < numAttributes; i++) {
/* Fix for databases that uppercase column names */
// String attribName = attributeCaseFix(md.getColumnName(i + 1));
String attribName = attributeCaseFix(columnNames.get(i));
switch (attributeTypes[i]) {
case Attribute.NOMINAL:
attribInfo.addElement(new Attribute(attribName, nominalStrings[i]));
break;
case Attribute.NUMERIC:
attribInfo.addElement(new Attribute(attribName));
break;
case Attribute.STRING:
Attribute att = new Attribute(attribName, (FastVector) null);
attribInfo.addElement(att);
for (int n = 0; n < nominalStrings[i].size(); n++) {
att.addStringValue((String) nominalStrings[i].elementAt(n));
}
break;
case Attribute.DATE:
attribInfo.addElement(new Attribute(attribName, (String) null));
break;
default:
throw new Exception("Unknown attribute type");
}
}
Instances result = new Instances("QueryResult", attribInfo, instances.size());
for (int i = 0; i < instances.size(); i++) {
result.add((Instance) instances.elementAt(i));
}
close(rs);
return result;
}
/** 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) {}
;
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>columns</b> representing individual </b>instances</b> and <b>rows</b> representing
* <b>attributes</b> (e.g. as is common with microarray data)
*/
public Instances tableColsToInstances(Table t, String relationName) {
System.err.print("Converting table cols to instances...");
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int r = 0; r < t.numRows; r++) {
if (rowIsNumeric(t, r)) {
isNominal.add(false);
atts.addElement(new Attribute(t.rowNames[r]));
allAttVals.add(null); // No enumeration of attribute values.
} else {
// It's nominal... determine the range of values and create a nominal attribute...
isNominal.add(true);
FastVector attVals = getRowValues(t, r);
atts.addElement(new Attribute(t.rowNames[r], attVals));
// Save it for later
allAttVals.add(attVals);
}
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < t.numCols; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
// For each attribute fill in the numeric or attributeValue index...
for (int r = 0; r < t.numRows; r++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[r] = Instance.missingValue();
else if (isNominal.get(r)) {
vals[r] = allAttVals.get(r).indexOf(val);
} else {
vals[r] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int c = 0; c < t.colNames.length; c++) {
instanceNames.add(t.colNames[c]);
newData.instance(c).setValue(attrIdx, t.colNames[c]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>rows</b> representing individual </b>instances</b> and <b>columns</b> representing
* <b>attributes</b>
*/
public Instances tableRowsToNominalInstances(Table t, String relationName) {
System.err.print("Converting table rows to instances...");
// Set up attributes, which for rowInstances will be the colNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int c = 0; c < t.numCols; c++) {
// It's nominal... determine the range of values
isNominal.add(true);
FastVector attVals = getColValues(t, c);
atts.addElement(new Attribute(t.colNames[c], attVals));
// Save it for later
allAttVals.add(attVals);
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
// Fill the instances with data...
// For each instance...
for (int r = 0; r < t.numRows; r++) {
double[] vals = new double[data.numAttributes()];
// for each attribute
for (int c = 0; c < t.numCols; c++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[c] = Instance.missingValue();
else if (isNominal.get(c)) {
vals[c] = allAttVals.get(c).indexOf(val);
} else {
vals[c] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int r = 0; r < t.rowNames.length; r++) {
instanceNames.add(t.rowNames[r]);
newData.instance(r).setValue(attrIdx, t.rowNames[r]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/**
* If we know in advance that the table is numeric, can optimize a lot... For example, on 9803 x
* 294 table, TableFileLoader.readNumeric takes 6s compared to 12s for WekaMine readFromTable.
*/
public static Instances readNumeric(String fileName, String relationName, String delimiter)
throws Exception {
int numAttributes = FileUtils.fastCountLines(fileName) - 1; // -1 exclude heading.
String[] attrNames = new String[numAttributes];
// Read the col headings and figure out the number of columns in the table..
BufferedReader reader = new BufferedReader(new FileReader(fileName), 4194304);
String line = reader.readLine();
String[] instanceNames = parseColNames(line, delimiter);
int numInstances = instanceNames.length;
System.err.print("reading " + numAttributes + " x " + numInstances + " table..");
// Create an array to hold the data as we read it in...
double dataArray[][] = new double[numAttributes][numInstances];
// Populate the matrix with values...
String valToken = "";
try {
int rowIdx = 0;
while ((line = reader.readLine()) != null) {
String[] tokens = line.split(delimiter, -1);
attrNames[rowIdx] = tokens[0].trim();
for (int colIdx = 0; colIdx < (tokens.length - 1); colIdx++) {
valToken = tokens[colIdx + 1];
double value;
if (valToken.equals("null")) {
value = Instance.missingValue();
} else if (valToken.equals("?")) {
value = Instance.missingValue();
} else if (valToken.equals("NA")) {
value = Instance.missingValue();
} else if (valToken.equals("")) {
value = Instance.missingValue();
// }else value = DoubleParser.lightningParse(valToken); // faster double parser with
// MANY assumptions
} else value = Double.parseDouble(valToken);
dataArray[rowIdx][colIdx] = value;
}
rowIdx++;
}
} catch (NumberFormatException e) {
System.err.println(e.toString());
System.err.println("Parsing line: " + line);
System.err.println("Parsing token: " + valToken);
}
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
for (int a = 0; a < numAttributes; a++) {
atts.addElement(new Attribute(attrNames[a]));
}
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
System.err.print("creating instances..");
// System.err.println("DEBUG: numAttributes "+numAttributes);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < numInstances; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
for (int r = 0; r < numAttributes; r++) {
double val = dataArray[r][c];
vals[r] = val;
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
// System.err.println("DEBUG: data.relationNAme"+data.relationName());
System.err.print("add feature names..");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
for (int c = 0; c < numInstances; c++) {
newData.instance(c).setValue(attrIdx, instanceNames[c]);
}
data = newData;
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
return (data);
}
