/*
* 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., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* StringToWordVector.java
* Copyright (C) 2002 University of Waikato
*
* Updated 12/Dec/2001 by Gordon Paynter (gordon.paynter@ucr.edu)
* Added parameters for delimiter set,
* number of words to add, and input range.
*/
package weka.filters.unsupervised.attribute;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Iterator;
import java.util.Random;
import java.util.StringTokenizer;
import java.util.TreeMap;
import java.util.Vector;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
/**
* Converts String attributes into a set of attributes representing word
* occurrence information from the text contained in the strings. The set of
* words (attributes) is determined by the first batch filtered (typically
* training data).
*
* @author Len Trigg (len@reeltwo.com)
* @author Stuart Inglis (stuart@reeltwo.com)
* @version $Revision: 1.1.1.1 $
*/
public class StringToWordVector extends Filter
implements UnsupervisedFilter, OptionHandler {
/** Delimiters used in tokenization */
private String delimiters = " \n\t.,:'\"()?!";
/** Range of columns to convert to word vectors */
protected Range m_SelectedRange = null;
/** Contains a mapping of valid words to attribute indexes */
private TreeMap m_Dictionary = new TreeMap();
/** True if the first batch has been done */
private boolean m_FirstBatchDone = false;
/** True if output instances should contain word frequency rather than boolean 0 or 1. */
private boolean m_OutputCounts = false;
/**
* The default number of words (per class if there is a class attribute
* assigned) to attempt to keep.
*/
private int m_WordsToKeep = 1000;
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector newVector = new Vector(3);
newVector.addElement(new Option(
"\tOutput word counts rather than boolean word presence.\n",
"C", 0, "-C"));
newVector.addElement(new Option(
"\tString containing the set of delimiter characters\n"
+ "\t(default: \" \\n\\t.,:'\\\"()?!\")",
"D", 1, "-D <delimiter set>"));
newVector.addElement(new Option(
"\tSpecify list of string attributes to convert to words (as weka Range).\n"
+ "\t(default: select all string attributes)",
"R", 1, "-R <index1,index2-index4,...>"));
newVector.addElement(new Option(
"\tSpecify approximate number of word fields to create.\n"
+ "\tSurplus words will be discarded..\n"
+ "\t(default: 1000)",
"W", 1, "-W <number of words to keep>"));
return newVector.elements();
}
/**
* Parses a given list of options controlling the behaviour of this object.
* Valid options are:<p>
*
* -C<br>
* Output word counts rather than boolean word presence.<p>
*
* -D delimiter_charcters <br>
* Specify set of delimiter characters
* (default: " \n\t.,:'\\\"()?!\"<p>
*
* -R index1,index2-index4,...<br>
* Specify list of string attributes to convert to words.
* (default: all string attributes)<p>
*
* -W number_of_words_to_keep <br>
* Specify number of word fields to create.
* Other, less useful words will be discarded.
* (default: 1000)<p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String value = Utils.getOption('D', options);
if (value.length() != 0) {
setDelimiters(value);
}
value = Utils.getOption('R', options);
if (value.length() != 0) {
setSelectedRange(value);
}
value = Utils.getOption('W', options);
if (value.length() != 0) {
setWordsToKeep(Integer.valueOf(value).intValue());
}
setOutputWordCounts(Utils.getFlag('C', options));
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options = new String [11];
int current = 0;
options[current++] = "-D";
options[current++] = getDelimiters();
if (getSelectedRange() != null) {
options[current++] = "-R";
m_SelectedRange.setUpper(getInputFormat().numAttributes() - 1);
options[current++] = getSelectedRange().getRanges();
}
options[current++] = "-W";
options[current++] = String.valueOf(getWordsToKeep());
if (getOutputWordCounts()) {
options[current++] = "-C";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Default constructor. Targets 1000 words in the output.
*/
public StringToWordVector() {
}
/**
* Constructor that allows specification of the target number of words
* in the output.
*
* @param wordsToKeep the number of words in the output vector (per class
* if assigned).
*/
public StringToWordVector(int wordsToKeep) {
m_WordsToKeep = wordsToKeep;
}
/**
* Used to store word counts for dictionary selection based on
* a threshold.
*/
private class Count implements Serializable {
public int count;
public Count(int c) { count = c; }
}
/**
* Sets the format of the input instances.
*
* @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
* @exception Exception if the input format can't be set
* successfully
*/
public boolean setInputFormat(Instances instanceInfo)
throws Exception {
super.setInputFormat(instanceInfo);
m_FirstBatchDone = false;
return false;
}
/**
* Input an instance for filtering. Filter requires all
* training 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 structure has been defined.
*/
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (m_FirstBatchDone) {
convertInstance(instance);
return true;
} else {
bufferInput(instance);
return false;
}
}
/**
* 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.
*
* @return true if there are instances pending output.
* @exception IllegalStateException if no input structure has been defined.
*/
public boolean batchFinished() {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
// Determine the dictionary
if (!m_FirstBatchDone) {
determineDictionary();
}
// Convert pending input instances.
for(int i = 0; i < getInputFormat().numInstances(); i++) {
convertInstance(getInputFormat().instance(i));
}
flushInput();
m_NewBatch = true;
m_FirstBatchDone = true;
return (numPendingOutput() != 0);
}
/**
* Gets whether output instances contain 0 or 1 indicating word
* presence, or word counts.
*
* @return true if word counts should be output.
*/
public boolean getOutputWordCounts() {
return m_OutputCounts;
}
/**
* Sets whether output instances contain 0 or 1 indicating word
* presence, or word counts.
*
* @param outputWordCounts true if word counts should be output.
*/
public void setOutputWordCounts(boolean outputWordCounts) {
m_OutputCounts = outputWordCounts;
}
/**
* Get the value of delimiters.
*
* @return Value of delimiters.
*/
public String getDelimiters() {
return delimiters;
}
/**
* Set the value of delimiters.
*
* @param newdelimiters Value to assign to delimiters.
*/
public void setDelimiters(String newDelimiters) {
delimiters = newDelimiters;
}
/**
* Get the value of m_SelectedRange.
*
* @return Value of m_SelectedRange.
*/
public Range getSelectedRange() {
return m_SelectedRange;
}
/**
* Set the value of m_SelectedRange.
*
* @param newSelectedRange Value to assign to m_SelectedRange.
*/
public void setSelectedRange(String newSelectedRange) {
m_SelectedRange = new Range(newSelectedRange);
}
/**
* Gets the number of words (per class if there is a class attribute
* assigned) to attempt to keep.
*
* @return the target number of words in the output vector (per class if
* assigned).
*/
public int getWordsToKeep() {
return m_WordsToKeep;
}
/**
* Sets the number of words (per class if there is a class attribute
* assigned) to attempt to keep.
*
* @param newWordsToKeep the target number of words in the output
* vector (per class if assigned).
*/
public void setWordsToKeep(int newWordsToKeep) {
m_WordsToKeep = newWordsToKeep;
}
private static void sortArray(int [] array) {
int i, j, h, N = array.length - 1;
for (h = 1; h <= N / 9; h = 3 * h + 1);
for (; h > 0; h /= 3) {
for (i = h + 1; i <= N; i++) {
int v = array[i];
j = i;
while (j > h && array[j - h] > v ) {
array[j] = array[j - h];
j -= h;
}
array[j] = v;
}
}
}
private void determineSelectedRange() {
Instances inputFormat = getInputFormat();
// Calculate the default set of fields to convert
if (m_SelectedRange == null) {
StringBuffer fields = new StringBuffer();
for (int j = 0; j < inputFormat.numAttributes(); j++) {
if (inputFormat.attribute(j).type() == Attribute.STRING)
fields.append((j + 1) + ",");
}
m_SelectedRange = new Range(fields.toString());
}
m_SelectedRange.setUpper(inputFormat.numAttributes() - 1);
// Prevent the user from converting non-string fields
StringBuffer fields = new StringBuffer();
for (int j = 0; j < inputFormat.numAttributes(); j++) {
if (m_SelectedRange.isInRange(j)
&& inputFormat.attribute(j).type() == Attribute.STRING)
fields.append((j + 1) + ",");
}
m_SelectedRange.setRanges(fields.toString());
// System.err.println("Selected Range: " + getSelectedRange().getRanges());
}
private void determineDictionary() {
// System.err.println("Creating dictionary");
int classInd = getInputFormat().classIndex();
int values = 1;
if (classInd != -1) {
values = getInputFormat().attribute(classInd).numValues();
}
TreeMap dictionaryArr [] = new TreeMap[values];
for (int i = 0; i < values; i++) {
dictionaryArr[i] = new TreeMap();
}
// Make sure we know which fields to convert
determineSelectedRange();
// Tokenize all training text into an orderedMap of "words".
for (int i = 0; i < getInputFormat().numInstances(); i++) {
/*
if (i % 10 == 0) {
System.err.print( i + " " + getInputFormat().numInstances() + "\r");
System.err.flush();
}
*/
Instance instance = getInputFormat().instance(i);
for (int j = 0; j < instance.numAttributes(); j++) {
if (m_SelectedRange.isInRange(j) && (instance.isMissing(j) == false)) {
//getInputFormat().attribute(j).type() == Attribute.STRING
StringTokenizer st = new StringTokenizer(instance.stringValue(j),
delimiters);
while (st.hasMoreTokens()) {
String word = st.nextToken().intern();
int vInd = 0;
if (classInd != -1) {
vInd = (int)instance.classValue();
}
Count count = (Count)dictionaryArr[vInd].get(word);
if (count == null) {
dictionaryArr[vInd].put(word, new Count(1));
} else {
count.count ++;
}
}
}
}
}
int totalsize = 0;
int prune[] = new int[values];
for (int z = 0; z < values; z++) {
totalsize += dictionaryArr[z].size();
int array[] = new int[dictionaryArr[z].size()];
int pos = 0;
Iterator it = dictionaryArr[z].keySet().iterator();
while (it.hasNext()) {
String word = (String)it.next();
Count count = (Count)dictionaryArr[z].get(word);
array[pos] = count.count;
pos++;
}
// sort the array
sortArray(array);
if (array.length < m_WordsToKeep) {
// if there aren't enough words, set the threshold to 1
prune[z] = 1;
} else {
// otherwise set it to be at least 1
prune[z] = Math.max(1, array[array.length - m_WordsToKeep]);
}
}
/*
for (int z=0;z<values;z++) {
System.err.println(dictionaryArr[z].size()+" "+totalsize);
}
*/
// Convert the dictionary into an attribute index
// and create one attribute per word
FastVector attributes = new FastVector(totalsize +
getInputFormat().numAttributes());
// Add the non-converted attributes
int classIndex = -1;
for (int i = 0; i < getInputFormat().numAttributes(); i++) {
if (!m_SelectedRange.isInRange(i)) {
if (getInputFormat().classIndex() == i) {
classIndex = attributes.size();
}
attributes.addElement(getInputFormat().attribute(i).copy());
}
}
// Add the word vector attributes
TreeMap newDictionary = new TreeMap();
int index = attributes.size();
for(int z = 0; z < values; z++) {
/*
System.err.print("\nCreating word index...");
if (values > 1) {
System.err.print(" for class id=" + z);
}
System.err.flush();
*/
Iterator it = dictionaryArr[z].keySet().iterator();
while (it.hasNext()) {
String word = (String)it.next();
Count count = (Count)dictionaryArr[z].get(word);
if (count.count >= prune[z]) {
// System.err.println(word+" "+newDictionary.get(word));
if(newDictionary.get(word) == null) {
/*
if (values > 1) {
System.err.print(getInputFormat().classAttribute().value(z) + " ");
}
System.err.println(word);
*/
newDictionary.put(word, new Integer(index++));
attributes.addElement(new Attribute(word));
}
}
}
}
attributes.trimToSize();
m_Dictionary = newDictionary;
//System.err.println("done: " + index + " words in total.");
// Set the filter's output format
Instances outputFormat = new Instances(getInputFormat().relationName(),
attributes, 0);
outputFormat.setClassIndex(classIndex);
setOutputFormat(outputFormat);
}
private void convertInstance(Instance instance) {
// Convert the instance into a sorted set of indexes
TreeMap contained = new TreeMap();
// Copy all non-converted attributes from input to output
int firstCopy = 0;
for (int i = 0; i < getInputFormat().numAttributes(); i++) {
if (!m_SelectedRange.isInRange(i)) {
if (getInputFormat().attribute(i).type() != Attribute.STRING) {
// Add simple nominal and numeric attributes directly
if (instance.value(i) != 0.0) {
contained.put(new Integer(firstCopy),
new Double(instance.value(i)));
}
} else {
if (instance.isMissing(i)) {
contained.put(new Integer(firstCopy),
new Double(Instance.missingValue()));
} else {
// If this is a string attribute, we have to first add
// this value to the range of possible values, then add
// its new internal index.
if (outputFormatPeek().attribute(firstCopy).numValues() == 0) {
// Note that the first string value in a
// SparseInstance doesn't get printed.
outputFormatPeek().attribute(firstCopy)
.addStringValue("Hack to defeat SparseInstance bug");
}
int newIndex = outputFormatPeek().attribute(firstCopy)
.addStringValue(instance.stringValue(i));
contained.put(new Integer(firstCopy),
new Double(newIndex));
}
}
firstCopy++;
}
}
for (int j = 0; j < instance.numAttributes(); j++) {
//if ((getInputFormat().attribute(j).type() == Attribute.STRING)
if (m_SelectedRange.isInRange(j)
&& (instance.isMissing(j) == false)) {
StringTokenizer st = new StringTokenizer(instance.stringValue(j),
delimiters);
while (st.hasMoreTokens()) {
String word = st.nextToken();
Integer index = (Integer) m_Dictionary.get(word);
if (index != null) {
if (m_OutputCounts) { // Separate if here rather than two lines down to avoid hashtable lookup
Double count = (Double)contained.get(index);
if (count != null) {
contained.put(index, new Double(count.doubleValue() + 1.0));
} else {
contained.put(index, new Double(1));
}
} else {
contained.put(index, new Double(1));
}
}
}
}
}
// Convert the set to structures needed to create a sparse instance.
double [] values = new double [contained.size()];
int [] indices = new int [contained.size()];
Iterator it = contained.keySet().iterator();
for (int i = 0; it.hasNext(); i++) {
Integer index = (Integer)it.next();
Double value = (Double)contained.get(index);
values[i] = value.doubleValue();
indices[i] = index.intValue();
}
Instance inst = new SparseInstance(instance.weight(), values, indices,
outputFormatPeek().numAttributes());
inst.setDataset(outputFormatPeek());
push(inst);
//System.err.print("#"); System.err.flush();
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter:
* use -h for help
*/
public static void main(String [] argv) {
try {
if (Utils.getFlag('b', argv)) {
Filter.batchFilterFile(new StringToWordVector(), argv);
} else {
Filter.filterFile(new StringToWordVector(), argv);
}
} catch (Exception ex) {
ex.printStackTrace();
System.out.println(ex.getMessage());
}
}
}