/*
* 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.
*/
/*
* BarHillelMetricMatlab.java
* Copyright (C) 2002 Sugato Basu
*
*/
package weka.core.metrics;
import weka.core.*;
import java.util.*;
import java.io.*;
/**
* Class for performing RCA according to Bar-Hillel's algorithm. <p>
*
*
* @author Sugato Basu
* @version $Revision: 1.3 $
*/
public class BarHillelMetricMatlab extends OfflineLearnableMetric
implements OptionHandler {
/** number of instances used to train metric */
int m_numInstances = -1;
/** number of attributes in each instance */
int m_numAttribs = -1;
/** instances used to train metric */
Instances m_trainInstances = null;
/** chunklet assignments of instances */
int [] m_chunkletAssignments = null;
/** full matrix returned by Matlab code */
protected double [][] m_attrMatrix = null;
/** We can have different ways of converting from distance to similarity */
public static final int CONVERSION_LAPLACIAN = 1;
public static final int CONVERSION_UNIT = 2;
public static final int CONVERSION_EXPONENTIAL = 4;
public static final Tag[] TAGS_CONVERSION = {
new Tag(CONVERSION_UNIT, "similarity = 1-distance"),
new Tag(CONVERSION_LAPLACIAN, "similarity=1/(1+distance)"),
new Tag(CONVERSION_EXPONENTIAL, "similarity=exp(-distance)")
};
/** The method of converting, by default laplacian */
protected int m_conversionType = CONVERSION_LAPLACIAN;
/** Name of the Matlab program file that computes RCA */
protected String m_RCAMFile = new String("/var/local/RCAmetric.m");
/** A timestamp suffix for matching vectors with attributes */
String m_timestamp = null;
/** Name of the file where attribute names will be stored */
// String m_rcaAttributeMatrixFilename = null;
// String m_rcaAttributeMatrixFilenameBase = new String("/var/local/RCAattributeMatrix");
String m_rcaAttributeMatrixFilename = new String("/var/local/RCAattributeMatrix");
/** Name of the file where dataMatrix will be stored */
public String m_dataFilename = new String("/var/local/RCAdataMatrix");
/** Name of the file where chunklet assignments will be stored */
// public String m_chunkletAssignmentFilename = null;
// public String m_chunkletAssignmentFilenameBase = new String("/var/local/RCAchunkletAssignment");
public String m_chunkletAssignmentFilename = new String("/var/local/RCAchunkletAssignment");
/**
* Create a new metric.
* @param numAttributes the number of attributes that the metric will work on
*/
public BarHillelMetricMatlab(int numAttributes) throws Exception {
buildMetric(numAttributes);
}
/** Create a default new metric */
public BarHillelMetricMatlab() {
}
/**
* Creates a new metric which takes specified attributes.
*
* @param _attrIdxs An array containing attribute indeces that will
* be used in the metric
*/
public BarHillelMetricMatlab(int[] _attrIdxs) throws Exception {
setAttrIdxs(_attrIdxs);
buildMetric(_attrIdxs.length);
}
/**
* Reset all values that have been learned
*/
public void resetMetric() throws Exception {
m_trained = false;
if (m_attrMatrix != null) {
for (int i = 0; i < m_attrMatrix.length; i++) {
for (int j = 0; j < m_attrMatrix.length; j++) {
if (i == j) {
m_attrMatrix[i][j] = 1;
} else {
m_attrMatrix[i][j] = 0;
}
}
}
}
}
/**
* Generates a new Metric. Has to initialize all fields of the metric
* with default values.
*
* @param numAttributes the number of attributes that the metric will work on
* @exception Exception if the distance metric has not been
* generated successfully.
*/
public void buildMetric(int numAttributes) throws Exception {
m_numAttributes = numAttributes;
m_attrMatrix = new double[numAttributes][numAttributes];
m_attrIdxs = new int[numAttributes];
for (int i = 0; i < numAttributes; i++) {
for (int j = 0; j < numAttributes; j++) {
if (i == j) {
m_attrMatrix[i][j] = 1;
} else {
m_attrMatrix[i][j] = 0;
}
}
m_attrIdxs[i] = i;
}
}
/**
* Generates a new Metric. Has to initialize all fields of the metric
* with default values
*
* @param options an array of options suitable for passing to setOptions.
* May be null.
* @exception Exception if the distance metric has not been
* generated successfully.
*/
public void buildMetric(int numAttributes, String[] options) throws Exception {
buildMetric(numAttributes);
}
/**
* Create a new metric for operating on specified instances
* @param data instances that the metric will be used on
*/
public void buildMetric(Instances data) throws Exception {
m_classIndex = data.classIndex();
m_numAttributes = data.numAttributes();
m_trainInstances = data;
m_numInstances = data.numInstances();
if (m_classIndex != m_numAttributes-1 && m_classIndex != -1) {
throw new Exception("Class attribute (" + m_classIndex + ") should be the last attribute!!!");
}
if (m_classIndex != -1) {
m_numAttributes--;
}
System.out.println("About to build metric with " + m_numAttributes + " attributes, trainable=" + m_trainable);
buildMetric(m_numAttributes);
}
/**
* Returns a distance value between two instances.
* @param instance1 First instance.
* @param instance2 Second instance.
* @exception Exception if distance could not be estimated.
*/
public double distance(Instance instance1, Instance instance2) throws Exception {
if (instance1 instanceof SparseInstance && instance2 instanceof SparseInstance) {
throw new Exception ("Not handled now!!\n\n");
} else if (instance1 instanceof SparseInstance) {
throw new Exception ("Not handled now!!\n\n");
} else if (instance2 instanceof SparseInstance) {
throw new Exception ("Not handled now!!\n\n");
} else {
// distance is (x-y)^T*A*(x-y)
// RETURNS SQUARE DISTANCE FOR EFFICIENCY!!
double [] values1 = instance1.toDoubleArray();
double [] values2 = instance2.toDoubleArray();
double distance = 0;
for (int col = 0; col < m_numAttribs; col++) {
double innerProduct = 0;
for (int row = 0; row < m_numAttribs; row++) {
if (col == m_classIndex || row == m_classIndex) {
continue;
}
innerProduct += (values1[row]-values2[row]) * m_attrMatrix[row][col];
}
distance += innerProduct * (values1[col]-values2[col]);
}
// distance = Math.sqrt(distance);
return distance;
}
}
/** Return the penalty contribution - distance*distance */
public double penalty(Instance instance1,
Instance instance2) throws Exception {
double distance = distance(instance1, instance2);
return distance * distance;
}
/** Return the penalty contribution - distance*distance */
public double penaltySymmetric(Instance instance1,
Instance instance2) throws Exception {
double distance = distance(instance1, instance2);
return distance * distance;
}
/**
* Set the type of distance to similarity conversion. Values other
* than CONVERSION_LAPLACIAN, CONVERSION_UNIT, or CONVERSION_EXPONENTIAL will be ignored
*
* @param type type of the similarity to distance conversion to use
*/
public void setConversionType(SelectedTag conversionType) {
if (conversionType.getTags() == TAGS_CONVERSION) {
m_conversionType = conversionType.getSelectedTag().getID();
}
}
/**
* return the type of distance to similarity conversion
* @return one of CONVERSION_LAPLACIAN, CONVERSION_UNIT, or CONVERSION_EXPONENTIAL
*/
public SelectedTag getConversionType() {
return new SelectedTag(m_conversionType, TAGS_CONVERSION);
}
/** The computation of a metric can be either based on distance, or on similarity
* @returns true because euclidean metrict fundamentally computes distance
*/
public boolean isDistanceBased() {
return true;
}
/**
* Returns a similarity estimate between two instances. Similarity is obtained by
* inverting the distance value using one of three methods:
* CONVERSION_LAPLACIAN, CONVERSION_EXPONENTIAL, CONVERSION_UNIT.
* @param instance1 First instance.
* @param instance2 Second instance.
* @exception Exception if similarity could not be estimated.
*/
public double similarity(Instance instance1, Instance instance2) throws Exception {
switch (m_conversionType) {
case CONVERSION_LAPLACIAN:
return 1 / (1 + distance(instance1, instance2));
case CONVERSION_UNIT:
return 2 * (1 - distance(instance1, instance2));
case CONVERSION_EXPONENTIAL:
return Math.exp(-distance(instance1, instance2));
default:
throw new Exception ("Unknown distance to similarity conversion method");
}
}
public void buildAttributeMatrix (Instances data, int [] clusterAssignments) throws Exception {
m_chunkletAssignments = clusterAssignments;
m_numInstances = data.numInstances();
m_numAttribs = data.instance(1).numAttributes();
if (m_timestamp == null) {
m_timestamp = getLogTimestamp();
}
// m_rcaAttributeMatrixFilename = new String(m_rcaAttributeMatrixFilenameBase + m_timestamp);
// m_chunkletAssignmentFilename = new String(m_chunkletAssignmentFilenameBase + m_timestamp);
System.out.println("About to run RCA on " + m_numInstances + " instances, each with " + m_numAttribs + " attributes");
dumpInstances(m_dataFilename);
dumpChunklets(m_chunkletAssignmentFilename);
prepareMatlab(m_RCAMFile);
runMatlab(m_RCAMFile, "/var/local/RCAmatlab.output");
System.out.println("Done running Matlab code ... now parsing matlab output files");
m_attrMatrix = readMatrix(m_rcaAttributeMatrixFilename);
if (m_attrMatrix == null) {
System.out.println("WARNING!! Could not parse matlab output file!!\n\n");
}
else {
System.out.println("Successfully parsed matlab output file and learned metric");
}
}
/**
* Read column vectors from a text file
* @param name file name
* @return a <code>double[][]</code> value
* @exception Exception if an error occurs
* @returns double[][] array corresponding to vectors
*/
public double[][] readMatrix(String name) throws Exception {
BufferedReader r = new BufferedReader(new FileReader(name));
double[][] vectors = new double[m_numAttribs][m_numAttribs];
int i = 0, j = 0;
String s = null;
// number of rows -- ignore
s = r.readLine();
// number of columns -- ignore
s = r.readLine();
// try {
// int numRows = (int)Double.parseDouble(s);
// } catch (Exception e) {
// System.err.println("Couldn't parse " + s + " as Double");
// }
// try {
// int numCols = (int)Double.parseDouble(s);
// } catch (Exception e) {
// System.err.println("Couldn't parse " + s + " as Double");
// }
while ((s = r.readLine()) != null) {
StringTokenizer tokenizer = new StringTokenizer(s);
while (tokenizer.hasMoreTokens()) {
String value = tokenizer.nextToken();
try {
vectors[i][j] = Double.parseDouble(value);
} catch (Exception e) {
System.err.println("Couldn't parse " + value + " as double");
}
j++;
if (j > m_numAttribs) {
System.err.println("Too many columns (" + j + " instead of " + m_numAttribs + ") in line: " + s);
}
}
if (j != m_numAttribs) {
System.err.println("Too few columns (" + j + " instead of " + m_numAttribs + ") in line: " + s);
}
j = 0;
i++;
if (i > m_numAttribs) {
System.err.println("Too many rows: " + i + ", expecting " + m_numAttribs + " attributes");
}
}
if (i != m_numAttribs) {
System.err.println("Too few rows: " + i + " expecting " + m_numAttribs + " attributes");
}
return vectors;
}
/**
* Dump data matrix into a file
*/
private void dumpInstances(String tempFile) {
try {
PrintWriter writer = new PrintWriter(new BufferedOutputStream(new FileOutputStream(tempFile)));
for (int k = 0; k < m_numInstances; k++) {
Instance instance = m_trainInstances.instance(k);
for (int j = 0; j < m_numAttribs; j++) {
writer.print(instance.value(j) + " ");
}
writer.println();
}
writer.close();
} catch (Exception e) {
System.err.println("Could not create a temporary file for dumping the data matrix: " + e);
}
}
/**
* Dump chunklet vector into a file
*/
private void dumpChunklets (String tempFile) {
try {
PrintWriter writer = new PrintWriter(new BufferedOutputStream(new FileOutputStream(tempFile)));
for (int k = 0; k < m_chunkletAssignments.length; k++) {
writer.print(m_chunkletAssignments[k] + " ");
}
writer.println();
writer.close();
} catch (Exception e) {
System.err.println("Could not create a temporary file for dumping the data matrix: " + e);
}
}
/** Create matlab m-file for ICA
* @param filename file where matlab script is created
*/
public void prepareMatlab(String filename) {
try{
PrintWriter writer = new PrintWriter(new BufferedOutputStream(new FileOutputStream(filename)));
writer.println("addpath /var/local;");
writer.println("addpath /u/ml/software/metric-learning/rca;");
writer.println("data = load('" + m_dataFilename + "');");
writer.println("chunklets = load('" + m_chunkletAssignmentFilename + "');");
writer.println("[A] = RCA(data,chunklets);");
writer.println("[AnumRows, AnumCols] = size(A);");
writer.println("save " + m_rcaAttributeMatrixFilename + " AnumRows AnumCols A -ASCII -DOUBLE");
writer.close();
}
catch (Exception e) {
System.err.println("Could not create matlab file: " + e);
}
}
/** Run matlab in command line with a given argument
* @param inFile file to be input to Matlab
* @param outFile file where results are stored
*/
public static void runMatlab(String inFile, String outFile) {
// call matlab to do the dirty work
try {
int exitValue;
do {
System.out.println("Starting to run matlab");
Process proc = Runtime.getRuntime().exec("matlab -nodesktop -nosplash < " + inFile + " > " + outFile);
exitValue = proc.waitFor();
if (exitValue != 0) {
System.err.println("WARNING!!!!! Matlab returned exit value 1, trying again in 5 mins!");
Thread.sleep(300000);
}
} while (exitValue != 0);
System.out.println("End of running matlab, exitValue = " + exitValue);
}
catch (Exception e) {
System.err.println("Problems running matlab: " + e);
}
}
/** Get a timestamp string as a weak uniqueid
* @returns a timestamp string in the form "mmddhhmmssS"
*/
public static String getLogTimestamp() {
Calendar cal = Calendar.getInstance(TimeZone.getDefault());
String DATE_FORMAT = "MMddHHmmssS";
java.text.SimpleDateFormat sdf = new java.text.SimpleDateFormat(DATE_FORMAT);
sdf.setTimeZone(TimeZone.getDefault());
return (sdf.format(cal.getTime()));
}
/**
* Given a cluster of instances, return the centroid of that cluster
* @param instances objects belonging to a cluster
* @param fastMode whether fast mode should be used for SparseInstances
* @param normalized normalize centroids for SPKMeans
* @return a centroid instance for the given cluster
*/
public Instance getCentroidInstance(Instances instances, boolean fastMode, boolean normalized) {
System.out.println("\n\nWARNING!! Not implemented!!\n\n");
return null;
}
/** Get the values of the partial derivates for the metric components
* for a particular instance pair
@param instance1 the first instance
@param instance2 the first instance
*/
public double[] getGradients(Instance instance1, Instance instance2) throws Exception {
System.out.println("\n\nWARNING!! Not implemented!!\n\n");
return null;
}
/**
* Create an instance with features corresponding to components of the two given instances
* @param instance1 first instance
* @param instance2 second instance
*/
public Instance createDiffInstance (Instance instance1, Instance instance2) {
System.out.println("\n\nWARNING!! Not implemented!!\n\n");
return null;
}
/**
* Train the distance metric. A specific metric will take care of
* its own training either via a metric learner or by itself.
*/
public void learnMetric(Instances data) throws Exception {
System.out.println("\n\nWARNING!! Not implemented!!\n\n");
}
/**
* Returns a distance value between two instances.
* @param instance1 First instance.
* @param instance2 Second instance.
* @exception Exception if distance could not be estimated.
*/
public double distanceNonWeighted(Instance instance1, Instance instance2) throws Exception {
System.out.println("\n\nWARNING!! Not implemented!!\n\n");
return -1;
}
/**
* Returns a similarity estimate between two instances without using the weights.
* @param instance1 First instance.
* @param instance2 Second instance.
* @exception Exception if similarity could not be estimated.
*/
public double similarityNonWeighted(Instance instance1, Instance instance2) throws Exception {
switch (m_conversionType) {
case CONVERSION_LAPLACIAN:
return 1 / (1 + distanceNonWeighted(instance1, instance2));
case CONVERSION_UNIT:
return 2 * (1 - distanceNonWeighted(instance1, instance2));
case CONVERSION_EXPONENTIAL:
return Math.exp(-distanceNonWeighted(instance1, instance2));
default:
throw new Exception ("Unknown distance to similarity conversion method");
}
}
/**
* Gets the current settings of WeightedEuclideanP.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String [] getOptions() {
String [] options = new String [45];
int current = 0;
if (m_conversionType == CONVERSION_EXPONENTIAL) {
options[current++] = "-E";
} else if (m_conversionType == CONVERSION_UNIT) {
options[current++] = "-U";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -N <br>
* Normalize the euclidean distance by vectors lengths
*
* -E <br>
* Use exponential conversion from distance to similarity
* (default laplacian conversion) <p>
*
* -U <br>
* Use unit conversion from similarity to distance (dist=1-sim)
* (default laplacian conversion) <p>
*
* -R <br>
* The metric is trainable and will be trained using the current MetricLearner
* (default non-trainable)
*
* @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 {
if (Utils.getFlag('E', options)) {
setConversionType(new SelectedTag(CONVERSION_EXPONENTIAL, TAGS_CONVERSION));
} else if (Utils.getFlag('U', options)) {
setConversionType(new SelectedTag(CONVERSION_UNIT, TAGS_CONVERSION));
} else {
setConversionType(new SelectedTag(CONVERSION_LAPLACIAN, TAGS_CONVERSION));
}
if (Utils.getFlag('R', options)) {
setTrainable(Utils.getFlag('R', options));
setExternal(Utils.getFlag('X', options));
}
Utils.checkForRemainingOptions(options);
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(4);
newVector.addElement(new Option("\tNormalize the euclidean distance by vectors lengths\n",
"N", 0, "-N"));
newVector.addElement(new Option("\tUse exponential conversion from similarity to distance\n",
"E", 0, "-E"));
newVector.addElement(new Option("\tUse unit conversion from similarity to distance\n",
"U", 0, "-U"));
newVector.addElement(new Option("\tTrain the metric\n",
"R", 0, "-R"));
newVector.addElement(new Option("\tUse the metric learner for similarity calculations(\"external\")",
"X", 0, "-X"));
newVector.addElement(new Option(
"\tFull class name of metric learner to use, followed\n"
+ "\tby scheme options. (required)\n"
+ "\teg: \"weka.core.metrics.ClassifierMetricLearner -B weka.classifiers.function.SMO\"",
"L", 1, "-L <classifier specification>"));
return newVector.elements();
}
/**
* Main method for testing this class
* @param argv should contain the command line arguments to the
* evaluator/transformer (see AttributeSelection)
*/
public static void main(String [] argv) {
}
}