/*
* RapidMiner
*
* Copyright (C) 2001-2011 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.features.transformation;
import java.util.Iterator;
import java.util.List;
import java.util.Random;
import Jama.Matrix;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetPassThroughRule;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.GenerateModelTransformationRule;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.RandomGenerator;
import com.rapidminer.tools.math.matrix.CovarianceMatrix;
/**
* Generalized Hebbian Algorithm (GHA) is an iterative method to compute
* principal components. From a computational point of view, it can be
* advantageous to solve the eigenvalue problem by iterative methods which do
* not need to compute the covariance matrix directly. This is useful when the
* ExampleSet contains many Attributes (hundreds, thousands). The operator
* outputs a <code>GHAModel</code>. With the <code>ModelApplier</code> you
* can transform the features.
*
* @author Daniel Hakenjos, Ingo Mierswa
*/
public class GHA extends Operator {
/** The parameter name for "Number of components to compute. If '-1' the number of attributes is taken.'" */
public static final String PARAMETER_NUMBER_OF_COMPONENTS = "number_of_components";
/** The parameter name for "Number of Iterations to apply the update rule." */
public static final String PARAMETER_NUMBER_OF_ITERATIONS = "number_of_iterations";
/** The parameter name for "The learning rate for GHA (small)" */
public static final String PARAMETER_LEARNING_RATE = "learning_rate";
private InputPort exampleSetInput = getInputPorts().createPort("example set input");
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set output");
private OutputPort originalOutput = getOutputPorts().createPort("original");
private OutputPort modelOutput = getOutputPorts().createPort("preprocessing model");
public GHA(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, Ontology.NUMERICAL));
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, exampleSetOutput, SetRelation.SUBSET) {
@Override
public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) {
int numberOfAttributes = metaData.getNumberOfRegularAttributes();
metaData.clearRegular();
// adding newly generated components
try {
int numberOfGeneratedAttributes = getParameterAsInt(PARAMETER_NUMBER_OF_COMPONENTS);
numberOfGeneratedAttributes = (numberOfGeneratedAttributes == -1) ? numberOfAttributes : numberOfGeneratedAttributes;
for (int i = 1; i <= numberOfGeneratedAttributes; i++) {
AttributeMetaData generatedAttribute = new AttributeMetaData("pc_" + i, Ontology.NUMERICAL);
metaData.addAttribute(generatedAttribute);
}
// knowing all remaining regular attributes
metaData.attributesAreKnown();
return metaData;
} catch (UndefinedParameterError e) {
return metaData;
}
}
});
getTransformer().addPassThroughRule(exampleSetInput, originalOutput);
getTransformer().addRule(new GenerateModelTransformationRule(exampleSetInput, modelOutput, GHAModel.class));
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData();
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
exampleSet.recalculateAllAttributeStatistics();
// 1) check whether all attributes are numerical
double[] means = new double[exampleSet.getAttributes().size()];
int a = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
if (!attribute.isNumerical()) {
throw new UserError(this, 104, "GHA", attribute.getName());
}
means[a] = exampleSet.getStatistics(attribute, Statistics.AVERAGE);
a++;
}
// 2) create data and subtract the mean
log("Initialising the weight matrix...");
double[][] data = new double[exampleSet.size()][exampleSet.getAttributes().size()];
Iterator<Example> reader = exampleSet.iterator();
Example example;
for (int sample = 0; sample < exampleSet.size(); sample++) {
example = reader.next();
int d = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
data[sample][d] = example.getValue(attribute) - means[d];
d++;
}
checkForStop();
}
// init
double learningRate = getParameterAsDouble(PARAMETER_LEARNING_RATE);
int numberOfComponents = getParameterAsInt(PARAMETER_NUMBER_OF_COMPONENTS);
if (numberOfComponents < 0)
numberOfComponents = exampleSet.getAttributes().size();
int numberOfIterations = getParameterAsInt(PARAMETER_NUMBER_OF_ITERATIONS);
double[][] randomMatrix = new double[numberOfComponents][exampleSet.getAttributes().size()];
for (int i = 0; i < randomMatrix.length; i++) {
for (int j = 0; j < randomMatrix[i].length; j++) {
randomMatrix[i][j] = random.nextDouble();
}
}
Matrix W = new Matrix(randomMatrix);
W.timesEquals(0.1d);
log("Training with learning rate: " + learningRate);
train(data, W, numberOfIterations, learningRate, random);
log("Creating the model...");
// compute eigenvalues
// --> create covariancematrix
// --> multiply with eigenvector
// --> calculate eigenvalue
Matrix covarianceMatrix = CovarianceMatrix.getCovarianceMatrix(exampleSet);
Matrix tmp = W.times(covarianceMatrix);
double[][] weights = W.getArray();
double[][] tmparray = tmp.getArray();
double[] eigenvalues = new double[numberOfComponents];
for (int i = 0; i < weights.length; i++) {
double nr = 0;
eigenvalues[i] = 0.0d;
for (int j = 0; j < weights[0].length; j++) {
tmparray[i][j] = tmparray[i][j] / weights[i][j];
if (tmparray[i][j] > 0.0d) {
nr += 1.0d;
eigenvalues[i] += tmparray[i][j];
}
}
nr = Math.max(nr, 1.0d);
eigenvalues[i] = eigenvalues[i] / nr;
}
GHAModel model = new GHAModel(exampleSet, eigenvalues, W.getArray(), means);
if (exampleSetOutput.isConnected())
exampleSetOutput.deliver(model.apply((ExampleSet)exampleSet.clone()));
originalOutput.deliver(exampleSet);
modelOutput.deliver(model);
}
private void train(double[][] data, Matrix W, int numberOfIterations, double learningRate, Random random) throws OperatorException {
int sample;
Matrix x;
Matrix y;
int iterlog = 1;
while ((numberOfIterations / iterlog > 10) && (numberOfIterations / (iterlog * 10) >= 3)) {
iterlog *= 10;
}
for (int iter = 1; iter <= numberOfIterations; iter++) {
if (iter % iterlog == 0) {
log("Iteration " + iter);
}
sample = (int) (random.nextDouble() * data.length);
// sample as matrix
x = new Matrix(data[sample], data[sample].length);
// create output y
// y = W'*x;
y = W.times(x);
// double[rows][columns]
double[][] yyT = y.times(y.transpose()).getArray();
// lower triangular
for (int row = 0; row < yyT.length; row++) {
for (int col = row + 1; col < yyT.length; col++) {
yyT[row][col] = 0.0d;
}
}
// the lower triangular matrix
Matrix LT = new Matrix(yyT);
// W = W + beta*(x*y' - W*tril(y*y'));
// beta = options.rate*options.annealfunc(iter);
Matrix tmp1 = y.times(x.transpose());
Matrix tmp2 = LT.times(W);
tmp1 = tmp1.minus(tmp2);
tmp1.timesEquals(learningRate);
W.plusEquals(tmp1);
double[][] w = W.getArray();
for (int i = 0; i < w.length; i++) {
for (int j = 0; j < w[0].length; j++) {
if ((Double.isInfinite(w[i][j])) || (Double.isNaN(w[i][j]))) {
throw new OperatorException("Lost convergence at iterator " + (iter + 1) + ". Lower learning rate?");
}
}
}
checkForStop();
// all ok continue iteration
}
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_OF_COMPONENTS, "Number of components to compute. If \'-1\' nr of attributes is taken.'", -1, Integer.MAX_VALUE, -1);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_OF_ITERATIONS, "Number of Iterations to apply the update rule.", 0, Integer.MAX_VALUE, 10);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_LEARNING_RATE, "The learning rate for GHA (small)", 0.0d, Double.POSITIVE_INFINITY, 0.01d);
types.add(type);
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
}