mirror of https://github.com/jlizier/jidt
Added unit tests for conditional TE continuous calculators.
Added some more tests for TE Kraskov also.
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joseph.lizier
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926f22992e
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package infodynamics.measures.continuous;
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import junit.framework.TestCase;
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import infodynamics.utils.MatrixUtils;
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import infodynamics.utils.RandomGenerator;
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public abstract class ConditionalTransferEntropyAbstractTester extends TestCase {
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/**
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* Confirm that the local values average correctly back to the average value
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*
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* @param teCalc a pre-constructed ConditionalTransferEntropyCalculator object
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* @param timeSteps number of time steps for the random data
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* @param k dest history length for the TE calculator to use
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*/
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public void testLocalsAverageCorrectly(ConditionalTransferEntropyCalculator teCalc,
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int timeSteps, int k)
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throws Exception {
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teCalc.initialise(k, 1, 1);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[] sourceData = rg.generateNormalData(timeSteps,
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0, 1);
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double[] destData = rg.generateNormalData(timeSteps,
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0, 1);
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double[] condData = rg.generateNormalData(timeSteps,
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0, 1);
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teCalc.setObservations(sourceData, destData, condData);
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//teCalc.setDebug(true);
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double te = teCalc.computeAverageLocalOfObservations();
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//teCalc.setDebug(false);
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double[] teLocal = teCalc.computeLocalOfPreviousObservations();
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System.out.printf("Average was %.5f\n", te);
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assertEquals(te, MatrixUtils.mean(teLocal, k, timeSteps-k), 0.00001);
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}
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/**
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* Confirm that significance testing doesn't alter the average that
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* would be returned.
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*
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* @param teCalc a pre-constructed ConditionalTransferEntropyCalculator object
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* @param timeSteps number of time steps for the random data
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* @param k history length for the TE calculator to use
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* @throws Exception
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*/
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public void testComputeSignificanceDoesntAlterAverage(ConditionalTransferEntropyCalculator teCalc,
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int timeSteps, int k) throws Exception {
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teCalc.initialise(k, 1, 1);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[] sourceData = rg.generateNormalData(timeSteps,
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0, 1);
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double[] destData = rg.generateNormalData(timeSteps,
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0, 1);
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double[] condData = rg.generateNormalData(timeSteps,
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0, 1);
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teCalc.setObservations(sourceData, destData, condData);
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//teCalc.setDebug(true);
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double te = teCalc.computeAverageLocalOfObservations();
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//teCalc.setDebug(false);
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//double[] teLocal = teCalc.computeLocalOfPreviousObservations();
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System.out.printf("Average was %.5f\n", te);
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// Now look at statistical significance tests
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int[][] newOrderings = rg.generateDistinctRandomPerturbations(
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timeSteps - k, 100);
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teCalc.computeSignificance(newOrderings);
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// And compute the average value again to check that it's consistent:
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for (int i = 0; i < 10; i++) {
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double averageCheck1 = teCalc.computeAverageLocalOfObservations();
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assertEquals(te, averageCheck1);
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}
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}
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/**
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* Confirm that univariate method signature calls fail if the calculator
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* was not initialised for univariate conditional data.
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*
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* @param teCalc
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*/
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public void testUnivariateCallFailsIfWrongInitialisation(ConditionalTransferEntropyCalculator teCalc) throws Exception {
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teCalc.initialise(1, 1, 1);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[] sourceData = rg.generateNormalData(10,
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0, 1);
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double[] destData = rg.generateNormalData(10,
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0, 1);
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double[] condData = rg.generateNormalData(10,
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0, 1);
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boolean gotException = false;
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try {
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teCalc.setObservations(sourceData, destData, condData);
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} catch (Exception e) {
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gotException = true;
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}
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assert(gotException);
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}
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/**
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* Confirm the workings of the conditional TE calculator
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* by calculating pairwise TE with a long k, then computing
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* conditional TE with a shorter history length k, but
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* other conditional variables copying the values of those further past
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* values of the destination.
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*
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* @param teCalc a pre-constructed TransferEntropyCalculator object
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* @param condTeCalc a pre-constructed ConditionalTransferEntropyCalculator object
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* of the same estimator type
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* @param timeSteps number of time steps for the random data
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* @param k history length for the TE calculator to use
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* @throws Exception
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*/
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public void testConditionalAgainstOrdinaryTE(
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TransferEntropyCalculator teCalc,
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ConditionalTransferEntropyCalculator condTeCalc,
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int timeSteps, int k) throws Exception {
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if (k < 2) {
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throw new Exception("Need k >= 2 for testConditionalAgainstOrdinaryTE");
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}
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[] sourceData = rg.generateNormalData(timeSteps,
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0, 1);
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double[] destData = rg.generateNormalData(timeSteps,
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0, 1);
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// First compute ordinary TE
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teCalc.initialise(k);
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teCalc.setObservations(sourceData, destData);
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double te = teCalc.computeAverageLocalOfObservations();
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System.out.printf("TE(k=%d): Average was %.5f\n", k, te);
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// Next compute conditional TE with some older
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// parts of the destination as conditional variables
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// instead of in the destination past.
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int[] condDims = {k-1};
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int[] condTaus = {1};
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int[] condDelays = {2};
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condTeCalc.initialise(1, 1, 1, 1, 1, condDims, condTaus, condDelays);
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// Don't need to extract the data ourselves:
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// double[][] condData =
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// MatrixUtils.makeDelayEmbeddingVector(destData, k-1,
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// k-2, destData.length-k+1); // Need an extra unused one here
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condTeCalc.setObservations(sourceData, destData, destData);
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double condTe = condTeCalc.computeAverageLocalOfObservations();
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System.out.printf("CondTE(k=%d): Average was %.5f\n", k, condTe);
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assertEquals(teCalc.getNumObservations(), condTeCalc.getNumObservations());
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assertEquals(te, condTe, 0.000000001);
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}
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}
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@ -0,0 +1,30 @@
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package infodynamics.measures.continuous.gaussian;
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import infodynamics.measures.continuous.ConditionalTransferEntropyAbstractTester;
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public class ConditionalTransferEntropyGaussianTester extends
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ConditionalTransferEntropyAbstractTester {
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public void testUnivariateMethodSignatureFails() throws Exception {
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ConditionalTransferEntropyCalculatorGaussian teCalc = new ConditionalTransferEntropyCalculatorGaussian();
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super.testUnivariateCallFailsIfWrongInitialisation(teCalc);
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}
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public void testLocalsAverageCorrectly() throws Exception {
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ConditionalTransferEntropyCalculatorGaussian teCalc = new ConditionalTransferEntropyCalculatorGaussian();
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super.testLocalsAverageCorrectly(teCalc, 100, 2);
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}
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public void testComputeSignificanceDoesntAlterAverage() throws Exception {
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ConditionalTransferEntropyCalculatorGaussian teCalc = new ConditionalTransferEntropyCalculatorGaussian();
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super.testComputeSignificanceDoesntAlterAverage(teCalc, 100, 2);
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}
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public void testAgainstApparentTE() throws Exception {
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TransferEntropyCalculatorGaussian teCalc = new TransferEntropyCalculatorGaussian();
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ConditionalTransferEntropyCalculatorGaussian condTeCalc = new ConditionalTransferEntropyCalculatorGaussian();
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testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 2);
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testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 3);
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testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 4);
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}
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}
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@ -2,12 +2,22 @@ package infodynamics.measures.continuous.gaussian;
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import java.util.Vector;
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import infodynamics.measures.continuous.TransferEntropyAbstractTester;
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import infodynamics.utils.ArrayFileReader;
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import infodynamics.utils.MatrixUtils;
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import infodynamics.utils.RandomGenerator;
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import junit.framework.TestCase;
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public class TransferEntropyGaussianTester extends TestCase {
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public class TransferEntropyGaussianTester extends TransferEntropyAbstractTester {
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public void testLocalsAverageCorrectly() throws Exception {
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TransferEntropyCalculatorGaussian teCalc = new TransferEntropyCalculatorGaussian();
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super.testLocalsAverageCorrectly(teCalc, 100, 2);
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}
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public void testComputeSignificanceDoesntAlterAverage() throws Exception {
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TransferEntropyCalculatorGaussian teCalc = new TransferEntropyCalculatorGaussian();
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super.testComputeSignificanceDoesntAlterAverage(teCalc, 100, 2);
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}
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public void testEmbedding() throws Exception {
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ArrayFileReader afr = new ArrayFileReader("demos/data/2coupledRandomCols-1.txt");
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@ -0,0 +1,49 @@
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package infodynamics.measures.continuous.kraskov;
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import infodynamics.measures.continuous.ConditionalTransferEntropyAbstractTester;
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public class ConditionalTransferEntropyKraskovTester extends
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ConditionalTransferEntropyAbstractTester {
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public void testUnivariateMethodSignatureFails() throws Exception {
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ConditionalTransferEntropyCalculatorKraskov teCalc = new ConditionalTransferEntropyCalculatorKraskov();
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String kraskov_K = "4";
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teCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
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kraskov_K);
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super.testUnivariateCallFailsIfWrongInitialisation(teCalc);
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}
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public void testLocalsAverageCorrectly() throws Exception {
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ConditionalTransferEntropyCalculatorKraskov teCalc = new ConditionalTransferEntropyCalculatorKraskov();
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String kraskov_K = "4";
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teCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
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kraskov_K);
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super.testLocalsAverageCorrectly(teCalc, 100, 2);
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}
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public void testComputeSignificanceDoesntAlterAverage() throws Exception {
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ConditionalTransferEntropyCalculatorKraskov teCalc = new ConditionalTransferEntropyCalculatorKraskov();
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String kraskov_K = "4";
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teCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
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kraskov_K);
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super.testComputeSignificanceDoesntAlterAverage(teCalc, 100, 2);
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}
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public void testAgainstApparentTE() throws Exception {
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TransferEntropyCalculatorKraskov teCalc = new TransferEntropyCalculatorKraskov();
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String kraskov_K = "4";
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teCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
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kraskov_K);
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ConditionalTransferEntropyCalculatorKraskov condTeCalc = new ConditionalTransferEntropyCalculatorKraskov();
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condTeCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
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kraskov_K);
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testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 2);
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testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 3);
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testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 4);
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}
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}
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