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Added unit tests for conditional TE continuous calculators. 

Added some more tests for TE Kraskov also.
This commit is contained in:
joseph.lizier 2014-04-17 06:00:54 +00:00
parent a63e104dc6
commit 926f22992e
4 changed files with 261 additions and 2 deletions
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170
java/unittests/infodynamics/measures/continuous/ConditionalTransferEntropyAbstractTester.java Executable file
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package infodynamics.measures.continuous;
import junit.framework.TestCase;
import infodynamics.utils.MatrixUtils;
import infodynamics.utils.RandomGenerator;
public abstract class ConditionalTransferEntropyAbstractTester extends TestCase {
/**
* Confirm that the local values average correctly back to the average value
*
* @param teCalc a pre-constructed ConditionalTransferEntropyCalculator object
* @param timeSteps number of time steps for the random data
* @param k dest history length for the TE calculator to use
*/
public void testLocalsAverageCorrectly(ConditionalTransferEntropyCalculator teCalc,
int timeSteps, int k)
throws Exception {
teCalc.initialise(k, 1, 1);
// generate some random data
RandomGenerator rg = new RandomGenerator();
double[] sourceData = rg.generateNormalData(timeSteps,
0, 1);
double[] destData = rg.generateNormalData(timeSteps,
0, 1);
double[] condData = rg.generateNormalData(timeSteps,
0, 1);
teCalc.setObservations(sourceData, destData, condData);
//teCalc.setDebug(true);
double te = teCalc.computeAverageLocalOfObservations();
//teCalc.setDebug(false);
double[] teLocal = teCalc.computeLocalOfPreviousObservations();
System.out.printf("Average was %.5f\n", te);
assertEquals(te, MatrixUtils.mean(teLocal, k, timeSteps-k), 0.00001);
}
/**
* Confirm that significance testing doesn't alter the average that
* would be returned.
*
* @param teCalc a pre-constructed ConditionalTransferEntropyCalculator object
* @param timeSteps number of time steps for the random data
* @param k history length for the TE calculator to use
* @throws Exception
*/
public void testComputeSignificanceDoesntAlterAverage(ConditionalTransferEntropyCalculator teCalc,
int timeSteps, int k) throws Exception {
teCalc.initialise(k, 1, 1);
// generate some random data
RandomGenerator rg = new RandomGenerator();
double[] sourceData = rg.generateNormalData(timeSteps,
0, 1);
double[] destData = rg.generateNormalData(timeSteps,
0, 1);
double[] condData = rg.generateNormalData(timeSteps,
0, 1);
teCalc.setObservations(sourceData, destData, condData);
//teCalc.setDebug(true);
double te = teCalc.computeAverageLocalOfObservations();
//teCalc.setDebug(false);
//double[] teLocal = teCalc.computeLocalOfPreviousObservations();
System.out.printf("Average was %.5f\n", te);
// Now look at statistical significance tests
int[][] newOrderings = rg.generateDistinctRandomPerturbations(
timeSteps - k, 100);
teCalc.computeSignificance(newOrderings);
// And compute the average value again to check that it's consistent:
for (int i = 0; i < 10; i++) {
double averageCheck1 = teCalc.computeAverageLocalOfObservations();
assertEquals(te, averageCheck1);
}
}
/**
* Confirm that univariate method signature calls fail if the calculator
* was not initialised for univariate conditional data.
*
* @param teCalc
*/
public void testUnivariateCallFailsIfWrongInitialisation(ConditionalTransferEntropyCalculator teCalc) throws Exception {
teCalc.initialise(1, 1, 1);
// generate some random data
RandomGenerator rg = new RandomGenerator();
double[] sourceData = rg.generateNormalData(10,
0, 1);
double[] destData = rg.generateNormalData(10,
0, 1);
double[] condData = rg.generateNormalData(10,
0, 1);
boolean gotException = false;
try {
teCalc.setObservations(sourceData, destData, condData);
} catch (Exception e) {
gotException = true;
}
assert(gotException);
}
/**
* Confirm the workings of the conditional TE calculator
* by calculating pairwise TE with a long k, then computing
* conditional TE with a shorter history length k, but
* other conditional variables copying the values of those further past
* values of the destination.
*
* @param teCalc a pre-constructed TransferEntropyCalculator object
* @param condTeCalc a pre-constructed ConditionalTransferEntropyCalculator object
* of the same estimator type
* @param timeSteps number of time steps for the random data
* @param k history length for the TE calculator to use
* @throws Exception
*/
public void testConditionalAgainstOrdinaryTE(
TransferEntropyCalculator teCalc,
ConditionalTransferEntropyCalculator condTeCalc,
int timeSteps, int k) throws Exception {
if (k < 2) {
throw new Exception("Need k >= 2 for testConditionalAgainstOrdinaryTE");
}
// generate some random data
RandomGenerator rg = new RandomGenerator();
double[] sourceData = rg.generateNormalData(timeSteps,
0, 1);
double[] destData = rg.generateNormalData(timeSteps,
0, 1);
// First compute ordinary TE
teCalc.initialise(k);
teCalc.setObservations(sourceData, destData);
double te = teCalc.computeAverageLocalOfObservations();
System.out.printf("TE(k=%d): Average was %.5f\n", k, te);
// Next compute conditional TE with some older
// parts of the destination as conditional variables
// instead of in the destination past.
int[] condDims = {k-1};
int[] condTaus = {1};
int[] condDelays = {2};
condTeCalc.initialise(1, 1, 1, 1, 1, condDims, condTaus, condDelays);
// Don't need to extract the data ourselves:
// double[][] condData =
// MatrixUtils.makeDelayEmbeddingVector(destData, k-1,
// k-2, destData.length-k+1); // Need an extra unused one here
condTeCalc.setObservations(sourceData, destData, destData);
double condTe = condTeCalc.computeAverageLocalOfObservations();
System.out.printf("CondTE(k=%d): Average was %.5f\n", k, condTe);
assertEquals(teCalc.getNumObservations(), condTeCalc.getNumObservations());
assertEquals(te, condTe, 0.000000001);
}
}

30
java/unittests/infodynamics/measures/continuous/gaussian/ConditionalTransferEntropyGaussianTester.java Executable file
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package infodynamics.measures.continuous.gaussian;
import infodynamics.measures.continuous.ConditionalTransferEntropyAbstractTester;
public class ConditionalTransferEntropyGaussianTester extends
ConditionalTransferEntropyAbstractTester {
public void testUnivariateMethodSignatureFails() throws Exception {
ConditionalTransferEntropyCalculatorGaussian teCalc = new ConditionalTransferEntropyCalculatorGaussian();
super.testUnivariateCallFailsIfWrongInitialisation(teCalc);
}
public void testLocalsAverageCorrectly() throws Exception {
ConditionalTransferEntropyCalculatorGaussian teCalc = new ConditionalTransferEntropyCalculatorGaussian();
super.testLocalsAverageCorrectly(teCalc, 100, 2);
}
public void testComputeSignificanceDoesntAlterAverage() throws Exception {
ConditionalTransferEntropyCalculatorGaussian teCalc = new ConditionalTransferEntropyCalculatorGaussian();
super.testComputeSignificanceDoesntAlterAverage(teCalc, 100, 2);
}
public void testAgainstApparentTE() throws Exception {
TransferEntropyCalculatorGaussian teCalc = new TransferEntropyCalculatorGaussian();
ConditionalTransferEntropyCalculatorGaussian condTeCalc = new ConditionalTransferEntropyCalculatorGaussian();
testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 2);
testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 3);
testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 4);
}
}

14
java/unittests/infodynamics/measures/continuous/gaussian/TransferEntropyGaussianTester.java
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@ -2,12 +2,22 @@ package infodynamics.measures.continuous.gaussian;
import java.util.Vector;
import infodynamics.measures.continuous.TransferEntropyAbstractTester;
import infodynamics.utils.ArrayFileReader;
import infodynamics.utils.MatrixUtils;
import infodynamics.utils.RandomGenerator;
import junit.framework.TestCase;
public class TransferEntropyGaussianTester extends TestCase {
public class TransferEntropyGaussianTester extends TransferEntropyAbstractTester {
public void testLocalsAverageCorrectly() throws Exception {
TransferEntropyCalculatorGaussian teCalc = new TransferEntropyCalculatorGaussian();
super.testLocalsAverageCorrectly(teCalc, 100, 2);
}
public void testComputeSignificanceDoesntAlterAverage() throws Exception {
TransferEntropyCalculatorGaussian teCalc = new TransferEntropyCalculatorGaussian();
super.testComputeSignificanceDoesntAlterAverage(teCalc, 100, 2);
}
public void testEmbedding() throws Exception {
ArrayFileReader afr = new ArrayFileReader("demos/data/2coupledRandomCols-1.txt");

49
java/unittests/infodynamics/measures/continuous/kraskov/ConditionalTransferEntropyKraskovTester.java Executable file
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package infodynamics.measures.continuous.kraskov;
import infodynamics.measures.continuous.ConditionalTransferEntropyAbstractTester;
public class ConditionalTransferEntropyKraskovTester extends
ConditionalTransferEntropyAbstractTester {
public void testUnivariateMethodSignatureFails() throws Exception {
ConditionalTransferEntropyCalculatorKraskov teCalc = new ConditionalTransferEntropyCalculatorKraskov();
String kraskov_K = "4";
teCalc.setProperty(
ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
kraskov_K);
super.testUnivariateCallFailsIfWrongInitialisation(teCalc);
}
public void testLocalsAverageCorrectly() throws Exception {
ConditionalTransferEntropyCalculatorKraskov teCalc = new ConditionalTransferEntropyCalculatorKraskov();
String kraskov_K = "4";
teCalc.setProperty(
ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
kraskov_K);
super.testLocalsAverageCorrectly(teCalc, 100, 2);
}
public void testComputeSignificanceDoesntAlterAverage() throws Exception {
ConditionalTransferEntropyCalculatorKraskov teCalc = new ConditionalTransferEntropyCalculatorKraskov();
String kraskov_K = "4";
teCalc.setProperty(
ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
kraskov_K);
super.testComputeSignificanceDoesntAlterAverage(teCalc, 100, 2);
}
public void testAgainstApparentTE() throws Exception {
TransferEntropyCalculatorKraskov teCalc = new TransferEntropyCalculatorKraskov();
String kraskov_K = "4";
teCalc.setProperty(
ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
kraskov_K);
ConditionalTransferEntropyCalculatorKraskov condTeCalc = new ConditionalTransferEntropyCalculatorKraskov();
condTeCalc.setProperty(
ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
kraskov_K);
testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 2);
testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 3);
testConditionalAgainstOrdinaryTE(teCalc, condTeCalc, 100, 4);
}
}