mirror of https://github.com/jlizier/jidt
Added unit tests for Kraskov Conditional MI against TEs computed by Wibral et al.'s TRENTOOL
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joseph.lizier
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@ -1,5 +1,8 @@
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package infodynamics.measures.continuous.kraskov;
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import infodynamics.utils.ArrayFileReader;
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import infodynamics.utils.MatrixUtils;
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public class ConditionalMutualInfoMultiVariateTester
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extends infodynamics.measures.continuous.ConditionalMutualInfoMultiVariateAbstractTester {
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@ -63,4 +66,176 @@ public class ConditionalMutualInfoMultiVariateTester
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checkComputeSignificanceDoesntAlterAverage(2);
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}
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/**
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* Utility function to run Kraskov conditional MI algorithm 1
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* as transfer entropy for data with known results
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* from TRENTOOL.
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*
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* @param var1 source multivariate data set
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* @param var2 dest multivariate data set
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* @param kNNs array of Kraskov k nearest neighbours parameter to check
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* @param expectedResults array of expected results for each k
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*/
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protected void checkTEForGivenData(double[][] var1, double[][] var2,
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int[] kNNs, double[] expectedResults) throws Exception {
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ConditionalMutualInfoCalculatorMultiVariateKraskov condMiCalc = getNewCalc(1);
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// Normalise the data ourselves rather than letting the calculator do it -
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// this ensures the extra values in the time series (e.g. last value in source)
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// are taken into account, in line with TRENTOOL
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var1 = MatrixUtils.normaliseIntoNewArray(var1);
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var2 = MatrixUtils.normaliseIntoNewArray(var2);
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for (int kIndex = 0; kIndex < kNNs.length; kIndex++) {
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int k = kNNs[kIndex];
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condMiCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K,
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Integer.toString(k));
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// We already normalised above, and this will do a different
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// normalisation without taking the extra values in to account if we did it
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condMiCalc.setProperty(
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ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_NORMALISE,
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Boolean.toString(false));
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// No longer need to set this property as it's set by default:
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//condMiCalc.setProperty(ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_NORM_TYPE,
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// EuclideanUtils.NORM_MAX_NORM_STRING);
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condMiCalc.setObservations(MatrixUtils.selectRows(var1, 0, var1.length - 1),
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MatrixUtils.selectRows(var2, 1, var2.length - 1),
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MatrixUtils.selectRows(var2, 0, var2.length - 1));
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double condMi = condMiCalc.computeAverageLocalOfObservations();
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//miCalc.setDebug(false);
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System.out.printf("k=%d: Average MI %.8f (expected %.8f)\n",
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k, condMi, expectedResults[kIndex]);
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// 6 decimal places is Matlab accuracy
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assertEquals(expectedResults[kIndex], condMi, 0.000001);
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}
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}
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/**
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* Test the computed univariate TE as a conditional MI
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* against that calculated by Wibral et al.'s TRENTOOL
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* on the same data.
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*
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* To run TRENTOOL (http://www.trentool.de/) for this
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* data, run its TEvalues.m matlab script on the multivariate source
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* and dest data sets as:
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* TEvalues(source, dest, 1, 1, 1, kraskovK, 0)
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* with these values ensuring source-dest lag 1, history k=1,
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* embedding lag 1, no dynamic correlation exclusion
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*
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* @throws Exception if file not found
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*
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*/
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public void testUnivariateTEforCoupledVariablesFromFile() throws Exception {
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// Test set 1:
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ArrayFileReader afr = new ArrayFileReader("demos/data/2coupledRandomCols-1.txt");
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double[][] data = afr.getDouble2DMatrix();
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// Use various Kraskov k nearest neighbours parameter
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int[] kNNs = {4};
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// Expected values from TRENTOOL:
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double[] expectedFromTRENTOOL = {0.3058006};
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System.out.println("Kraskov Cond MI as TE comparison 1 - univariate coupled data 1");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
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MatrixUtils.selectColumns(data, new int[] {1}),
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kNNs, expectedFromTRENTOOL);
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// And now in the reverse direction:
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expectedFromTRENTOOL = new double[] {-0.0029744};
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System.out.println(" reverse direction:");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
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MatrixUtils.selectColumns(data, new int[] {0}),
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kNNs, expectedFromTRENTOOL);
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}
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/**
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* Test the computed univariate TE as a conditional MI
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* against that calculated by Wibral et al.'s TRENTOOL
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* on the same data.
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*
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* To run TRENTOOL (http://www.trentool.de/) for this
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* data, run its TEvalues.m matlab script on the multivariate source
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* and dest data sets as:
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* TEvalues(source, dest, 1, 1, 1, kraskovK, 0)
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* with these values ensuring source-dest lag 1, history k=1,
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* embedding lag 1, no dynamic correlation exclusion
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*
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* @throws Exception if file not found
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*
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*/
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public void testUnivariateTEforCoupledLogisticMapFromFile() throws Exception {
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// Test set 1:
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ArrayFileReader afr = new ArrayFileReader("demos/data/coupledLogisticMapXY.txt");
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double[][] data = afr.getDouble2DMatrix();
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// Use various Kraskov k nearest neighbours parameter
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int[] kNNs = {4};
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// Expected values from TRENTOOL:
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double[] expectedFromTRENTOOL = {0.508417};
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System.out.println("Kraskov Cond MI as TE comparison 1 - univariate coupled logistic map data 1");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
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MatrixUtils.selectColumns(data, new int[] {1}),
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kNNs, expectedFromTRENTOOL);
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// And now in the reverse direction:
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expectedFromTRENTOOL = new double[] {0.016257};
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System.out.println(" reverse direction:");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
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MatrixUtils.selectColumns(data, new int[] {0}),
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kNNs, expectedFromTRENTOOL);
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}
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/**
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* Test the computed univariate TE as a conditional MI
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* against that calculated by Wibral et al.'s TRENTOOL
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* on the same data.
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*
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* To run TRENTOOL (http://www.trentool.de/) for this
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* data, run its TEvalues.m matlab script on the multivariate source
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* and dest data sets as:
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* TEvalues(source, dest, 1, 1, 1, kraskovK, 0)
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* with these values ensuring source-dest lag 1, history k=1,
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* embedding lag 1, no dynamic correlation exclusion
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*
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* @throws Exception if file not found
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*
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*/
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public void testUnivariateTEforRandomDataFromFile() throws Exception {
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// Test set 1:
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ArrayFileReader afr = new ArrayFileReader("demos/data/4randomCols-1.txt");
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double[][] data = afr.getDouble2DMatrix();
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// Use various Kraskov k nearest neighbours parameter
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int[] kNNs = {4};
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// Expected values from TRENTOOL:
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double[] expectedFromTRENTOOL = {-0.0096556};
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System.out.println("Kraskov Cond MI as TE comparison 1 - univariate random data 1");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
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MatrixUtils.selectColumns(data, new int[] {1}),
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kNNs, expectedFromTRENTOOL);
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// And now for other columns
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expectedFromTRENTOOL = new double[] {0.0175389};
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System.out.println(" reverse direction:");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
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MatrixUtils.selectColumns(data, new int[] {2}),
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kNNs, expectedFromTRENTOOL);
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}
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}
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