mirror of https://github.com/jlizier/jidt
Added more unit tests for Kraskov conditional MI against TRENTOOL (here for multivariate data, run with TRENTOOL as univariate TE but with history lengths k=2, l=2)
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joseph.lizier
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afe224ab11
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4671b15d61
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@ -69,7 +69,7 @@ public class ConditionalMutualInfoMultiVariateTester
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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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* from TRENTOOL. (with default parameter settings k=1, l=1)
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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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@ -78,9 +78,31 @@ public class ConditionalMutualInfoMultiVariateTester
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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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checkTEForGivenData(var1, var2, 1, 1, kNNs, expectedResults);
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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 historyK history length k of destination
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* @param historyL history length l of source
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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 historyK, int historyL, int[] kNNs, double[] expectedResults) throws Exception {
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ConditionalMutualInfoCalculatorMultiVariateKraskov condMiCalc = getNewCalc(1);
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// Which is the first time index for the dest next state?
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// It depends on the values of k and l for embedding the past state
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// of destination and source.
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int firstDestTimeIndex = Math.max(historyK, historyL);
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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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@ -100,9 +122,44 @@ public class ConditionalMutualInfoMultiVariateTester
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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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condMiCalc.initialise(var1[0].length * historyL,
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var2[0].length, var2[0].length * historyK);
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// Construct the joint vectors of the source states
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double[][] sources = null;
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if (historyL == 1) {
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sources = MatrixUtils.selectRows(var1, firstDestTimeIndex - historyL,
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var1.length - firstDestTimeIndex);
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} else {
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// Build the storage for the source states
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int sourceVars = var1[0].length;
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sources = new double[var1.length - firstDestTimeIndex][sourceVars * historyL];
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for (int t = 0; t < historyL; t++) {
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MatrixUtils.copyIntoMatrix(
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var1, firstDestTimeIndex - historyL + t, 0,
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sources, 0, t*sourceVars,
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var1.length - firstDestTimeIndex, sourceVars);
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}
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}
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// Construct the joint vectors of the conditionals
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double[][] conditionals = null;
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if (historyK == 1) {
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conditionals = MatrixUtils.selectRows(var2, firstDestTimeIndex - historyK,
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var2.length - firstDestTimeIndex);
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} else {
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// Build the storage for the conditional observations
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int destVars = var2[0].length;
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conditionals = new double[var2.length - firstDestTimeIndex][destVars * historyK];
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for (int t = 0; t < historyK; t++) {
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MatrixUtils.copyIntoMatrix(
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var2, firstDestTimeIndex - historyK + t, 0,
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conditionals, 0, t*destVars,
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var2.length - firstDestTimeIndex, destVars);
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}
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}
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// And set the observations using these
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condMiCalc.setObservations(sources,
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MatrixUtils.selectRows(var2, firstDestTimeIndex, var2.length - firstDestTimeIndex),
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conditionals);
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double condMi = condMiCalc.computeAverageLocalOfObservations();
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//miCalc.setDebug(false);
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@ -224,7 +281,7 @@ public class ConditionalMutualInfoMultiVariateTester
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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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System.out.println("Kraskov Cond MI as TE comparison 1 - univariate random data 1 (col 0->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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@ -232,10 +289,113 @@ public class ConditionalMutualInfoMultiVariateTester
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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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System.out.println(" (col 1->2):");
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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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// And now for other columns
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expectedFromTRENTOOL = new double[] {0.0026367};
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System.out.println(" (col 1->0):");
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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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// And now for other columns
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expectedFromTRENTOOL = new double[] {-0.00012474};
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System.out.println(" (col 0->2):");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
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MatrixUtils.selectColumns(data, new int[] {2}),
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kNNs, expectedFromTRENTOOL);
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// And now for other columns
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expectedFromTRENTOOL = new double[] {-5.4437e-03};
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System.out.println(" (col 2->0):");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {2}),
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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 multivariate 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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* It's multivariate because we use embedding dimension 2 on both source
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* and destination.
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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, 2, 1, 1, kraskovK, 0)
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* with these values ensuring source-dest lag 1, history k=2,
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* history embedding dimension l=2 on source as well.
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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 testMultivariateTEforCoupledDataFromFile() throws Exception {
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// Test set 1:
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ArrayFileReader afr = new ArrayFileReader("demos/data/4ColsPairedOneStepNoisyDependence-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 = {0.1400645};
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System.out.println("Kraskov Cond MI as TE - multivariate coupled data 1, k=2,l=2");
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System.out.println(" (0->2)");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
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MatrixUtils.selectColumns(data, new int[] {2}),
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2, 2,
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kNNs, expectedFromTRENTOOL);
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// And now for reverse direction:
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expectedFromTRENTOOL = new double[] {-0.0181459};
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System.out.println(" (2->0):");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {2}),
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MatrixUtils.selectColumns(data, new int[] {0}),
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2, 2,
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kNNs, expectedFromTRENTOOL);
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// And now for other columns:
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expectedFromTRENTOOL = new double[] {0.1639186};
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System.out.println(" (1->3):");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
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MatrixUtils.selectColumns(data, new int[] {3}),
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2, 2,
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kNNs, expectedFromTRENTOOL);
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// And in reverse:
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expectedFromTRENTOOL = new double[] {0.0036976};
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System.out.println(" (3->1):");
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checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {3}),
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MatrixUtils.selectColumns(data, new int[] {1}),
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2, 2,
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kNNs, expectedFromTRENTOOL);
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// -------------
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// And finally, confirm that we get different results for k=1,l=1,
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// which match TRENTOOL
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expectedFromTRENTOOL = new double[] {0.0072169};
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System.out.println(" (0->1) but with k=1,l=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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1, 1,
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kNNs, expectedFromTRENTOOL);
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// And in reverse
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expectedFromTRENTOOL = new double[] {0.0011738};
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System.out.println(" (1->2) but with k=1,l=1:");
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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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1, 1,
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kNNs, expectedFromTRENTOOL);
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}
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}
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