/** * Calculates the AUC measurment. * * @param algorithm The optimisation algorithm with a NNTrainingProblem. * @return A Vector with the AUC for each NN output. */ @Override public Vector getValue(Algorithm algorithm) { Vector solution = (Vector) algorithm.getBestSolution().getPosition(); NNTrainingProblem problem = (NNTrainingProblem) algorithm.getOptimisationProblem(); StandardPatternDataTable generalisationSet = problem.getGeneralisationSet(); NeuralNetwork neuralNetwork = problem.getNeuralNetwork(); neuralNetwork.setWeights(solution); // Arrange outputs and target values into ArrayLists. ArrayList<ArrayList<Real>> targets = new ArrayList<ArrayList<Real>>(); ArrayList<ArrayList<Real>> outputs = new ArrayList<ArrayList<Real>>(); // case of multiple outputs if (generalisationSet.getRow(0).getTarget() instanceof Vector) { int size = ((Vector) generalisationSet.getRow(0).getTarget()).size(); for (int i = 0; i < size; ++i) { targets.add(new ArrayList<Real>()); outputs.add(new ArrayList<Real>()); } for (StandardPattern pattern : generalisationSet) { Vector target = (Vector) pattern.getTarget(); Vector output = neuralNetwork.evaluatePattern(pattern); for (int curOutput = 0; curOutput < target.size(); ++curOutput) { targets.get(curOutput).add((Real) target.get(curOutput)); outputs.get(curOutput).add((Real) output.get(curOutput)); } } } // case of single output else { targets.add(new ArrayList<Real>()); outputs.add(new ArrayList<Real>()); for (StandardPattern pattern : generalisationSet) { Real target = (Real) pattern.getTarget(); Vector output = neuralNetwork.evaluatePattern(pattern); targets.get(0).add(target); outputs.get(0).add((Real) output.get(0)); } } // Calculate the Vector of AUC values Vector results = Vector.of(); for (int curOutput = 0; curOutput < outputs.size(); ++curOutput) { results.add(Real.valueOf(areaUnderCurve(targets.get(curOutput), outputs.get(curOutput)))); } return results; }