void HierarchyAutoSearchCrossValidationDriver::resetForOptimal( CyclicCoordinateDescent& ccd, CrossValidationSelector& selector, const CCDArguments& arguments) { ccd.setWeights(NULL); ccd.setHyperprior(maxPoint); ccd.setClassHyperprior(maxPointClass); ccd.resetBeta(); // Cold-start }
MaxPoint GridSearchCrossValidationDriver::doCrossValidationLoop( CyclicCoordinateDescent& ccd, AbstractSelector& selector, const CCDArguments& allArguments, int nThreads, std::vector<CyclicCoordinateDescent*>& ccdPool, std::vector<AbstractSelector*>& selectorPool) { const auto& arguments = allArguments.crossValidation; // std::vector<double> weights; for (int step = 0; step < gridSize; step++) { std::vector<double> predLogLikelihood; double point = computeGridPoint(step); ccd.setHyperprior(point); selector.reseed(); double pointEstimate = doCrossValidationStep(ccd, selector, allArguments, step, nThreads, ccdPool, selectorPool, predLogLikelihood); double value = pointEstimate / (double(arguments.foldToCompute) / double(arguments.fold)); gridPoint.push_back(point); gridValue.push_back(value); } // Report results double maxPoint; double maxValue; findMax(&maxPoint, &maxValue); // std::ostringstream stream; // stream << std::endl; // stream << "Maximum predicted log likelihood (" << maxValue << ") found at:" << std::endl; // stream << "\t" << maxPoint << " (variance)" << std::endl; // if (!allArguments.useNormalPrior) { // double lambda = convertVarianceToHyperparameter(maxPoint); // stream << "\t" << lambda << " (lambda)" << std::endl; // } // logger->writeLine(stream); std::vector<double> point(1, maxPoint); return MaxPoint{point, maxValue}; //return std::vector<double>(1, maxPoint); }
// This is specific to auto-search std::vector<double> AutoSearchCrossValidationDriver::doCrossValidationLoop( CyclicCoordinateDescent& ccd, AbstractSelector& selector, const CCDArguments& allArguments, int nThreads, std::vector<CyclicCoordinateDescent*>& ccdPool, std::vector<AbstractSelector*>& selectorPool) { const auto& arguments = allArguments.crossValidation; double tryvalue = (arguments.startingVariance > 0) ? arguments.startingVariance : modelData.getNormalBasedDefaultVar(); std::ostringstream stream; stream << "Starting var = " << tryvalue; if (arguments.startingVariance == -1) { stream << " (default)"; } logger->writeLine(stream); const double tolerance = 1E-2; // TODO Make Cyclops argument int nDim = ccd.getHyperprior().size(); std::vector<double> currentOptimal(nDim, tryvalue); bool globalFinished = false; std::vector<double> savedOptimal; while (!globalFinished) { if (nDim > 1) { savedOptimal = currentOptimal; // make copy } for (int dim = 0; dim < nDim; ++dim) { // Local search UniModalSearch searcher(10, 0.01, log(1.5)); int step = 0; bool dimFinished = false; while (!dimFinished) { ccd.setHyperprior(dim, currentOptimal[dim]); selector.reseed(); std::vector<double> predLogLikelihood; // Newly re-located code double pointEstimate = doCrossValidationStep(ccd, selector, allArguments, step, nThreads, ccdPool, selectorPool, predLogLikelihood); double stdDevEstimate = computeStDev(predLogLikelihood, pointEstimate); std::ostringstream stream; stream << "AvgPred = " << pointEstimate << " with stdev = " << stdDevEstimate << std::endl; searcher.tried(currentOptimal[dim], pointEstimate, stdDevEstimate); pair<bool,double> next = searcher.step(); stream << "Completed at " << currentOptimal[dim] << std::endl; stream << "Next point at " << next.second << " and " << next.first; logger->writeLine(stream); currentOptimal[dim] = next.second; if (!next.first) { dimFinished = true; } std::ostringstream stream1; stream1 << searcher; logger->writeLine(stream1); step++; if (step >= maxSteps) { std::ostringstream stream; stream << "Max steps reached!"; logger->writeLine(stream); dimFinished = true; } } } if (nDim == 1) { globalFinished = true; } else { double diff = 0.0; for (int i = 0; i < nDim; ++i) { diff += std::abs((currentOptimal[i] - savedOptimal[i]) / savedOptimal[i]); } std::ostringstream stream; stream << "Absolute percent difference in cycle: " << diff << std::endl; globalFinished = (diff < tolerance); } } return currentOptimal; }
void HierarchyAutoSearchCrossValidationDriver::drive( CyclicCoordinateDescent& ccd, AbstractSelector& selector, const CCDArguments& arguments) { // TODO Check that selector is type of CrossValidationSelector std::vector<real> weights; double tryvalue = modelData.getNormalBasedDefaultVar(); double tryvalueClass = tryvalue; // start with same variance at the class and element level; // for hierarchy class variance UniModalSearch searcher(10, 0.01, log(1.5)); UniModalSearch searcherClass(10, 0.01, log(1.5)); // Need a better way to do this. // const double eps = 0.05; //search stopper std::ostringstream stream; stream << "Default var = " << tryvalue; logger->writeLine(stream); bool finished = false; bool drugLevelFinished = false; bool classLevelFinished = false; int step = 0; while (!finished) { // More hierarchy logic ccd.setHyperprior(tryvalue); ccd.setClassHyperprior(tryvalueClass); std::vector<double> predLogLikelihood; // Newly re-located code double pointEstimate = doCrossValidation(ccd, selector, arguments, step, predLogLikelihood); double stdDevEstimate = computeStDev(predLogLikelihood, pointEstimate); std::ostringstream stream; stream << "AvgPred = " << pointEstimate << " with stdev = " << stdDevEstimate; logger->writeLine(stream); // alternate adapting the class and element level, unless one is finished if ((step % 2 == 0 && !drugLevelFinished) || classLevelFinished){ searcher.tried(tryvalue, pointEstimate, stdDevEstimate); pair<bool,double> next = searcher.step(); tryvalue = next.second; std::ostringstream stream; stream << "Next point at " << next.second << " and " << next.first; logger->writeLine(stream); if (!next.first) { drugLevelFinished = true; } } else { searcherClass.tried(tryvalueClass, pointEstimate, stdDevEstimate); pair<bool,double> next = searcherClass.step(); tryvalueClass = next.second; std::ostringstream stream; stream << "Next Class point at " << next.second << " and " << next.first; logger->writeLine(stream); if (!next.first) { classLevelFinished = true; } } // if everything is finished, end. if (drugLevelFinished && classLevelFinished){ finished = true; } std::ostringstream stream2; stream2 << searcher; logger->writeLine(stream2); step++; if (step >= maxSteps) { std::ostringstream stream; stream << "Max steps reached!"; logger->writeLine(stream); finished = true; } } maxPoint = tryvalue; maxPointClass = tryvalueClass; // Report results std::ostringstream stream2; stream2 << std::endl; stream2 << "Maximum predicted log likelihood estimated at:" << std::endl; stream2 << "\t" << maxPoint << " (variance)" << std::endl; stream2 << "class level = " << maxPointClass; logger->writeLine(stream2); if (!arguments.useNormalPrior) { double lambda = convertVarianceToHyperparameter(maxPoint); std::ostringstream stream; stream << "\t" << lambda << " (lambda)"; logger->writeLine(stream); } std::ostringstream stream3; logger->writeLine(stream3); }