std::vector<DataPoint> Analysis_Impl::getDataPoints(const std::string& tag) const {
DataPointVector result;
BOOST_FOREACH(const DataPoint& dataPoint,m_dataPoints) {
if (dataPoint.isTag(tag)) {
result.push_back(dataPoint);
}
}
return result;
}
std::vector<DataPoint> Analysis_Impl::failedDataPoints() const {
DataPointVector result;
BOOST_FOREACH(const DataPoint& dataPoint,m_dataPoints) {
if (dataPoint.isComplete() && dataPoint.failed()) {
result.push_back(dataPoint);
}
}
return result;
}
std::vector<DataPoint> Analysis_Impl::getDataPoints(
const std::vector<QVariant>& variableValues) const
{
DataPointVector result;
BOOST_FOREACH(const DataPoint& dataPoint, m_dataPoints) {
if (dataPoint.matches(variableValues)) {
result.push_back(dataPoint);
}
}
return result;
}
std::vector<DataPoint> Analysis_Impl::dataPointsNeedingDetails() const {
DataPointVector result;
BOOST_FOREACH(const DataPoint& dataPoint, m_dataPoints) {
if (dataPoint.isComplete() &&
(dataPoint.runType() == DataPointRunType::CloudDetailed) &&
dataPoint.directory().empty())
{
result.push_back(dataPoint);
}
}
return result;
}
boost::optional<DataPoint> Analysis_Impl::getDataPoint(
const std::vector<Measure>& measures) const
{
OptionalDataPoint result;
std::vector<QVariant> variableValues = problem().getVariableValues(measures);
if (variableValues.size() == measures.size()) {
// problem was able to match all measures
DataPointVector intermediate = getDataPoints(variableValues);
OS_ASSERT(intermediate.size() < 2u);
if (intermediate.size() == 1u) {
result = intermediate[0];
}
}
return result;
}
boost::optional<DataPoint> Analysis_Impl::getDataPoint(
const std::vector<DiscretePerturbation>& perturbations) const
{
OptionalDataPoint result;
std::vector<QVariant> variableValues = problem().getVariableValues(perturbations);
if (variableValues.size() == perturbations.size()) {
// problem was able to match all perturbations
DataPointVector intermediate = getDataPoints(variableValues);
BOOST_ASSERT(intermediate.size() < 2u);
if (intermediate.size() == 1u) {
result = intermediate[0];
}
}
return result;
}
bool Analysis_Impl::addDataPoint(const DataPoint& dataPoint) {
if (m_dataPointsAreInvalid) {
LOG(Info,"Current data points are invalid. Call removeAllDataPoints before adding new ones.");
return false;
}
if (!(dataPoint.problem().uuid() == problem().uuid())) {
LOG(Error,"Cannot add given DataPoint to Analysis '" << name() <<
"', because it is not associated with Problem '" << problem().name() << "'.");
return false;
}
DataPointVector existingDataPoints = getDataPoints(dataPoint.variableValues());
if (existingDataPoints.size() > 0) {
BOOST_ASSERT(existingDataPoints.size() == 1); // dataPoint must be fully specified to be valid
LOG(Info,"DataPoint not added to Analysis '" << name() << "', because it already exists.");
return false;
}
m_dataPoints.push_back(dataPoint);
connectChild(m_dataPoints.back(),true);
onChange(AnalysisObject_Impl::Benign);
return true;
}
boost::optional<DataPoint> DakotaAlgorithm_Impl::createNextDataPoint(
Analysis& analysis,const DakotaParametersFile& params)
{
OS_ASSERT(analysis.algorithm().get() == getPublicObject<DakotaAlgorithm>());
// TODO: Update iteration counter.
OptionalDataPoint result = analysis.problem().createDataPoint(params,
getPublicObject<DakotaAlgorithm>());
if (result) {
bool added = analysis.addDataPoint(*result);
if (!added) {
// get equivalent point already in analysis
DataPointVector candidates = analysis.getDataPoints(result->variableValues());
OS_ASSERT(candidates.size() == 1u);
result = candidates[0];
}
std::stringstream ss;
ss << name() << "_" << m_iter;
result->addTag(ss.str());
}
return result;
}
TEST_F(AnalysisDriverFixture, DDACE_LatinHypercube_Continuous) {
{
// GET SIMPLE PROJECT
SimpleProject project = getCleanSimpleProject("DDACE_LatinHypercube_Continuous");
Analysis analysis = project.analysis();
// SET PROBLEM
Problem problem = retrieveProblem("Continuous",true,false);
analysis.setProblem(problem);
// DEFINE SEED
Model model = model::exampleModel();
openstudio::path p = toPath("./example.osm");
model.save(p,true);
FileReference seedModel(p);
analysis.setSeed(seedModel);
// CREATE ANALYSIS
DDACEAlgorithmOptions algOptions(DDACEAlgorithmType::lhs);
DDACEAlgorithm algorithm(algOptions);
analysis.setAlgorithm(algorithm);
// RUN ANALYSIS
AnalysisDriver driver = project.analysisDriver();
AnalysisRunOptions runOptions = standardRunOptions(project.projectDir());
CurrentAnalysis currentAnalysis = driver.run(analysis,runOptions);
EXPECT_TRUE(driver.waitForFinished());
boost::optional<runmanager::JobErrors> jobErrors = currentAnalysis.dakotaJobErrors();
ASSERT_TRUE(jobErrors);
EXPECT_FALSE(jobErrors->errors().empty()); // require specification of number of samples
EXPECT_TRUE(driver.currentAnalyses().empty());
Table summary = currentAnalysis.analysis().summaryTable();
EXPECT_EQ(1u,summary.nRows()); // no points
project.clearAllResults();
algOptions.setSamples(4);
EXPECT_EQ(4,analysis.algorithm()->cast<DDACEAlgorithm>().ddaceAlgorithmOptions().samples());
currentAnalysis = driver.run(analysis,runOptions);
EXPECT_TRUE(driver.waitForFinished());
jobErrors = currentAnalysis.dakotaJobErrors();
ASSERT_TRUE(jobErrors);
EXPECT_TRUE(jobErrors->errors().empty());
EXPECT_TRUE(driver.currentAnalyses().empty());
summary = currentAnalysis.analysis().summaryTable();
EXPECT_EQ(5u,summary.nRows());
summary.save(project.projectDir() / toPath("summary.csv"));
BOOST_FOREACH(const DataPoint& dataPoint,analysis.dataPoints()) {
EXPECT_TRUE(dataPoint.isComplete());
EXPECT_FALSE(dataPoint.failed());
// EXPECT_FALSE(dataPoint.responseValues().empty());
}
ASSERT_TRUE(analysis.algorithm());
EXPECT_TRUE(analysis.algorithm()->isComplete());
EXPECT_FALSE(analysis.algorithm()->failed());
{
AnalysisRecord analysisRecord = project.analysisRecord();
Analysis analysisCopy = analysisRecord.analysis();
ASSERT_TRUE(analysisCopy.algorithm());
EXPECT_TRUE(analysisCopy.algorithm()->isComplete());
EXPECT_FALSE(analysisCopy.algorithm()->failed());
}
}
LOG(Info,"Restart from existing project.");
// Get existing project
SimpleProject project = getSimpleProject("DDACE_LatinHypercube_Continuous");
EXPECT_FALSE(project.analysisIsLoaded()); // make sure starting fresh
Analysis analysis = project.analysis();
EXPECT_FALSE(analysis.isDirty());
// Add custom data point
std::vector<QVariant> values;
values.push_back(0.0);
values.push_back(0.8);
values.push_back(int(0));
OptionalDataPoint dataPoint = analysis.problem().createDataPoint(values);
ASSERT_TRUE(dataPoint);
analysis.addDataPoint(*dataPoint);
EXPECT_EQ(1u,analysis.dataPointsToQueue().size());
ASSERT_TRUE(analysis.algorithm());
EXPECT_TRUE(analysis.algorithm()->isComplete());
EXPECT_FALSE(analysis.algorithm()->failed());
EXPECT_TRUE(analysis.isDirty());
EXPECT_FALSE(analysis.resultsAreInvalid());
EXPECT_FALSE(analysis.dataPointsAreInvalid());
// get last modified time of a file in a completed data point to make sure nothing is re-run
DataPointVector completePoints = analysis.completeDataPoints();
ASSERT_FALSE(completePoints.empty());
OptionalFileReference inputFileRef = completePoints[0].osmInputData();
ASSERT_TRUE(inputFileRef);
QFileInfo inputFileInfo(toQString(inputFileRef->path()));
QDateTime inputFileModifiedTestTime = inputFileInfo.lastModified();
EXPECT_EQ(1u,analysis.dataPointsToQueue().size());
AnalysisDriver driver = project.analysisDriver();
CurrentAnalysis currentAnalysis = driver.run(
analysis,
standardRunOptions(project.projectDir()));
EXPECT_TRUE(driver.waitForFinished());
boost::optional<runmanager::JobErrors> jobErrors = currentAnalysis.dakotaJobErrors();
EXPECT_FALSE(jobErrors); // should not try to re-run DakotaAlgorithm
EXPECT_TRUE(driver.currentAnalyses().empty());
EXPECT_TRUE(analysis.dataPointsToQueue().empty());
Table summary = currentAnalysis.analysis().summaryTable();
EXPECT_EQ(6u,summary.nRows());
summary.save(project.projectDir() / toPath("summary_post_restart.csv"));
// RunManager should not re-run any data points
EXPECT_EQ(inputFileModifiedTestTime,inputFileInfo.lastModified());
}
int DesignOfExperiments_Impl::createNextIteration(Analysis& analysis) {
int result(0);
// to make sure problem type check has already occurred. this is stated usage in header.
OS_ASSERT(analysis.algorithm().get() == getPublicObject<DesignOfExperiments>());
// nothing else is supported yet
DesignOfExperimentsOptions options = designOfExperimentsOptions();
OS_ASSERT(options.designType() == DesignOfExperimentsType::FullFactorial);
if (isComplete()) {
LOG(Info,"Algorithm is already marked as complete. Returning without creating new points.");
return result;
}
if (options.maxIter() && options.maxIter().get() < 1) {
LOG(Info,"Maximum iterations set to less than one. No DataPoints will be added to Analysis '"
<< analysis.name() << "', and the Algorithm will be marked complete.");
markComplete();
return result;
}
OptionalInt mxSim = options.maxSims();
DataPointVector dataPoints = analysis.getDataPoints("DOE");
int totPoints = dataPoints.size();
if (mxSim && (totPoints >= *mxSim)) {
LOG(Info,"Analysis '" << analysis.name() << "' already contains " << totPoints
<< " DataPoints added by the DesignOfExperiments algorithm, which meets or exceeds the "
<< "maximum number specified in this algorithm's options object, " << *mxSim << ". "
<< "No data points will be added and the Algorithm will be marked complete.");
markComplete();
return result;
}
m_iter = 1;
// determine all combinations
std::vector< std::vector<QVariant> > variableValues;
for (const Variable& variable : analysis.problem().variables()) {
// variable must be DiscreteVariable, otherwise !isCompatibleProblemType(analysis.problem())
DiscreteVariable discreteVariable = variable.cast<DiscreteVariable>();
IntVector dvValues = discreteVariable.validValues(true);
std::vector< std::vector<QVariant> > currentValues = variableValues;
for (IntVector::const_iterator it = dvValues.begin(), itEnd = dvValues.end();
it != itEnd; ++it)
{
std::vector< std::vector<QVariant> > nextSet = currentValues;
if (currentValues.empty()) {
variableValues.push_back(std::vector<QVariant>(1u,QVariant(*it)));
}
else {
for (std::vector<QVariant>& point : nextSet) {
point.push_back(QVariant(*it));
}
if (it == dvValues.begin()) {
variableValues = nextSet;
}
else {
variableValues.insert(variableValues.end(),nextSet.begin(),nextSet.end());
}
}
}
}
// create data points and add to analysis
for (const std::vector<QVariant>& value : variableValues) {
DataPoint dataPoint = analysis.problem().createDataPoint(value).get();
dataPoint.addTag("DOE");
bool added = analysis.addDataPoint(dataPoint);
if (added) {
++result;
++totPoints;
if (mxSim && (totPoints == mxSim.get())) {
break;
}
}
}
if (result == 0) {
LOG(Trace,"No new points were added, so marking this DesignOfExperiments complete.");
markComplete();
}
return result;
}
