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Use state elimination to eliminate chains of non-Markovian states in MA

tempestpy_adaptions
Matthias Volk 5 years ago
parent
9fc473383f
commit
c8158018b8
5 changed files with 543 additions and 259 deletions
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  1. 155
      resources/examples/testfiles/ma/chain_elimination1.drn
  2. 34
      resources/examples/testfiles/ma/chain_elimination2.drn
  3. 343
      src/storm/transformer/NonMarkovianChainTransformer.cpp
  4. 7
      src/storm/transformer/NonMarkovianChainTransformer.h
  5. 249
      src/test/storm/transformer/NonMarkovianChainTransformerTest.cpp

155
resources/examples/testfiles/ma/chain_elimination1.drn
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@ -0,0 +1,155 @@
// Exported by storm
// Original model type: Markov Automaton
@type: Markov Automaton
@parameters
@reward_models
@nr_states
43
@model
state 0 !0 init
action 0
1 : 1
state 1 !0.0003
action 0
2 : 0.3333333333
3 : 0.3333333333
4 : 0.3333333333
state 2 !0
action 0
5 : 1
state 3 !0
action 0
6 : 1
state 4 !0
action 0
7 : 1
state 5 !0
action 0
8 : 1
state 6 !0
action 0
9 : 1
state 7 !0
action 0
10 : 1
state 8 !0
action 0
12 : 1
state 9 !0
action 0
14 : 1
state 10 !0.1002 Fail
action 0
11 : 0.000998003992
13 : 0.000998003992
15 : 0.998003992
state 11 !0 Fail
action 0
18 : 1
state 12 !0.1002 Fail
action 0
16 : 0.000998003992
19 : 0.000998003992
20 : 0.998003992
state 13 !0 Fail
action 0
21 : 1
state 14 !0.1002 Fail
action 0
17 : 0.000998003992
22 : 0.000998003992
23 : 0.998003992
state 15 !0 Fail
action 0
24 : 1
state 16 !0 Fail
action 0
25 : 1
state 17 !0 Fail
action 0
25 : 1
state 18 !0 Fail
action 0
26 : 1
state 19 !0 Fail
action 0
26 : 1
state 20 !0 Fail
action 0
27 : 1
state 21 !0 Fail
action 0
28 : 1
state 22 !0 Fail
action 0
28 : 1
state 23 !0 Fail
action 0
27 : 1
state 24 !0 Fail
action 0
1 : 1
state 25 !0.2001 Fail
action 0
31 : 0.0004997501249
32 : 0.4997501249
33 : 0.4997501249
state 26 !0.2001 Fail
action 0
29 : 0.0004997501249
34 : 0.4997501249
35 : 0.4997501249
state 27 !0 Fail
action 0
24 : 1
state 28 !0.2001 Fail
action 0
30 : 0.0004997501249
36 : 0.4997501249
37 : 0.4997501249
state 29 !0 Fail
action 0
38 : 1
state 30 !0 Fail
action 0
38 : 1
state 31 !0 Fail
action 0
38 : 1
state 32 !0 Fail
action 0
14 : 1
state 33 !0 Fail
action 0
12 : 1
state 34 !0 Fail
action 0
12 : 1
state 35 !0 Fail
action 0
39 : 1
state 36 !0 Fail
action 0
14 : 1
state 37 !0 Fail
action 0
39 : 1
state 38 !0.3 Fail
action 0
40 : 0.3333333333
41 : 0.3333333333
42 : 0.3333333333
state 39 !0 Fail
action 0
10 : 1
state 40 !0 Fail
action 0
25 : 1
state 41 !0 Fail
action 0
28 : 1
state 42 !0 Fail
action 0
26 : 1

34
resources/examples/testfiles/ma/chain_elimination2.drn
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@ -0,0 +1,34 @@
// Exported by storm
// Original model type: Markov Automaton
@type: Markov Automaton
@parameters
@reward_models
@nr_states
10
@model
state 0 !0.1 init
1 : 1
state 1 !0
2 : 1
state 2 !0
3 : 1
state 3 !0
4 : 0.1
5 : 0.9
state 4 !0.2 Fail
5 : 0.5
6 : 0.5
state 5 !0.1
0 : 1
state 6 !0.2
0 : 0.5
7 : 0.5
state 7 !0.2 Fail
6 : 0.5
8 : 0.5
state 8 !0 Fail
9 : 1
state 9 !0 Fail
3 : 1

343
src/storm/transformer/NonMarkovianChainTransformer.cpp
View File

@ -1,40 +1,35 @@
#include <queue>
#include "NonMarkovianChainTransformer.h"
#include "storm/logic/Formulas.h"
#include "storm/logic/FragmentSpecification.h"
#include <queue>
#include "storm/storage/sparse/ModelComponents.h"
#include "storm/adapters/RationalFunctionAdapter.h"
#include "storm/exceptions/InvalidModelException.h"
#include "storm/logic/Formulas.h"
#include "storm/logic/FragmentSpecification.h"
#include "storm/models/sparse/Ctmc.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include <storm/solver/stateelimination/NondeterministicModelStateEliminator.h>
#include "storm/storage/FlexibleSparseMatrix.h"
#include "storm/storage/sparse/ModelComponents.h"
#include "storm/utility/constants.h"
#include "storm/utility/ConstantsComparator.h"
#include "storm/utility/vector.h"
#include "storm/utility/macros.h"
#include "storm/utility/graph.h"
namespace storm {
namespace transformer {
template<typename ValueType, typename RewardModelType>
std::shared_ptr<models::sparse::Model<ValueType, RewardModelType>>
NonMarkovianChainTransformer<ValueType, RewardModelType>::eliminateNonmarkovianStates(
std::shared_ptr<models::sparse::MarkovAutomaton<ValueType, RewardModelType>> ma,
NonMarkovianChainTransformer<ValueType, RewardModelType>::eliminateNonmarkovianStates(std::shared_ptr<models::sparse::MarkovAutomaton<ValueType, RewardModelType>> ma,
EliminationLabelBehavior labelBehavior) {
// TODO reward models
STORM_LOG_WARN_COND(labelBehavior == EliminationLabelBehavior::KeepLabels, "Labels are not preserved! Results may be incorrect. Continue at your own caution.");
if (labelBehavior == EliminationLabelBehavior::DeleteLabels) {
STORM_PRINT("Use Label Deletion" << std::endl)
}
if (labelBehavior == EliminationLabelBehavior::MergeLabels) {
STORM_PRINT("Use Label Merging" << std::endl)
}
STORM_LOG_WARN("Reward Models and Choice Labelings are ignored!");
if (ma->isClosed() && ma->getMarkovianStates().full()) {
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> components(
ma->getTransitionMatrix(), ma->getStateLabeling(), ma->getRewardModels(), false);
STORM_LOG_THROW(ma->isClosed(), storm::exceptions::InvalidModelException, "MA should be closed first.");
if (ma->getMarkovianStates().full()) {
// Is already a CTMC
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> components(ma->getTransitionMatrix(), ma->getStateLabeling(), ma->getRewardModels(), false);
components.exitRates = ma->getExitRates();
if (ma->hasChoiceLabeling()) {
components.choiceLabeling = ma->getChoiceLabeling();
@ -45,243 +40,98 @@ namespace storm {
if (ma->hasChoiceOrigins()) {
components.choiceOrigins = ma->getChoiceOrigins();
}
return std::make_shared<storm::models::sparse::MarkovAutomaton<ValueType, RewardModelType>>(
std::move(components));
return std::make_shared<storm::models::sparse::Ctmc<ValueType, RewardModelType>>(std::move(components));
}
std::map<uint_fast64_t, uint_fast64_t> eliminationMapping;
std::set<uint_fast64_t> statesToKeep;
std::queue<uint_fast64_t> changedStates;
std::queue<uint_fast64_t> queue;
// Initialize
storm::storage::FlexibleSparseMatrix<ValueType> flexibleMatrix(ma->getTransitionMatrix());
storm::storage::FlexibleSparseMatrix<ValueType> flexibleBackwardTransitions(ma->getTransitionMatrix().transpose(), true);
storm::models::sparse::StateLabeling stateLabeling = ma->getStateLabeling();
// TODO: update reward models and choice labels according to kept states
STORM_LOG_WARN_COND(ma->getRewardModels().empty(), "Reward models are not preserved in chain elimination.");
std::unordered_map<std::string, RewardModelType> rewardModels;
STORM_LOG_WARN_COND(!ma->hasChoiceLabeling(), "Choice labels are not preserved in chain elimination.");
STORM_LOG_WARN_COND(!ma->hasStateValuations(), "State valuations are not preserved in chain elimination.");
STORM_LOG_WARN_COND(!ma->hasChoiceOrigins(), "Choice origins are not preserved in chain elimination.");
storm::storage::SparseMatrix<ValueType> backwards = ma->getBackwardTransitions();
// Eliminate all probabilistic states by state elimination
auto actionRewards = std::vector<ValueType>(ma->getTransitionMatrix().getRowCount(), storm::utility::zero<ValueType>());
storm::solver::stateelimination::NondeterministicModelStateEliminator<ValueType> stateEliminator(flexibleMatrix, flexibleBackwardTransitions, actionRewards);
storm::storage::BitVector keepStates(ma->getNumberOfStates(), true);
// Determine the state remapping
for (uint_fast64_t base_state = 0; base_state < ma->getNumberOfStates(); ++base_state) {
STORM_LOG_ASSERT(!ma->isHybridState(base_state), "Base state is hybrid.");
if (ma->isMarkovianState(base_state)) {
queue.push(base_state);
while (!queue.empty()) {
auto currState = queue.front();
queue.pop();
auto currLabels = ma->getLabelsOfState(currState);
// Get predecessors from matrix
typename storm::storage::SparseMatrix<ValueType>::rows entriesInRow = backwards.getRow(currState);
for (uint_fast64_t state = 0; state < ma->getNumberOfStates(); ++state) {
STORM_LOG_ASSERT(!ma->isHybridState(state), "State is hybrid.");
if (ma->isProbabilisticState(state)) {
// Only eliminate immediate states (and no Markovian states)
if (ma->getNumberOfChoices(state) <= 1) {
// Eliminate only if no non-determinism occurs
STORM_LOG_ASSERT(ma->getNumberOfChoices(state) == 1, "State " << state << " has no choices.");
STORM_LOG_ASSERT(flexibleMatrix.getRowGroupSize(state) == 1, "State " << state << " has too many rows.");
if (labelBehavior == EliminationLabelBehavior::KeepLabels) {
// Only eliminate if eliminated state and all its successors have the same labels
bool sameLabels = true;
auto currLabels = stateLabeling.getLabelsOfState(state);
typename storm::storage::FlexibleSparseMatrix<ValueType>::row_type entriesInRow = flexibleMatrix.getRow(state, 0); // Row group
for (auto entryIt = entriesInRow.begin(); entryIt != entriesInRow.end(); ++entryIt) {
uint_fast64_t predecessor = entryIt->getColumn();
if (!ma->isMarkovianState(predecessor) && !statesToKeep.count(predecessor)) {
if (labelBehavior == EliminationLabelBehavior::DeleteLabels || labelBehavior == EliminationLabelBehavior::MergeLabels ||
currLabels == ma->getLabelsOfState(predecessor)) {
// If labels are not to be preserved or states are labeled the same
if (!eliminationMapping.count(predecessor)) {
eliminationMapping[predecessor] = base_state;
queue.push(predecessor);
} else if (eliminationMapping[predecessor] != base_state) {
eliminationMapping.erase(predecessor);
statesToKeep.insert(predecessor);
changedStates.push(predecessor);
}
} else {
// Labels are to be preserved and states have different labels
if (eliminationMapping.count(predecessor)) {
eliminationMapping.erase(predecessor);
}
statesToKeep.insert(predecessor);
changedStates.push(predecessor);
}
}
if (currLabels != stateLabeling.getLabelsOfState(entryIt->getColumn())) {
STORM_LOG_TRACE("Do not eliminate state " << state << " because labels of state " << entryIt->getColumn() << " are different.");
sameLabels = false;
break;
}
}
}
}
// Correct the mapping with the states which have to be kept
while (!changedStates.empty()) {
uint_fast64_t base_state = changedStates.front();
queue.push(base_state);
while (!queue.empty()) {
auto currState = queue.front();
queue.pop();
auto currLabels = ma->getLabelsOfState(currState);
// Get predecessors from matrix
typename storm::storage::SparseMatrix<ValueType>::rows entriesInRow = backwards.getRow(currState);
for (auto entryIt = entriesInRow.begin(); entryIt != entriesInRow.end(); ++entryIt) {
uint_fast64_t predecessor = entryIt->getColumn();
if (!ma->isMarkovianState(predecessor) && !statesToKeep.count(predecessor)) {
if (labelBehavior == EliminationLabelBehavior::DeleteLabels || labelBehavior == EliminationLabelBehavior::MergeLabels ||
currLabels == ma->getLabelsOfState(predecessor)) {
// If labels are not to be preserved or states are labeled the same
if (!eliminationMapping.count(predecessor)) {
eliminationMapping[predecessor] = base_state;
queue.push(predecessor);
} else if (eliminationMapping[predecessor] != base_state) {
eliminationMapping.erase(predecessor);
statesToKeep.insert(predecessor);
changedStates.push(predecessor);
if (!sameLabels) {
continue;
}
} else {
// Labels are to be preserved and states have different labels
if (eliminationMapping.count(predecessor)) {
eliminationMapping.erase(predecessor);
}
statesToKeep.insert(predecessor);
changedStates.push(predecessor);
}
}
}
}
changedStates.pop();
}
// At this point, we hopefully have a valid mapping which eliminates a lot of states
STORM_LOG_TRACE("Elimination Mapping:");
for (auto entry : eliminationMapping) {
STORM_LOG_TRACE(std::to_string(entry.first) << " -> " << std::to_string(entry.second));
// As helper for the labeling we create a bitvector representing all successor states
storm::storage::BitVector successors(stateLabeling.getNumberOfItems());
typename storm::storage::FlexibleSparseMatrix<ValueType>::row_type entriesInRow = flexibleMatrix.getRow(state, 0); // Row group
for (auto entryIt = entriesInRow.begin(); entryIt != entriesInRow.end(); ++entryIt) {
successors.set(entryIt->getColumn());
}
STORM_LOG_INFO("Eliminating " << eliminationMapping.size() << " states" << std::endl);
// TODO explore if one can construct elimination mapping and state remapping in one step
uint64_t newStateCount = ma->getNumberOfStates() - eliminationMapping.size();
std::vector<std::set<std::string>> labelMap(newStateCount, std::set<std::string>());
// Construct a mapping of old state space to new one
std::vector<uint_fast64_t> stateRemapping(ma->getNumberOfStates(), -1);
uint_fast64_t currentNewState = 0;
for (uint_fast64_t state = 0; state < ma->getNumberOfStates(); ++state) {
if (eliminationMapping.count(state) > 0) {
if (stateRemapping[eliminationMapping[state]] == uint_fast64_t(-1)) {
stateRemapping[eliminationMapping[state]] = currentNewState;
stateRemapping[state] = currentNewState;
++currentNewState;
queue.push(eliminationMapping[state]);
} else {
stateRemapping[state] = stateRemapping[eliminationMapping[state]];
}
if (labelBehavior == EliminationLabelBehavior::DeleteLabels && ma->getInitialStates().get(state)) {
// Keep initial label for 'delete' behavior
labelMap[stateRemapping[eliminationMapping[state]]].insert("init");
}
if (labelBehavior == EliminationLabelBehavior::MergeLabels) {
//add all labels to the label set for the representative
for (auto const &label : ma->getLabelsOfState(state)) {
labelMap[stateRemapping[eliminationMapping[state]]].insert(label);
}
// Add labels from eliminated state to successors
for (std::string const& label : stateLabeling.getLabelsOfState(state)) {
storm::storage::BitVector states = stateLabeling.getStates(label);
// Add successor states for this label as well
stateLabeling.setStates(label, states | successors);
}
} else {
if (stateRemapping[state] == uint_fast64_t(-1)) {
stateRemapping[state] = currentNewState;
queue.push(state);
++currentNewState;
}
if (labelBehavior == EliminationLabelBehavior::DeleteLabels && ma->getInitialStates().get(state)) {
// Keep initial label for 'delete' behavior
labelMap[stateRemapping[state]].insert("init");
}
if (labelBehavior == EliminationLabelBehavior::MergeLabels) {
for (auto const &label : ma->getLabelsOfState(state)) {
labelMap[stateRemapping[state]].insert(label);
}
}
}
}
// Build the new MA
storm::storage::SparseMatrix<ValueType> newTransitionMatrix;
storm::models::sparse::StateLabeling newStateLabeling(newStateCount);
storm::storage::BitVector newMarkovianStates(ma->getNumberOfStates() - eliminationMapping.size(),false);
std::vector<ValueType> newExitRates;
//TODO choice labeling
boost::optional<storm::models::sparse::ChoiceLabeling> newChoiceLabeling;
// Initialize the matrix builder and helper variables
storm::storage::SparseMatrixBuilder<ValueType> matrixBuilder = storm::storage::SparseMatrixBuilder<ValueType>(
0, 0, 0, false, true, 0);
for (auto const &label : ma->getStateLabeling().getLabels()) {
if (!newStateLabeling.containsLabel(label)) {
newStateLabeling.addLabel(label);
}
}
uint_fast64_t currentRow = 0;
uint_fast64_t state = 0;
while (!queue.empty()) {
state = queue.front();
queue.pop();
std::set<std::string> labelSet = ma->getLabelsOfState(state);
if (labelBehavior == EliminationLabelBehavior::DeleteLabels) {
labelSet.insert(labelMap[stateRemapping[state]].begin(), labelMap[stateRemapping[state]].end());
STORM_LOG_ASSERT(labelBehavior == EliminationLabelBehavior::DeleteLabels, "Unknown label behavior.");
if (stateLabeling.getStateHasLabel("init", state)) {
// Add "init" label of eliminated state to successor states
storm::storage::BitVector states = stateLabeling.getStates("init");
// Add successor states for this label as well
stateLabeling.setStates("init", states | successors);
}
if (labelBehavior == EliminationLabelBehavior::MergeLabels) {
labelSet = labelMap[stateRemapping[state]];
}
for (auto const &label : labelSet) {
if (!newStateLabeling.containsLabel(label)) {
newStateLabeling.addLabel(label);
}
newStateLabeling.addLabelToState(label, stateRemapping[state]);
}
// Use a set to not include redundant rows
std::set<std::map<uint_fast64_t, ValueType>> rowSet;
for (uint_fast64_t row = 0; row < ma->getTransitionMatrix().getRowGroupSize(state); ++row) {
std::map<uint_fast64_t, ValueType> transitions;
for (typename storm::storage::SparseMatrix<ValueType>::const_iterator itEntry = ma->getTransitionMatrix().getRow(
state, row).begin();
itEntry != ma->getTransitionMatrix().getRow(state, row).end(); ++itEntry) {
uint_fast64_t newId = stateRemapping[itEntry->getColumn()];
if (transitions.count(newId) == 0) {
transitions[newId] = itEntry->getValue();
} else {
transitions[newId] += itEntry->getValue();
// Eliminate this probabilistic state
stateEliminator.eliminateState(state, true);
keepStates.set(state, false);
}
}
rowSet.insert(transitions);
}
// correctly set rates
auto rate = storm::utility::zero<ValueType>();
// Create the new matrix
auto keptRows = ma->getTransitionMatrix().getRowFilter(keepStates);
storm::storage::SparseMatrix<ValueType> matrix = flexibleMatrix.createSparseMatrix(keptRows, keepStates);
if (ma->isMarkovianState(state)) {
newMarkovianStates.set(stateRemapping[state], true);
rate = ma->getExitRates().at(state);
}
newExitRates.push_back(rate);
// Build matrix
matrixBuilder.newRowGroup(currentRow);
for (auto const &row : rowSet) {
for (auto const &transition : row) {
matrixBuilder.addNextValue(currentRow, transition.first, transition.second);
STORM_LOG_TRACE(stateRemapping[state] << "->" << transition.first << " : " << transition.second
<< std::endl);
}
++currentRow;
}
}
// explicitly force dimensions of the matrix in case a column is missing
newTransitionMatrix = matrixBuilder.build(newStateCount, newStateCount, newStateCount);
// TODO: obtain the reward model for the resulting system
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> newComponents = storm::storage::sparse::ModelComponents<ValueType, RewardModelType>(
std::move(newTransitionMatrix), std::move(newStateLabeling));
// Prepare model components
storm::storage::BitVector markovianStates = ma->getMarkovianStates() % keepStates;
storm::models::sparse::StateLabeling labeling = stateLabeling.getSubLabeling(keepStates);
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> components(matrix, labeling, ma->getRewardModels(), false, markovianStates);
std::vector<ValueType> exitRates(markovianStates.size());
storm::utility::vector::selectVectorValues(exitRates, keepStates, ma->getExitRates());
components.exitRates = exitRates;
newComponents.rateTransitions = false;
newComponents.markovianStates = std::move(newMarkovianStates);
newComponents.exitRates = std::move(newExitRates);
auto model = std::make_shared<storm::models::sparse::MarkovAutomaton<ValueType, RewardModelType >>(
std::move(newComponents));
// Build transformed model
auto model = std::make_shared<storm::models::sparse::MarkovAutomaton<ValueType, RewardModelType>>(std::move(components));
if (model->isConvertibleToCtmc()) {
return model->convertToCtmc();
} else {
@ -290,50 +140,49 @@ namespace storm {
}
template<typename ValueType, typename RewardModelType>
bool NonMarkovianChainTransformer<ValueType, RewardModelType>::preservesFormula(
storm::logic::Formula const &formula) {
bool NonMarkovianChainTransformer<ValueType, RewardModelType>::preservesFormula(storm::logic::Formula const& formula) {
storm::logic::FragmentSpecification fragment = storm::logic::propositional();
fragment.setProbabilityOperatorsAllowed(true);
fragment.setGloballyFormulasAllowed(true);
fragment.setReachabilityProbabilityFormulasAllowed(true);
fragment.setUntilFormulasAllowed(true);
fragment.setBoundedUntilFormulasAllowed(true);
fragment.setTimeBoundedUntilFormulasAllowed(true);
fragment.setNextFormulasAllowed(false);
fragment.setStepBoundedUntilFormulasAllowed(false);
return formula.isInFragment(fragment);
}
template<typename ValueType, typename RewardModelType>
std::vector<std::shared_ptr<storm::logic::Formula const>>
NonMarkovianChainTransformer<ValueType, RewardModelType>::checkAndTransformFormulas(
std::vector<std::shared_ptr<storm::logic::Formula const>> const &formulas) {
NonMarkovianChainTransformer<ValueType, RewardModelType>::checkAndTransformFormulas(std::vector<std::shared_ptr<storm::logic::Formula const>> const& formulas) {
std::vector<std::shared_ptr<storm::logic::Formula const>> result;
for (auto const &f : formulas) {
for (auto const& f : formulas) {
if (preservesFormula(*f)) {
result.push_back(f);
} else {
STORM_LOG_INFO("Non-Markovian chain elimination does not preserve formula " << *f);
STORM_LOG_WARN("Non-Markovian chain elimination does not preserve formula " << *f);
}
}
return result;
}
template
class NonMarkovianChainTransformer<double>;
template class NonMarkovianChainTransformer<double>;
template
class NonMarkovianChainTransformer<double, storm::models::sparse::StandardRewardModel<storm::Interval>>;
#ifdef STORM_HAVE_CARL
template class NonMarkovianChainTransformer<double, storm::models::sparse::StandardRewardModel<storm::Interval>>;
template
class NonMarkovianChainTransformer<storm::RationalFunction>;
#ifdef STORM_HAVE_CARL
template class NonMarkovianChainTransformer<storm::RationalFunction>;
template
class NonMarkovianChainTransformer<storm::RationalNumber>;
template class NonMarkovianChainTransformer<storm::RationalNumber>;
#endif
}
}

7
src/storm/transformer/NonMarkovianChainTransformer.h
View File

@ -21,13 +21,10 @@ namespace storm {
* If no non-determinism occurs, a CTMC is generated.
*
* @param ma The input Markov Automaton.
* @param preserveLabels If set, the procedure considers the labels of non-Markovian states when eliminating states.
* @param labelBehavior How the labels of non-Markovian states should be treated when eliminating states.
* @return A reference to the new model after eliminating non-Markovian states.
*/
static std::shared_ptr<models::sparse::Model<ValueType, RewardModelType>> eliminateNonmarkovianStates(
std::shared_ptr<models::sparse::MarkovAutomaton < ValueType, RewardModelType>> ma,
EliminationLabelBehavior labelBehavior = EliminationLabelBehavior::KeepLabels
);
static std::shared_ptr<models::sparse::Model<ValueType, RewardModelType>> eliminateNonmarkovianStates(std::shared_ptr<models::sparse::MarkovAutomaton<ValueType, RewardModelType>> ma, EliminationLabelBehavior labelBehavior = EliminationLabelBehavior::KeepLabels);
/**
* Check if the property specified by the given formula is preserved by the transformation.

249
src/test/storm/transformer/NonMarkovianChainTransformerTest.cpp
View File

@ -0,0 +1,249 @@
#include "test/storm_gtest.h"
#include "storm-config.h"
#include "storm/api/storm.h"
#include "storm/modelchecker/results/ExplicitQuantitativeCheckResult.h"
#include "storm-parsers/api/storm-parsers.h"
#include "storm-parsers/parser/PrismParser.h"
#include "storm/storage/jani/Property.h"
TEST(NonMarkovianChainTransformerTest, StreamExampleTest) {
storm::prism::Program program = storm::parser::PrismParser::parse(STORM_TEST_RESOURCES_DIR "/ma/stream2.ma");
std::string formulasString = "Pmin=? [ F \"done\"];Pmin=? [ F<=1 \"done\"];Tmin=? [ F \"done\" ]";
auto formulas = storm::api::extractFormulasFromProperties(storm::api::parsePropertiesForPrismProgram(formulasString, program));
auto model = storm::api::buildSparseModel<double>(program, formulas)->template as<storm::models::sparse::MarkovAutomaton<double>>();
EXPECT_EQ(12ul, model->getNumberOfStates());
EXPECT_EQ(14ul, model->getNumberOfTransitions());
ASSERT_TRUE(model->isOfType(storm::models::ModelType::MarkovAutomaton));
size_t initState = 0;
auto labeling = model->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("done", 10));
auto result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[1], true));
EXPECT_NEAR(0.6487584849, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[2], true));
EXPECT_NEAR(0.7888888889, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Keep labels
auto transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::KeepLabels);
ASSERT_EQ(9ul, transformed.first->getNumberOfStates());
ASSERT_EQ(11ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("done", 8));
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.6487584849, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(0.7888888889, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Merge labels
transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::MergeLabels);
ASSERT_EQ(8ul, transformed.first->getNumberOfStates());
ASSERT_EQ(10ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("done", 7));
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.6487584849, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(0.7888888889, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Delete labels
transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::DeleteLabels);
ASSERT_EQ(8ul, transformed.first->getNumberOfStates());
ASSERT_EQ(10ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("done", 7));
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.6487584849, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(0.7888888889, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
}
TEST(NonMarkovianChainTransformerTest, ChainElimination1ExampleTest) {
auto model = storm::parser::DirectEncodingParser<double>::parseModel(STORM_TEST_RESOURCES_DIR "/ma/chain_elimination1.drn")->template as<storm::models::sparse::MarkovAutomaton<double>>();
std::string formulasString = "Pmin=? [ F \"Fail\"];Pmin=? [ F<=300 \"Fail\"];Tmin=? [ F \"Fail\" ]";
auto formulas = storm::api::extractFormulasFromProperties(storm::api::parseProperties(formulasString));
EXPECT_EQ(43ul, model->getNumberOfStates());
EXPECT_EQ(59ul, model->getNumberOfTransitions());
ASSERT_TRUE(model->isOfType(storm::models::ModelType::MarkovAutomaton));
size_t initState = 0;
auto labeling = model->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
for (size_t i = 10; i < 43; ++i) {
ASSERT_TRUE(labeling.getStateHasLabel("Fail", i));
}
auto result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[1], true));
EXPECT_NEAR(0.08606881472, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[2], true));
EXPECT_NEAR(3333.333333, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Keep labels
auto transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::KeepLabels);
ASSERT_EQ(13ul, transformed.first->getNumberOfStates());
ASSERT_EQ(29ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
for (size_t i = 5; i < 13; ++i) {
ASSERT_TRUE(labeling.getStateHasLabel("Fail", i));
}
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.08606881472, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(3333.333333, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Merge labels
transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::MergeLabels);
ASSERT_EQ(8ul, transformed.first->getNumberOfStates());
ASSERT_EQ(24ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
for (size_t i = 0; i < 8; ++i) {
ASSERT_TRUE(labeling.getStateHasLabel("Fail", i));
}
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.08606881472, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(3333.333333, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Delete labels
transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::DeleteLabels);
ASSERT_EQ(8ul, transformed.first->getNumberOfStates());
ASSERT_EQ(24ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_FALSE(labeling.getStateHasLabel("Fail", 0));
for (size_t i = 1; i < 8; ++i) {
ASSERT_TRUE(labeling.getStateHasLabel("Fail", i));
}
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.08606881472, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(3333.333333, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
}
TEST(NonMarkovianChainTransformerTest, ChainElimination2ExampleTest) {
auto model = storm::parser::DirectEncodingParser<double>::parseModel(STORM_TEST_RESOURCES_DIR "/ma/chain_elimination2.drn")->template as<storm::models::sparse::MarkovAutomaton<double>>();
std::string formulasString = "Pmin=? [ F \"Fail\"];Pmin=? [ F<=300 \"Fail\"];Tmin=? [ F \"Fail\" ]";
auto formulas = storm::api::extractFormulasFromProperties(storm::api::parseProperties(formulasString));
EXPECT_EQ(10ul, model->getNumberOfStates());
EXPECT_EQ(14ul, model->getNumberOfTransitions());
ASSERT_TRUE(model->isOfType(storm::models::ModelType::MarkovAutomaton));
size_t initState = 0;
auto labeling = model->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 4));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 7));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 8));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 9));
auto result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[1], true));
EXPECT_NEAR(0.791015319, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(formulas[2], true));
EXPECT_NEAR(190, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Keep labels
auto transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::KeepLabels);
ASSERT_EQ(7ul, transformed.first->getNumberOfStates());
ASSERT_EQ(11ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 2));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 5));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 6));
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.791015319, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(190, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Merge labels
transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::MergeLabels);
ASSERT_EQ(5ul, transformed.first->getNumberOfStates());
ASSERT_EQ(10ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 1));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 2));
ASSERT_FALSE(labeling.getStateHasLabel("Fail", 3));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 4));
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.791015319, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(190, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
// Delete labels
transformed = storm::api::eliminateNonMarkovianChains(model, formulas, storm::transformer::EliminationLabelBehavior::DeleteLabels);
ASSERT_EQ(5ul, transformed.first->getNumberOfStates());
ASSERT_EQ(10ul, transformed.first->getNumberOfTransitions());
labeling = transformed.first->getStateLabeling();
ASSERT_TRUE(labeling.getStateHasLabel("init", initState));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 1));
ASSERT_FALSE(labeling.getStateHasLabel("Fail", 2));
ASSERT_FALSE(labeling.getStateHasLabel("Fail", 3));
ASSERT_TRUE(labeling.getStateHasLabel("Fail", 4));
EXPECT_EQ(2ul, transformed.second.size());
result = storm::api::verifyWithSparseEngine(transformed.first, storm::api::createTask<double>(transformed.second[0], true));
EXPECT_EQ(1, result->asExplicitQuantitativeCheckResult<double>()[initState]);
result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[1], true));
EXPECT_NEAR(0.791015319, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
//result = storm::api::verifyWithSparseEngine(model, storm::api::createTask<double>(transformed.second[2], true));
//EXPECT_NEAR(190, result->asExplicitQuantitativeCheckResult<double>()[initState], 1e-6);
}
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