Frank Dellaert
GitHub
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3f6ae48dfb
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* DiscreteSearch.cpp
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*
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* @date January, 2025
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* @author Frank Dellaert
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*/
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#include <gtsam/discrete/DiscreteSearch.h>
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namespace gtsam {
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using Solution = DiscreteSearch::Solution;
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/**
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* @brief Represents a node in the search tree for discrete search algorithms.
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*
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* @details Each SearchNode contains a partial assignment of discrete variables,
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* the current error, a bound on the final error, and the index of the next
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* conditional to be assigned.
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*/
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struct SearchNode {
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DiscreteValues assignment; ///< Partial assignment of discrete variables.
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double error; ///< Current error for the partial assignment.
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double bound; ///< Lower bound on the final error for unassigned variables.
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int nextConditional; ///< Index of the next conditional to be assigned.
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/**
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* @brief Construct the root node for the search.
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*/
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static SearchNode Root(size_t numConditionals, double bound) {
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return {DiscreteValues(), 0.0, bound,
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static_cast<int>(numConditionals) - 1};
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}
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struct Compare {
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bool operator()(const SearchNode& a, const SearchNode& b) const {
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return a.bound > b.bound; // smallest bound -> highest priority
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}
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};
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/**
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* @brief Checks if the node represents a complete assignment.
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*
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* @return True if all variables have been assigned, false otherwise.
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*/
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inline bool isComplete() const { return nextConditional < 0; }
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/**
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* @brief Expands the node by assigning the next variable.
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*
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* @param conditional The discrete conditional representing the next variable
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* to be assigned.
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* @param fa The frontal assignment for the next variable.
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* @return A new SearchNode representing the expanded state.
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*/
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SearchNode expand(const DiscreteConditional& conditional,
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const DiscreteValues& fa) const {
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// Combine the new frontal assignment with the current partial assignment
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DiscreteValues newAssignment = assignment;
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for (auto& [key, value] : fa) {
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newAssignment[key] = value;
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}
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return {newAssignment, error + conditional.error(newAssignment), 0.0,
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nextConditional - 1};
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}
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/**
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* @brief Prints the SearchNode to an output stream.
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*
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* @param os The output stream.
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* @param node The SearchNode to be printed.
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* @return The output stream.
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*/
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friend std::ostream& operator<<(std::ostream& os, const SearchNode& node) {
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os << "SearchNode(error=" << node.error << ", bound=" << node.bound << ")";
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return os;
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}
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};
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struct CompareSolution {
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bool operator()(const Solution& a, const Solution& b) const {
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return a.error < b.error;
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}
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};
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// Define the Solutions class
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class Solutions {
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private:
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size_t maxSize_;
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std::priority_queue<Solution, std::vector<Solution>, CompareSolution> pq_;
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public:
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Solutions(size_t maxSize) : maxSize_(maxSize) {}
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/// Add a solution to the priority queue, possibly evicting the worst one.
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/// Return true if we added the solution.
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bool maybeAdd(double error, const DiscreteValues& assignment) {
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const bool full = pq_.size() == maxSize_;
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if (full && error >= pq_.top().error) return false;
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if (full) pq_.pop();
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pq_.emplace(error, assignment);
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return true;
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}
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/// Check if we have any solutions
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bool empty() const { return pq_.empty(); }
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// Method to print all solutions
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friend std::ostream& operator<<(std::ostream& os, const Solutions& sn) {
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os << "Solutions (top " << sn.pq_.size() << "):\n";
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auto pq = sn.pq_;
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while (!pq.empty()) {
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os << pq.top() << "\n";
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pq.pop();
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}
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return os;
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}
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/// Check if (partial) solution with given bound can be pruned. If we have
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/// room, we never prune. Otherwise, prune if lower bound on error is worse
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/// than our current worst error.
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bool prune(double bound) const {
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if (pq_.size() < maxSize_) return false;
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return bound >= pq_.top().error;
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}
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// Method to extract solutions in ascending order of error
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std::vector<Solution> extractSolutions() {
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std::vector<Solution> result;
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while (!pq_.empty()) {
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result.push_back(pq_.top());
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pq_.pop();
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}
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std::sort(
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result.begin(), result.end(),
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[](const Solution& a, const Solution& b) { return a.error < b.error; });
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return result;
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}
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};
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DiscreteSearch::DiscreteSearch(const DiscreteBayesNet& bayesNet) {
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std::vector<DiscreteConditional::shared_ptr> conditionals;
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for (auto& factor : bayesNet) conditionals_.push_back(factor);
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costToGo_ = computeCostToGo(conditionals_);
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}
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DiscreteSearch::DiscreteSearch(const DiscreteBayesTree& bayesTree) {
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std::function<void(const DiscreteBayesTree::sharedClique&)>
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collectConditionals = [&](const auto& clique) {
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if (!clique) return;
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for (const auto& child : clique->children) collectConditionals(child);
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conditionals_.push_back(clique->conditional());
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};
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for (const auto& root : bayesTree.roots()) collectConditionals(root);
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costToGo_ = computeCostToGo(conditionals_);
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}
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struct SearchNodeQueue
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: public std::priority_queue<SearchNode, std::vector<SearchNode>,
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SearchNode::Compare> {
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void expandNextNode(
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const std::vector<DiscreteConditional::shared_ptr>& conditionals,
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const std::vector<double>& costToGo, Solutions* solutions) {
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// Pop the partial assignment with the smallest bound
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SearchNode current = top();
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pop();
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// If we already have K solutions, prune if we cannot beat the worst one.
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if (solutions->prune(current.bound)) {
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return;
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}
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// Check if we have a complete assignment
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if (current.isComplete()) {
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solutions->maybeAdd(current.error, current.assignment);
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return;
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}
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// Expand on the next factor
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const auto& conditional = conditionals[current.nextConditional];
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for (auto& fa : conditional->frontalAssignments()) {
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auto childNode = current.expand(*conditional, fa);
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if (childNode.nextConditional >= 0)
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childNode.bound = childNode.error + costToGo[childNode.nextConditional];
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// Again, prune if we cannot beat the worst solution
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if (!solutions->prune(childNode.bound)) {
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emplace(childNode);
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}
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}
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}
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};
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std::vector<Solution> DiscreteSearch::run(size_t K) const {
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Solutions solutions(K);
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SearchNodeQueue expansions;
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expansions.push(SearchNode::Root(conditionals_.size(),
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costToGo_.empty() ? 0.0 : costToGo_.back()));
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#ifdef DISCRETE_SEARCH_DEBUG
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size_t numExpansions = 0;
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#endif
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// Perform the search
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while (!expansions.empty()) {
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expansions.expandNextNode(conditionals_, costToGo_, &solutions);
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#ifdef DISCRETE_SEARCH_DEBUG
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++numExpansions;
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#endif
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}
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#ifdef DISCRETE_SEARCH_DEBUG
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std::cout << "Number of expansions: " << numExpansions << std::endl;
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#endif
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// Extract solutions from bestSolutions in ascending order of error
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return solutions.extractSolutions();
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}
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std::vector<double> DiscreteSearch::computeCostToGo(
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const std::vector<DiscreteConditional::shared_ptr>& conditionals) {
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std::vector<double> costToGo;
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double error = 0.0;
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for (const auto& conditional : conditionals) {
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Ordering ordering(conditional->begin(), conditional->end());
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auto maxx = conditional->max(ordering);
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error -= std::log(maxx->evaluate({}));
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costToGo.push_back(error);
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}
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return costToGo;
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}
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} // namespace gtsam
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@ -0,0 +1,78 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* DiscreteSearch.cpp
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*
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* @date January, 2025
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* @author Frank Dellaert
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*/
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#include <gtsam/discrete/DiscreteBayesNet.h>
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#include <gtsam/discrete/DiscreteBayesTree.h>
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#include <queue>
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namespace gtsam {
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/**
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* DiscreteSearch: Search for the K best solutions.
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*/
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class GTSAM_EXPORT DiscreteSearch {
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public:
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/**
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* @brief A solution to a discrete search problem.
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*/
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struct Solution {
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double error;
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DiscreteValues assignment;
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Solution(double err, const DiscreteValues& assign)
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: error(err), assignment(assign) {}
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friend std::ostream& operator<<(std::ostream& os, const Solution& sn) {
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os << "[ error=" << sn.error << " assignment={" << sn.assignment << "}]";
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return os;
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}
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};
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/**
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* Construct from a DiscreteBayesNet and K.
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*/
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DiscreteSearch(const DiscreteBayesNet& bayesNet);
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/**
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* Construct from a DiscreteBayesTree and K.
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*/
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DiscreteSearch(const DiscreteBayesTree& bayesTree);
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/**
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* @brief Search for the K best solutions.
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*
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* This method performs a search to find the K best solutions for the given
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* DiscreteBayesNet. It uses a priority queue to manage the search nodes,
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* expanding nodes with the smallest bound first. The search continues until
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* all possible nodes have been expanded or pruned.
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*
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* @return A vector of the K best solutions found during the search.
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*/
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std::vector<Solution> run(size_t K = 1) const;
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private:
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/// Compute the cumulative cost-to-go for each conditional slot.
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static std::vector<double> computeCostToGo(
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const std::vector<DiscreteConditional::shared_ptr>& conditionals);
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/// Expand the next node in the search tree.
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void expandNextNode() const;
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std::vector<DiscreteConditional::shared_ptr> conditionals_;
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std::vector<double> costToGo_;
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};
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} // namespace gtsam
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@ -26,12 +26,24 @@ using std::stringstream;
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namespace gtsam {
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/* ************************************************************************ */
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static void stream(std::ostream& os, const DiscreteValues& x,
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const KeyFormatter& keyFormatter) {
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for (const auto& kv : x)
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os << "(" << keyFormatter(kv.first) << ", " << kv.second << ")";
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}
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/* ************************************************************************ */
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std::ostream& operator<<(std::ostream& os, const DiscreteValues& x) {
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stream(os, x, DefaultKeyFormatter);
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return os;
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}
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/* ************************************************************************ */
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void DiscreteValues::print(const string& s,
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const KeyFormatter& keyFormatter) const {
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cout << s << ": ";
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for (auto&& kv : *this)
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cout << "(" << keyFormatter(kv.first) << ", " << kv.second << ")";
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stream(cout, *this, keyFormatter);
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cout << endl;
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}
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@ -64,6 +64,9 @@ class GTSAM_EXPORT DiscreteValues : public Assignment<Key> {
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/// @name Standard Interface
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/// @{
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/// ostream operator:
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friend std::ostream& operator<<(std::ostream& os, const DiscreteValues& x);
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// insert in base class;
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std::pair<iterator, bool> insert( const value_type& value ){
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return Base::insert(value);
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@ -0,0 +1,61 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* AsiaExample.h
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*
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* @date Jan, 2025
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* @author Frank Dellaert
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*/
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#include <gtsam/discrete/DiscreteBayesNet.h>
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#include <gtsam/inference/Symbol.h>
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namespace gtsam {
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namespace asia_example {
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static const Key D = Symbol('D', 1), X = Symbol('X', 2), E = Symbol('E', 3),
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B = Symbol('B', 4), L = Symbol('L', 5), T = Symbol('T', 6),
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S = Symbol('S', 7), A = Symbol('A', 8);
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static const DiscreteKey Dyspnea(D, 2), XRay(X, 2), Either(E, 2),
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Bronchitis(B, 2), LungCancer(L, 2), Tuberculosis(T, 2), Smoking(S, 2),
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Asia(A, 2);
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// Function to construct the Asia priors
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DiscreteBayesNet createPriors() {
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DiscreteBayesNet priors;
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priors.add(Smoking % "50/50");
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priors.add(Asia, "99/1");
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return priors;
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}
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// Function to construct the incomplete Asia example
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DiscreteBayesNet createFragment() {
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DiscreteBayesNet fragment;
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fragment.add((Either | Tuberculosis, LungCancer) = "F T T T");
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fragment.add(LungCancer | Smoking = "99/1 90/10");
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fragment.add(Tuberculosis | Asia = "99/1 95/5");
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for (const auto& factor : createPriors()) fragment.push_back(factor);
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return fragment;
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}
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// Function to construct the Asia example
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DiscreteBayesNet createAsiaExample() {
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DiscreteBayesNet asia;
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asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
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asia.add(XRay | Either = "95/5 2/98");
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asia.add(Bronchitis | Smoking = "70/30 40/60");
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for (const auto& factor : createFragment()) asia.push_back(factor);
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return asia;
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}
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} // namespace asia_example
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} // namespace gtsam
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@ -23,40 +23,19 @@
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#include <gtsam/discrete/DiscreteBayesNet.h>
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#include <gtsam/discrete/DiscreteFactorGraph.h>
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#include <gtsam/discrete/DiscreteMarginals.h>
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#include <gtsam/inference/Symbol.h>
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#include <iostream>
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#include <string>
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#include <vector>
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using namespace std;
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#include "AsiaExample.h"
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using namespace gtsam;
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static const DiscreteKey Asia(0, 2), Smoking(4, 2), Tuberculosis(3, 2),
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LungCancer(6, 2), Bronchitis(7, 2), Either(5, 2), XRay(2, 2), Dyspnea(1, 2);
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using ADT = AlgebraicDecisionTree<Key>;
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// Function to construct the Asia example
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DiscreteBayesNet constructAsiaExample() {
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DiscreteBayesNet asia;
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asia.add(Asia, "99/1");
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asia.add(Smoking % "50/50"); // Signature version
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asia.add(Tuberculosis | Asia = "99/1 95/5");
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asia.add(LungCancer | Smoking = "99/1 90/10");
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asia.add(Bronchitis | Smoking = "70/30 40/60");
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asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
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asia.add(XRay | Either = "95/5 2/98");
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asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
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return asia;
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}
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/* ************************************************************************* */
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TEST(DiscreteBayesNet, bayesNet) {
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using ADT = AlgebraicDecisionTree<Key>;
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DiscreteBayesNet bayesNet;
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DiscreteKey Parent(0, 2), Child(1, 2);
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@ -86,11 +65,12 @@ TEST(DiscreteBayesNet, bayesNet) {
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/* ************************************************************************* */
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TEST(DiscreteBayesNet, Asia) {
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DiscreteBayesNet asia = constructAsiaExample();
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using namespace asia_example;
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const DiscreteBayesNet asia = createAsiaExample();
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// Convert to factor graph
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DiscreteFactorGraph fg(asia);
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LONGS_EQUAL(3, fg.back()->size());
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LONGS_EQUAL(1, fg.back()->size());
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// Check the marginals we know (of the parent-less nodes)
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DiscreteMarginals marginals(fg);
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@ -99,7 +79,7 @@ TEST(DiscreteBayesNet, Asia) {
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EXPECT(assert_equal(vs, marginals.marginalProbabilities(Smoking)));
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// Create solver and eliminate
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const Ordering ordering{0, 1, 2, 3, 4, 5, 6, 7};
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const Ordering ordering{A, D, T, X, S, E, L, B};
|
||||
DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
|
||||
DiscreteConditional expected2(Bronchitis % "11/9");
|
||||
EXPECT(assert_equal(expected2, *chordal->back()));
|
||||
|
|
@ -144,55 +124,50 @@ TEST(DiscreteBayesNet, Sugar) {
|
|||
|
||||
/* ************************************************************************* */
|
||||
TEST(DiscreteBayesNet, Dot) {
|
||||
DiscreteBayesNet fragment;
|
||||
fragment.add(Asia % "99/1");
|
||||
fragment.add(Smoking % "50/50");
|
||||
using namespace asia_example;
|
||||
const DiscreteBayesNet fragment = createFragment();
|
||||
|
||||
fragment.add(Tuberculosis | Asia = "99/1 95/5");
|
||||
fragment.add(LungCancer | Smoking = "99/1 90/10");
|
||||
fragment.add((Either | Tuberculosis, LungCancer) = "F T T T");
|
||||
|
||||
string actual = fragment.dot();
|
||||
EXPECT(actual ==
|
||||
"digraph {\n"
|
||||
" size=\"5,5\";\n"
|
||||
"\n"
|
||||
" var0[label=\"0\"];\n"
|
||||
" var3[label=\"3\"];\n"
|
||||
" var4[label=\"4\"];\n"
|
||||
" var5[label=\"5\"];\n"
|
||||
" var6[label=\"6\"];\n"
|
||||
"\n"
|
||||
" var3->var5\n"
|
||||
" var6->var5\n"
|
||||
" var4->var6\n"
|
||||
" var0->var3\n"
|
||||
"}");
|
||||
std::string expected =
|
||||
"digraph {\n"
|
||||
" size=\"5,5\";\n"
|
||||
"\n"
|
||||
" var4683743612465315848[label=\"A8\"];\n"
|
||||
" var4971973988617027587[label=\"E3\"];\n"
|
||||
" var5476377146882523141[label=\"L5\"];\n"
|
||||
" var5980780305148018695[label=\"S7\"];\n"
|
||||
" var6052837899185946630[label=\"T6\"];\n"
|
||||
"\n"
|
||||
" var4683743612465315848->var6052837899185946630\n"
|
||||
" var5980780305148018695->var5476377146882523141\n"
|
||||
" var6052837899185946630->var4971973988617027587\n"
|
||||
" var5476377146882523141->var4971973988617027587\n"
|
||||
"}";
|
||||
std::string actual = fragment.dot();
|
||||
EXPECT(actual.compare(expected) == 0);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Check markdown representation looks as expected.
|
||||
TEST(DiscreteBayesNet, markdown) {
|
||||
DiscreteBayesNet fragment;
|
||||
fragment.add(Asia % "99/1");
|
||||
fragment.add(Smoking | Asia = "8/2 7/3");
|
||||
using namespace asia_example;
|
||||
DiscreteBayesNet priors = createPriors();
|
||||
|
||||
string expected =
|
||||
std::string expected =
|
||||
"`DiscreteBayesNet` of size 2\n"
|
||||
"\n"
|
||||
" *P(Smoking):*\n\n"
|
||||
"|Smoking|value|\n"
|
||||
"|:-:|:-:|\n"
|
||||
"|0|0.5|\n"
|
||||
"|1|0.5|\n"
|
||||
"\n"
|
||||
" *P(Asia):*\n\n"
|
||||
"|Asia|value|\n"
|
||||
"|:-:|:-:|\n"
|
||||
"|0|0.99|\n"
|
||||
"|1|0.01|\n"
|
||||
"\n"
|
||||
" *P(Smoking|Asia):*\n\n"
|
||||
"|*Asia*|0|1|\n"
|
||||
"|:-:|:-:|:-:|\n"
|
||||
"|0|0.8|0.2|\n"
|
||||
"|1|0.7|0.3|\n\n";
|
||||
auto formatter = [](Key key) { return key == 0 ? "Asia" : "Smoking"; };
|
||||
string actual = fragment.markdown(formatter);
|
||||
"|1|0.01|\n\n";
|
||||
auto formatter = [](Key key) { return key == A ? "Asia" : "Smoking"; };
|
||||
std::string actual = priors.markdown(formatter);
|
||||
EXPECT(actual == expected);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,111 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
/*
|
||||
* testDiscreteSearch.cpp
|
||||
*
|
||||
* @date January, 2025
|
||||
* @author Frank Dellaert
|
||||
*/
|
||||
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/base/Testable.h>
|
||||
#include <gtsam/discrete/DiscreteSearch.h>
|
||||
|
||||
#include "AsiaExample.h"
|
||||
|
||||
using namespace gtsam;
|
||||
|
||||
// Create Asia Bayes net, FG, and Bayes tree once
|
||||
namespace asia {
|
||||
using namespace asia_example;
|
||||
static const DiscreteBayesNet bayesNet = createAsiaExample();
|
||||
static const DiscreteFactorGraph factorGraph(bayesNet);
|
||||
static const DiscreteValues mpe = factorGraph.optimize();
|
||||
static const Ordering ordering{D, X, B, E, L, T, S, A};
|
||||
static const DiscreteBayesTree bayesTree =
|
||||
*factorGraph.eliminateMultifrontal(ordering);
|
||||
} // namespace asia
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(DiscreteBayesNet, EmptyKBest) {
|
||||
DiscreteBayesNet net; // no factors
|
||||
DiscreteSearch search(net);
|
||||
auto solutions = search.run(3);
|
||||
// Expect one solution with empty assignment, error=0
|
||||
EXPECT_LONGS_EQUAL(1, solutions.size());
|
||||
EXPECT_DOUBLES_EQUAL(0, std::fabs(solutions[0].error), 1e-9);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(DiscreteBayesNet, AsiaKBest) {
|
||||
const DiscreteSearch search(asia::bayesNet);
|
||||
|
||||
// Ask for the MPE
|
||||
auto mpe = search.run();
|
||||
|
||||
EXPECT_LONGS_EQUAL(1, mpe.size());
|
||||
// Regression test: check the MPE solution
|
||||
EXPECT_DOUBLES_EQUAL(1.236627, std::fabs(mpe[0].error), 1e-5);
|
||||
|
||||
// Check it is equal to MPE via inference
|
||||
EXPECT(assert_equal(asia::mpe, mpe[0].assignment));
|
||||
|
||||
// Ask for top 4 solutions
|
||||
auto solutions = search.run(4);
|
||||
|
||||
EXPECT_LONGS_EQUAL(4, solutions.size());
|
||||
// Regression test: check the first and last solution
|
||||
EXPECT_DOUBLES_EQUAL(1.236627, std::fabs(solutions[0].error), 1e-5);
|
||||
EXPECT_DOUBLES_EQUAL(2.201708, std::fabs(solutions[3].error), 1e-5);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(DiscreteBayesTree, EmptyTree) {
|
||||
DiscreteBayesTree bt;
|
||||
|
||||
DiscreteSearch search(bt);
|
||||
auto solutions = search.run(3);
|
||||
|
||||
// We expect exactly 1 solution with error = 0.0 (the empty assignment).
|
||||
EXPECT_LONGS_EQUAL(1, solutions.size());
|
||||
EXPECT_DOUBLES_EQUAL(0, std::fabs(solutions[0].error), 1e-9);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(DiscreteBayesTree, AsiaTreeKBest) {
|
||||
DiscreteSearch search(asia::bayesTree);
|
||||
|
||||
// Ask for MPE
|
||||
auto mpe = search.run();
|
||||
|
||||
EXPECT_LONGS_EQUAL(1, mpe.size());
|
||||
// Regression test: check the MPE solution
|
||||
EXPECT_DOUBLES_EQUAL(1.236627, std::fabs(mpe[0].error), 1e-5);
|
||||
|
||||
// Check it is equal to MPE via inference
|
||||
EXPECT(assert_equal(asia::mpe, mpe[0].assignment));
|
||||
|
||||
// Ask for top 4 solutions
|
||||
auto solutions = search.run(4);
|
||||
|
||||
EXPECT_LONGS_EQUAL(4, solutions.size());
|
||||
// Regression test: check the first and last solution
|
||||
EXPECT_DOUBLES_EQUAL(1.236627, std::fabs(solutions[0].error), 1e-5);
|
||||
EXPECT_DOUBLES_EQUAL(2.201708, std::fabs(solutions[3].error), 1e-5);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
return TestRegistry::runAllTests(tr);
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
Loading…
Reference in New Issue