Asia example
Frank Dellaert
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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 incomplete Asia example
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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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@ -29,40 +29,13 @@
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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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namespace keys {
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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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}
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static const DiscreteKey Dyspnea(keys::D, 2), XRay(keys::X, 2),
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Either(keys::E, 2), Bronchitis(keys::B, 2), LungCancer(keys::L, 2),
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Tuberculosis(keys::T, 2), Smoking(keys::S, 2), Asia(keys::A, 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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// Add in topological sort order, parents last:
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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((Either | Tuberculosis, LungCancer) = "F T T T");
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asia.add(Bronchitis | Smoking = "70/30 40/60");
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asia.add(LungCancer | Smoking = "99/1 90/10");
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asia.add(Tuberculosis | Asia = "99/1 95/5");
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asia.add(Smoking % "50/50"); // Signature version
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asia.add(Asia, "99/1");
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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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@ -92,7 +65,8 @@ 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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@ -105,8 +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{keys::A, keys::D, keys::T, keys::X,
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keys::S, keys::E, keys::L, keys::B};
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const Ordering ordering{A, D, T, X, S, E, L, B};
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DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
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DiscreteConditional expected2(Bronchitis % "11/9");
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EXPECT(assert_equal(expected2, *chordal->back()));
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@ -151,319 +124,53 @@ TEST(DiscreteBayesNet, Sugar) {
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/* ************************************************************************* */
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TEST(DiscreteBayesNet, Dot) {
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DiscreteBayesNet fragment;
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fragment.add(Asia % "99/1");
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fragment.add(Smoking % "50/50");
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using namespace asia_example;
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const DiscreteBayesNet fragment = createFragment();
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fragment.add(Tuberculosis | Asia = "99/1 95/5");
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fragment.add(LungCancer | Smoking = "99/1 90/10");
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fragment.add((Either | Tuberculosis, LungCancer) = "F T T T");
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string actual = fragment.dot();
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EXPECT(actual ==
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"digraph {\n"
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" size=\"5,5\";\n"
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"\n"
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" var4683743612465315848[label=\"A8\"];\n"
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" var4971973988617027587[label=\"E3\"];\n"
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" var5476377146882523141[label=\"L5\"];\n"
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" var5980780305148018695[label=\"S7\"];\n"
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" var6052837899185946630[label=\"T6\"];\n"
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"\n"
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" var6052837899185946630->var4971973988617027587\n"
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" var5476377146882523141->var4971973988617027587\n"
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" var5980780305148018695->var5476377146882523141\n"
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" var4683743612465315848->var6052837899185946630\n"
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"}");
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std::string expected =
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"digraph {\n"
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" size=\"5,5\";\n"
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"\n"
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" var4683743612465315848[label=\"A8\"];\n"
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" var4971973988617027587[label=\"E3\"];\n"
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" var5476377146882523141[label=\"L5\"];\n"
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" var5980780305148018695[label=\"S7\"];\n"
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" var6052837899185946630[label=\"T6\"];\n"
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"\n"
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" var4683743612465315848->var6052837899185946630\n"
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" var5980780305148018695->var5476377146882523141\n"
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" var6052837899185946630->var4971973988617027587\n"
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" var5476377146882523141->var4971973988617027587\n"
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"}";
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std::string actual = fragment.dot();
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EXPECT(actual.compare(expected) == 0);
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}
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/* ************************************************************************* */
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// Check markdown representation looks as expected.
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TEST(DiscreteBayesNet, markdown) {
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DiscreteBayesNet fragment;
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fragment.add(Asia % "99/1");
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fragment.add(Smoking | Asia = "8/2 7/3");
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using namespace asia_example;
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DiscreteBayesNet priors = createPriors();
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string expected =
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std::string expected =
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"`DiscreteBayesNet` of size 2\n"
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"\n"
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" *P(Smoking):*\n\n"
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"|Smoking|value|\n"
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"|:-:|:-:|\n"
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"|0|0.5|\n"
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"|1|0.5|\n"
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"\n"
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" *P(Asia):*\n\n"
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"|Asia|value|\n"
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"|:-:|:-:|\n"
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"|0|0.99|\n"
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"|1|0.01|\n"
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"\n"
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" *P(Smoking|Asia):*\n\n"
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"|*Asia*|0|1|\n"
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"|:-:|:-:|:-:|\n"
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"|0|0.8|0.2|\n"
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"|1|0.7|0.3|\n\n";
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auto formatter = [](Key key) { return key == keys::A ? "Asia" : "Smoking"; };
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string actual = fragment.markdown(formatter);
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"|1|0.01|\n\n";
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auto formatter = [](Key key) { return key == A ? "Asia" : "Smoking"; };
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std::string actual = priors.markdown(formatter);
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EXPECT(actual == expected);
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}
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/* ************************************************************************* */
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <map>
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#include <queue>
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#include <vector>
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using Value = size_t;
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// ----------------------------------------------------------------------------
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// 1) SearchNode: store partial assignment and next factor to expand
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// ----------------------------------------------------------------------------
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struct SearchNode {
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DiscreteValues assignment;
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double error;
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double bound;
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int nextConditional; // index into conditionals
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/// if nextConditional < 0, we've assigned everything.
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bool isComplete() const { return nextConditional < 0; }
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/// lower bound on final error for unassigned variables. Stub=0.
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double computeBound() const {
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// Real code might do partial factor analysis or heuristics.
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return 0.0;
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}
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/// Expand this node by assigning the next variable
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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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SearchNode child;
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child.assignment = assignment;
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for (auto& kv : fa) {
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child.assignment[kv.first] = kv.second;
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}
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// Compute the incremental error for this factor
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child.error = error + conditional.error(child.assignment);
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// Compute new bound
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child.bound = child.error + computeBound();
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// Next factor index
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child.nextConditional = nextConditional - 1;
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return child;
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}
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friend std::ostream& operator<<(std::ostream& os, const SearchNode& sn) {
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os << "[ error=" << sn.error << " bound=" << sn.bound
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<< " nextConditional=" << sn.nextConditional << " assignment={"
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<< sn.assignment << "}]";
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return os;
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}
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};
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// ----------------------------------------------------------------------------
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// 2) Priority functor to make a min-heap by bound
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// ----------------------------------------------------------------------------
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struct CompareByBound {
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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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// 4) A Solution
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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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struct CompareByError {
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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>, CompareByError> 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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void print() const {
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auto pq = pq_;
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while (!pq.empty()) {
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const Solution& best = pq.top();
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std::cout << "Error: " << best.error << ", Values: " << best.assignment
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<< std::endl;
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pq.pop();
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}
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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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double worstError = pq_.top().error;
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return (bound >= worstError);
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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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/**
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* BestKSearch: Search for the K best solutions.
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*/
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class BestKSearch {
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public:
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/**
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* Construct from a DiscreteBayesNet and K.
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*/
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BestKSearch(const DiscreteBayesNet& bayesNet, size_t K)
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: bayesNet_(bayesNet), solutions_(K) {
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// Copy out the conditionals
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for (auto& factor : bayesNet_) {
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conditionals_.push_back(factor);
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}
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// Create the root node: no variables assigned, nextConditional = last.
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SearchNode root{
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.assignment = DiscreteValues(),
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.error = 0.0,
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.nextConditional = static_cast<int>(conditionals_.size()) - 1};
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root.bound = root.computeBound();
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std::cout << "Root: " << root << std::endl;
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expansions_.push(root);
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}
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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() {
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size_t numExpansions = 0;
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while (!expansions_.empty()) {
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expandNextNode();
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numExpansions++;
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}
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std::cout << "Expansions: " << numExpansions << std::endl;
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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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private:
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//
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void expandNextNode() {
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// Pop the partial assignment with the smallest bound
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SearchNode current = expansions_.top();
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expansions_.pop();
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std::cout << "Expanding: " << current << std::endl;
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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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std::cout << "Pruning: bound=" << current.bound << std::endl;
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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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const bool added = solutions_.maybeAdd(current.error, current.assignment);
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if (added) {
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std::cout << "Best solutions so far:" << std::endl;
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solutions_.print();
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}
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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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std::cout << "Frontal assignment: " << fa << std::endl;
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auto childNode = current.expand(*conditional, fa);
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// Again, prune if we cannot beat the worst solution
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if (solutions_.prune(current.bound)) {
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std::cout << "Pruning: bound=" << childNode.bound << std::endl;
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continue;
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}
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expansions_.push(childNode);
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}
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}
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const DiscreteBayesNet& bayesNet_;
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std::vector<std::shared_ptr<DiscreteConditional>> conditionals_;
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std::priority_queue<SearchNode, std::vector<SearchNode>, CompareByBound>
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expansions_;
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Solutions solutions_;
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};
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// ----------------------------------------------------------------------------
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// Example “Unit Tests” (trivial stubs)
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// ----------------------------------------------------------------------------
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TEST(DiscreteBayesNet, EmptyKBest) {
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DiscreteBayesNet net; // no factors
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BestKSearch search(net, 3);
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auto solutions = search.run();
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// Expect one solution with empty assignment, error=0
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EXPECT_LONGS_EQUAL(1, solutions.size());
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EXPECT_DOUBLES_EQUAL(0, std::fabs(solutions[0].error), 1e-9);
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}
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TEST(DiscreteBayesNet, AsiaKBest) {
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DiscreteBayesNet asia = constructAsiaExample();
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BestKSearch search(asia, 4);
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auto solutions = search.run();
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EXPECT(!solutions.empty());
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// Regression test: check the first solution
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EXPECT_DOUBLES_EQUAL(1.236627, std::fabs(solutions[0].error), 1e-5);
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}
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|
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/* ************************************************************************* */
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int main() {
|
||||
TestResult tr;
|
||||
|
|
|
|||
Loading…
Reference in New Issue