testDecisionTree.cpp

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/* ----------------------------------------------------------------------------
 
 * 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
 
 * -------------------------------------------------------------------------- */
 
/*
 * @file    testDecisionTree.cpp
 * @brief   DecisionTree unit tests
 * @author  Frank Dellaert
 * @author  Can Erdogan
 * @date    Jan 30, 2012
 */
 
// #define DT_DEBUG_MEMORY
#define DISABLE_DOT
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/serializationTestHelpers.h>
#include <gtsam/discrete/DecisionTree-inl.h>
#include <gtsam/discrete/Ring.h>
#include <gtsam/discrete/Signature.h>
#include <gtsam/inference/Symbol.h>
 
#include <iomanip>
 
using std::vector;
using std::string;
using std::map;
using namespace gtsam;
 
template <typename T>
void dot(const T& f, const string& filename) {
#ifndef DISABLE_DOT
  f.dot(filename);
#endif
}
 
#define DOT(x) (dot(x, #x))
 
struct Crazy {
  int a;
  double b;
};
 
struct CrazyDecisionTree : public DecisionTree<string, Crazy> {
  void print(const std::string& s = "") const {
    auto keyFormatter = [](const std::string& s) { return s; };
    auto valueFormatter = [](const Crazy& v) {
      std::stringstream ss;
      ss << "{" << v.a << "," << std::setw(4) << std::setprecision(2) << v.b << "}";
      return ss.str();
    };
    DecisionTree<string, Crazy>::print("", keyFormatter, valueFormatter);
  }
  bool equals(const CrazyDecisionTree& other, double tol = 1e-9) const {
    auto compare = [tol](const Crazy& v, const Crazy& w) {
      return v.a == w.a && std::abs(v.b - w.b) < tol;
    };
    return DecisionTree<string, Crazy>::equals(other, compare);
  }
};
 
// traits
namespace gtsam {
template <>
struct traits<CrazyDecisionTree> : public Testable<CrazyDecisionTree> {};
}  // namespace gtsam
 
GTSAM_CONCEPT_TESTABLE_INST(CrazyDecisionTree)
 
/* ************************************************************************** */
// Test char labels and int range
/* ************************************************************************** */
 
// Create a decision stump one one variable 'a' with values 10 and 20.
TEST(DecisionTree, Constructor) {
  DecisionTree<char, int> tree('a', 10, 20);
 
  // Evaluate the tree on an assignment to the variable.
  EXPECT_LONGS_EQUAL(10, tree({{'a', 0}}));
  EXPECT_LONGS_EQUAL(20, tree({{'a', 1}}));
}
 
/* ************************************************************************** */
// Test string labels and int range
/* ************************************************************************** */
 
struct DT : public DecisionTree<string, int> {
  using Base = DecisionTree<string, int>;
  using DecisionTree::DecisionTree;
  DT() = default;
 
  DT(const Base& dt) : Base(dt) {}
 
  void print(const std::string& s = "") const {
    auto keyFormatter = [](const std::string& s) { return s; };
    auto valueFormatter = [](const int& v) {
      return std::to_string(v);
    };
    std::cout << s;
    Base::print("", keyFormatter, valueFormatter);
  }
 
  bool equals(const Base& other, double tol = 1e-9) const {
    auto compare = [](const int& v, const int& w) { return v == w; };
    return Base::equals(other, compare);
  }
};
 
// traits
namespace gtsam {
template <>
struct traits<DT> : public Testable<DT> {};
}  // namespace gtsam
 
GTSAM_CONCEPT_TESTABLE_INST(DT)
 
/* ************************************************************************** */
// Check that creating decision trees respects key order.
TEST(DecisionTree, ConstructorOrder) {
  // Create labels
  string A("A"), B("B");
 
  const std::vector<int> ys1 = {1, 2, 3, 4};
  DT tree1({{B, 2}, {A, 2}}, ys1); // faster version, as B is "higher" than A!
 
  const std::vector<int> ys2 = {1, 3, 2, 4};
  DT tree2({{A, 2}, {B, 2}}, ys2); // slower version !
 
  // Both trees will be the same, tree is order from high to low labels.
  // Choice(B)
  //  0 Choice(A)
  //  0 0 Leaf 1
  //  0 1 Leaf 2
  //  1 Choice(A)
  //  1 0 Leaf 3
  //  1 1 Leaf 4
 
  EXPECT(tree2.equals(tree1));
 
  // Check the values are as expected by calling the () operator:
  EXPECT_LONGS_EQUAL(1, tree1({{A, 0}, {B, 0}}));
  EXPECT_LONGS_EQUAL(3, tree1({{A, 0}, {B, 1}}));
  EXPECT_LONGS_EQUAL(2, tree1({{A, 1}, {B, 0}}));
  EXPECT_LONGS_EQUAL(4, tree1({{A, 1}, {B, 1}}));
}
 
/* ************************************************************************** */
// test DT
TEST(DecisionTree, Example) {
  // Create labels
  string A("A"), B("B"), C("C");
 
  // Create assignments using brace initialization:
  Assignment<string> x00{{A, 0}, {B, 0}};
  Assignment<string> x01{{A, 0}, {B, 1}};
  Assignment<string> x10{{A, 1}, {B, 0}};
  Assignment<string> x11{{A, 1}, {B, 1}};
 
  // empty
  DT empty;
 
  // A
  DT a(A, 0, 5);
  LONGS_EQUAL(0, a(x00))
  LONGS_EQUAL(5, a(x10))
  DOT(a);
 
  // pruned
  DT p(A, 2, 2);
  LONGS_EQUAL(2, p(x00))
  LONGS_EQUAL(2, p(x10))
  DOT(p);
 
  // \neg B
  DT notb(B, 5, 0);
  LONGS_EQUAL(5, notb(x00))
  LONGS_EQUAL(5, notb(x10))
  DOT(notb);
 
  // Check supplying empty trees yields an exception
  CHECK_EXCEPTION(gtsam::apply(empty, &Ring::id), std::runtime_error);
  CHECK_EXCEPTION(gtsam::apply(empty, a, &Ring::mul), std::runtime_error);
  CHECK_EXCEPTION(gtsam::apply(a, empty, &Ring::mul), std::runtime_error);
 
  // apply, two nodes, in natural order
  DT anotb = apply(a, notb, &Ring::mul);
  LONGS_EQUAL(0, anotb(x00))
  LONGS_EQUAL(0, anotb(x01))
  LONGS_EQUAL(25, anotb(x10))
  LONGS_EQUAL(0, anotb(x11))
  DOT(anotb);
 
  // check pruning
  DT pnotb = apply(p, notb, &Ring::mul);
  LONGS_EQUAL(10, pnotb(x00))
  LONGS_EQUAL(0, pnotb(x01))
  LONGS_EQUAL(10, pnotb(x10))
  LONGS_EQUAL(0, pnotb(x11))
  DOT(pnotb);
 
  // check pruning
  DT zeros = apply(DT(A, 0, 0), notb, &Ring::mul);
  LONGS_EQUAL(0, zeros(x00))
  LONGS_EQUAL(0, zeros(x01))
  LONGS_EQUAL(0, zeros(x10))
  LONGS_EQUAL(0, zeros(x11))
  DOT(zeros);
 
  // apply, two nodes, in switched order
  DT notba = apply(a, notb, &Ring::mul);
  LONGS_EQUAL(0, notba(x00))
  LONGS_EQUAL(0, notba(x01))
  LONGS_EQUAL(25, notba(x10))
  LONGS_EQUAL(0, notba(x11))
  DOT(notba);
 
  // Test choose 0
  DT actual0 = notba.choose(A, 0);
#ifdef GTSAM_DT_MERGING
  EXPECT(assert_equal(DT(0.0), actual0));
#else
  EXPECT(assert_equal(DT({0.0, 0.0}), actual0));
#endif
  DOT(actual0);
 
  // Test choose 1
  DT actual1 = notba.choose(A, 1);
  EXPECT(assert_equal(DT(B, 25, 0), actual1));
  DOT(actual1);
 
  // apply, two nodes at same level
  DT a_and_a = apply(a, a, &Ring::mul);
  LONGS_EQUAL(0, a_and_a(x00))
  LONGS_EQUAL(0, a_and_a(x01))
  LONGS_EQUAL(25, a_and_a(x10))
  LONGS_EQUAL(25, a_and_a(x11))
  DOT(a_and_a);
 
  // create a function on C
  DT c(C, 0, 5);
 
  // and a model assigning stuff to C
  Assignment<string> x101;
  x101[A] = 1, x101[B] = 0, x101[C] = 1;
 
  // mul notba with C
  DT notbac = apply(notba, c, &Ring::mul);
  LONGS_EQUAL(125, notbac(x101))
  DOT(notbac);
 
  // mul now in different order
  DT acnotb = apply(apply(a, c, &Ring::mul), notb, &Ring::mul);
  LONGS_EQUAL(125, acnotb(x101))
  DOT(acnotb);
}
 
/* ************************************************************************** */
// Test that we can create two trees out of one, using a function that returns a pair.
TEST(DecisionTree, Split) {
  // Create labels
  string A("A"), B("B");
 
  // Create a decision tree
  DT original(A, DT(B, 1, 2), DT(B, 3, 4));
 
  // Define a function that returns an int/bool pair
  auto split_function = [](const int& value) -> std::pair<int, bool> {
    return {value*3, value*3 % 2 == 0};
  };
 
  // Split the original tree into two new trees
  auto [la,lb] = original.split<int,bool>(split_function);
 
  // Check the first resulting tree
  EXPECT_LONGS_EQUAL(3, la(Assignment<string>{{A, 0}, {B, 0}}));
  EXPECT_LONGS_EQUAL(6, la(Assignment<string>{{A, 0}, {B, 1}}));
  EXPECT_LONGS_EQUAL(9, la(Assignment<string>{{A, 1}, {B, 0}}));
  EXPECT_LONGS_EQUAL(12, la(Assignment<string>{{A, 1}, {B, 1}}));
 
  // Check the second resulting tree
  EXPECT(!lb(Assignment<string>{{A, 0}, {B, 0}}));
  EXPECT(lb(Assignment<string>{{A, 0}, {B, 1}}));
  EXPECT(!lb(Assignment<string>{{A, 1}, {B, 0}}));
  EXPECT(lb(Assignment<string>{{A, 1}, {B, 1}}));
}
 
 
/* ************************************************************************** */
// Test that we can create a tree by modifying an rvalue.
TEST(DecisionTree, Consume) {
  // Create labels
  string A("A"), B("B");
 
  // Create a decision tree
  DT original(A, DT(B, 1, 2), DT(B, 3, 4));
 
  DT modified([](int i){return i*2;}, std::move(original));
 
  // Check the first resulting tree
  EXPECT_LONGS_EQUAL(2, modified(Assignment<string>{{A, 0}, {B, 0}}));
  EXPECT_LONGS_EQUAL(4, modified(Assignment<string>{{A, 0}, {B, 1}}));
  EXPECT_LONGS_EQUAL(6, modified(Assignment<string>{{A, 1}, {B, 0}}));
  EXPECT_LONGS_EQUAL(8, modified(Assignment<string>{{A, 1}, {B, 1}}));
 
  // Check original was moved
  EXPECT(original.root_ == nullptr);
}
 
/* ************************************************************************** */
// test Conversion of values
bool bool_of_int(const int& y) { return y != 0; };
typedef DecisionTree<string, bool> StringBoolTree;
 
TEST(DecisionTree, ConvertValuesOnly) {
  // Create labels
  string A("A"), B("B");
 
  // apply, two nodes, in natural order
  DT f1 = apply(DT(A, 0, 5), DT(B, 5, 0), &Ring::mul);
 
  // convert
  StringBoolTree f2(f1, bool_of_int);
 
  // Check a value
  Assignment<string> x00 {{A, 0}, {B, 0}};
  EXPECT(!f2(x00));
}
 
/* ************************************************************************** */
// test Conversion of both values and labels.
enum Label { U, V, X, Y, Z };
typedef DecisionTree<Label, bool> LabelBoolTree;
 
TEST(DecisionTree, ConvertBoth) {
  // Create labels
  string A("A"), B("B");
 
  // apply, two nodes, in natural order
  DT f1 = apply(DT(A, 0, 5), DT(B, 5, 0), &Ring::mul);
 
  // convert
  map<string, Label> ordering;
  ordering[A] = X;
  ordering[B] = Y;
  LabelBoolTree f2(f1, ordering, &bool_of_int);
 
  // Check some values
  Assignment<Label> x00, x01, x10, x11;
  x00 = {{X, 0}, {Y, 0}};
  x01 = {{X, 0}, {Y, 1}};
  x10 = {{X, 1}, {Y, 0}};
  x11 = {{X, 1}, {Y, 1}};
 
  EXPECT(!f2(x00));
  EXPECT(!f2(x01));
  EXPECT(f2(x10));
  EXPECT(!f2(x11));
}
 
/* ************************************************************************** */
// test Compose expansion
TEST(DecisionTree, Compose) {
  // Create labels
  string A("A"), B("B"), C("C");
 
  // Put two stumps on A together
  DT f1(B, DT(A, 0, 1), DT(A, 2, 3));
 
  // Create from string
  vector<DT::LabelC> keys{DT::LabelC(A, 2), DT::LabelC(B, 2)};
  DT f2(keys, "0 2 1 3");
  EXPECT(assert_equal(f2, f1, 1e-9));
 
  // Put this AB tree together with another one
  DT f3(keys, "4 6 5 7");
  DT f4(C, f1, f3);
  DOT(f4);
 
  // a bigger tree
  keys.push_back(DT::LabelC(C, 2));
  DT f5(keys, "0 4 2 6 1 5 3 7");
  EXPECT(assert_equal(f5, f4, 1e-9));
  DOT(f5);
}
 
/* ************************************************************************** */
// Check we can create a decision tree of containers.
TEST(DecisionTree, Containers) {
  using Container = std::vector<double>;
  using StringContainerTree = DecisionTree<string, Container>;
 
  // Check default constructor
  StringContainerTree tree;
 
  // Create small two-level tree
  string A("A"), B("B");
  DT stringIntTree(B, DT(A, 0, 1), DT(A, 2, 3));
 
  // Check conversion
  auto container_of_int = [](const int& i) {
    Container c;
    c.emplace_back(i);
    return c;
  };
  StringContainerTree converted(stringIntTree, container_of_int);
}
 
/* ************************************************************************** */
// Test visit.
TEST(DecisionTree, visit) {
  // Create small two-level tree
  string A("A"), B("B");
  DT tree(B, DT(A, 0, 1), DT(A, 2, 3));
  double sum = 0.0;
  auto visitor = [&](int y) { sum += y; };
  tree.visit(visitor);
  EXPECT_DOUBLES_EQUAL(6.0, sum, 1e-9);
}
 
/* ************************************************************************** */
// Test visit, with Choices argument.
TEST(DecisionTree, visitWith) {
  // Create small two-level tree
  string A("A"), B("B");
  DT tree(B, DT(A, 0, 1), DT(A, 2, 3));
  double sum = 0.0;
  auto visitor = [&](const Assignment<string>& choices, int y) { sum += y; };
  tree.visitWith(visitor);
  EXPECT_DOUBLES_EQUAL(6.0, sum, 1e-9);
}
 
/* ************************************************************************** */
// Test visit, with Choices argument.
TEST(DecisionTree, VisitWithPruned) {
  // Create pruned tree
  std::pair<string, size_t> A("A", 2), B("B", 2), C("C", 2);
  std::vector<std::pair<string, size_t>> labels = {C, B, A};
  std::vector<int> nodes = {0, 0, 2, 3, 4, 4, 6, 7};
  DT tree(labels, nodes);
 
  std::vector<Assignment<string>> choices;
  auto func = [&](const Assignment<string>& choice, const int& d) {
    choices.push_back(choice);
  };
  tree.visitWith(func);
 
#ifdef GTSAM_DT_MERGING
  EXPECT_LONGS_EQUAL(6, choices.size());
#else
  EXPECT_LONGS_EQUAL(8, choices.size());
#endif
 
  Assignment<string> expectedAssignment;
 
#ifdef GTSAM_DT_MERGING
  expectedAssignment = {{"B", 0}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(0));
 
  expectedAssignment = {{"A", 0}, {"B", 1}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(1));
 
  expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(2));
 
  expectedAssignment = {{"B", 0}, {"C", 1}};
  EXPECT(expectedAssignment == choices.at(3));
 
  expectedAssignment = {{"A", 0}, {"B", 1}, {"C", 1}};
  EXPECT(expectedAssignment == choices.at(4));
 
  expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 1}};
  EXPECT(expectedAssignment == choices.at(5));
#else
  expectedAssignment = {{"A", 0}, {"B", 0}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(0));
 
  expectedAssignment = {{"A", 1}, {"B", 0}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(1));
 
  expectedAssignment = {{"A", 0}, {"B", 1}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(2));
 
  expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 0}};
  EXPECT(expectedAssignment == choices.at(3));
 
  expectedAssignment = {{"A", 0}, {"B", 0}, {"C", 1}};
  EXPECT(expectedAssignment == choices.at(4));
 
  expectedAssignment = {{"A", 1}, {"B", 0}, {"C", 1}};
  EXPECT(expectedAssignment == choices.at(5));
#endif
}
 
/* ************************************************************************** */
// Test fold.
TEST(DecisionTree, fold) {
  // Create small two-level tree
  string A("A"), B("B");
  DT tree(B, DT(A, 1, 1), DT(A, 2, 3));
  auto add = [](const int& y, double x) { return y + x; };
  double sum = tree.fold(add, 0.0);
#ifdef GTSAM_DT_MERGING
  EXPECT_DOUBLES_EQUAL(6.0, sum, 1e-9);  // Note, not 7, due to merging!
#else
  EXPECT_DOUBLES_EQUAL(7.0, sum, 1e-9);
#endif
}
 
/* ************************************************************************** */
// Test retrieving all labels.
TEST(DecisionTree, labels) {
  // Create small two-level tree
  string A("A"), B("B");
  DT tree(B, DT(A, 0, 1), DT(A, 2, 3));
  auto labels = tree.labels();
  EXPECT_LONGS_EQUAL(2, labels.size());
}
 
/* ************************************************************************** */
// Test unzip method.
TEST(DecisionTree, unzip) {
  using DTP = DecisionTree<string, std::pair<int, string>>;
  using DT1 = DecisionTree<string, int>;
  using DT2 = DecisionTree<string, string>;
 
  // Create small two-level tree
  string A("A"), B("B"), C("C");
  DTP tree(B, DTP(A, {0, "zero"}, {1, "one"}),
           DTP(A, {2, "two"}, {1337, "l33t"}));
 
  const auto [dt1, dt2] = unzip(tree);
 
  DT1 tree1(B, DT1(A, 0, 1), DT1(A, 2, 1337));
  DT2 tree2(B, DT2(A, "zero", "one"), DT2(A, "two", "l33t"));
 
  EXPECT(tree1.equals(dt1));
  EXPECT(tree2.equals(dt2));
}
 
/* ************************************************************************** */
// Test thresholding.
TEST(DecisionTree, threshold) {
  // Create three level tree
  const vector<DT::LabelC> keys{DT::LabelC("C", 2), DT::LabelC("B", 2),
                                DT::LabelC("A", 2)};
  DT tree(keys, "0 1 2 3 4 5 6 7");
 
  // Check number of leaves equal to zero
  auto count = [](const int& value, int count) {
    return value == 0 ? count + 1 : count;
  };
  EXPECT_LONGS_EQUAL(1, tree.fold(count, 0));
 
  // Now threshold
  auto threshold = [](int value) { return value < 5 ? 0 : value; };
  DT thresholded(tree, threshold);
 
#ifdef GTSAM_DT_MERGING
  // Check number of leaves equal to zero now = 2
  // Note: it is 2, because the pruned branches are counted as 1!
  EXPECT_LONGS_EQUAL(2, thresholded.fold(count, 0));
#else
  // if GTSAM_DT_MERGING is disabled, the count will be larger
  EXPECT_LONGS_EQUAL(5, thresholded.fold(count, 0));
#endif
}
 
/* ************************************************************************** */
// Test apply with assignment.
TEST(DecisionTree, ApplyWithAssignment) {
  // Create three level tree
  const vector<DT::LabelC> keys{DT::LabelC("C", 2), DT::LabelC("B", 2),
                                DT::LabelC("A", 2)};
  DT tree(keys, "1 2 3 4 5 6 7 8");
 
  DecisionTree<string, double> probTree(
      keys, "0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08");
  double threshold = 0.045;
 
  // We test pruning one tree by indexing into another.
  auto pruner = [&](const Assignment<string>& choices, const int& x) {
    // Prune out all the leaves with even numbers
    if (probTree(choices) < threshold) {
      return 0;
    } else {
      return x;
    }
  };
  DT prunedTree = tree.apply(pruner);
 
  DT expectedTree(keys, "0 0 0 0 5 6 7 8");
  EXPECT(assert_equal(expectedTree, prunedTree));
 
  size_t count = 0;
  auto counter = [&](const Assignment<string>& choices, const int& x) {
    count += 1;
    return x;
  };
  DT prunedTree2 = prunedTree.apply(counter);
 
#ifdef GTSAM_DT_MERGING
  // Check if apply doesn't enumerate all leaves.
  EXPECT_LONGS_EQUAL(5, count);
#else
  // if GTSAM_DT_MERGING is disabled, the count will be full
  EXPECT_LONGS_EQUAL(8, count);
#endif
}
 
/* ************************************************************************* */
int main() {
  TestResult tr;
  return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */