testDecisionTreeFactor.cpp
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1 /* ----------------------------------------------------------------------------
2 
3  * GTSAM Copyright 2010, Georgia Tech Research Corporation,
4  * Atlanta, Georgia 30332-0415
5  * All Rights Reserved
6  * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
7 
8  * See LICENSE for the license information
9 
10  * -------------------------------------------------------------------------- */
11 
12 /*
13  * testDecisionTreeFactor.cpp
14  *
15  * @date Feb 5, 2012
16  * @author Frank Dellaert
17  * @author Duy-Nguyen Ta
18  */
19 
20 #include <CppUnitLite/TestHarness.h>
21 #include <gtsam/base/Testable.h>
22 #include <gtsam/base/TestableAssertions.h>
23 #include <gtsam/base/serializationTestHelpers.h>
24 #include <gtsam/discrete/DecisionTreeFactor.h>
25 #include <gtsam/discrete/DiscreteDistribution.h>
26 #include <gtsam/discrete/DiscreteFactor.h>
27 #include <gtsam/discrete/Signature.h>
28 #include <gtsam/inference/Key.h>
29 #include <gtsam/inference/Ordering.h>
30 
31 using namespace std;
32 using namespace gtsam;
33 
35 DecisionTreeFactor create(const Signature& signature) {
36  DecisionTreeFactor p(signature.discreteKeys(), signature.cpt());
37  return p;
38 }
39 
40 /* ************************************************************************* */
41 TEST(DecisionTreeFactor, ConstructorsMatch) {
42  // Declare two keys
43  DiscreteKey X(0, 2), Y(1, 3);
44 
45  // Create with vector and with string
46  const std::vector<double> table{2, 5, 3, 6, 4, 7};
47  DecisionTreeFactor f1({X, Y}, table);
48  DecisionTreeFactor f2({X, Y}, "2 5 3 6 4 7");
49  EXPECT(assert_equal(f1, f2));
50 }
51 
52 /* ************************************************************************* */
53 TEST(DecisionTreeFactor, constructors) {
54  // Declare a bunch of keys
55  DiscreteKey X(0, 2), Y(1, 3), Z(2, 2);
56 
57  // Create factors
58  DecisionTreeFactor f1(X, {2, 8});
59  DecisionTreeFactor f2(X & Y, "2 5 3 6 4 7");
60  DecisionTreeFactor f3(X & Y & Z, "2 5 3 6 4 7 25 55 35 65 45 75");
61  EXPECT_LONGS_EQUAL(1, f1.size());
62  EXPECT_LONGS_EQUAL(2, f2.size());
63  EXPECT_LONGS_EQUAL(3, f3.size());
64 
65  DiscreteValues x121{{0, 1}, {1, 2}, {2, 1}};
66  EXPECT_DOUBLES_EQUAL(8, f1(x121), 1e-9);
67  EXPECT_DOUBLES_EQUAL(7, f2(x121), 1e-9);
68  EXPECT_DOUBLES_EQUAL(75, f3(x121), 1e-9);
69 
70  // Assert that error = -log(value)
71  EXPECT_DOUBLES_EQUAL(-log(f1(x121)), f1.error(x121), 1e-9);
72 
73  // Construct from DiscreteConditional
74  DiscreteConditional conditional(X | Y = "1/1 2/3 1/4");
75  DecisionTreeFactor f4(conditional);
76  EXPECT_DOUBLES_EQUAL(0.8, f4(x121), 1e-9);
77 }
78 
79 /* ************************************************************************* */
80 TEST(DecisionTreeFactor, Error) {
81  // Declare a bunch of keys
82  DiscreteKey X(0, 2), Y(1, 3), Z(2, 2);
83 
84  // Create factors
85  DecisionTreeFactor f(X & Y & Z, "2 5 3 6 4 7 25 55 35 65 45 75");
86 
87  auto errors = f.errorTree();
88  // regression
89  AlgebraicDecisionTree<Key> expected(
90  {X, Y, Z},
91  vector<double>{-0.69314718, -1.6094379, -1.0986123, -1.7917595,
92  -1.3862944, -1.9459101, -3.2188758, -4.0073332, -3.5553481,
93  -4.1743873, -3.8066625, -4.3174881});
94  EXPECT(assert_equal(expected, errors, 1e-6));
95 }
96 
97 /* ************************************************************************* */
98 TEST(DecisionTreeFactor, multiplication) {
99  DiscreteKey v0(0, 2), v1(1, 2), v2(2, 2);
100 
101  // Multiply with a DiscreteDistribution, i.e., Bayes Law!
102  DiscreteDistribution prior(v1 % "1/3");
103  DecisionTreeFactor f1(v0 & v1, "1 2 3 4");
104  DecisionTreeFactor expected(v0 & v1, "0.25 1.5 0.75 3");
105  CHECK(assert_equal(expected, static_cast<DecisionTreeFactor>(prior) * f1));
106  CHECK(assert_equal(expected, f1 * prior));
107 
108  // Multiply two factors
109  DecisionTreeFactor f2(v1 & v2, "5 6 7 8");
110  DecisionTreeFactor actual = f1 * f2;
111  DecisionTreeFactor expected2(v0 & v1 & v2, "5 6 14 16 15 18 28 32");
112  CHECK(assert_equal(expected2, actual));
113 }
114 
115 /* ************************************************************************* */
116 TEST(DecisionTreeFactor, Divide) {
117  DiscreteKey A(0, 2), S(1, 2);
118  DecisionTreeFactor pA = create(A % "99/1"), pS = create(S % "50/50");
119  DecisionTreeFactor joint = pA * pS;
120 
121  DecisionTreeFactor s = joint / pA;
122 
123  // Factors are not equal due to difference in keys
124  EXPECT(assert_inequal(pS, s));
125 
126  // The underlying data should be the same
127 #ifdef GTSAM_DT_MERGING
128  using ADT = AlgebraicDecisionTree<Key>;
129  EXPECT(assert_equal(ADT(pS), ADT(s)));
130 #endif
131 
132  KeySet keys(joint.keys());
133  keys.insert(pA.keys().begin(), pA.keys().end());
134  EXPECT(assert_inequal(KeySet(pS.keys()), keys));
135 }
136 
137 /* ************************************************************************* */
138 TEST(DecisionTreeFactor, sum_max) {
139  DiscreteKey v0(0, 3), v1(1, 2);
140  DecisionTreeFactor f1(v0 & v1, "1 2 3 4 5 6");
141 
142  DecisionTreeFactor expected(v1, "9 12");
143  auto actual = std::dynamic_pointer_cast<DecisionTreeFactor>(f1.sum(1));
144  CHECK(actual);
145  CHECK(assert_equal(expected, *actual, 1e-5));
146 
147  DecisionTreeFactor expected2(v1, "5 6");
148  auto actual2 = std::dynamic_pointer_cast<DecisionTreeFactor>(f1.max(1));
149  CHECK(actual2);
150  CHECK(assert_equal(expected2, *actual2));
151 
152  DecisionTreeFactor f2(v1 & v0, "1 2 3 4 5 6");
153  auto actual22 = std::dynamic_pointer_cast<DecisionTreeFactor>(f2.sum(1));
154  CHECK(actual22);
155 }
156 
157 /* ************************************************************************* */
158 // Check enumerate yields the correct list of assignment/value pairs.
159 TEST(DecisionTreeFactor, enumerate) {
160  DiscreteKey A(12, 3), B(5, 2);
161  DecisionTreeFactor f(A & B, "1 2 3 4 5 6");
162  auto actual = f.enumerate();
163  std::vector<std::pair<DiscreteValues, double>> expected;
164  DiscreteValues values;
165  for (size_t a : {0, 1, 2}) {
166  for (size_t b : {0, 1}) {
167  values[12] = a;
168  values[5] = b;
169  expected.emplace_back(values, f(values));
170  }
171  }
172  EXPECT(actual == expected);
173 }
174 
175 namespace pruning_fixture {
176 
177 DiscreteKey A(1, 2), B(2, 2), C(3, 2);
178 DecisionTreeFactor f(A& B& C, "1 5 3 7 2 6 4 8");
179 
180 DiscreteKey D(4, 2);
181 DecisionTreeFactor factor(
182  D& C & B & A,
183  "0.0 0.0 0.0 0.60658897 0.61241912 0.61241969 0.61247685 0.61247742 0.0 "
184  "0.0 0.0 0.99995287 1.0 1.0 1.0 1.0");
185 
186 } // namespace pruning_fixture
187 
188 /* ************************************************************************* */
189 // Check if computing the correct threshold works.
190 TEST(DecisionTreeFactor, ComputeThreshold) {
191  using namespace pruning_fixture;
192 
193  // Only keep the leaves with the top 5 values.
194  double threshold = f.computeThreshold(5);
195  EXPECT_DOUBLES_EQUAL(4.0, threshold, 1e-9);
196 
197  // Check for more extreme pruning where we only keep the top 2 leaves
198  threshold = f.computeThreshold(2);
199  EXPECT_DOUBLES_EQUAL(7.0, threshold, 1e-9);
200 
201  threshold = factor.computeThreshold(5);
202  EXPECT_DOUBLES_EQUAL(0.99995287, threshold, 1e-9);
203 
204  threshold = factor.computeThreshold(3);
205  EXPECT_DOUBLES_EQUAL(1.0, threshold, 1e-9);
206 
207  threshold = factor.computeThreshold(6);
208  EXPECT_DOUBLES_EQUAL(0.61247742, threshold, 1e-9);
209 }
210 
211 /* ************************************************************************* */
212 // Check pruning of the decision tree works as expected.
213 TEST(DecisionTreeFactor, Prune) {
214  using namespace pruning_fixture;
215 
216  // Only keep the leaves with the top 5 values.
217  size_t maxNrAssignments = 5;
218  auto pruned5 = f.prune(maxNrAssignments);
219 
220  // Pruned leaves should be 0
221  DecisionTreeFactor expected(A & B & C, "0 5 0 7 0 6 4 8");
222  EXPECT(assert_equal(expected, pruned5));
223 
224  // Check for more extreme pruning where we only keep the top 2 leaves
225  maxNrAssignments = 2;
226  auto pruned2 = f.prune(maxNrAssignments);
227  DecisionTreeFactor expected2(A & B & C, "0 0 0 7 0 0 0 8");
228  EXPECT(assert_equal(expected2, pruned2));
229 
230  DecisionTreeFactor expected3(D & C & B & A,
231  "0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 "
232  "0.999952870000 1.0 1.0 1.0 1.0");
233  maxNrAssignments = 5;
234  auto pruned3 = factor.prune(maxNrAssignments);
235  EXPECT(assert_equal(expected3, pruned3));
236 
237  // Edge case where the number of hypotheses are less than maxNrAssignments
238  DecisionTreeFactor f(A, "0.50001 0.49999");
239  auto pruned4 = f.prune(10);
240  DecisionTreeFactor expected4(A, "0.50001 0.49999");
241  EXPECT(assert_equal(expected4, pruned4));
242 }
243 
244 /* ************************************************************************** */
245 // Asia Bayes Network
246 /* ************************************************************************** */
247 
248 #define DISABLE_DOT
249 
250 void maybeSaveDotFile(const DecisionTreeFactor& f, const string& filename) {
251 #ifndef DISABLE_DOT
252  std::vector<std::string> names = {"A", "S", "T", "L", "B", "E", "X", "D"};
253  auto formatter = [&](Key key) { return names[key]; };
254  f.dot(filename, formatter, true);
255 #endif
256 }
257 
258 /* ************************************************************************* */
259 // test Asia Joint
260 TEST(DecisionTreeFactor, joint) {
261  DiscreteKey A(0, 2), S(1, 2), T(2, 2), L(3, 2), B(4, 2), E(5, 2), X(6, 2),
262  D(7, 2);
263 
264  gttic_(asiaCPTs);
265  DecisionTreeFactor pA = create(A % "99/1");
266  DecisionTreeFactor pS = create(S % "50/50");
267  DecisionTreeFactor pT = create(T | A = "99/1 95/5");
268  DecisionTreeFactor pL = create(L | S = "99/1 90/10");
269  DecisionTreeFactor pB = create(B | S = "70/30 40/60");
270  DecisionTreeFactor pE = create((E | T, L) = "F T T T");
271  DecisionTreeFactor pX = create(X | E = "95/5 2/98");
272  DecisionTreeFactor pD = create((D | E, B) = "9/1 2/8 3/7 1/9");
273 
274  // Create joint
275  gttic_(asiaJoint);
276  DecisionTreeFactor joint = pA;
277  maybeSaveDotFile(joint, "Asia-A");
278  joint = joint * pS;
279  maybeSaveDotFile(joint, "Asia-AS");
280  joint = joint * pT;
281  maybeSaveDotFile(joint, "Asia-AST");
282  joint = joint * pL;
283  maybeSaveDotFile(joint, "Asia-ASTL");
284  joint = joint * pB;
285  maybeSaveDotFile(joint, "Asia-ASTLB");
286  joint = joint * pE;
287  maybeSaveDotFile(joint, "Asia-ASTLBE");
288  joint = joint * pX;
289  maybeSaveDotFile(joint, "Asia-ASTLBEX");
290  joint = joint * pD;
291  maybeSaveDotFile(joint, "Asia-ASTLBEXD");
292 
293  // Check that discrete keys are as expected
294  EXPECT(assert_equal(joint.discreteKeys(), {A, S, T, L, B, E, X, D}));
295 
296  // Check that summing out variables maintains the keys even if merged, as is
297  // the case with S.
298  auto noAB = joint.sum(Ordering{A.first, B.first});
299  EXPECT(assert_equal(noAB->discreteKeys(), {S, T, L, E, X, D}));
300 }
301 
302 /* ************************************************************************* */
303 TEST(DecisionTreeFactor, DotWithNames) {
304  DiscreteKey A(12, 3), B(5, 2);
305  DecisionTreeFactor f(A & B, "1 2 3 4 5 6");
306  auto formatter = [](Key key) { return key == 12 ? "A" : "B"; };
307 
308  for (bool showZero : {true, false}) {
309  string actual = f.dot(formatter, showZero);
310  // pretty weak test, as ids are pointers and not stable across platforms.
311  string expected = "digraph G {";
312  EXPECT(actual.substr(0, 11) == expected);
313  }
314 }
315 
316 /* ************************************************************************* */
317 // Check markdown representation looks as expected.
318 TEST(DecisionTreeFactor, markdown) {
319  DiscreteKey A(12, 3), B(5, 2);
320  DecisionTreeFactor f(A & B, "1 2 3 4 5 6");
321  string expected =
322  "|A|B|value|\n"
323  "|:-:|:-:|:-:|\n"
324  "|0|0|1|\n"
325  "|0|1|2|\n"
326  "|1|0|3|\n"
327  "|1|1|4|\n"
328  "|2|0|5|\n"
329  "|2|1|6|\n";
330  auto formatter = [](Key key) { return key == 12 ? "A" : "B"; };
331  string actual = f.markdown(formatter);
332  EXPECT(actual == expected);
333 }
334 
335 /* ************************************************************************* */
336 // Check markdown representation with a value formatter.
337 TEST(DecisionTreeFactor, markdownWithValueFormatter) {
338  DiscreteKey A(12, 3), B(5, 2);
339  DecisionTreeFactor f(A & B, "1 2 3 4 5 6");
340  string expected =
341  "|A|B|value|\n"
342  "|:-:|:-:|:-:|\n"
343  "|Zero|-|1|\n"
344  "|Zero|+|2|\n"
345  "|One|-|3|\n"
346  "|One|+|4|\n"
347  "|Two|-|5|\n"
348  "|Two|+|6|\n";
349  auto keyFormatter = [](Key key) { return key == 12 ? "A" : "B"; };
350  DecisionTreeFactor::Names names{{12, {"Zero", "One", "Two"}},
351  {5, {"-", "+"}}};
352  string actual = f.markdown(keyFormatter, names);
353  EXPECT(actual == expected);
354 }
355 
356 /* ************************************************************************* */
357 // Check html representation with a value formatter.
358 TEST(DecisionTreeFactor, htmlWithValueFormatter) {
359  DiscreteKey A(12, 3), B(5, 2);
360  DecisionTreeFactor f(A & B, "1 2 3 4 5 6");
361  string expected =
362  "<div>\n"
363  "<table class='DecisionTreeFactor'>\n"
364  " <thead>\n"
365  " <tr><th>A</th><th>B</th><th>value</th></tr>\n"
366  " </thead>\n"
367  " <tbody>\n"
368  " <tr><th>Zero</th><th>-</th><td>1</td></tr>\n"
369  " <tr><th>Zero</th><th>+</th><td>2</td></tr>\n"
370  " <tr><th>One</th><th>-</th><td>3</td></tr>\n"
371  " <tr><th>One</th><th>+</th><td>4</td></tr>\n"
372  " <tr><th>Two</th><th>-</th><td>5</td></tr>\n"
373  " <tr><th>Two</th><th>+</th><td>6</td></tr>\n"
374  " </tbody>\n"
375  "</table>\n"
376  "</div>";
377  auto keyFormatter = [](Key key) { return key == 12 ? "A" : "B"; };
378  DecisionTreeFactor::Names names{{12, {"Zero", "One", "Two"}},
379  {5, {"-", "+"}}};
380  string actual = f.html(keyFormatter, names);
381  EXPECT(actual == expected);
382 }
383 
384 /* ************************************************************************* */
385 int main() {
386  TestResult tr;
387  return TestRegistry::runAllTests(tr);
388 }
389 /* ************************************************************************* */
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