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