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libmdlp (#31)

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Add mdlp as library in lib/
Fix tests to reach 99.1% of coverage

Reviewed-on: #31
This commit is contained in:
Ricardo Montañana Gómez
2024-11-23 17:22:41 +00:00
parent f0f3d9ad6e
commit 86f2bc44fc
26 changed files with 5183 additions and 261 deletions
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81
tests/TestBayesNetwork.cc
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@@ -186,11 +186,11 @@ TEST_CASE("Test Bayesian Network", "[Network]")
auto str = net.graph("Test Graph");
REQUIRE(str.size() == 7);
REQUIRE(str[0] == "digraph BayesNet {\nlabel=<BayesNet Test Graph>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
REQUIRE(str[1] == "A [shape=circle] \n");
REQUIRE(str[2] == "A -> B");
REQUIRE(str[3] == "A -> C");
REQUIRE(str[4] == "B [shape=circle] \n");
REQUIRE(str[5] == "C [shape=circle] \n");
REQUIRE(str[1] == "\"A\" [shape=circle] \n");
REQUIRE(str[2] == "\"A\" -> \"B\"");
REQUIRE(str[3] == "\"A\" -> \"C\"");
REQUIRE(str[4] == "\"B\" [shape=circle] \n");
REQUIRE(str[5] == "\"C\" [shape=circle] \n");
REQUIRE(str[6] == "}\n");
}
SECTION("Test predict")
@@ -257,9 +257,9 @@ TEST_CASE("Test Bayesian Network", "[Network]")
REQUIRE(node->getCPT().equal(node2->getCPT()));
}
}
SECTION("Test oddities")
SECTION("Network oddities")
{
INFO("Test oddities");
INFO("Network oddities");
buildModel(net, raw.features, raw.className);
// predict without fitting
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
@@ -329,6 +329,14 @@ TEST_CASE("Test Bayesian Network", "[Network]")
std::string invalid_state = "Feature sepallength not found in states";
REQUIRE_THROWS_AS(net4.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, std::map<std::string, std::vector<int>>(), raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net4.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, std::map<std::string, std::vector<int>>(), raw.smoothing), invalid_state);
// Try to add node or edge to a fitted network
auto net5 = bayesnet::Network();
buildModel(net5, raw.features, raw.className);
net5.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE_THROWS_AS(net5.addNode("A"), std::logic_error);
REQUIRE_THROWS_WITH(net5.addNode("A"), "Cannot add node to a fitted network. Initialize first.");
REQUIRE_THROWS_AS(net5.addEdge("A", "B"), std::logic_error);
REQUIRE_THROWS_WITH(net5.addEdge("A", "B"), "Cannot add edge to a fitted network. Initialize first.");
}
}
@@ -373,7 +381,7 @@ TEST_CASE("Dump CPT", "[Network]")
0.3333
0.3333
0.3333
[ CPUFloatType{3} ]
[ CPUDoubleType{3} ]
* petallength: (4) : [4, 3, 3]
(1,.,.) =
0.9388 0.1000 0.2000
@@ -394,7 +402,7 @@ TEST_CASE("Dump CPT", "[Network]")
0.0204 0.1000 0.2000
0.1250 0.0526 0.1667
0.2000 0.0606 0.8235
[ CPUFloatType{4,3,3} ]
[ CPUDoubleType{4,3,3} ]
* petalwidth: (3) : [3, 6, 3]
(1,.,.) =
0.5000 0.0417 0.0714
@@ -419,12 +427,12 @@ TEST_CASE("Dump CPT", "[Network]")
0.1111 0.0909 0.8000
0.0667 0.2000 0.8667
0.0303 0.2500 0.7500
[ CPUFloatType{3,6,3} ]
[ CPUDoubleType{3,6,3} ]
* sepallength: (3) : [3, 3]
0.8679 0.1321 0.0377
0.0943 0.3019 0.0566
0.0377 0.5660 0.9057
[ CPUFloatType{3,3} ]
[ CPUDoubleType{3,3} ]
* sepalwidth: (6) : [6, 3, 3]
(1,.,.) =
0.0392 0.5000 0.2857
@@ -455,7 +463,7 @@ TEST_CASE("Dump CPT", "[Network]")
0.5098 0.0833 0.1429
0.5000 0.0476 0.1250
0.2857 0.0571 0.1132
[ CPUFloatType{6,3,3} ]
[ CPUDoubleType{6,3,3} ]
)";
REQUIRE(res == expected);
}
@@ -525,6 +533,7 @@ TEST_CASE("Test Smoothing A", "[Network]")
}
}
}
TEST_CASE("Test Smoothing B", "[Network]")
{
auto net = bayesnet::Network();
@@ -549,19 +558,41 @@ TEST_CASE("Test Smoothing B", "[Network]")
{ "C", {0, 1} }
};
auto weights = std::vector<double>(C.size(), 1);
// Simple
std::cout << "LAPLACE\n";
// See https://www.overleaf.com/read/tfnhpfysfkfx#2d576c example for calculations
INFO("Test Smoothing B - Laplace");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
std::cout << net.dump_cpt();
std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
std::cout << "ORIGINAL\n";
auto laplace_values = std::vector<std::vector<float>>({ {0.377418, 0.622582}, {0.217821, 0.782179} });
auto laplace_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(laplace_score.at(i).at(j) == Catch::Approx(laplace_values.at(i).at(j)).margin(threshold));
}
}
INFO("Test Smoothing B - Original");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::ORIGINAL);
std::cout << net.dump_cpt();
std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
std::cout << "CESTNIK\n";
auto original_values = std::vector<std::vector<float>>({ {0.344769, 0.655231}, {0.0421263, 0.957874} });
auto original_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(original_score.at(i).at(j) == Catch::Approx(original_values.at(i).at(j)).margin(threshold));
}
}
INFO("Test Smoothing B - Cestnik");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
std::cout << net.dump_cpt();
std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
}
auto cestnik_values = std::vector<std::vector<float>>({ {0.353422, 0.646578}, {0.12364, 0.87636} });
auto cestnik_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(cestnik_score.at(i).at(j) == Catch::Approx(cestnik_values.at(i).at(j)).margin(threshold));
}
}
INFO("Test Smoothing B - No smoothing");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::NONE);
auto nosmooth_values = std::vector<std::vector<float>>({ {0.342465753, 0.65753424}, {0.0, 1.0} });
auto nosmooth_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(nosmooth_score.at(i).at(j) == Catch::Approx(nosmooth_values.at(i).at(j)).margin(threshold));
}
}
}