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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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375
tests/TestBoostA2DE.cc
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@@ -27,189 +27,192 @@ TEST_CASE("Build basic model", "[BoostA2DE]")
auto score = clf.score(raw.Xv, raw.yv);
REQUIRE(score == Catch::Approx(0.919271).epsilon(raw.epsilon));
}
// TEST_CASE("Feature_select IWSS", "[BoostAODE]")
// {
// auto raw = RawDatasets("glass", true);
// auto clf = bayesnet::BoostAODE();
// clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
// REQUIRE(clf.getNumberOfNodes() == 90);
// REQUIRE(clf.getNumberOfEdges() == 153);
// REQUIRE(clf.getNotes().size() == 2);
// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
// }
// TEST_CASE("Feature_select FCBF", "[BoostAODE]")
// {
// auto raw = RawDatasets("glass", true);
// auto clf = bayesnet::BoostAODE();
// clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
// REQUIRE(clf.getNumberOfNodes() == 90);
// REQUIRE(clf.getNumberOfEdges() == 153);
// REQUIRE(clf.getNotes().size() == 2);
// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
// }
// TEST_CASE("Test used features in train note and score", "[BoostAODE]")
// {
// auto raw = RawDatasets("diabetes", true);
// auto clf = bayesnet::BoostAODE(true);
// clf.setHyperparameters({
// {"order", "asc"},
// {"convergence", true},
// {"select_features","CFS"},
// });
// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
// REQUIRE(clf.getNumberOfNodes() == 72);
// REQUIRE(clf.getNumberOfEdges() == 120);
// REQUIRE(clf.getNotes().size() == 2);
// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
// REQUIRE(clf.getNotes()[1] == "Number of models: 8");
// auto score = clf.score(raw.Xv, raw.yv);
// auto scoret = clf.score(raw.Xt, raw.yt);
// REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
// REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
// }
// TEST_CASE("Voting vs proba", "[BoostAODE]")
// {
// auto raw = RawDatasets("iris", true);
// auto clf = bayesnet::BoostAODE(false);
// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
// auto score_proba = clf.score(raw.Xv, raw.yv);
// auto pred_proba = clf.predict_proba(raw.Xv);
// clf.setHyperparameters({
// {"predict_voting",true},
// });
// auto score_voting = clf.score(raw.Xv, raw.yv);
// auto pred_voting = clf.predict_proba(raw.Xv);
// REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
// REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
// REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
// REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
// REQUIRE(clf.dump_cpt() == "");
// REQUIRE(clf.topological_order() == std::vector<std::string>());
// }
// TEST_CASE("Order asc, desc & random", "[BoostAODE]")
// {
// auto raw = RawDatasets("glass", true);
// std::map<std::string, double> scores{
// {"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
// };
// for (const std::string& order : { "asc", "desc", "rand" }) {
// auto clf = bayesnet::BoostAODE();
// clf.setHyperparameters({
// {"order", order},
// {"bisection", false},
// {"maxTolerance", 1},
// {"convergence", false},
// });
// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
// auto score = clf.score(raw.Xv, raw.yv);
// auto scoret = clf.score(raw.Xt, raw.yt);
// INFO("BoostAODE order: " + order);
// REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
// REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
// }
// }
// TEST_CASE("Oddities", "[BoostAODE]")
// {
// auto clf = bayesnet::BoostAODE();
// auto raw = RawDatasets("iris", true);
// auto bad_hyper = nlohmann::json{
// { { "order", "duck" } },
// { { "select_features", "duck" } },
// { { "maxTolerance", 0 } },
// { { "maxTolerance", 5 } },
// };
// for (const auto& hyper : bad_hyper.items()) {
// INFO("BoostAODE hyper: " + hyper.value().dump());
// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
// }
// REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
// auto bad_hyper_fit = nlohmann::json{
// { { "select_features","IWSS" }, { "threshold", -0.01 } },
// { { "select_features","IWSS" }, { "threshold", 0.51 } },
// { { "select_features","FCBF" }, { "threshold", 1e-8 } },
// { { "select_features","FCBF" }, { "threshold", 1.01 } },
// };
// for (const auto& hyper : bad_hyper_fit.items()) {
// INFO("BoostAODE hyper: " + hyper.value().dump());
// clf.setHyperparameters(hyper.value());
// REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing, std::invalid_argument);
// }
// }
// TEST_CASE("Bisection Best", "[BoostAODE]")
// {
// auto clf = bayesnet::BoostAODE();
// auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
// clf.setHyperparameters({
// {"bisection", true},
// {"maxTolerance", 3},
// {"convergence", true},
// {"block_update", false},
// {"convergence_best", false},
// });
// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
// REQUIRE(clf.getNumberOfNodes() == 210);
// REQUIRE(clf.getNumberOfEdges() == 378);
// REQUIRE(clf.getNotes().size() == 1);
// REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
// auto score = clf.score(raw.X_test, raw.y_test);
// auto scoret = clf.score(raw.X_test, raw.y_test);
// REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
// REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
// }
// TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
// {
// auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
// auto clf = bayesnet::BoostAODE(true);
// auto hyperparameters = nlohmann::json{
// {"bisection", true},
// {"maxTolerance", 3},
// {"convergence", true},
// {"convergence_best", true},
// };
// clf.setHyperparameters(hyperparameters);
// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
// auto score_best = clf.score(raw.X_test, raw.y_test);
// REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
// // Now we will set the hyperparameter to use the last accuracy
// hyperparameters["convergence_best"] = false;
// clf.setHyperparameters(hyperparameters);
// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
// auto score_last = clf.score(raw.X_test, raw.y_test);
// REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
// }
// TEST_CASE("Block Update", "[BoostAODE]")
// {
// auto clf = bayesnet::BoostAODE();
// auto raw = RawDatasets("mfeat-factors", true, 500);
// clf.setHyperparameters({
// {"bisection", true},
// {"block_update", true},
// {"maxTolerance", 3},
// {"convergence", true},
// });
// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
// REQUIRE(clf.getNumberOfNodes() == 868);
// REQUIRE(clf.getNumberOfEdges() == 1724);
// REQUIRE(clf.getNotes().size() == 3);
// REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
// REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
// REQUIRE(clf.getNotes()[2] == "Number of models: 4");
// auto score = clf.score(raw.X_test, raw.y_test);
// auto scoret = clf.score(raw.X_test, raw.y_test);
// REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
// REQUIRE(scoret == Catch::Approx(0.99f).epsilon(raw.epsilon));
// //
// // std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
// // std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
// // std::cout << "Notes size " << clf.getNotes().size() << std::endl;
// // for (auto note : clf.getNotes()) {
// // std::cout << note << std::endl;
// // }
// // std::cout << "Score " << score << std::endl;
// }
TEST_CASE("Feature_select IWSS", "[BoostA2DE]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostA2DE();
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 140);
REQUIRE(clf.getNumberOfEdges() == 294);
REQUIRE(clf.getNotes().size() == 4);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
REQUIRE(clf.getNotes()[3] == "Number of models: 14");
}
TEST_CASE("Feature_select FCBF", "[BoostA2DE]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostA2DE();
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 110);
REQUIRE(clf.getNumberOfEdges() == 231);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
REQUIRE(clf.getNotes()[3] == "Number of models: 11");
}
TEST_CASE("Test used features in train note and score", "[BoostA2DE]")
{
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::BoostA2DE(true);
clf.setHyperparameters({
{"order", "asc"},
{"convergence", true},
{"select_features","CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 144);
REQUIRE(clf.getNumberOfEdges() == 288);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 16");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.856771).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.856771).epsilon(raw.epsilon));
}
TEST_CASE("Voting vs proba", "[BoostA2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostA2DE(false);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score_proba = clf.score(raw.Xv, raw.yv);
auto pred_proba = clf.predict_proba(raw.Xv);
clf.setHyperparameters({
{"predict_voting",true},
});
auto score_voting = clf.score(raw.Xv, raw.yv);
auto pred_voting = clf.predict_proba(raw.Xv);
REQUIRE(score_proba == Catch::Approx(0.98).epsilon(raw.epsilon));
REQUIRE(score_voting == Catch::Approx(0.946667).epsilon(raw.epsilon));
REQUIRE(pred_voting[83][2] == Catch::Approx(0.53508).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.48394).epsilon(raw.epsilon));
REQUIRE(clf.dump_cpt() == "");
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("Order asc, desc & random", "[BoostA2DE]")
{
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{
{"asc", 0.752336f }, { "desc", 0.813084f }, { "rand", 0.850467 }
};
for (const std::string& order : { "asc", "desc", "rand" }) {
auto clf = bayesnet::BoostA2DE();
clf.setHyperparameters({
{"order", order},
{"bisection", false},
{"maxTolerance", 1},
{"convergence", false},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("BoostA2DE order: " + order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}
TEST_CASE("Oddities2", "[BoostA2DE]")
{
auto clf = bayesnet::BoostA2DE();
auto raw = RawDatasets("iris", true);
auto bad_hyper = nlohmann::json{
{ { "order", "duck" } },
{ { "select_features", "duck" } },
{ { "maxTolerance", 0 } },
{ { "maxTolerance", 5 } },
};
for (const auto& hyper : bad_hyper.items()) {
INFO("BoostA2DE hyper: " + hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
auto bad_hyper_fit = nlohmann::json{
{ { "select_features","IWSS" }, { "threshold", -0.01 } },
{ { "select_features","IWSS" }, { "threshold", 0.51 } },
{ { "select_features","FCBF" }, { "threshold", 1e-8 } },
{ { "select_features","FCBF" }, { "threshold", 1.01 } },
};
for (const auto& hyper : bad_hyper_fit.items()) {
INFO("BoostA2DE hyper: " + hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
}
}
TEST_CASE("No features selected", "[BoostA2DE]")
{
// Check that the note "No features selected in initialization" is added
//
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostA2DE();
clf.setHyperparameters({ {"select_features","FCBF"}, {"threshold", 1 } });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getNotes()[0] == "No features selected in initialization");
}
TEST_CASE("Bisection Best", "[BoostA2DE]")
{
auto clf = bayesnet::BoostA2DE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
clf.setHyperparameters({
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"block_update", false},
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 480);
REQUIRE(clf.getNumberOfEdges() == 1152);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes().at(1) == "Pairs not used in train: 83");
REQUIRE(clf.getNotes().at(2) == "Number of models: 32");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(0.966667f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.966667f).epsilon(raw.epsilon));
}
TEST_CASE("Block Update", "[BoostA2DE]")
{
auto clf = bayesnet::BoostA2DE();
auto raw = RawDatasets("spambase", true, 500);
clf.setHyperparameters({
{"bisection", true},
{"block_update", true},
{"maxTolerance", 3},
{"convergence", true},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 58);
REQUIRE(clf.getNumberOfEdges() == 165);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 1588");
REQUIRE(clf.getNotes()[2] == "Number of models: 1");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
//
// std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
// std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
// std::cout << "Notes size " << clf.getNotes().size() << std::endl;
// for (auto note : clf.getNotes()) {
// std::cout << note << std::endl;
// }
// std::cout << "Score " << score << std::endl;
}
TEST_CASE("Test graph b2a2de", "[BoostA2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostA2DE();
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto graph = clf.graph();
REQUIRE(graph.size() == 26);
REQUIRE(graph[0] == "digraph BayesNet {\nlabel=<BayesNet BoostA2DE_0>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
REQUIRE(graph[1] == "\"class\" [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n");
}