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Add selectKParis method

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Ricardo Montañana Gómez
2024-05-16 11:17:21 +02:00
parent 8784a24898
commit 2e3e0e0fc2
8 changed files with 224 additions and 26 deletions
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34
tests/TestBoostA2DE.cc
View File

@@ -8,21 +8,35 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include "bayesnet/utils/BayesMetrics.h"
#include "bayesnet/ensembles/BoostA2DE.h"
#include "TestUtils.h"
TEST_CASE("Feature_select CFS", "[BoostA2DE]")
TEST_CASE("Build basic model", "[BoostA2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostA2DE();
clf.setHyperparameters({ {"select_features", "CFS"} });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 0);
REQUIRE(clf.getNumberOfEdges() == 0);
// REQUIRE(clf.getNotes().size() == 2);
// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
auto raw = RawDatasets("diabetes", true);
bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
auto expected = std::map<std::pair<int, int>, double>{
{ { 0, 1 }, 0.0 },
{ { 0, 2 }, 0.287696 },
{ { 0, 3 }, 0.403749 },
{ { 1, 2 }, 1.17112 },
{ { 1, 3 }, 1.31852 },
{ { 2, 3 }, 0.210068 },
};
for (int i = 0; i < raw.features.size() - 1; ++i) {
for (int j = i + 1; j < raw.features.size(); ++j) {
double result = metrics.conditionalMutualInformation(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights);
// REQUIRE(result == Catch::Approx(expected.at({ i, j })).epsilon(raw.epsilon));
auto clf = bayesnet::SPnDE({ i, j });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
auto score = clf.score(raw.Xt, raw.yt);
std::cout << " i " << i << " j " << j << " cmi "
<< std::setw(8) << std::setprecision(6) << fixed << result
<< " score = " << score << std::endl;
}
}
}
// TEST_CASE("Feature_select IWSS", "[BoostAODE]")
// {

6
tests/TestBoostAODE.cc
View File

@@ -104,7 +104,7 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("BoostAODE order: " + order);
INFO("BoostAODE order: " << order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
@@ -120,7 +120,7 @@ TEST_CASE("Oddities", "[BoostAODE]")
{ { "maxTolerance", 5 } },
};
for (const auto& hyper : bad_hyper.items()) {
INFO("BoostAODE hyper: " + hyper.value().dump());
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);
@@ -131,7 +131,7 @@ TEST_CASE("Oddities", "[BoostAODE]")
{ { "select_features","FCBF" }, { "threshold", 1.01 } },
};
for (const auto& hyper : bad_hyper_fit.items()) {
INFO("BoostAODE hyper: " + hyper.value().dump());
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), std::invalid_argument);
}