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Add Ensemble tests

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This commit is contained in:
Ricardo Montañana Gómez
2024-04-08 19:09:51 +02:00
parent d12a779bd9
commit a1178554ff
7 changed files with 242 additions and 97 deletions
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113
tests/TestBayesModels.cc
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@@ -2,6 +2,7 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "bayesnet/classifiers/KDB.h"
#include "bayesnet/classifiers/TAN.h"
#include "bayesnet/classifiers/SPODE.h"
@@ -87,62 +88,7 @@ TEST_CASE("Get num features & num edges", "[Models]")
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 8);
}
TEST_CASE("BoostAODE feature_select CFS", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "CFS"} });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
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");
}
TEST_CASE("BoostAODE feature_select IWSS", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("BoostAODE feature_select FCBF", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("BoostAODE test used features in train note and score", "[Models]")
{
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.featuresv, raw.classNamev, raw.statesv);
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(0.80078).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.80078).epsilon(raw.epsilon));
}
TEST_CASE("Model predict_proba", "[Models]")
{
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting");
@@ -230,25 +176,7 @@ TEST_CASE("Model predict_proba", "[Models]")
delete clf;
}
}
TEST_CASE("BoostAODE voting-proba", "[Models]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostAODE(false);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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(0.552091).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
REQUIRE(clf.dump_cpt() == "");
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("AODE voting-proba", "[Models]")
{
auto raw = RawDatasets("glass", true);
@@ -294,22 +222,21 @@ TEST_CASE("KDB with hyperparameters", "[Models]")
REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
}
TEST_CASE("BoostAODE order asc, desc & random", "[Models]")
TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
{
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},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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));
}
}
auto clf = bayesnet::AODE();
auto raw = RawDatasets("iris", true);
std::string message = "Ensemble has not been fitted";
REQUIRE_THROWS_AS(clf.predict(raw.Xv), std::logic_error);
REQUIRE_THROWS_AS(clf.predict_proba(raw.Xv), std::logic_error);
REQUIRE_THROWS_AS(clf.predict(raw.Xt), std::logic_error);
REQUIRE_THROWS_AS(clf.predict_proba(raw.Xt), std::logic_error);
REQUIRE_THROWS_AS(clf.score(raw.Xv, raw.yv), std::logic_error);
REQUIRE_THROWS_AS(clf.score(raw.Xt, raw.yt), std::logic_error);
REQUIRE_THROWS_WITH(clf.predict(raw.Xv), message);
REQUIRE_THROWS_WITH(clf.predict_proba(raw.Xv), message);
REQUIRE_THROWS_WITH(clf.predict(raw.Xt), message);
REQUIRE_THROWS_WITH(clf.predict_proba(raw.Xt), message);
REQUIRE_THROWS_WITH(clf.score(raw.Xv, raw.yv), message);
REQUIRE_THROWS_WITH(clf.score(raw.Xt, raw.yt), message);
}