rmontanana/BayesNet
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Update tests

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This commit is contained in:
Ricardo Montañana Gómez 2024-04-01 11:51:29 +02:00
parent bc0b938cfc
commit 8c61840d81
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
2 changed files with 42 additions and 33 deletions
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35
.vscode/c_cpp_properties.json vendored
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@ -3,7 +3,7 @@
{
"name": "Mac",
"includePath": [
"${workspaceFolder}/**"
"/home/rmontanana/Code/BayesNet/**"
],
"defines": [],
"macFrameworkPath": [
@ -11,7 +11,38 @@
],
"cStandard": "c17",
"cppStandard": "c++17",
"compileCommands": "${workspaceFolder}/cmake-build-release/compile_commands.json"
"compileCommands": "/home/rmontanana/Code/BayesNet/cmake-build-release/compile_commands.json",
"compileCommandsInCppPropertiesJson": "${workspaceFolder}/cmake-build-release/compile_commands.json",
"intelliSenseMode": "macos-clang-arm64",
"mergeConfigurations": false,
"browse": {
"path": [
"/home/rmontanana/Code/BayesNet/**",
"${workspaceFolder}"
],
"limitSymbolsToIncludedHeaders": true
}
},
{
"name": "Linux",
"includePath": [
"/home/rmontanana/Code/BayesNet/**",
"/home/rmontanana/Code/libtorch/include/torch/csrc/api/include/"
],
"defines": [],
"cStandard": "c17",
"cppStandard": "c++17",
"intelliSenseMode": "linux-gcc-x64",
"mergeConfigurations": false,
"compilerPath": "/usr/bin/g++",
"browse": {
"path": [
"/home/rmontanana/Code/BayesNet/**",
"${workspaceFolder}"
],
"limitSymbolsToIncludedHeaders": true
},
"configurationProvider": "ms-vscode.cmake-tools"
}
],
"version": 4

40
tests/TestBayesModels.cc
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@ -19,13 +19,13 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[BayesNet]")
{
map <pair<std::string, std::string>, float> scores{
// Diabetes
{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODE"}, 0.82161}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f}, {{"diabetes", "BoostAODE"}, 0.83984f},
// Ecoli
{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
{{"ecoli", "AODELd"}, 0.8869f}, {{"ecoli", "KDBLd"}, 0.875f}, {{"ecoli", "SPODELd"}, 0.84226f}, {{"ecoli", "TANLd"}, 0.86905f}, {{"ecoli", "BoostAODE"}, 0.89583f},
// Glass
{{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
{{"glass", "AODE"}, 0.79439}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
{{"glass", "AODELd"}, 0.79439f}, {{"glass", "KDBLd"}, 0.85047f}, {{"glass", "SPODELd"}, 0.79439f}, {{"glass", "TANLd"}, 0.86449f}, {{"glass", "BoostAODE"}, 0.84579f},
// Iris
{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
@ -49,7 +49,7 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[BayesNet]")
auto raw = RawDatasets(file_name, discretize);
clf->fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto score = clf->score(raw.Xt, raw.yt);
INFO("File: " + file_name);
INFO("Classifier: " + name + " File: " + file_name);
REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
}
}
@ -106,20 +106,18 @@ TEST_CASE("BoostAODE test used features in train note and score", "[BayesNet]")
clf.setHyperparameters({
{"order", "asc"},
{"convergence", true},
{"repeatSparent",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() == 3);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
REQUIRE(clf.getNotes()[1] == "Used features in train: 7 of 8");
REQUIRE(clf.getNotes()[2] == "Number of models: 8");
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(0.8138).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.8138).epsilon(raw.epsilon));
REQUIRE(score == Catch::Approx(0.82031).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.82031).epsilon(raw.epsilon));
}
TEST_CASE("Model predict_proba", "[BayesNet]")
{
@ -232,7 +230,7 @@ TEST_CASE("BoostAODE order asc, desc & random", "[BayesNet]")
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{
{"asc", 0.83178f }, { "desc", 0.84579f }, { "rand", 0.83645f }
{"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
};
for (const std::string& order : { "asc", "desc", "rand" }) {
auto clf = bayesnet::BoostAODE();
@ -242,28 +240,8 @@ TEST_CASE("BoostAODE order asc, desc & random", "[BayesNet]")
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("order: " + order);
INFO("BoostAODE order: " + order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}
TEST_CASE("BoostAODE predict_single", "[BayesNet]")
{
auto raw = RawDatasets("glass", true);
std::map<bool, double> scores{
{true, 0.84579f }, { false, 0.80841f }
};
for (const bool kind : { true, false}) {
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({
{"predict_single", kind}, {"order", "desc" },
});
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("kind: " + std::string(kind ? "true" : "false"));
REQUIRE(score == Catch::Approx(scores[kind]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[kind]).epsilon(raw.epsilon));
}
}