rmontanana/BayesNet
rmontanana/BayesNet
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bisection proposal #24

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rmontanana merged 23 commits from bisection into main 2024-04-08 14:29:26 +00:00
Conversation 0 Commits 23 Files Changed 42 +22 -21
2 changed files with 22 additions and 21 deletions
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3
.vscode/c_cpp_properties.json vendored
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@ -27,7 +27,8 @@
"name": "Linux",
"includePath": [
"/home/rmontanana/Code/BayesNet/**",
"/home/rmontanana/Code/libtorch/include/torch/csrc/api/include/"
"/home/rmontanana/Code/libtorch/include/torch/csrc/api/include/",
"/home/rmontanana/Code/BayesNet/lib/"
],
"defines": [],
"cStandard": "c17",

40
tests/TestBayesModels.cc
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@ -98,26 +98,26 @@ TEST_CASE("BoostAODE feature_select CFS", "[Models]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
// TEST_CASE("BoostAODE test used features in train note and score", "[BayesNet]")
// {
// 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: 7 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(0.82031).epsilon(raw.epsilon));
// REQUIRE(scoret == Catch::Approx(0.82031).epsilon(raw.epsilon));
// }
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(0.82031).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.82031).epsilon(raw.epsilon));
}
TEST_CASE("Model predict_proba", "[Models]")
{
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting");