Add tests to 90% coverage
This commit is contained in:
@@ -4,20 +4,17 @@
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <type_traits>
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include <catch2/matchers/catch_matchers.hpp>
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#include "bayesnet/ensembles/BoostAODE.h"
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#include "TestUtils.h"
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#include "bayesnet/ensembles/BoostAODE.h"
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TEST_CASE("Feature_select CFS", "[BoostAODE]")
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{
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TEST_CASE("Feature_select CFS", "[BoostAODE]") {
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::BoostAODE();
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clf.setHyperparameters({ {"select_features", "CFS"} });
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clf.setHyperparameters({{"select_features", "CFS"}});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 153);
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@@ -25,11 +22,10 @@ TEST_CASE("Feature_select CFS", "[BoostAODE]")
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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}
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TEST_CASE("Feature_select IWSS", "[BoostAODE]")
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{
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TEST_CASE("Feature_select IWSS", "[BoostAODE]") {
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::BoostAODE();
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clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
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clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 153);
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@@ -37,11 +33,10 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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}
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TEST_CASE("Feature_select FCBF", "[BoostAODE]")
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{
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TEST_CASE("Feature_select FCBF", "[BoostAODE]") {
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::BoostAODE();
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clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
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clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 153);
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@@ -49,15 +44,14 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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}
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TEST_CASE("Test used features in train note and score", "[BoostAODE]")
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{
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TEST_CASE("Test used features in train note and score", "[BoostAODE]") {
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auto raw = RawDatasets("diabetes", true);
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auto clf = bayesnet::BoostAODE(true);
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clf.setHyperparameters({
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{"order", "asc"},
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{"convergence", true},
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{"select_features","CFS"},
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});
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{"select_features", "CFS"},
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});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 72);
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REQUIRE(clf.getNumberOfEdges() == 120);
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@@ -69,16 +63,15 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
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REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
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}
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TEST_CASE("Voting vs proba", "[BoostAODE]")
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{
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TEST_CASE("Voting vs proba", "[BoostAODE]") {
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::BoostAODE(false);
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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auto score_proba = clf.score(raw.Xv, raw.yv);
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auto pred_proba = clf.predict_proba(raw.Xv);
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clf.setHyperparameters({
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{"predict_voting",true},
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});
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{"predict_voting", true},
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});
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auto score_voting = clf.score(raw.Xv, raw.yv);
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auto pred_voting = clf.predict_proba(raw.Xv);
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REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
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@@ -88,20 +81,17 @@ TEST_CASE("Voting vs proba", "[BoostAODE]")
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REQUIRE(clf.dump_cpt().size() == 7004);
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REQUIRE(clf.topological_order() == std::vector<std::string>());
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}
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TEST_CASE("Order asc, desc & random", "[BoostAODE]")
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{
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TEST_CASE("Order asc, desc & random", "[BoostAODE]") {
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auto raw = RawDatasets("glass", true);
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std::map<std::string, double> scores{
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{"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
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};
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for (const std::string& order : { "asc", "desc", "rand" }) {
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std::map<std::string, double> scores{{"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112}};
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for (const std::string &order : {"asc", "desc", "rand"}) {
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auto clf = bayesnet::BoostAODE();
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clf.setHyperparameters({
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{"order", order},
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{"bisection", false},
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{"maxTolerance", 1},
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{"convergence", false},
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});
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});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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auto score = clf.score(raw.Xv, raw.yv);
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auto scoret = clf.score(raw.Xt, raw.yt);
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@@ -110,44 +100,43 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
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REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
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}
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}
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TEST_CASE("Oddities", "[BoostAODE]")
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{
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TEST_CASE("Oddities", "[BoostAODE]") {
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auto clf = bayesnet::BoostAODE();
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auto raw = RawDatasets("iris", true);
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auto bad_hyper = nlohmann::json{
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{ { "order", "duck" } },
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{ { "select_features", "duck" } },
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{ { "maxTolerance", 0 } },
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{ { "maxTolerance", 7 } },
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{{"order", "duck"}},
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{{"select_features", "duck"}},
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{{"maxTolerance", 0}},
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{{"maxTolerance", 7}},
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};
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for (const auto& hyper : bad_hyper.items()) {
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for (const auto &hyper : bad_hyper.items()) {
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INFO("BoostAODE hyper: " << hyper.value().dump());
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REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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}
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REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
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REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
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auto bad_hyper_fit = nlohmann::json{
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{ { "select_features","IWSS" }, { "threshold", -0.01 } },
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{ { "select_features","IWSS" }, { "threshold", 0.51 } },
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{ { "select_features","FCBF" }, { "threshold", 1e-8 } },
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{ { "select_features","FCBF" }, { "threshold", 1.01 } },
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{{"select_features", "IWSS"}, {"threshold", -0.01}},
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{{"select_features", "IWSS"}, {"threshold", 0.51}},
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{{"select_features", "FCBF"}, {"threshold", 1e-8}},
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{{"select_features", "FCBF"}, {"threshold", 1.01}},
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};
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for (const auto& hyper : bad_hyper_fit.items()) {
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for (const auto &hyper : bad_hyper_fit.items()) {
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INFO("BoostAODE hyper: " << hyper.value().dump());
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clf.setHyperparameters(hyper.value());
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REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
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REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
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std::invalid_argument);
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}
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auto bad_hyper_fit2 = nlohmann::json{
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{ { "alpha_block", true }, { "block_update", true } },
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{ { "bisection", false }, { "block_update", true } },
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{{"alpha_block", true}, {"block_update", true}},
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{{"bisection", false}, {"block_update", true}},
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};
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for (const auto& hyper : bad_hyper_fit2.items()) {
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for (const auto &hyper : bad_hyper_fit2.items()) {
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INFO("BoostAODE hyper: " << hyper.value().dump());
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REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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}
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}
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TEST_CASE("Bisection Best", "[BoostAODE]")
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{
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TEST_CASE("Bisection Best", "[BoostAODE]") {
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auto clf = bayesnet::BoostAODE();
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auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
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clf.setHyperparameters({
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@@ -156,7 +145,7 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
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{"convergence", true},
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{"block_update", false},
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{"convergence_best", false},
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});
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});
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clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 210);
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REQUIRE(clf.getNumberOfEdges() == 378);
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@@ -167,8 +156,7 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
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REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
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}
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TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
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{
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TEST_CASE("Bisection Best vs Last", "[BoostAODE]") {
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auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
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auto clf = bayesnet::BoostAODE(true);
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auto hyperparameters = nlohmann::json{
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@@ -188,8 +176,7 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
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auto score_last = clf.score(raw.X_test, raw.y_test);
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REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
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}
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TEST_CASE("Block Update", "[BoostAODE]")
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{
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TEST_CASE("Block Update", "[BoostAODE]") {
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auto clf = bayesnet::BoostAODE();
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auto raw = RawDatasets("mfeat-factors", true, 500);
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clf.setHyperparameters({
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@@ -197,7 +184,7 @@ TEST_CASE("Block Update", "[BoostAODE]")
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{"block_update", true},
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{"maxTolerance", 3},
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{"convergence", true},
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});
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});
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clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 868);
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REQUIRE(clf.getNumberOfEdges() == 1724);
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@@ -218,18 +205,18 @@ TEST_CASE("Block Update", "[BoostAODE]")
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// }
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// std::cout << "Score " << score << std::endl;
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}
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TEST_CASE("Alphablock", "[BoostAODE]")
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{
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TEST_CASE("Alphablock", "[BoostAODE]") {
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auto clf_alpha = bayesnet::BoostAODE();
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auto clf_no_alpha = bayesnet::BoostAODE();
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auto raw = RawDatasets("diabetes", true);
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clf_alpha.setHyperparameters({
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{"alpha_block", true},
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});
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});
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clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
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auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
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REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
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REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
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}
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}
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