485 lines
27 KiB
C++
485 lines
27 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <catch2/catch_approx.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 "TestUtils.h"
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#include "bayesnet/classifiers/KDB.h"
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#include "bayesnet/classifiers/KDBLd.h"
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#include "bayesnet/classifiers/SPODE.h"
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#include "bayesnet/classifiers/SPODELd.h"
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#include "bayesnet/classifiers/TAN.h"
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#include "bayesnet/classifiers/TANLd.h"
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#include "bayesnet/classifiers/XSPODE.h"
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#include "bayesnet/ensembles/AODE.h"
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#include "bayesnet/ensembles/AODELd.h"
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#include "bayesnet/ensembles/BoostAODE.h"
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const std::string ACTUAL_VERSION = "1.2.0";
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TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
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{
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map<pair<std::string, std::string>, float> scores{// Diabetes
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{{"diabetes", "AODE"}, 0.82161},
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{{"diabetes", "KDB"}, 0.852865},
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{{"diabetes", "XSPODE"}, 0.631510437f},
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{{"diabetes", "SPODE"}, 0.802083},
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{{"diabetes", "TAN"}, 0.821615},
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{{"diabetes", "AODELd"}, 0.8125f},
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{{"diabetes", "KDBLd"}, 0.804688f},
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{{"diabetes", "SPODELd"}, 0.7890625f},
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{{"diabetes", "TANLd"}, 0.8125f},
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{{"diabetes", "BoostAODE"}, 0.83984f},
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// Ecoli
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{{"ecoli", "AODE"}, 0.889881},
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{{"ecoli", "KDB"}, 0.889881},
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{{"ecoli", "XSPODE"}, 0.696428597f},
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{{"ecoli", "SPODE"}, 0.880952},
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{{"ecoli", "TAN"}, 0.892857},
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{{"ecoli", "AODELd"}, 0.875f},
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{{"ecoli", "KDBLd"}, 0.872024f},
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{{"ecoli", "SPODELd"}, 0.839285731f},
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{{"ecoli", "TANLd"}, 0.869047642f},
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{{"ecoli", "BoostAODE"}, 0.89583f},
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// Glass
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{{"glass", "AODE"}, 0.79439},
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{{"glass", "KDB"}, 0.827103},
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{{"glass", "XSPODE"}, 0.775701},
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{{"glass", "SPODE"}, 0.775701},
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{{"glass", "TAN"}, 0.827103},
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{{"glass", "AODELd"}, 0.799065411f},
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{{"glass", "KDBLd"}, 0.864485979f},
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{{"glass", "SPODELd"}, 0.780373812f},
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{{"glass", "TANLd"}, 0.831775725f},
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{{"glass", "BoostAODE"}, 0.84579f},
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// Iris
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{{"iris", "AODE"}, 0.973333},
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{{"iris", "KDB"}, 0.973333},
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{{"iris", "XSPODE"}, 0.853333354f},
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{{"iris", "SPODE"}, 0.973333},
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{{"iris", "TAN"}, 0.973333},
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{{"iris", "AODELd"}, 0.973333},
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{{"iris", "KDBLd"}, 0.973333},
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{{"iris", "SPODELd"}, 0.96f},
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{{"iris", "TANLd"}, 0.97333f},
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{{"iris", "BoostAODE"}, 0.98f} };
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std::map<std::string, std::unique_ptr<bayesnet::BaseClassifier>> models;
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models["AODE"] = std::make_unique<bayesnet::AODE>();
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models["AODELd"] = std::make_unique<bayesnet::AODELd>();
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models["BoostAODE"] = std::make_unique<bayesnet::BoostAODE>();
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models["KDB"] = std::make_unique<bayesnet::KDB>(2);
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models["KDBLd"] = std::make_unique<bayesnet::KDBLd>(2);
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models["XSPODE"] = std::make_unique<bayesnet::XSpode>(1);
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models["SPODE"] = std::make_unique<bayesnet::SPODE>(1);
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models["SPODELd"] = std::make_unique<bayesnet::SPODELd>(1);
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models["TAN"] = std::make_unique<bayesnet::TAN>();
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models["TANLd"] = std::make_unique<bayesnet::TANLd>();
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std::string name = GENERATE("AODE", "AODELd", "KDB", "KDBLd", "SPODE", "XSPODE", "SPODELd", "TAN", "TANLd");
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auto clf = std::move(models[name]);
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SECTION("Test " + name + " classifier")
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{
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for (const std::string& file_name : { "glass", "iris", "ecoli", "diabetes" }) {
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auto discretize = name.substr(name.length() - 2) != "Ld";
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auto raw = RawDatasets(file_name, discretize);
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clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
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auto score = clf->score(raw.Xt, raw.yt);
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// std::cout << "Classifier: " << name << " File: " << file_name << " Score: " << score << " expected = " <<
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// scores[{file_name, name}] << std::endl;
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INFO("Classifier: " << name << " File: " << file_name);
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REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
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REQUIRE(clf->getStatus() == bayesnet::NORMAL);
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}
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}
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SECTION("Library check version")
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{
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INFO("Checking version of " << name << " classifier");
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REQUIRE(clf->getVersion() == ACTUAL_VERSION);
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}
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}
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TEST_CASE("Models features & Graph", "[Models]")
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{
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auto graph = std::vector<std::string>(
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{ "digraph BayesNet {\nlabel=<BayesNet Test>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n",
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"\"class\" [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n",
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"\"class\" -> \"sepallength\"", "\"class\" -> \"sepalwidth\"", "\"class\" -> \"petallength\"",
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"\"class\" -> \"petalwidth\"", "\"petallength\" [shape=circle] \n", "\"petallength\" -> \"sepallength\"",
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"\"petalwidth\" [shape=circle] \n", "\"sepallength\" [shape=circle] \n", "\"sepallength\" -> \"sepalwidth\"",
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"\"sepalwidth\" [shape=circle] \n", "\"sepalwidth\" -> \"petalwidth\"", "}\n" });
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SECTION("Test TAN")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::TAN();
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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() == 5);
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REQUIRE(clf.getNumberOfEdges() == 7);
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REQUIRE(clf.getNumberOfStates() == 19);
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REQUIRE(clf.getClassNumStates() == 3);
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REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ",
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"petallength -> sepallength, ", "petalwidth -> ",
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"sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
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REQUIRE(clf.graph("Test") == graph);
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}
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SECTION("Test TANLd")
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{
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auto clf = bayesnet::TANLd();
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auto raw = RawDatasets("iris", false);
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 7);
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REQUIRE(clf.getNumberOfStates() == 26);
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REQUIRE(clf.getClassNumStates() == 3);
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REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ",
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"petallength -> sepallength, ", "petalwidth -> ",
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"sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
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REQUIRE(clf.graph("Test") == graph);
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}
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}
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TEST_CASE("Get num features & num edges", "[Models]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::KDB(2);
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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() == 5);
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REQUIRE(clf.getNumberOfEdges() == 8);
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}
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TEST_CASE("Model predict_proba", "[Models]")
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{
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std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting", "TANLd", "SPODELd", "KDBLd");
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auto res_prob_tan = std::vector<std::vector<double>>({ {0.00375671, 0.994457, 0.00178621},
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{0.00137462, 0.992734, 0.00589123},
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{0.00137462, 0.992734, 0.00589123},
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{0.00137462, 0.992734, 0.00589123},
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{0.00218225, 0.992877, 0.00494094},
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{0.00494209, 0.0978534, 0.897205},
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{0.0054192, 0.974275, 0.0203054},
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{0.00433012, 0.985054, 0.0106159},
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{0.000860806, 0.996922, 0.00221698} });
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auto res_prob_spode = std::vector<std::vector<double>>({ {0.00419032, 0.994247, 0.00156265},
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{0.00172808, 0.993433, 0.00483862},
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{0.00172808, 0.993433, 0.00483862},
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{0.00172808, 0.993433, 0.00483862},
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{0.00279211, 0.993737, 0.00347077},
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{0.0120674, 0.357909, 0.630024},
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{0.00386239, 0.913919, 0.0822185},
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{0.0244389, 0.966447, 0.00911374},
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{0.003135, 0.991799, 0.0050661} });
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auto res_prob_baode = std::vector<std::vector<double>>({ {0.0112349, 0.962274, 0.0264907},
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{0.00371025, 0.950592, 0.0456973},
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{0.00371025, 0.950592, 0.0456973},
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{0.00371025, 0.950592, 0.0456973},
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{0.00369275, 0.84967, 0.146637},
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{0.0252205, 0.113564, 0.861215},
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{0.0284828, 0.770524, 0.200993},
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{0.0213182, 0.857189, 0.121493},
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{0.00868436, 0.949494, 0.0418215} });
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auto res_prob_tanld = std::vector<std::vector<double>>({ {0.000597557, 0.9957, 0.00370254},
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{0.000731377, 0.997914, 0.0013544},
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{0.000731377, 0.997914, 0.0013544},
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{0.000731377, 0.997914, 0.0013544},
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{0.000838614, 0.998122, 0.00103923},
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{0.00130852, 0.0659492, 0.932742},
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{0.00365946, 0.979412, 0.0169281},
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{0.00435035, 0.986248, 0.00940212},
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{0.000583815, 0.997746, 0.00167066} });
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auto res_prob_spodeld = std::vector<std::vector<double>>({ {0.000908024, 0.993742, 0.00535024 },
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{0.00187726, 0.99167, 0.00645308 },
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{0.00187726, 0.99167, 0.00645308 },
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{0.00187726, 0.99167, 0.00645308 },
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{0.00287539, 0.993736, 0.00338846 },
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{0.00294402, 0.268495, 0.728561 },
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{0.0132381, 0.873282, 0.113479 },
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{0.0159412, 0.969228, 0.0148308 },
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{0.00203487, 0.989762, 0.00820356 } });
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auto res_prob_kdbld = std::vector<std::vector<double>>({ {0.000738981, 0.997208, 0.00205272 },
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{0.00087708, 0.996687, 0.00243633 },
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{0.00087708, 0.996687, 0.00243633 },
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{0.00087708, 0.996687, 0.00243633 },
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{0.000738981, 0.997208, 0.00205272 },
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{0.00512442, 0.0455504, 0.949325 },
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{0.0023632, 0.976631, 0.0210063 },
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{0.00189194, 0.992853, 0.00525538 },
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{0.00189194, 0.992853, 0.00525538, } });
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auto res_prob_voting = std::vector<std::vector<double>>(
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{ {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 0, 1}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0} });
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std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan},
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{"SPODE", res_prob_spode},
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{"BoostAODEproba", res_prob_baode},
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{"BoostAODEvoting", res_prob_voting},
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{"TANLd", res_prob_tanld},
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{"SPODELd", res_prob_spodeld},
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{"KDBLd", res_prob_kdbld} };
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std::map<std::string, std::unique_ptr<bayesnet::BaseClassifier>> models;
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models["TAN"] = std::make_unique<bayesnet::TAN>();
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models["SPODE"] = std::make_unique<bayesnet::SPODE>(0);
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models["BoostAODEproba"] = std::make_unique<bayesnet::BoostAODE>(false);
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models["BoostAODEvoting"] = std::make_unique<bayesnet::BoostAODE>(true);
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models["TANLd"] = std::make_unique<bayesnet::TANLd>();
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models["SPODELd"] = std::make_unique<bayesnet::SPODELd>(0);
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models["KDBLd"] = std::make_unique<bayesnet::KDBLd>(2);
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int init_index = 78;
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SECTION("Test " + model + " predict_proba")
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{
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INFO("Testing " << model << " predict_proba");
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auto ld_model = model.substr(model.length() - 2) == "Ld";
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auto discretize = !ld_model;
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auto raw = RawDatasets("iris", discretize);
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auto& clf = *models[model];
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
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auto yt_pred_proba = clf.predict_proba(raw.Xt);
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auto yt_pred = clf.predict(raw.Xt);
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std::vector<int> y_pred;
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std::vector<std::vector<double>> y_pred_proba;
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if (!ld_model) {
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y_pred = clf.predict(raw.Xv);
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y_pred_proba = clf.predict_proba(raw.Xv);
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REQUIRE(y_pred.size() == y_pred_proba.size());
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REQUIRE(y_pred.size() == yt_pred.size(0));
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REQUIRE(y_pred.size() == yt_pred_proba.size(0));
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REQUIRE(y_pred_proba[0].size() == 3);
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REQUIRE(y_pred.size() == raw.yv.size());
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REQUIRE(yt_pred_proba.size(1) == y_pred_proba[0].size());
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for (int i = 0; i < 9; ++i) {
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auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
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int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
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REQUIRE(predictedClass == y_pred[i]);
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// Check predict is coherent with predict_proba
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REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
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for (int j = 0; j < yt_pred_proba.size(1); j++) {
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REQUIRE(yt_pred_proba[i][j].item<double>() == Catch::Approx(y_pred_proba[i][j]).epsilon(raw.epsilon));
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}
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}
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// Check predict_proba values for vectors and tensors
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for (int i = 0; i < 9; i++) {
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REQUIRE(y_pred[i] == yt_pred[i].item<int>());
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for (int j = 0; j < 3; j++) {
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REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
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REQUIRE(res_prob[model][i][j] ==
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Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
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}
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}
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} else {
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// Check predict_proba values for vectors and tensors
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auto predictedClasses = yt_pred_proba.argmax(1);
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// std::cout << model << std::endl;
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for (int i = 0; i < 9; i++) {
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REQUIRE(predictedClasses[i].item<int>() == yt_pred[i].item<int>());
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// std::cout << "{";
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for (int j = 0; j < 3; j++) {
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// std::cout << yt_pred_proba[i + init_index][j].item<double>() << ", ";
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REQUIRE(res_prob[model][i][j] ==
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Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
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}
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// std::cout << "\b\b}," << std::endl;
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}
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}
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}
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}
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TEST_CASE("AODE voting-proba", "[Models]")
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{
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::AODE(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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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(0.79439f).epsilon(raw.epsilon));
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REQUIRE(score_voting == Catch::Approx(0.78972f).epsilon(raw.epsilon));
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REQUIRE(pred_voting[67][0] == Catch::Approx(0.888889).epsilon(raw.epsilon));
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REQUIRE(pred_proba[67][0] == Catch::Approx(0.702184).epsilon(raw.epsilon));
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REQUIRE(clf.topological_order() == std::vector<std::string>());
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}
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TEST_CASE("SPODELd dataset", "[Models]")
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{
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auto raw = RawDatasets("iris", false);
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auto clf = bayesnet::SPODELd(0);
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// raw.dataset.to(torch::kFloat32);
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clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing);
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auto score = clf.score(raw.Xt, raw.yt);
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
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auto scoret = clf.score(raw.Xt, raw.yt);
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REQUIRE(score == Catch::Approx(0.97333f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.97333f).epsilon(raw.epsilon));
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}
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TEST_CASE("KDB with hyperparameters", "[Models]")
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{
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::KDB(2);
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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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clf.setHyperparameters({
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{"k", 3},
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{"theta", 0.7},
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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 scoret = clf.score(raw.Xv, raw.yv);
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REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
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|
}
|
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TEST_CASE("Incorrect type of data for Ld models", "[Models]")
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|
{
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auto raw = RawDatasets("iris", true);
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auto clfs = bayesnet::SPODELd(0);
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|
REQUIRE_THROWS_AS(clfs.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
|
|
auto clft = bayesnet::TANLd();
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|
REQUIRE_THROWS_AS(clft.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
|
|
auto clfk = bayesnet::KDBLd(0);
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|
REQUIRE_THROWS_AS(clfk.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
|
|
}
|
|
TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
|
|
{
|
|
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);
|
|
}
|
|
TEST_CASE("TAN & SPODE with hyperparameters", "[Models]")
|
|
{
|
|
auto raw = RawDatasets("iris", true);
|
|
auto clf = bayesnet::TAN();
|
|
clf.setHyperparameters({
|
|
{"parent", 1},
|
|
});
|
|
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
|
auto score = clf.score(raw.Xv, raw.yv);
|
|
REQUIRE(score == Catch::Approx(0.973333).epsilon(raw.epsilon));
|
|
auto clf2 = bayesnet::SPODE(0);
|
|
clf2.setHyperparameters({
|
|
{"parent", 1},
|
|
});
|
|
clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
|
auto score2 = clf2.score(raw.Xv, raw.yv);
|
|
REQUIRE(score2 == Catch::Approx(0.973333).epsilon(raw.epsilon));
|
|
}
|
|
TEST_CASE("TAN & SPODE with invalid hyperparameters", "[Models]")
|
|
{
|
|
auto raw = RawDatasets("iris", true);
|
|
auto clf = bayesnet::TAN();
|
|
clf.setHyperparameters({
|
|
{"parent", 5},
|
|
});
|
|
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
|
|
std::invalid_argument);
|
|
auto clf2 = bayesnet::SPODE(0);
|
|
clf2.setHyperparameters({
|
|
{"parent", 5},
|
|
});
|
|
REQUIRE_THROWS_AS(clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
|
|
std::invalid_argument);
|
|
}
|
|
TEST_CASE("Check proposal checkInput", "[Models]")
|
|
{
|
|
class testProposal : public bayesnet::Proposal {
|
|
public:
|
|
testProposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_)
|
|
: Proposal(dataset_, features_, className_)
|
|
{
|
|
}
|
|
void test_X_y(const torch::Tensor& X, const torch::Tensor& y) { checkInput(X, y); }
|
|
};
|
|
auto raw = RawDatasets("iris", true);
|
|
auto clf = testProposal(raw.dataset, raw.features, raw.className);
|
|
torch::Tensor X = torch::randint(0, 3, { 10, 4 });
|
|
torch::Tensor y = torch::rand({ 10 });
|
|
INFO("Check X is not float");
|
|
REQUIRE_THROWS_AS(clf.test_X_y(X, y), std::invalid_argument);
|
|
X = torch::rand({ 10, 4 });
|
|
INFO("Check y is not integer");
|
|
REQUIRE_THROWS_AS(clf.test_X_y(X, y), std::invalid_argument);
|
|
y = torch::randint(0, 3, { 10 });
|
|
INFO("X and y are correct");
|
|
REQUIRE_NOTHROW(clf.test_X_y(X, y));
|
|
}
|
|
TEST_CASE("Check KDB loop detection", "[Models]")
|
|
{
|
|
class testKDB : public bayesnet::KDB {
|
|
public:
|
|
testKDB() : KDB(2, 0) {}
|
|
void test_add_m_edges(std::vector<std::string> features_, int idx, std::vector<int>& S, torch::Tensor& weights)
|
|
{
|
|
features = features_;
|
|
add_m_edges(idx, S, weights);
|
|
}
|
|
};
|
|
auto clf = testKDB();
|
|
auto features = std::vector<std::string>{ "A", "B", "C" };
|
|
int idx = 0;
|
|
std::vector<int> S = { 0 };
|
|
torch::Tensor weights = torch::tensor({
|
|
{ 1.0, 10.0, 0.0 }, // row0 -> picks col1
|
|
{ 0.0, 1.0, 10.0 }, // row1 -> picks col2
|
|
{ 10.0, 0.0, 1.0 }, // row2 -> picks col0
|
|
});
|
|
REQUIRE_NOTHROW(clf.test_add_m_edges(features, 0, S, weights));
|
|
REQUIRE_NOTHROW(clf.test_add_m_edges(features, 1, S, weights));
|
|
}
|
|
TEST_CASE("Local discretization hyperparameters", "[Models]")
|
|
{
|
|
auto raw = RawDatasets("iris", false);
|
|
auto clfs = bayesnet::SPODELd(0);
|
|
clfs.setHyperparameters({
|
|
{"max_iterations", 7},
|
|
{"verbose_convergence", true},
|
|
});
|
|
REQUIRE_NOTHROW(clfs.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
|
|
REQUIRE(clfs.getStatus() == bayesnet::NORMAL);
|
|
auto clfk = bayesnet::KDBLd(0);
|
|
clfk.setHyperparameters({
|
|
{"k", 3},
|
|
{"theta", 1e-4},
|
|
});
|
|
REQUIRE_NOTHROW(clfk.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
|
|
REQUIRE(clfk.getStatus() == bayesnet::NORMAL);
|
|
auto clfa = bayesnet::AODELd();
|
|
clfa.setHyperparameters({
|
|
{"ld_proposed_cuts", 9},
|
|
{"ld_algorithm", "BINQ"},
|
|
});
|
|
REQUIRE_NOTHROW(clfa.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
|
|
REQUIRE(clfa.getStatus() == bayesnet::NORMAL);
|
|
auto clft = bayesnet::TANLd();
|
|
clft.setHyperparameters({
|
|
{"ld_proposed_cuts", 7},
|
|
{"mdlp_max_depth", 5},
|
|
{"mdlp_min_length", 3},
|
|
{"ld_algorithm", "MDLP"},
|
|
});
|
|
REQUIRE_NOTHROW(clft.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
|
|
REQUIRE(clft.getStatus() == bayesnet::NORMAL);
|
|
clft.setHyperparameters({
|
|
{"ld_proposed_cuts", 9},
|
|
{"ld_algorithm", "BINQ"},
|
|
});
|
|
REQUIRE_NOTHROW(clft.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
|
|
REQUIRE(clft.getStatus() == bayesnet::NORMAL);
|
|
clft.setHyperparameters({
|
|
{"ld_proposed_cuts", 5},
|
|
{"ld_algorithm", "BINU"},
|
|
});
|
|
REQUIRE_NOTHROW(clft.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
|
|
REQUIRE(clft.getStatus() == bayesnet::NORMAL);
|
|
}
|