Add tests to 90% coverage
This commit is contained in:
@@ -4,83 +4,111 @@
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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/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/TAN.h"
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#include "bayesnet/classifiers/SPODE.h"
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#include "bayesnet/classifiers/XSPODE.h"
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#include "bayesnet/classifiers/TANLd.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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#include "TestUtils.h"
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const std::string ACTUAL_VERSION = "1.0.6";
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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{
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// Diabetes
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{{"diabetes", "AODE"}, 0.82161}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "XSPODE"}, 0.631510437f}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
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{{"diabetes", "AODELd"}, 0.8125f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.7890625f}, {{"diabetes", "TANLd"}, 0.803385437f}, {{"diabetes", "BoostAODE"}, 0.83984f},
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// Ecoli
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{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "XSPODE"}, 0.696428597f}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
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{{"ecoli", "AODELd"}, 0.875f}, {{"ecoli", "KDBLd"}, 0.880952358f}, {{"ecoli", "SPODELd"}, 0.839285731f}, {{"ecoli", "TANLd"}, 0.848214269f}, {{"ecoli", "BoostAODE"}, 0.89583f},
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// Glass
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{{"glass", "AODE"}, 0.79439}, {{"glass", "KDB"}, 0.827103}, {{"glass", "XSPODE"}, 0.775701}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
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{{"glass", "AODELd"}, 0.799065411f}, {{"glass", "KDBLd"}, 0.82710278f}, {{"glass", "SPODELd"}, 0.780373812f}, {{"glass", "TANLd"}, 0.869158864f}, {{"glass", "BoostAODE"}, 0.84579f},
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// Iris
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{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "XSPODE"}, 0.853333354f}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
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{{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
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};
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std::map<std::string, bayesnet::BaseClassifier*> models{
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{"AODE", new bayesnet::AODE()}, {"AODELd", new bayesnet::AODELd()},
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{"BoostAODE", new bayesnet::BoostAODE()},
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{"KDB", new bayesnet::KDB(2)}, {"KDBLd", new bayesnet::KDBLd(2)},
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{"XSPODE", new bayesnet::XSpode(1)}, {"SPODE", new bayesnet::SPODE(1)}, {"SPODELd", new bayesnet::SPODELd(1)},
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{"TAN", new bayesnet::TAN()}, {"TANLd", new bayesnet::TANLd()}
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};
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TEST_CASE("Test Bayesian Classifiers score & version", "[Models]") {
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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"}, 0.80208f},
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{{"diabetes", "SPODELd"}, 0.7890625f},
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{{"diabetes", "TANLd"}, 0.803385437f},
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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"}, 0.880952358f},
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{{"ecoli", "SPODELd"}, 0.839285731f},
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{{"ecoli", "TANLd"}, 0.848214269f},
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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"}, 0.82710278f},
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{{"glass", "SPODELd"}, 0.780373812f},
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{{"glass", "TANLd"}, 0.869158864f},
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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, bayesnet::BaseClassifier *> models{{"AODE", new bayesnet::AODE()},
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{"AODELd", new bayesnet::AODELd()},
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{"BoostAODE", new bayesnet::BoostAODE()},
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{"KDB", new bayesnet::KDB(2)},
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{"KDBLd", new bayesnet::KDBLd(2)},
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{"XSPODE", new bayesnet::XSpode(1)},
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{"SPODE", new bayesnet::SPODE(1)},
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{"SPODELd", new bayesnet::SPODELd(1)},
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{"TAN", new bayesnet::TAN()},
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{"TANLd", new 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 = 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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SECTION("Test " + name + " classifier") {
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for (const std::string &file_name : {"glass", "iris", "ecoli", "diabetes"}) {
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auto clf = models[name];
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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 = " << scores[{file_name, name}] << std::endl;
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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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SECTION("Library check version") {
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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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delete clf;
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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>({ "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\"", "\"class\" -> \"petalwidth\"", "\"petallength\" [shape=circle] \n",
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"\"petallength\" -> \"sepallength\"", "\"petalwidth\" [shape=circle] \n", "\"sepallength\" [shape=circle] \n",
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"\"sepallength\" -> \"sepalwidth\"", "\"sepalwidth\" [shape=circle] \n", "\"sepalwidth\" -> \"petalwidth\"", "}\n"
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}
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);
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SECTION("Test TAN")
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{
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TEST_CASE("Models features & Graph", "[Models]") {
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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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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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@@ -88,11 +116,12 @@ TEST_CASE("Models features & Graph", "[Models]")
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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, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
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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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SECTION("Test TANLd") {
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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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@@ -100,12 +129,13 @@ TEST_CASE("Models features & Graph", "[Models]")
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REQUIRE(clf.getNumberOfEdges() == 7);
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REQUIRE(clf.getNumberOfStates() == 27);
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REQUIRE(clf.getClassNumStates() == 3);
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REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
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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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TEST_CASE("Get num features & num edges", "[Models]") {
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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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@@ -113,60 +143,49 @@ TEST_CASE("Get num features & num edges", "[Models]")
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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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TEST_CASE("Model predict_proba", "[Models]") {
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std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting");
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auto res_prob_tan = std::vector<std::vector<double>>({
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{ 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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});
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auto res_prob_spode = std::vector<std::vector<double>>({
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{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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});
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auto res_prob_baode = std::vector<std::vector<double>>({
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{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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});
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auto res_prob_voting = std::vector<std::vector<double>>({
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{0, 1, 0},
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{0, 1, 0},
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{0, 1, 0},
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{0, 1, 0},
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{0, 1, 0},
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{0, 0, 1},
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{0, 1, 0},
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{0, 1, 0},
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{0, 1, 0}
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});
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std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
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std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
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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_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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std::map<std::string, bayesnet::BaseClassifier *> models{{"TAN", new bayesnet::TAN()},
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{"SPODE", new bayesnet::SPODE(0)},
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{"BoostAODEproba", new bayesnet::BoostAODE(false)},
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{"BoostAODEvoting", new bayesnet::BoostAODE(true)}};
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int init_index = 78;
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auto raw = RawDatasets("iris", true);
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SECTION("Test " + model + " predict_proba")
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{
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SECTION("Test " + model + " predict_proba") {
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auto clf = models[model];
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clf->fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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auto y_pred_proba = clf->predict_proba(raw.Xv);
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@@ -194,23 +213,23 @@ TEST_CASE("Model predict_proba", "[Models]")
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REQUIRE(y_pred[i] == yt_pred[i].item<int>());
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for (int j = 0; j < 3; j++) {
|
||||
REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
|
||||
REQUIRE(res_prob[model][i][j] == Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
|
||||
REQUIRE(res_prob[model][i][j] ==
|
||||
Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
delete clf;
|
||||
}
|
||||
}
|
||||
|
||||
TEST_CASE("AODE voting-proba", "[Models]")
|
||||
{
|
||||
TEST_CASE("AODE voting-proba", "[Models]") {
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||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::AODE(false);
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score_proba = clf.score(raw.Xv, raw.yv);
|
||||
auto pred_proba = clf.predict_proba(raw.Xv);
|
||||
clf.setHyperparameters({
|
||||
{"predict_voting",true},
|
||||
});
|
||||
{"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(0.79439f).epsilon(raw.epsilon));
|
||||
@@ -219,8 +238,7 @@ TEST_CASE("AODE voting-proba", "[Models]")
|
||||
REQUIRE(pred_proba[67][0] == Catch::Approx(0.702184).epsilon(raw.epsilon));
|
||||
REQUIRE(clf.topological_order() == std::vector<std::string>());
|
||||
}
|
||||
TEST_CASE("SPODELd dataset", "[Models]")
|
||||
{
|
||||
TEST_CASE("SPODELd dataset", "[Models]") {
|
||||
auto raw = RawDatasets("iris", false);
|
||||
auto clf = bayesnet::SPODELd(0);
|
||||
// raw.dataset.to(torch::kFloat32);
|
||||
@@ -231,8 +249,7 @@ TEST_CASE("SPODELd dataset", "[Models]")
|
||||
REQUIRE(score == Catch::Approx(0.97333f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.97333f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("KDB with hyperparameters", "[Models]")
|
||||
{
|
||||
TEST_CASE("KDB with hyperparameters", "[Models]") {
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::KDB(2);
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
@@ -240,20 +257,18 @@ TEST_CASE("KDB with hyperparameters", "[Models]")
|
||||
clf.setHyperparameters({
|
||||
{"k", 3},
|
||||
{"theta", 0.7},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto scoret = clf.score(raw.Xv, raw.yv);
|
||||
REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Incorrect type of data for SPODELd", "[Models]")
|
||||
{
|
||||
TEST_CASE("Incorrect type of data for SPODELd", "[Models]") {
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::SPODELd(0);
|
||||
REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
|
||||
}
|
||||
TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
|
||||
{
|
||||
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";
|
||||
@@ -270,35 +285,55 @@ TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
|
||||
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]")
|
||||
{
|
||||
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]")
|
||||
{
|
||||
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);
|
||||
});
|
||||
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);
|
||||
}
|
||||
});
|
||||
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));
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user