127 lines
5.3 KiB
C++
127 lines
5.3 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_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/matchers/catch_matchers.hpp>
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#include <stdexcept>
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#include "bayesnet/classifiers/XSPODE.h"
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#include "TestUtils.h"
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TEST_CASE("fit vector test", "[XSPODE]") {
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auto raw = RawDatasets("iris", true);
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auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
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for (int i = 0; i < 4; ++i) {
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auto clf = bayesnet::XSpode(i);
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 9);
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REQUIRE(clf.getNotes().size() == 0);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
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}
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}
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TEST_CASE("fit dataset test", "[XSPODE]") {
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auto raw = RawDatasets("iris", true);
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auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
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for (int i = 0; i < 4; ++i) {
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auto clf = bayesnet::XSpode(i);
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clf.fit(raw.dataset, raw.features, raw.className, raw.states,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 9);
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REQUIRE(clf.getNotes().size() == 0);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
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}
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}
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TEST_CASE("tensors dataset predict & predict_proba", "[XSPODE]") {
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auto raw = RawDatasets("iris", true);
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auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
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auto probs_expected = std::vector<std::vector<float>>({
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{0.999017, 0.000306908, 0.000676449},
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{0.99831, 0.00119304, 0.000497099},
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{0.998432, 0.00078416, 0.00078416},
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{0.998801, 0.000599438, 0.000599438}
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});
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for (int i = 0; i < 4; ++i) {
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auto clf = bayesnet::XSpode(i);
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 9);
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REQUIRE(clf.getNotes().size() == 0);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
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// Get the first 4 lines of X_test to do predict_proba
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auto X_reduced = raw.X_test.slice(1, 0, 4);
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auto proba = clf.predict_proba(X_reduced);
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for (int p = 0; p < 3; ++p) {
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REQUIRE(proba[0][p].item<double>() == Catch::Approx(probs_expected.at(i).at(p)));
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}
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}
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}
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TEST_CASE("mfeat-factors dataset test", "[XSPODE]") {
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auto raw = RawDatasets("mfeat-factors", true);
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auto scores = std::vector<float>({0.9825, 0.9775, 0.9775, 0.99});
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for (int i = 0; i < 4; ++i) {
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auto clf = bayesnet::XSpode(i);
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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() == 217);
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REQUIRE(clf.getNumberOfEdges() == 433);
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REQUIRE(clf.getNotes().size() == 0);
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REQUIRE(clf.getNumberOfStates() == 652320);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
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}
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}
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TEST_CASE("Laplace predict", "[XSPODE]") {
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auto raw = RawDatasets("iris", true);
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auto scores = std::vector<float>({0.966666639, 1.0f, 0.933333337, 1.0f});
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for (int i = 0; i < 4; ++i) {
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auto clf = bayesnet::XSpode(0);
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clf.setHyperparameters({ {"parent", i} });
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::LAPLACE);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 9);
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REQUIRE(clf.getNotes().size() == 0);
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REQUIRE(clf.getNumberOfStates() == 64);
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REQUIRE(clf.getNFeatures() == 4);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
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}
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}
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TEST_CASE("Not fitted model predict", "[XSPODE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::XSpode(0);
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REQUIRE_THROWS_AS(clf.predict(std::vector<int>({1,2,3})), std::logic_error);
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}
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TEST_CASE("Test instance predict", "[XSPODE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::XSpode(0);
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::ORIGINAL);
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REQUIRE(clf.predict(std::vector<int>({1,2,3,4})) == 1);
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REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f));
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// Cestnik is not defined in the classifier so it should imply alpha_ = 0
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clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::CESTNIK);
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REQUIRE(clf.predict(std::vector<int>({1,2,3,4})) == 0);
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REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f));
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}
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TEST_CASE("Test to_string and fitx", "[XSPODE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::XSpode(0);
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auto weights = torch::full({raw.Xt.size(1)}, 1.0 / raw.Xt.size(1), torch::kFloat64);
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clf.fitx(raw.Xt, raw.yt, weights, bayesnet::Smoothing_t::ORIGINAL);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 9);
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REQUIRE(clf.getNotes().size() == 0);
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REQUIRE(clf.getNumberOfStates() == 64);
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REQUIRE(clf.getNFeatures() == 4);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.966666639f));
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REQUIRE(clf.to_string().size() == 1966);
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REQUIRE(clf.graph("Not yet implemented") == std::vector<std::string>({"Not yet implemented"}));
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}
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