88 lines
3.9 KiB
C++
88 lines
3.9 KiB
C++
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do
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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 <vector>
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#include <map>
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#include <string>
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#include "KDB.h"
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#include "TAN.h"
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#include "SPODE.h"
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#include "AODE.h"
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#include "platformUtils.h"
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TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
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{
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map <pair<string, string>, float> scores = {
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{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
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{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
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{{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
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{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333}
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};
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string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto [Xd, y, features, className, states] = loadFile(file_name);
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SECTION("Test TAN classifier (" + file_name + ")")
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{
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auto clf = bayesnet::TAN();
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clf.fit(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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//scores[{file_name, "TAN"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(1e-6));
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}
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SECTION("Test KDB classifier (" + file_name + ")")
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{
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auto clf = bayesnet::KDB(2);
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clf.fit(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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//scores[{file_name, "KDB"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
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}]).epsilon(1e-6));
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}
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SECTION("Test SPODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::SPODE(1);
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clf.fit(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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// scores[{file_name, "SPODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(1e-6));
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}
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SECTION("Test AODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::AODE();
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clf.fit(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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// scores[{file_name, "AODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(1e-6));
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}
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// for (auto scores : scores) {
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// cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
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// }
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}
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TEST_CASE("Models features")
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{
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auto graph = vector<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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auto clf = bayesnet::TAN();
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auto [Xd, y, features, className, states] = loadFile("iris");
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clf.fit(Xd, y, features, className, states);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 7);
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REQUIRE(clf.show() == vector<string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
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REQUIRE(clf.graph("Test") == graph);
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}
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TEST_CASE("Get num features & num edges")
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{
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auto [Xd, y, features, className, states] = loadFile("iris");
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auto clf = bayesnet::KDB(2);
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clf.fit(Xd, y, features, className, states);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 8);
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} |