80 lines
3.3 KiB
C++
80 lines
3.3 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 "STree.h"
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#include "SVC.h"
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#include "RandomForest.h"
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#include "ODTE.h"
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#include "TestUtils.h"
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TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
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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", "STree"}, 0}, {{"diabetes", "ODTE"}, 0.84635}, {{"diabetes", "SVC"}, 0}, {{"diabetes", "RandomForest"}, 1.0},
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// Ecoli
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{{"ecoli", "STree"}, 0}, {{"ecoli", "ODTE"}, 0.84821}, {{"ecoli", "SVC"}, 0.}, {{"ecoli", "RandomForest"}, 1.0},
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// Glass
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{{"glass", "STree"}, 0}, {{"glass", "ODTE"}, 0.77103}, {{"glass", "SVC"}, 0}, {{"glass", "RandomForest"}, 1.0},
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// Iris
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{{"iris", "STree"}, 0}, {{"iris", "ODTE"}, 0.98667}, {{"iris", "SVC"}, 0}, {{"iris", "RandomForest"}, 1.0},
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};
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std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto raw = RawDatasets(file_name, false);
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SECTION("Test STree classifier (" + file_name + ")")
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{
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auto clf = pywrap::STree();
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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REQUIRE(score == Catch::Approx(scores[{file_name, "STree"}]).epsilon(raw.epsilon));
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}
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SECTION("Test ODTE classifier (" + file_name + ")")
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{
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auto clf = pywrap::ODTE();
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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scores[{file_name, "ODTE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "ODTE"}]).epsilon(raw.epsilon));
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}
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SECTION("Test SVC classifier (" + file_name + ")")
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{
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auto clf = pywrap::SVC();
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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scores[{file_name, "SVC"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SVC"}]).epsilon(raw.epsilon));
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}
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SECTION("Test RandomForest classifier (" + file_name + ")")
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{
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auto clf = pywrap::RandomForest();
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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scores[{file_name, "RandomForest"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "RandomForest"}]).epsilon(raw.epsilon));
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}
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for (auto scores : scores) {
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std::cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
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}
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}
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TEST_CASE("Classifiers features", "[PyClassifiers]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = pywrap::STree();
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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REQUIRE(clf.getNumberOfNodes() == 0);
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REQUIRE(clf.getNumberOfEdges() == 0);
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}
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TEST_CASE("Get num features & num edges", "[PyClassifiers]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = pywrap::ODTE();
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 8);
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} |