Add tests for Classifier class
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@@ -20,4 +20,67 @@ TEST_CASE("Test Cannot build dataset with wrong data tensor", "[Classifier]")
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auto yshort = torch::zeros({ 149 }, torch::kInt32);
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REQUIRE_THROWS_AS(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
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REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
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}
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TEST_CASE("Invalid data type", "[Classifier]")
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{
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auto model = bayesnet::TAN();
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auto raw = RawDatasets("iris", false);
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REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
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REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "dataset (X, y) must be of type Integer");
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}
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TEST_CASE("Invalid number of features", "[Classifier]")
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{
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auto model = bayesnet::TAN();
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auto raw = RawDatasets("iris", true);
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auto Xt = torch::cat({ raw.Xt, torch::zeros({ 1, 150 }, torch::kInt32) }, 0);
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REQUIRE_THROWS_AS(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
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REQUIRE_THROWS_WITH(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "Classifier: X 5 and features 4 must have the same number of features");
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}
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TEST_CASE("Invalid class name", "[Classifier]")
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{
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auto model = bayesnet::TAN();
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auto raw = RawDatasets("iris", true);
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REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), std::invalid_argument);
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REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), "class name not found in states");
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}
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TEST_CASE("Invalid feature name", "[Classifier]")
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{
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auto model = bayesnet::TAN();
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auto raw = RawDatasets("iris", true);
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auto statest = raw.statest;
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statest.erase("petallength");
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REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), std::invalid_argument);
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REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), "feature [petallength] not found in states");
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}
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TEST_CASE("Topological order", "[Classifier]")
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{
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auto model = bayesnet::TAN();
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auto raw = RawDatasets("iris", true);
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model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto order = model.topological_order();
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REQUIRE(order.size() == 4);
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REQUIRE(order[0] == "petallength");
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REQUIRE(order[1] == "sepallength");
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REQUIRE(order[2] == "sepalwidth");
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REQUIRE(order[3] == "petalwidth");
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}
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TEST_CASE("Not fitted model", "[Classifier]")
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{
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auto model = bayesnet::TAN();
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auto raw = RawDatasets("iris", true);
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auto message = "Classifier has not been fitted";
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// tensors
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REQUIRE_THROWS_AS(model.predict(raw.Xt), std::logic_error);
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REQUIRE_THROWS_WITH(model.predict(raw.Xt), message);
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REQUIRE_THROWS_AS(model.predict_proba(raw.Xt), std::logic_error);
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REQUIRE_THROWS_WITH(model.predict_proba(raw.Xt), message);
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REQUIRE_THROWS_AS(model.score(raw.Xt, raw.yt), std::logic_error);
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REQUIRE_THROWS_WITH(model.score(raw.Xt, raw.yt), message);
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// vectors
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REQUIRE_THROWS_AS(model.predict(raw.Xv), std::logic_error);
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REQUIRE_THROWS_WITH(model.predict(raw.Xv), message);
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REQUIRE_THROWS_AS(model.predict_proba(raw.Xv), std::logic_error);
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REQUIRE_THROWS_WITH(model.predict_proba(raw.Xv), message);
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REQUIRE_THROWS_AS(model.score(raw.Xv, raw.yv), std::logic_error);
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REQUIRE_THROWS_WITH(model.score(raw.Xv, raw.yv), message);
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}
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@@ -246,7 +246,7 @@ TEST_CASE("BoostAODE voting-proba", "[Models]")
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REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
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REQUIRE(pred_voting[83][2] == Catch::Approx(0.552091).epsilon(raw.epsilon));
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REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
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clf.dump_cpt();
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REQUIRE(clf.dump_cpt() == "");
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REQUIRE(clf.topological_order() == std::vector<std::string>());
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}
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TEST_CASE("AODE voting-proba", "[Models]")
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