Fix fit & predict with discretized datasets
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@@ -13,7 +13,7 @@
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#include "pyclfs/ODTE.h"
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#include "TestUtils.h"
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const std::string ACTUAL_VERSION = "1.0.4";
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const std::string ACTUAL_VERSION = "1.0.5";
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TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
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{
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@@ -60,19 +60,27 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
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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 raw = RawDatasets("iris", false);
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auto clf = pywrap::STree();
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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REQUIRE(clf.getNumberOfNodes() == 3);
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REQUIRE(clf.getNumberOfEdges() == 2);
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REQUIRE(clf.getNumberOfNodes() == 5);
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REQUIRE(clf.getNumberOfEdges() == 3);
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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 raw = RawDatasets("iris", false);
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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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REQUIRE(clf.getNumberOfNodes() == 10);
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REQUIRE(clf.getNumberOfEdges() == 10);
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REQUIRE(clf.getNumberOfNodes() == 50);
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REQUIRE(clf.getNumberOfEdges() == 30);
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}
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TEST_CASE("Classifier with discretized dataset", "[PyClassifiers]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = pywrap::SVC();
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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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REQUIRE(score == Catch::Approx(0.96667f).epsilon(raw.epsilon));
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}
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// TEST_CASE("XGBoost", "[PyClassifiers]")
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// {
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