block_update #26
@ -33,6 +33,11 @@ TEST_CASE("Show", "[Ensemble]")
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{
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auto clf = bayesnet::BoostAODE();
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auto raw = RawDatasets("iris", true);
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clf.setHyperparameters({
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{"bisection", false},
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{"maxTolerance", 1},
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{"convergence", false},
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});
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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std::vector<std::string> expected = {
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"class -> sepallength, sepalwidth, petallength, petalwidth, ",
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@ -115,15 +115,15 @@ TEST_CASE("Model predict_proba", "[Models]")
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{0.003135, 0.991799, 0.0050661}
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});
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auto res_prob_baode = std::vector<std::vector<double>>({
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{0.00803291, 0.9676, 0.0243672},
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{0.00398714, 0.945126, 0.050887},
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{0.00398714, 0.945126, 0.050887},
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{0.00398714, 0.945126, 0.050887},
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{0.00189227, 0.859575, 0.138533},
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{0.0118341, 0.442149, 0.546017},
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{0.0216135, 0.785781, 0.192605},
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{0.0204803, 0.844276, 0.135244},
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{0.00576313, 0.961665, 0.0325716},
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{0.0112349, 0.962274, 0.0264907},
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{0.00371025, 0.950592, 0.0456973},
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{0.00371025, 0.950592, 0.0456973},
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{0.00371025, 0.950592, 0.0456973},
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{0.00369275, 0.84967, 0.146637},
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{0.0252205, 0.113564, 0.861215},
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{0.0284828, 0.770524, 0.200993},
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{0.0213182, 0.857189, 0.121493},
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{0.00868436, 0.949494, 0.0418215}
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});
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auto res_prob_voting = std::vector<std::vector<double>>({
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{0, 1, 0},
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@ -131,8 +131,8 @@ TEST_CASE("Model predict_proba", "[Models]")
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{0, 1, 0},
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{0, 1, 0},
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{0, 1, 0},
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{0, 0.447909, 0.552091},
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{0, 0.811482, 0.188517},
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{0, 0, 1},
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{0, 1, 0},
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{0, 1, 0},
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{0, 1, 0}
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});
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@ -155,7 +155,7 @@ TEST_CASE("Model predict_proba", "[Models]")
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REQUIRE(y_pred.size() == raw.yv.size());
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REQUIRE(y_pred_proba[0].size() == 3);
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REQUIRE(yt_pred_proba.size(1) == y_pred_proba[0].size());
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for (int i = 0; i < y_pred_proba.size(); ++i) {
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for (int i = 0; i < 9; ++i) {
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auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
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int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
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REQUIRE(predictedClass == y_pred[i]);
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@ -166,7 +166,7 @@ TEST_CASE("Model predict_proba", "[Models]")
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}
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}
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// Check predict_proba values for vectors and tensors
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for (int i = 0; i < res_prob.size(); i++) {
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for (int i = 0; i < 9; i++) {
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REQUIRE(y_pred[i] == yt_pred[i].item<int>());
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for (int j = 0; j < 3; j++) {
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REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
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@ -27,7 +27,7 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 153);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with IWSS");
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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}
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TEST_CASE("Feature_select FCBF", "[BoostAODE]")
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@ -76,8 +76,8 @@ TEST_CASE("Voting vs proba", "[BoostAODE]")
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auto pred_voting = clf.predict_proba(raw.Xv);
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REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
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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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REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
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REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
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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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@ -91,6 +91,9 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
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auto clf = bayesnet::BoostAODE();
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clf.setHyperparameters({
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{"order", order},
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{"bisection", false},
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{"maxTolerance", 1},
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{"convergence", false},
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});
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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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