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Set smoothing as fit parameter

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 11:40:45 +02:00
parent 27a3e5a5e0
commit b34869cc61
30 changed files with 168 additions and 178 deletions
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22
tests/TestBoostAODE.cc
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@@ -18,7 +18,7 @@ TEST_CASE("Feature_select CFS", "[BoostAODE]")
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "CFS"} });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
@@ -30,7 +30,7 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
@@ -42,7 +42,7 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
@@ -58,7 +58,7 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
{"convergence", true},
{"select_features","CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 72);
REQUIRE(clf.getNumberOfEdges() == 120);
REQUIRE(clf.getNotes().size() == 2);
@@ -73,7 +73,7 @@ TEST_CASE("Voting vs proba", "[BoostAODE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostAODE(false);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score_proba = clf.score(raw.Xv, raw.yv);
auto pred_proba = clf.predict_proba(raw.Xv);
clf.setHyperparameters({
@@ -102,7 +102,7 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
{"maxTolerance", 1},
{"convergence", false},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("BoostAODE order: " << order);
@@ -134,7 +134,7 @@ TEST_CASE("Oddities", "[BoostAODE]")
for (const auto& hyper : bad_hyper_fit.items()) {
INFO("BoostAODE hyper: " << hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states), std::invalid_argument);
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
}
}
@@ -149,7 +149,7 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
{"block_update", false},
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 210);
REQUIRE(clf.getNumberOfEdges() == 378);
REQUIRE(clf.getNotes().size() == 1);
@@ -170,13 +170,13 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
{"convergence_best", true},
};
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_best = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
// Now we will set the hyperparameter to use the last accuracy
hyperparameters["convergence_best"] = false;
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
}
@@ -191,7 +191,7 @@ TEST_CASE("Block Update", "[BoostAODE]")
{"maxTolerance", 3},
{"convergence", true},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 868);
REQUIRE(clf.getNumberOfEdges() == 1724);
REQUIRE(clf.getNotes().size() == 3);