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Add tests to 90% coverage

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This commit is contained in:
Ricardo Montañana Gómez
2025-03-14 14:53:22 +01:00
parent c234308701
commit 400967b4e3
14 changed files with 943 additions and 463 deletions
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59
tests/TestXSPnDE.cc
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@@ -37,16 +37,12 @@ static void check_spnde_pair(
// Basic checks
REQUIRE(clf.getNumberOfNodes() == 5); // for iris: 4 features + 1 class
// For XSpnde, edges are often computed as 3*nFeatures - 4. For iris nFeatures=4 => 3*4 -4 = 8
REQUIRE(clf.getNumberOfEdges() == 8);
REQUIRE(clf.getNotes().size() == 0);
// Evaluate on test set
float sc = clf.score(raw.X_test, raw.y_test);
// If you know the exact expected accuracy for each pair, use:
// REQUIRE(sc == Catch::Approx(someValue));
// Otherwise, just check it's > some threshold:
REQUIRE(sc >= 0.90f); // placeholder; you can pick your own threshold
REQUIRE(sc >= 0.93f);
}
// ------------------------------------------------------------
@@ -55,13 +51,10 @@ static void check_spnde_pair(
TEST_CASE("fit vector test (XSPNDE)", "[XSPNDE]") {
auto raw = RawDatasets("iris", true);
// We’ll test a couple of two-superparent pairs, e.g. (0,1) and (2,3).
// You can add more if you like, e.g. (0,2), (1,3), etc.
std::vector<std::pair<int,int>> parentPairs = {
{0,1}, {2,3}
};
for (auto &p : parentPairs) {
// We’re doing the “vector” version
check_spnde_pair(p.first, p.second, raw, /*fitVector=*/true, /*fitTensor=*/false);
}
}
@@ -77,7 +70,6 @@ TEST_CASE("fit dataset test (XSPNDE)", "[XSPNDE]") {
{0,2}, {1,3}
};
for (auto &p : parentPairs) {
// Now do the “dataset” version
check_spnde_pair(p.first, p.second, raw, /*fitVector=*/false, /*fitTensor=*/false);
}
}
@@ -88,14 +80,12 @@ TEST_CASE("fit dataset test (XSPNDE)", "[XSPNDE]") {
TEST_CASE("tensors dataset predict & predict_proba (XSPNDE)", "[XSPNDE]") {
auto raw = RawDatasets("iris", true);
// Let’s test a single pair or multiple pairs. For brevity:
std::vector<std::pair<int,int>> parentPairs = {
{0,3}
{0,3}, {1,2}
};
for (auto &p : parentPairs) {
bayesnet::XSpnde clf(p.first, p.second);
// Fit using the “tensor” approach
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
@@ -106,15 +96,46 @@ TEST_CASE("tensors dataset predict & predict_proba (XSPNDE)", "[XSPNDE]") {
float sc = clf.score(raw.X_test, raw.y_test);
REQUIRE(sc >= 0.90f);
// You can also test predict_proba on a small slice:
// e.g. the first 3 samples in X_test
auto X_reduced = raw.X_test.slice(1, 0, 3);
auto proba = clf.predict_proba(X_reduced);
// If you know exact probabilities, compare them with Catch::Approx.
// For example:
// REQUIRE(proba[0][0].item<double>() == Catch::Approx(0.98));
// etc.
}
}
TEST_CASE("Check hyperparameters", "[XSPNDE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpnde(0, 1);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto clf2 = bayesnet::XSpnde(2, 3);
clf2.setHyperparameters({{"parent1", 0}, {"parent2", 1}});
clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.to_string() == clf2.to_string());
}
TEST_CASE("Check different smoothing", "[XSPNDE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpnde(0, 1);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::ORIGINAL);
auto clf2 = bayesnet::XSpnde(0, 1);
clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::LAPLACE);
auto clf3 = bayesnet::XSpnde(0, 1);
clf3.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::NONE);
auto score = clf.score(raw.X_test, raw.y_test);
auto score2 = clf2.score(raw.X_test, raw.y_test);
auto score3 = clf3.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0).epsilon(raw.epsilon));
REQUIRE(score2 == Catch::Approx(0.7333333).epsilon(raw.epsilon));
REQUIRE(score3 == Catch::Approx(0.966667).epsilon(raw.epsilon));
}
TEST_CASE("Check rest", "[XSPNDE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpnde(0, 1);
REQUIRE_THROWS_AS(clf.predict_proba(std::vector<int>({1,2,3,4})), std::logic_error);
clf.fitx(raw.Xt, raw.yt, raw.weights, bayesnet::Smoothing_t::ORIGINAL);
REQUIRE(clf.getNFeatures() == 4);
REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f).epsilon(raw.epsilon));
REQUIRE(clf.predict({1,2,3,4}) == 1);
}