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Update version, changelog, and Xsp2de clf name

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This commit is contained in:
Ricardo Montañana Gómez
2025-03-16 18:55:24 +01:00
parent 70c7d3dd3d
commit 9ee388561f
7 changed files with 61 additions and 53 deletions
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2
tests/TestBayesModels.cc
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@@ -20,7 +20,7 @@
#include "bayesnet/ensembles/AODELd.h"
#include "bayesnet/ensembles/BoostAODE.h"
const std::string ACTUAL_VERSION = "1.0.6";
const std::string ACTUAL_VERSION = "1.0.7";
TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
{

30
tests/TestXSPnDE.cc
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@@ -7,7 +7,7 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "bayesnet/classifiers/XSPnDE.h" // <-- your new 2-superparent classifier
#include "bayesnet/classifiers/XSP2DE.h" // <-- your new 2-superparent classifier
#include "TestUtils.h" // for RawDatasets, etc.
// Helper function to handle each (sp1, sp2) pair in tests
@@ -19,7 +19,7 @@ static void check_spnde_pair(
bool fitTensor)
{
// Create our classifier
bayesnet::XSpnde clf(sp1, sp2);
bayesnet::XSp2de clf(sp1, sp2);
// Option A: fit with vector-based data
if (fitVector) {
@@ -48,7 +48,7 @@ static void check_spnde_pair(
// ------------------------------------------------------------
// 1) Fit vector test
// ------------------------------------------------------------
TEST_CASE("fit vector test (XSPNDE)", "[XSPNDE]") {
TEST_CASE("fit vector test (XSP2DE)", "[XSP2DE]") {
auto raw = RawDatasets("iris", true);
std::vector<std::pair<int,int>> parentPairs = {
@@ -62,7 +62,7 @@ TEST_CASE("fit vector test (XSPNDE)", "[XSPNDE]") {
// ------------------------------------------------------------
// 2) Fit dataset test
// ------------------------------------------------------------
TEST_CASE("fit dataset test (XSPNDE)", "[XSPNDE]") {
TEST_CASE("fit dataset test (XSP2DE)", "[XSP2DE]") {
auto raw = RawDatasets("iris", true);
// Again test multiple pairs:
@@ -77,7 +77,7 @@ TEST_CASE("fit dataset test (XSPNDE)", "[XSPNDE]") {
// ------------------------------------------------------------
// 3) Tensors dataset predict & predict_proba
// ------------------------------------------------------------
TEST_CASE("tensors dataset predict & predict_proba (XSPNDE)", "[XSPNDE]") {
TEST_CASE("tensors dataset predict & predict_proba (XSP2DE)", "[XSP2DE]") {
auto raw = RawDatasets("iris", true);
std::vector<std::pair<int,int>> parentPairs = {
@@ -85,7 +85,7 @@ TEST_CASE("tensors dataset predict & predict_proba (XSPNDE)", "[XSPNDE]") {
};
for (auto &p : parentPairs) {
bayesnet::XSpnde clf(p.first, p.second);
bayesnet::XSp2de clf(p.first, p.second);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
@@ -100,26 +100,26 @@ TEST_CASE("tensors dataset predict & predict_proba (XSPNDE)", "[XSPNDE]") {
auto proba = clf.predict_proba(X_reduced);
}
}
TEST_CASE("Check hyperparameters", "[XSPNDE]")
TEST_CASE("Check hyperparameters", "[XSP2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpnde(0, 1);
auto clf = bayesnet::XSp2de(0, 1);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto clf2 = bayesnet::XSpnde(2, 3);
auto clf2 = bayesnet::XSp2de(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]")
TEST_CASE("Check different smoothing", "[XSP2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpnde(0, 1);
auto clf = bayesnet::XSp2de(0, 1);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::ORIGINAL);
auto clf2 = bayesnet::XSpnde(0, 1);
auto clf2 = bayesnet::XSp2de(0, 1);
clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::LAPLACE);
auto clf3 = bayesnet::XSpnde(0, 1);
auto clf3 = bayesnet::XSp2de(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);
@@ -128,10 +128,10 @@ TEST_CASE("Check different smoothing", "[XSPNDE]")
REQUIRE(score2 == Catch::Approx(0.7333333).epsilon(raw.epsilon));
REQUIRE(score3 == Catch::Approx(0.966667).epsilon(raw.epsilon));
}
TEST_CASE("Check rest", "[XSPNDE]")
TEST_CASE("Check rest", "[XSP2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpnde(0, 1);
auto clf = bayesnet::XSp2de(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);