Add SPnDE n=2

tests/CMakeLists.txt

@@ -9,7 +9,7 @@ if(ENABLE_TESTING)
         ${CMAKE_BINARY_DIR}/configured_files/include
     )
     file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
-    add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc 
+    add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc TestXSPnDE.cc
         TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestXBAODE.cc TestA2DE.cc 
         TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc TestMST.cc TestXSPODE.cc ${BayesNet_SOURCES})
     target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" fimdlp PRIVATE Catch2::Catch2WithMain)
@@ -18,6 +18,7 @@ if(ENABLE_TESTING)
     add_test(NAME BoostA2DE COMMAND TestBayesNet "[BoostA2DE]")
     add_test(NAME BoostAODE COMMAND TestBayesNet "[BoostAODE]")
     add_test(NAME XSPODE COMMAND TestBayesNet "[XSPODE]")
+    add_test(NAME XSPnDE COMMAND TestBayesNet "[XSPnDE]")
     add_test(NAME XBAODE COMMAND TestBayesNet "[XBAODE]")
     add_test(NAME Classifier COMMAND TestBayesNet "[Classifier]")
     add_test(NAME Ensemble COMMAND TestBayesNet "[Ensemble]")

tests/TestXSPnDE.cc

@@ -0,0 +1,120 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#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 "TestUtils.h"                   // for RawDatasets, etc.
+
+// Helper function to handle each (sp1, sp2) pair in tests
+static void check_spnde_pair(
+    int sp1, 
+    int sp2, 
+    RawDatasets &raw, 
+    bool fitVector, 
+    bool fitTensor)
+{
+  // Create our classifier
+  bayesnet::XSpnde clf(sp1, sp2);
+
+  // Option A: fit with vector-based data
+  if (fitVector) {
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+  }
+  // Option B: fit with the whole dataset in torch::Tensor form
+  else if (fitTensor) {
+    // your “tensor” version of fit
+    clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
+  }
+  // Option C: or you might do the “dataset” version:
+  else {
+    clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing);
+  }
+
+  // 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
+}
+
+// ------------------------------------------------------------
+// 1) Fit vector test
+// ------------------------------------------------------------
+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);
+  }
+}
+
+// ------------------------------------------------------------
+// 2) Fit dataset test
+// ------------------------------------------------------------
+TEST_CASE("fit dataset test (XSPNDE)", "[XSPNDE]") {
+  auto raw = RawDatasets("iris", true);
+
+  // Again test multiple pairs:
+  std::vector<std::pair<int,int>> parentPairs = {
+    {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);
+  }
+}
+
+// ------------------------------------------------------------
+// 3) Tensors dataset predict & predict_proba
+// ------------------------------------------------------------
+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}
+  };
+
+  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);
+    REQUIRE(clf.getNumberOfEdges() == 8);
+    REQUIRE(clf.getNotes().size() == 0);
+
+    // Check the score
+    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.
+  }
+}
+