From 0e24135d4609a5af47799fd5a76e378805018b33 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Wed, 15 May 2024 11:09:23 +0200
Subject: [PATCH] Complete Conditional Mutual Information and test

---
 bayesnet/utils/BayesMetrics.cc |  2 +-
 tests/TestBayesMetrics.cc      | 34 +++++++++++-----------------------
 2 files changed, 12 insertions(+), 24 deletions(-)

diff --git a/bayesnet/utils/BayesMetrics.cc b/bayesnet/utils/BayesMetrics.cc
index f3fa20c..3e63038 100644
--- a/bayesnet/utils/BayesMetrics.cc
+++ b/bayesnet/utils/BayesMetrics.cc
@@ -204,7 +204,7 @@ namespace bayesnet {
     // I(X;Y|C) = H(Y|C) - H(Y|X,C)
     double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
     {
-        return conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights);
+        return std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);
     }
     /*
     Compute the maximum spanning tree considering the weights as distances
diff --git a/tests/TestBayesMetrics.cc b/tests/TestBayesMetrics.cc
index 47ddf11..b7d5c76 100644
--- a/tests/TestBayesMetrics.cc
+++ b/tests/TestBayesMetrics.cc
@@ -101,32 +101,20 @@ TEST_CASE("Entropy Test", "[Metrics]")
 }
 TEST_CASE("Conditional Entropy", "[Metrics]")
 {
-    auto raw = RawDatasets("mfeat-factors", true);
+    auto raw = RawDatasets("iris", true);
     bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
-    bayesnet::Metrics metrics2(raw.dataset, raw.features, raw.className, raw.classNumStates);
-    auto feature0 = raw.dataset.index({ 0, "..." });
-    auto feature1 = raw.dataset.index({ 1, "..." });
-    auto feature2 = raw.dataset.index({ 2, "..." });
-    auto feature3 = raw.dataset.index({ 3, "..." });
-    platform::Timer timer;
-    double result, greatest = 0;
-    int best_i, best_j;
-    timer.start();
+    auto expected = std::map<std::pair<int, int>, double>{
+        { { 0, 1 }, 0.0 },
+        { { 0, 2 }, 0.287696 },
+        { { 0, 3 }, 0.403749 },
+        { { 1, 2 }, 1.17112 },
+        { { 1, 3 }, 1.31852 },
+        { { 2, 3 }, 0.210068 },
+    };
     for (int i = 0; i < raw.features.size() - 1; ++i) {
-        if (i % 50 == 0) {
-            std::cout << "i=" << i << " Time=" << timer.getDurationString(true) << std::endl;
-        }
         for (int j = i + 1; j < raw.features.size(); ++j) {
-            result = metrics.conditionalMutualInformation(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights);
-            if (result > greatest) {
-                greatest = result;
-                best_i = i;
-                best_j = j;
-            }
+            double result = metrics.conditionalMutualInformation(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights);
+            REQUIRE(result == Catch::Approx(expected.at({ i, j })).epsilon(raw.epsilon));
         }
     }
-    timer.stop();
-    std::cout << "CMI(" << best_i << "," << best_j << ")=" << greatest << "\n";
-    std::cout << "Time=" << timer.getDurationString() << std::endl;
-    // Se pueden precalcular estos valores y utilizarlos en el algoritmo como entrada
 }
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