Complete Conditional Mutual Information and test

.gitmodules

@@ -13,3 +13,8 @@
 	url = https://github.com/rmontanana/folding
 	main = main
 	update = merge
+[submodule "tests/lib/catch2"]
+	path = tests/lib/catch2
+        url = https://github.com/catchorg/Catch2.git
+	main = main
+	update = merge

bayesnet/utils/BayesMetrics.cc

@@ -4,6 +4,9 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <map>
+#include <unordered_map>
+#include <tuple>
 #include "Mst.h"
 #include "BayesMetrics.h"
 namespace bayesnet {
@@ -105,6 +108,8 @@ namespace bayesnet {
         }
         return matrix;
     }
+    // Measured in nats (natural logarithm (log) base e)
+    // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
     double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
         torch::Tensor counts = feature.bincount(weights);
@@ -143,11 +148,64 @@ namespace bayesnet {
         }
         return entropyValue;
     }
+    // H(Y|X,C) = sum_{x in X, c in C} p(x,c) H(Y|X=x,C=c)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
+    {
+        // Ensure the tensors are of the same length
+        assert(firstFeature.size(0) == secondFeature.size(0) && firstFeature.size(0) == labels.size(0) && firstFeature.size(0) == weights.size(0));
+
+        // Convert tensors to vectors for easier processing
+        auto firstFeatureData = firstFeature.accessor<int, 1>();
+        auto secondFeatureData = secondFeature.accessor<int, 1>();
+        auto labelsData = labels.accessor<int, 1>();
+        auto weightsData = weights.accessor<double, 1>();
+
+        int numSamples = firstFeature.size(0);
+
+        // Maps for joint and marginal probabilities
+        std::map<std::tuple<int, int, int>, double> jointCount;
+        std::map<std::tuple<int, int>, double> marginalCount;
+
+        // Compute joint and marginal counts
+        for (int i = 0; i < numSamples; ++i) {
+            auto keyJoint = std::make_tuple(firstFeatureData[i], labelsData[i], secondFeatureData[i]);
+            auto keyMarginal = std::make_tuple(firstFeatureData[i], labelsData[i]);
+
+            jointCount[keyJoint] += weightsData[i];
+            marginalCount[keyMarginal] += weightsData[i];
+        }
+
+        // Total weight sum
+        double totalWeight = torch::sum(weights).item<double>();
+        if (totalWeight == 0)
+            return 0;
+
+        // Compute the conditional entropy
+        double conditionalEntropy = 0.0;
+
+        for (const auto& [keyJoint, jointFreq] : jointCount) {
+            auto [x, c, y] = keyJoint;
+            auto keyMarginal = std::make_tuple(x, c);
+
+            double p_xc = marginalCount[keyMarginal] / totalWeight;
+            double p_y_given_xc = jointFreq / marginalCount[keyMarginal];
+
+            if (p_y_given_xc > 0) {
+                conditionalEntropy -= (jointFreq / totalWeight) * std::log(p_y_given_xc);
+            }
+        }
+        return conditionalEntropy;
+    }
     // I(X;Y) = H(Y) - H(Y|X)
     double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
         return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
     }
+    // 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 std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);
+    }
     /*
     Compute the maximum spanning tree considering the weights as distances
     and the indices of the weights as nodes of this square matrix using

bayesnet/utils/BayesMetrics.h

@@ -18,12 +18,16 @@ namespace bayesnet {
         std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
         std::vector<double> getScoresKBest() const;
         double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+        double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
         torch::Tensor conditionalEdge(const torch::Tensor& weights);
         std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        // Measured in nats (natural logarithm (log) base e)
+        // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
+        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
     protected:
         torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
         std::string className;
-        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
         std::vector<std::string> features;
         template <class T>
         std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)

tests/TestBayesMetrics.cc

@@ -9,6 +9,7 @@
 #include <catch2/generators/catch_generators.hpp>
 #include "bayesnet/utils/BayesMetrics.h"
 #include "TestUtils.h"
+#include "Timer.h"
 
 
 TEST_CASE("Metrics Test", "[Metrics]")
@@ -83,4 +84,37 @@ TEST_CASE("Select all features ordered by Mutual Information", "[Metrics]")
     auto kBest = metrics.SelectKBestWeighted(raw.weights, true, 0);
     REQUIRE(kBest.size() == raw.features.size());
     REQUIRE(kBest == std::vector<int>({ 1, 0, 3, 2 }));
+}
+TEST_CASE("Entropy Test", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    auto result = metrics.entropy(raw.dataset.index({ 0, "..." }), raw.weights);
+    REQUIRE(result == Catch::Approx(0.9848175048828125).epsilon(raw.epsilon));
+    auto data = torch::tensor({ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 }, torch::kInt32);
+    auto weights = torch::tensor({ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }, torch::kFloat32);
+    result = metrics.entropy(data, weights);
+    REQUIRE(result == Catch::Approx(0.61086434125900269).epsilon(raw.epsilon));
+    data = torch::tensor({ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 }, torch::kInt32);
+    result = metrics.entropy(data, weights);
+    REQUIRE(result == Catch::Approx(0.693147180559945).epsilon(raw.epsilon));
+}
+TEST_CASE("Conditional Entropy", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    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) {
+        for (int j = i + 1; j < raw.features.size(); ++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));
+        }
+    }
 }

tests/TestBoostAODE.cc

@@ -45,7 +45,7 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
     REQUIRE(clf.getNumberOfNodes() == 90);
     REQUIRE(clf.getNumberOfEdges() == 153);
     REQUIRE(clf.getNotes().size() == 2);
-    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with FCBF");
+    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
 }
 TEST_CASE("Test used features in train note and score", "[BoostAODE]")
@@ -65,8 +65,8 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
     REQUIRE(clf.getNotes()[1] == "Number of models: 8");
     auto score = clf.score(raw.Xv, raw.yv);
     auto scoret = clf.score(raw.Xt, raw.yt);
-    REQUIRE(score == Catch::Approx(0.80078).epsilon(raw.epsilon));
+    REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
-    REQUIRE(scoret == Catch::Approx(0.80078).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
 }
 TEST_CASE("Voting vs proba", "[BoostAODE]")
 {
@@ -149,15 +149,14 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
         {"convergence_best", false},
         });
     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
-    REQUIRE(clf.getNumberOfNodes() == 75);
+    REQUIRE(clf.getNumberOfNodes() == 210);
-    REQUIRE(clf.getNumberOfEdges() == 135);
+    REQUIRE(clf.getNumberOfEdges() == 378);
-    REQUIRE(clf.getNotes().size() == 2);
+    REQUIRE(clf.getNotes().size() == 1);
-    REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 9 models eliminated");
+    REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
-    REQUIRE(clf.getNotes().at(1) == "Number of models: 5");
     auto score = clf.score(raw.X_test, raw.y_test);
     auto scoret = clf.score(raw.X_test, raw.y_test);
-    REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
+    REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
-    REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
 }
 TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
 {
@@ -172,13 +171,13 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
     clf.setHyperparameters(hyperparameters);
     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
     auto score_best = clf.score(raw.X_test, raw.y_test);
-    REQUIRE(score_best == Catch::Approx(0.993355f).epsilon(raw.epsilon));
+    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);
     auto score_last = clf.score(raw.X_test, raw.y_test);
-    REQUIRE(score_last == Catch::Approx(0.996678f).epsilon(raw.epsilon));
+    REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
 }
 
 TEST_CASE("Block Update", "[BoostAODE]")

tests/Timer.h

@@ -0,0 +1,41 @@
+#pragma once
+#include <chrono>
+#include <string>
+#include <sstream>
+
+namespace platform {
+    class Timer {
+    private:
+        std::chrono::high_resolution_clock::time_point begin;
+        std::chrono::high_resolution_clock::time_point end;
+    public:
+        Timer() = default;
+        ~Timer() = default;
+        void start() { begin = std::chrono::high_resolution_clock::now(); }
+        void stop() { end = std::chrono::high_resolution_clock::now(); }
+        double getDuration()
+        {
+            stop();
+            std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (end - begin);
+            return time_span.count();
+        }
+        double getLapse()
+        {
+            std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (std::chrono::high_resolution_clock::now() - begin);
+            return time_span.count();
+        }
+        std::string getDurationString(bool lapse = false)
+        {
+            double duration = lapse ? getLapse() : getDuration();
+            return translate2String(duration);
+        }
+        std::string translate2String(double duration)
+        {
+            double durationShow = duration > 3600 ? duration / 3600 : duration > 60 ? duration / 60 : duration;
+            std::string durationUnit = duration > 3600 ? "h" : duration > 60 ? "m" : "s";
+            std::stringstream ss;
+            ss << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit;
+            return ss.str();
+        }
+    };
+} /* namespace platform */