Initial pass at implementing test fixtures

src/HealthGPS.Tests/CMakeLists.txt

@@ -39,6 +39,7 @@ target_sources(
             "RelativeRiskLookup.Test.cpp"
             "RuntimeMetric.Test.cpp"
             "Scenario.Test.cpp"
+            "StaticLinearModel.Test.cpp"
             "TestMain.cpp"
             "WeightModel.Test.cpp"
             "CountryModule.h"

src/HealthGPS.Tests/StaticLinearModel.Test.cpp

@@ -0,0 +1,186 @@
+#include "pch.h"
+
+#include "HealthGPS/static_linear_model.h"
+
+// Create a test fixture for the StaticLinearModel parameters
+class ModelParameters : public ::testing::Test {
+    public:
+        hgps::RiskFactorSexAgeTable expected;
+        std::vector<hgps::core::Identifier> names;
+        std::vector<hgps::LinearModelParams> models;
+        std::vector<double> lambda;
+        std::vector<double> stddev;
+        Eigen::Matrix2d correlation;
+        Eigen::MatrixXd cholesky;
+        std::vector<hgps::LinearModelParams> policy_models;
+        std::vector<hgps::core::DoubleInterval> policy_ranges;
+        Eigen::MatrixXd policy_cholesky;
+
+        void SetUp() override {
+            auto expected = hgps::RiskFactorSexAgeTable{};
+            std::vector<double> factorsMale = {200.,40.,30.,2000.,1.,1000.,3.,50.};
+            std::vector<double> factorsFemale = {200.,40.,30.,2000.,1.,1000.,3.,50.};
+
+            std::unordered_map<hgps::core::Identifier, std::vector<double>> factorsMale = {
+                {hgps::core::Identifier{"0"}, factorsMale},
+                {hgps::core::Identifier{"1"}, factorsMale}
+            };
+
+            std::unordered_map<hgps::core::Identifier, std::vector<double>> factorsFemale = {
+                {hgps::core::Identifier{"0"}, factorsFemale},
+                {hgps::core::Identifier{"1"}, factorsFemale}
+            };
+
+            expected.emplace_row(hgps::core::Gender::male, factorsMale);
+            expected.emplace_row(hgps::core::Gender::female, factorsFemale);
+
+            std::vector<hgps::core::Identifier> names = {"FoodCarbohydrate", "FoodFat"};
+
+            // Define models
+            // We will define 2 RiskFactorModels: carb_model and fat_model 
+            std::vector<hgps::LinearModelParams> models;
+            hgps::LinearModelParams carb_model;
+            carb_model.intercept = -0.241505734056409;
+            std::unordered_map<hgps::core::Identifier, double> coefficients = {
+                {"Gender", 0.00191412437431455},
+                {"Age", -0.000545257990453302},
+                {"Age2", 3.06434596341924e-6},
+                {"Age3", 5.24468215546687e-9},
+                {"Sector", 0.0967308121487847},
+                {"Income", 0.0810023788842083}
+            };
+            carb_model.coefficients = coefficients;
+
+            hgps::LinearModelParams fat_model;
+            fat_model.intercept = -0.693521187677;
+            coefficients = {
+                {"Gender", 0.00385126434589752},
+                {"Age", -0.00433711587715954},
+                {"Age2", 3.11799231385975e-5},
+                {"Age3", 3.4291809335544e-8},
+                {"Sector", -0.0629531974852436},
+                {"Income", 0.373256996730791}
+            };
+            fat_model.coefficients = coefficients;
+
+            models.emplace_back(std::move(carb_model));
+            models.emplace_back(std::move(fat_model));
+
+            std::vector<double> lambda = {0.262626262626263, 0.141414141414141};
+            std::vector<double> stddev = {0.337810256621877, 0.436763527499362};
+
+            Eigen::Matrix2d correlation;
+            correlation << 1.0, 0.322065425, 0.322065425,1.0;
+            auto cholesky = Eigen::MatrixXd{Eigen::LLT<Eigen::Matrix2d>{correlation}.matrixL()};
+
+            Eigen::MatrixXd policy_covariance;
+            policy_covariance << 0.1,0.1,0.1,0.41;
+            auto policy_cholesky = Eigen::MatrixXd{Eigen::LLT<Eigen::MatrixXd>{policy_covariance}.matrixL()};
+
+            // Define policy models
+            std::vector<hgps::LinearModelParams> policy_models;
+            std::vector<hgps::core::DoubleInterval> policy_ranges;
+
+            // We need 2 policy models for the 2 risk factor models
+            hgps::LinearModelParams carb_policy_model;
+            carb_policy_model.intercept = -1.29385215316281;
+            coefficients = {
+                {"Gender", 0.00845121355575458},
+                {"Age", 0.000386003599279214},
+                {"Sector", 0.0621763530502617},
+                {"Income", 0.0560558815729017}
+            };
+            carb_policy_model.coefficients = coefficients;
+            std::unordered_map<hgps::core::Identifier, double> log_coefficients = {
+                {"FoodCarbohydrate", 0.577638667323293},
+                {"FoodFat", -0.0801300827856402},
+                {"FoodProtein", -0.480946893299471},
+                {"FoodSodium", -0.0396319735508116}
+            };
+            carb_policy_model.log_coefficients = log_coefficients;
+            policy_ranges.emplace_back(hgps::core::DoubleInterval{-2.31063, 0.37526});
+
+            hgps::LinearModelParams fat_policy_model;
+            fat_policy_model.intercept = -0.734121205356728;
+            coefficients = {
+                {"Gender", 0.023422206650121},
+                {"Age", 0.000557687923688474},
+                {"Sector", 0.00166651997223223},
+                {"Income", -0.498755023022772}
+            }
+            fat_policy_model.coefficients = coefficients;
+            log_coefficients = {
+                {"FoodCarbohydrate", 0.652615267035516},
+                {"FoodFat", 0.743457905132894},
+                {"FoodProtein", -1.2648185364167},
+                {"FoodSodium", -0.17181077457271}
+            };
+            fat_policy_model.log_coefficients = log_coefficients;
+            policy_ranges.emplace_back(hgps::core::DoubleInterval{-4.87205, -0.10346});
+
+            policy_models.emplace_back(std::move(carb_policy_model));
+            policy_models.emplace_back(std::move(fat_policy_model));
+
+            const double info_speed = 0.1;
+
+            std::unordered_map<hgps::core::Identifier, std::unordered_map<hgps::core::Gender, double>> rural_prevalence;
+            rural_prevalence["Under18"] = {{hgps::core::Gender::female, 0.65},{hgps::core::Gender::male, 0.65}};
+            rural_prevalence["Over18"] = {{hgps::core::Gender::female, 0.6},{hgps::core::Gender::male, 0.6}};
+
+            std::unordered_map<hgps::core::Income, hgps::LinearModelParams> income_models;
+
+            hgps::LinearModelParams income_model_low;
+            income_model_low.intercept = 0.0;
+            coefficients = {}
+            income_model_low.coefficients = coefficients;
+
+            hgps::LinearModelParams income_model_middle;
+            income_model_middle.intercept = -0.0023671151435328;
+            coefficients = {
+                {"Gender", 0.0244784583947992},
+                {"Over18", 0.276669591410528},
+                {"Sector", -0.491794449118805}
+            };
+            income_model_middle.coefficients = coefficients;
+
+            hgps::LinearModelParams income_model_high;
+            income_model_high.intercept = 0.0187952471153142;
+            coefficients = {
+                {"Gender", 0.02018185354194},
+                {"Over18", 0.607099252829797},
+                {"Sector", -1.5870837069653}
+            };
+            income_model_high.coefficients = coefficients;
+
+            income_models.emplace(hgps::core::Income::low, std::move(income_model_low));
+            income_models.emplace(hgps::core::Income::middle, std::move(income_model_middle));
+            income_models.emplace(hgps::core::Income::high, std::move(income_model_high));
+
+            const double physical_activity_stddev = 0.06;
+
+    };
+};
+
+// Create test fixture for a StaticLinearModel class instance
+class StaticLinearModelTestFixture : public::testing::Test {
+    public:
+        StaticLinearModel testModel(
+            expected,
+            names,
+            models,
+            lambda,
+            stddev,
+            cholesky,
+            policy_models,
+            policy_ranges,
+            policy_cholesky,
+            info_speed,
+            rural_prevalence,
+            income_models,
+            physical_activity_stddev
+        );
+};
+
+TEST_F(StaticLinearModelTestFixture, InitialiseFactors) {
+    // Test logic goes here
+}