Merge branch 'GPAnalyzer' into GPLoop-amanXSimon-CGPA

CMakeLists.txt

@@ -135,7 +135,8 @@ if (${BUILD_MAIN})
         string(REPLACE ".cpp" "" EXE_NAME ${EXE_SOURCE})
         # Create list of source files (currently just the one .cpp file)
         # Create executable and link to includes / libraries
-        add_executable(${EXE_NAME} ${CMAKE_SOURCE_DIR}/source/${EXE_SOURCE} ${CMAKE_SOURCE_DIR}/source/core/Entity.cpp)
+        add_executable(${EXE_NAME} ${CMAKE_SOURCE_DIR}/source/${EXE_SOURCE} ${CMAKE_SOURCE_DIR}/source/core/Entity.cpp
+                source/Agents/GP/GPAgentAnalyze.h)
         target_include_directories(${EXE_NAME}
                 PRIVATE ${CMAKE_SOURCE_DIR}/source
                 )

source/Agents/GP/GPAgentAnalyze.h

@@ -0,0 +1,104 @@
+//
+// A class that analyzes the data of the best agent in the GP algorithm
+// and saves it to a csv file
+//
+
+#pragma once
+
+#include <vector>
+#include <iostream>
+#include <string>
+#include <fstream>
+
+namespace cowboys {
+
+    class GPAgentAnalyzer {
+    private:
+        /// The average fitness of the best agent
+        std::vector<double> average_fitness;
+
+        /// The max fitness of the best agent
+        std::vector<double> max_fitness;
+
+        /// The weighted score of the best agent
+        std::vector<double> elite_score;
+
+        /// The average score of the best agent
+        std::vector<double> average_score;
+
+        /// The number of agents with the max fitness
+        std::vector<double> max_agents;
+
+    public:
+        /**
+         * @brief Construct a new GP Agent Analyzer object
+         */
+        GPAgentAnalyzer() = default;
+
+        /**
+         * @brief Destroy the GP Agent Analyzer object
+         */
+        ~GPAgentAnalyzer() = default;
+
+        /**
+         * @brief Adds the average fitness of the best agent
+         * @param fitness
+         */
+        void addAverageFitness(double fitness) {
+            average_fitness.push_back(fitness);
+        }
+
+        /**
+         * @brief Adds the max fitness of the best agent
+         * @param fitness
+         */
+        void addMaxFitness(double fitness) {
+            max_fitness.push_back(fitness);
+        }
+
+        /**
+         * @brief Adds the weighted score of the best agent
+         * @param score
+         */
+        void addEliteScore(double score) {
+            elite_score.push_back(score);
+        }
+
+        /**
+         * @brief Adds the average score of the best agent
+         * @param score
+         */
+        void addAverageScore(double score) {
+            average_score.push_back(score);
+        }
+
+        /**
+         * @brief Adds the number of agents with the max fitness
+         * @param num_agents
+         */
+        void addNumAgentsWithMaxFitness(double num_agents) {
+            max_agents.push_back(num_agents);
+        }
+
+        /**
+         * @brief Saves the data to a csv file
+         */
+        void saveToFile() {
+            // create a new file
+            std::ofstream file("gp_agent_analyzer.csv");
+
+            // write the data to the file
+            file << "average_fitness,max_fitness,average_elite_score,best_agent_weighted_score,agents_with_max_fitness\n";
+
+            for (int i = 0; i < average_fitness.size(); i++) {
+                file << average_fitness[i] << "," << max_fitness[i] << "," << elite_score[i] << "," << average_score[i] << "," << max_agents[i] << "\n";
+            }
+            std::cout << "Saved GP Agent Analyzer data to gp_agent_analyzer.csv" << std::endl;
+
+            // close the file
+            file.close();
+        }
+    };
+
+}  // namespace cowboys
+

source/Agents/GP/GPTrainingLoop.hpp

@@ -21,12 +21,12 @@
 
 
 #include "tinyxml2.h"
-
+#include "GPAgentAnalyze.h"
 
 
 namespace cowboys {
 
-    constexpr unsigned int TRAINING_SEED = 10; ///< If this is 0, then a random seed will be used
+    constexpr unsigned int TRAINING_SEED = 0; ///< If this is 0, then a random seed will be used
 
     template<class AgentType, class EnvironmentType>
     class GPTrainingLoop {
@@ -36,6 +36,7 @@ namespace cowboys {
         std::vector<std::vector<cowboys::GPAgentBase *>> agents;
         std::vector<std::vector<double>> TEMPAgentFitness;
 
+        GPAgentAnalyzer analyzer;
 
         tinyxml2::XMLDocument topAgentsDoc;
         tinyxml2::XMLDocument lastGenerationsTopAgentsDoc; // <- Saves the last 5 generations
@@ -487,6 +488,7 @@ namespace cowboys {
             saveDataParams.countMaxAgents = countMaxAgents;
             SaveDataCheckPoint(saveDataParams);
 
+
             GpLoopMutateHelper();
             resetEnvironments();
 
@@ -495,6 +497,7 @@ namespace cowboys {
                     generationEndTime - generationStartTime);
             std::cout << "Generation " << generation << " took " << generationDuration.count() / 1000000.0 << " seconds"
                       << std::endl;
+          analyzer.saveToFile();
 
           }
 
@@ -597,6 +600,7 @@ namespace cowboys {
 
           std::cout << "@@@@@@@@@@@@@@@@@@@@@@  " << "DataSaved" << "  @@@@@@@@@@@@@@@@@@@@@@" << std::endl;
 
+//            analyzer.saveToFile(getSystemPath() / "fitness.csv");
           lastGenerationsTopAgentsDoc.Clear();
           ResetMainTagLastGenerations();
         }
@@ -663,7 +667,11 @@ namespace cowboys {
 
           std::cout << "Generation " << generation << " complete" << std::endl;
           std::cout << "Average fitness: " << averageFitness << " ";
+          analyzer.addAverageFitness(averageFitness);
+
           std::cout << "Max fitness: " << maxFitness << std::endl;
+          analyzer.addMaxFitness(maxFitness);
+
 
 
           std::string tagName = "generation_" + std::to_string(generation);
@@ -723,12 +731,13 @@ namespace cowboys {
             std::cout << std::endl;
           }
 
-          std::cout << "with an average score of " << TEMPAgentFitness[bestAgent.first][bestAgent.second] << std::endl;
+          std::cout << "with best agent weighted score of " << TEMPAgentFitness[bestAgent.first][bestAgent.second] << std::endl;
           std::cout << std::endl;
-
+          analyzer.addAverageScore(TEMPAgentFitness[bestAgent.first][bestAgent.second]);
 
           std::cout << "Number of agents with max fitness: " << countMaxAgents << std::endl;
           std::cout << "------------------------------------------------------------------" << std::endl;
+          analyzer.addNumAgentsWithMaxFitness(countMaxAgents);
           return countMaxAgents;
         }
 
@@ -867,7 +876,7 @@ namespace cowboys {
           averageEliteFitness /= ELITE_POPULATION_SIZE;
 
           std::cout << " --- average elite score " << averageEliteFitness << "------ " << std::endl;
-
+          analyzer.addEliteScore(averageEliteFitness);
 
           const int MIDDLE_MUTATE_ENDBOUND = int(sortedAgents.size() * (1 - UNFIT_POPULATION_PERCENT));
           const int MIDDLE_MUTATE_STARTBOUND = int(ELITE_POPULATION_PERCENT * sortedAgents.size());

source/Worlds/MazeWorld.hpp

@@ -31,11 +31,13 @@ class MazeWorld : public WorldBase {
 
   MazeWorld(unsigned int seed = 0) : WorldBase(seed) {
     // Create cell types
+
     floor_id =
         AddCellType("floor", "Floor that you can easily walk over.", ' ');
     wall_id = AddCellType(
         "wall", "Impenetrable wall that you must find a way around.", '#');
 
+
     // Set cell type properties
     type_options.at(floor_id).SetProperty(CellType::CELL_WALL);
     // Load map

source/gp_train_main.cpp

@@ -27,7 +27,6 @@ int main() {
 
     cowboys::GPTrainingLoop <cowboys::CGPAgent, cse491::MazeWorld> loop(false);
 
-
     loop.Initialize(13, 200);
     loop.Run(6, 50, num_threads, true);