Refactor mpi classes

src/CMakeLists.txt

@@ -29,7 +29,7 @@ add_executable(
 target_link_libraries(b_best Boost::boost "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
 # b_grid
-set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp GridFunctions.cpp)
+set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp)
 list(TRANSFORM grid_sources PREPEND grid/)
 add_executable(b_grid commands/b_grid.cpp ${grid_sources} 
     common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp

src/grid/GridBase.h

@@ -32,12 +32,22 @@ namespace platform {
         };
         ~GridBase() = default;
         virtual void go(struct ConfigMPI& config_mpi) = 0;
-        virtual json build_tasks_mpi() = 0;
     protected:
+        virtual json build_tasks() = 0;
         struct ConfigGrid config;
         Timer timer; // used to measure the time of the whole process
         const std::string separator = "|";
         bayesnet::Smoothing_t smooth_type{ bayesnet::Smoothing_t::NONE };
     };
+    class MPI_Base {
+    public:
+        static std::string get_color_rank(int rank)
+        {
+            auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(),  Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN(), Colors::YELLOW(), Colors::BLACK() };
+            std::string id = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
+            auto idx = rank % id.size();
+            return *(colors.begin() + rank % colors.size()) + id[idx];
+        }
+    };
 } /* namespace platform */
 #endif

src/grid/GridConfig.h

@@ -45,31 +45,5 @@ namespace platform {
     const int TAG_RESULT = 2;
     const int TAG_TASK = 3;
     const int TAG_END = 4;
-    /* *************************************************************************************************************
-    //
-    // MPI Common Functions
-    //
-    ************************************************************************************************************* */
-    std::string get_color_rank(int rank);
-    /* *************************************************************************************************************
-    //
-    // MPI Experiment Functions
-    //
-    ************************************************************************************************************* */
-    json mpi_experiment_producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result);
-    void mpi_experiment_consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result);
-    void join_results_folds(json& results, json& all_results, std::string& model);
-    json store_experiment_result(std::vector<std::string>& names, Task_Result& result, json& results);
-    void mpi_experiment_consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tass, int n_task, Datasets& datasets, Task_Result* result);
-    /* *************************************************************************************************************
-    //
-    // MPI Search Functions
-    //
-    ************************************************************************************************************* */
-    json mpi_search_producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result);
-    void mpi_search_consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result);
-    void select_best_results_folds(json& results, json& all_results, std::string& model);
-    json store_search_result(std::vector<std::string>& names, Task_Result& result, json& results);
-    void mpi_search_consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result);
 } /* namespace platform */
 #endif

src/grid/GridExperiment.cpp

@@ -21,7 +21,7 @@ namespace platform {
         }
         return json();
     }
-    json GridExperiment::build_tasks_mpi()
+    json GridExperiment::build_tasks()
     {
         auto tasks = json::array();
         auto grid = GridData(Paths::grid_input(config.model));
@@ -113,7 +113,7 @@ namespace platform {
         json tasks;
         if (config_mpi.rank == config_mpi.manager) {
             timer.start();
-            tasks = build_tasks_mpi();
+            tasks = build_tasks();
             auto tasks_str = tasks.dump();
             tasks_size = tasks_str.size();
             msg = new char[tasks_size + 1];
@@ -137,7 +137,7 @@ namespace platform {
             // 2a. Producer delivers the tasks to the consumers
             //
             auto datasets_names = std::vector<std::string>();
-            json all_results = mpi_experiment_producer(datasets_names, tasks, config_mpi, MPI_Result);
+            json all_results = MPI_EXPERIMENT::producer(datasets_names, tasks, config_mpi, MPI_Result);
             std::cout << separator << std::endl;
             //
             // 3. Manager select the bests sccores for each dataset
@@ -152,7 +152,7 @@ namespace platform {
             //
             // 2b. Consumers prostore_search_resultcess the tasks and send the results to the producer
             //
-            mpi_experiment_consumer(datasets, tasks, config, config_mpi, MPI_Result);
+            MPI_EXPERIMENT::consumer(datasets, tasks, config, config_mpi, MPI_Result);
         }
     }
     json GridExperiment::initializeResults()

src/grid/GridExperiment.h

@@ -23,22 +23,15 @@ namespace platform {
     private:
         void save(json& results);
         json initializeResults();
-        json build_tasks_mpi();
+        json build_tasks();
     };
     /* *************************************************************************************************************
     //
     // MPI Search Functions
     //
     ************************************************************************************************************* */
-    class MPI_EXPERIMENT {
+    class MPI_EXPERIMENT :public MPI_Base {
     public:
-        static std::string get_color_rank(int rank)
-        {
-            auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(),  Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN(), Colors::YELLOW(), Colors::BLACK() };
-            std::string id = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-            auto idx = rank % id.size();
-            return *(colors.begin() + rank % colors.size()) + id[idx];
-        }
         static json producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
         {
             Task_Result result;
@@ -53,7 +46,7 @@ namespace platform {
                 MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
                 if (status.MPI_TAG == TAG_RESULT) {
                     //Store result
-                    store_search_result(names, result, results);
+                    store_result(names, result, results);
                 }
                 MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
             }
@@ -65,7 +58,7 @@ namespace platform {
                 MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
                 if (status.MPI_TAG == TAG_RESULT) {
                     //Store result
-                    store_search_result(names, result, results);
+                    store_result(names, result, results);
                 }
                 MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
             }
@@ -88,7 +81,7 @@ namespace platform {
                 if (status.MPI_TAG == TAG_END) {
                     break;
                 }
-                mpi_experiment_consumer_go(config, config_mpi, tasks, task, datasets, &result);
+                consumer_go(config, config_mpi, tasks, task, datasets, &result);
                 //
                 // 2b.3 Consumers send the result to the producer
                 //
@@ -125,7 +118,7 @@ namespace platform {
                 results[dataset] = json_best;
             }
         }
-        static json store_search_result(std::vector<std::string>& names, Task_Result& result, json& results)
+        static json store_result(std::vector<std::string>& names, Task_Result& result, json& results)
         {
             json json_result = {
                 { "score", result.score },

src/grid/GridFunctions.cpp

@@ -1,240 +0,0 @@
-
-#include <iostream>
-#include <torch/torch.h>
-#include <folding.hpp>
-#include "main/Models.h"
-#include "common/Paths.h"
-#include "common/Colors.h"
-#include "common/Utils.h"
-#include "GridConfig.h"
-namespace platform {
-    using json = nlohmann::ordered_json;
-    std::string get_color_rank(int rank)
-    {
-        auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(),  Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN(), Colors::YELLOW(), Colors::BLACK() };
-        std::string id = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-        auto idx = rank % id.size();
-        return *(colors.begin() + rank % colors.size()) + id[idx];
-    }
-    /* *************************************************************************************************************
-    //
-    // MPI Experiment Functions
-    //
-    ************************************************************************************************************* */
-    json mpi_experiment_producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
-    {
-        Task_Result result;
-        json results;
-        int num_tasks = tasks.size();
-
-        //
-        // 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
-        //
-        for (int i = 0; i < num_tasks; ++i) {
-            MPI_Status status;
-            MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
-            if (status.MPI_TAG == TAG_RESULT) {
-                //Store result
-                store_experiment_result(names, result, results);
-            }
-            MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
-        }
-        //
-        // 2a.2 Producer will send the end message to all the consumers
-        //
-        for (int i = 0; i < config_mpi.n_procs - 1; ++i) {
-            MPI_Status status;
-            MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
-            if (status.MPI_TAG == TAG_RESULT) {
-                //Store result
-                store_experiment_result(names, result, results);
-            }
-            MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
-        }
-        return results;
-    }
-    void mpi_experiment_consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
-    {
-        Task_Result result;
-        //
-        // 2b.1 Consumers announce to the producer that they are ready to receive a task
-        //
-        MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_QUERY, MPI_COMM_WORLD);
-        int task;
-        while (true) {
-            MPI_Status status;
-            //
-            // 2b.2 Consumers receive the task from the producer and process it
-            //
-            MPI_Recv(&task, 1, MPI_INT, config_mpi.manager, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
-            if (status.MPI_TAG == TAG_END) {
-                break;
-            }
-            mpi_experiment_consumer_go(config, config_mpi, tasks, task, datasets, &result);
-            //
-            // 2b.3 Consumers send the result to the producer
-            //
-            MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_RESULT, MPI_COMM_WORLD);
-        }
-    }
-    void join_results_folds(json& results, json& all_results, std::string& model)
-    {
-        Timer timer;
-        auto grid = GridData(Paths::grid_input(model));
-        //
-        // Select the best result of the computed outer folds
-        //
-        for (const auto& result : all_results.items()) {
-            // each result has the results of all the outer folds as each one were a different task
-            double best_score = 0.0;
-            json best;
-            for (const auto& result_fold : result.value()) {
-                double score = result_fold["score"].get<double>();
-                if (score > best_score) {
-                    best_score = score;
-                    best = result_fold;
-                }
-            }
-            auto dataset = result.key();
-            auto combinations = grid.getGrid(dataset);
-            json json_best = {
-                    { "score", best_score },
-                    { "hyperparameters", combinations[best["combination"].get<int>()] },
-                    { "date", get_date() + " " + get_time() },
-                    { "grid", grid.getInputGrid(dataset) },
-                    { "duration", timer.translate2String(best["time"].get<double>()) }
-            };
-            results[dataset] = json_best;
-        }
-    }
-    json store_experiment_result(std::vector<std::string>& names, Task_Result& result, json& results)
-    {
-        json json_result = {
-            { "score", result.score },
-            { "combination", result.idx_combination },
-            { "fold", result.n_fold },
-            { "time", result.time },
-            { "dataset", result.idx_dataset }
-        };
-        auto name = names[result.idx_dataset];
-        if (!results.contains(name)) {
-            results[name] = json::array();
-        }
-        results[name].push_back(json_result);
-        return results;
-    }
-    void mpi_experiment_consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result)
-    {
-        //
-        // initialize
-        //
-        Timer timer;
-        timer.start();
-        json task = tasks[n_task];
-        auto model = config.model;
-        auto grid = GridData(Paths::grid_input(model));
-        auto dataset_name = task["dataset"].get<std::string>();
-        auto idx_dataset = task["idx_dataset"].get<int>();
-        auto seed = task["seed"].get<int>();
-        auto n_fold = task["fold"].get<int>();
-        bool stratified = config.stratified;
-        bayesnet::Smoothing_t smooth;
-        if (config.smooth_strategy == "ORIGINAL")
-            smooth = bayesnet::Smoothing_t::ORIGINAL;
-        else if (config.smooth_strategy == "LAPLACE")
-            smooth = bayesnet::Smoothing_t::LAPLACE;
-        else if (config.smooth_strategy == "CESTNIK")
-            smooth = bayesnet::Smoothing_t::CESTNIK;
-        //
-        // Generate the hyperparameters combinations
-        //
-        auto& dataset = datasets.getDataset(dataset_name);
-        auto combinations = grid.getGrid(dataset_name);
-        dataset.load();
-        auto [X, y] = dataset.getTensors();
-        auto features = dataset.getFeatures();
-        auto className = dataset.getClassName();
-        //
-        // Start working on task
-        //
-        folding::Fold* fold;
-        if (stratified)
-            fold = new folding::StratifiedKFold(config.n_folds, y, seed);
-        else
-            fold = new folding::KFold(config.n_folds, y.size(0), seed);
-        auto [train, test] = fold->getFold(n_fold);
-        auto [X_train, X_test, y_train, y_test] = dataset.getTrainTestTensors(train, test);
-        auto states = dataset.getStates(); // Get the states of the features Once they are discretized
-        float best_fold_score = 0.0;
-        int best_idx_combination = -1;
-        json best_fold_hyper;
-        for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
-            auto hyperparam_line = combinations[idx_combination];
-            auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
-            folding::Fold* nested_fold;
-            if (config.stratified)
-                nested_fold = new folding::StratifiedKFold(config.nested, y_train, seed);
-            else
-                nested_fold = new folding::KFold(config.nested, y_train.size(0), seed);
-            double score = 0.0;
-            for (int n_nested_fold = 0; n_nested_fold < config.nested; n_nested_fold++) {
-                //
-                // Nested level fold
-                //
-                auto [train_nested, test_nested] = nested_fold->getFold(n_nested_fold);
-                auto train_nested_t = torch::tensor(train_nested);
-                auto test_nested_t = torch::tensor(test_nested);
-                auto X_nested_train = X_train.index({ "...", train_nested_t });
-                auto y_nested_train = y_train.index({ train_nested_t });
-                auto X_nested_test = X_train.index({ "...", test_nested_t });
-                auto y_nested_test = y_train.index({ test_nested_t });
-                //
-                // Build Classifier with selected hyperparameters
-                //
-                auto clf = Models::instance()->create(config.model);
-                auto valid = clf->getValidHyperparameters();
-                hyperparameters.check(valid, dataset_name);
-                clf->setHyperparameters(hyperparameters.get(dataset_name));
-                //
-                // Train model
-                //
-                clf->fit(X_nested_train, y_nested_train, features, className, states, smooth);
-                //
-                // Test model
-                //
-                score += clf->score(X_nested_test, y_nested_test);
-            }
-            delete nested_fold;
-            score /= config.nested;
-            if (score > best_fold_score) {
-                best_fold_score = score;
-                best_idx_combination = idx_combination;
-                best_fold_hyper = hyperparam_line;
-            }
-        }
-        delete fold;
-        //
-        // Build Classifier with the best hyperparameters to obtain the best score
-        //
-        auto hyperparameters = platform::HyperParameters(datasets.getNames(), best_fold_hyper);
-        auto clf = Models::instance()->create(config.model);
-        auto valid = clf->getValidHyperparameters();
-        hyperparameters.check(valid, dataset_name);
-        clf->setHyperparameters(best_fold_hyper);
-        clf->fit(X_train, y_train, features, className, states, smooth);
-        best_fold_score = clf->score(X_test, y_test);
-        //
-        // Return the result
-        //
-        result->idx_dataset = task["idx_dataset"].get<int>();
-        result->idx_combination = best_idx_combination;
-        result->score = best_fold_score;
-        result->n_fold = n_fold;
-        result->time = timer.getDuration();
-        //
-        // Update progress bar
-        //
-        std::cout << get_color_rank(config_mpi.rank) << std::flush;
-    }
-
-}

src/grid/GridSearch.cpp

@@ -53,7 +53,7 @@ namespace platform {
         }
         return datasets_names;
     }
-    json GridSearch::build_tasks_mpi()
+    json GridSearch::build_tasks()
     {
         auto tasks = json::array();
         auto grid = GridData(Paths::grid_input(config.model));
@@ -145,7 +145,7 @@ namespace platform {
         json tasks;
         if (config_mpi.rank == config_mpi.manager) {
             timer.start();
-            tasks = build_tasks_mpi();
+            tasks = build_tasks();
             auto tasks_str = tasks.dump();
             tasks_size = tasks_str.size();
             msg = new char[tasks_size + 1];

src/grid/GridSearch.h

@@ -26,14 +26,14 @@ namespace platform {
         void save(json& results);
         json initializeResults();
         std::vector<std::string> filterDatasets(Datasets& datasets) const;
-        json build_tasks_mpi();
+        json build_tasks();
     };
     /* *************************************************************************************************************
     //
     // MPI Search Functions
     //
     ************************************************************************************************************* */
-    class MPI_SEARCH {
+    class MPI_SEARCH :public MPI_Base {
     public:
         static json producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
         {
@@ -49,7 +49,7 @@ namespace platform {
                 MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
                 if (status.MPI_TAG == TAG_RESULT) {
                     //Store result
-                    store_search_result(names, result, results);
+                    store_result(names, result, results);
                 }
                 MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
             }
@@ -61,7 +61,7 @@ namespace platform {
                 MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
                 if (status.MPI_TAG == TAG_RESULT) {
                     //Store result
-                    store_search_result(names, result, results);
+                    store_result(names, result, results);
                 }
                 MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
             }
@@ -84,7 +84,7 @@ namespace platform {
                 if (status.MPI_TAG == TAG_END) {
                     break;
                 }
-                mpi_experiment_consumer_go(config, config_mpi, tasks, task, datasets, &result);
+                consumer_go(config, config_mpi, tasks, task, datasets, &result);
                 //
                 // 2b.3 Consumers send the result to the producer
                 //
@@ -121,7 +121,7 @@ namespace platform {
                 results[dataset] = json_best;
             }
         }
-        static json store_search_result(std::vector<std::string>& names, Task_Result& result, json& results)
+        static json store_result(std::vector<std::string>& names, Task_Result& result, json& results)
         {
             json json_result = {
                 { "score", result.score },