Continue with grid_experiment refactor
This commit is contained in:
@@ -29,7 +29,7 @@ add_executable(
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target_link_libraries(b_best Boost::boost "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
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# b_grid
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set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp)
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set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp GridBase.cpp)
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list(TRANSFORM grid_sources PREPEND grid/)
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add_executable(b_grid commands/b_grid.cpp ${grid_sources}
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common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp
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22
src/grid/GridBase.cpp
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22
src/grid/GridBase.cpp
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@@ -0,0 +1,22 @@
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#include "common/DotEnv.h"
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#include "common/Paths.h"
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#include "GridBase.h"
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namespace platform {
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GridBase::GridBase(struct ConfigGrid& config)
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{
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this->config = config;
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if (config.smooth_strategy == "ORIGINAL")
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smooth_type = bayesnet::Smoothing_t::ORIGINAL;
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else if (config.smooth_strategy == "LAPLACE")
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smooth_type = bayesnet::Smoothing_t::LAPLACE;
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else if (config.smooth_strategy == "CESTNIK")
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smooth_type = bayesnet::Smoothing_t::CESTNIK;
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else {
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std::cerr << "GridBase: Unknown smoothing strategy: " << config.smooth_strategy << std::endl;
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exit(1);
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}
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}
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}
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@@ -6,6 +6,7 @@
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#include <nlohmann/json.hpp>
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#include "common/Datasets.h"
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#include "common/Timer.h"
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#include "common/Colors.h"
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#include "main/HyperParameters.h"
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#include "GridData.h"
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#include "GridConfig.h"
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@@ -16,24 +17,11 @@ namespace platform {
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using json = nlohmann::ordered_json;
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class GridBase {
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public:
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explicit GridBase(struct ConfigGrid& config)
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{
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this->config = config;
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if (config.smooth_strategy == "ORIGINAL")
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smooth_type = bayesnet::Smoothing_t::ORIGINAL;
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else if (config.smooth_strategy == "LAPLACE")
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smooth_type = bayesnet::Smoothing_t::LAPLACE;
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else if (config.smooth_strategy == "CESTNIK")
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smooth_type = bayesnet::Smoothing_t::CESTNIK;
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else {
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std::cerr << "GridBase: Unknown smoothing strategy: " << config.smooth_strategy << std::endl;
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exit(1);
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}
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};
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explicit GridBase(struct ConfigGrid& config);
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~GridBase() = default;
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virtual void go(struct ConfigMPI& config_mpi) = 0;
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protected:
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virtual json build_tasks() = 0;
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virtual void save(json& results) = 0;
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struct ConfigGrid config;
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Timer timer; // used to measure the time of the whole process
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const std::string separator = "|";
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@@ -23,6 +23,16 @@ namespace platform {
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}
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json GridExperiment::build_tasks()
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{
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/*
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* Each task is a json object with the following structure:
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* {
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* "dataset": "dataset_name",
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* "idx_dataset": idx_dataset, // used to identify the dataset in the results
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* // this index is relative to the list of used datasets in the actual run not to the whole datasets list
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* "seed": # of seed to use,
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* "fold": # of fold to process
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* }
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*/
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auto tasks = json::array();
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auto grid = GridData(Paths::grid_input(config.model));
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auto datasets = Datasets(false, Paths::datasets());
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@@ -57,104 +67,6 @@ namespace platform {
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std::cout << separator << std::endl << separator << std::flush;
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return tasks;
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}
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void GridExperiment::go(struct ConfigMPI& config_mpi)
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{
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/*
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* Each task is a json object with the following structure:
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* {
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* "dataset": "dataset_name",
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* "idx_dataset": idx_dataset, // used to identify the dataset in the results
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* // this index is relative to the list of used datasets in the actual run not to the whole datasets list
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* "seed": # of seed to use,
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* "fold": # of fold to process
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* }
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*
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* This way a task consists in process all combinations of hyperparameters for a dataset, seed and fold
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*
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* The overall process consists in these steps:
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* 0. Create the MPI result type & tasks
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* 0.1 Create the MPI result type
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* 0.2 Manager creates the tasks
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* 1. Manager will broadcast the tasks to all the processes
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* 1.1 Broadcast the number of tasks
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* 1.2 Broadcast the length of the following string
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* 1.2 Broadcast the tasks as a char* string
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* 2a. Producer delivers the tasks to the consumers
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* 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
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* 2a.2 Producer will send the end message to all the consumers
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* 2b. Consumers process the tasks and send the results to the producer
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* 2b.1 Consumers announce to the producer that they are ready to receive a task
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* 2b.2 Consumers receive the task from the producer and process it
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* 2b.3 Consumers send the result to the producer
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* 3. Manager select the bests scores for each dataset
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* 3.1 Loop thru all the results obtained from each outer fold (task) and select the best
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* 3.2 Save the results
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*/
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//
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// 0.1 Create the MPI result type
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//
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Task_Result result;
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int tasks_size;
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MPI_Datatype MPI_Result;
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MPI_Datatype type[5] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_INT, MPI_DOUBLE, MPI_DOUBLE };
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int blocklen[5] = { 1, 1, 1, 1, 1 };
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MPI_Aint disp[5];
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disp[0] = offsetof(Task_Result, idx_dataset);
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disp[1] = offsetof(Task_Result, idx_combination);
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disp[2] = offsetof(Task_Result, n_fold);
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disp[3] = offsetof(Task_Result, score);
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disp[4] = offsetof(Task_Result, time);
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MPI_Type_create_struct(5, blocklen, disp, type, &MPI_Result);
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MPI_Type_commit(&MPI_Result);
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//
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// 0.2 Manager creates the tasks
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//
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char* msg;
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json tasks;
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if (config_mpi.rank == config_mpi.manager) {
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timer.start();
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tasks = build_tasks();
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auto tasks_str = tasks.dump();
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tasks_size = tasks_str.size();
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msg = new char[tasks_size + 1];
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strcpy(msg, tasks_str.c_str());
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}
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//
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// 1. Manager will broadcast the tasks to all the processes
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//
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MPI_Bcast(&tasks_size, 1, MPI_INT, config_mpi.manager, MPI_COMM_WORLD);
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if (config_mpi.rank != config_mpi.manager) {
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msg = new char[tasks_size + 1];
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}
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MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD);
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tasks = json::parse(msg);
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delete[] msg;
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auto env = platform::DotEnv();
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auto datasets = Datasets(config.discretize, Paths::datasets(), env.get("discretize_algo"));
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if (config_mpi.rank == config_mpi.manager) {
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//
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// 2a. Producer delivers the tasks to the consumers
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//
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auto datasets_names = std::vector<std::string>();
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json all_results = MPI_EXPERIMENT::producer(datasets_names, tasks, config_mpi, MPI_Result);
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std::cout << separator << std::endl;
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//
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// 3. Manager select the bests sccores for each dataset
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//
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auto results = initializeResults();
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//select_best_results_folds(results, all_results, config.model);
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//
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// 3.2 Save the results
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//
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save(results);
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} else {
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//
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// 2b. Consumers prostore_search_resultcess the tasks and send the results to the producer
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//
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MPI_EXPERIMENT::consumer(datasets, tasks, config, config_mpi, MPI_Result);
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}
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}
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json GridExperiment::initializeResults()
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{
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// Load previous results if continue is set
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@@ -5,7 +5,6 @@
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#include <mpi.h>
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#include <nlohmann/json.hpp>
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#include "common/Datasets.h"
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#include "common/Timer.h"
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#include "main/HyperParameters.h"
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#include "GridData.h"
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#include "GridBase.h"
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@@ -17,9 +16,9 @@ namespace platform {
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class GridExperiment : public GridBase {
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public:
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explicit GridExperiment(struct ConfigGrid& config);
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void go(struct ConfigMPI& config_mpi);
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~GridExperiment() = default;
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json loadResults();
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void go(struct ConfigMPI& config_mpi);
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private:
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void save(json& results);
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json initializeResults();
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@@ -27,7 +26,7 @@ namespace platform {
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};
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/* *************************************************************************************************************
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//
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// MPI Search Functions
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// MPI Experiment Functions
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//
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************************************************************************************************************* */
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class MPI_EXPERIMENT :public MPI_Base {
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@@ -4,7 +4,6 @@
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#include <folding.hpp>
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#include "main/Models.h"
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#include "common/Paths.h"
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#include "common/Colors.h"
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#include "common/Utils.h"
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#include "GridSearch.h"
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@@ -55,6 +54,16 @@ namespace platform {
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}
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json GridSearch::build_tasks()
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{
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/*
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* Each task is a json object with the following structure:
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* {
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* "dataset": "dataset_name",
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* "idx_dataset": idx_dataset, // used to identify the dataset in the results
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* // this index is relative to the list of used datasets in the actual run not to the whole datasets list
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* "seed": # of seed to use,
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* "fold": # of fold to process
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* }
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*/
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auto tasks = json::array();
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auto grid = GridData(Paths::grid_input(config.model));
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auto datasets = Datasets(false, Paths::datasets());
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@@ -18,10 +18,10 @@ namespace platform {
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class GridSearch : public GridBase {
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public:
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explicit GridSearch(struct ConfigGrid& config);
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void go(struct ConfigMPI& config_mpi);
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~GridSearch() = default;
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json loadResults();
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static inline std::string NO_CONTINUE() { return "NO_CONTINUE"; }
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void go(struct ConfigMPI& config_mpi);
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private:
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void save(json& results);
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json initializeResults();
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