Continue grid Experiment
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@@ -19,6 +19,41 @@ namespace platform {
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}
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return json();
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}
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json GridSearch::build_tasks(Datasets& datasets)
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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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* 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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auto tasks = json::array();
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auto grid = GridData(Paths::grid_input(config.model));
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auto all_datasets = datasets.getNames();
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auto datasets_names = filterDatasets(datasets);
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for (int idx_dataset = 0; idx_dataset < datasets_names.size(); ++idx_dataset) {
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auto dataset = datasets_names[idx_dataset];
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for (const auto& seed : config.seeds) {
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auto combinations = grid.getGrid(dataset);
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for (int n_fold = 0; n_fold < config.n_folds; n_fold++) {
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json task = {
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{ "dataset", dataset },
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{ "idx_dataset", idx_dataset},
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{ "seed", seed },
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{ "fold", n_fold},
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};
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tasks.push_back(task);
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}
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}
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}
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shuffle_and_progress_bar(tasks);
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return tasks;
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}
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std::vector<std::string> GridSearch::filterDatasets(Datasets& datasets) const
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{
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// Load datasets
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@@ -93,66 +128,7 @@ namespace platform {
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};
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file << output.dump(4);
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}
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//
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//
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//
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json GridSearch::producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
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{
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Task_Result result;
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json results;
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int num_tasks = tasks.size();
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//
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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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//
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for (int i = 0; i < num_tasks; ++i) {
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MPI_Status status;
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MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
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if (status.MPI_TAG == TAG_RESULT) {
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//Store result
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store_result(names, result, results);
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}
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MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
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}
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//
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// 2a.2 Producer will send the end message to all the consumers
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//
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for (int i = 0; i < config_mpi.n_procs - 1; ++i) {
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MPI_Status status;
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MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
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if (status.MPI_TAG == TAG_RESULT) {
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//Store result
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store_result(names, result, results);
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}
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MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
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}
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return results;
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}
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void GridSearch::consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
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{
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Task_Result result;
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//
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// 2b.1 Consumers announce to the producer that they are ready to receive a task
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//
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MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_QUERY, MPI_COMM_WORLD);
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int task;
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while (true) {
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MPI_Status status;
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//
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// 2b.2 Consumers receive the task from the producer and process it
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//
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MPI_Recv(&task, 1, MPI_INT, config_mpi.manager, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
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if (status.MPI_TAG == TAG_END) {
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break;
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}
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consumer_go(config, config_mpi, tasks, task, datasets, &result);
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//
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// 2b.3 Consumers send the result to the producer
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//
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MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_RESULT, MPI_COMM_WORLD);
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}
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}
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void GridSearch::select_best_results_folds(json& results, json& all_results, std::string& model)
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void GridSearch::compile_results(json& results, json& all_results, std::string& model)
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{
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Timer timer;
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auto grid = GridData(Paths::grid_input(model));
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