From 722da7f7814eb5302417cb47b1da9de3fc15051a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Thu, 4 Jan 2024 01:21:56 +0100 Subject: [PATCH] Keep only mpi b_grid compute --- src/Platform/GridSearch.cc | 527 ++++--------------------------------- src/Platform/GridSearch.h | 10 +- src/Platform/b_grid.cc | 43 ++- 3 files changed, 69 insertions(+), 511 deletions(-) diff --git a/src/Platform/GridSearch.cc b/src/Platform/GridSearch.cc index 94bd617..3e9ae3d 100644 --- a/src/Platform/GridSearch.cc +++ b/src/Platform/GridSearch.cc @@ -30,7 +30,7 @@ namespace platform { } std::string get_color_rank(int rank) { - auto colors = { Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() }; + auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() }; return *(colors.begin() + rank % colors.size()); } GridSearch::GridSearch(struct ConfigGrid& config) : config(config) @@ -77,32 +77,7 @@ namespace platform { } return datasets_names; } - void showProgressComb(const int num, const int n_folds, const int total, const std::string& color) - { - int spaces = int(log(total) / log(10)) + 1; - int magic = n_folds * 3 + 22 + 2 * spaces; - std::string prefix = num == 1 ? "" : string(magic, '\b') + string(magic + 1, ' ') + string(magic + 1, '\b'); - std::cout << prefix << color << "(" << setw(spaces) << num << "/" << setw(spaces) << total << ") " << Colors::RESET() << flush; - } - void showProgressFold(int fold, const std::string& color, const std::string& phase) - { - std::string prefix = phase == "a" ? "" : "\b\b\b\b"; - std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush; - } - std::string getColor(bayesnet::status_t status) - { - switch (status) { - case bayesnet::NORMAL: - return Colors::GREEN(); - case bayesnet::WARNING: - return Colors::YELLOW(); - case bayesnet::ERROR: - return Colors::RED(); - default: - return Colors::RESET(); - } - } - json GridSearch::build_tasks_mpi() + json GridSearch::build_tasks_mpi(int rank) { auto tasks = json::array(); auto grid = GridData(Paths::grid_input(config.model)); @@ -124,10 +99,10 @@ namespace platform { } } } - // It's important to shuffle the array so heavy datasets are spread across the Workers + // Shuffle the array so heavy datasets are spread across the workers std::mt19937 g{ 271 }; // Use fixed seed to obtain the same shuffle std::shuffle(tasks.begin(), tasks.end(), g); - std::cout << "Tasks size: " << tasks.size() << std::endl; + std::cout << get_color_rank(rank) << "* Number of tasks: " << tasks.size() << std::endl; std::cout << "|"; for (int i = 0; i < tasks.size(); ++i) { std::cout << (i + 1) % 10; @@ -226,24 +201,6 @@ namespace platform { // Update progress bar std::cout << get_color_rank(config_mpi.rank) << "*" << std::flush; } - // std::pair GridSearch::part_range_mpi(int n_tasks, int nprocs, int rank) - // { - // int assigned = 0; - // int remainder = n_tasks % nprocs; - // int start = 0; - // if (rank < remainder) { - // assigned = n_tasks / nprocs + 1; - // } else { - // assigned = n_tasks / nprocs; - // start = remainder; - // } - // start += rank * assigned; - // int end = start + assigned; - // if (rank == nprocs - 1) { - // end = n_tasks; - // } - // return { start, end }; - // } json store_result(std::vector& names, Task_Result& result, json& results) { json json_result = { @@ -266,6 +223,9 @@ namespace platform { 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); @@ -275,7 +235,9 @@ namespace platform { } MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD); } - // Send end message to all workers but the manager + // + // 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); @@ -287,9 +249,8 @@ namespace platform { } return results; } - json select_best_results_folds(json& all_results, std::string& model) + void select_best_results_folds(json& results, json& all_results, std::string& model) { - json results; Timer timer; auto grid = GridData(Paths::grid_input(model)); // @@ -317,33 +278,39 @@ namespace platform { }; results[dataset] = json_best; } - return results; } void consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result) { Task_Result result; - // Anounce to the producer + // + // 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; } - // Process task process_task_mpi_consumer(config, config_mpi, tasks, task, datasets, &result); - // Send result to producer + // + // 2b.3 Consumers send the result to the producer + // MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_RESULT, MPI_COMM_WORLD); } } - void GridSearch::go_producer_consumer(struct ConfigMPI& config_mpi) + void GridSearch::go(struct ConfigMPI& config_mpi) { /* * Each task is a json object with the following structure: * { * "dataset": "dataset_name", - * "idx_dataset": idx_dataset, + * "idx_dataset": idx_dataset, // used to identify the dataset in the results + * // this index is relative to the used datasets in the actual run not to the whole datasets * "seed": # of seed to use, * "Fold": # of fold to process * } @@ -356,14 +323,16 @@ namespace platform { * 1.1 Broadcast the number of tasks * 1.2 Broadcast the length of the following string * 1.2 Broadcast the tasks as a char* string - * 2. Workers will receive the tasks and start the process - * 2.1 A method will tell each worker the range of tasks to process - * 2.2 Each worker will process the tasks and generate the best score for each task - * 3. Manager gather the scores from all the workers and find out the best hyperparameters for each dataset - * 3.1 Obtain the maximum size of the results message of all the workers - * 3.2 Gather all the results from the workers into the manager - * 3.3 Compile the results from all the workers - * 3.4 Filter the best hyperparameters for each dataset + * 2a. Producer delivers the tasks to the consumers + * 2a.1 Producer will loop to send all the tasks to the consumers and receive the results + * 2a.2 Producer will send the end message to all the consumers + * 2b. Consumers process the tasks and send the results to the producer + * 2b.1 Consumers announce to the producer that they are ready to receive a task + * 2b.2 Consumers receive the task from the producer and process it + * 2b.3 Consumers send the result to the producer + * 3. Manager select the bests sccores for each dataset + * 3.1 Loop thru all the results obtained from each outer fold (task) and select the best + * 3.2 Save the results */ // // 0.1 Create the MPI result type @@ -388,7 +357,7 @@ namespace platform { json tasks; if (config_mpi.rank == config_mpi.manager) { timer.start(); - tasks = build_tasks_mpi(); + tasks = build_tasks_mpi(config_mpi.rank); auto tasks_str = tasks.dump(); tasks_size = tasks_str.size(); msg = new char[tasks_size + 1]; @@ -404,429 +373,33 @@ namespace platform { MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD); tasks = json::parse(msg); delete[] msg; - // - // 2. All Workers will receive the tasks and start the process - // auto datasets = Datasets(config.discretize, Paths::datasets()); if (config_mpi.rank == config_mpi.manager) { + // + // 2a. Producer delivers the tasks to the consumers + // auto datasets_names = filterDatasets(datasets); json all_results = producer(datasets_names, tasks, config_mpi, MPI_Result); - json results = select_best_results_folds(all_results, config.model); + std::cout << get_color_rank(config_mpi.rank) << "|" << std::endl; + // + // 3. Manager select the bests sccores for each dataset + // + auto results = initializeResults(); + select_best_results_folds(results, all_results, config.model); + // + // 3.2 Save the results + // save(results); - std::cout << Colors::RESET() << "|" << std::endl; } else { + // + // 2b. Consumers process the tasks and send the results to the producer + // consumer(datasets, tasks, config, config_mpi, MPI_Result); } } - // void GridSearch::go_mpi(struct ConfigMPI& config_mpi) - // { - // /* - // * Each task is a json object with the following structure: - // * { - // * "dataset": "dataset_name", - // * "seed": # of seed to use, - // * "model": "model_name", - // * "Fold": # of fold to process - // * } - // * - // * The overall process consists in these steps: - // * 1. Manager will broadcast the tasks to all the processes - // * 1.1 Broadcast the number of tasks - // * 1.2 Broadcast the length of the following string - // * 1.2 Broadcast the tasks as a char* string - // * 2. Workers will receive the tasks and start the process - // * 2.1 A method will tell each worker the range of tasks to process - // * 2.2 Each worker will process the tasks and generate the best score for each task - // * 3. Manager gather the scores from all the workers and find out the best hyperparameters for each dataset - // * 3.1 Obtain the maximum size of the results message of all the workers - // * 3.2 Gather all the results from the workers into the manager - // * 3.3 Compile the results from all the workers - // * 3.4 Filter the best hyperparameters for each dataset - // */ - // char* msg; - // int tasks_size; - // if (config_mpi.rank == config_mpi.manager) { - // timer.start(); - // auto tasks = build_tasks_mpi(); - // auto tasks_str = tasks.dump(); - // tasks_size = tasks_str.size(); - // msg = new char[tasks_size + 1]; - // strcpy(msg, tasks_str.c_str()); - // } - // // - // // 1. Manager will broadcast the tasks to all the processes - // // - // MPI_Bcast(&tasks_size, 1, MPI_INT, config_mpi.manager, MPI_COMM_WORLD); - // if (config_mpi.rank != config_mpi.manager) { - // msg = new char[tasks_size + 1]; - // } - // MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD); - // json tasks = json::parse(msg); - // delete[] msg; - // // - // // 2. All Workers will receive the tasks and start the process - // // - // int num_tasks = tasks.size(); - // // 2.1 A method will tell each worker the range of tasks to process - // auto [start, end] = part_range_mpi(num_tasks, config_mpi.n_procs, config_mpi.rank); - // // 2.2 Each worker will process the tasks and return the best scores obtained - // auto datasets = Datasets(config.discretize, Paths::datasets()); - // json results; - // for (int i = start; i < end; ++i) { - // // Process task - // process_task_mpi(config_mpi, tasks[i], datasets, results); - // } - // int size = results.dump().size() + 1; - // int max_size = 0; - // // - // // 3. Manager gather the scores from all the workers and find out the best hyperparameters for each dataset - // // - // //3.1 Obtain the maximum size of the results message of all the workers - // MPI_Allreduce(&size, &max_size, 1, MPI_INT, MPI_MAX, MPI_COMM_WORLD); - // // Assign the memory to the message and initialize it to 0s - // char* total = NULL; - // msg = new char[max_size]; - // strncpy(msg, results.dump().c_str(), size); - // if (config_mpi.rank == config_mpi.manager) { - // total = new char[max_size * config_mpi.n_procs]; - // } - // // 3.2 Gather all the results from the workers into the manager - // MPI_Gather(msg, max_size, MPI_CHAR, total, max_size, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD); - // delete[] msg; - // if (config_mpi.rank == config_mpi.manager) { - // std::cout << Colors::RESET() << "|" << std::endl; - // json total_results; - // json best_results; - // // 3.3 Compile the results from all the workers - // for (int i = 0; i < config_mpi.n_procs; ++i) { - // json partial_results = json::parse(total + i * max_size); - // for (auto& [dataset, folds] : partial_results.items()) { - // for (auto& [fold, result] : folds.items()) { - // total_results[dataset][fold] = result; - // } - // } - // } - // delete[] total; - // // 3.4 Filter the best hyperparameters for each dataset - // auto grid = GridData(Paths::grid_input(config.model)); - // for (auto& [dataset, folds] : total_results.items()) { - // double best_score = 0.0; - // double duration = 0.0; - // json best_hyper; - // for (auto& [fold, result] : folds.items()) { - // duration += result["duration"].get(); - // if (result["score"] > best_score) { - // best_score = result["score"]; - // best_hyper = result["hyperparameters"]; - // } - // } - // auto timer = Timer(); - // json result = { - // { "score", best_score }, - // { "hyperparameters", best_hyper }, - // { "date", get_date() + " " + get_time() }, - // { "grid", grid.getInputGrid(dataset) }, - // { "duration", timer.translate2String(duration) } - // }; - // best_results[dataset] = result; - // } - // save(best_results); - // } - // } - // void GridSearch::go() - // { - // timer.start(); - // auto grid_type = config.nested == 0 ? "Single" : "Nested"; - // auto datasets = Datasets(config.discretize, Paths::datasets()); - // auto datasets_names = processDatasets(datasets); - // json results = initializeResults(); - // std::cout << "***************** Starting " << grid_type << " Gridsearch *****************" << std::endl; - // std::cout << "input file=" << Paths::grid_input(config.model) << std::endl; - // auto grid = GridData(Paths::grid_input(config.model)); - // Timer timer_dataset; - // double bestScore = 0; - // json bestHyperparameters; - // for (const auto& dataset : datasets_names) { - // if (!config.quiet) - // std::cout << "- " << setw(20) << left << dataset << " " << right << flush; - // auto combinations = grid.getGrid(dataset); - // timer_dataset.start(); - // if (config.nested == 0) - // // for dataset // for hyperparameters // for seed // for fold - // tie(bestScore, bestHyperparameters) = processFileSingle(dataset, datasets, combinations); - // else - // // for dataset // for seed // for fold // for hyperparameters // for nested fold - // tie(bestScore, bestHyperparameters) = processFileNested(dataset, datasets, combinations); - // if (!config.quiet) { - // std::cout << "end." << " Score: " << Colors::IBLUE() << setw(9) << setprecision(7) << fixed - // << bestScore << Colors::BLUE() << " [" << bestHyperparameters.dump() << "]" - // << Colors::RESET() << ::endl; - // } - // json result = { - // { "score", bestScore }, - // { "hyperparameters", bestHyperparameters }, - // { "date", get_date() + " " + get_time() }, - // { "grid", grid.getInputGrid(dataset) }, - // { "duration", timer_dataset.getDurationString() } - // }; - // results[dataset] = result; - // // Save partial results - // save(results); - // } - // // Save final results - // save(results); - // std::cout << "***************** Ending " << grid_type << " Gridsearch *******************" << std::endl; - // } - // pair GridSearch::processFileSingle(std::string fileName, Datasets& datasets, vector& combinations) - // { - // int num = 0; - // double bestScore = 0.0; - // json bestHyperparameters; - // auto totalComb = combinations.size(); - // for (const auto& hyperparam_line : combinations) { - // if (!config.quiet) - // showProgressComb(++num, config.n_folds, totalComb, Colors::CYAN()); - // auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line); - // // Get dataset - // auto [X, y] = datasets.getTensors(fileName); - // auto states = datasets.getStates(fileName); - // auto features = datasets.getFeatures(fileName); - // auto className = datasets.getClassName(fileName); - // double totalScore = 0.0; - // int numItems = 0; - // for (const auto& seed : config.seeds) { - // if (!config.quiet) - // std::cout << "(" << seed << ") doing Fold: " << flush; - // Fold* fold; - // if (config.stratified) - // fold = new StratifiedKFold(config.n_folds, y, seed); - // else - // fold = new KFold(config.n_folds, y.size(0), seed); - // for (int nfold = 0; nfold < config.n_folds; nfold++) { - // auto clf = Models::instance()->create(config.model); - // auto valid = clf->getValidHyperparameters(); - // hyperparameters.check(valid, fileName); - // clf->setHyperparameters(hyperparameters.get(fileName)); - // auto [train, test] = fold->getFold(nfold); - // auto train_t = torch::tensor(train); - // auto test_t = torch::tensor(test); - // auto X_train = X.index({ "...", train_t }); - // auto y_train = y.index({ train_t }); - // auto X_test = X.index({ "...", test_t }); - // auto y_test = y.index({ test_t }); - // // Train model - // if (!config.quiet) - // showProgressFold(nfold + 1, getColor(clf->getStatus()), "a"); - // clf->fit(X_train, y_train, features, className, states); - // // Test model - // if (!config.quiet) - // showProgressFold(nfold + 1, getColor(clf->getStatus()), "b"); - // totalScore += clf->score(X_test, y_test); - // numItems++; - // if (!config.quiet) - // std::cout << "\b\b\b, \b" << flush; - // } - // delete fold; - // } - // double score = numItems == 0 ? 0.0 : totalScore / numItems; - // if (score > bestScore) { - // bestScore = score; - // bestHyperparameters = hyperparam_line; - // } - // } - // return { bestScore, bestHyperparameters }; - // } - // pair GridSearch::processFileNested(std::string fileName, Datasets& datasets, vector& combinations) - // { - // // Get dataset - // auto [X, y] = datasets.getTensors(fileName); - // auto states = datasets.getStates(fileName); - // auto features = datasets.getFeatures(fileName); - // auto className = datasets.getClassName(fileName); - // int spcs_combinations = int(log(combinations.size()) / log(10)) + 1; - // double goatScore = 0.0; - // json goatHyperparameters; - // // for dataset // for seed // for fold // for hyperparameters // for nested fold - // for (const auto& seed : config.seeds) { - // Fold* fold; - // if (config.stratified) - // fold = new StratifiedKFold(config.n_folds, y, seed); - // else - // fold = new KFold(config.n_folds, y.size(0), seed); - // double bestScore = 0.0; - // json bestHyperparameters; - // std::cout << "(" << seed << ") doing Fold: " << flush; - // for (int nfold = 0; nfold < config.n_folds; nfold++) { - // if (!config.quiet) - // std::cout << Colors::GREEN() << nfold + 1 << " " << flush; - // // First level fold - // auto [train, test] = fold->getFold(nfold); - // auto train_t = torch::tensor(train); - // auto test_t = torch::tensor(test); - // auto X_train = X.index({ "...", train_t }); - // auto y_train = y.index({ train_t }); - // auto X_test = X.index({ "...", test_t }); - // auto y_test = y.index({ test_t }); - // auto num = 0; - // json result_fold; - // double hypScore = 0.0; - // double bestHypScore = 0.0; - // json bestHypHyperparameters; - // for (const auto& hyperparam_line : combinations) { - // std::cout << "[" << setw(spcs_combinations) << ++num << "/" << setw(spcs_combinations) - // << combinations.size() << "] " << std::flush; - // Fold* nested_fold; - // if (config.stratified) - // nested_fold = new StratifiedKFold(config.nested, y_train, seed); - // else - // nested_fold = new KFold(config.nested, y_train.size(0), seed); - // 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_nexted_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 hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line); - // auto clf = Models::instance()->create(config.model); - // auto valid = clf->getValidHyperparameters(); - // hyperparameters.check(valid, fileName); - // clf->setHyperparameters(hyperparameters.get(fileName)); - // // Train model - // if (!config.quiet) - // showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "a"); - // clf->fit(X_nexted_train, y_nested_train, features, className, states); - // // Test model - // if (!config.quiet) - // showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "b"); - // hypScore += clf->score(X_nested_test, y_nested_test); - // if (!config.quiet) - // std::cout << "\b\b\b, \b" << flush; - // } - // int magic = 3 * config.nested + 2 * spcs_combinations + 4; - // std::cout << string(magic, '\b') << string(magic, ' ') << string(magic, '\b') << flush; - // delete nested_fold; - // hypScore /= config.nested; - // if (hypScore > bestHypScore) { - // bestHypScore = hypScore; - // bestHypHyperparameters = hyperparam_line; - // } - // } - // // Build Classifier with selected hyperparameters - // auto clf = Models::instance()->create(config.model); - // clf->setHyperparameters(bestHypHyperparameters); - // // Train model - // if (!config.quiet) - // showProgressFold(nfold + 1, getColor(clf->getStatus()), "a"); - // clf->fit(X_train, y_train, features, className, states); - // // Test model - // if (!config.quiet) - // showProgressFold(nfold + 1, getColor(clf->getStatus()), "b"); - // double score = clf->score(X_test, y_test); - // if (!config.quiet) - // std::cout << string(2 * config.nested - 1, '\b') << "," << string(2 * config.nested, ' ') << string(2 * config.nested - 1, '\b') << flush; - // if (score > bestScore) { - // bestScore = score; - // bestHyperparameters = bestHypHyperparameters; - // } - // } - // if (bestScore > goatScore) { - // goatScore = bestScore; - // goatHyperparameters = bestHyperparameters; - // } - // delete fold; - // } - // return { goatScore, goatHyperparameters }; - // } - // void GridSearch::process_task_mpi(struct ConfigMPI& config_mpi, json& task, Datasets& datasets, json& results) - // { - // // Process the task and store the result in the results json - // Timer timer; - // timer.start(); - // auto grid = GridData(Paths::grid_input(config.model)); - // auto dataset = task["dataset"].get(); - // auto seed = task["seed"].get(); - // auto n_fold = task["fold"].get(); - // // Generate the hyperparamters combinations - // auto combinations = grid.getGrid(dataset); - // auto [X, y] = datasets.getTensors(dataset); - // auto states = datasets.getStates(dataset); - // auto features = datasets.getFeatures(dataset); - // auto className = datasets.getClassName(dataset); - // // - // // Start working on task - // // - // Fold* fold; - // if (config.stratified) - // fold = new StratifiedKFold(config.n_folds, y, seed); - // else - // fold = new KFold(config.n_folds, y.size(0), seed); - // auto [train, test] = fold->getFold(n_fold); - // auto train_t = torch::tensor(train); - // auto test_t = torch::tensor(test); - // auto X_train = X.index({ "...", train_t }); - // auto y_train = y.index({ train_t }); - // auto X_test = X.index({ "...", test_t }); - // auto y_test = y.index({ test_t }); - // auto num = 0; - // double best_fold_score = 0.0; - // json best_fold_hyper; - // for (const auto& hyperparam_line : combinations) { - // auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line); - // Fold* nested_fold; - // if (config.stratified) - // nested_fold = new StratifiedKFold(config.nested, y_train, seed); - // else - // nested_fold = new 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); - // clf->setHyperparameters(hyperparameters.get(dataset)); - // // Train model - // clf->fit(X_nested_train, y_nested_train, features, className, states); - // // 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_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); - // clf->setHyperparameters(best_fold_hyper); - // clf->fit(X_train, y_train, features, className, states); - // best_fold_score = clf->score(X_test, y_test); - // // Save results - // results[dataset][std::to_string(n_fold)]["score"] = best_fold_score; - // results[dataset][std::to_string(n_fold)]["hyperparameters"] = best_fold_hyper; - // results[dataset][std::to_string(n_fold)]["seed"] = seed; - // results[dataset][std::to_string(n_fold)]["duration"] = timer.getDuration(); - // std::cout << get_color_rank(config_mpi.rank) << "*" << std::flush; - // } json GridSearch::initializeResults() { - // Load previous results + // Load previous results if continue is set json results; if (config.continue_from != NO_CONTINUE()) { if (!config.quiet) diff --git a/src/Platform/GridSearch.h b/src/Platform/GridSearch.h index 36760b6..ec1b3cb 100644 --- a/src/Platform/GridSearch.h +++ b/src/Platform/GridSearch.h @@ -44,9 +44,7 @@ namespace platform { class GridSearch { public: explicit GridSearch(struct ConfigGrid& config); - // void go(); - // void go_mpi(struct ConfigMPI& config_mpi); - void go_producer_consumer(struct ConfigMPI& config_mpi); + void go(struct ConfigMPI& config_mpi); ~GridSearch() = default; json loadResults(); static inline std::string NO_CONTINUE() { return "NO_CONTINUE"; } @@ -54,12 +52,8 @@ namespace platform { void save(json& results); json initializeResults(); std::vector filterDatasets(Datasets& datasets) const; - // pair processFileSingle(std::string fileName, Datasets& datasets, std::vector& combinations); - // pair processFileNested(std::string fileName, Datasets& datasets, std::vector& combinations); struct ConfigGrid config; - // pair part_range_mpi(int n_tasks, int nprocs, int rank); - json build_tasks_mpi(); - // void process_task_mpi(struct ConfigMPI& config_mpi, json& task, Datasets& datasets, json& results); + json build_tasks_mpi(int rank); Timer timer; // used to measure the time of the whole process }; } /* namespace platform */ diff --git a/src/Platform/b_grid.cc b/src/Platform/b_grid.cc index 1d42543..c54a21c 100644 --- a/src/Platform/b_grid.cc +++ b/src/Platform/b_grid.cc @@ -32,7 +32,6 @@ void manageArguments(argparse::ArgumentParser& program) group.add_argument("--report").help("Report the computed hyperparameters").default_value(false).implicit_value(true); group.add_argument("--compute").help("Perform computation of the grid output hyperparameters").default_value(false).implicit_value(true); program.add_argument("--discretize").help("Discretize input datasets").default_value((bool)stoi(env.get("discretize"))).implicit_value(true); - program.add_argument("--mpi").help("Use MPI computing grid").default_value(false).implicit_value(true); program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true); program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true); program.add_argument("--continue").help("Continue computing from that dataset").default_value(platform::GridSearch::NO_CONTINUE()); @@ -108,8 +107,8 @@ void list_results(json& results, std::string& model) + " Nested: " + (results["nested"].get() == 0 ? "False" : to_string(results["nested"].get())) ); std::cout << std::string(MAXL, '*') << std::endl; - int spaces = 0; - int hyperparameters_spaces = 0; + int spaces = 7; + int hyperparameters_spaces = 15; for (const auto& item : results["results"].items()) { auto key = item.key(); auto value = item.value(); @@ -128,11 +127,10 @@ void list_results(json& results, std::string& model) int index = 0; for (const auto& item : results["results"].items()) { auto color = odd ? Colors::CYAN() : Colors::BLUE(); - auto key = item.key(); auto value = item.value(); std::cout << color; std::cout << std::setw(3) << std::right << index++ << " "; - std::cout << left << setw(spaces) << key << " " << value["date"].get() + std::cout << left << setw(spaces) << item.key() << " " << value["date"].get() << " " << setw(8) << right << value["duration"].get() << " " << setw(8) << setprecision(6) << fixed << right << value["score"].get() << " " << value["hyperparameters"].dump() << std::endl; odd = !odd; @@ -171,11 +169,6 @@ int main(int argc, char** argv) } auto excluded = program.get("exclude"); config.excluded = json::parse(excluded); - if (program.get("mpi")) { - if (!compute || config.nested == 0) { - throw std::runtime_error("Cannot use --mpi without --compute or without --nested"); - } - } } catch (const exception& err) { cerr << err.what() << std::endl; @@ -195,23 +188,21 @@ int main(int argc, char** argv) list_dump(config.model); } else { if (compute) { - if (program.get("mpi")) { - struct platform::ConfigMPI mpi_config; - mpi_config.manager = 0; // which process is the manager - MPI_Init(&argc, &argv); - MPI_Comm_rank(MPI_COMM_WORLD, &mpi_config.rank); - MPI_Comm_size(MPI_COMM_WORLD, &mpi_config.n_procs); - grid_search.go_producer_consumer(mpi_config); - if (mpi_config.rank == mpi_config.manager) { - auto results = grid_search.loadResults(); - list_results(results, config.model); - std::cout << "Process took " << timer.getDurationString() << std::endl; - } - MPI_Finalize(); - // } else { - // grid_search.go(); - // std::cout << "Process took " << timer.getDurationString() << std::endl; + struct platform::ConfigMPI mpi_config; + mpi_config.manager = 0; // which process is the manager + MPI_Init(&argc, &argv); + MPI_Comm_rank(MPI_COMM_WORLD, &mpi_config.rank); + MPI_Comm_size(MPI_COMM_WORLD, &mpi_config.n_procs); + if (mpi_config.n_procs < 2) { + throw std::runtime_error("Cannot use --compute with less than 2 mpi processes, try mpirun -np 2 ..."); } + grid_search.go(mpi_config); + if (mpi_config.rank == mpi_config.manager) { + auto results = grid_search.loadResults(); + list_results(results, config.model); + std::cout << "Process took " << timer.getDurationString() << std::endl; + } + MPI_Finalize(); } else { // List results auto results = grid_search.loadResults();