Fix syntax errors
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21c4c6df51
commit
343269d48c
GPG Key ID:
46064262FD9A7ADE
@ -28,6 +28,11 @@ namespace platform {
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oss << std::put_time(timeinfo, "%H:%M:%S");
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return oss.str();
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}
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std::string get_color_rank(int rank)
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{
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auto colors = { Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() };
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return *(colors.begin() + rank % colors.size());
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}
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GridSearch::GridSearch(struct ConfigGrid& config) : config(config)
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{
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}
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@ -104,20 +109,16 @@ namespace platform {
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auto datasets = Datasets(false, Paths::datasets());
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auto all_datasets = datasets.getNames();
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auto datasets_names = processDatasets(datasets);
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for (const auto& dataset : datasets_names) {
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for (int idx_dataset = 0; idx_dataset < all_datasets.size(); ++idx_dataset) {
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auto dataset = all_datasets[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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auto it = find(all_datasets.begin(), all_datasets.end(), dataset);
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if (it == all_datasets.end()) {
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throw std::invalid_argument("Dataset " + dataset + " not found");
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}
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auto idx_dataset = std::distance(all_datasets.begin(), it);
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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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{ "fold", n_fold},
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};
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tasks.push_back(task);
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}
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@ -134,8 +135,96 @@ namespace platform {
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std::cout << "|" << std::endl << "|" << std::flush;
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return tasks;
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}
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void process_task_mpi(struct ConfigMPI& config_mpi, int task, Task_Result* result)
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void process_task_mpi_consumer(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result)
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{
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// initialize
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Timer timer;
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timer.start();
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json task = tasks[n_task];
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auto model = config.model;
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auto grid = GridData(Paths::grid_input(model));
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auto dataset = task["dataset"].get<std::string>();
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auto idx_dataset = task["idx_dataset"].get<int>();
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auto seed = task["seed"].get<int>();
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auto n_fold = task["fold"].get<int>();
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bool stratified = config.stratified;
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// Generate the hyperparamters combinations
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auto combinations = grid.getGrid(dataset);
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auto [X, y] = datasets.getTensors(dataset);
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auto states = datasets.getStates(dataset);
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auto features = datasets.getFeatures(dataset);
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auto className = datasets.getClassName(dataset);
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//
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// Start working on task
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//
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Fold* fold;
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if (stratified)
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fold = new StratifiedKFold(config.n_folds, y, seed);
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else
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fold = new KFold(config.n_folds, y.size(0), seed);
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auto [train, test] = fold->getFold(n_fold);
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auto train_t = torch::tensor(train);
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auto test_t = torch::tensor(test);
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auto X_train = X.index({ "...", train_t });
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auto y_train = y.index({ train_t });
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auto X_test = X.index({ "...", test_t });
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auto y_test = y.index({ test_t });
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auto num = 0;
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double best_fold_score = 0.0;
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int best_idx_combination = -1;
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json best_fold_hyper;
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for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
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auto hyperparam_line = combinations[idx_combination];
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auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
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Fold* nested_fold;
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if (config.stratified)
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nested_fold = new StratifiedKFold(config.nested, y_train, seed);
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else
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nested_fold = new KFold(config.nested, y_train.size(0), seed);
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double score = 0.0;
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for (int n_nested_fold = 0; n_nested_fold < config.nested; n_nested_fold++) {
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// Nested level fold
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auto [train_nested, test_nested] = nested_fold->getFold(n_nested_fold);
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auto train_nested_t = torch::tensor(train_nested);
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auto test_nested_t = torch::tensor(test_nested);
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auto X_nested_train = X_train.index({ "...", train_nested_t });
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auto y_nested_train = y_train.index({ train_nested_t });
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auto X_nested_test = X_train.index({ "...", test_nested_t });
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auto y_nested_test = y_train.index({ test_nested_t });
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// Build Classifier with selected hyperparameters
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auto clf = Models::instance()->create(config.model);
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auto valid = clf->getValidHyperparameters();
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hyperparameters.check(valid, dataset);
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clf->setHyperparameters(hyperparameters.get(dataset));
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// Train model
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clf->fit(X_nested_train, y_nested_train, features, className, states);
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// Test model
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score += clf->score(X_nested_test, y_nested_test);
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}
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delete nested_fold;
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score /= config.nested;
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if (score > best_fold_score) {
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best_fold_score = score;
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best_idx_combination = idx_combination;
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best_fold_hyper = hyperparam_line;
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}
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}
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delete fold;
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// Build Classifier with the best hyperparameters to obtain the best score
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auto hyperparameters = platform::HyperParameters(datasets.getNames(), best_fold_hyper);
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auto clf = Models::instance()->create(config.model);
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auto valid = clf->getValidHyperparameters();
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hyperparameters.check(valid, dataset);
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clf->setHyperparameters(best_fold_hyper);
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clf->fit(X_train, y_train, features, className, states);
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best_fold_score = clf->score(X_test, y_test);
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// Return the result
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result->idx_dataset = task["idx_dataset"].get<int>();
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result->idx_combination = best_idx_combination;
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result->score = best_fold_score;
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result->time = timer.getDuration();
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// Update progress bar
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std::cout << get_color_rank(config_mpi.rank) << "*" << std::flush;
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}
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std::pair<int, int> GridSearch::part_range_mpi(int n_tasks, int nprocs, int rank)
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{
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@ -155,14 +244,10 @@ namespace platform {
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}
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return { start, end };
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}
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std::string get_color_rank(int rank)
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{
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auto colors = { Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() };
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return *(colors.begin() + rank % colors.size());
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}
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void producer(json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
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json producer(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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for (int i = 0; i < num_tasks; ++i) {
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MPI_Status status;
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@ -183,8 +268,17 @@ namespace platform {
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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 consumer(json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
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json select_best_results_folds(json& all_results)
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{
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json results;
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//
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// Select the best result of the computed outer folds
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//
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return results;
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}
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void 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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// Anounce to the producer
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@ -197,7 +291,7 @@ namespace platform {
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break;
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}
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// Process task
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process_task_mpi(config_mpi, task, &result);
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process_task_mpi_consumer(config, config_mpi, tasks, task, datasets, &result);
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// Send result to producer
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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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@ -236,21 +330,23 @@ namespace platform {
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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[3] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_DOUBLE };
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int blocklen[3] = { 1, 1, 1 };
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MPI_Aint disp[3];
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MPI_Datatype type[4] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_DOUBLE, MPI_DOUBLE };
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int blocklen[4] = { 1, 1, 1, 1 };
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MPI_Aint disp[4];
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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, score);
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MPI_Type_create_struct(3, blocklen, disp, type, &MPI_Result);
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disp[3] = offsetof(Task_Result, time);
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MPI_Type_create_struct(4, 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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auto tasks = build_tasks_mpi();
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tasks = build_tasks_mpi();
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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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@ -264,15 +360,18 @@ namespace platform {
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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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json tasks = json::parse(msg);
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tasks = json::parse(msg);
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delete[] msg;
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//
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// 2. All Workers will receive the tasks and start the process
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//
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auto datasets = Datasets(config.discretize, Paths::datasets());
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if (config_mpi.rank == config_mpi.manager) {
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producer(tasks, config_mpi, MPI_Result);
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auto all_results = producer(tasks, config_mpi, MPI_Result);
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auto results = select_best_results_folds(all_results);
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save(results);
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} else {
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consumer(tasks, config_mpi, MPI_Result);
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consumer(datasets, tasks, config, config_mpi, MPI_Result);
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}
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}
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void GridSearch::go_mpi(struct ConfigMPI& config_mpi)
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@ -34,6 +34,7 @@ namespace platform {
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uint idx_dataset;
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uint idx_combination;
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double score;
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double time;
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} Task_Result;
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const int TAG_QUERY = 1;
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const int TAG_RESULT = 2;
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