438 lines
18 KiB
C++
438 lines
18 KiB
C++
#include <iostream>
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#include <cstddef>
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#include <torch/torch.h>
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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 "GridSearch.h"
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namespace platform {
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std::string get_date()
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{
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time_t rawtime;
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tm* timeinfo;
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time(&rawtime);
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timeinfo = std::localtime(&rawtime);
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std::ostringstream oss;
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oss << std::put_time(timeinfo, "%Y-%m-%d");
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return oss.str();
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}
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std::string get_time()
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{
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time_t rawtime;
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tm* timeinfo;
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time(&rawtime);
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timeinfo = std::localtime(&rawtime);
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std::ostringstream oss;
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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::WHITE(), 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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json GridSearch::loadResults()
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{
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std::ifstream file(Paths::grid_output(config.model));
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if (file.is_open()) {
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return json::parse(file);
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}
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return json();
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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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auto datasets_names = datasets.getNames();
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if (config.continue_from != NO_CONTINUE()) {
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// Continue previous execution:
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if (std::find(datasets_names.begin(), datasets_names.end(), config.continue_from) == datasets_names.end()) {
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throw std::invalid_argument("Dataset " + config.continue_from + " not found");
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}
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// Remove datasets already processed
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std::vector<string>::iterator it = datasets_names.begin();
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while (it != datasets_names.end()) {
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if (*it != config.continue_from) {
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it = datasets_names.erase(it);
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} else {
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if (config.only)
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++it;
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else
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break;
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}
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}
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}
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// Exclude datasets
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for (const auto& name : config.excluded) {
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auto dataset = name.get<std::string>();
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auto it = std::find(datasets_names.begin(), datasets_names.end(), dataset);
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if (it == datasets_names.end()) {
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throw std::invalid_argument("Dataset " + dataset + " already excluded or doesn't exist!");
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}
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datasets_names.erase(it);
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}
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return datasets_names;
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}
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json GridSearch::build_tasks_mpi(int rank)
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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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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 the array so heavy datasets are spread across the workers
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std::mt19937 g{ 271 }; // Use fixed seed to obtain the same shuffle
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std::shuffle(tasks.begin(), tasks.end(), g);
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std::cout << get_color_rank(rank) << "* Number of tasks: " << tasks.size() << std::endl;
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std::cout << "|";
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for (int i = 0; i < tasks.size(); ++i) {
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std::cout << (i + 1) % 10;
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}
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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_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_name = 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& dataset = datasets.getDataset(dataset_name);
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auto combinations = grid.getGrid(dataset_name);
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auto [X, y] = dataset.getTensors();
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auto states = dataset.getStates();
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auto features = dataset.getFeatures();
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auto className = dataset.getClassName();
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//
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// Start working on task
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//
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folding::Fold* fold;
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if (stratified)
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fold = new folding::StratifiedKFold(config.n_folds, y, seed);
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else
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fold = new folding::KFold(config.n_folds, y.size(0), seed);
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auto [train, test] = fold->getFold(n_fold);
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auto [X_train, X_test, y_train, y_test] = dataset.getTrainTestTensors(train, test);
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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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folding::Fold* nested_fold;
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if (config.stratified)
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nested_fold = new folding::StratifiedKFold(config.nested, y_train, seed);
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else
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nested_fold = new folding::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_name);
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clf->setHyperparameters(hyperparameters.get(dataset_name));
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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_name);
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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->n_fold = n_fold;
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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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json store_result(std::vector<std::string>& names, Task_Result& result, json& results)
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{
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json json_result = {
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{ "score", result.score },
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{ "combination", result.idx_combination },
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{ "fold", result.n_fold },
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{ "time", result.time },
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{ "dataset", result.idx_dataset }
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};
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auto name = names[result.idx_dataset];
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if (!results.contains(name)) {
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results[name] = json::array();
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}
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results[name].push_back(json_result);
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return results;
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}
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json 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 select_best_results_folds(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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//
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// Select the best result of the computed outer folds
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//
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for (const auto& result : all_results.items()) {
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// each result has the results of all the outer folds as each one were a different task
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double best_score = 0.0;
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json best;
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for (const auto& result_fold : result.value()) {
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double score = result_fold["score"].get<double>();
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if (score > best_score) {
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best_score = score;
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best = result_fold;
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}
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}
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auto dataset = result.key();
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auto combinations = grid.getGrid(dataset);
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json json_best = {
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{ "score", best_score },
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{ "hyperparameters", combinations[best["combination"].get<int>()] },
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{ "date", get_date() + " " + get_time() },
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{ "grid", grid.getInputGrid(dataset) },
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{ "duration", timer.translate2String(best["time"].get<double>()) }
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};
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results[dataset] = json_best;
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}
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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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//
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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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process_task_mpi_consumer(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::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 used datasets in the actual run not to the whole datasets
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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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* 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 sccores 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_mpi(config_mpi.rank);
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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("discretiz_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 = filterDatasets(datasets);
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json all_results = producer(datasets_names, tasks, config_mpi, MPI_Result);
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std::cout << get_color_rank(config_mpi.rank) << "|" << 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 process the tasks and send the results to the producer
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//
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consumer(datasets, tasks, config, config_mpi, MPI_Result);
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}
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}
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json GridSearch::initializeResults()
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{
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// Load previous results if continue is set
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json results;
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if (config.continue_from != NO_CONTINUE()) {
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if (!config.quiet)
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std::cout << "* Loading previous results" << std::endl;
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try {
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std::ifstream file(Paths::grid_output(config.model));
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if (file.is_open()) {
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results = json::parse(file);
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results = results["results"];
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}
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}
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catch (const std::exception& e) {
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std::cerr << "* There were no previous results" << std::endl;
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std::cerr << "* Initizalizing new results" << std::endl;
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results = json();
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}
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}
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return results;
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}
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void GridSearch::save(json& results)
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{
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std::ofstream file(Paths::grid_output(config.model));
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json output = {
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{ "model", config.model },
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{ "score", config.score },
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{ "discretize", config.discretize },
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{ "stratified", config.stratified },
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{ "n_folds", config.n_folds },
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{ "seeds", config.seeds },
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{ "date", get_date() + " " + get_time()},
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{ "nested", config.nested},
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{ "platform", config.platform },
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{ "duration", timer.getDurationString(true)},
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{ "results", results }
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};
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file << output.dump(4);
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}
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} /* namespace platform */ |