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Refactor gridsearch output

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This commit is contained in:
Ricardo Montañana Gómez
2024-12-17 10:49:58 +01:00
parent 70ea32dc9a
commit e966c880e6
1 changed files with 12 additions and 8 deletions
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20
src/grid/GridSearch.cpp
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@@ -12,8 +12,10 @@ namespace platform {
std::string get_color_rank(int rank)
{
auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() };
return *(colors.begin() + rank % colors.size());
auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN(), Colors::YELLOW(), Colors::BLACK() };
std::string id = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
auto idx = rank % id.size();
return *(colors.begin() + rank % colors.size()) + id[idx];
}
GridSearch::GridSearch(struct ConfigGrid& config) : config(config)
{
@@ -81,10 +83,10 @@ namespace platform {
}
}
}
// Shuffle the array so heavy datasets are spread across the workers
// Shuffle the array so heavy datasets are eas ier 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 << get_color_rank(rank) << "* Number of tasks: " << tasks.size() << std::endl;
std::cout << "* Number of tasks: " << tasks.size() << std::endl;
std::cout << separator;
for (int i = 0; i < tasks.size(); ++i) {
std::cout << (i + 1) % 10;
@@ -179,7 +181,7 @@ namespace platform {
result->n_fold = n_fold;
result->time = timer.getDuration();
// Update progress bar
std::cout << get_color_rank(config_mpi.rank) << "*" << std::flush;
std::cout << get_color_rank(config_mpi.rank) << std::flush;
}
json store_result(std::vector<std::string>& names, Task_Result& result, json& results)
{
@@ -290,11 +292,13 @@ namespace platform {
* {
* "dataset": "dataset_name",
* "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
* // this index is relative to the list of used datasets in the actual run not to the whole datasets list
* "seed": # of seed to use,
* "Fold": # of fold to process
* }
*
* This way a task consists in process all combinations of hyperparameters for a dataset, seed and fold
*
* The overall process consists in these steps:
* 0. Create the MPI result type & tasks
* 0.1 Create the MPI result type
@@ -310,7 +314,7 @@ namespace platform {
* 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. Manager select the bests scores 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
*/
@@ -362,7 +366,7 @@ namespace platform {
//
auto datasets_names = filterDatasets(datasets);
json all_results = producer(datasets_names, tasks, config_mpi, MPI_Result);
std::cout << get_color_rank(config_mpi.rank) << separator << std::endl;
std::cout << separator << std::endl;
//
// 3. Manager select the bests sccores for each dataset
//