/**
* Initializes the biobjective observer. Possible options:
* - log_nondominated : none (don't log nondominated solutions)
* - log_nondominated : final (log only the final nondominated solutions; default value)
* - log_nondominated : all (log every solution that is nondominated at creation time)
* - log_decision_variables : none (don't output decision variables)
* - log_decision_variables : log_dim (output decision variables only for dimensions lower or equal to 5; default value)
* - log_decision_variables : all (output all decision variables)
* - compute_indicators : 0 / 1 (whether to compute and output performance indicators; default value is 1)
* - produce_all_data: 0 / 1 (whether to produce all data; if set to 1, overwrites other options and is equivalent to
* setting log_nondominated to all, log_decision_variables to log_dim and compute_indicators to 1; if set to 0, it
* does not change the values of other options; default value is 0)
*/
static void observer_biobj(coco_observer_t *self, const char *options) {
observer_biobj_t *data;
char string_value[COCO_PATH_MAX];
data = coco_allocate_memory(sizeof(*data));
data->log_nondom_mode = FINAL;
if (coco_options_read_string(options, "log_nondominated", string_value) > 0) {
if (strcmp(string_value, "none") == 0)
data->log_nondom_mode = NONE;
else if (strcmp(string_value, "all") == 0)
data->log_nondom_mode = ALL;
}
data->log_vars_mode = LOW_DIM;
if (coco_options_read_string(options, "log_decision_variables", string_value) > 0) {
if (strcmp(string_value, "none") == 0)
data->log_vars_mode = NEVER;
else if (strcmp(string_value, "all") == 0)
data->log_vars_mode = ALWAYS;
}
if (coco_options_read_int(options, "compute_indicators", &(data->compute_indicators)) == 0)
data->compute_indicators = 1;
if (coco_options_read_int(options, "produce_all_data", &(data->produce_all_data)) == 0)
data->produce_all_data = 0;
if (data->produce_all_data) {
data->log_vars_mode = LOW_DIM;
data->compute_indicators = 1;
data->log_nondom_mode = ALL;
}
if (data->compute_indicators) {
data->previous_function = -1;
}
self->logger_initialize_function = logger_biobj;
self->data_free_function = NULL;
self->data = data;
if ((data->log_nondom_mode == NONE) && (!data->compute_indicators)) {
/* No logging required */
self->is_active = 0;
}
}
/**
* @brief Initializes the bi-objective observer.
*
* Possible options:
* - log_nondominated : none (don't log nondominated solutions)
* - log_nondominated : final (log only the final nondominated solutions)
* - log_nondominated : all (log every solution that is nondominated at creation time; default value)
* - log_decision_variables : none (don't output decision variables)
* - log_decision_variables : log_dim (output decision variables only for dimensions lower or equal to 5;
* default value)
* - log_decision_variables : all (output all decision variables)
* - compute_indicators : 0 / 1 (whether to compute and output performance indicators; default value is 1)
* - produce_all_data: 0 / 1 (whether to produce all data; if set to 1, overwrites other options and is
* equivalent to setting log_nondominated to all, log_decision_variables to log_dim and compute_indicators
* to 1; if set to 0, it does not change the values of other options; default value is 0)
*/
static void observer_biobj(coco_observer_t *observer, const char *options) {
observer_biobj_data_t *observer_biobj;
char string_value[COCO_PATH_MAX];
observer_biobj = (observer_biobj_data_t *) coco_allocate_memory(sizeof(*observer_biobj));
observer_biobj->log_nondom_mode = LOG_NONDOM_ALL;
if (coco_options_read_string(options, "log_nondominated", string_value) > 0) {
if (strcmp(string_value, "none") == 0)
observer_biobj->log_nondom_mode = LOG_NONDOM_NONE;
else if (strcmp(string_value, "final") == 0)
observer_biobj->log_nondom_mode = LOG_NONDOM_FINAL;
}
observer_biobj->log_vars_mode = LOG_VARS_LOW_DIM;
if (coco_options_read_string(options, "log_decision_variables", string_value) > 0) {
if (strcmp(string_value, "none") == 0)
observer_biobj->log_vars_mode = LOG_VARS_NEVER;
else if (strcmp(string_value, "all") == 0)
observer_biobj->log_vars_mode = LOG_VARS_ALWAYS;
}
if (coco_options_read_int(options, "compute_indicators", &(observer_biobj->compute_indicators)) == 0)
observer_biobj->compute_indicators = 1;
if (coco_options_read_int(options, "produce_all_data", &(observer_biobj->produce_all_data)) == 0)
observer_biobj->produce_all_data = 0;
if (observer_biobj->produce_all_data) {
observer_biobj->log_vars_mode = LOG_VARS_LOW_DIM;
observer_biobj->compute_indicators = 1;
observer_biobj->log_nondom_mode = LOG_NONDOM_ALL;
}
if (observer_biobj->compute_indicators) {
observer_biobj->previous_function = -1;
}
observer->logger_initialize_function = logger_biobj;
observer->data_free_function = NULL;
observer->data = observer_biobj;
if ((observer_biobj->log_nondom_mode == LOG_NONDOM_NONE) && (!observer_biobj->compute_indicators)) {
/* No logging required */
observer->is_active = 0;
}
}
/**
* Currently, three observers are supported:
* - "bbob" is the observer for single-objective (both noisy and noiseless) problems with known optima, which
* creates *.info, *.dat, *.tdat and *.rdat files and logs the distance to the optimum.
* - "bbob-biobj" is the observer for bi-objective problems, which creates *.info and *.dat files for the
* given indicators, as well as an archive folder with *.dat files containing nondominated solutions.
* - "toy" is a simple observer that logs when a target has been hit.
*
* @param observer_name A string containing the name of the observer. Currently supported observer names are
* "bbob", "bbob-biobj", "toy". "no_observer", "" or NULL return NULL.
* @param observer_options A string of pairs "key: value" used to pass the options to the observer. Some
* observer options are general, while others are specific to some observers. Here we list only the general
* options, see observer_bbob, observer_biobj and observer_toy for options of the specific observers.
* - "result_folder: NAME" determines the output folder. If the folder with the given name already exists,
* first NAME_001 will be tried, then NAME_002 and so on. The default value is "results".
* - "algorithm_name: NAME", where NAME is a short name of the algorithm that will be used in plots (no
* spaces are allowed). The default value is "ALG".
* - "algorithm_info: STRING" stores the description of the algorithm. If it contains spaces, it must be
* surrounded by double quotes. The default value is "" (no description).
* - "precision_x: VALUE" defines the precision used when outputting variables and corresponds to the number
* of digits to be printed after the decimal point. The default value is 8.
* - precision_f: VALUE defines the precision used when outputting f values and corresponds to the number of
* digits to be printed after the decimal point. The default value is 15.
* @return The constructed observer object or NULL if observer_name equals NULL, "" or "no_observer".
*/
coco_observer_t *coco_observer(const char *observer_name, const char *observer_options) {
coco_observer_t *observer;
char *result_folder, *algorithm_name, *algorithm_info;
int precision_x, precision_f;
if (0 == strcmp(observer_name, "no_observer")) {
return NULL;
} else if (strlen(observer_name) == 0) {
coco_warning("Empty observer_name has no effect. To prevent this warning use 'no_observer' instead");
return NULL;
}
result_folder = (char *) coco_allocate_memory(COCO_PATH_MAX);
algorithm_name = (char *) coco_allocate_memory(COCO_PATH_MAX);
algorithm_info = (char *) coco_allocate_memory(5 * COCO_PATH_MAX);
/* Read result_folder, algorithm_name and algorithm_info from the observer_options and use
* them to initialize the observer */
if (coco_options_read_string(observer_options, "result_folder", result_folder) == 0) {
strcpy(result_folder, "exdata-default");
}
coco_create_unique_path(&result_folder);
coco_info("Results will be output to folder %s", result_folder);
if (coco_options_read_string(observer_options, "algorithm_name", algorithm_name) == 0) {
strcpy(algorithm_name, "ALG");
}
if (coco_options_read_string(observer_options, "algorithm_info", algorithm_info) == 0) {
strcpy(algorithm_info, "");
}
precision_x = 8;
if (coco_options_read_int(observer_options, "precision_x", &precision_x) != 0) {
if ((precision_x < 1) || (precision_x > 32))
precision_x = 8;
}
precision_f = 15;
if (coco_options_read_int(observer_options, "precision_f", &precision_f) != 0) {
if ((precision_f < 1) || (precision_f > 32))
precision_f = 15;
}
observer = coco_observer_allocate(result_folder, algorithm_name, algorithm_info, precision_x, precision_f);
coco_free_memory(result_folder);
coco_free_memory(algorithm_name);
coco_free_memory(algorithm_info);
/* Here each observer must have an entry */
if (0 == strcmp(observer_name, "toy")) {
observer_toy(observer, observer_options);
} else if (0 == strcmp(observer_name, "bbob")) {
observer_bbob(observer, observer_options);
} else if (0 == strcmp(observer_name, "bbob-biobj")) {
observer_biobj(observer, observer_options);
} else {
coco_warning("Unknown observer!");
return NULL;
}
return observer;
}
