/**
* @brief Returns the best known value for indicator_name matching the given key if the key is found, and
* throws a coco_error otherwise.
*/
static double suite_biobj_get_best_value(const char *indicator_name, const char *key) {
size_t i, count;
double best_value = 0;
char *curr_key;
if (strcmp(indicator_name, "hyp") == 0) {
curr_key = coco_allocate_string(COCO_PATH_MAX);
count = sizeof(suite_biobj_best_values_hyp) / sizeof(char *);
for (i = 0; i < count; i++) {
sscanf(suite_biobj_best_values_hyp[i], "%s %lf", curr_key, &best_value);
if (strcmp(curr_key, key) == 0) {
coco_free_memory(curr_key);
return best_value;
}
}
coco_free_memory(curr_key);
coco_warning("suite_biobj_get_best_value(): best value of %s could not be found; set to 1.0", key);
return 1.0;
} else {
coco_error("suite_biobj_get_best_value(): indicator %s not supported", indicator_name);
return 0; /* Never reached */
}
coco_error("suite_biobj_get_best_value(): unexpected exception");
return 0; /* Never reached */
}
/**
* Wraps the observer around the problem if the observer is not NULL and invokes initialization of the
* corresponding logger.
*
* @param problem The given COCO problem.
* @param observer The COCO observer that will wrap the problem.
* @returns The observed problem in the form of a new COCO problem instance or the same problem if the
* observer is NULL.
*/
coco_problem_t *coco_problem_add_observer(coco_problem_t *problem, coco_observer_t *observer) {
if ((observer == NULL) || (observer->is_active == 0)) {
coco_warning("The problem will not be observed. %s", observer == NULL ? "(observer == NULL)" : "(observer not active)");
return problem;
}
return observer->logger_initialize_function(observer, problem);
}
/**
* Layer added to the transformed-problem evaluate_function by the logger
*/
static void logger_bbob2009_evaluate(coco_problem_t *self, const double *x, double *y) {
logger_bbob2009_t *data = coco_transformed_get_data(self);
coco_problem_t * inner_problem = coco_transformed_get_inner_problem(self);
if (!data->is_initialized) {
logger_bbob2009_initialize(data, inner_problem);
}
if (bbob2009_logger_verbosity > 2 && data->number_of_evaluations == 0) {
if (inner_problem->suite_dep_index >= 0) {
printf("%4ld: ", inner_problem->suite_dep_index);
}
printf("on problem %s ... ", coco_problem_get_id(inner_problem));
}
coco_evaluate_function(inner_problem, x, y);
data->last_fvalue = y[0];
data->written_last_eval = 0;
if (data->number_of_evaluations == 0 || y[0] < data->best_fvalue) {
size_t i;
data->best_fvalue = y[0];
for (i = 0; i < self->number_of_variables; i++)
data->best_solution[i] = x[i];
}
data->number_of_evaluations++;
/* Add sanity check for optimal f value */
/* assert(y[0] >= data->optimal_fvalue); */
if (!bbob2009_raisedOptValWarning && y[0] < data->optimal_fvalue) {
coco_warning("Observed fitness is smaller than supposed optimal fitness.");
bbob2009_raisedOptValWarning = 1;
}
/* Add a line in the .dat file for each logging target reached. */
if (y[0] - data->optimal_fvalue <= data->f_trigger) {
logger_bbob2009_write_data(data->fdata_file, data->number_of_evaluations, y[0], data->best_fvalue,
data->optimal_fvalue, x, self->number_of_variables);
logger_bbob2009_update_f_trigger(data, y[0]);
}
/* Add a line in the .tdat file each time an fevals trigger is reached. */
if (data->number_of_evaluations >= data->t_trigger) {
data->written_last_eval = 1;
logger_bbob2009_write_data(data->tdata_file, data->number_of_evaluations, y[0], data->best_fvalue,
data->optimal_fvalue, x, self->number_of_variables);
logger_bbob2009_update_t_trigger(data, self->number_of_variables);
}
/* Flush output so that impatient users can see progress. */
fflush(data->fdata_file);
}
/**
* @brief Creates the transformation.
*/
static coco_problem_t *transform_vars_asymmetric(coco_problem_t *inner_problem, const double beta) {
transform_vars_asymmetric_data_t *data;
coco_problem_t *problem;
data = (transform_vars_asymmetric_data_t *) coco_allocate_memory(sizeof(*data));
data->x = coco_allocate_vector(inner_problem->number_of_variables);
data->beta = beta;
problem = coco_problem_transformed_allocate(inner_problem, data, transform_vars_asymmetric_free, "transform_vars_asymmetric");
problem->evaluate_function = transform_vars_asymmetric_evaluate;
if (coco_problem_best_parameter_not_zero(inner_problem)) {
coco_warning("transform_vars_asymmetric(): 'best_parameter' not updated, set to NAN");
coco_vector_set_to_nan(inner_problem->best_parameter, inner_problem->number_of_variables);
}
return problem;
}
static coco_problem_t *logger_bbob2009(coco_problem_t *inner_problem, const char *alg_name) {
logger_bbob2009_t *data;
coco_problem_t *self;
data = coco_allocate_memory(sizeof(*data));
data->alg_name = coco_strdup(alg_name);
if (bbob2009_logger_is_open)
coco_error("The current bbob2009_logger (observer) must be closed before a new one is opened");
/* This is the name of the folder which happens to be the algName */
data->path = coco_strdup(alg_name);
data->index_file = NULL;
data->fdata_file = NULL;
data->tdata_file = NULL;
data->rdata_file = NULL;
data->number_of_variables = inner_problem->number_of_variables;
if (inner_problem->best_value == NULL) {
/* coco_error("Optimal f value must be defined for each problem in order for the logger to work properly"); */
/* Setting the value to 0 results in the assertion y>=optimal_fvalue being susceptible to failure */
coco_warning("undefined optimal f value. Set to 0");
data->optimal_fvalue = 0;
} else {
data->optimal_fvalue = *(inner_problem->best_value);
}
bbob2009_raisedOptValWarning = 0;
data->idx_f_trigger = INT_MAX;
data->idx_t_trigger = 0;
data->idx_tdim_trigger = 0;
data->f_trigger = DBL_MAX;
data->t_trigger = 0;
data->number_of_evaluations = 0;
data->best_solution = coco_allocate_vector(inner_problem->number_of_variables);
/* TODO: the following inits are just to be in the safe side and
* should eventually be removed. Some fields of the bbob2009_logger struct
* might be useless
*/
data->function_id = coco_problem_get_suite_dep_function_id(inner_problem);
data->instance_id = coco_problem_get_suite_dep_instance_id(inner_problem);
data->written_last_eval = 1;
data->last_fvalue = DBL_MAX;
data->is_initialized = 0;
self = coco_transformed_allocate(inner_problem, data, logger_bbob2009_free);
self->evaluate_function = logger_bbob2009_evaluate;
bbob2009_logger_is_open = 1;
return self;
}
/**
* Initializes the toy logger.
*/
static coco_problem_t *logger_toy(coco_observer_t *observer, coco_problem_t *problem) {
logger_toy_t *logger;
coco_problem_t *self;
FILE *output_file;
if (problem->number_of_objectives != 1) {
coco_warning("logger_toy(): The toy logger shouldn't be used to log a problem with %d objectives", problem->number_of_objectives);
}
logger = coco_allocate_memory(sizeof(*logger));
logger->observer = observer;
logger->next_target = 0;
logger->number_of_evaluations = 0;
output_file = ((observer_toy_t *) logger->observer->data)->log_file;
fprintf(output_file, "\n%s, %s\n", coco_problem_get_id(problem), coco_problem_get_name(problem));
self = coco_transformed_allocate(problem, logger, NULL);
self->evaluate_function = logger_toy_evaluate;
return self;
}
/**
* @brief Computes the instance number of the second problem/objective so that the resulting bi-objective
* problem has more than a single optimal solution.
*
* Starts by setting instance2 = instance1 + 1 and increases this number until an appropriate instance has
* been found (or until a maximum number of tries has been reached, in which case it throws a coco_error).
* An appropriate instance is the one for which the resulting bi-objective problem (in any considered
* dimension) has the ideal and nadir points apart enough in the objective space and the extreme optimal
* points apart enough in the decision space. When the instance has been found, it is output through
* coco_warning, so that the user can see it and eventually manually add it to suite_biobj_instances.
*/
static size_t suite_biobj_get_new_instance(coco_suite_t *suite,
const size_t instance,
const size_t instance1,
const size_t num_bbob_functions,
const size_t *bbob_functions) {
size_t instance2 = 0;
size_t num_tries = 0;
const size_t max_tries = 1000;
const double apart_enough = 1e-4;
int appropriate_instance_found = 0, break_search, warning_produced = 0;
coco_problem_t *problem1, *problem2, *problem = NULL;
size_t d, f1, f2, i;
size_t function1, function2, dimension;
double norm;
suite_biobj_t *data;
assert(suite->data);
data = (suite_biobj_t *) suite->data;
while ((!appropriate_instance_found) && (num_tries < max_tries)) {
num_tries++;
instance2 = instance1 + num_tries;
break_search = 0;
/* An instance is "appropriate" if the ideal and nadir points in the objective space and the two
* extreme optimal points in the decisions space are apart enough for all problems (all dimensions
* and function combinations); therefore iterate over all dimensions and function combinations */
for (f1 = 0; (f1 < num_bbob_functions) && !break_search; f1++) {
function1 = bbob_functions[f1];
for (f2 = f1; (f2 < num_bbob_functions) && !break_search; f2++) {
function2 = bbob_functions[f2];
for (d = 0; (d < suite->number_of_dimensions) && !break_search; d++) {
dimension = suite->dimensions[d];
if (dimension == 0) {
if (!warning_produced)
coco_warning("suite_biobj_get_new_instance(): remove filtering of dimensions to get generally acceptable instances!");
warning_produced = 1;
continue;
}
problem1 = coco_get_bbob_problem(function1, dimension, instance1);
problem2 = coco_get_bbob_problem(function2, dimension, instance2);
if (problem) {
coco_problem_stacked_free(problem);
problem = NULL;
}
problem = coco_problem_stacked_allocate(problem1, problem2);
/* Check whether the ideal and nadir points are too close in the objective space */
norm = mo_get_norm(problem->best_value, problem->nadir_value, 2);
if (norm < 1e-1) { /* TODO How to set this value in a sensible manner? */
coco_debug(
"suite_biobj_get_new_instance(): The ideal and nadir points of %s are too close in the objective space",
problem->problem_id);
coco_debug("norm = %e, ideal = %e\t%e, nadir = %e\t%e", norm, problem->best_value[0],
problem->best_value[1], problem->nadir_value[0], problem->nadir_value[1]);
break_search = 1;
}
/* Check whether the extreme optimal points are too close in the decision space */
norm = mo_get_norm(problem1->best_parameter, problem2->best_parameter, problem->number_of_variables);
if (norm < apart_enough) {
coco_debug(
"suite_biobj_get_new_instance(): The extremal optimal points of %s are too close in the decision space",
problem->problem_id);
coco_debug("norm = %e", norm);
break_search = 1;
}
}
}
}
/* Clean up */
if (problem) {
coco_problem_stacked_free(problem);
problem = NULL;
}
if (break_search) {
/* The search was broken, continue with next instance2 */
continue;
} else {
/* An appropriate instance was found */
appropriate_instance_found = 1;
coco_info("suite_biobj_set_new_instance(): Instance %lu created from instances %lu and %lu", instance,
instance1, instance2);
/* Save the instance to new_instances */
for (i = 0; i < data->max_new_instances; i++) {
if (data->new_instances[i][0] == 0) {
data->new_instances[i][0] = instance;
data->new_instances[i][1] = instance1;
data->new_instances[i][2] = instance2;
break;
};
}
}
}
if (!appropriate_instance_found) {
coco_error("suite_biobj_get_new_instance(): Could not find suitable instance %lu in $lu tries", instance, num_tries);
return 0; /* Never reached */
}
return instance2;
}
/**
* @brief Returns the numbers defined by the ranges.
*
* Reads ranges from a string of positive ranges separated by commas. For example: "-3,5-6,8-". Returns the
* numbers that are defined by the ranges if min and max are used as their extremes. If the ranges with open
* beginning/end are not allowed, use 0 as min/max. The returned string has an appended 0 to mark its end.
* A maximum of max_count values is returned. If there is a problem with one of the ranges, the parsing stops
* and the current result is returned. The memory of the returned object needs to be freed by the caller.
*/
static size_t *coco_string_parse_ranges(const char *string,
const size_t min,
const size_t max,
const char *name,
const size_t max_count) {
char *ptr, *dash = NULL;
char **ranges, **numbers;
size_t i, j, count;
size_t num[2];
size_t *result;
size_t i_result = 0;
char *str = coco_strdup(string);
/* Check for empty string */
if ((str == NULL) || (strlen(str) == 0)) {
coco_warning("coco_string_parse_ranges(): cannot parse empty ranges");
coco_free_memory(str);
return NULL;
}
ptr = str;
/* Check for disallowed characters */
while (*ptr != '\0') {
if ((*ptr != '-') && (*ptr != ',') && !isdigit((unsigned char )*ptr)) {
coco_warning("coco_string_parse_ranges(): problem parsing '%s' - cannot parse ranges with '%c'", str,
*ptr);
coco_free_memory(str);
return NULL;
} else
ptr++;
}
/* Check for incorrect boundaries */
if ((max > 0) && (min > max)) {
coco_warning("coco_string_parse_ranges(): incorrect boundaries");
coco_free_memory(str);
return NULL;
}
result = coco_allocate_vector_size_t(max_count + 1);
/* Split string to ranges w.r.t commas */
ranges = coco_string_split(str, ',');
coco_free_memory(str);
if (ranges) {
/* Go over the current range */
for (i = 0; *(ranges + i); i++) {
ptr = *(ranges + i);
/* Count the number of '-' */
count = 0;
while (*ptr != '\0') {
if (*ptr == '-') {
if (count == 0)
/* Remember the position of the first '-' */
dash = ptr;
count++;
}
ptr++;
}
/* Point again to the start of the range */
ptr = *(ranges + i);
/* Check for incorrect number of '-' */
if (count > 1) {
coco_warning("coco_string_parse_ranges(): problem parsing '%s' - too many '-'s", string);
/* Cleanup */
for (j = i; *(ranges + j); j++)
coco_free_memory(*(ranges + j));
coco_free_memory(ranges);
if (i_result == 0) {
coco_free_memory(result);
return NULL;
}
result[i_result] = 0;
return result;
} else if (count == 0) {
/* Range is in the format: n (no range) */
num[0] = (size_t) strtol(ptr, NULL, 10);
num[1] = num[0];
} else {
/* Range is in one of the following formats: n-m / -n / n- / - */
/* Split current range to numbers w.r.t '-' */
numbers = coco_string_split(ptr, '-');
j = 0;
if (numbers) {
/* Read the numbers */
for (j = 0; *(numbers + j); j++) {
assert(j < 2);
num[j] = (size_t) strtol(*(numbers + j), NULL, 10);
coco_free_memory(*(numbers + j));
}
}
coco_free_memory(numbers);
if (j == 0) {
/* Range is in the format - (open ends) */
if ((min == 0) || (max == 0)) {
coco_warning("coco_string_parse_ranges(): '%s' ranges cannot have an open ends; some ranges ignored", name);
/* Cleanup */
for (j = i; *(ranges + j); j++)
coco_free_memory(*(ranges + j));
coco_free_memory(ranges);
if (i_result == 0) {
coco_free_memory(result);
return NULL;
}
result[i_result] = 0;
return result;
}
num[0] = min;
num[1] = max;
} else if (j == 1) {
if (dash - *(ranges + i) == 0) {
/* Range is in the format -n */
if (min == 0) {
coco_warning("coco_string_parse_ranges(): '%s' ranges cannot have an open beginning; some ranges ignored", name);
/* Cleanup */
for (j = i; *(ranges + j); j++)
coco_free_memory(*(ranges + j));
coco_free_memory(ranges);
if (i_result == 0) {
coco_free_memory(result);
return NULL;
}
result[i_result] = 0;
return result;
}
num[1] = num[0];
num[0] = min;
} else {
/* Range is in the format n- */
if (max == 0) {
coco_warning("coco_string_parse_ranges(): '%s' ranges cannot have an open end; some ranges ignored", name);
/* Cleanup */
for (j = i; *(ranges + j); j++)
coco_free_memory(*(ranges + j));
coco_free_memory(ranges);
if (i_result == 0) {
coco_free_memory(result);
return NULL;
}
result[i_result] = 0;
return result;
}
num[1] = max;
}
}
/* if (j == 2), range is in the format n-m and there is nothing to do */
}
/* Make sure the boundaries are taken into account */
if ((min > 0) && (num[0] < min)) {
num[0] = min;
coco_warning("coco_string_parse_ranges(): '%s' ranges adjusted to be >= %lu", name,
(unsigned long) min);
}
if ((max > 0) && (num[1] > max)) {
num[1] = max;
coco_warning("coco_string_parse_ranges(): '%s' ranges adjusted to be <= %lu", name,
(unsigned long) max);
}
if (num[0] > num[1]) {
coco_warning("coco_string_parse_ranges(): '%s' ranges not within boundaries; some ranges ignored", name);
/* Cleanup */
for (j = i; *(ranges + j); j++)
coco_free_memory(*(ranges + j));
coco_free_memory(ranges);
if (i_result == 0) {
coco_free_memory(result);
return NULL;
}
result[i_result] = 0;
return result;
}
/* Write in result */
for (j = num[0]; j <= num[1]; j++) {
if (i_result > max_count - 1)
break;
result[i_result++] = j;
}
coco_free_memory(*(ranges + i));
*(ranges + i) = NULL;
}
}
coco_free_memory(ranges);
if (i_result == 0) {
coco_free_memory(result);
return NULL;
}
result[i_result] = 0;
return result;
}
/**
* 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;
}
int coco_archive_add_solution(coco_archive_t *archive, const double y1, const double y2, const char *text) {
coco_archive_avl_item_t* insert_item;
avl_node_t *node, *next_node;
int update = 0;
int dominance;
double *y = coco_allocate_vector(2);
y[0] = y1;
y[1] = y2;
insert_item = coco_archive_node_item_create(y, archive->ideal, archive->nadir,
archive->number_of_objectives, text);
coco_free_memory(y);
/* Find the first point that is not worse than the new point (NULL if such point does not exist) */
node = avl_item_search_right(archive->tree, insert_item, NULL);
if (node == NULL) {
/* The new point is an extreme point */
update = 1;
next_node = archive->tree->head;
} else {
dominance = mo_get_dominance(insert_item->normalized_y, ((coco_archive_avl_item_t*) node->item)->normalized_y,
archive->number_of_objectives);
if (dominance > -1) {
update = 1;
next_node = node->next;
if (dominance == 1) {
/* The new point dominates the next point, remove the next point */
assert((node != archive->extreme1) && (node != archive->extreme2));
avl_node_delete(archive->tree, node);
}
} else {
/* The new point is dominated or equal to an existing one, ignore */
update = 0;
}
}
if (!update) {
coco_archive_node_item_free(insert_item, NULL);
} else {
/* Perform tree update */
while (next_node != NULL) {
/* Check the dominance relation between the new node and the next node. There are only two possibilities:
* dominance = 0: the new node and the next node are nondominated
* dominance = 1: the new node dominates the next node */
node = next_node;
dominance = mo_get_dominance(insert_item->normalized_y, ((coco_archive_avl_item_t*) node->item)->normalized_y,
archive->number_of_objectives);
if (dominance == 1) {
next_node = node->next;
/* The new point dominates the next point, remove the next point */
assert((node != archive->extreme1) && (node != archive->extreme2));
avl_node_delete(archive->tree, node);
} else {
break;
}
}
if(avl_item_insert(archive->tree, insert_item) == NULL) {
coco_warning("Solution %s did not update the archive", text);
update = 0;
}
archive->is_up_to_date = 0;
}
return update;
}
