Hyperparameters

While scikit_FIBERS has a number of available hyperparameters only a few are considered to be essential or useful to check or set.

• Essential hyperparameters are given in the first table.

  • For survival data without covariate columns, the fitness_metric should be set to ‘log_rank’ and covariates should be set to None.

  • For survival data with covariate colums (that need to be adjusted for), the fitness_metric should be set to either ‘residuals’ or to ‘log_rank_residuals’ (otherwise referred to as the ‘product’ fitness metric), and covariates should be set to a list of all covariate column lables in the dataset.

  • For survival data without censoring, censor_label should be set to None, otherwise it should be set the censoring column lable.

  • While outcome_type is also an important hyperparameter to check, currently only the ‘survival’ option (for survial data analysis) has been fully implemented and tested.

Hyperparameter	Description	Type/Options	Default Value
outcome_label	Data column label for time-to-event (outcome)	str	‘Duration’
outcome_type	Defines the type of outcome in the dataset	‘survival’,’class’	‘survival’
censor_label	Data column label for censoring	str/None	‘Censoring’, None
fitness_metric	Pre-fitness metric driving fitness ranking	‘log_rank’,’residuals’,’log_rank_residuals’	‘log_rank’
covariates	List of data column labels to be treated as covariates (i.e. not considered for bin inclusion)	list, None	None

• This second table includes hyperparameters that are not essential but can have a significant impact on algorithm performance.

  • In general, setting iterations and pop_size to larger integers is expected to improve training performance, but will require longer run times.

  • The group_thresh hyperparameter controls the adaptive burden thresholding in FIBERS, where ‘None’ activates this mechanism, and an integer value (e.g. 0), will enforce a specific burden threshold for all discovered bins. Related to this, max_thresh controls the maximum burden threshold allowed in bins, assuming group_thresh = None. Also related to adaptive burden thresholding, the thresh_evolve_prob (set between 0.0 and 1.0) can lead to better performance when set closer to 1.0, but requires significantly more runtime.

  • The group_strata_min hyperparameter (set as > 0.0 and < 0.5) enforces a minimum instance count balance between risk groups, where 0.5 describes two risk groups with the same instance count.

  • The manual_bin_init hyperparameter allows users to load an existing set of candidate bins for FIBERS to start learning from, rather than starting from randomly initialized bins. The value of using this function depends on the quality of the loaded bins, however utilizing expert (i.e. domain) knowledge to design candidate bins before running FIBERS has the potential to dramatically improve or speed up learning in larger or more complex tasks.

  • Lastly, pop_clean = ‘group_strata’ applies a post-hoc cleaning of the bin population, removing any remaining bins that have a risk group instance count ratio below the group_strata_min.

Hyperparameter	Description	Type/Options	Default Value
iterations	Number of training/optimization cycles	int	100
pop_size	Maximum bin population size at end of each cycle	int	50
group_thresh	Optionally specify a group threshold for bins to use	int, None	None
max_thresh	Maximum group threshold for adaptive thresholding	int	5
thresh_evolve_prob	Probability that an optimization cycle will evolve vs. deterministically select a group threshold for new bin evaluation	float	0.5
group_strata_min	Min. cutoff for group strata sizes below which a pre-fitness penalty is applied to bin	float	0.2
manual_bin_init	Dataframe of FIBERS-formatted bin population used to initialize the bin population	dataframe, None	None
pop_clean	Optional bin population cleanup strategy	‘group_strata’, None	None

• The remaining hyperparameters in the table below can largely be left to their default values by most users.

Hyperparameter	Description	Type/Options	Default Value
tournament_prop	Population proportion randomly selected for tournament	float	0.2
crossover_prob	Uniform crossover operator probability	float	0.5
min_mutation_prob	Minimum mutation operator probability	float	0.1
max_mutation_prob	Maximum mutation operator probability	float	0.3
merge_prob	Merge operator probability	float	0.1
new_gen	Proportion of pop_size. Determines the number of offspring to generate each generation	float	1.0
elitism	Proportion of pop_size protected from deletion	float	0.1
diversity_pressure	Number of bin similarity clusters driving bin deletion to encourage bin diversity	int	3
min_bin_size	Min. number of features to be specified in a bin	int	1
max_bin_size	Max. number of features to be specified in a bin	int/None	None
max_bin_init_size	Max. number of features in initialized bins	int	10
log_rank_weightning	Optional weighting of log-rank test	‘wilcoxon’, ‘tarone-ware’, ‘peto’, ‘fleming-harrington’, None	None
penalty	Penalty multiplier applied to pre-fitness when bin’s group strata ratio goes below the minimum	float	0.5
min_thresh	Minimum group threshold for adaptive thresholding	int	0
int_thresh	Boolean indicating that adaptive bin thresholds are limited to positive intergers	Boolean	True
random_seed	Seed value used to generate random numbers or make random selections	int, None	None
report	List of integers, indicating iterations where the population will be printed out for viewing	list, None	None
verbose	Boolean flag to run in ‘verbose’ mode - display run details	Boolean	False