Source code for immuneML.hyperparameter_optimization.strategy.HPOptimizationStrategy

import abc

from immuneML.hyperparameter_optimization.HPSetting import HPSetting
from immuneML.hyperparameter_optimization.HPSettingResult import HPSettingResult

[docs]class HPOptimizationStrategy(metaclass=abc.ABCMeta): """ hyper-parameter optimization strategy is a base class of all different hyper-parameter optimization approaches, such as grid search, random search, bayesian optimization etc. HPOptimizationStrategy internally keeps a dict of settings that were tried out and the metric value that was obtained on the validation set which it then uses to determine the next step """ def __init__(self, hp_settings: list, search_criterion=max): self.hp_settings = {hp_setting.get_key(): hp_setting for hp_setting in hp_settings} self.search_space_metric = {hp_setting.get_key(): None for hp_setting in hp_settings} self.search_criterion = search_criterion
[docs] @abc.abstractmethod def generate_next_setting(self, hp_setting: HPSetting = None, metric: dict = None): """ generator function which returns the next hyper-parameter setting to be evaluated :param hp_setting: previous setting (None if it's the first iteration) :param metric: performance metric from the previous setting per label :return: new hp_setting or None (if the end is reached) """ pass
[docs] @abc.abstractmethod def get_optimal_hps(self) -> HPSetting: pass
[docs] @abc.abstractmethod def get_all_hps(self) -> HPSettingResult: pass
[docs] @abc.abstractmethod def clone(self): pass