lore_sa.neighgen.GeneticGenerator

class lore_sa.neighgen.GeneticGenerator(bbox=None, dataset=None, encoder=None, ocr=0.1, alpha1=0.5, alpha2=0.5, metric=<function neuclidean>, ngen=100, mutpb=0.2, cxpb=0.5, tournsize=3, halloffame_ratio=0.1, random_seed=None)[source]

Random Generator creates neighbor instances by generating random values starting from an input instance and pruning the generation around a fitness function based on proximity to the instance to explain

__init__(bbox=None, dataset=None, encoder=None, ocr=0.1, alpha1=0.5, alpha2=0.5, metric=<function neuclidean>, ngen=100, mutpb=0.2, cxpb=0.5, tournsize=3, halloffame_ratio=0.1, random_seed=None)[source]
Parameters

  • bbox – the Black Box model to explain

  • dataset – the dataset with the descriptor of the original dataset

  • encoder – an encoder to transfrom the data from/to the black box model

  • ocr – acronym for One Class Ratio, it is the ratio of the number of instances of the minority class

  • alpha1 – the weight of the similarity of the features from the given instance. The sum of alpha1 and alpha2 must be 1

  • alpha2 – the weight of the similiarity of the target class from the given instance. The sum of alpha1 and alpha2 must be 1

  • metric – the distance metric to use to compute the distance between instances

  • ngen – the number of generations to run

  • mutpb – probability of mutation of a specific feature

  • cxpb –

  • tournsize –

  • halloffame_ratio –

  • random_seed – initial seed for the random number generator

Methods

__init__([bbox, dataset, encoder, ocr, ...])

param bbox

the Black Box model to explain

add_halloffame(population, halloffame)

balance_neigh(x, Z, num_samples)

check_generated([filter_function, check_fuction])

It contains the logic to check the requirements for generated data

clone(x)

fit(toolbox, population_size)

fitness_equal(x, x1)

fitness_notequal(x, x1)

generate(x, num_instances, descriptor)

The generation is based on the strategy of generating a number of instances for the same class as the input instance and a number of instances for a different class.

generate_synthetic_instance([from_z, mutpb])

mutate(toolbox, x)

random_init()

record_init(x)

setup_toolbox(x, evaluate, population_size)

setup_toolbox_noteq(x, x1, evaluate, ...)

abstract check_generated(filter_function=None, check_fuction=None)

It contains the logic to check the requirements for generated data

generate(x, num_instances, descriptor)[source]

The generation is based on the strategy of generating a number of instances for the same class as the input instance and a number of instances for a different class. The generation of the instances for each subgroup is done through a genetic algorithm based on two fitness fuctions: one for the same class and one for the different class. :param x: the input instance :param num_instances: how many elements to generate :param descriptor: the descriptor of the dataset :return: