lore_sa.neighgen.RandomGenerator

class lore_sa.neighgen.RandomGenerator(bbox: AbstractBBox, dataset: Dataset, encoder: EncDec, ocr=0.1, mutpb=0.1)[source]

Random Generator creates neighbor instances by generating random values starting from the instance to explain

__init__(bbox: AbstractBBox, dataset: Dataset, encoder: EncDec, ocr=0.1, mutpb=0.1)[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

Methods

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

param bbox:

the Black Box model to explain

balance_neigh(z, Z, num_samples)

check_generated([filter_function, check_fuction])

It contains the logic to check the requirements for generated data

generate(x, num_instances, descriptor, encoder)

random generation of new instances.

generate_synthetic_instance([from_z, mutpb])

Generate a single synthetic instance.
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, encoder)[source]

random generation of new instances. The starting instance x is only used to detect the value type of each feature, in order to generate new values only for numeric features.

Parameters:

  • x[dict] – the starting instance from the real dataset

  • num_instances[int] – the number of instances to generate

  • descriptor[dict] – data descriptor as generated from a Dataset object

  • encoder[EncDec] – the encoder eventually to encode the instance

The list (or range) associated to each key is used to randomly choice an element within the list.

Return [instances]:

an array of instances with the new data generated

generate_synthetic_instance(from_z=None, mutpb=1.0)

Generate a single synthetic instance.

This method creates one synthetic instance by randomly sampling or mutating feature values. For categorical features, it randomly selects from valid values. For numerical features, it samples from the feature’s range.

Parameters:

  • from_z (np.array, optional) – Starting instance in encoded space to mutate. If None, generates a completely random instance. If provided, features are mutated with probability mutpb.

  • mutpb (float, optional) – Mutation probability for each feature (0 to 1). Only used when from_z is provided. Default is 1.0 (mutate all features).

Returns:

A single synthetic instance in encoded space, shape (n_encoded_features,)

Return type:

np.array

Note

The method respects feature types and valid ranges from the dataset descriptor. For categorical features, it ensures the one-hot encoding constraint (exactly one category is active).