BNSA (Binary Negative Selection Algorithm)

Constructor RNSA

The BNSA (Binary Negative Selection Algorithm) class has the purpose of classifying and identifying anomalies through the self and not self methods.

class BNSA(
    self,
    N: int = 100,
    aff_thresh: float = 0.1,
    max_discards: int = 1000,
    seed: int = None,
    no_label_sample_selection: Literal[
        "max_average_difference", "max_nearest_difference"
    ] = "max_average_difference",
)

Attributes:

• N (int): Number of detectors. Defaults to 100.
• aff_thresh (float): The variable ('affinity threshold') represents the percentage of dissimilarity between the T cell and the own samples. The default value is 10% (0.1), while a value of 1.0 represents 100% dissimilarity.

note

Setting the difference percentage too high can result in the inability to generate detectors for non-self.

• max_discards (int): This parameter indicates the maximum number of detector discards in sequence, which aims to avoid a possible infinite loop if a radius is defined that it is not possible to generate non-self detectors. Defaults to 1000.
• seed (int): Seed for the random generation of values in the detectors. Defaults to None.
• no_label_sample_selection (str): Method for selecting labels for samples designated as non-members by all non-member detectors. Available method types:
  • (max_average_difference): Selects the class with the highest average difference among the detectors.
  • (max_nearest_difference): Selects the class with the highest difference between the nearest and farthest detector from the sample.

Other variables initiated:

• detectors (dict): This variable stores a list of detectors by class.

• classes (npt.NDArray): list of output classes.

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Function fit(...)

The fit(...) function generates the detectors for non-fits with respect to the samples:

def fit(self, X: npt.NDArray, y: npt.NDArray, verbose: bool = True)

In it, training is performed according to X and y, using the negative selection method(NegativeSelect).

The input parameters are:

• X: array with the characteristics of the samples with N samples (rows) and N characteristics (columns).

• y: array with the output classes arranged in N samples that are related to X.

• verbose: boolean with default value True, determines if the feedback from the detector generation will be printed.

Returns the instance of the class.

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Function predict(...)

The predict(...) function performs class prediction using the generated detectors:

def predict(self, X: npt.NDArray) -> npt.NDArray:

The input parameter is:

• X: array with the characteristics for the prediction, with N samples (Rows) and N columns.

Returns:

• C: prediction array, with the output classes for the given characteristics.
• None: if there are no detectors.

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Function score(...)

The function score(...) calculates the accuracy of the trained model by making predictions and computing accuracy.

def score(self, X: npt.NDArray, y: list) -> float:

It returns the accuracy as a float type.

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Private Methods

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Function __assign_class_to_non_self_sample(...)

The function __assign_class_to_non_self_sample(...), determines the class of a sample when all detectors classify it as "non-self". Classification is performed using the max_average_difference and max_nearest_difference methods.

def __assign_class_to_non_self_sample(self, line: npt.NDArray, c: list):

The input parameter is:

• line (npt.NDArray): Sample to be classified.
• c (list): List of predictions to be updated with the new classification.

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