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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