Artificial Immune Recognition System Base

Base class for algorithm AIRS.

BaseAIRS(BaseClassifier, ABC)

The base class contains functions that are used by more than one class in the package, and therefore are considered essential for the overall functioning of the system.

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def _check_and_raise_exceptions_fit(...)

Verify the fit parameters and throw exceptions if the verification is not successful.

@staticmethod
def _check_and_raise_exceptions_fit(
    X: npt.NDArray = None,
    y: npt.NDArray = None,
    algorithm: Literal[
        "continuous-features", "binary-features"
    ] = "continuous-features"
):

Parameters:

• X (npt.NDArray): Training array, containing the samples and their characteristics, [N samples (rows)][N features (columns)].
• y (npt.NDArray): Array of target classes of X with [N samples (lines)].
• algorithm (Literal["continuous-features", "binary-features"], optional): Specifies the type of algorithm to use, depending on whether the input data has continuous or binary features.

Raises

• TypeError: If X or y are not ndarrays or have incompatible shapes.
• ValueError If class is BNSA and X contains values that are not composed only of 0 and 1.

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def _check_and_raise_exceptions_predict(...)

Verify the predict parameters and throw exceptions if the verification is not successful.

@staticmethod
def _check_and_raise_exceptions_predict(
    X: npt.NDArray = None,
    expected: int = 0,
    algorithm: Literal[
        "continuous-features", "binary-features"
    ] = "continuous-features"
) -> None:

Parameters:

• X (npt.NDArray): Training array, containing the samples and their characteristics, [N samples (rows)][N features (columns)].
• expected (int): Expected number of features per sample (columns in X).
• algorithm (Literal["continuous-features", "binary-features"], optional): Specifies the type of algorithm to use, depending on whether the input data has continuous or binary features.

Raises

• TypeError If X is not a ndarray or list.
• FeatureDimensionMismatch If the number of features in X does not match the expected number.
• ValueError If algorithm is binary-features and X contains values that are not composed only of 0 and 1.

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