Base class for classification algorithm.

class BaseClassifier(ABC):

Base class for classification algorithms, defining the abstract methods fit and predict, and implementing the get_params method.

Abstract methods

def fit(...)

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

Fit the model to the training data.

Implementation:

• RNSA
• BNSA

def predict(...)

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

Performs label prediction for the given data.

Implementation:

• RNSA
• BNSA

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Methods

def score(...)

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

Score function calculates forecast accuracy.

This function performs the prediction of X and checks how many elements are equal between vector y and y_predicted. This function was added for compatibility with some scikit-learn functions.

Parameters:

• X: np.ndarray Feature set with shape (n_samples, n_features).
• y: np.ndarray True values with shape (n_samples,).

Returns:

• accuracy: float The accuracy of the model.

Function _slice_index_list_by_class(...)

The function __slice_index_list_by_class(...), separates the indices of the lines according to the output class, to go through the sample array, only in the positions that the output is the class that is being trained:

def __slice_index_list_by_class(self, y: npt.NDArray) -> dict:

Returns a dictionary with the classes as key and the indices in X of the samples.

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Function get_params(...)

def get_params(self, deep: bool = True) -> dict:

The get_params function Returns a dictionary with the object's main parameters.

This function is required to ensure compatibility with scikit-learn functions.

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