Multiclass

This file contains internal utility functions designed to simplify data manipulation and processing in multiclass classification scenarios within the AISP package.

def slice_index_list_by_class(...)

def slice_index_list_by_class(classes: Union[npt.NDArray, list], y: npt.NDArray) -> dict

The function slice_index_list_by_class(...), separates the indices of the lines
according to the output class, to loop through the sample array, only in positions where
the output is the class being trained.

Parameters:

• classes (list or npt.NDArray): list with unique classes.
• y (npt.NDArray): Receives a y[N sample] array with the output classes of the X sample array.

returns:

• dict: A dictionary with the list of array positions(y), with the classes as key.

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def predict_knn_affinity(...)

def predict_knn_affinity(
    X: npt.NDArray,
    k: int,
    all_cell_vectors: List[Tuple[Union[str, int], npt.NDArray]],
    affinity_func: Callable[[npt.NDArray, npt.NDArray], float]
) -> npt.NDArray

Function to predict classes using k-nearest neighbors and trained cells.

Parameters:

• X (npt.NDArray): Input data to be classified.
• k (int): Number of nearest neighbors to consider for prediction.
• all_cell_vectors (List[Tuple[Union[str, int], npt.NDArray]]): List of tuples containing (class_name, cell vector) pairs.
• affinity_func (Callable[[npt.NDArray, npt.NDArray], float]): Function that takes two vectors and returns an affinity value.

Returns:

• npt.NDArray: Array of predicted labels for each sample in X, based on the k nearest neighbors.