Source code for verona.evaluation.metrics.time

from typing import Literal, Union

import numpy as np




def get_mae(predictions: np.array, ground_truths: np.array,
            reduction: Literal['mean', 'none'] = 'mean') -> Union[float, np.array]:
    """
    Calculates the Mean Absolute Error (MAE) between the predicted and real times.

    Args:
        predictions (np.array): NumPy Array containing the predicted times as floats.
        ground_truths (np.array): NumPy Array containing the real times as floats.
        reduction (Literal['mean', 'none'], optional): Determines the type of reduction applied to the MAE.
            If ``'mean'``, calculates the average MAE for all pairs of prediction and ground truth.
            If ``'none'``, returns all MAE values for the individual pairs without reduction.
            Default is ``'mean'``

    Returns:
        Union[float, np.array]: MAE as a single float if reduction is 'mean', or as a NumPy Array if reduction is ``'none'``.
    """

    mae = np.abs(predictions - ground_truths)

    if reduction == 'mean':
        mae = np.mean(mae).item()

    return mae





def get_mse(predictions: np.array, ground_truths: np.array,
            reduction: Literal['mean', 'none'] = 'mean') -> Union[float, np.array]:
    """
    Calculates the Mean Square Error (MSE) between the predicted and real times.

    Args:
        predictions (np.array): NumPy Array containing the predicted times as floats.
        ground_truths (np.array): NumPy Array containing the real times as floats.
        reduction (Literal['mean', 'none'], optional): Determines the type of reduction applied to the MSE.
            If ``'mean'``, calculates the average MSE for all pairs of prediction and ground truth.
            If ``'none'``, returns all MSE values for the individual pairs without reduction.
            DEfault is ``'mean'``.

    Returns:
        Union[float, np.array]: MSE as a single float if reduction is 'mean', or as a NumPy Array if reduction is ``'none'``.
    """

    mse = np.power(predictions - ground_truths, 2)

    if reduction == 'mean':
        mse = np.mean(mse).item()

    return mse