"""DashAI MAE regression metric implementation."""
import numpy as np
from sklearn.metrics import mean_absolute_error
from DashAI.back.dataloaders.classes.dashai_dataset import DashAIDataset
from DashAI.back.metrics.regression_metric import RegressionMetric, prepare_to_metric
[docs]class MAE(RegressionMetric):
"""Mean Absolute Error metric for regression tasks."""
@staticmethod
def score(true_values: DashAIDataset, predicted_values: np.ndarray) -> float:
"""Calculate the MAE between true values and predicted values.
Parameters
----------
true_values : DashAIDataset
A DashAI dataset with true values.
predicted_values : np.ndarray
A one-dimensional array with the predicted values
for each instance.
Returns
-------
float
MAE score between true values and predicted values
"""
true_values, pred_values = prepare_to_metric(true_values, predicted_values)
return mean_absolute_error(true_values, pred_values)