"""DashAI recall classification metric implementation."""
import numpy as np
from sklearn.metrics import recall_score
from DashAI.back.dataloaders.classes.dashai_dataset import DashAIDataset
from DashAI.back.metrics.classification_metric import (
ClassificationMetric,
prepare_to_metric,
)
[docs]class Recall(ClassificationMetric):
"""Recall metric to classification tasks."""
@staticmethod
def score(true_labels: DashAIDataset, probs_pred_labels: np.ndarray) -> float:
"""Calculate recall between true labels and predicted labels.
Parameters
----------
true_labels : DashAIDataset
A DashAI dataset with labels.
probs_pred_labels : np.ndarray
A two-dimensional matrix in which each column represents a class and the row
values represent the probability that an example belongs to the class
associated with the column.
Returns
-------
float
recall score between true labels and predicted labels
"""
true_labels, pred_labels = prepare_to_metric(true_labels, probs_pred_labels)
multiclass = len(np.unique(true_labels)) > 2
if multiclass:
return recall_score(true_labels, pred_labels, average="macro")
else:
return recall_score(true_labels, pred_labels, average="binary")