"""DashAI precision classification metric implementation."""
import numpy as np
from sklearn.metrics import precision_score
from DashAI.back.dataloaders.classes.dashai_dataset import DashAIDataset
from DashAI.back.metrics.classification_metric import (
ClassificationMetric,
prepare_to_metric,
)
[docs]class Precision(ClassificationMetric):
"""Precision metric to classification tasks."""
@staticmethod
def score(
true_labels: DashAIDataset, probs_pred_labels: np.ndarray, multiclass=None
) -> float:
"""Calculate precision 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.
multiclass : bool, optional
Whether the task is a multiclass classification. If None, it will be
determined automatically from the number of unique labels.
Returns
-------
float
Precision score between true labels and predicted labels
"""
true_labels, pred_labels = prepare_to_metric(true_labels, probs_pred_labels)
# Use the provided multiclass parameter or determine it using is_multiclass
if multiclass is None:
multiclass = ClassificationMetric.is_multiclass(true_labels)
if multiclass:
return precision_score(true_labels, pred_labels, average="macro")
else:
return precision_score(true_labels, pred_labels, average="binary")