---
title: PR AUC
emoji: 🤗 
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: "This metric computes the area under the curve (AUC) for the Precision-Recall Curve (PR). summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight."
---

# Metric Card for PR AUC
## Metric Description
This metric computes the area under the curve (AUC) for the Precision-Recall Curve (PR). summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight.

You should use this metric:
- when your data is heavily imbalanced. As mentioned before, it was discussed extensively in this article by Takaya Saito and Marc Rehmsmeier. The intuition is the following: since PR AUC focuses mainly on the positive class (PPV and TPR) it cares less about the frequent negative class.
- when you care more about positive than negative class. If you care more about the positive class and hence PPV and TPR you should go with Precision-Recall curve and PR AUC (average precision).

## How to Use
*Give general statement of how to use the metric*
This metric requires references and prediction scores:
```python
>>> average_precision_score = evaluate.load("pr_auc")
>>> refs = np.array([0, 0, 1, 1])
>>> pred_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> results = average_precision_score.compute(references=refs, prediction_scores=pred_scores)
>>> print(round(results['average_precision'], 2))
0.83
```
Default implementation of this metric is binary. If using multiclass, see examples below.

### Inputs
- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
Args:
- **`references`** (array-like of shape (n_samples,) or (n_samples, n_classes)): True binary labels or binary label indicators.
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by decision_function on some classifiers).
- **`average`** (`str`): Type of average, and is ignored in the binary use case. Defaults to `'macro'`. Options are:
    - `'micro'`: Calculate metrics globally by considering each element of the label indicator matrix as a label.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- **`pos_label`** (`int`, `float`, `bool` or `str`): The label of the positive class. Only applied to binary y_true. For multilabel-indicator y_true, pos_label is fixed to 1.
- **`sample_weight`** (array-like of shape (n_samples,)): Sample weights. Defaults to None.
### Output Values
This metric returns a dict containing the `average_precision` score. The score is a `float`.

The output therefore generally takes the following format:
```python
{'average_precision': 0.778}
```

PR AUC scores can take on any value between `0` and `1`, inclusive.

#### Values from Popular Papers

### Examples
Example 1, the **binary** use case:
```python
>>> average_precision_score = evaluate.load("pr_auc")
>>> refs = np.array([0, 0, 1, 1])
>>> pred_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> results = average_precision_score.compute(references=refs, prediction_scores=pred_scores)
>>> print(round(results['average_precision'], 2))
0.83
```

Example 2, the **multiclass** use case:
```python
>>> average_precision_score = evaluate.load("pr_auc")
>>> refs = np.array([0, 0, 1, 1, 2, 2])
>>> pred_scores = np.array([[0.7, 0.2, 0.1],
...                         [0.4, 0.3, 0.3],
...                         [0.1, 0.8, 0.1],
...                         [0.2, 0.3, 0.5],
...                         [0.4, 0.4, 0.2],
...                         [0.1, 0.2, 0.7]])
>>> results = average_precision_score.compute(references=refs, prediction_scores=pred_scores)
>>> print(round(results['average_precision'], 2))
0.77
```

## Limitations and Bias


## Citation


## Further References
This implementation is a wrapper around the [Scikit-learn implementation]("https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html"). Much of the documentation here was adapted from their existing documentation, as well.