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Calculate common OOD detection metrics

# OOD Detection Metrics

Functions for computing metrics commonly used in the field of out-of-distribution (OOD) detection.

## Installation

`pip install ood-metrics`

## Metrics functions

### AUROC

Calculate and return the area under the ROC curve using unthresholded predictions on the data and a binary true label.

```from ood_metrics import auroc

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

print(auroc(scores, labels))
# 0.75
```

### AUPR

Calculate and return the area under the Precision Recall curve using unthresholded predictions on the data and a binary true label.

```from ood_metrics import aupr

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

print(aupr(scores, labels))
# 0.25
```

### FPR @ 95% TPR

Return the FPR when TPR is at least 95%.

```from ood_metrics import fpr_at_95_tpr

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

print(fpr_at_95_tpr(scores, labels))
# 0.25
```

### Detection Error

Return the misclassification probability when TPR is 95%.

```from ood_metrics import detection_error

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

print(detection_error(scores, labels))
# 0.125
```

### Calculate all stats

Using predictions and labels, return a dictionary containing all novelty detection performance statistics.

```from ood_metrics import calc_metrics

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

print(calc_metrics(scores, labels))
# {
#     'fpr_at_95_tpr': 0.25,
#     'detection_error': 0.125,
#     'auroc': 0.75,
#     'aupr_in': 0.25,
#     'aupr_out': 0.94375
# }
```

## Plotting functions

### Plot ROC

Plot an ROC curve based on unthresholded predictions and true binary labels.

```from ood_metrics import plot_roc

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

plot_roc(scores, labels)
# Generate Matplotlib AUROC plot
```

### Plot PR

Plot an Precision-Recall curve based on unthresholded predictions and true binary labels.

```from ood_metrics import plot_pr

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

plot_pr(scores, labels)
# Generate Matplotlib Precision-Recall plot
```

### Plot Barcode

Plot a visualization showing inliers and outliers sorted by their prediction of novelty.

```from ood_metrics import plot_barcode

labels = [0, 0, 0, 1, 0]
scores = [0.1, 0.3, 0.6, 0.9, 1.3]

plot_barcode(scores, labels)
# Shows visualization of sort order of labels occording to the scores.
```

## Project details

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