---
title: Coord_eval
datasets:
- GeoBenchmark
tags:
- evaluate
- metric
description: 'TODO: add a description here'
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
---

# Metric Card for Coord_eval

***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*

## Metric Description
Coordinates Accuracy aims to evaluate model performance in coordinates prediction tasks where the model has to predict a coordinate of a geographic entity in the form **(__lat__, __long__)**. It checks if the coordinates generated are inside a circle of radius __d__ and centered at gold coordinates.

## How to Use
This metric takes 2 mandatory arguments : `generations` (a list of string of generated coordinates), `golds` (a list of list of floats of gold coordinates)

```python
import evaluate
coord_acc = evaluate.load("rfr2003/coord_eval")
results = coord_acc.compute(generations=["(12.7, 67.8)", "(16.7, 89.6)"], golds=[[12.7, 67.8], [10.9, 80.6]], d_range=20)
print(results)
{'coord_accuracy': 0.5}
```

This metric also accepts an optional argument: 


`d` (int): Radius of the circle. The default value is `20`.


### Output Values

This metric outputs a dictionary with the following values:

`coord_accuracy`: The coordinates accuracy between `generations` and `golds`, which ranges from 0.0 to 1.0. 

#### Values from Popular Papers


### Examples

```python
import evaluate
coord_acc = evaluate.load("rfr2003/coord_eval")
results = coord_acc.compute(generations=["(12.7, 67.8)", "(16.7, 89.6)"], golds=[[12.7, 67.8], [10.9, 80.6]], d_range=20)
print(results)
{'coord_accuracy': 0.5}
```

## Limitations and Bias


## Citation


## Further References