---
title: relation_extraction
datasets:
- none
tags:
- evaluate
- metric
description: >-
  This metric is used for evaluating the F1 accuracy of input references and
  predictions.
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
license: apache-2.0
---

# Metric Card for relation_extraction evalutation
This metric is used for evaluating the quality of relation extraction output. By calculating the Micro and Macro F1 score of every relation extraction outputs to ensure the quality.


## Metric Description
This metric can be used in relation extraction evaluation.

## How to Use
This metric takes 3 inputs, prediction, references(ground truth) and mode.Predictions and references are a list of list of dictionary of entity's name and entity's type. And mode define the evaluation type:
```python
import evaluate
metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phip igments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict")
```

### Inputs
- **predictions** (`list` of `list` of `dictionary`): a list of list of dictionary with every element's relation_type and their entity name
- **references** (`list` of `list` of `dictionary`): a list of list of dictionary with every element's relation_type and their entity name
- **mode** (`str`): define strict or boundaries mode for evaluation, strict mode consider "head_type" and "tail_type", boundaries mode doesn't consider "head_type" and "tail_type"
- **show** (`bool`): False for only output all relation_type score. True for output every relation_type score, default False
- **relation_types** (`list`): define which relation type that need to be evaluate and show, if not given, it will construct relation_types from ground truth, default []
 
### Output Values

**output** (`dictionary` of `dictionary`s) with multiple key-value pairs
- **ALL** (`dictionary`): score of all of the relation type
  - **tp** : true positive count
  - **fp** : false positive count
  - **fn** : false negative count
  - **p** : precision
  - **r** : recall
  - **f1** : micro f1 score
  - **Macro_f1** : macro f1 score
  - **Macro_p** : macro precision
  - **Macro_r** : macro recall
- **{selected relation type}** (`dictionary`): score of selected relation type
  - **tp** : true positive count
  - **fp** : false positive count
  - **fn** : false negative count
  - **p** : precision
  - **r** : recall
  - **f1** : micro f1 score
 
Output Example:
```python
{'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0, 'Macro_f1': 50.0, 'Macro_p': 50.0, 'Macro_r': 50.0}
```

Remind : Macro_f1、Macro_p、Macro_r、p、r、f1 are always a number between 0 and 1. And tp、fp、fn depend on how many data inputs.

### Examples
Example1 : only one prediction and reference, mode=strict, show=False, only output total relation score
```python
metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phipigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {'head': 'A醛賦活緊緻精華', 'tail': 'Serum', 'head_type': 'product', 'tail_type': 'category', 'type': 'belongs_to'},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", show=False, relation_types=[])
print(evaluation_scores)
>>> {'tp': 1, 'fp': 1, 'fn': 2, 'p': 50.0, 'r': 33.333333333333336, 'f1': 40.0, 'Macro_f1': 25.0, 'Macro_p': 25.0, 'Macro_r': 25.0}
```

Example2 : only one prediction and reference, mode=boundaries, show=False, only output total relation score
```python
metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phipigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {'head': 'A醛賦活緊緻精華', 'tail': 'Serum', 'head_type': 'product', 'tail_type': 'category', 'type': 'belongs_to'},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", show=False, relation_types=[])
print(evaluation_scores)
>>> {'tp': 1, 'fp': 1, 'fn': 2, 'p': 50.0, 'r': 33.333333333333336, 'f1': 40.0, 'Macro_f1': 25.0, 'Macro_p': 25.0, 'Macro_r': 25.0}
```

Example3 : two or more prediction and reference, mode=boundaries, show=True, output all relation type score
```python
metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phipigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ],
  [
    {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
    {'head': 'A醛賦活緊緻精華', 'tail': 'Serum', 'head_type': 'product', 'tail_type': 'category', 'type': 'belongs_to'},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ],
  [
    {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
    {'head': 'SNTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references, mode="boundaries", show=True, relation_types=[])
print(evaluation_scores)
>>> {'sell': {'tp': 3, 'fp': 1, 'fn': 0, 'p': 75.0, 'r': 100.0, 'f1': 85.71428571428571}, 'belongs_to': {'tp': 0, 'fp': 0, 'fn': 1, 'p': 0, 'r': 0, 'f1': 0}, 'ALL': {'tp': 3, 'fp': 1, 'fn': 1, 'p': 75.0, 'r': 75.0, 'f1': 75.0, 'Macro_f1': 42.857142857142854, 'Macro_p': 37.5, 'Macro_r': 50.0}}
```

Example 4 : two or more prediction and reference, mode=boundaries, show=True, output all relation type score, relation_types  = ["belongs_to"], only consider belongs_to type score
```python
metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phipigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ],
  [
    {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
    {'head': 'A醛賦活緊緻精華', 'tail': 'Serum', 'head_type': 'product', 'tail_type': 'category', 'type': 'belongs_to'},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ],
  [
    {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
    {'head': 'SNTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references, mode="boundaries", show=True, relation_types=["belongs_to"])
print(evaluation_scores)  
>>> {'belongs_to': {'tp': 0, 'fp': 0, 'fn': 1, 'p': 0, 'r': 0, 'f1': 0}, 'ALL': {'tp': 0, 'fp': 0, 'fn': 1, 'p': 0, 'r': 0, 'f1': 0, 'Macro_f1': 0.0, 'Macro_p': 0.0, 'Macro_r': 0.0}}
```

## Limitations and Bias
This metric has strict and boundaries mode, also can select relation_types for different type evaluation. Make sure to select suitable evaluation parameters. F1 score may be totally different.
Prediction and reference entity_name should be exactly the same regardless of case and spaces. If prediction is not exactly the same as the reference one. It will count as fp or fn. 

## Citation
```bibtex
@Paper{
    author = {Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari},
    title = {Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!},
    year = {2020},
    link = https://arxiv.org/abs/2009.10684
}
```
## Further References
This evaluation metric implementation uses 
*https://github.com/btaille/sincere/blob/master/code/utils/evaluation.py*