Metadata-Version: 2.1
Name: multilabel-eval-metrics
Version: 0.0.1
Summary: Quickly evaluate multi-label classifiers in various metrics
Home-page: http://github.com/dhchenx/multilabel-eval-metrics
Author: Donghua Chen
Author-email: douglaschan@126.com
License: MIT
Project-URL: Bug Reports, https://github.com/dhchenx/multilabel-eval-metrics/issues
Project-URL: Source, https://github.com/dhchenx/multilabel-eval-metrics
Keywords: multi-label-classifier,metrics
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
License-File: LICENSE

## Evaluation metrics for multi-label classification models
This toolkit is used to focus on different evaluation metrics that can be used for evaluating the performance of a multilabel classifier. 

### Intro

The evaluation metrics for multi-label classification can be broadly classified into two categories:

- Example-Based Evaluation Metrics
- Label Based Evaluation Metrics.

### Metrics

Exact Match Ratio (EMR)
1/0 Loss
Hamming Loss
Example-Based Accuracy
Example-Based Precision
Label Based Metrics
Macro Averaged Accuracy
Macro Averaged Precision
Macro Averaged Recall
Micro Averaged Accuracy
Micro Averaged Precision
Micro Averaged Recall
α- Evaluation Score

[Reference](https://medium.datadriveninvestor.com/a-survey-of-evaluation-metrics-for-multilabel-classification-bb16e8cd41cd)

### Examples

```python
from multilabel_eval_metrics import *
import numpy as np
if __name__=="__main__":
    y_true = np.array([[0, 1], [1, 1], [1, 1], [0, 1], [1, 0]])
    y_pred = np.array([[1, 1], [1, 0], [1, 1], [0, 1], [1, 0]])
    print(y_true)
    print(y_pred)
    result=MultiLabelMetrics(y_true,y_pred).get_metric_summary(show=True)
```

### License

The `multilabel-eval-metrics` toolkit is provided by [Donghua Chen](https://github.com/dhchenx) with MIT License.



