rpfnet 0.0.7

RPFNet: Attack-Agnostic Tabular Data Poisoning Detection via Meta-Learned Relational Fingerprints

**Status: Alpha**

APIs may change and detection thresholds are still being refined.

Results should not be considered production-stable.



This is the python library that can be used for the api requests so that users can call to check for poisons in their own datasets.



---



## Quick Start



```python

from poison_detector import api



#UCI dataset

report = api.analyze('uci', 73)

print(report)

clean_uci_df = api.clean('uci', 73)



#CSV file

report = api.analyze('csv', 'path/to/file/dataset.csv')

print(report)

clean_csv_df = api.clean('csv', 'path/to/file/dataset.csv')



#URL

report = api.analyze('url', 'https://webpath/to/file/dataset')

print(report)

clean_url_csv_df = api.clean('url', 'https://webpath/to/file/dataset')

```



---



```markdown

## Supported Sources



- "uci" - UCI Machine Learning repository (by dataset ID).

- "csv" - Local CSV files.

- "url" - URL where an csv file is set for the dataset.



for url's in google drive you can use the following format



- https://drive.google.com/uc?id=FILE_ID&export=download

```