Status: Beta - APIs may change and detection thresholds are still being refined. Results should not be considered production-stable.
RPFNet is a Python library for detecting data poisoning attacks in tabular datasets. It supports batch analysis, streaming detection, and clean dataset export.
Installation
pip install rpfnet
pip install ucimlrepo # only needed for UCI datasets
Quick Start
from RPFNet import api
# UCI dataset (by ID)
report = api.analyze('uci', 73)
print(f"Flagged: {report['n_flagged']} / {report['n_rows']} ({report['pct_flagged']}%)")
# Local CSV
report = api.analyze('csv', 'dataset.csv')
# Remote URL
report = api.analyze('url', 'https://example.com/dataset.csv')
# Google Drive: https://drive.google.com/uc?id=FILE_ID&export=download
Report Keys
| Key | Description |
|---|---|
n_rows |
Total rows analyzed |
n_flagged |
Rows flagged as potentially poisoned |
n_clean |
Rows considered clean |
pct_flagged |
Percentage flagged |
estimated_rate |
Auto-estimated contamination rate |
mode |
Scoring mode (rpfnet_hybrid or isolation_forest) |
scores |
Per-row anomaly scores (0–1) |
flags |
Per-row binary flags (1 = flagged) |
flagged_indices |
Row indices of flagged samples |
clean_indices |
Row indices of clean samples |
dataframe |
Original data annotated with _poison_score and _poison_flag |
Export Clean Data
# Returns original DataFrame with poisoned rows removed
clean_df = api.clean('uci', 73)
clean_df = api.clean('csv', 'dataset.csv')
clean_df = api.clean('url', 'https://example.com/dataset.csv')
print(f"Kept {len(clean_df)} clean rows")
clean_df.to_csv('clean_dataset.csv', index=False)
Streaming Detection
For real-time or incremental data pipelines, RPFNet supports row-by-row streaming.
import pandas as pd
from RPFNet import api
# Existing dataset - used as baseline
existing = pd.read_csv('dataset.csv')
api.analyze('stream', existing)
# New rows scored against that baseline
for _, row in new_data.iterrows():
result = api.analyze('stream', row)
if result['status'] == 'warming_up':
continue
if result['poison_flag']:
print(f"Poisoned row detected! score={result['score']:.4f}")
# Export clean rows from the buffer
clean_df = api.clean('stream')
print(f"Clean rows in buffer: {len(clean_df)}")
# Utilities
api.analyze('stream') # check buffer status
api.stream_retrain() # force retrain after concept drift
api.stream_reset() # wipe buffer and start fresh
Streaming Result Keys
| Key | Description |
|---|---|
status |
warming_up, active |
score |
Anomaly score for this row |
threshold |
Current decision threshold |
poison_flag |
True if row is flagged |
n_samples |
Rows in current buffer |
buffer_pct_flagged |
% flagged in buffer window |
Supported Sources
| Source | Argument | Example |
|---|---|---|
'uci' |
Dataset ID (int) | api.analyze('uci', 73) |
'csv' |
File path (str) | api.analyze('csv', 'data.csv') |
'url' |
URL string (str) | api.analyze('url', 'https://...') |
'stream' |
DataFrame / row / None | api.analyze('stream', df) |
1 maintainer
Metadata
1 maintainer
Release files for rpfnet 0.1.14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rpfnet-0.1.14.tar.gz | 419.3 kB | Details |
Release files / rpfnet-0.1.14.tar.gz
| Download URL | rpfnet-0.1.14.tar.gz |
|---|---|
| Size | 419.3 kB |
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