| Name | qde JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | Quality Data Extractor (QDE): CES & OES filtering for synthetic data |
| upload_time | 2025-08-31 12:51:06 |
| maintainer | None |
| docs_url | None |
| author | Pragati Sachdeva |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Pragati Sachdeva
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
| keywords |
synthetic data
data augmentation
filtering
feature selection
gans
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
# Quality Data Extractor (QDE)
[](https://pypi.org/project/qde/)
[](https://pypi.org/project/qde/)
[](LICENSE)
[](https://doi.org/10.1109/ACCESS.2025.3603435)
**QDE (Quality Data Extractor)** is a Python framework for **post-generation filtration of synthetic data**.
It introduces two filtering strategies:
- **CES (Comprehensive Extraction Strategy)**
- **OES (Optimal Extraction Strategy)**
These strategies help researchers and practitioners filter synthetic datasets to retain samples that improve downstream model accuracy.
📄 Published in *IEEE Access* (2025):
[Sachdeva, P., Malhotra, A., & Gupta, K. — *Quality Data Extractor (QDE): Elevating Synthetic Data Augmentation through Post-Generation Filtration*](https://doi.org/10.1109/ACCESS.2025.3603435)
---
## 🚀 Installation
From **PyPI**:
```bash
pip install qde
```
---
## 🔧 Quick Start
```python
import qde
from qde import QDE
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_iris
import numpy as np
# Example: use Iris dataset
X, y = load_iris(return_X_y=True)
train_X, train_y = X[:80], y[:80]
synth_X, synth_y = X[80:110], y[80:110] # pretend this is synthetic
test_X, test_y = X[110:], y[110:]
# Initialize QDE with CES
q = QDE(default_strategy="ces")
q.fit(train_X, train_y, synth_X, synth_y, test_X, test_y, encode_labels=True)
# Extract filtered synthetic samples
result, X_sel, y_sel = q.extract(estimator=GaussianNB())
print("Selected indices:", result.indices)
print("Filtered accuracy:", result.meta["filtered-accuracy"])
```
---
## 🖥️ Command-Line Interface (CLI)
QDE also ships a CLI:
```bash
qde strategies
# -> ces
# -> oes
qde run --train train.csv --synth synth.csv --test test.csv --target target --strategy ces
```
---
## 📖 Documentation
- **CES**
Adds synthetic samples one by one, retaining only those that do not reduce baseline accuracy.
- **OES**
Selects samples using distance-based neighborhood filtering (configurable with `--k-neighbors` and `--distance-mode`).
#### ✅ Each run outputs
- `SelectionResult.indices` → indices of accepted synthetic samples
- `meta` → metadata (strategy, accuracy metrics, etc.)
## 🛠️ Development
Clone the repo and install in editable mode:
``` bash
git clone https://github.com/pragatischdv/quality-data-extractor
cd quality-data-extractor
pip install -e .
```
## 📄 Citation
If you use QDE in your research, please cite:
```
@ARTICLE{11142788,
author={Sachdeva, Pragati and Malhotra, Amarjit and Gupta, Karan},
journal={IEEE Access},
title={Quality Data Extractor (QDE): Elevating Synthetic Data Augmentation through Post-Generation Filtration},
year={2025},
doi={10.1109/ACCESS.2025.3603435}}
```
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"description": "# Quality Data Extractor (QDE)\r\n\r\n[](https://pypi.org/project/qde/)\r\n[](https://pypi.org/project/qde/)\r\n[](LICENSE)\r\n[](https://doi.org/10.1109/ACCESS.2025.3603435)\r\n\r\n**QDE (Quality Data Extractor)** is a Python framework for **post-generation filtration of synthetic data**. \r\n\r\nIt introduces two filtering strategies:\r\n\r\n- **CES (Comprehensive Extraction Strategy)**\r\n- **OES (Optimal Extraction Strategy)**\r\n\r\nThese strategies help researchers and practitioners filter synthetic datasets to retain samples that improve downstream model accuracy.\r\n\r\n\ud83d\udcc4 Published in *IEEE Access* (2025): \r\n[Sachdeva, P., Malhotra, A., & Gupta, K. \u2014 *Quality Data Extractor (QDE): Elevating Synthetic Data Augmentation through Post-Generation Filtration*](https://doi.org/10.1109/ACCESS.2025.3603435)\r\n\r\n---\r\n\r\n## \ud83d\ude80 Installation\r\n\r\nFrom **PyPI**:\r\n\r\n```bash\r\npip install qde\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udd27 Quick Start\r\n\r\n```python\r\nimport qde\r\nfrom qde import QDE\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.datasets import load_iris\r\nimport numpy as np\r\n\r\n# Example: use Iris dataset\r\nX, y = load_iris(return_X_y=True)\r\ntrain_X, train_y = X[:80], y[:80]\r\nsynth_X, synth_y = X[80:110], y[80:110] # pretend this is synthetic\r\ntest_X, test_y = X[110:], y[110:]\r\n\r\n# Initialize QDE with CES\r\nq = QDE(default_strategy=\"ces\")\r\nq.fit(train_X, train_y, synth_X, synth_y, test_X, test_y, encode_labels=True)\r\n\r\n# Extract filtered synthetic samples\r\nresult, X_sel, y_sel = q.extract(estimator=GaussianNB())\r\nprint(\"Selected indices:\", result.indices)\r\nprint(\"Filtered accuracy:\", result.meta[\"filtered-accuracy\"])\r\n```\r\n---\r\n## \ud83d\udda5\ufe0f Command-Line Interface (CLI)\r\n\r\nQDE also ships a CLI:\r\n\r\n```bash\r\nqde strategies\r\n# -> ces\r\n# -> oes\r\n\r\nqde run --train train.csv --synth synth.csv --test test.csv --target target --strategy ces\r\n\r\n```\r\n---\r\n## \ud83d\udcd6 Documentation\r\n\r\n- **CES** \r\n Adds synthetic samples one by one, retaining only those that do not reduce baseline accuracy.\r\n\r\n- **OES** \r\n Selects samples using distance-based neighborhood filtering (configurable with `--k-neighbors` and `--distance-mode`).\r\n\r\n#### \u2705 Each run outputs\r\n\r\n- `SelectionResult.indices` \u2192 indices of accepted synthetic samples \r\n- `meta` \u2192 metadata (strategy, accuracy metrics, etc.)\r\n\r\n## \ud83d\udee0\ufe0f Development\r\n\r\nClone the repo and install in editable mode:\r\n\r\n``` bash\r\ngit clone https://github.com/pragatischdv/quality-data-extractor\r\ncd quality-data-extractor\r\npip install -e .\r\n```\r\n\r\n## \ud83d\udcc4 Citation\r\n\r\nIf you use QDE in your research, please cite:\r\n\r\n```\r\n@ARTICLE{11142788,\r\n author={Sachdeva, Pragati and Malhotra, Amarjit and Gupta, Karan},\r\n journal={IEEE Access}, \r\n title={Quality Data Extractor (QDE): Elevating Synthetic Data Augmentation through Post-Generation Filtration}, \r\n year={2025},\r\n doi={10.1109/ACCESS.2025.3603435}}\r\n```\r\n\r\n\r\n",
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