semvec/pypi-packages huggingface dataset:
graphflex 0.1.1
Flexible Framework for Graph Feature Engineering
License: MIT
Requires Python: >=3.10
Keywords: graph, feafture-engineering

# GraphFlex

**Flexible Framework for Graph Feature Engineering**

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---
GraphFlex is a modular and extensible framework for graph-based feature engineering in Python. It allows seamless integration of graph datasets with traditional machine learning pipelines using familiar tools like `scikit-learn`.

> 🔗 Homepage & Documentation: [GraphFlex on GitHub](https://github.com/predict-idlab/graphflex)

## 📦 Installation

```bash
pip install graphflex
```
### Optional Dependencies

GraphFlex supports several optional extras. Install them with:

```bash
pip install "graphflex[dgl]"
pip install "graphflex[neo4j]"
pip install "graphflex[rdflib]"
pip install "graphflex[full]"  # all optional features
```

---

## 🔍 Example Usage

```python
# GraphFlex pipeline
from graphflex import GraphFlex
from graphflex.functions.postprocessing.filter import NonUniqueFeatureFilter
from graphflex.functions.feature import MeanStdFeature
from graphflex.functions.edgenode import NumericalEdgeNode

connect = Connector(...) #use defined connector here
gflex = GraphFlex(
    connector=connect,
    node_feature=MeanStdFeature(),
    edge_node_feature=NumericalEdgeNode(),
    post_processor=NonUniqueFeatureFilter()
)
nodes = ...
feature_matrix = gflex.fit_transform(nodes)
```
---

## ✨ Features

- Plug-and-play feature extraction for graph nodes
- Compatible with `scikit-learn` pipelines
- Support for multiple graph backends (DGL, RDFLib-HDT, Neo4j, ...)
- Built-in feature functions and postprocessing modules
- Easily extendable with custom logic
---

## 📚 Documentation

For full documentation, examples, and API reference, visit the [GraphFlex repository](https://github.com/predict-idlab/graphflex).

---

## ⚙ Dependencies

- Python ≥ 3.10
- `numpy`, `pandas`, `scikit-learn`, `tqdm`
- Optional: `dgl`, `torch`, `torchdata`, `rdflib-hdt`, `neo4j`, `PyYAML`, `pydantic`

---

## 👤 Author

**Bram Steenwinckel** – [bram.steenwinckel@ugent.be](mailto:bram.steenwinckel@ugent.be)

---

## 📄 License
This project is licensed under the MIT License.
pytorch-complex 0.2.1
PyTorch wrapper for complex-valued operations. Usage: Similar to PyTorch. For using the Complex features of this library, just change the regular torch imports with torchcomplex imports. For example: import torchcomplex.nn as nn instead of import torch.nn as nn Then, simply nn.Conv2d for both torch and torchcomplex, for 2D Convolution
License: MIT
Requires Python: <4.0,>=3.11

# pytorch-complex

Install it using pip: 

pip install pytorch-complex

Usage:
Similar to PyTorch.
For using the Complex features of this library, just change the regular torch imports with torchcomplex imports.
For example:
import torchcomplex.nn as nn  instead of import torch.nn as nn
Then, simply nn.Conv2d for both torch and torchcomplex, for 2D Convolution

## Credits

If you like this repository, please click on Star!

If you use this package or benift from the codes of this repo, please cite the following in your publications:

> [Soumick Chatterjee, Pavan Tummala, Oliver Speck, Andreas Nürnberger: Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN (IEEE IPAS, Dec 2023)](https://arxiv.org/abs/2302.04584)

BibTeX entry:

```bibtex
@inproceedings{chatterjee2022complex,
  title={Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN},
  author={Chatterjee, Soumick and Tummala, Pavan and Speck, Oliver and N{\"u}rnberger, Andreas},
  booktitle={2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}
```
Thank you so much for your support.