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Approximate fuzzy subgraph matching in polynomial time

Project description

Fornax

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An implementation of NeMa: Fast Graph Search with Label Similarity using python3 and sqlite or postgres.

FORNAX

Install

Via pip:

pip install fornax

Note that Fornax requires numpy to be installed (part of the SciPy ecosystem) which in turn has non python dependencies. The available options for installing SciPy packages are listed here.

We recomend installing fornax via anaconda

conda create --name myenv python=3.6
source activate myenv
pip install fornax

Install (Dev)

From the root directory:

# install dev dependencies
pip install -r requirements/dev.txt

# install fornax
pip install -e .

View the Docs

View the docs at: fornax.readthedocs.io

Test

From the root directory

python run_test.py

Tutorials

See the tutorials for a full working example.

  • Part 1 - Download a small graph dataset
  • Part 2 - Search the dataset using fornax

Install Tutorial Dependencies (using conda)

The following tutorials use jupyter notebooks to create a worked example. We recommend you use the anaconda python distribution to run the notebooks.

conda env create -f environment.yml

Run the Tutorials

source activate fornax_tutorial
cd docs/tutorial
jupyter-notebook

Documentation

Build the Docs

# install docs dependencies
pip install -r requirements/docs.txt
# install fornax
pip install .

# build
cd docs
make html

View the Docs Locally

cd _build/html
python3 -m http.server

navigate to 0.0.0.0:8000 in your browser.

Project details


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