Skip to main content

No project description provided

Project description

mG

Tests

Usage

  • Install libmg by running pip install git+https://github.com/Unicam-mG/mG.git
  • Create a Dataset object containing the Graph instances to process
  • Define dictionaries of Psi, Phi, Sigma objects as needed by your application
  • Define a CompilationConfig that is appropriate for your Dataset
  • Create a GNNCompiler using the dictionaries and the CompilationConfig
  • Create an appropriate Loader for your Dataset: use the SingleGraphLoader if your Dataset contains a single graph and use the MultipleGraphLoader otherwise.
  • Build a model from your mG formulas using the model = GNNCompiler.compile(expr) method.
  • Use output = model.predict(loader.load(), steps=loader.steps_per_epoch) or a loop like
    for x, y in loader.load():
        output = model(x)
    
    to run your model on the dataset.
  • Check the tests folder for some examples of the above steps.

Compatibility

Python 3.10

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

libmg-1.0.1.tar.gz (45.9 kB view details)

Uploaded Source

Built Distribution

libmg-1.0.1-py3-none-any.whl (51.6 kB view details)

Uploaded Python 3

File details

Details for the file libmg-1.0.1.tar.gz.

File metadata

  • Download URL: libmg-1.0.1.tar.gz
  • Upload date:
  • Size: 45.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.0 CPython/3.10.12 Linux/6.2.0-36-generic

File hashes

Hashes for libmg-1.0.1.tar.gz
Algorithm Hash digest
SHA256 a8ae3a3f7cfbedc74a1c6da697e57fa1e7889cdf9cb92ed791a94e709318dd30
MD5 9eeb50422c18ac1e1509843a79a030ac
BLAKE2b-256 9258ca24b8a1c9c1b1e916e1e3d4fb162f76fbd236a89d191c67f1a9ffb7a887

See more details on using hashes here.

File details

Details for the file libmg-1.0.1-py3-none-any.whl.

File metadata

  • Download URL: libmg-1.0.1-py3-none-any.whl
  • Upload date:
  • Size: 51.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.0 CPython/3.10.12 Linux/6.2.0-36-generic

File hashes

Hashes for libmg-1.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 3feedef2fab9fd14d213a6c9cc02ff6fc8244690588e975988e03cae64c5973c
MD5 29092035ab2946d35c6aa88e7722a02f
BLAKE2b-256 e4321d588587ed6073d676e0ae08318f57f2318ee33443f45b42dc888abeb705

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page