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Get Started

Basic Install by pip

You can install the stable release on PyPI:

$ pip install pygmtools

or get the latest version by running:

$ pip install -U https://github.com/Thinklab-SJTU/pygmtools/archive/master.zip # with --user for user install (no root)

The following packages are required, and shall be automatically installed by pip:

Python >= 3.5
requests >= 2.25.1
scipy >= 1.4.1
Pillow >= 7.2.0
numpy >= 1.18.5
easydict >= 1.7
appdirs >= 1.4.4
tqdm >= 4.64.1

Note that we support different deep learning architectures pytorch, paddle, jittor. You may see ./numerical_backends for the introduction and examples on different backends.

Example: Matching Isomorphic Graphs with numpy backend

Here we provide a basic example of matching two isomorphic graphs (i.e. two graphs have the same nodes and edges, but the node permutations are unknown) with the default numpy backend to show the usage of pygmtools.

Step 0: Import packages and set backend

>>> import numpy as np
>>> import pygmtools as pygm
>>> pygm.BACKEND = 'numpy'
>>> np.random.seed(1)

Step 1: Generate a batch of isomorphic graphs

>>> batch_size = 3
>>> X_gt = np.zeros((batch_size, 4, 4))
>>> X_gt[:, np.arange(0, 4, dtype=np.int64), np.random.permutation(4)] = 1
>>> A1 = np.random.rand(batch_size, 4, 4)
>>> A2 = np.matmul(np.matmul(X_gt.transpose((0, 2, 1)), A1), X_gt)
>>> n1 = n2 = np.repeat([4], batch_size)

Step 2: Build an affinity matrix and select an affinity function

>>> conn1, edge1, ne1 = pygm.utils.dense_to_sparse(A1)
>>> conn2, edge2, ne2 = pygm.utils.dense_to_sparse(A2)
>>> import functools
>>> gaussian_aff = functools.partial(pygm.utils.gaussian_aff_fn, sigma=1.) # set affinity function
>>> K = pygm.utils.build_aff_mat(None, edge1, conn1, None, edge2, conn2, n1, ne1, n2, ne2, edge_aff_fn=gaussian_aff)

Step 3: Solve graph matching by RRWM

>>> X = pygm.rrwm(K, n1, n2, beta=100)
>>> X = pygm.hungarian(X)
>>> X # X is the permutation matrix
[[[0. 0. 0. 1.]
  [0. 0. 1. 0.]
  [1. 0. 0. 0.]
  [0. 1. 0. 0.]]

 [[0. 0. 0. 1.]
  [0. 0. 1. 0.]
  [1. 0. 0. 0.]
  [0. 1. 0. 0.]]

 [[0. 0. 0. 1.]
  [0. 0. 1. 0.]
  [1. 0. 0. 0.]
  [0. 1. 0. 0.]]]

Final Step: Evaluate the accuracy

>>> (X * X_gt).sum() / X_gt.sum()
1.0

What's Next

Please checkout ../auto_examples/index to see how to apply pygmtools to tackle real-world problems. You may see ../api/pygmtools for the API documentation.