dict-lookup-mpnn-problem 0.1.1

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This project implements a graph generator inspired by the DictionaryLookup Benchmark from "How Attentive are Graph Neural Networks".

The problem is described as follows:

1. A bi-partite graph, G, is provided, with nodes split into two sets TOP, BOT.

2. The set TOP is a subset of Keys.

3. The set BOT is a bijection Keys -> Values represented as a subset of Keys x Values.

• For example if ("A", 1) is in BOT then no other element can have "A" for a key or 1 as a value.

4. The goal is to map each key in TOP to the approriate value via BOT(key).

The encoding provided by this repo is to take: Key = 1..n_keys and Values = 1..n_vals for some integers, n_vals and n_vals. The primary interface to this code is the gen_problems function which takes these in along with a random seed and provides an infinite sequence of dictionary lookup problems.

from dict_lookup_mpnn_problem import gen_problems

problems = gen_problems(n_keys=2, n_vals=3, seed=12)
p1 = next(problems)

• Here p1 is a Problem object with three relevant attributes:
  1. adj: Adjacency matrix (it's block anti-diagonal).
  2. nodes: Array of node features (see below).
  3. answers: What value the ith node should decode to.
• The feature encoding is the 1-hot encoding of the key concatenated with the 1-hot encoding of the value. If no value is present, the all zeros vector is used.

An example output for n_keys=2 and n_vals=3 is provided below.

Problem(nodes=array([[1., 0., 0., 0., 0.],
                     [0., 1., 0., 0., 0.],
                     [1., 0., 1., 0., 0.],
                     [0., 1., 0., 0., 1.]]),
        adj=array([[0., 0., 1., 1.], 
                   [0., 0., 1., 1.],
                   [1., 1., 0., 0.],
                   [1., 1., 0., 0.]]),
        answers=array([0, 2]),
        n_keys=2)