decagon-pytorch/tests/triacontagon/test_batch.py

from triacontagon.batch import Batcher
from triacontagon.data import Data
from triacontagon.decode import dedicom_decoder
import torch


def test_batcher_01():
    d = Data()
    d.add_vertex_type('Gene', 5)

    d.add_edge_type('Gene-Gene', 0, 0, [
        torch.tensor([
            [0, 1, 0, 1, 0],
            [0, 0, 0, 0, 1],
            [1, 0, 0, 0, 0],
            [0, 0, 1, 0, 0],
            [0, 0, 0, 1, 0]
        ]).to_sparse()
    ], dedicom_decoder)

    b = Batcher(d, batch_size=1)

    visited = set()
    for t in b:
        print(t)
        k = tuple(t.edges[0].tolist())
        visited.add(k)

    assert visited == { (0, 1), (0, 3),
        (1, 4), (2, 0), (3, 2), (4, 3) }


def test_batcher_02():
    d = Data()
    d.add_vertex_type('Gene', 5)

    d.add_edge_type('Gene-Gene', 0, 0, [
        torch.tensor([
            [0, 1, 0, 1, 0],
            [0, 0, 0, 0, 1],
            [1, 0, 0, 0, 0],
            [0, 0, 1, 0, 0],
            [0, 0, 0, 1, 0]
        ]).to_sparse(),

        torch.tensor([
            [1, 0, 1, 0, 0],
            [0, 0, 0, 1, 0],
            [0, 0, 0, 0, 1],
            [0, 1, 0, 0, 0],
            [0, 0, 1, 0, 0]
        ]).to_sparse()
    ], dedicom_decoder)

    b = Batcher(d, batch_size=1)

    visited = set()
    for t in b:
        print(t)
        k = (t.relation_type_index,) + \
            tuple(t.edges[0].tolist())
        visited.add(k)

    assert visited == { (0, 0, 1), (0, 0, 3),
        (0, 1, 4), (0, 2, 0), (0, 3, 2), (0, 4, 3),
        (1, 0, 0), (1, 0, 2), (1, 1, 3), (1, 2, 4),
        (1, 3, 1), (1, 4, 2) }


def test_batcher_03():
        d = Data()
        d.add_vertex_type('Gene', 5)
        d.add_vertex_type('Drug', 4)

        d.add_edge_type('Gene-Gene', 0, 0, [
            torch.tensor([
                [0, 1, 0, 1, 0],
                [0, 0, 0, 0, 1],
                [1, 0, 0, 0, 0],
                [0, 0, 1, 0, 0],
                [0, 0, 0, 1, 0]
            ]).to_sparse(),

            torch.tensor([
                [1, 0, 1, 0, 0],
                [0, 0, 0, 1, 0],
                [0, 0, 0, 0, 1],
                [0, 1, 0, 0, 0],
                [0, 0, 1, 0, 0]
            ]).to_sparse()
        ], dedicom_decoder)

        d.add_edge_type('Gene-Drug', 0, 1, [
            torch.tensor([
                [0, 1, 0, 0],
                [1, 0, 0, 1],
                [0, 1, 0, 0],
                [0, 0, 1, 0],
                [0, 1, 1, 0]
            ]).to_sparse()
        ], dedicom_decoder)

        b = Batcher(d, batch_size=1)

        visited = set()
        for t in b:
            print(t)
            k = (t.vertex_type_row, t.vertex_type_column,
                t.relation_type_index,) + \
                    tuple(t.edges[0].tolist())
            visited.add(k)

        assert visited == { (0, 0, 0, 0, 1), (0, 0, 0, 0, 3),
            (0, 0, 0, 1, 4), (0, 0, 0, 2, 0), (0, 0, 0, 3, 2), (0, 0, 0, 4, 3),
            (0, 0, 1, 0, 0), (0, 0, 1, 0, 2), (0, 0, 1, 1, 3), (0, 0, 1, 2, 4),
            (0, 0, 1, 3, 1), (0, 0, 1, 4, 2),
            (0, 1, 0, 0, 1), (0, 1, 0, 1, 0), (0, 1, 0, 1, 3),
            (0, 1, 0, 2, 1), (0, 1, 0, 3, 2), (0, 1, 0, 4, 1),
            (0, 1, 0, 4, 2) }