IF YOU WOULD LIKE TO GET AN ACCOUNT, please write an email to s dot adaszewski at gmail dot com. User accounts are meant only to report issues and/or generate pull requests. This is a purpose-specific Git hosting for ADARED projects. Thank you for your understanding!
sadaszewski1
/
decagon-pytorch
forked from sadaszewski/decagon-pytorch
1
0
Fork 0
Code Issues 0 Pull Requests 0 Releases 0 Wiki Activity
Browse Source

More batcher tests.

master
Stanislaw Adaszewski 5 years ago
parent
14e3d14b36
commit
8f86fc0833
1 changed files with 125 additions and 45 deletions
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +125
    -45
      tests/triacontagon/test_batch.py

+ 125
- 45
tests/triacontagon/test_batch.py View File

@@ -68,52 +68,132 @@ def test_batcher_02():
def test_batcher_03(): 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)
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) }
def test_batcher_04():
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=3)
visited = set()
for t in b:
print(t)
for e in t.edges:
k = tuple(e.tolist())
visited.add(k)
assert visited == { (0, 1), (0, 3),
(1, 4), (2, 0), (3, 2), (4, 3) }
def test_batcher_05():
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=5)
visited = set()
for t in b:
print(t)
for e in t.edges:
k = (t.vertex_type_row, t.vertex_type_column, k = (t.vertex_type_row, t.vertex_type_column,
t.relation_type_index,) + \ t.relation_type_index,) + \
tuple(t.edges[0].tolist())
tuple(e.tolist())
visited.add(k) 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) }
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) }

Write Preview
Loading…
Cancel
Save