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Add extra checks in Data.

master
Stanislaw Adaszewski 3 years ago
parent
commit
9ff1038728
5 changed files with 79 additions and 30 deletions
  1. +19
    -3
      src/triacontagon/data.py
  2. +10
    -2
      src/triacontagon/sampling.py
  3. +23
    -0
      src/triacontagon/util.py
  4. +10
    -10
      tests/triacontagon/test_batch.py
  5. +17
    -15
      tests/triacontagon/test_model.py

+ 19
- 3
src/triacontagon/data.py View File

@@ -9,7 +9,8 @@ from typing import Callable, \
Tuple, \
List
import types
from .util import _nonzero_sum
from .util import _nonzero_sum, \
_diag
import torch
@@ -61,13 +62,28 @@ class Data(object):
name = str(name)
vertex_type_row = int(vertex_type_row)
vertex_type_column = int(vertex_type_column)
if not isinstance(adjacency_matrices, list):
raise TypeError('adjacency_matrices must be a list of tensors')
if not isinstance(decoder_factory, types.FunctionType):
raise TypeError('decoder_factory must be a function')
if not callable(decoder_factory):
raise TypeError('decoder_factory must be callable')
if (vertex_type_row, vertex_type_column) in self.edge_types:
raise KeyError('Edge type for given combination of row and column already exists')
if vertex_type_row == vertex_type_column and \
any(torch.any(_diag(adj_mat).to(torch.bool)) \
for adj_mat in adjacency_matrices):
raise ValueError('Adjacency matrices for same row/column vertex types must have empty diagonals')
if any(adj_mat.shape[0] != self.vertex_types[vertex_type_row].count \
or adj_mat.shape[1] != self.vertex_types[vertex_type_column].count \
for adj_mat in adjacency_matrices):
raise ValueError('Adjacency matrices must have as many rows as row vertex type count and as many columns as column vertex type count')
total_connectivity = _nonzero_sum(adjacency_matrices)
self.edge_types[vertex_type_row, vertex_type_column] = \
EdgeType(name, vertex_type_row, vertex_type_column,
adjacency_matrices, decoder_factory, total_connectivity)

+ 10
- 2
src/triacontagon/sampling.py View File

@@ -120,13 +120,18 @@ def get_true_classes(adj_mat: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
return true_classes, row_count
def negative_sample_adj_mat(adj_mat: torch.Tensor) -> torch.Tensor:
def negative_sample_adj_mat(adj_mat: torch.Tensor,
remove_diagonal: bool=False) -> torch.Tensor:
if not isinstance(adj_mat, torch.Tensor):
raise ValueError('adj_mat must be a torch.Tensor, got: %s' % adj_mat.__class__.__name__)
edges_pos, degrees = get_edges_and_degrees(adj_mat)
true_classes, row_count = get_true_classes(adj_mat)
if remove_diagonal:
true_classes = torch.cat([ torch.arange(len(adj_mat)).view(-1, 1),
true_classes ], dim=1)
# true_classes = edges_pos[:, 1].view(-1, 1)
# print('true_classes:', true_classes)
@@ -164,7 +169,10 @@ def negative_sample_data(data: Data) -> Data:
for key, et in data.edge_types.items():
adjacency_matrices_neg = []
for adj_mat in et.adjacency_matrices:
adj_mat_neg = negative_sample_adj_mat(adj_mat)
remove_diagonal = True \
if et.vertex_type_row == et.vertex_type_column \
else False
adj_mat_neg = negative_sample_adj_mat(adj_mat, remove_diagonal)
adjacency_matrices_neg.append(adj_mat_neg)
res.add_edge_type(et.name,
et.vertex_type_row, et.vertex_type_column,


+ 23
- 0
src/triacontagon/util.py View File

@@ -4,6 +4,29 @@ from typing import List, \
import time
def _diag(x: torch.Tensor, make_sparse: bool=False):
if len(x.shape) < 1 or len(x.shape) > 2:
raise ValueError('Matrix or vector expected')
if not x.is_sparse and not make_sparse:
return torch.diag(x)
if len(x.shape) == 1:
indices = torch.arange(len(x)).view(1, -1)
indices = torch.cat([ indices, indices ])
return _sparse_coo_tensor(indices, x.to_dense(), (len(x),) * 2)
values = x.values()
indices = x.indices()
mask = torch.nonzero(indices[0] == indices[1], as_tuple=True)[0]
indices = torch.flatten(indices[0, mask])
order = torch.argsort(indices)
values = values[mask][order]
res = torch.zeros(min(x.shape[0], x.shape[1]), dtype=values.dtype)
res[indices] = values
return res
def _equal(x: torch.Tensor, y: torch.Tensor):
if x.is_sparse ^ y.is_sparse:
raise ValueError('Cannot mix sparse and dense tensors')


+ 10
- 10
tests/triacontagon/test_batch.py View File

@@ -33,7 +33,7 @@ def test_same_data_org_02():
torch.tensor([
[0, 0, 0, 1],
[1, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 1],
[1, 0, 1, 0]
]).to_sparse()
], dedicom_decoder)
@@ -46,7 +46,7 @@ def test_same_data_org_02():
torch.tensor([
[0, 0, 0, 1],
[1, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 1],
[1, 0, 0, 0]
]).to_sparse()
], dedicom_decoder)
@@ -94,7 +94,7 @@ def test_batcher_02():
]).to_sparse(),
torch.tensor([
[1, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
@@ -113,7 +113,7 @@ def test_batcher_02():
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, 0, 2), (1, 0, 4), (1, 1, 3), (1, 2, 4),
(1, 3, 1), (1, 4, 2) }
@@ -132,7 +132,7 @@ def test_batcher_03():
]).to_sparse(),
torch.tensor([
[1, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
@@ -162,7 +162,7 @@ def test_batcher_03():
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, 0, 2), (0, 0, 1, 0, 4), (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),
@@ -211,7 +211,7 @@ def test_batcher_05():
]).to_sparse(),
torch.tensor([
[1, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
@@ -242,7 +242,7 @@ def test_batcher_05():
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, 0, 2), (0, 0, 1, 0, 4), (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),
@@ -264,7 +264,7 @@ def test_dual_batcher_01():
]).to_sparse(),
torch.tensor([
[1, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
@@ -306,7 +306,7 @@ def test_dual_batcher_01():
expected = { (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, 0, 2), (0, 0, 1, 0, 4), (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),


+ 17
- 15
tests/triacontagon/test_model.py View File

@@ -13,10 +13,10 @@ def test_per_layer_required_vertices_01():
d.add_vertex_type('Drug', 5)
d.add_edge_type('Gene-Gene', 0, 0, [ torch.tensor([
[1, 0, 0, 1],
[0, 1, 1, 0],
[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 1]
[1, 0, 0, 0],
[0, 1, 0, 0]
]).to_sparse() ], dedicom_decoder)
d.add_edge_type('Gene-Drug', 0, 1, [ torch.tensor([
@@ -27,11 +27,11 @@ def test_per_layer_required_vertices_01():
]).to_sparse() ], dedicom_decoder)
d.add_edge_type('Drug-Drug', 1, 1, [ torch.tensor([
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 1],
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]
[0, 1, 0, 0, 1],
[1, 1, 0, 1, 0]
]).to_sparse() ], dedicom_decoder)
batch = TrainingBatch(0, 1, 0, torch.tensor([
@@ -48,10 +48,10 @@ def test_model_convolve_01():
d.add_vertex_type('Drug', 5)
d.add_edge_type('Gene-Gene', 0, 0, [ torch.tensor([
[1, 0, 0, 1],
[0, 1, 1, 0],
[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 1]
[1, 0, 0, 0],
[0, 1, 0, 0]
], dtype=torch.float).to_sparse() ], dedicom_decoder)
d.add_edge_type('Gene-Drug', 0, 1, [ torch.tensor([
@@ -62,11 +62,11 @@ def test_model_convolve_01():
], dtype=torch.float).to_sparse() ], dedicom_decoder)
d.add_edge_type('Drug-Drug', 1, 1, [ torch.tensor([
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 1, 0, 1, 0]
], dtype=torch.float).to_sparse() ], dedicom_decoder)
model = Model(d, [9, 32, 64], keep_prob=1.0,
@@ -90,8 +90,10 @@ def test_model_decode_01():
d = Data()
d.add_vertex_type('Gene', 100)
gene_gene = torch.rand(100, 100).round()
gene_gene = gene_gene - torch.diag(torch.diag(gene_gene))
d.add_edge_type('Gene-Gene', 0, 0, [
torch.rand(100, 100).round().to_sparse()
gene_gene.to_sparse()
], dedicom_decoder)
b = TrainingBatch(0, 0, 0, torch.tensor([


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