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New implementation for one node type normalization seems to work.

master
Stanislaw Adaszewski преди 4 години
родител
ревизия
6f80b65a46
променени са 2 файла, в които са добавени 41 реда и са изтрити 18 реда
  1. +15
    -7
      src/icosagon/normalize.py
  2. +26
    -11
      tests/icosagon/test_normalize.py

+ 15
- 7
src/icosagon/normalize.py Целия файл

@@ -46,11 +46,15 @@ def norm_adj_mat_one_node_type_sparse(adj_mat):
adj_mat = adj_mat.coalesce()
indices = adj_mat.indices()
values = adj_mat.values()
degrees = torch.zeros(adj_mat.shape[0])
degrees = degrees.index_add(0, indices[0], values.to(degrees.dtype))
print('degrees:', degrees)
print('values:', values)
values = values.to(degrees.dtype) / degrees[indices[0]]
degrees_row = torch.zeros(adj_mat.shape[0])
degrees_row = degrees_row.index_add(0, indices[0], values.to(degrees_row.dtype))
degrees_col = torch.zeros(adj_mat.shape[1])
degrees_col = degrees_col.index_add(0, indices[1], values.to(degrees_col.dtype))
# degrees_row = torch.sqrt(degrees_row)
# degrees_col = torch.sqrt(degrees_col)
# print('degrees:', degrees)
# print('values:', values)
values = values.to(degrees_row.dtype) / torch.sqrt(degrees_row[indices[0]] * degrees_col[indices[1]])
adj_mat = torch.sparse_coo_tensor(indices=indices, values=values, size=adj_mat.shape)
return adj_mat
@@ -68,8 +72,12 @@ def norm_adj_mat_one_node_type_dense(adj_mat):
raise ValueError('adj_mat must be a square matrix')
adj_mat = adj_mat + torch.eye(adj_mat.shape[0], dtype=adj_mat.dtype)
degrees = adj_mat.sum(1).view(-1, 1).to(torch.float32)
adj_mat = adj_mat.to(degrees.dtype) / degrees
degrees_row = adj_mat.sum(1).view(-1, 1).to(torch.float32)
degrees_col = adj_mat.sum(0).view(1, -1).to(torch.float32)
degrees_row = torch.sqrt(degrees_row)
degrees_col = torch.sqrt(degrees_col)
adj_mat = adj_mat.to(degrees_row.dtype) / degrees_row
adj_mat = adj_mat / degrees_col
return adj_mat


+ 26
- 11
tests/icosagon/test_normalize.py Целия файл

@@ -6,6 +6,7 @@ import decagon_pytorch.normalize
import torch
import pytest
import numpy as np
from math import sqrt
def test_add_eye_sparse_01():
@@ -53,7 +54,7 @@ def test_norm_adj_mat_one_node_type_sparse_02():
adj_mat_sparse = adj_mat_dense.to_sparse()
adj_mat_sparse = norm_adj_mat_one_node_type_sparse(adj_mat_sparse)
adj_mat_dense = norm_adj_mat_one_node_type_dense(adj_mat_dense)
assert torch.all(adj_mat_sparse.to_dense() == adj_mat_dense)
assert torch.all(adj_mat_sparse.to_dense() - adj_mat_dense < 0.000001)
def test_norm_adj_mat_one_node_type_dense_01():
@@ -68,28 +69,42 @@ def test_norm_adj_mat_one_node_type_dense_02():
[1, 0, 1, 0], # 3
[1, 1, 0, 1], # 4
[0, 0, 1, 0] # 2
# 3 3 4 2
])
expect = np.array([
[1/3, 1/3, 1/3, 0],
[1/3, 1/3, 1/3, 0],
[1/4, 1/4, 1/4, 1/4],
[0, 0, 1/2, 1/2]
expect_denom = np.array([
[ 3, 3, sqrt(3)*2, sqrt(6) ],
[ 3, 3, sqrt(3)*2, sqrt(6) ],
[ sqrt(3)*2, sqrt(3)*2, 4, sqrt(2)*2 ],
[ sqrt(6), sqrt(6), sqrt(2)*2, 2 ]
], dtype=np.float32)
expect = (adj_mat.detach().cpu().numpy().astype(np.float32) + np.eye(4)) / expect_denom
# expect = np.array([
# [1/3, 1/3, 1/3, 0],
# [1/3, 1/3, 1/3, 0],
# [1/4, 1/4, 1/4, 1/4],
# [0, 0, 1/2, 1/2]
# ], dtype=np.float32)
res = decagon_pytorch.normalize.norm_adj_mat_one_node_type(adj_mat)
res = res.todense().astype(np.float32)
print('res:', res)
print('expect:', expect)
assert torch.all(res == expect)
assert np.all(res - expect < 0.000001)
@pytest.mark.skip
def test_norm_adj_mat_one_node_type_dense_03():
adj_mat = torch.rand((10, 10))
adj_mat = (adj_mat > .5)
# adj_mat = torch.rand((10, 10))
adj_mat = torch.tensor([
[0, 1, 1, 0, 0],
[1, 0, 1, 0, 1],
[1, 1, 0, .5, .5],
[0, 0, .5, 0, 1],
[0, 1, .5, 1, 0]
])
# adj_mat = (adj_mat > .5)
adj_mat_dec = decagon_pytorch.normalize.norm_adj_mat_one_node_type(adj_mat)
adj_mat_ico = norm_adj_mat_one_node_type_dense(adj_mat)
adj_mat_dec = adj_mat_dec.todense()
adj_mat_ico = adj_mat_ico.detach().cpu().numpy()
print('adj_mat_dec:', adj_mat_dec)
print('adj_mat_ico:', adj_mat_ico)
assert np.all(adj_mat_dec == adj_mat_ico)
assert np.all(adj_mat_dec - adj_mat_ico < 0.000001)

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