diff --git a/tests/triacontagon/test_normalize.py b/tests/triacontagon/test_normalize.py new file mode 100644 index 0000000..e8de28f --- /dev/null +++ b/tests/triacontagon/test_normalize.py @@ -0,0 +1,185 @@ +from triacontagon.normalize import add_eye_sparse, \ + norm_adj_mat_one_node_type_sparse, \ + norm_adj_mat_one_node_type_dense, \ + norm_adj_mat_one_node_type, \ + norm_adj_mat_two_node_types_sparse, \ + norm_adj_mat_two_node_types_dense, \ + norm_adj_mat_two_node_types +import decagon_pytorch.normalize +import torch +import pytest +import numpy as np +from math import sqrt + + +def test_add_eye_sparse_01(): + adj_mat_dense = torch.rand((10, 10)) + adj_mat_sparse = adj_mat_dense.to_sparse() + + adj_mat_dense += torch.eye(10) + adj_mat_sparse = add_eye_sparse(adj_mat_sparse) + + assert torch.all(adj_mat_sparse.to_dense() == adj_mat_dense) + + +def test_add_eye_sparse_02(): + adj_mat_dense = torch.rand((10, 20)) + adj_mat_sparse = adj_mat_dense.to_sparse() + + with pytest.raises(ValueError): + _ = add_eye_sparse(adj_mat_sparse) + + +def test_add_eye_sparse_03(): + adj_mat_dense = torch.rand((10, 10)) + + with pytest.raises(ValueError): + _ = add_eye_sparse(adj_mat_dense) + + +def test_add_eye_sparse_04(): + adj_mat_dense = np.random.rand(10, 10) + + with pytest.raises(ValueError): + _ = add_eye_sparse(adj_mat_dense) + + +def test_norm_adj_mat_one_node_type_sparse_01(): + adj_mat = torch.rand((10, 10)) + adj_mat = (adj_mat > .5).to(torch.float32) + adj_mat = adj_mat.to_sparse() + _ = norm_adj_mat_one_node_type_sparse(adj_mat) + + +def test_norm_adj_mat_one_node_type_sparse_02(): + adj_mat_dense = torch.rand((10, 10)) + adj_mat_dense = (adj_mat_dense > .5).to(torch.float32) + 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 < 0.000001) + + +def test_norm_adj_mat_one_node_type_dense_01(): + adj_mat = torch.rand((10, 10)) + adj_mat = (adj_mat > .5) + _ = norm_adj_mat_one_node_type_dense(adj_mat) + + +def test_norm_adj_mat_one_node_type_dense_02(): + adj_mat = torch.tensor([ + [0, 1, 1, 0], # 3 + [1, 0, 1, 0], # 3 + [1, 1, 0, 1], # 4 + [0, 0, 1, 0] # 2 + # 3 3 4 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 np.all(res - expect < 0.000001) + + +def test_norm_adj_mat_one_node_type_dense_03(): + # 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 < 0.000001) + + +def test_norm_adj_mat_two_node_types_sparse_01(): + adj_mat = torch.rand((10, 20)) + adj_mat = (adj_mat > .5) + adj_mat = adj_mat.to_sparse() + _ = norm_adj_mat_two_node_types_sparse(adj_mat) + + +def test_norm_adj_mat_two_node_types_sparse_02(): + adj_mat_dense = torch.rand((10, 20)) + adj_mat_dense = (adj_mat_dense > .5) + adj_mat_sparse = adj_mat_dense.to_sparse() + adj_mat_sparse = norm_adj_mat_two_node_types_sparse(adj_mat_sparse) + adj_mat_dense = norm_adj_mat_two_node_types_dense(adj_mat_dense) + assert torch.all(adj_mat_sparse.to_dense() - adj_mat_dense < 0.000001) + + +def test_norm_adj_mat_two_node_types_dense_01(): + adj_mat = torch.rand((10, 20)) + adj_mat = (adj_mat > .5) + _ = norm_adj_mat_two_node_types_dense(adj_mat) + + +def test_norm_adj_mat_two_node_types_dense_02(): + adj_mat = torch.tensor([ + [0, 1, 1, 0], # 2 + [1, 0, 1, 0], # 2 + [1, 1, 0, 1], # 3 + [0, 0, 1, 0] # 1 + # 2 2 3 1 + ]) + expect_denom = np.array([ + [ 2, 2, sqrt(6), sqrt(2) ], + [ 2, 2, sqrt(6), sqrt(2) ], + [ sqrt(6), sqrt(6), 3, sqrt(3) ], + [ sqrt(2), sqrt(2), sqrt(3), 1 ] + ], dtype=np.float32) + expect = adj_mat.detach().cpu().numpy().astype(np.float32) / expect_denom + res = decagon_pytorch.normalize.norm_adj_mat_two_node_types(adj_mat) + res = res.todense().astype(np.float32) + print('res:', res) + print('expect:', expect) + assert np.all(res - expect < 0.000001) + + +def test_norm_adj_mat_two_node_types_dense_03(): + 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_dec = decagon_pytorch.normalize.norm_adj_mat_two_node_types(adj_mat) + adj_mat_ico = norm_adj_mat_two_node_types_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 < 0.000001) + + +def test_norm_adj_mat_two_node_types_dense_04(): + adj_mat = torch.rand((10, 20)) + adj_mat_dec = decagon_pytorch.normalize.norm_adj_mat_two_node_types(adj_mat) + adj_mat_ico = norm_adj_mat_two_node_types_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 < 0.000001)