Add test_normalize.

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)