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- from triacontagon.data import Data
- from triacontagon.sampling import fixed_unigram_candidate_sampler, \
- get_true_classes, \
- negative_sample_adj_mat, \
- negative_sample_data
- from triacontagon.decode import dedicom_decoder
- import torch
- import time
-
-
- def test_fixed_unigram_candidate_sampler_01():
- true_classes = torch.tensor([[-1],
- [-1],
- [ 3],
- [ 2],
- [-1]])
- num_repeats = torch.tensor([0, 0, 1, 1, 0])
- unigrams = torch.tensor([0., 0., 1., 1., 0.], dtype=torch.float64)
- distortion = 0.75
- res = fixed_unigram_candidate_sampler(true_classes, num_repeats,
- unigrams, distortion)
- print('res:', res)
-
-
- def test_get_true_classes_01():
- adj_mat = torch.tensor([
- [0, 1, 0, 1, 0],
- [0, 0, 0, 0, 1],
- [1, 1, 0, 0, 0],
- [0, 0, 1, 0, 1],
- [0, 1, 0, 0, 0]
- ], dtype=torch.float).to_sparse()
-
- true_classes, row_count = get_true_classes(adj_mat)
- print('true_classes:', true_classes)
-
- true_classes = torch.repeat_interleave(true_classes, row_count, dim=0)
-
- assert torch.all(true_classes == torch.tensor([
- [1, 3],
- [1, 3],
- [4, -1],
- [0, 1],
- [0, 1],
- [2, 4],
- [2, 4],
- [1, -1]
- ]))
-
-
- def test_get_true_classes_02():
- adj_mat = torch.rand(2000, 2000).round().to_sparse()
-
- t = time.time()
- true_classes, row_count = get_true_classes(adj_mat)
- print('Elapsed:', time.time() - t)
-
- print('true_classes.shape:', true_classes.shape)
-
-
- def test_negative_sample_adj_mat_01():
- adj_mat = torch.tensor([
- [0, 1, 0, 1, 0],
- [0, 0, 0, 0, 1],
- [1, 1, 0, 0, 0],
- [0, 0, 1, 0, 1],
- [0, 1, 0, 0, 0]
- ])
-
- print('adj_mat:', adj_mat)
-
- adj_mat_neg = negative_sample_adj_mat(adj_mat.to_sparse())
-
- print('adj_mat_neg:', adj_mat_neg.to_dense())
-
-
- def test_negative_sample_data_01():
- 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, 1, 0, 0, 0],
- [0, 0, 1, 0, 1],
- [0, 1, 0, 0, 0]
- ], dtype=torch.float).to_sparse()
- ], dedicom_decoder)
-
- d_neg = negative_sample_data(d)
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