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test_fastmodel.py 1.5KB

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  1. from icosagon.fastmodel import FastModel
  2. from icosagon.data import Data
  3. from icosagon.trainprep import prepare_training, \
  4. TrainValTest
  5. import torch
  6. import time
  7. def _make_symmetric(x: torch.Tensor):
  8. x = (x + x.transpose(0, 1)) / 2
  9. return x
  10. def _symmetric_random(n_rows, n_columns):
  11. return _make_symmetric(torch.rand((n_rows, n_columns),
  12. dtype=torch.float32).round().to_sparse())
  13. def _some_data_with_interactions():
  14. d = Data()
  15. d.add_node_type('Gene', 1000)
  16. d.add_node_type('Drug', 100)
  17. fam = d.add_relation_family('Drug-Gene', 1, 0, True)
  18. fam.add_relation_type('Target',
  19. torch.rand((100, 1000), dtype=torch.float32).round().to_sparse())
  20. fam = d.add_relation_family('Gene-Gene', 0, 0, True)
  21. fam.add_relation_type('Interaction',
  22. _symmetric_random(1000, 1000))
  23. fam = d.add_relation_family('Drug-Drug', 1, 1, True)
  24. for i in range(500):
  25. fam.add_relation_type('Side Effect: Nausea %d' % i,
  26. _symmetric_random(100, 100))
  27. fam.add_relation_type('Side Effect: Infertility %d' % i,
  28. _symmetric_random(100, 100))
  29. fam.add_relation_type('Side Effect: Death %d' % i,
  30. _symmetric_random(100, 100))
  31. return d
  32. def test_fast_model_01():
  33. d = _some_data_with_interactions()
  34. prep_d = prepare_training(d, TrainValTest(.8, .1, .1))
  35. model = FastModel(prep_d)
  36. for i in range(10):
  37. t = time.time()
  38. _ = model(None)
  39. print('Model forward took:', time.time() - t)