Skip to content

GitLab

Commit 6f1329ef authored by Dingquan Yu's avatar Dingquan Yu Committed by Jennifer Wei
Browse files

Update tests and comments

parent 9a6eb649
Show whitespace changes
Inline Side-by-side
Showing with 99 additions and 38 deletions
tests/test_permutation.py
View file @ 6f1329ef
......@@ -48,7 +48,8 @@ class TestPermutation(unittest.TestCase):
self.chain_a_num_res = 9
self.chain_b_num_res = 13
# below create default fake ground truth structures for a hetero-pentamer A2B3
self.residue_index = list(range(self.chain_a_num_res)) * 2 + list(range(self.chain_b_num_res)) * 3
self.residue_index = list(
range(self.chain_a_num_res)) * 2 + list(range(self.chain_b_num_res)) * 3
self.num_res = self.chain_a_num_res * 2 + self.chain_b_num_res * 3
self.asym_id = torch.tensor([[1] * self.chain_a_num_res + [2] * self.chain_a_num_res + [
3] * self.chain_b_num_res + [4] * self.chain_b_num_res + [5] * self.chain_b_num_res], device=device)
......@@ -56,7 +57,6 @@ class TestPermutation(unittest.TestCase):
self.entity_id = torch.tensor([[1] * (self.chain_a_num_res * 2) + [2] * (self.chain_b_num_res * 3)],
device=device)
# @unittest.skip("skip for now")
def test_1_selecting_anchors(self):
batch = {
'asym_id': self.asym_id,
......@@ -64,7 +64,8 @@ class TestPermutation(unittest.TestCase):
'entity_id': self.entity_id,
'seq_length': torch.tensor([57])
}
anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(batch, batch['asym_id'])
anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(
batch, batch['asym_id'])
anchor_gt_asym = int(anchor_gt_asym)
anchor_pred_asym = {int(i) for i in anchor_pred_asym}
expected_anchors = {1, 2}
......@@ -76,8 +77,31 @@ class TestPermutation(unittest.TestCase):
self.assertEqual(anchor_pred_asym, expected_anchors & anchor_pred_asym)
self.assertEqual(set(), anchor_pred_asym & expected_non_anchors)
# @unittest.skip("skip for now")
def test_2_permutation_pentamer(self):
"""
Test the permutation results on a pentamer A2B3, in which protein A has 9 residues
and protein B has 13 residues.
Expected outputs:
Only protein A should be selected as an anchor thus, in the output list, either [(0,1), (1,0)] or [(0,0), (1,1)] are allowed
The 3 chains from protein B should ALWAYS be aligned in a way that predicted b1 to be aligned with ground truth b1, pred b2 to ground truth b2
as shown below:
predicted structure: a2 - a1 - b2 - b3 - b1
indexes in the predicted list: 0 1 2 3 4
ground truth structure: a1 - a2 - b1 - b2 - b3
indexes in the ground truth list: 0 1 2 3 4
then the 2 protein A chains are free to be aligned by either order, thus either [(0,1),(1,0)] or [(0,0),(1,1)] is valid.
However, the 3 protein B chains should be strictly aligned in the following order:
[(2,3), (3,4), (4,1)], regardless of how protein A chains are aligned.
Therefore, the only 2 correct permutations are :
[(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)] and
[(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]
"""
batch = {
'asym_id': self.asym_id,
'sym_id': self.sym_id,
......@@ -95,16 +119,22 @@ class TestPermutation(unittest.TestCase):
chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
chain_b3_pos = torch.matmul(torch.matmul(
chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
# Below permutate predicted chain positions
pred_atom_position = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
# here the b2 chain from the ground truth is deliberately put in b1 chain's position, and predicted b3 chain to b2's position
# and predicted b1 chain to b3's position
pred_atom_position = torch.cat(
(chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
pred_atom_mask = torch.ones((1, self.num_res, 37))
out = {
'final_atom_positions': pred_atom_position,
'final_atom_mask': pred_atom_mask
}
true_atom_position = torch.cat((chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
true_atom_position = torch.cat(
(chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1, self.chain_b_num_res, 37)),
......@@ -115,12 +145,33 @@ class TestPermutation(unittest.TestCase):
aligns, per_asym_residue_index = compute_permutation_alignment(out, batch,
batch)
possible_outcome = [[(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)], [(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]]
wrong_outcome = [[(0, 1), (1, 0), (2, 4), (3, 2), (4, 3)], [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]]
self.assertIn(aligns, possible_outcome)
self.assertNotIn(aligns, wrong_outcome)
# @unittest.skip("Test needs to be fixed post-refactor")
expected_asym_residue_index = {
1: torch.tensor(list(range(self.chain_a_num_res))),
2: torch.tensor(list(range(self.chain_a_num_res))),
3: torch.tensor(list(range(self.chain_b_num_res))),
4: torch.tensor(list(range(self.chain_b_num_res))),
5: torch.tensor(list(range(self.chain_b_num_res)))
}
chain_a_permutated_chain_b_permutated = [
(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)]
chain_a_not_permutated_chain_b_permutated = [
(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]
chain_a_permutated_chain_b_not_permuated = [
(0, 1), (1, 0), (2, 2), (3, 3), (4, 4)]
chain_a_not_permutated_chain_b_not_permuated = [
(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
# test on the permutation alignments
self.assertIn(aligns, [chain_a_permutated_chain_b_permutated,
chain_a_not_permutated_chain_b_permutated])
self.assertNotIn(aligns, [chain_a_permutated_chain_b_not_permuated,
chain_a_not_permutated_chain_b_not_permuated])
# test on the per_aysm_residue_index
for k, v in expected_asym_residue_index.items():
self.assertTrue(torch.equal(v, per_asym_residue_index[k]))
def test_3_merge_labels(self):
nres_pad = 325 - 57 # suppose the cropping size is 325
batch = {
......@@ -140,17 +191,21 @@ class TestPermutation(unittest.TestCase):
chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
chain_b3_pos = torch.matmul(torch.matmul(
chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
# Below permutate predicted chain positions
pred_atom_position = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
pred_atom_position = torch.cat(
(chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
pred_atom_mask = torch.ones((1, self.num_res, 37))
pred_atom_position = pad_features(pred_atom_position, nres_pad, pad_dim=1)
pred_atom_position = pad_features(
pred_atom_position, nres_pad, pad_dim=1)
pred_atom_mask = pad_features(pred_atom_mask, nres_pad, pad_dim=1)
out = {
'final_atom_positions': pred_atom_position,
'final_atom_mask': pred_atom_mask
}
true_atom_position = torch.cat((chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
true_atom_position = torch.cat(
(chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1, self.chain_b_num_res, 37)),
......@@ -168,14 +223,18 @@ class TestPermutation(unittest.TestCase):
'aatype': torch.randint(21, size=(1, 325)),
'seq_length': torch.tensor([57])
}
fake_input_features['asym_id'] = fake_input_features['asym_id'].reshape(1, 325)
fake_input_features["residue_index"] = pad_features(torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1)
fake_input_features['all_atom_positions'] = pad_features(true_atom_position, nres_pad, pad_dim=1)
fake_input_features['all_atom_mask'] = pad_features(true_atom_mask, nres_pad=nres_pad, pad_dim=1)
fake_input_features['asym_id'] = fake_input_features['asym_id'].reshape(
1, 325)
fake_input_features["residue_index"] = pad_features(
torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1)
fake_input_features['all_atom_positions'] = pad_features(
true_atom_position, nres_pad, pad_dim=1)
fake_input_features['all_atom_mask'] = pad_features(
true_atom_mask, nres_pad=nres_pad, pad_dim=1)
# NOTE
# batch: simulates ground_truth features
# fake_input_features: simulates the data that gonna be used as input for model.forward(fake_input_features)
# fake_input_features: simulates the data that are going be used as input for model.forward(fake_input_features)
# out: simulates the output of model.forward(fake_input_features)
aligns, per_asym_residue_index = compute_permutation_alignment(out,
fake_input_features,
......@@ -185,9 +244,11 @@ class TestPermutation(unittest.TestCase):
labels = merge_labels(per_asym_residue_index, labels, aligns,
original_nres=batch['aatype'].shape[-1])
self.assertTrue(torch.equal(labels['residue_index'], batch['residue_index']))
self.assertTrue(torch.equal(
labels['residue_index'], batch['residue_index']))
expected_permutated_gt_pos = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos),
dim=1)
self.assertTrue(torch.equal(labels['all_atom_positions'], expected_permutated_gt_pos))
self.assertTrue(torch.equal(
labels['all_atom_positions'], expected_permutated_gt_pos))
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment