def __getitem__(self, i):
""" Gets alignment pair.
Parameters
----------
i : int
Index of item
Returns
-------
gene : torch.Tensor
Encoded representation of protein of interest
pos : torch.Tensor
Encoded representation of protein that aligns with `gene`.
states : torch.Tensor
Alignment string
alignment_matrix : torch.Tensor
Ground truth alignment matrix
"""
gene = self.pairs.loc[i, 0]
pos = self.pairs.loc[i, 1]
assert len(gene) == len(pos)
alnstr = list(zip(list(gene), list(pos)))
states = torch.Tensor(list(map(state_f, alnstr)))
gene = self.tokenizer(str.encode(gene.replace('-', '')))
pos = self.tokenizer(str.encode(pos.replace('-', '')))
gene = torch.Tensor(gene).long()
pos = torch.Tensor(pos).long()
alignment_matrix = torch.from_numpy(states2matrix(states))
return gene, pos, states, alignment_matrix
def test_states2matrix_swap_y(self):
s = "::1122::"
s = np.array(list(map(tmstate_f, s)))
npt.assert_allclose(s, np.array([1, 1, 0, 0, 2, 2, 1, 1]))
M = states2matrix(s, sparse=True)
res_coords = list(zip(list(M.row), list(M.col)))
exp_coords = [(0, 0), (1, 1), (2, 1), (3, 1), (3, 2), (3, 3), (4, 4),
(5, 5)]
self.assertListEqual(res_coords, exp_coords)
def test_states2matrix_only_matches(self):
s = ":11::11:"
s = np.array(list(map(tmstate_f, s)))
npt.assert_allclose(s, np.array([1, 0, 0, 1, 1, 0, 0, 1]))
M = states2matrix(s, sparse=True)
res_coords = list(zip(list(M.row), list(M.col)))
exp_coords = [(0, 0), (1, 0), (2, 0), (3, 1), (4, 2), (5, 2), (6, 2),
(7, 3)]
self.assertListEqual(res_coords, exp_coords)
def __getitem__(self, i):
""" Gets alignment pair.
Parameters
----------
i : int
Index of item
Returns
-------
gene : torch.Tensor
Encoded representation of protein of interest
pos : torch.Tensor
Encoded representation of protein that aligns with `gene`.
states : torch.Tensor
Alignment string
alignment_matrix : torch.Tensor
Ground truth alignment matrix
path_matrix : torch.Tensor
Pairwise path distances, where the smallest distance
to the path is computed for every element in the matrix.
"""
gene = self.pairs.iloc[i]['chain1']
pos = self.pairs.iloc[i]['chain2']
st = self.pairs.iloc[i]['alignment']
states = list(map(tmstate_f, st))
if self.clip_ends:
gene, pos, states, st = clip_boundaries(gene, pos, states, st)
if self.pad_ends:
states = [m] + states + [m]
states = torch.Tensor(states).long()
gene = self.tokenizer(str.encode(gene))
pos = self.tokenizer(str.encode(pos))
gene = torch.Tensor(gene).long()
pos = torch.Tensor(pos).long()
alignment_matrix = torch.from_numpy(states2matrix(states))
path_matrix = torch.empty(*alignment_matrix.shape)
g_mask = torch.ones(*alignment_matrix.shape)
if self.construct_paths:
pi = states2edges(states)
path_matrix = torch.from_numpy(path_distance_matrix(pi))
path_matrix = reshape(path_matrix, len(gene), len(pos))
if self.mask_gaps:
g_mask = torch.from_numpy(gap_mask(st)).bool()
alignment_matrix = reshape(alignment_matrix, len(gene), len(pos))
g_mask = reshape(g_mask, len(gene), len(pos))
if not self.return_names:
return gene, pos, states, alignment_matrix, path_matrix, g_mask
else:
gene_name = self.pairs.iloc[i]['chain1_name']
pos_name = self.pairs.iloc[i]['chain2_name']
return (gene, pos, states, alignment_matrix, path_matrix, g_mask,
gene_name, pos_name)
def test_states2matrix_zinc(self):
s = ':1111::::1:'
# x = 'RGCFH '
# y = 'YGSVHASERH'
s = np.array(list(map(tmstate_f, s)))
states2matrix(s, sparse=True)