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ddi_dataset.py
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ddi_dataset.py
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import numpy as np
import torch.utils.data
def ddi_collate_paired_batch(paired_batch):
pos_batch = []
neg_batch = []
seg_pos_neg = []
pos_se_i = 0
for ddi_pair in paired_batch:
pos_ddi, neg_ddis = ddi_pair
pos_batch += [pos_ddi] # flatten negative instances
neg_batch += neg_ddis
*_, pos_ses, _ = pos_ddi
for _ in range(len(pos_ses)):
seg_pos_neg += [pos_se_i] * len(neg_ddis)
pos_se_i += 1
seg_pos_neg = torch.LongTensor(np.array(seg_pos_neg))
pos_batch = ddi_collate_batch(pos_batch, return_label=False)
neg_batch = ddi_collate_batch(neg_batch, return_label=False)
return pos_batch, neg_batch, seg_pos_neg
def ddi_collate_batch(batch, return_label):
drug1, drug2, se_idx_lists, label = list(zip(*batch))
ddi_idxs1, ddi_idxs2 = collate_drug_pairs(drug1, drug2)
drug1 = (*collate_drugs(drug1), *ddi_idxs1)
drug2 = (*collate_drugs(drug2), *ddi_idxs2)
se_idx, se_seg = collate_side_effect(se_idx_lists)
if return_label:
label = np.hstack([
[label_i] * len(ses_i) for ses_i, label_i in zip(se_idx_lists, label)])
return (*drug1, *drug2, se_idx, se_seg, label)
else:
return (*drug1, *drug2, se_idx, se_seg)
def collate_drug_pairs(drugs1, drugs2):
n_atom1 = [d['n_atom'] for d in drugs1]
n_atom2 = [d['n_atom'] for d in drugs2]
c_atom1 = [sum(n_atom1[:k]) for k in range(len(n_atom1))]
c_atom2 = [sum(n_atom2[:k]) for k in range(len(n_atom2))]
ddi_seg_i1, ddi_seg_i2, ddi_idx_j1, ddi_idx_j2 = zip(*[
(i1 + c1, i2 + c2, i2, i1)
for l1, l2, c1, c2 in zip(n_atom1, n_atom2, c_atom1, c_atom2)
for i1 in range(l1) for i2 in range(l2)])
ddi_seg_i1 = torch.LongTensor(ddi_seg_i1)
ddi_idx_j1 = torch.LongTensor(ddi_idx_j1)
ddi_seg_i2 = torch.LongTensor(ddi_seg_i2)
ddi_idx_j2 = torch.LongTensor(ddi_idx_j2)
return (ddi_seg_i1, ddi_idx_j1), (ddi_seg_i2, ddi_idx_j2)
def collate_side_effect(se_idx_lists):
se_idx = torch.LongTensor(np.hstack(se_idx_lists).astype(np.int64))
se_seg = np.hstack([[i] * len(ses_i) for i, ses_i in enumerate(se_idx_lists)])
se_seg = torch.LongTensor(se_seg)
return se_idx, se_seg
def collate_drugs(drugs):
c_atoms = [sum(d['n_atom'] for d in drugs[:k]) for k in range(len(drugs))]
atom_feat = torch.FloatTensor(np.vstack([d['atom_feat'] for d in drugs]))
atom_type = torch.LongTensor(np.hstack([d['atom_type'] for d in drugs]))
bond_type = torch.LongTensor(np.hstack([d['bond_type'] for d in drugs]))
bond_seg_i = torch.LongTensor(np.hstack([
np.array(d['bond_seg_i']) + c for d, c in zip(drugs, c_atoms)]))
bond_idx_j = torch.LongTensor(np.hstack([
np.array(d['bond_idx_j']) + c for d, c in zip(drugs, c_atoms)]))
batch_seg_m = torch.LongTensor(np.hstack([
[k] * d['n_atom'] for k, d in enumerate(drugs)]))
return batch_seg_m, atom_type, atom_feat, bond_type, bond_seg_i, bond_idx_j
class PolypharmacyDataset(torch.utils.data.Dataset):
def __init__(
self,
drug_structure_dict,
se_idx_dict,
se_pos_dps=None,
se_neg_dps=None,
negative_sampling=False,
negative_sample_ratio=1,
n_max_batch_se=1,
paired_input=False):
assert se_pos_dps
assert se_neg_dps or negative_sampling
assert not (se_neg_dps and negative_sampling)
assert type(negative_sample_ratio) is int and negative_sample_ratio >= 1
self.negative_sampling = negative_sampling
self.paired_input = paired_input
self.se_idx_dict = se_idx_dict
"""
print("Se idx dict ")
with open("se_idx_dict.txt", "w") as filename:
for se in se_idx_dict:
print(se, se_idx_dict[se], file=filename)
"""
self.drug_structure_dict = drug_structure_dict
"""
print("Drug struct dict ")
with open("drug_struct_dict.txt", "w") as filename1:
for drug in drug_structure_dict:
print(drug, drug_structure_dict[se], file=filename1)
"""
self.drug_idx_list = list(drug_structure_dict.keys())
self.n_inst_batch_se = n_max_batch_se
self.n_corrupt = negative_sample_ratio
self.pos_ddis = self.collate_given_positive_set(se_pos_dps, se_idx_dict, negative_sampling)
self.neg_ddis = self.collate_given_negative_set(se_neg_dps, se_idx_dict)
self.feeding_insts = None
self.prepare_feeding_insts()
def collate_given_negative_set(self, se_neg_dps, se_idx_dict):
''' From se -> dps mapping to dp -> ses mapping '''
neg_ddis = {}
if se_neg_dps:
for se, dps in se_neg_dps.items():
for dp in dps:
if dp not in neg_ddis:
neg_ddis[dp] = []
neg_ddis[dp] += [se_idx_dict[se]]
return neg_ddis
def mapping_transpose(self, se_dps_dict):
''' From `se -> dps` mapping to `dp -> ses` mapping '''
dp_ses_dict = {}
for se, dps in se_dps_dict.items():
for dp in dps:
if dp not in dp_ses_dict:
dp_ses_dict[dp] = []
dp_ses_dict[dp] += [se]
return dp_ses_dict
def collate_given_positive_set(self, se_pos_dps, se_idx_dict, negative_sampling):
''' From se -> dps mapping to dp -> ses mapping '''
pos_ddis = {}
flip_drug_pair = lambda dp: tuple(reversed(dp))
for se, dps in se_pos_dps.items():
if negative_sampling:
dps = dps + list(map(flip_drug_pair, dps))
for dp in dps:
if dp not in pos_ddis:
pos_ddis[dp] = []
pos_ddis[dp] += [se_idx_dict[se]]
return pos_ddis
def prepare_feeding_insts(self):
def collect_with_proper_size_se(ddis, inst_label):
''' To reduce the duplicated computing on same graph pair for different labels. '''
# split number of ses in k * batch(ses) to account
# for d-d with many vs few ses
ddis = dict(ddis)
inst_list = []
for dp, ses in ddis.items():
n_se_batch = int(np.ceil(len(ses) / self.n_inst_batch_se))
for i in range(n_se_batch):
start = i * self.n_inst_batch_se
end = (i+1) * self.n_inst_batch_se
inst_list += [(*dp, ses[start: end], inst_label)]
return inst_list
pos_insts = collect_with_proper_size_se(self.pos_ddis, inst_label=True)
if self.negative_sampling:
feeding_insts = []
rand_drugs = list(np.random.choice(
self.drug_idx_list, size=self.n_corrupt * len(pos_insts)))
if not self.paired_input:
feeding_insts = pos_insts
for pos_inst in pos_insts:
d1, _, ses, _ = pos_inst
corr_insts = [(d1, rand_drugs.pop(), ses, False) for _ in range(self.n_corrupt)]
if self.paired_input:
paired_feed = (pos_inst, corr_insts)
feeding_insts += [paired_feed]
else:
feeding_insts += corr_insts
else:
neg_insts = collect_with_proper_size_se(self.neg_ddis, inst_label=False)
feeding_insts = pos_insts + neg_insts
self.feeding_insts = feeding_insts
def __len__(self):
return len(self.feeding_insts)
def __getitem__(self, idx):
instance = self.feeding_insts[idx]
# drug lookup
if self.paired_input:
pos_inst, neg_insts = instance
pos_inst = self.drug_structure_lookup(pos_inst)
neg_insts = list(map(self.drug_structure_lookup, neg_insts))
return pos_inst, neg_insts
else:
instance = self.drug_structure_lookup(instance)
return instance
def drug_structure_lookup(self, instance):
drug_idx1, drug_idx2, se_idx_lists, label = instance
drug1 = self.drug_structure_dict[drug_idx1]
drug2 = self.drug_structure_dict[drug_idx2]
return drug1, drug2, se_idx_lists, label