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Add first triacontagon experiment.

master
Stanislaw Adaszewski 6 months ago
parent
commit
ebcd38c910
2 changed files with 148 additions and 1 deletions
  1. +146
    -0
      experiments/triacontagon_run/triacontagon_run.py
  2. +2
    -1
      src/triacontagon/util.py

+ 146
- 0
experiments/triacontagon_run/triacontagon_run.py View File

@@ -0,0 +1,146 @@
#!/usr/bin/env python3
from triacontagon.data import Data
from triacontagon.split import split_data
from triacontagon.model import Model
from triacontagon.loop import TrainLoop
from triacontagon.decode import dedicom_decoder
from triacontagon.util import common_one_hot_encoding
import os
import pandas as pd
from bisect import bisect_left
import torch
import sys
def index(a, x):
i = bisect_left(a, x)
if i != len(a) and a[i] == x:
return i
raise ValueError
def load_data(dev):
path = '/pstore/data/data_science/ref/decagon'
df_combo = pd.read_csv(os.path.join(path, 'bio-decagon-combo.csv'))
df_effcat = pd.read_csv(os.path.join(path, 'bio-decagon-effectcategories.csv'))
df_mono = pd.read_csv(os.path.join(path, 'bio-decagon-mono.csv'))
df_ppi = pd.read_csv(os.path.join(path, 'bio-decagon-ppi.csv'))
df_tgtall = pd.read_csv(os.path.join(path, 'bio-decagon-targets-all.csv'))
df_tgt = pd.read_csv(os.path.join(path, 'bio-decagon-targets.csv'))
lst = [ 'df_combo', 'df_effcat', 'df_mono', 'df_ppi', 'df_tgtall', 'df_tgt' ]
for nam in lst:
print(f'len({nam}): {len(locals()[nam])}')
print(f'{nam}.columns: {locals()[nam].columns}')
genes = set()
genes = genes.union(df_ppi['Gene 1']).union(df_ppi['Gene 2']) \
.union(df_tgtall['Gene']).union(df_tgt['Gene'])
genes = sorted(genes)
print('len(genes):', len(genes))
drugs = set()
drugs = drugs.union(df_combo['STITCH 1']).union(df_combo['STITCH 2']) \
.union(df_mono['STITCH']).union(df_tgtall['STITCH']).union(df_tgt['STITCH'])
drugs = sorted(drugs)
print('len(drugs):', len(drugs))
data = Data()
data.add_vertex_type('Gene', len(genes))
data.add_vertex_type('Drug', len(drugs))
print('Preparing PPI...')
print('Indexing rows...')
rows = [index(genes, g) for g in df_ppi['Gene 1']]
print('Indexing cols...')
cols = [index(genes, g) for g in df_ppi['Gene 2']]
indices = list(zip(rows, cols))
indices = torch.tensor(indices).transpose(0, 1)
values = torch.ones(len(rows))
print('indices.shape:', indices.shape, 'values.shape:', values.shape)
adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(genes),) * 2,
device=dev)
adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
print('adj_mat created')
data.add_edge_type('PPI', 0, 0, [ adj_mat ], dedicom_decoder)
print('OK')
print('Preparing Drug-Gene (Target) edges...')
rows = [index(drugs, d) for d in df_tgtall['STITCH']]
cols = [index(genes, g) for g in df_tgtall['Gene']]
indices = list(zip(rows, cols))
indices = torch.tensor(indices).transpose(0, 1)
values = torch.ones(len(rows))
adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(drugs), len(genes)),
device=dev)
data.add_edge_type('Drug-Gene', 1, 0, [ adj_mat ], dedicom_decoder)
data.add_edge_type('Gene-Drug', 0, 1, [ adj_mat.transpose(0, 1) ], dedicom_decoder)
print('OK')
print('Preparing Drug-Drug (Side Effect) edges...')
fam = data.add_relation_family('Drug-Drug (Side Effect)', 1, 1, True)
print('# of side effects:', len(df_combo), 'unique:', len(df_combo['Polypharmacy Side Effect'].unique()))
adjacency_matrices = []
side_effect_names = []
for eff, df in df_combo.groupby('Polypharmacy Side Effect'):
sys.stdout.write('.') # print(eff, '...')
sys.stdout.flush()
rows = [index(drugs, d) for d in df['STITCH 1']]
cols = [index(drugs, d) for d in df['STITCH 2']]
indices = list(zip(rows, cols))
indices = torch.tensor(indices).transpose(0, 1)
values = torch.ones(len(rows))
adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(drugs), len(drugs)),
device=dev)
adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
adjacency_matrices.append(adj_mat)
side_effect_names.append(df['Polypharmacy Side Effect'])
fam.add_edge_type('Drug-Drug', 1, 1, adjacency_matrices, dedicom_decoder)
print()
print('OK')
return data
def _wrap(obj, method_name):
orig_fn = getattr(obj, method_name)
def fn(*args, **kwargs):
print(f'{method_name}() :: ENTER')
res = orig_fn(*args, **kwargs)
print(f'{method_name}() :: EXIT')
return res
setattr(obj, method_name, fn)
def main():
dev = torch.device('cuda:0')
data = load_data(dev)
train_data, val_data, test_data = split_data(data, (.8, .1, .1))
n = sum(vt.count for vt in data.vertex_types)
model = Model(data, [n, 32, 64], keep_prob=.9,
conv_activation=torch.sigmoid,
dec_activation=torch.sigmoid).to(dev)
initial_repr = common_one_hot_encoding([ vt.count \
for vt in data.vertex_types ], device=dev)
loop = TrainLoop(model, val_data, test_data,
initial_repr, max_epochs=50, batch_size=512,
loss=torch.nn.functional.binary_cross_entropy_with_logits,
lr=0.001)
loop.run()
with open('/pstore/data/data_science/year/2020/adaszews/models/triacontagon/basic_run.pth', 'wb') as f:
torch.save(model.state_dict(), f)
if __name__ == '__main__':
main()

+ 2
- 1
src/triacontagon/util.py View File

@@ -225,7 +225,7 @@ def _select_rows(a: torch.Tensor, rows: torch.Tensor):
return res
def common_one_hot_encoding(vertex_type_counts: List[int]) -> \
def common_one_hot_encoding(vertex_type_counts: List[int], device=None) -> \
List[torch.Tensor]:
tot = sum(vertex_type_counts)
@@ -241,6 +241,7 @@ def common_one_hot_encoding(vertex_type_counts: List[int]) -> \
])
val = torch.ones(cnt)
x = _sparse_coo_tensor(ind, val, size=(cnt, tot))
x = x.to(device)
res.append(x)
ofs += cnt


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