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Performance tests.

master
Stanislaw Adaszewski 3 years ago
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4 changed files with 630 additions and 0 deletions
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  1. +438
    -0
      docs/nodes-involved.svg
  2. +131
    -0
      experiments/decagon_run_effcat/decagon_run_effcat.py
  3. +28
    -0
      src/icosagon/compile.py
  4. +33
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      tests/icosagon/test_trainloop.py

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docs/nodes-involved.svg View File

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+ 131
- 0
experiments/decagon_run_effcat/decagon_run_effcat.py View File

@@ -0,0 +1,131 @@
#!/usr/bin/env python3
from icosagon.data import Data
from icosagon.trainprep import TrainValTest, \
prepare_training
from icosagon.model import Model
from icosagon.trainloop import TrainLoop
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_node_type('Gene', len(genes))
data.add_node_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')
fam = data.add_relation_family('PPI', 0, 0, True)
rel = fam.add_relation_type('PPI', adj_mat)
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)
fam = data.add_relation_family('Drug-Gene (Target)', 1, 0, True)
rel = fam.add_relation_type('Drug-Gene (Target)', adj_mat)
print('OK')
df_combo_effcat = df_combo.merge(df_effcat, left_on='Polypharmacy Side Effect', right_on='Side Effect')
disease_classes = []
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()))
for discls, df in df_combo_effcat.groupby('Disease Class'):
disease_classes.append(discls)
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
rel = fam.add_relation_type(df['Polypharmacy Side Effect'], adj_mat)
print()
print('len(disease_classes):', len(disease_classes))
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)
prep_d = prepare_training(data, TrainValTest(.8, .1, .1))
_wrap(Model, 'build')
model = Model(prep_d)
model = model.to(dev)
# model = torch.nn.DataParallel(model, ['cuda:0', 'cuda:1'])
_wrap(TrainLoop, 'build')
_wrap(TrainLoop, 'run_epoch')
loop = TrainLoop(model, batch_size=512, shuffle=True)
loop.run_epoch()
if __name__ == '__main__':
main()

+ 28
- 0
src/icosagon/compile.py View File

@@ -0,0 +1,28 @@
#
# The goal of this module is to make Icosagon more efficient.
# It takes the nice Icosagon model architecture and tries to
# formulate it in terms of batch matrix multiplications instead
# of using Python for loops.
#
from .weights import init_glorot
from .input
import torch
class EncodeLayer(object):
def __init__(self, num_relation_types, input_dim, output_dim):
weights = [ init_glorot(input_dim, output_dim) \
for _ in range(num_relation_types) ]
weights = torch.cat(weights)
class Compiler(object):
def __init__(self, data: Data, layer_dimensions: List[int] = [32, 64]) -> None:
self.data = data
self.layer_dimensions = layer_dimensions
self.build()
def build(self) -> None:
for fam in data.relation_families:
init_glorot(in_channels, out_channels)

+ 33
- 0
tests/icosagon/test_trainloop.py View File

@@ -79,3 +79,36 @@ def test_timing_01():
for _ in range(1300):
_ = torch.sparse.mm(adj_mat, rep)
print('Elapsed:', time.time() - t)
def test_timing_02():
adj_mat = (torch.rand(2000, 2000) < .001).to(torch.float32)
adj_mat_batch = [adj_mat.view(1, 2000, 2000)] * 1300
adj_mat_batch = torch.cat(adj_mat_batch)
rep = torch.eye(2000).requires_grad_(True)
t = time.time()
res = torch.matmul(adj_mat_batch, rep)
print('Elapsed:', time.time() - t)
print('res.shape:', res.shape)
def test_timing_03():
adj_mat = (torch.rand(2000, 2000) < .001).to(torch.float32)
adj_mat_batch = [adj_mat.view(1, 2000, 2000).to_sparse()] * 1300
adj_mat_batch = torch.cat(adj_mat_batch)
rep = torch.eye(2000).requires_grad_(True)
rep_batch = [rep.view(1, 2000, 2000)] * 1300
rep_batch = torch.cat(rep_batch)
t = time.time()
with pytest.raises(RuntimeError):
_ = torch.bmm(adj_mat_batch, rep)
print('Elapsed:', time.time() - t)
def test_timing_04():
adj_mat = (torch.rand(2000, 2000) < .0001).to(torch.float32).to_sparse()
rep = torch.eye(2000).requires_grad_(True)
t = time.time()
for _ in range(1300):
_ = torch.sparse.mm(adj_mat, rep)
print('Elapsed:', time.time() - t)

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