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Add input to icosagon.

master
Stanislaw Adaszewski 4 years ago
parent
commit
d4dd1f2923
4 changed files with 184 additions and 0 deletions
  1. +0
    -0
      src/icosagon/convlayer.py
  2. +2
    -0
      src/icosagon/declayer.py
  3. +76
    -0
      src/icosagon/input.py
  4. +106
    -0
      tests/icosagon/test_input.py

src/icosagon/layer.py → src/icosagon/convlayer.py View File


+ 2
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src/icosagon/declayer.py View File

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# from .layer import DecagonLayer
# from .input import OneHotInputLayer

+ 76
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src/icosagon/input.py View File

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#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#
import torch
from typing import Union, \
List
from .data import Data
class InputLayer(torch.nn.Module):
def __init__(self, data: Data, output_dim: Union[int, List[int]] = None, **kwargs) -> None:
output_dim = output_dim or \
list(map(lambda a: a.count, data.node_types))
if not isinstance(output_dim, list):
output_dim = [output_dim,] * len(data.node_types)
super().__init__(**kwargs)
self.output_dim = output_dim
self.data = data
self.is_sparse=False
self.node_reps = None
self.build()
def build(self) -> None:
self.node_reps = []
for i, nt in enumerate(self.data.node_types):
reps = torch.rand(nt.count, self.output_dim[i])
reps = torch.nn.Parameter(reps)
self.register_parameter('node_reps[%d]' % i, reps)
self.node_reps.append(reps)
def forward(self, x) -> List[torch.nn.Parameter]:
return self.node_reps
def __repr__(self) -> str:
s = ''
s += 'Icosagon input layer with output_dim: %s\n' % self.output_dim
s += ' # of node types: %d\n' % len(self.data.node_types)
for nt in self.data.node_types:
s += ' - %s (%d)\n' % (nt.name, nt.count)
return s.strip()
class OneHotInputLayer(torch.nn.Module):
def __init__(self, data: Data, **kwargs) -> None:
output_dim = [ a.count for a in data.node_types ]
super().__init__(**kwargs)
self.output_dim = output_dim
self.data = data
self.is_sparse=True
self.node_reps = None
self.build()
def build(self) -> None:
self.node_reps = []
for i, nt in enumerate(self.data.node_types):
reps = torch.eye(nt.count).to_sparse()
reps = torch.nn.Parameter(reps)
self.register_parameter('node_reps[%d]' % i, reps)
self.node_reps.append(reps)
def forward(self, x) -> List[torch.nn.Parameter]:
return self.node_reps
def __repr__(self) -> str:
s = ''
s += 'One-hot Icosagon input layer\n'
s += ' # of node types: %d\n' % len(self.data.node_types)
for nt in self.data.node_types:
s += ' - %s (%d)\n' % (nt.name, nt.count)
return s.strip()

+ 106
- 0
tests/icosagon/test_input.py View File

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from icosagon.input import InputLayer, \
OneHotInputLayer
from icosagon.data import Data
import torch
import pytest
def _some_data():
d = Data()
d.add_node_type('Gene', 1000)
d.add_node_type('Drug', 100)
d.add_relation_type('Target', 1, 0, torch.rand(100, 1000))
d.add_relation_type('Interaction', 0, 0, torch.rand(1000, 1000))
d.add_relation_type('Side Effect: Nausea', 1, 1, torch.rand(100, 100))
d.add_relation_type('Side Effect: Infertility', 1, 1, torch.rand(100, 100))
d.add_relation_type('Side Effect: Death', 1, 1, torch.rand(100, 100))
return d
def _some_data_with_interactions():
d = Data()
d.add_node_type('Gene', 1000)
d.add_node_type('Drug', 100)
d.add_relation_type('Target', 1, 0,
torch.rand((100, 1000), dtype=torch.float32).round())
d.add_relation_type('Interaction', 0, 0,
torch.rand((1000, 1000), dtype=torch.float32).round())
d.add_relation_type('Side Effect: Nausea', 1, 1,
torch.rand((100, 100), dtype=torch.float32).round())
d.add_relation_type('Side Effect: Infertility', 1, 1,
torch.rand((100, 100), dtype=torch.float32).round())
d.add_relation_type('Side Effect: Death', 1, 1,
torch.rand((100, 100), dtype=torch.float32).round())
return d
def test_input_layer_01():
d = _some_data()
for output_dim in [32, 64, 128]:
layer = InputLayer(d, output_dim)
assert layer.output_dim[0] == output_dim
assert len(layer.node_reps) == 2
assert layer.node_reps[0].shape == (1000, output_dim)
assert layer.node_reps[1].shape == (100, output_dim)
assert layer.data == d
def test_input_layer_02():
d = _some_data()
layer = InputLayer(d, 32)
res = layer(None)
assert isinstance(res[0], torch.Tensor)
assert isinstance(res[1], torch.Tensor)
assert res[0].shape == (1000, 32)
assert res[1].shape == (100, 32)
assert torch.all(res[0] == layer.node_reps[0])
assert torch.all(res[1] == layer.node_reps[1])
def test_input_layer_03():
if torch.cuda.device_count() == 0:
pytest.skip('No CUDA devices on this host')
d = _some_data()
layer = InputLayer(d, 32)
device = torch.device('cuda:0')
layer = layer.to(device)
print(list(layer.parameters()))
# assert layer.device.type == 'cuda:0'
assert layer.node_reps[0].device == device
assert layer.node_reps[1].device == device
def test_one_hot_input_layer_01():
d = _some_data()
layer = OneHotInputLayer(d)
assert layer.output_dim == [1000, 100]
assert len(layer.node_reps) == 2
assert layer.node_reps[0].shape == (1000, 1000)
assert layer.node_reps[1].shape == (100, 100)
assert layer.data == d
assert layer.is_sparse
def test_one_hot_input_layer_02():
d = _some_data()
layer = OneHotInputLayer(d)
res = layer(None)
assert isinstance(res[0], torch.Tensor)
assert isinstance(res[1], torch.Tensor)
assert res[0].shape == (1000, 1000)
assert res[1].shape == (100, 100)
assert torch.all(res[0].to_dense() == layer.node_reps[0].to_dense())
assert torch.all(res[1].to_dense() == layer.node_reps[1].to_dense())
def test_one_hot_input_layer_03():
if torch.cuda.device_count() == 0:
pytest.skip('No CUDA devices on this host')
d = _some_data()
layer = OneHotInputLayer(d)
device = torch.device('cuda:0')
layer = layer.to(device)
print(list(layer.parameters()))
# assert layer.device.type == 'cuda:0'
assert layer.node_reps[0].device == device
assert layer.node_reps[1].device == device

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