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Move back to using single-level dictionary for Data.relation_types.

master
Stanislaw Adaszewski 4 vuotta sitten
vanhempi
commit
bd894a0257
5 muutettua tiedostoa jossa 49 lisäystä ja 78 poistoa
  1. +11
    -22
      src/icosagon/convlayer.py
  2. +15
    -23
      src/icosagon/data.py
  3. +17
    -27
      src/icosagon/declayer.py
  4. +4
    -4
      src/icosagon/trainprep.py
  5. +2
    -2
      tests/icosagon/test_convlayer.py

+ 11
- 22
src/icosagon/convlayer.py Näytä tiedosto

@@ -51,34 +51,23 @@ class DecagonLayer(torch.nn.Module):
self.build()
def build(self):
n = len(self.data.node_types)
rel_types = self.data.relation_types
self.next_layer_repr = [ [] for _ in range(n) ]
self.next_layer_repr = [ [] for _ in range(len(self.data.node_types)) ]
for node_type_row in range(n):
if node_type_row not in rel_types:
for (node_type_row, node_type_column), rels in self.data.relation_types.items():
if len(rels) == 0:
continue
for node_type_column in range(n):
if node_type_column not in rel_types[node_type_row]:
continue
rels = rel_types[node_type_row][node_type_column]
if len(rels) == 0:
continue
convolutions = []
convolutions = []
for r in rels:
conv = DropoutGraphConvActivation(self.input_dim[node_type_column],
self.output_dim[node_type_row], r.adjacency_matrix,
self.keep_prob, self.rel_activation)
for r in rels:
conv = DropoutGraphConvActivation(self.input_dim[node_type_column],
self.output_dim[node_type_row], r.adjacency_matrix,
self.keep_prob, self.rel_activation)
convolutions.append(conv)
convolutions.append(conv)
self.next_layer_repr[node_type_row].append(
Convolutions(node_type_column, convolutions))
self.next_layer_repr[node_type_row].append(
Convolutions(node_type_column, convolutions))
def __call__(self, prev_layer_repr):
next_layer_repr = [ [] for _ in range(len(self.data.node_types)) ]


+ 15
- 23
src/icosagon/data.py Näytä tiedosto

@@ -7,6 +7,9 @@
from collections import defaultdict
from dataclasses import dataclass
import torch
from typing import List, \
Dict, \
Tuple
@dataclass
@@ -25,9 +28,12 @@ class RelationType(object):
class Data(object):
node_types: List[NodeType]
relation_types: Dict[Tuple[int, int], List[RelationType]]
def __init__(self) -> None:
self.node_types = []
self.relation_types = defaultdict(lambda: defaultdict(list))
self.relation_types = defaultdict(list)
def add_node_type(self, name: str, count: int) -> None:
name = str(name)
@@ -73,14 +79,14 @@ class Data(object):
adjacency_matrix_backward is not None:
raise ValueError('Relation between nodes of the same type must be expressed using a single matrix')
self.relation_types[node_type_row][node_type_column].append(
self.relation_types[node_type_row, node_type_column].append(
RelationType(name, node_type_row, node_type_column,
adjacency_matrix, False))
if node_type_row != node_type_column and two_way:
if adjacency_matrix_backward is None:
adjacency_matrix_backward = adjacency_matrix.transpose(0, 1)
self.relation_types[node_type_column][node_type_row].append(
self.relation_types[node_type_column, node_type_row].append(
RelationType(name, node_type_column, node_type_row,
adjacency_matrix_backward, True))
@@ -99,16 +105,15 @@ class Data(object):
s_1 = ''
count = 0
for i in range(n):
for j in range(n):
if i not in self.relation_types or \
j not in self.relation_types[i]:
for node_type_row in range(n):
for node_type_column in range(n):
if (node_type_row, node_type_column) not in self.relation_types:
continue
s_1 += ' - ' + self.node_types[i].name + ' -- ' + \
self.node_types[j].name + ':\n'
s_1 += ' - ' + self.node_types[node_type_row].name + ' -- ' + \
self.node_types[node_type_column].name + ':\n'
for r in self.relation_types[i][j]:
for r in self.relation_types[node_type_row, node_type_column]:
if r.is_autogenerated:
continue
s_1 += ' - ' + r.name + '\n'
@@ -118,16 +123,3 @@ class Data(object):
s += s_1
return s.strip()
# n = sum(map(len, self.relation_types))
#
# for i in range(n):
# for j in range(n):
# key = (i, j)
# if key not in self.relation_types:
# continue
# rels = self.relation_types[key]
#
# for r in rels:
#
# return s.strip()

+ 17
- 27
src/icosagon/declayer.py Näytä tiedosto

@@ -44,37 +44,27 @@ class DecodeLayer(torch.nn.Module):
def build(self) -> None:
self.decoders = {}
n = len(self.data.node_types)
relation_types = self.data.relation_types
for node_type_row in range(n):
if node_type_row not in relation_types:
for (node_type_row, node_type_column), rels in self.data.relation_types.items():
if len(rels) == 0:
continue
for node_type_column in range(n):
if node_type_column not in relation_types[node_type_row]:
continue
rels = relation_types[node_type_row][node_type_column]
if len(rels) == 0:
continue
if isinstance(self.decoder_class, dict):
if (node_type_row, node_type_column) in self.decoder_class:
decoder_class = self.decoder_class[node_type_row, node_type_column]
elif (node_type_column, node_type_row) in self.decoder_class:
decoder_class = self.decoder_class[node_type_column, node_type_row]
else:
raise KeyError('Decoder not specified for edge type: %s -- %s' % (
self.data.node_types[node_type_row].name,
self.data.node_types[node_type_column].name))
if isinstance(self.decoder_class, dict):
if (node_type_row, node_type_column) in self.decoder_class:
decoder_class = self.decoder_class[node_type_row, node_type_column]
elif (node_type_column, node_type_row) in self.decoder_class:
decoder_class = self.decoder_class[node_type_column, node_type_row]
else:
decoder_class = self.decoder_class
raise KeyError('Decoder not specified for edge type: %s -- %s' % (
self.data.node_types[node_type_row].name,
self.data.node_types[node_type_column].name))
else:
decoder_class = self.decoder_class
self.decoders[node_type_row, node_type_column] = \
decoder_class(self.input_dim[node_type_row],
num_relation_types = len(rels),
keep_prob = self.keep_prob,
activation = self.activation)
self.decoders[node_type_row, node_type_column] = \
decoder_class(self.input_dim[node_type_row],
num_relation_types = len(rels),
keep_prob = self.keep_prob,
activation = self.activation)
def forward(self, last_layer_repr: List[torch.Tensor]) -> Dict[Tuple[int, int], List[torch.Tensor]]:
res = {}


+ 4
- 4
src/icosagon/trainprep.py Näytä tiedosto

@@ -46,7 +46,7 @@ class PreparedRelationType(object):
@dataclass
class PreparedData(object):
node_types: List[NodeType]
relation_types: Dict[int, Dict[int, List[PreparedRelationType]]]
relation_types: Dict[Tuple[int, int], List[PreparedRelationType]]
def train_val_test_split_edges(edges: torch.Tensor,
@@ -137,9 +137,9 @@ def prepare_training(data: Data) -> PreparedData:
if not isinstance(data, Data):
raise ValueError('data must be of class Data')
relation_types = defaultdict(lambda: defaultdict(list))
for (node_type_row, node_type_column), rels in data.relation_types:
relation_types = defaultdict(list)
for (node_type_row, node_type_column), rels in data.relation_types.items():
for r in rels:
relation_types[node_type_row][node_type_column].append(
relation_types[node_type_row, node_type_column].append(
prep_relation_type(r))
return PreparedData(data.node_types, relation_types)

+ 2
- 2
tests/icosagon/test_convlayer.py Näytä tiedosto

@@ -86,7 +86,7 @@ def test_decagon_layer_04():
in_layer = OneHotInputLayer(d)
multi_dgca = MultiDGCA([10], 32,
[r.adjacency_matrix for r in d.relation_types[0][0]],
[r.adjacency_matrix for r in d.relation_types[0, 0]],
keep_prob=1., activation=lambda x: x)
d_layer = DecagonLayer(in_layer.output_dim, 32, d,
@@ -129,7 +129,7 @@ def test_decagon_layer_05():
in_layer = OneHotInputLayer(d)
multi_dgca = MultiDGCA([100, 100], 32,
[r.adjacency_matrix for r in d.relation_types[0][0]],
[r.adjacency_matrix for r in d.relation_types[0, 0]],
keep_prob=1., activation=lambda x: x)
d_layer = DecagonLayer(in_layer.output_dim, output_dim=32, data=d,


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