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Work on icosagon.trainprep.

master
Stanislaw Adaszewski 4 years ago
parent
3795545674
commit
7ed4bc373a
7 changed files with 499 additions and 6 deletions
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  1. +252
    -6
      docs/decagon-diagram.svg
  2. +6
    -0
      src/icosagon/__init__.py
  3. +6
    -0
      src/icosagon/data.py
  4. +29
    -0
      src/icosagon/normalize.py
  5. +42
    -0
      src/icosagon/sampling.py
  6. +106
    -0
      src/icosagon/trainprep.py
  7. +58
    -0
      tests/icosagon/test_trainprep.py

+ 252
- 6
docs/decagon-diagram.svg View File

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+ 6
- 0
src/icosagon/__init__.py View File

@@ -1 +1,7 @@
#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#
from .data import Data from .data import Data

+ 6
- 0
src/icosagon/data.py View File

@@ -1,3 +1,9 @@
#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#
from collections import defaultdict from collections import defaultdict
from dataclasses import dataclass from dataclasses import dataclass
import torch import torch


+ 29
- 0
src/icosagon/normalize.py View File

@@ -0,0 +1,29 @@
#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#
import numpy as np
import scipy.sparse as sp
def norm_adj_mat_one_node_type(adj):
adj = sp.coo_matrix(adj)
assert adj.shape[0] == adj.shape[1]
adj_ = adj + sp.eye(adj.shape[0])
rowsum = np.array(adj_.sum(1))
degree_mat_inv_sqrt = np.power(rowsum, -0.5).flatten()
degree_mat_inv_sqrt = sp.diags(degree_mat_inv_sqrt)
adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt)
return adj_normalized
def norm_adj_mat_two_node_types(adj):
adj = sp.coo_matrix(adj)
rowsum = np.array(adj.sum(1))
colsum = np.array(adj.sum(0))
rowdegree_mat_inv = sp.diags(np.nan_to_num(np.power(rowsum, -0.5)).flatten())
coldegree_mat_inv = sp.diags(np.nan_to_num(np.power(colsum, -0.5)).flatten())
adj_normalized = rowdegree_mat_inv.dot(adj).dot(coldegree_mat_inv).tocoo()
return adj_normalized

+ 42
- 0
src/icosagon/sampling.py View File

@@ -0,0 +1,42 @@
#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#
import numpy as np
import torch
import torch.utils.data
from typing import List, \
Union
def fixed_unigram_candidate_sampler(
true_classes: Union[np.array, torch.Tensor],
num_samples: int,
unigrams: List[Union[int, float]],
distortion: float = 1.):
if isinstance(true_classes, torch.Tensor):
true_classes = true_classes.detach().cpu().numpy()
if true_classes.shape[0] != num_samples:
raise ValueError('true_classes must be a 2D matrix with shape (num_samples, num_true)')
unigrams = np.array(unigrams)
if distortion != 1.:
unigrams = unigrams.astype(np.float64) ** distortion
# print('unigrams:', unigrams)
indices = np.arange(num_samples)
result = np.zeros(num_samples, dtype=np.int64)
while len(indices) > 0:
# print('len(indices):', len(indices))
sampler = torch.utils.data.WeightedRandomSampler(unigrams, len(indices))
candidates = np.array(list(sampler))
candidates = np.reshape(candidates, (len(indices), 1))
# print('candidates:', candidates)
# print('true_classes:', true_classes[indices, :])
result[indices] = candidates.T
mask = (candidates == true_classes[indices, :])
mask = mask.sum(1).astype(np.bool)
# print('mask:', mask)
indices = indices[mask]
return result

+ 106
- 0
src/icosagon/trainprep.py View File

@@ -0,0 +1,106 @@
#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#
from .sampling import fixed_unigram_candidate_sampler
import torch
from dataclasses import dataclass
from typing import Any, \
List, \
Tuple, \
Dict
from .data import NodeType
from collections import defaultdict
@dataclass
class TrainValTest(object):
train: Any
val: Any
test: Any
@dataclass
class PreparedEdges(object):
positive: TrainValTest
negative: TrainValTest
@dataclass
class PreparedRelationType(object):
name: str
node_type_row: int
node_type_column: int
adj_mat_train: torch.Tensor
edges_pos: TrainValTest
edges_neg: TrainValTest
@dataclass
class PreparedData(object):
node_types: List[NodeType]
relation_types: Dict[int, Dict[int, List[PreparedRelationType]]]
def train_val_test_split_edges(edges: torch.Tensor,
ratios: TrainValTest) -> TrainValTest:
if not isinstance(edges, torch.Tensor):
raise ValueError('edges must be a torch.Tensor')
if len(edges.shape) != 2 or edges.shape[1] != 2:
raise ValueError('edges shape must be (num_edges, 2)')
if not isinstance(ratios, TrainValTest):
raise ValueError('ratios must be a TrainValTest')
if ratios.train + ratios.val + ratios.test != 1.0:
raise ValueError('Train, validation and test ratios must add up to 1')
order = torch.randperm(len(edges))
edges = edges[order, :]
n = round(len(edges) * ratios.train)
edges_train = edges[:n]
n_1 = round(len(edges) * (ratios.train + ratios.val))
edges_val = edges[n:n_1]
edges_test = edges[n_1:]
return TrainValTest(edges_train, edges_val, edges_test)
def prepare_adj_mat(adj_mat: torch.Tensor,
ratios: TrainValTest) -> Tuple[TrainValTest, TrainValTest]:
degrees = adj_mat.sum(0)
edges_pos = torch.nonzero(adj_mat)
neg_neighbors = fixed_unigram_candidate_sampler(edges_pos[:, 1],
len(edges), degrees, 0.75)
edges_neg = torch.cat((edges_pos[:, 0], neg_neighbors.view(-1, 1)), 1)
edges_pos = train_val_test_split_edges(edges_pos, ratios)
edges_neg = train_val_test_split_edges(edges_neg, ratios)
return edges_pos, edges_neg
def prepare_relation(r, ratios):
adj_mat = r.adjacency_matrix
edges_pos, edges_neg = prepare_adj_mat(adj_mat)
adj_mat_train = torch.sparse_coo_tensor(indices = edges_pos[0].transpose(0, 1),
values=torch.ones(len(edges_pos[0]), dtype=adj_mat.dtype))
return PreparedRelation(r.name, r.node_type_row, r.node_type_column,
adj_mat_train, edges_pos, edges_neg)
def prepare_training(data):
relation_types = defaultdict(lambda: defaultdict(list))
for (node_type_row, node_type_column), rels in data.relation_types:
for r in rels:
relation_types[node_type_row][node_type_column].append(
prep_relation(r))
return PreparedData(data.node_types, relation_types)

+ 58
- 0
tests/icosagon/test_trainprep.py View File

@@ -0,0 +1,58 @@
from icosagon.trainprep import TrainValTest, \
train_val_test_split_edges
import torch
import pytest
import numpy as np
def test_train_val_test_split_edges_01():
edges = torch.randint(0, 10, (10, 2))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(edges, TrainValTest(.5, .5, .5))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(edges, TrainValTest(.2, .2, .2))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(edges, None)
with pytest.raises(ValueError):
_ = train_val_test_split_edges(edges, (.8, .1, .1))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(np.random.randint(0, 10, (10, 2)), TrainValTest(.8, .1, .1))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(torch.randint(0, 10, (10, 3)), TrainValTest(.8, .1, .1))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(torch.randint(0, 10, (10, 2, 1)), TrainValTest(.8, .1, .1))
with pytest.raises(ValueError):
_ = train_val_test_split_edges(None, TrainValTest(.8, .2, .2))
res = train_val_test_split_edges(edges, TrainValTest(.8, .1, .1))
assert res.train.shape == (8, 2) and res.val.shape == (1, 2) and \
res.test.shape == (1, 2)
res = train_val_test_split_edges(edges, TrainValTest(.8, .0, .2))
assert res.train.shape == (8, 2) and res.val.shape == (0, 2) and \
res.test.shape == (2, 2)
res = train_val_test_split_edges(edges, TrainValTest(.8, .2, .0))
assert res.train.shape == (8, 2) and res.val.shape == (2, 2) and \
res.test.shape == (0, 2)
res = train_val_test_split_edges(edges, TrainValTest(.0, .5, .5))
assert res.train.shape == (0, 2) and res.val.shape == (5, 2) and \
res.test.shape == (5, 2)
res = train_val_test_split_edges(edges, TrainValTest(.0, .0, 1.))
assert res.train.shape == (0, 2) and res.val.shape == (0, 2) and \
res.test.shape == (10, 2)
res = train_val_test_split_edges(edges, TrainValTest(.0, 1., .0))
assert res.train.shape == (0, 2) and res.val.shape == (10, 2) and \
res.test.shape == (0, 2)
# if ratios.train + ratios.val + ratios.test != 1.0:
# raise ValueError('Train, validation and test ratios must add up to 1')
#
# order = torch.randperm(len(edges))
# edges = edges[order, :]
# n = round(len(edges) * ratios.train)
# edges_train = edges[:n]
# n_1 = round(len(edges) * (ratios.train + ratios.val))
# edges_val = edges[n:n_1]
# edges_test = edges[n_1:]
#
# return TrainValTest(edges_train, edges_val, edges_test)

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