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import os.path as osp
from typing import Any, Callable, List, Optional, Union
import numpy as np
import torch
from torch import Tensor
from torch_geometric.data import Data, InMemoryDataset
from torch_geometric.utils import stochastic_blockmodel_graph
class StochasticBlockModelDataset(InMemoryDataset):
r"""A synthetic graph dataset generated by the stochastic block model.
The node features of each block are sampled from normal distributions where
the centers of clusters are vertices of a hypercube, as computed by the
:meth:`sklearn.datasets.make_classification` method.
Args:
root (str): Root directory where the dataset should be saved.
block_sizes ([int] or LongTensor): The sizes of blocks.
edge_probs ([[float]] or FloatTensor): The density of edges going from
each block to each other block. Must be symmetric if the graph is
undirected.
num_graphs (int, optional): The number of graphs. (default: :obj:`1`)
num_channels (int, optional): The number of node features. If given
as :obj:`None`, node features are not generated.
(default: :obj:`None`)
is_undirected (bool, optional): Whether the graph to generate is
undirected. (default: :obj:`True`)
transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes
in an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed
before being saved to disk. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**kwargs (optional): The keyword arguments that are passed down to the
:meth:`sklearn.datasets.make_classification` method for drawing
node features.
"""
def __init__(
self,
root: str,
block_sizes: Union[List[int], Tensor],
edge_probs: Union[List[List[float]], Tensor],
num_graphs: int = 1,
num_channels: Optional[int] = None,
is_undirected: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
**kwargs: Any,
) -> None:
if not isinstance(block_sizes, torch.Tensor):
block_sizes = torch.tensor(block_sizes, dtype=torch.long)
if not isinstance(edge_probs, torch.Tensor):
edge_probs = torch.tensor(edge_probs, dtype=torch.float)
assert num_graphs > 0
self.block_sizes = block_sizes
self.edge_probs = edge_probs
self.num_graphs = num_graphs
self.num_channels = num_channels
self.is_undirected = is_undirected
self.kwargs = {
'n_informative': num_channels,
'n_redundant': 0,
'flip_y': 0.0,
'shuffle': False,
}
self.kwargs.update(kwargs)
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.__class__.__name__, 'processed')
@property
def processed_file_names(self) -> str:
block_sizes = self.block_sizes.view(-1).tolist()
hash1 = '-'.join([f'{x:.1f}' for x in block_sizes])
edge_probs = self.edge_probs.view(-1).tolist()
hash2 = '-'.join([f'{x:.1f}' for x in edge_probs])
return f'data_{self.num_channels}_{hash1}_{hash2}_{self.num_graphs}.pt'
def process(self) -> None:
from sklearn.datasets import make_classification
edge_index = stochastic_blockmodel_graph(
self.block_sizes, self.edge_probs, directed=not self.is_undirected)
num_samples = int(self.block_sizes.sum())
num_classes = self.block_sizes.size(0)
data_list = []
for _ in range(self.num_graphs):
x = None
if self.num_channels is not None:
x, y_not_sorted = make_classification(
n_samples=num_samples,
n_features=self.num_channels,
n_classes=num_classes,
weights=self.block_sizes / num_samples,
**self.kwargs,
)
x = x[np.argsort(y_not_sorted)]
x = torch.from_numpy(x).to(torch.float)
y = torch.arange(num_classes).repeat_interleave(self.block_sizes)
data = Data(x=x, edge_index=edge_index, y=y)
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
self.save(data_list, self.processed_paths[0])
class RandomPartitionGraphDataset(StochasticBlockModelDataset):
r"""The random partition graph dataset from the `"How to Find Your
Friendly Neighborhood: Graph Attention Design with Self-Supervision"
<https://openreview.net/forum?id=Wi5KUNlqWty>`_ paper.
This is a synthetic graph of communities controlled by the node homophily
and the average degree, and each community is considered as a class.
The node features are sampled from normal distributions where the centers
of clusters are vertices of a hypercube, as computed by the
:meth:`sklearn.datasets.make_classification` method.
Args:
root (str): Root directory where the dataset should be saved.
num_classes (int): The number of classes.
num_nodes_per_class (int): The number of nodes per class.
node_homophily_ratio (float): The degree of node homophily.
average_degree (float): The average degree of the graph.
num_graphs (int, optional): The number of graphs. (default: :obj:`1`)
num_channels (int, optional): The number of node features. If given
as :obj:`None`, node features are not generated.
(default: :obj:`None`)
is_undirected (bool, optional): Whether the graph to generate is
undirected. (default: :obj:`True`)
transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes
in an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
**kwargs (optional): The keyword arguments that are passed down
to :meth:`sklearn.datasets.make_classification` method in
drawing node features.
"""
def __init__(
self,
root: str,
num_classes: int,
num_nodes_per_class: int,
node_homophily_ratio: float,
average_degree: float,
num_graphs: int = 1,
num_channels: Optional[int] = None,
is_undirected: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
**kwargs: Any,
) -> None:
self._num_classes = num_classes
self.num_nodes_per_class = num_nodes_per_class
self.node_homophily_ratio = node_homophily_ratio
self.average_degree = average_degree
# (p_in + (C - 1) * p_out) / C = |E|/|V|^2
# i.e., p_in + (C - 1) * p_out = average_degree / num_nodes_per_class
ec_over_v2 = average_degree / num_nodes_per_class
p_in = node_homophily_ratio * ec_over_v2
p_out = (ec_over_v2 - p_in) / (num_classes - 1)
block_sizes = [num_nodes_per_class for _ in range(num_classes)]
edge_probs = [[p_out for _ in range(num_classes)]
for _ in range(num_classes)]
for r in range(num_classes):
edge_probs[r][r] = p_in
super().__init__(root, block_sizes, edge_probs, num_graphs,
num_channels, is_undirected, transform, pre_transform,
**kwargs)
@property
def processed_file_names(self) -> str:
return (f'data_{self.num_channels}_{self._num_classes}_'
f'{self.num_nodes_per_class}_{self.node_homophily_ratio:.1f}_'
f'{self.average_degree:.1f}_{self.num_graphs}.pt')
def process(self) -> None:
return super().process()
|
