import os.path as osp
from typing import Callable, Optional
import tensorlayerx as tlx
from gammagl.data import InMemoryDataset, download_url
from gammagl.io.npz import read_npz
from gammagl.utils import get_train_val_test_split
[docs]
class Amazon(InMemoryDataset):
r"""The Amazon Computers and Amazon Photo networks from the
`"Pitfalls of Graph Neural Network Evaluation"
<https://arxiv.org/abs/1811.05868>`_ paper.
Nodes represent goods and edges represent that two goods are frequently
bought together.
Given product reviews as bag-of-words node features, the task is to
map goods to their respective product category.
Parameters
----------
root: str, optional
Root directory where the dataset should be saved.
name: str, optional
The name of the dataset (:obj:`"Computers"`,
:obj:`"Photo"`).
transform: callable, optional
A function/transform that takes in an
:obj:`gammagl.data.Graph` 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:`gammagl.data.Graph` 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`)
train_ratio : float, optional
Ratio of training samples.
(default: :obj:`0.1`)
val_ratio : float, optional
Ratio of validation samples.
(default: :obj:`0.15`)
Stats:
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - Name
- #nodes
- #edges
- #features
- #classes
* - Computers
- 13,752
- 491,722
- 767
- 10
* - Photo
- 7,650
- 238,162
- 745
- 8
"""
url = 'https://github.com/shchur/gnn-benchmark/raw/master/data/npz/'
def __init__(self, root: str = None, name: str = 'computers',
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
train_ratio: float = 0.1,
val_ratio: float = 0.15):
self.name = name.lower()
assert self.name in ['computers', 'photo']
super().__init__(root, transform, pre_transform, force_reload = force_reload)
self.data, self.slices = self.load_data(self.processed_paths[0])
data = self.get(0)
data.train_mask, data.val_mask, data.test_mask = get_train_val_test_split(self.data, train_ratio, val_ratio)
self.data, self.slices = self.collate([data])
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name.capitalize(), 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name.capitalize(), 'processed')
@property
def raw_file_names(self) -> str:
return f'amazon_electronics_{self.name.lower()}.npz'
@property
def processed_file_names(self) -> str:
return tlx.BACKEND + 'data.pt'
[docs]
def download(self):
download_url(self.url + self.raw_file_names, self.raw_dir)
[docs]
def process(self):
data = read_npz(self.raw_paths[0])
data = data if self.pre_transform is None else self.pre_transform(data)
self.save_data(self.collate([data]), self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.__class__.__name__}{self.name.capitalize()}()'
# data=Amazon(root='./Amazon/',name='photo')
# data.process()