wehub-resource-sync
280 lines
11 KiB
Python
280 lines
11 KiB
Python
from functools import partial
|
|
from typing import Optional
|
|
|
|
from easygraph.datapipe import load_from_pickle
|
|
from easygraph.datapipe import norm_ft
|
|
from easygraph.datapipe import to_bool_tensor
|
|
from easygraph.datapipe import to_long_tensor
|
|
from easygraph.datapipe import to_tensor
|
|
from easygraph.datasets.hypergraph.hypergraph_dataset_base import BaseData
|
|
|
|
|
|
class CocitationCora(BaseData):
|
|
r"""The Co-citation Cora dataset is a citation network dataset for vertex classification task.
|
|
More details see the `HyperGCN <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper.
|
|
|
|
The content of the Co-citation Cora dataset includes the following:
|
|
|
|
- ``num_classes``: The number of classes: :math:`7`.
|
|
- ``num_vertices``: The number of vertices: :math:`2,708`.
|
|
- ``num_edges``: The number of edges: :math:`1,579`.
|
|
- ``dim_features``: The dimension of features: :math:`1,433`.
|
|
- ``features``: The vertex feature matrix. ``torch.Tensor`` with size :math:`(2,708 \times 1,433)`.
|
|
- ``edge_list``: The edge list. ``List`` with length :math:`1,579`.
|
|
- ``labels``: The label list. ``torch.LongTensor`` with size :math:`(2,708, )`.
|
|
- ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(2,708, )`.
|
|
- ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(2,708, )`.
|
|
- ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(2,708, )`.
|
|
|
|
Args:
|
|
``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to ``None``.
|
|
"""
|
|
|
|
def __init__(self, data_root: Optional[str] = None) -> None:
|
|
super().__init__("cocitation_cora", data_root)
|
|
self._content = {
|
|
"num_classes": 7,
|
|
"num_vertices": 2708,
|
|
"num_edges": 1579,
|
|
"dim_features": 1433,
|
|
"features": {
|
|
"upon": [
|
|
{
|
|
"filename": "features.pkl",
|
|
"md5": "14257c0e24b4eb741b469a351e524785",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_tensor, partial(norm_ft, ord=1)],
|
|
},
|
|
"edge_list": {
|
|
"upon": [
|
|
{
|
|
"filename": "edge_list.pkl",
|
|
"md5": "e43d1321880c8ecb2260d8fb7effd9ea",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
},
|
|
"labels": {
|
|
"upon": [
|
|
{
|
|
"filename": "labels.pkl",
|
|
"md5": "c8d11c452e0be69f79a47dd839279117",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_long_tensor],
|
|
},
|
|
"train_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "train_mask.pkl",
|
|
"md5": "111db6c6f986be2908378df7bdca7a9b",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
"val_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "val_mask.pkl",
|
|
"md5": "ffab1055193ffb2fe74822bb575d332a",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
"test_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "test_mask.pkl",
|
|
"md5": "ffab1055193ffb2fe74822bb575d332a",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
}
|
|
|
|
|
|
class CocitationCiteseer(BaseData):
|
|
r"""The Co-citation Citeseer dataset is a citation network dataset for vertex classification task.
|
|
More details see the `HyperGCN <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper.
|
|
|
|
The content of the Co-citation Citaseer dataset includes the following:
|
|
|
|
- ``num_classes``: The number of classes: :math:`6`.
|
|
- ``num_vertices``: The number of vertices: :math:`3,312`.
|
|
- ``num_edges``: The number of edges: :math:`1,079`.
|
|
- ``dim_features``: The dimension of features: :math:`3,703`.
|
|
- ``features``: The vertex feature matrix. ``torch.Tensor`` with size :math:`(3,312 \times 3,703)`.
|
|
- ``edge_list``: The edge list. ``List`` with length :math:`1,079`.
|
|
- ``labels``: The label list. ``torch.LongTensor`` with size :math:`(3,312, )`.
|
|
- ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(3,312, )`.
|
|
- ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(3,312, )`.
|
|
- ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(3,312, )`.
|
|
|
|
Args:
|
|
``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to ``None``.
|
|
"""
|
|
|
|
def __init__(self, data_root: Optional[str] = None) -> None:
|
|
super().__init__("cocitation_citeseer", data_root)
|
|
self._content = {
|
|
"num_classes": 6,
|
|
"num_vertices": 3312,
|
|
"num_edges": 1079,
|
|
"dim_features": 3703,
|
|
"features": {
|
|
"upon": [
|
|
{
|
|
"filename": "features.pkl",
|
|
"md5": "1ee0dc89e0d5f5ac9187b55a407683e8",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_tensor, partial(norm_ft, ord=1)],
|
|
},
|
|
"edge_list": {
|
|
"upon": [
|
|
{
|
|
"filename": "edge_list.pkl",
|
|
"md5": "6687b2e96159c534a424253f536b49ae",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
},
|
|
"labels": {
|
|
"upon": [
|
|
{
|
|
"filename": "labels.pkl",
|
|
"md5": "71069f78e83fa85dd6a4b9b6570447c2",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_long_tensor],
|
|
},
|
|
"train_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "train_mask.pkl",
|
|
"md5": "3b831318fc3d3e588bead5ba469fe38f",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
"val_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "val_mask.pkl",
|
|
"md5": "c22eb5b7493908042c7e039c8bb5a82e",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
"test_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "test_mask.pkl",
|
|
"md5": "c22eb5b7493908042c7e039c8bb5a82e",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
}
|
|
|
|
|
|
class CocitationPubmed(BaseData):
|
|
r"""The Co-citation PubMed dataset is a citation network dataset for vertex classification task.
|
|
More details see the `HyperGCN <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper.
|
|
|
|
The content of the Co-citation PubMed dataset includes the following:
|
|
|
|
- ``num_classes``: The number of classes: :math:`3`.
|
|
- ``num_vertices``: The number of vertices: :math:`19,717`.
|
|
- ``num_edges``: The number of edges: :math:`7,963`.
|
|
- ``dim_features``: The dimension of features: :math:`500`.
|
|
- ``features``: The vertex feature matrix. ``torch.Tensor`` with size :math:`(19,717 \times 500)`.
|
|
- ``edge_list``: The edge list. ``List`` with length :math:`7,963`.
|
|
- ``labels``: The label list. ``torch.LongTensor`` with size :math:`(19,717, )`.
|
|
- ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(19,717, )`.
|
|
- ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(19,717, )`.
|
|
- ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(19,717, )`.
|
|
|
|
Args:
|
|
``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to ``None``.
|
|
"""
|
|
|
|
def __init__(self, data_root: Optional[str] = None) -> None:
|
|
super().__init__("cocitation_pubmed", data_root)
|
|
self._content = {
|
|
"num_classes": 3,
|
|
"num_vertices": 19717,
|
|
"num_edges": 7963,
|
|
"dim_features": 500,
|
|
"features": {
|
|
"upon": [
|
|
{
|
|
"filename": "features.pkl",
|
|
"md5": "f89502c432ca451156a8235c5efc034e",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_tensor, partial(norm_ft, ord=1)],
|
|
},
|
|
"edge_list": {
|
|
"upon": [
|
|
{
|
|
"filename": "edge_list.pkl",
|
|
"md5": "c5fbedf63e5be527f200e8c4e0391b00",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
},
|
|
"labels": {
|
|
"upon": [
|
|
{
|
|
"filename": "labels.pkl",
|
|
"md5": "c039f778409a15f9b2ceefacad9c2202",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_long_tensor],
|
|
},
|
|
"train_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "train_mask.pkl",
|
|
"md5": "81b422937f3adccd89a334d7093b67d7",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
"val_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "val_mask.pkl",
|
|
"md5": "10717940ddbfa3e4f6c0b148bb394f79",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
"test_mask": {
|
|
"upon": [
|
|
{
|
|
"filename": "test_mask.pkl",
|
|
"md5": "10717940ddbfa3e4f6c0b148bb394f79",
|
|
}
|
|
],
|
|
"loader": load_from_pickle,
|
|
"preprocess": [to_bool_tensor],
|
|
},
|
|
}
|