chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from functools import partial
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from typing import Optional
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from easygraph.datapipe import load_from_pickle
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from easygraph.datapipe import norm_ft
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from easygraph.datapipe import to_bool_tensor
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from easygraph.datapipe import to_long_tensor
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from easygraph.datapipe import to_tensor
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from easygraph.datasets.hypergraph.hypergraph_dataset_base import BaseData
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__all__ = ["CoauthorshipCora", "CoauthorshipDBLP"]
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class CoauthorshipCora(BaseData):
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r"""The Co-authorship Cora dataset is a citation network dataset for vertex classification task.
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More details see the `HyperGCN <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper.
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The content of the Co-authorship Cora dataset includes the following:
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- ``num_classes``: The number of classes: :math:`7`.
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- ``num_vertices``: The number of vertices: :math:`2,708`.
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- ``num_edges``: The number of edges: :math:`1,072`.
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- ``dim_features``: The dimension of features: :math:`1,433`.
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- ``features``: The vertex feature matrix. ``torch.Tensor`` with size :math:`(2,708 \times 1,433)`.
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- ``edge_list``: The edge list. ``List`` with length :math:`1,072`.
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- ``labels``: The label list. ``torch.LongTensor`` with size :math:`(2,708, )`.
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- ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(2,708, )`.
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- ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(2,708, )`.
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- ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(2,708, )`.
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Args:
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``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``.
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"""
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def __init__(self, data_root: Optional[str] = None) -> None:
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super().__init__("coauthorship_cora", data_root)
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self._content = {
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"num_classes": 7,
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"num_vertices": 2708,
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"num_edges": 1072,
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"dim_features": 1433,
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"features": {
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"upon": [
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{
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"filename": "features.pkl",
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"md5": "14257c0e24b4eb741b469a351e524785",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_tensor, partial(norm_ft, ord=1)],
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},
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"edge_list": {
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"upon": [
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{
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"filename": "edge_list.pkl",
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"md5": "a17ff337f1b9099f5a9d4d670674e146",
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}
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],
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"loader": load_from_pickle,
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},
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"labels": {
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"upon": [
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{
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"filename": "labels.pkl",
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"md5": "c8d11c452e0be69f79a47dd839279117",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_long_tensor],
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},
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"train_mask": {
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"upon": [
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{
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"filename": "train_mask.pkl",
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"md5": "111db6c6f986be2908378df7bdca7a9b",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_bool_tensor],
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},
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"val_mask": {
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"upon": [
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{
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"filename": "val_mask.pkl",
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"md5": "ffab1055193ffb2fe74822bb575d332a",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_bool_tensor],
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},
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"test_mask": {
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"upon": [
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{
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"filename": "test_mask.pkl",
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"md5": "ffab1055193ffb2fe74822bb575d332a",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_bool_tensor],
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},
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}
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class CoauthorshipDBLP(BaseData):
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r"""The Co-authorship DBLP dataset is a citation network dataset for vertex classification task.
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More details see the `HyperGCN <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper.
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The content of the Co-authorship DBLP dataset includes the following:
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- ``num_classes``: The number of classes: :math:`6`.
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- ``num_vertices``: The number of vertices: :math:`41,302`.
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- ``num_edges``: The number of edges: :math:`22,363`.
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- ``dim_features``: The dimension of features: :math:`1,425`.
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- ``features``: The vertex feature matrix. ``torch.Tensor`` with size :math:`(41,302 \times 1,425)`.
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- ``edge_list``: The edge list. ``List`` with length :math:`22,363`.
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- ``labels``: The label list. ``torch.LongTensor`` with size :math:`(41,302, )`.
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- ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(41,302, )`.
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- ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(41,302, )`.
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- ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(41,302, )`.
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Args:
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``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.
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"""
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def __init__(self, data_root: Optional[str] = None) -> None:
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super().__init__("coauthorship_dblp", data_root)
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self._content = {
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"num_classes": 6,
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"num_vertices": 41302,
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"num_edges": 22363,
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"dim_features": 1425,
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"features": {
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"upon": [
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{
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"filename": "features.pkl",
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"md5": "b78fd31b2586d1e19a40b3f6cd9cc2e7",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_tensor, partial(norm_ft, ord=1)],
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},
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"edge_list": {
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"upon": [
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{
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"filename": "edge_list.pkl",
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"md5": "c6bf5f9f3b9683bcc9b7bcc9eb8707d8",
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}
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],
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"loader": load_from_pickle,
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},
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"labels": {
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"upon": [
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{
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"filename": "labels.pkl",
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"md5": "2e7a792ea018028d582af8f02f2058ca",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_long_tensor],
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},
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"train_mask": {
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"upon": [
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{
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"filename": "train_mask.pkl",
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"md5": "a842b795c7cac4c2f98a56cf599bc1de",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_bool_tensor],
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},
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"val_mask": {
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"upon": [
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{
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"filename": "val_mask.pkl",
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"md5": "2ec4b7df7c5e6b355067a22c391ad578",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_bool_tensor],
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},
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"test_mask": {
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"upon": [
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{
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"filename": "test_mask.pkl",
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"md5": "2ec4b7df7c5e6b355067a22c391ad578",
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}
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],
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"loader": load_from_pickle,
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"preprocess": [to_bool_tensor],
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},
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}
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