Source code for easygraph.datasets.hypergraph.cocitation

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


[docs]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], }, }
[docs]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:`3,327`. - ``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:`(3,327 \times 3,703)`. - ``edge_list``: The edge list. ``List`` with length :math:`1,079`. - ``labels``: The label list. ``torch.LongTensor`` with size :math:`(3,327, )`. - ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(3,327, )`. - ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(3,327, )`. - ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(3,327, )`. 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], }, }
[docs]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], }, }