wehub-resource-sync
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
from typing import Optional
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from easygraph.datapipe import load_from_pickle
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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.datasets.hypergraph.hypergraph_dataset_base import BaseData
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class Cooking200(BaseData):
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r"""The Cooking 200 dataset is collected from `Yummly.com <https://www.yummly.com/>`_ for vertex classification task.
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It is a hypergraph dataset, in which vertex denotes the dish and hyperedge denotes
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the ingredient. Each dish is also associated with category information, which indicates the dish's cuisine like
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Chinese, Japanese, French, and Russian.
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The content of the Cooking200 dataset includes the following:
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- ``num_classes``: The number of classes: :math:`20`.
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- ``num_vertices``: The number of vertices: :math:`7,403`.
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- ``num_edges``: The number of edges: :math:`2,755`.
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- ``edge_list``: The edge list. ``List`` with length :math:`(2,755)`.
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- ``labels``: The label list. ``torch.LongTensor`` with size :math:`(7,403)`.
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- ``train_mask``: The train mask. ``torch.BoolTensor`` with size :math:`(7,403)`.
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- ``val_mask``: The validation mask. ``torch.BoolTensor`` with size :math:`(7,403)`.
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- ``test_mask``: The test mask. ``torch.BoolTensor`` with size :math:`(7,403)`.
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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__("cooking_200", data_root)
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self._content = {
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"num_classes": 20,
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"num_vertices": 7403,
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"num_edges": 2755,
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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": "2cd32e13dd4e33576c43936542975220",
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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": "f1f3c0399c9c28547088f44e0bfd5c81",
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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": "66ea36bae024aaaed289e1998fe894bd",
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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": "6c0d3d8b752e3955c64788cc65dcd018",
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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": "0e1564904551ba493e1f8a09d103461e",
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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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