wehub-resource-sync
111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
import os
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import easygraph as eg
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import numpy as np
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import scipy.sparse as sp
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from easygraph.classes.graph import Graph
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from .graph_dataset_base import EasyGraphBuiltinDataset
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from .utils import data_type_dict
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from .utils import download
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from .utils import extract_archive
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from .utils import tensor
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class AmazonPhotoDataset(EasyGraphBuiltinDataset):
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r"""Amazon Electronics Photo co-purchase graph dataset.
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Nodes represent products, and edges link products frequently co-purchased.
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Node features are bag-of-words of product reviews. The task is to classify
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the product category.
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Statistics:
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- Nodes: 7,650
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- Edges: 119,081
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- Number of Classes: 8
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- Features: 745
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Parameters
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----------
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raw_dir : str, optional
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Raw file directory to download/contains the input data directory. Default: None
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force_reload : bool, optional
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Whether to reload the dataset. Default: False
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verbose : bool, optional
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Whether to print out progress information. Default: True
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transform : callable, optional
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A transform that takes in a :class:`~easygraph.Graph` object and returns
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a transformed version. The :class:`~easygraph.Graph` object will be
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transformed before every access.
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Examples
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--------
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>>> from easygraph.datasets import AmazonPhotoDataset
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>>> dataset = AmazonPhotoDataset()
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>>> g = dataset[0]
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>>> print(g.number_of_nodes())
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>>> print(g.number_of_edges())
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>>> print(g.nodes[0]['feat'].shape)
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>>> print(g.nodes[0]['label'])
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>>> print(dataset.num_classes)
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"""
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def __init__(self, raw_dir=None, force_reload=False, verbose=True, transform=None):
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name = "amazon_photo"
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url = "https://data.dgl.ai/dataset/amazon_co_buy_photo.zip"
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super(AmazonPhotoDataset, self).__init__(
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name=name,
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url=url,
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raw_dir=raw_dir,
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force_reload=force_reload,
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verbose=verbose,
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transform=transform,
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)
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def process(self):
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path = os.path.join(self.raw_path, "amazon_co_buy_photo.npz")
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data = np.load(path)
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adj = sp.csr_matrix(
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(data["adj_data"], data["adj_indices"], data["adj_indptr"]),
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shape=data["adj_shape"],
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)
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features = sp.csr_matrix(
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(data["attr_data"], data["attr_indices"], data["attr_indptr"]),
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shape=data["attr_shape"],
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).todense()
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labels = data["labels"]
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g = eg.Graph()
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g.add_edges_from(list(zip(*adj.nonzero())))
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for i in range(features.shape[0]):
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g.add_node(i, feat=np.array(features[i]).squeeze(), label=int(labels[i]))
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self._g = g
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self._num_classes = len(np.unique(labels))
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if self.verbose:
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print("Finished loading AmazonPhoto dataset.")
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print(f" NumNodes: {g.number_of_nodes()}")
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print(f" NumEdges: {g.number_of_edges()}")
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print(f" NumFeats: {features.shape[1]}")
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print(f" NumClasses: {self._num_classes}")
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def __getitem__(self, idx):
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assert idx == 0, "AmazonPhotoDataset only contains one graph"
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if self._g is None:
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raise ValueError("Graph has not been loaded or processed correctly.")
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return self._g if self._transform is None else self._transform(self._g)
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def __len__(self):
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return 1
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@property
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def num_classes(self):
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return self._num_classes
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