chore: import upstream snapshot with attribution
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wehub-resource-sync
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""" PPIDataset for inductive learning. """
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import json
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import os
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import networkx as nx
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import numpy as np
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from networkx.readwrite import json_graph
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from .. import backend as F
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from ..convert import from_networkx
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from .dgl_dataset import DGLBuiltinDataset
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from .utils import _get_dgl_url, load_graphs, load_info, save_graphs, save_info
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class PPIDataset(DGLBuiltinDataset):
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r"""Protein-Protein Interaction dataset for inductive node classification
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A toy Protein-Protein Interaction network dataset. The dataset contains
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24 graphs. The average number of nodes per graph is 2372. Each node has
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50 features and 121 labels. 20 graphs for training, 2 for validation
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and 2 for testing.
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Reference: `<http://snap.stanford.edu/graphsage/>`_
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Statistics:
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- Train examples: 20
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- Valid examples: 2
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- Test examples: 2
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Parameters
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----------
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mode : str
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Must be one of ('train', 'valid', 'test').
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Default: 'train'
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raw_dir : str
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Raw file directory to download/contains the input data directory.
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Default: ~/.dgl/
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force_reload : bool
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Whether to reload the dataset.
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Default: False
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verbose : bool
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Whether to print out progress information.
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Default: True.
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access.
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Attributes
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----------
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num_labels : int
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Number of labels for each node
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labels : Tensor
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Node labels
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features : Tensor
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Node features
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Examples
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--------
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>>> dataset = PPIDataset(mode='valid')
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>>> num_classes = dataset.num_classes
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>>> for g in dataset:
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.... feat = g.ndata['feat']
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.... label = g.ndata['label']
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.... # your code here
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>>>
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"""
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def __init__(
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self,
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mode="train",
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raw_dir=None,
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force_reload=False,
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verbose=False,
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transform=None,
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):
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assert mode in ["train", "valid", "test"]
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self.mode = mode
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_url = _get_dgl_url("dataset/ppi.zip")
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super(PPIDataset, self).__init__(
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name="ppi",
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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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graph_file = os.path.join(
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self.save_path, "{}_graph.json".format(self.mode)
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)
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label_file = os.path.join(
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self.save_path, "{}_labels.npy".format(self.mode)
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)
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feat_file = os.path.join(
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self.save_path, "{}_feats.npy".format(self.mode)
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)
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graph_id_file = os.path.join(
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self.save_path, "{}_graph_id.npy".format(self.mode)
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)
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g_data = json.load(open(graph_file))
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self._labels = np.load(label_file)
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self._feats = np.load(feat_file)
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self.graph = from_networkx(
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nx.DiGraph(json_graph.node_link_graph(g_data))
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)
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graph_id = np.load(graph_id_file)
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# lo, hi means the range of graph ids for different portion of the dataset,
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# 20 graphs for training, 2 for validation and 2 for testing.
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lo, hi = 1, 21
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if self.mode == "valid":
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lo, hi = 21, 23
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elif self.mode == "test":
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lo, hi = 23, 25
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graph_masks = []
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self.graphs = []
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for g_id in range(lo, hi):
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g_mask = np.where(graph_id == g_id)[0]
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graph_masks.append(g_mask)
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g = self.graph.subgraph(g_mask)
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g.ndata["feat"] = F.tensor(
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self._feats[g_mask], dtype=F.data_type_dict["float32"]
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)
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g.ndata["label"] = F.tensor(
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self._labels[g_mask], dtype=F.data_type_dict["float32"]
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)
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self.graphs.append(g)
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@property
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def graph_list_path(self):
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return os.path.join(
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self.save_path, "{}_dgl_graph_list.bin".format(self.mode)
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)
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@property
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def g_path(self):
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return os.path.join(
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self.save_path, "{}_dgl_graph.bin".format(self.mode)
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)
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@property
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def info_path(self):
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return os.path.join(self.save_path, "{}_info.pkl".format(self.mode))
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def has_cache(self):
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return (
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os.path.exists(self.graph_list_path)
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and os.path.exists(self.g_path)
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and os.path.exists(self.info_path)
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)
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def save(self):
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save_graphs(self.graph_list_path, self.graphs)
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save_graphs(self.g_path, self.graph)
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save_info(
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self.info_path, {"labels": self._labels, "feats": self._feats}
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)
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def load(self):
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self.graphs = load_graphs(self.graph_list_path)[0]
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g, _ = load_graphs(self.g_path)
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self.graph = g[0]
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info = load_info(self.info_path)
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self._labels = info["labels"]
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self._feats = info["feats"]
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@property
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def num_labels(self):
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return 121
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@property
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def num_classes(self):
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return 121
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def __len__(self):
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"""Return number of samples in this dataset."""
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return len(self.graphs)
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def __getitem__(self, item):
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"""Get the item^th sample.
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Parameters
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---------
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item : int
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The sample index.
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Returns
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-------
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:class:`dgl.DGLGraph`
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graph structure, node features and node labels.
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- ``ndata['feat']``: node features
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- ``ndata['label']``: node labels
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"""
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if self._transform is None:
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return self.graphs[item]
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else:
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return self._transform(self.graphs[item])
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class LegacyPPIDataset(PPIDataset):
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"""Legacy version of PPI Dataset"""
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def __getitem__(self, item):
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"""Get the item^th sample.
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Paramters
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---------
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idx : int
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The sample index.
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Returns
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-------
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(dgl.DGLGraph, Tensor, Tensor)
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The graph, features and its label.
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"""
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if self._transform is None:
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g = self.graphs[item]
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else:
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g = self._transform(self.graphs[item])
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return g, g.ndata["feat"], g.ndata["label"]
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