Source code for

""" CLUSTERDataset for inductive learning. """
import os

from .dgl_dataset import DGLBuiltinDataset
from .utils import _get_dgl_url, load_graphs

[docs]class CLUSTERDataset(DGLBuiltinDataset): r"""CLUSTER dataset for semi-supervised clustering task. Each graph contains 6 SBM clusters with sizes randomly selected between [5, 35] and probabilities p = 0.55, q = 0.25. The graphs are of sizes 40 -190 nodes. Each node can take an input feature value in {0, 1, 2, ..., 6} and values 1~6 correspond to classes 0~5 respectively, while value 0 means that the class of the node is unknown. There is only one labeled node that is randomly assigned to each community and most node features are set to 0. Reference `<>`_ Statistics: - Train examples: 10,000 - Valid examples: 1,000 - Test examples: 1,000 - Number of classes for each node: 6 Parameters ---------- mode : str Must be one of ('train', 'valid', 'test'). Default: 'train' raw_dir : str Raw file directory to download/contains the input data directory. Default: ~/.dgl/ force_reload : bool Whether to reload the dataset. Default: False verbose : bool Whether to print out progress information. Default: False transform : callable, optional A transform that takes in a :class:`~dgl.DGLGraph` object and returns a transformed version. The :class:`~dgl.DGLGraph` object will be transformed before every access. Attributes ---------- num_classes : int Number of classes for each node. Examples -------- >>> from import CLUSTERDataset >>> >>> trainset = CLUSTERDataset(mode='train') >>> >>> trainset.num_classes 6 >>> len(trainset) 10000 >>> trainset[0] Graph(num_nodes=117, num_edges=4104, ndata_schemes={'label': Scheme(shape=(), dtype=torch.int16), 'feat': Scheme(shape=(), dtype=torch.int64)} edata_schemes={'feat': Scheme(shape=(1,), dtype=torch.float32)}) """ def __init__( self, mode="train", raw_dir=None, force_reload=False, verbose=False, transform=None, ): self._url = _get_dgl_url("dataset/") self.mode = mode super(CLUSTERDataset, self).__init__( name="cluster", url=self._url, raw_dir=raw_dir, force_reload=force_reload, verbose=verbose, transform=transform, ) def process(self): self.load() def has_cache(self): graph_path = os.path.join( self.save_path, "CLUSTER_{}.bin".format(self.mode) ) return os.path.exists(graph_path) def load(self): graph_path = os.path.join( self.save_path, "CLUSTER_{}.bin".format(self.mode) ) self._graphs, _ = load_graphs(graph_path) @property def num_classes(self): r"""Number of classes for each node.""" return 6
[docs] def __len__(self): r"""The number of examples in the dataset.""" return len(self._graphs)
[docs] def __getitem__(self, idx): r"""Get the idx^th sample. Parameters --------- idx : int The sample index. Returns ------- :class:`dgl.DGLGraph` graph structure, node features, node labels and edge features. - ``ndata['feat']``: node features - ``ndata['label']``: node labels - ``edata['feat']``: edge features """ if self._transform is None: return self._graphs[idx] else: return self._transform(self._graphs[idx])