Source code for

""" PPIDataset for inductive learning. """
import json
import os

import networkx as nx
import numpy as np
from networkx.readwrite import json_graph

from .. import backend as F
from ..convert import from_networkx
from .dgl_dataset import DGLBuiltinDataset
from .utils import _get_dgl_url, load_graphs, load_info, save_graphs, save_info

[docs]class PPIDataset(DGLBuiltinDataset): r"""Protein-Protein Interaction dataset for inductive node classification A toy Protein-Protein Interaction network dataset. The dataset contains 24 graphs. The average number of nodes per graph is 2372. Each node has 50 features and 121 labels. 20 graphs for training, 2 for validation and 2 for testing. Reference: `<>`_ Statistics: - Train examples: 20 - Valid examples: 2 - Test examples: 2 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: True. 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_labels : int Number of labels for each node labels : Tensor Node labels features : Tensor Node features Examples -------- >>> dataset = PPIDataset(mode='valid') >>> num_classes = dataset.num_classes >>> for g in dataset: .... feat = g.ndata['feat'] .... label = g.ndata['label'] .... # your code here >>> """ def __init__( self, mode="train", raw_dir=None, force_reload=False, verbose=False, transform=None, ): assert mode in ["train", "valid", "test"] self.mode = mode _url = _get_dgl_url("dataset/") super(PPIDataset, self).__init__( name="ppi", url=_url, raw_dir=raw_dir, force_reload=force_reload, verbose=verbose, transform=transform, ) def process(self): graph_file = os.path.join( self.save_path, "{}_graph.json".format(self.mode) ) label_file = os.path.join( self.save_path, "{}_labels.npy".format(self.mode) ) feat_file = os.path.join( self.save_path, "{}_feats.npy".format(self.mode) ) graph_id_file = os.path.join( self.save_path, "{}_graph_id.npy".format(self.mode) ) g_data = json.load(open(graph_file)) self._labels = np.load(label_file) self._feats = np.load(feat_file) self.graph = from_networkx( nx.DiGraph(json_graph.node_link_graph(g_data)) ) graph_id = np.load(graph_id_file) # lo, hi means the range of graph ids for different portion of the dataset, # 20 graphs for training, 2 for validation and 2 for testing. lo, hi = 1, 21 if self.mode == "valid": lo, hi = 21, 23 elif self.mode == "test": lo, hi = 23, 25 graph_masks = [] self.graphs = [] for g_id in range(lo, hi): g_mask = np.where(graph_id == g_id)[0] graph_masks.append(g_mask) g = self.graph.subgraph(g_mask) g.ndata["feat"] = F.tensor( self._feats[g_mask], dtype=F.data_type_dict["float32"] ) g.ndata["label"] = F.tensor( self._labels[g_mask], dtype=F.data_type_dict["float32"] ) self.graphs.append(g) @property def graph_list_path(self): return os.path.join( self.save_path, "{}_dgl_graph_list.bin".format(self.mode) ) @property def g_path(self): return os.path.join( self.save_path, "{}_dgl_graph.bin".format(self.mode) ) @property def info_path(self): return os.path.join(self.save_path, "{}_info.pkl".format(self.mode)) def has_cache(self): return ( os.path.exists(self.graph_list_path) and os.path.exists(self.g_path) and os.path.exists(self.info_path) ) def save(self): save_graphs(self.graph_list_path, self.graphs) save_graphs(self.g_path, self.graph) save_info( self.info_path, {"labels": self._labels, "feats": self._feats} ) def load(self): self.graphs = load_graphs(self.graph_list_path)[0] g, _ = load_graphs(self.g_path) self.graph = g[0] info = load_info(self.info_path) self._labels = info["labels"] self._feats = info["feats"] @property def num_labels(self): return 121 @property def num_classes(self): return 121
[docs] def __len__(self): """Return number of samples in this dataset.""" return len(self.graphs)
[docs] def __getitem__(self, item): """Get the item^th sample. Parameters --------- item : int The sample index. Returns ------- :class:`dgl.DGLGraph` graph structure, node features and node labels. - ``ndata['feat']``: node features - ``ndata['label']``: node labels """ if self._transform is None: return self.graphs[item] else: return self._transform(self.graphs[item])
class LegacyPPIDataset(PPIDataset): """Legacy version of PPI Dataset""" def __getitem__(self, item): """Get the item^th sample. Paramters --------- idx : int The sample index. Returns ------- (dgl.DGLGraph, Tensor, Tensor) The graph, features and its label. """ if self._transform is None: g = self.graphs[item] else: g = self._transform(self.graphs[item]) return g, g.ndata["feat"], g.ndata["label"]