Source code for dig.xgraph.dataset.nlp_dataset

import os
import glob
import json
import torch
import pickle
import numpy as np
import os.path as osp
from import Data, InMemoryDataset
import traceback

def undirected_graph(data):
    A pre_transform function that transfers the directed graph into undirected graph.
        data ( Directed graph in the format :class:``.
        where the :obj:`data.x`, :obj:`data.edge_index` are required.
    data.edge_index =[torch.stack([data.edge_index[1], data.edge_index[0]], dim=0),
                                 data.edge_index], dim=1)
    return data

def split(data, batch):
    # i-th contains elements from slice[i] to slice[i+1]
    node_slice = torch.cumsum(torch.from_numpy(np.bincount(batch)), 0)
    node_slice =[torch.tensor([0]), node_slice])
    row, _ = data.edge_index
    edge_slice = torch.cumsum(torch.from_numpy(np.bincount(batch[row])), 0)
    edge_slice =[torch.tensor([0]), edge_slice])

    # Edge indices should start at zero for every graph.
    data.edge_index -= node_slice[batch[row]].unsqueeze(0)
    data.__num_nodes__ = np.bincount(batch).tolist()

    slices = dict()
    slices['x'] = node_slice
    slices['edge_index'] = edge_slice
    slices['y'] = torch.arange(0, batch[-1] + 2, dtype=torch.long)
    return data, slices

def read_file(folder, prefix, name):
    file_path = osp.join(folder, prefix + f'_{name}.txt')
    return np.genfromtxt(file_path, dtype=np.int64)

def read_sentigraph_data(folder: str, prefix: str):
    txt_files = glob.glob(os.path.join(folder, "{}_*.txt".format(prefix)))
    json_files = glob.glob(os.path.join(folder, "{}_*.json".format(prefix)))
    txt_names = [f.split(os.sep)[-1][len(prefix) + 1:-4] for f in txt_files]
    json_names = [f.split(os.sep)[-1][len(prefix) + 1:-5] for f in json_files]
    names = txt_names + json_names

    with open(os.path.join(folder, prefix+"_node_features.pkl"), 'rb') as f:
        x: np.array = pickle.load(f)
    x: torch.FloatTensor = torch.from_numpy(x)
    edge_index: np.array = read_file(folder, prefix, 'edge_index')
    edge_index: torch.tensor = torch.tensor(edge_index, dtype=torch.long).T
    batch: np.array = read_file(folder, prefix, 'node_indicator') - 1     # from zero
    y: np.array = read_file(folder, prefix, 'graph_labels')
    y: torch.tensor = torch.tensor(y, dtype=torch.long)

    supplement = dict()
    if 'split_indices' in names:
        split_indices: np.array = read_file(folder, prefix, 'split_indices')
        split_indices = torch.tensor(split_indices, dtype=torch.long)
        supplement['split_indices'] = split_indices
    if 'sentence_tokens' in names:
        with open(os.path.join(folder, prefix + '_sentence_tokens.json')) as f:
            sentence_tokens: dict = json.load(f)
        supplement['sentence_tokens'] = sentence_tokens

    data = Data(x=x, edge_index=edge_index, y=y)
    data, slices = split(data, batch)

    return data, slices, supplement

[docs]class SentiGraphDataset(InMemoryDataset): r""" The SentiGraph datasets from `Explainability in Graph Neural Networks: A Taxonomic Survey <>`_. The datasets take pretrained BERT as node feature extractor and dependency tree as edges to transfer the text sentiment datasets into graph classification datasets. The dataset `Graph-SST2 <>`_ should be downloaded to the proper directory before running. All the three datasets Graph-SST2, Graph-SST5, and Graph-Twitter can be download in this `link <>`_. Args: root (:obj:`str`): Root directory where the datasets are saved name (:obj:`str`): The name of the datasets. transform (:obj:`Callable`, :obj:`None`): A function/transform that takes in an :class:`` object and returns a transformed version. The data object will be transformed before every access. (default: :obj:`None`) pre_transform (:obj:`Callable`, :obj:`None`): A function/transform that takes in an :class:`` object and returns a transformed version. The data object will be transformed before being saved to disk. (default: :obj:`None`) .. note:: The default parameter of pre_transform is :func:`~undirected_graph` which transfers the directed graph in original data into undirected graph before being saved to disk. """ def __init__(self, root, name, transform=None, pre_transform=undirected_graph): = name super(SentiGraphDataset, self).__init__(root, transform, pre_transform), self.slices, self.supplement = torch.load(self.processed_paths[0]) @property def raw_dir(self): return osp.join(self.root,, 'raw') @property def processed_dir(self): return osp.join(self.root,, 'processed') @property def raw_file_names(self): return ['node_features', 'node_indicator', 'sentence_tokens', 'edge_index', 'graph_labels', 'split_indices'] @property def processed_file_names(self): return ['']
[docs] def process(self): # Read data into huge `Data` list. try:, self.slices, self.supplement \ = read_sentigraph_data(self.raw_dir, except Exception as e: print(e) print(traceback.format_exc()) if type(e) is FileNotFoundError: print("Please download the required datasets file to the root directory.") print("The google drive link is " "") raise SystemExit() if self.pre_filter is not None: data_list = [self.get(idx) for idx in range(len(self))] data_list = [data for data in data_list if self.pre_filter(data)], self.slices = self.collate(data_list) if self.pre_transform is not None: data_list = [self.get(idx) for idx in range(len(self))] data_list = [self.pre_transform(data) for data in data_list], self.slices = self.collate(data_list), self.slices, self.supplement), self.processed_paths[0])
if __name__ == '__main__': dataset = SentiGraphDataset(root='.datasets', name='Graph-SST2')