Source code for

""" GDELT dataset for temporal graph """
import os

import numpy as np

from .. import backend as F
from ..convert import graph as dgl_graph
from .dgl_dataset import DGLBuiltinDataset
from .utils import _get_dgl_url, load_info, loadtxt, save_info

[docs]class GDELTDataset(DGLBuiltinDataset): r"""GDELT dataset for event-based temporal graph The Global Database of Events, Language, and Tone (GDELT) dataset. This contains events happend all over the world (ie every protest held anywhere in Russia on a given day is collapsed to a single entry). This Dataset consists ofevents collected from 1/1/2018 to 1/31/2018 (15 minutes time granularity). Reference: - `Recurrent Event Network for Reasoning over Temporal Knowledge Graphs <>`_ - `The Global Database of Events, Language, and Tone (GDELT) <>`_ Statistics: - Train examples: 2,304 - Valid examples: 288 - Test examples: 384 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 ---------- start_time : int Start time of the temporal graph end_time : int End time of the temporal graph is_temporal : bool Does the dataset contain temporal graphs Examples ---------- >>> # get train, valid, test dataset >>> train_data = GDELTDataset() >>> valid_data = GDELTDataset(mode='valid') >>> test_data = GDELTDataset(mode='test') >>> >>> # length of train set >>> train_size = len(train_data) >>> >>> for g in train_data: .... e_feat = g.edata['rel_type'] .... # your code here .... >>> """ def __init__( self, mode="train", raw_dir=None, force_reload=False, verbose=False, transform=None, ): mode = mode.lower() assert mode in ["train", "valid", "test"], "Mode not valid." self.mode = mode self.num_nodes = 23033 _url = _get_dgl_url("dataset/") super(GDELTDataset, self).__init__( name="GDELT", url=_url, raw_dir=raw_dir, force_reload=force_reload, verbose=verbose, transform=transform, ) def process(self): file_path = os.path.join(self.raw_path, self.mode + ".txt") = loadtxt(file_path, delimiter="\t").astype(np.int64) # The source code is not released, but the paper indicates there're # totally 137 samples. The cutoff below has exactly 137 samples. self.time_index = np.floor([:, 3] / 15).astype(np.int64) self._start_time = self.time_index.min() self._end_time = self.time_index.max() @property def info_path(self): return os.path.join(self.save_path, self.mode + "_info.pkl") def has_cache(self): return os.path.exists(self.info_path) def save(self): save_info( self.info_path, { "data":, "time_index": self.time_index, "start_time": self.start_time, "end_time": self.end_time, }, ) def load(self): info = load_info(self.info_path), self.time_index, self._start_time, self._end_time = ( info["data"], info["time_index"], info["start_time"], info["end_time"], ) @property def start_time(self): r"""Start time of events in the temporal graph Returns ------- int """ return self._start_time @property def end_time(self): r"""End time of events in the temporal graph Returns ------- int """ return self._end_time
[docs] def __getitem__(self, t): r"""Get graph by with events before time `t + self.start_time` Parameters ---------- t : int Time, its value must be in range [0, `self.end_time` - `self.start_time`] Returns ------- :class:`dgl.DGLGraph` The graph contains: - ``edata['rel_type']``: edge type """ if t >= len(self) or t < 0: raise IndexError("Index out of range") i = t + self.start_time row_mask = self.time_index <= i edges =[row_mask][:, [0, 2]] rate =[row_mask][:, 1] g = dgl_graph((edges[:, 0], edges[:, 1])) g.edata["rel_type"] = F.tensor( rate.reshape(-1, 1), dtype=F.data_type_dict["int64"] ) if self._transform is not None: g = self._transform(g) return g
[docs] def __len__(self): r"""Number of graphs in the dataset. Return ------- int """ return self._end_time - self._start_time + 1
@property def is_temporal(self): r"""Does the dataset contain temporal graphs Returns ------- bool """ return True