"""ICEWS18 dataset for temporal graph"""
import numpy as np
import os
from .dgl_dataset import DGLBuiltinDataset
from .utils import loadtxt, _get_dgl_url, save_graphs, load_graphs
from ..convert import graph as dgl_graph
from .. import backend as F
[docs]class ICEWS18Dataset(DGLBuiltinDataset):
r""" ICEWS18 dataset for temporal graph
Integrated Crisis Early Warning System (ICEWS18)
Event data consists of coded interactions between socio-political
actors (i.e., cooperative or hostile actions between individuals,
groups, sectors and nation states). This Dataset consists of events
from 1/1/2018 to 10/31/2018 (24 hours time granularity).
Reference:
- `Recurrent Event Network for Reasoning over Temporal Knowledge Graphs <https://arxiv.org/abs/1904.05530>`_
- `ICEWS Coded Event Data <https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/28075>`_
Statistics：
- Train examples: 240
- Valid examples: 30
- Test examples: 34
- Nodes per graph: 23033
Parameters
----------
mode: str
Load train/valid/test data. Has to be one of ['train', 'valid', 'test']
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.
Attributes
-------
is_temporal : bool
Is the dataset contains temporal graphs
Examples
--------
>>> # get train, valid, test set
>>> train_data = ICEWS18Dataset()
>>> valid_data = ICEWS18Dataset(mode='valid')
>>> test_data = ICEWS18Dataset(mode='test')
>>>
>>> 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):
mode = mode.lower()
assert mode in ['train', 'valid', 'test'], "Mode not valid"
self.mode = mode
_url = _get_dgl_url('dataset/icews18.zip')
super(ICEWS18Dataset, self).__init__(name='ICEWS18',
url=_url,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose)
def process(self):
data = loadtxt(os.path.join(self.save_path, '{}.txt'.format(self.mode)),
delimiter='\t').astype(np.int64)
num_nodes = 23033
# The source code is not released, but the paper indicates there're
# totally 137 samples. The cutoff below has exactly 137 samples.
time_index = np.floor(data[:, 3] / 24).astype(np.int64)
start_time = time_index[time_index != -1].min()
end_time = time_index.max()
self._graphs = []
for i in range(start_time, end_time + 1):
row_mask = time_index <= i
edges = data[row_mask][:, [0, 2]]
rate = data[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'])
self._graphs.append(g)
def has_cache(self):
graph_path = os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.mode))
return os.path.exists(graph_path)
def save(self):
graph_path = os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.mode))
save_graphs(graph_path, self._graphs)
def load(self):
graph_path = os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.mode))
self._graphs = load_graphs(graph_path)[0]
[docs] def __getitem__(self, idx):
r""" Get graph by index
Parameters
----------
idx : int
Item index
Returns
-------
:class:`dgl.DGLGraph`
The graph contains:
- ``edata['rel_type']``: edge type
"""
return self._graphs[idx]
[docs] def __len__(self):
r"""Number of graphs in the dataset.
Return
-------
int
"""
return len(self._graphs)
@property
def is_temporal(self):
r"""Is the dataset contains temporal graphs
Returns
-------
bool
"""
return True
ICEWS18 = ICEWS18Dataset