import os
import os.path as osp
from itertools import product
from typing import Callable, List, Optional
import mlx.core as mx
import numpy as np
from mlx_graphs.data import HeteroGraphData
from mlx_graphs.datasets.dataset import Dataset
from mlx_graphs.datasets.utils import download, extract_archive
[docs]
class DBLP(Dataset):
"""
A subset of the DBLP computer science bibliography website, as
collected in the `"MAGNN: Metapath Aggregated Graph Neural Network for
Heterogeneous Graph Embedding" <https://arxiv.org/abs/2002.01680>`_ paper.
DBLP is a heterogeneous graph containing four types of entities - authors
(4,057 nodes), papers (14,328 nodes), terms (7,723 nodes), and conferences
(20 nodes).
The authors are divided into four research areas (database, data mining,
artificial intelligence, information retrieval).
Each author is described by a bag-of-words representation of their paper
keywords.
Args:
base_dir: directory where the dataset should be saved.
transform: A function/transform that takes in an
:obj:`HeteroGraphData` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform: A function/transform that takes in
an `HeteroGraphData` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
"""
def __init__(
self,
base_dir: Optional[str] = None,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
):
super().__init__(
name="DBLP",
base_dir=base_dir,
transform=transform,
pre_transform=pre_transform,
)
@property
def raw_path(self) -> str:
return f"{super(self.__class__, self).raw_path}"
@property
def raw_file_names(self) -> List[str]:
return [
"adjM.npz",
"features_0.npz",
"features_1.npz",
"features_2.npz",
"labels.npy",
"train_val_test_idx.npz",
]
def download(self):
url = "https://www.dropbox.com/s/yh4grpeks87ugr2/DBLP_processed.zip?dl=1"
path = download(url=url, path=self.raw_path)
new_path = path.split("?")[-2]
os.rename(path, new_path)
extract_archive(new_path, self.raw_path)
os.remove(new_path)
def process(self):
try:
import scipy.sparse as sp
except ImportError:
raise ImportError("scipy is required to download and process the raw data")
node_types = ["author", "paper", "term", "conference"]
node_features_dict = {}
for i, node_type in enumerate(node_types[:2]):
nodes = sp.load_npz(osp.join(self.raw_path, f"features_{i}.npz"))
node_features_dict[node_type] = mx.array(nodes.todense())
term = np.load(osp.join(self.raw_path, "features_2.npy"))
node_features_dict["term"] = mx.array(term).astype(mx.float32)
node_type_idx_ = np.load(osp.join(self.raw_path, "node_types.npy"))
node_type_idx = mx.array(node_type_idx_).astype(mx.int32)
# Conference nodes don't have features and hence adding
# it explicitly to a dictionary will not make sense.
# Either override the property in the class or set attribute separately
# for conference
conference_nodes = int((node_type_idx == 3).sum().item())
node_labels_dict = {}
y = np.load(osp.join(self.raw_path, "labels.npy"))
node_labels_dict["author"] = mx.array(y)
data = HeteroGraphData(
edge_index_dict={},
node_features_dict=node_features_dict,
edge_features_dict={},
node_labels_dict=node_labels_dict,
)
split = np.load(osp.join(self.raw_path, "train_val_test_idx.npz"))
for name in ["train", "val", "test"]:
idx = split[f"{name}_idx"]
idx = mx.array(idx, dtype=mx.int64)
mask = mx.zeros(data.num_nodes["author"], dtype=mx.bool_)
mask[idx] = True
setattr(data, f"author_{name}_mask", mask)
s = {}
N_a = data.num_nodes["author"]
N_p = data.num_nodes["paper"]
N_t = data.num_nodes["term"]
N_c = conference_nodes
s["author"] = (0, N_a)
s["paper"] = (N_a, N_a + N_p)
s["term"] = (N_a + N_p, N_a + N_p + N_t)
s["conference"] = (N_a + N_p + N_t, N_a + N_p + N_t + N_c)
A = sp.load_npz(osp.join(self.raw_path, "adjM.npz"))
for src, dst in product(node_types, node_types):
A_sub = A[s[src][0] : s[src][1], s[dst][0] : s[dst][1]].tocoo()
if A_sub.nnz > 0:
row = mx.array(A_sub.row, dtype=mx.int64)
col = mx.array(A_sub.col, dtype=mx.int64)
data.edge_index_dict[(src, "to", dst)] = mx.stack([row, col], axis=0)
if self.pre_transform is not None:
data = self.pre_transform(data)
self.graphs = [data]
