import os
import os.path as osp
from itertools import product
from typing import Callable, List, Optional
import numpy as np
import scipy.sparse as sp
import tensorlayerx as tlx
from gammagl.data import (HeteroGraph, InMemoryDataset, download_url,
extract_zip)
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class IMDB(InMemoryDataset):
r"""A subset of the Internet Movie Database (IMDB), as collected in the
`"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph
Embedding" <https://arxiv.org/abs/2002.01680>`_ paper.
IMDB is a heterogeneous graph containing three types of entities - movies
(4,278 nodes), actors (5,257 nodes), and directors (2,081 nodes).
The movies are divided into three classes (action, comedy, drama) according
to their genre.
Movie features correspond to elements of a bag-of-words representation of
its plot keywords.
Parameters
----------
root: str, optional
Root directory where the dataset should be saved.
transform: callable, optional
A function/transform that takes in an
:obj:`gammagl.data.HeteroGraph` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform: callable, optional
A function/transform that takes in
an :obj:`gammagl.data.HeteroGraph` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
url = 'https://www.dropbox.com/s/g0btk9ctr1es39x/IMDB_processed.zip?dl=1'
def __init__(self, root: str = None, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None, force_reload: bool = False):
super().__init__(root, transform, pre_transform, force_reload = force_reload)
self.data, self.slices = self.load_data(self.processed_paths[0])
@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'
]
@property
def processed_file_names(self) -> str:
return tlx.BACKEND + 'data.pt'
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def download(self):
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.remove(path)
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def process(self):
data = HeteroGraph()
node_types = ['movie', 'director', 'actor']
for i, node_type in enumerate(node_types):
x = sp.load_npz(osp.join(self.raw_dir, f'features_{i}.npz'))
data[node_type].x = tlx.convert_to_tensor(x.todense(), dtype=tlx.float32)
y = np.load(osp.join(self.raw_dir, 'labels.npy'))
data['movie'].y = tlx.convert_to_tensor(y, dtype=tlx.int64)
split = np.load(osp.join(self.raw_dir, 'train_val_test_idx.npz'))
for name in ['train', 'val', 'test']:
idx = split[f'{name}_idx']
mask = np.zeros(data['movie'].num_nodes, dtype=np.bool_)
mask[idx] = True
data['movie'][f'{name}_mask'] = tlx.convert_to_tensor(mask, dtype=tlx.bool)
s = {}
N_m = data['movie'].num_nodes
N_d = data['director'].num_nodes
N_a = data['actor'].num_nodes
s['movie'] = (0, N_m)
s['director'] = (N_m, N_m + N_d)
s['actor'] = (N_m + N_d, N_m + N_d + N_a)
A = sp.load_npz(osp.join(self.raw_dir, '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 = tlx.convert_to_tensor(A_sub.row, dtype=tlx.int64)
col = tlx.convert_to_tensor(A_sub.col, dtype=tlx.int64)
data[src, dst].edge_index = tlx.stack([row, col], axis=0)
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save_data(self.collate([data]), self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.__class__.__name__}()'