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import os.path as osp
from typing import Callable, Optional
import torch
from torch import Tensor
from torch_geometric.data import HeteroData, InMemoryDataset, download_url
class IGMCDataset(InMemoryDataset):
r"""The user-item heterogeneous rating datasets :obj:`"Douban"`,
:obj:`"Flixster"` and :obj:`"Yahoo-Music"` from the `"Inductive Matrix
Completion Based on Graph Neural Networks"
<https://arxiv.org/abs/1904.12058>`_ paper.
Nodes represent users and items.
Edges and features between users and items represent a (training) rating of
the item given by the user.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"Douban"`,
:obj:`"Flixster"`, :obj:`"Yahoo-Music"`).
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.HeteroData` 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:`torch_geometric.data.HeteroData` 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://github.com/muhanzhang/IGMC/raw/master/raw_data'
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name.lower().replace('-', '_')
assert self.name in ['flixster', 'douban', 'yahoo_music']
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0], data_cls=HeteroData)
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self) -> str:
return 'training_test_dataset.mat'
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
path = f'{self.url}/{self.name}/training_test_dataset.mat'
download_url(path, self.raw_dir)
@staticmethod
def load_matlab_file(path_file: str, name: str) -> Tensor:
import h5py
import numpy as np
db = h5py.File(path_file, 'r')
out = torch.from_numpy(np.asarray(db[name])).to(torch.float).t()
db.close()
return out
def process(self) -> None:
data = HeteroData()
M = self.load_matlab_file(self.raw_paths[0], 'M')
if self.name == 'flixster':
user_x = self.load_matlab_file(self.raw_paths[0], 'W_users')
item_x = self.load_matlab_file(self.raw_paths[0], 'W_movies')
elif self.name == 'douban':
user_x = self.load_matlab_file(self.raw_paths[0], 'W_users')
item_x = torch.eye(M.size(1))
elif self.name == 'yahoo_music':
user_x = torch.eye(M.size(0))
item_x = self.load_matlab_file(self.raw_paths[0], 'W_tracks')
data['user'].x = user_x
data['item'].x = item_x
train_mask = self.load_matlab_file(self.raw_paths[0], 'Otraining')
train_mask = train_mask.to(torch.bool)
edge_index = train_mask.nonzero().t()
rating = M[edge_index[0], edge_index[1]]
data['user', 'rates', 'item'].edge_index = edge_index
data['user', 'rates', 'item'].rating = rating
data['item', 'rated_by', 'user'].edge_index = edge_index.flip([0])
data['item', 'rated_by', 'user'].rating = rating
test_mask = self.load_matlab_file(self.raw_paths[0], 'Otest')
test_mask = test_mask.to(torch.bool)
edge_label_index = test_mask.nonzero().t()
edge_label = M[edge_label_index[0], edge_label_index[1]]
data['user', 'rates', 'item'].edge_label_index = edge_label_index
data['user', 'rates', 'item'].edge_label = edge_label
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.__class__.__name__}(name={self.name})'
|
