from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import InMemoryDataset, TemporalData, download_url
[docs]class MyketDataset(InMemoryDataset):
r"""The Myket Android Application Install dataset from the
`"Effect of Choosing Loss Function when Using T-Batching for Representation
Learning on Dynamic Networks" <https://arxiv.org/abs/2308.06862>`_ paper.
The dataset contains a temporal graph of application install interactions
in an Android application market.
Args:
root (str): Root directory where the dataset should be saved.
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` 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.Data` 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`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - Name
- #nodes
- #edges
- #features
- #classes
* - Myket
- 17,988
- 694,121
- 33
- 1
"""
url = ('https://raw.githubusercontent.com/erfanloghmani/'
'myket-android-application-market-dataset/main/data_int_index')
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0], data_cls=TemporalData)
@property
def raw_file_names(self) -> List[str]:
return ['myket.csv', 'app_info_sample.npy']
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
for file_name in self.raw_file_names:
download_url(f'{self.url}/{file_name}', self.raw_dir)
def process(self) -> None:
import pandas as pd
df = pd.read_csv(self.raw_paths[0], skiprows=1, header=None)
src = torch.from_numpy(df[0].values)
dst = torch.from_numpy(df[1].values)
t = torch.from_numpy(df[2].values)
x = torch.from_numpy(np.load(self.raw_paths[1])).to(torch.float)
msg = x[dst]
dst = dst + (int(src.max()) + 1)
data = TemporalData(src=src, dst=dst, t=t, msg=msg)
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])