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import json
import os
import os.path as osp
from typing import Callable, Dict, List, Literal, Optional
import torch
import tqdm
from torch import Tensor
from torch_geometric.data import (
HeteroData,
InMemoryDataset,
download_url,
extract_tar,
)
class OPFDataset(InMemoryDataset):
r"""The heterogeneous OPF data from the `"Large-scale Datasets for AC
Optimal Power Flow with Topological Perturbations"
<https://arxiv.org/abs/2406.07234>`_ paper.
:class:`OPFDataset` is a large-scale dataset of solved optimal power flow
problems, derived from the
`pglib-opf <https://github.com/power-grid-lib/pglib-opf>`_ dataset.
The physical topology of the grid is represented by the :obj:`"bus"` node
type, and the connecting AC lines and transformers. Additionally,
:obj:`"generator"`, :obj:`"load"`, and :obj:`"shunt"` nodes are connected
to :obj:`"bus"` nodes using a dedicated edge type each, *e.g.*,
:obj:`"generator_link"`.
Edge direction corresponds to the properties of the line, *e.g.*,
:obj:`b_fr` is the line charging susceptance at the :obj:`from`
(source/sender) bus.
Args:
root (str): Root directory where the dataset should be saved.
split (str, optional): If :obj:`"train"`, loads the training dataset.
If :obj:`"val"`, loads the validation dataset.
If :obj:`"test"`, loads the test dataset. (default: :obj:`"train"`)
case_name (str, optional): The name of the original pglib-opf case.
(default: :obj:`"pglib_opf_case14_ieee"`)
num_groups (int, optional): The dataset is divided into 20 groups with
each group containing 15,000 samples.
For large networks, this amount of data can be overwhelming.
The :obj:`num_groups` parameters controls the amount of data being
downloaded. Allowed values are :obj:`[1, 20]`.
(default: :obj:`20`)
topological_perturbations (bool, optional): Whether to use the dataset
with added topological perturbations. (default: :obj:`False`)
transform (callable, optional): A function/transform that takes in
a :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 a :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`)
pre_filter (callable, optional): A function that takes in a
:obj:`torch_geometric.data.HeteroData` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
url = 'https://storage.googleapis.com/gridopt-dataset'
def __init__(
self,
root: str,
split: Literal['train', 'val', 'test'] = 'train',
case_name: Literal[
'pglib_opf_case14_ieee',
'pglib_opf_case30_ieee',
'pglib_opf_case57_ieee',
'pglib_opf_case118_ieee',
'pglib_opf_case500_goc',
'pglib_opf_case2000_goc',
'pglib_opf_case6470_rte',
'pglib_opf_case4661_sdet'
'pglib_opf_case10000_goc',
'pglib_opf_case13659_pegase',
] = 'pglib_opf_case14_ieee',
num_groups: int = 20,
topological_perturbations: bool = False,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.split = split
self.case_name = case_name
self.num_groups = num_groups
self.topological_perturbations = topological_perturbations
self._release = 'dataset_release_1'
if topological_perturbations:
self._release += '_nminusone'
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
idx = self.processed_file_names.index(f'{split}.pt')
self.load(self.processed_paths[idx])
@property
def raw_dir(self) -> str:
return osp.join(self.root, self._release, self.case_name, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self._release, self.case_name,
f'processed_{self.num_groups}')
@property
def raw_file_names(self) -> List[str]:
return [f'{self.case_name}_{i}.tar.gz' for i in range(self.num_groups)]
@property
def processed_file_names(self) -> List[str]:
return ['train.pt', 'val.pt', 'test.pt']
def download(self) -> None:
for name in self.raw_file_names:
url = f'{self.url}/{self._release}/{name}'
path = download_url(url, self.raw_dir)
extract_tar(path, self.raw_dir)
def process(self) -> None:
train_data_list = []
val_data_list = []
test_data_list = []
for group in tqdm.tqdm(range(self.num_groups)):
tmp_dir = osp.join(
self.raw_dir,
'gridopt-dataset-tmp',
self._release,
self.case_name,
f'group_{group}',
)
for name in os.listdir(tmp_dir):
with open(osp.join(tmp_dir, name)) as f:
obj = json.load(f)
grid = obj['grid']
solution = obj['solution']
metadata = obj['metadata']
# Graph-level properties:
data = HeteroData()
data.x = torch.tensor(grid['context']).view(-1)
data.objective = torch.tensor(metadata['objective'])
# Nodes (only some have a target):
data['bus'].x = torch.tensor(grid['nodes']['bus'])
data['bus'].y = torch.tensor(solution['nodes']['bus'])
data['generator'].x = torch.tensor(grid['nodes']['generator'])
data['generator'].y = torch.tensor(
solution['nodes']['generator'])
data['load'].x = torch.tensor(grid['nodes']['load'])
data['shunt'].x = torch.tensor(grid['nodes']['shunt'])
# Edges (only ac lines and transformers have features):
data['bus', 'ac_line', 'bus'].edge_index = ( #
extract_edge_index(obj, 'ac_line'))
data['bus', 'ac_line', 'bus'].edge_attr = torch.tensor(
grid['edges']['ac_line']['features'])
data['bus', 'ac_line', 'bus'].edge_label = torch.tensor(
solution['edges']['ac_line']['features'])
data['bus', 'transformer', 'bus'].edge_index = ( #
extract_edge_index(obj, 'transformer'))
data['bus', 'transformer', 'bus'].edge_attr = torch.tensor(
grid['edges']['transformer']['features'])
data['bus', 'transformer', 'bus'].edge_label = torch.tensor(
solution['edges']['transformer']['features'])
data['generator', 'generator_link', 'bus'].edge_index = ( #
extract_edge_index(obj, 'generator_link'))
data['bus', 'generator_link', 'generator'].edge_index = ( #
extract_edge_index_rev(obj, 'generator_link'))
data['load', 'load_link', 'bus'].edge_index = ( #
extract_edge_index(obj, 'load_link'))
data['bus', 'load_link', 'load'].edge_index = ( #
extract_edge_index_rev(obj, 'load_link'))
data['shunt', 'shunt_link', 'bus'].edge_index = ( #
extract_edge_index(obj, 'shunt_link'))
data['bus', 'shunt_link', 'shunt'].edge_index = ( #
extract_edge_index_rev(obj, 'shunt_link'))
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
i = int(name.split('.')[0].split('_')[1])
train_limit = int(15_000 * self.num_groups * 0.9)
val_limit = train_limit + int(15_000 * self.num_groups * 0.05)
if i < train_limit:
train_data_list.append(data)
elif i < val_limit:
val_data_list.append(data)
else:
test_data_list.append(data)
self.save(train_data_list, self.processed_paths[0])
self.save(val_data_list, self.processed_paths[1])
self.save(test_data_list, self.processed_paths[2])
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({len(self)}, '
f'split={self.split}, '
f'case_name={self.case_name}, '
f'topological_perturbations={self.topological_perturbations})')
def extract_edge_index(obj: Dict, edge_name: str) -> Tensor:
return torch.tensor([
obj['grid']['edges'][edge_name]['senders'],
obj['grid']['edges'][edge_name]['receivers'],
])
def extract_edge_index_rev(obj: Dict, edge_name: str) -> Tensor:
return torch.tensor([
obj['grid']['edges'][edge_name]['receivers'],
obj['grid']['edges'][edge_name]['senders'],
])
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