Datasets:
File size: 20,696 Bytes
3875184 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 |
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
import json
import shutil
import datasets as hfd
import h5py
import pgzip as gzip
import pyarrow as pa
# ┌──────────────┐
# │ Metadata │
# └──────────────┘
@dataclass
class CaseSizes:
n_bus: int
n_load: int
n_gen: int
n_branch: int
CASENAME = "1888_rte"
SIZES = CaseSizes(n_bus=1888, n_load=1000, n_gen=290, n_branch=2531)
NUM_TRAIN = 371587
NUM_TEST = 92897
NUM_INFEASIBLE = 35516
SPLITFILES = {
"train/SOCOPF/dual.h5.gz": ["train/SOCOPF/dual/xaa", "train/SOCOPF/dual/xab", "train/SOCOPF/dual/xac"],
}
URL = "https://huggingface.co/datasets/PGLearn/PGLearn-Medium-1888_rte"
DESCRIPTION = """\
The 1888_rte PGLearn optimal power flow dataset, part of the PGLearn-Medium collection. \
"""
VERSION = hfd.Version("1.0.0")
DEFAULT_CONFIG_DESCRIPTION="""\
This configuration contains feasible input, primal solution, and dual solution data \
for the ACOPF, DCOPF, and SOCOPF formulations on the {case} system. For case data, \
download the case.json.gz file from the `script` branch of the repository. \
https://huggingface.co/datasets/PGLearn/PGLearn-Medium-1888_rte/blob/script/case.json.gz
"""
USE_ML4OPF_WARNING = """
================================================================================================
Loading PGLearn-Medium-1888_rte through the `datasets.load_dataset` function may be slow.
Consider using ML4OPF to directly convert to `torch.Tensor`; for more info see:
https://github.com/AI4OPT/ML4OPF?tab=readme-ov-file#manually-loading-data
Or, use `huggingface_hub.snapshot_download` and an HDF5 reader; for more info see:
https://huggingface.co/datasets/PGLearn/PGLearn-Medium-1888_rte#downloading-individual-files
================================================================================================
"""
CITATION = """\
@article{klamkinpglearn,
title={{PGLearn - An Open-Source Learning Toolkit for Optimal Power Flow}},
author={Klamkin, Michael and Tanneau, Mathieu and Van Hentenryck, Pascal},
year={2025},
}\
"""
IS_COMPRESSED = True
# ┌──────────────────┐
# │ Formulations │
# └──────────────────┘
def acopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool):
features = {}
if primal: features.update(acopf_primal_features(sizes))
if dual: features.update(acopf_dual_features(sizes))
if meta: features.update({f"ACOPF/{k}": v for k, v in META_FEATURES.items()})
return features
def dcopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool):
features = {}
if primal: features.update(dcopf_primal_features(sizes))
if dual: features.update(dcopf_dual_features(sizes))
if meta: features.update({f"DCOPF/{k}": v for k, v in META_FEATURES.items()})
return features
def socopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool):
features = {}
if primal: features.update(socopf_primal_features(sizes))
if dual: features.update(socopf_dual_features(sizes))
if meta: features.update({f"SOCOPF/{k}": v for k, v in META_FEATURES.items()})
return features
FORMULATIONS_TO_FEATURES = {
"ACOPF": acopf_features,
"DCOPF": dcopf_features,
"SOCOPF": socopf_features,
}
# ┌───────────────────┐
# │ BuilderConfig │
# └───────────────────┘
class PGLearnMedium1888_rteConfig(hfd.BuilderConfig):
"""BuilderConfig for PGLearn-Medium-1888_rte.
By default, primal solution data, metadata, input, casejson, are included for the train and test splits.
To modify the default configuration, pass attributes of this class to `datasets.load_dataset`:
Attributes:
formulations (list[str]): The formulation(s) to include, e.g. ["ACOPF", "DCOPF"]
primal (bool, optional): Include primal solution data. Defaults to True.
dual (bool, optional): Include dual solution data. Defaults to False.
meta (bool, optional): Include metadata. Defaults to True.
input (bool, optional): Include input data. Defaults to True.
casejson (bool, optional): Include case.json data. Defaults to True.
train (bool, optional): Include training samples. Defaults to True.
test (bool, optional): Include testing samples. Defaults to True.
infeasible (bool, optional): Include infeasible samples. Defaults to False.
"""
def __init__(self,
formulations: list[str],
primal: bool=True, dual: bool=False, meta: bool=True, input: bool = True, casejson: bool=True,
train: bool=True, test: bool=True, infeasible: bool=False,
compressed: bool=IS_COMPRESSED, **kwargs
):
super(PGLearnMedium1888_rteConfig, self).__init__(version=VERSION, **kwargs)
self.case = CASENAME
self.formulations = formulations
self.primal = primal
self.dual = dual
self.meta = meta
self.input = input
self.casejson = casejson
self.train = train
self.test = test
self.infeasible = infeasible
self.gz_ext = ".gz" if compressed else ""
@property
def size(self):
return SIZES
@property
def features(self):
features = {}
if self.casejson: features.update(case_features())
if self.input: features.update(input_features(SIZES))
for formulation in self.formulations:
features.update(FORMULATIONS_TO_FEATURES[formulation](SIZES, self.primal, self.dual, self.meta))
return hfd.Features(features)
@property
def splits(self):
splits: dict[hfd.Split, dict[str, str | int]] = {}
if self.train:
splits[hfd.Split.TRAIN] = {
"name": "train",
"num_examples": NUM_TRAIN
}
if self.test:
splits[hfd.Split.TEST] = {
"name": "test",
"num_examples": NUM_TEST
}
if self.infeasible:
splits[hfd.Split("infeasible")] = {
"name": "infeasible",
"num_examples": NUM_INFEASIBLE
}
return splits
@property
def urls(self):
urls: dict[str, None | str | list] = {
"case": None, "train": [], "test": [], "infeasible": [],
}
if self.casejson:
urls["case"] = f"case.json" + self.gz_ext
else:
urls.pop("case")
split_names = []
if self.train: split_names.append("train")
if self.test: split_names.append("test")
if self.infeasible: split_names.append("infeasible")
for split in split_names:
if self.input: urls[split].append(f"{split}/input.h5" + self.gz_ext)
for formulation in self.formulations:
if self.primal:
filename = f"{split}/{formulation}/primal.h5" + self.gz_ext
if filename in SPLITFILES: urls[split].append(SPLITFILES[filename])
else: urls[split].append(filename)
if self.dual:
filename = f"{split}/{formulation}/dual.h5" + self.gz_ext
if filename in SPLITFILES: urls[split].append(SPLITFILES[filename])
else: urls[split].append(filename)
if self.meta:
filename = f"{split}/{formulation}/meta.h5" + self.gz_ext
if filename in SPLITFILES: urls[split].append(SPLITFILES[filename])
else: urls[split].append(filename)
return urls
# ┌────────────────────┐
# │ DatasetBuilder │
# └────────────────────┘
class PGLearnMedium1888_rte(hfd.ArrowBasedBuilder):
"""DatasetBuilder for PGLearn-Medium-1888_rte.
The main interface is `datasets.load_dataset` with `trust_remote_code=True`, e.g.
```python
from datasets import load_dataset
ds = load_dataset("PGLearn/PGLearn-Medium-1888_rte", trust_remote_code=True,
# modify the default configuration by passing kwargs
formulations=["DCOPF"],
dual=False,
meta=False,
)
```
"""
DEFAULT_WRITER_BATCH_SIZE = 10000
BUILDER_CONFIG_CLASS = PGLearnMedium1888_rteConfig
DEFAULT_CONFIG_NAME=CASENAME
BUILDER_CONFIGS = [
PGLearnMedium1888_rteConfig(
name=CASENAME, description=DEFAULT_CONFIG_DESCRIPTION.format(case=CASENAME),
formulations=list(FORMULATIONS_TO_FEATURES.keys()),
primal=True, dual=True, meta=True, input=True, casejson=False,
train=True, test=True, infeasible=False,
)
]
def _info(self):
return hfd.DatasetInfo(
features=self.config.features, splits=self.config.splits,
description=DESCRIPTION + self.config.description,
homepage=URL, citation=CITATION,
)
def _split_generators(self, dl_manager: hfd.DownloadManager):
hfd.logging.get_logger().warning(USE_ML4OPF_WARNING)
filepaths = dl_manager.download_and_extract(self.config.urls)
splits: list[hfd.SplitGenerator] = []
if self.config.train:
splits.append(hfd.SplitGenerator(
name=hfd.Split.TRAIN,
gen_kwargs=dict(case_file=filepaths.get("case", None), data_files=tuple(filepaths["train"]), n_samples=NUM_TRAIN),
))
if self.config.test:
splits.append(hfd.SplitGenerator(
name=hfd.Split.TEST,
gen_kwargs=dict(case_file=filepaths.get("case", None), data_files=tuple(filepaths["test"]), n_samples=NUM_TEST),
))
if self.config.infeasible:
splits.append(hfd.SplitGenerator(
name=hfd.Split("infeasible"),
gen_kwargs=dict(case_file=filepaths.get("case", None), data_files=tuple(filepaths["infeasible"]), n_samples=NUM_INFEASIBLE),
))
return splits
def _generate_tables(self, case_file: str | None, data_files: tuple[hfd.utils.track.tracked_str | list[hfd.utils.track.tracked_str]], n_samples: int):
case_data: str | None = json.dumps(json.load(open_maybe_gzip_cat(case_file))) if case_file is not None else None
data: dict[str, h5py.File] = {}
for file in data_files:
v = h5py.File(open_maybe_gzip_cat(file), "r")
if isinstance(file, list):
k = "/".join(Path(file[0].get_origin()).parts[-3:-1]).split(".")[0]
else:
k = "/".join(Path(file.get_origin()).parts[-2:]).split(".")[0]
data[k] = v
for k in list(data.keys()):
if "/input" in k: data[k.split("/", 1)[1]] = data.pop(k)
batch_size = self._writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
for i in range(0, n_samples, batch_size):
effective_batch_size = min(batch_size, n_samples - i)
sample_data = {
f"{dk}/{k}":
hfd.features.features.numpy_to_pyarrow_listarray(v[i:i + effective_batch_size, ...])
for dk, d in data.items() for k, v in d.items() if f"{dk}/{k}" in self.config.features
}
if case_data is not None:
sample_data["case/json"] = pa.array([case_data] * effective_batch_size)
yield i, pa.Table.from_pydict(sample_data)
for f in data.values():
f.close()
# ┌──────────────┐
# │ Features │
# └──────────────┘
FLOAT_TYPE = "float32"
INT_TYPE = "int64"
BOOL_TYPE = "bool"
STRING_TYPE = "string"
def case_features():
# FIXME: better way to share schema of case data -- need to treat jagged arrays
return {
"case/json": hfd.Value(STRING_TYPE),
}
META_FEATURES = {
"meta/seed": hfd.Value(dtype=INT_TYPE),
"meta/formulation": hfd.Value(dtype=STRING_TYPE),
"meta/primal_objective_value": hfd.Value(dtype=FLOAT_TYPE),
"meta/dual_objective_value": hfd.Value(dtype=FLOAT_TYPE),
"meta/primal_status": hfd.Value(dtype=STRING_TYPE),
"meta/dual_status": hfd.Value(dtype=STRING_TYPE),
"meta/termination_status": hfd.Value(dtype=STRING_TYPE),
"meta/build_time": hfd.Value(dtype=FLOAT_TYPE),
"meta/extract_time": hfd.Value(dtype=FLOAT_TYPE),
"meta/solve_time": hfd.Value(dtype=FLOAT_TYPE),
}
def input_features(sizes: CaseSizes):
return {
"input/pd": hfd.Sequence(length=sizes.n_load, feature=hfd.Value(dtype=FLOAT_TYPE)),
"input/qd": hfd.Sequence(length=sizes.n_load, feature=hfd.Value(dtype=FLOAT_TYPE)),
"input/gen_status": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=BOOL_TYPE)),
"input/branch_status": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=BOOL_TYPE)),
"input/seed": hfd.Value(dtype=INT_TYPE),
}
def acopf_primal_features(sizes: CaseSizes):
return {
"ACOPF/primal/vm": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/va": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/primal/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
}
def acopf_dual_features(sizes: CaseSizes):
return {
"ACOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/kcl_q": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/vm": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/ohm_pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/ohm_qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/ohm_qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/sm_fr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/sm_to": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"ACOPF/dual/slack_bus": hfd.Value(dtype=FLOAT_TYPE),
}
def dcopf_primal_features(sizes: CaseSizes):
return {
"DCOPF/primal/va": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
}
def dcopf_dual_features(sizes: CaseSizes):
return {
"DCOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"DCOPF/dual/slack_bus": hfd.Value(dtype=FLOAT_TYPE),
}
def socopf_primal_features(sizes: CaseSizes):
return {
"SOCOPF/primal/w": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/wr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/primal/wi": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
}
def socopf_dual_features(sizes: CaseSizes):
return {
"SOCOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/kcl_q": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/w": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/ohm_pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/ohm_qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/ohm_qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/jabr": hfd.Array2D(shape=(sizes.n_branch, 4), dtype=FLOAT_TYPE),
"SOCOPF/dual/sm_fr": hfd.Array2D(shape=(sizes.n_branch, 3), dtype=FLOAT_TYPE),
"SOCOPF/dual/sm_to": hfd.Array2D(shape=(sizes.n_branch, 3), dtype=FLOAT_TYPE),
"SOCOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/wr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/wi": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
"SOCOPF/dual/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)),
}
# ┌───────────────┐
# │ Utilities │
# └───────────────┘
def open_maybe_gzip_cat(path: str | list):
if isinstance(path, list):
dest = Path(path[0]).parent.with_suffix(".h5")
if not dest.exists():
with open(dest, "wb") as dest_f:
for piece in path:
with open(piece, "rb") as piece_f:
shutil.copyfileobj(piece_f, dest_f)
shutil.rmtree(Path(piece).parent)
path = dest.as_posix()
return gzip.open(path, "rb") if path.endswith(".gz") else open(path, "rb")
|