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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 = "6470_rte"
SIZES = CaseSizes(n_bus=6470, n_load=3670, n_gen=761, n_branch=9005)
NUM_TRAIN = 73912
NUM_TEST = 18478
NUM_INFEASIBLE = 7628
SPLITFILES = {
"train/SOCOPF/dual.h5.gz": ["train/SOCOPF/dual/xaa", "train/SOCOPF/dual/xab"],
}
URL = "https://huggingface.co/datasets/PGLearn/PGLearn-Large-6470_rte"
DESCRIPTION = """\
The 6470_rte PGLearn optimal power flow dataset, part of the PGLearn-Large 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-Large-6470_rte/blob/script/case.json.gz
"""
USE_ML4OPF_WARNING = """
================================================================================================
Loading PGLearn-Large-6470_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-Large-6470_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 PGLearnLarge6470_rteConfig(hfd.BuilderConfig):
"""BuilderConfig for PGLearn-Large-6470_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(PGLearnLarge6470_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 PGLearnLarge6470_rte(hfd.ArrowBasedBuilder):
"""DatasetBuilder for PGLearn-Large-6470_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-Large-6470_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 = PGLearnLarge6470_rteConfig
DEFAULT_CONFIG_NAME=CASENAME
BUILDER_CONFIGS = [
PGLearnLarge6470_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")
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