Convert dataset to Parquet (part 00010-of-00011)

infeasible/ACOPF/meta.h5.gz → 1888_rte/test-00077-of-00106.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b2c6cf37eff40ed8fc1cd9a2ac11f7fffb20d0a0cb2240b6f351812dd01b3a35
-size 1174026
+oid sha256:5acf26c648a4823d0a8739b7034391b3f1e85ea632bd2cf52a968a164d9f2700
+size 484194525

case.json.gz → 1888_rte/test-00078-of-00106.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:04bef6d8d495d8968d096fdb549b8e646430bfcbd07037199ba0c3d483634c39
-size 1448847
+oid sha256:bc491c87023d2ac3daf06a109542f844a65ca8a38eb2d21c0930788f52c392ae
+size 484182538

infeasible/DCOPF/dual.h5.gz → 1888_rte/test-00079-of-00106.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a14b5a5e883f7f9c885e69b7c63624434d4affa38a71dd9843c9a9f0439a3693
-size 133608111
+oid sha256:f71f84edccf667c48e7bd61d9e884dbaa353555bd3436d1473f494c8a9fe5c0b
+size 484231867

infeasible/DCOPF/meta.h5.gz → 1888_rte/test-00080-of-00106.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:73f147a445190cc6de1a6efdbcaf395e351720c61f4b71eda47624110b2a8172
-size 1188365
+oid sha256:179379a9b1ddb2c2f58631657f441776663bd96c2851bed424b8bcee341f5434
+size 484252131

1888_rte/test-00081-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d95c599e97957c09320192429c316ff366d72f5844d36d0e25aa615bc5bc01f
+size 484270118

1888_rte/test-00082-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:443160958f16c2ffc9473b84b55fcfea1adabbd3d929449a1707457894a59cc3
+size 484008809

1888_rte/test-00083-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:510f8c2258b689a43176462dd5d105a4a78c1af7a92049e81d78a5bbcb6033c3
+size 484346179

1888_rte/test-00084-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6345c4d20cb7a8d8eeafe66d44cafa46861aa11cbecab5d37b0da19417293136
+size 484263539

1888_rte/test-00085-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1c7d72614779de8e4a26969b5230e94bb9f4ccd51de75a4cc81c93541f74391
+size 484162680

1888_rte/test-00086-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87ab687417a26cec0c668aecfdba464453bc06acbd1c3d3d7059538a772038d0
+size 484131364

1888_rte/test-00087-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74e1113323b4aa49fe786393970b55a4ddf0e4983929425fb0e68833de9cad54
+size 484146897

1888_rte/test-00088-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ef7208238c3f0980d5940ec65a7e6b9b5e101ddbd07548cf93b5727f654142e
+size 484014126

1888_rte/test-00089-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:738e7b16c3c682dbe628a4dfc0717b886014f82665ea22617d1f82363a7c0488
+size 484035704

1888_rte/test-00090-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a4c9676fe0fbd3aefb9993b0272a64e8423efd087cd9dcd724052c54b3e9e851
+size 484126856

1888_rte/test-00091-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfddc95b58664a4d22378bf7337e18f136e5786b9ab31a88f53bb8e2206d2c6f
+size 484132532

1888_rte/test-00092-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbef47cbffff7233dcb8ebc36fe1781087f8800800d80fbb910d8993d1328a3c
+size 484229583

1888_rte/test-00093-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13af02a56451f49f176b7e71d58f0ea3e9c681c0b2c45b62db28ddc6fbc5c9fa
+size 484222804

1888_rte/test-00094-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8610c4204b61e9fe735f78591f82b406cc287aa590016838de80cd98f01770ee
+size 484258237

1888_rte/test-00095-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d29ad618db82f3a61b77e80ae3810ecbcb731eddce8f7878103c3f2e4e2e8a00
+size 484302220

1888_rte/test-00096-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7805430898242c20fa1404c61ccdab53400c35e314e5d04cc9b1fb2a63e7fefb
+size 484165646

1888_rte/test-00097-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6cdd51bf40954ded4cc117570d2f42cb8069df321fda9b53f7d8d041bf14539
+size 484112826

1888_rte/test-00098-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51596ae2ce5a4e206fed9d88d1b32b86c5a337a86bf96ff08e6eb931cfebe609
+size 484237234

1888_rte/test-00099-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5aca615fdda4bfd3a3143b7de6b12295ab893f6f2e769638a6dfd541ff7bd445
+size 484302042

1888_rte/test-00100-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15fe28f38c44679ecf544f638874c59531d7052bbb936a9558c13cc0a44f2e70
+size 484129090

1888_rte/test-00101-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c76e5c3050579404b6d9ef3df384f3db7c7ee361f63e32286cc78bc0b8535de5
+size 484367186

1888_rte/test-00102-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2be65e9e383240301242a8707ac927c8e2adefb6a1eb96539664f74fa8cb8e3
+size 484177704

1888_rte/test-00103-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56d4b183119a844403f7733e376afd6716e1d3e1337bdf2be3fd7d6ecff75093
+size 484192797

1888_rte/test-00104-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eda253e6a680961122edac643021fc700fafb0c657ff6ec6b944a4da45c5a512
+size 484235175

1888_rte/test-00105-of-00106.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03ed565c053f43d1aaa7f8ca9adf4970b9d23a5f0d1097c3839605a94605034e
+size 484175696

PGLearn-Medium-1888_rte.py

@@ -1,429 +0,0 @@
-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")

README.md

@@ -288,6 +288,14 @@ dataset_info:
   - name: test
     num_bytes: 52865882822
     num_examples: 92897
-  download_size: 221548942567
+  download_size: 256704088692
   dataset_size: 264328845025
+configs:
+- config_name: 1888_rte
+  data_files:
+  - split: train
+    path: 1888_rte/train-*
+  - split: test
+    path: 1888_rte/test-*
+  default: true
 ---

config.toml

@@ -1,42 +0,0 @@
-# Name of the reference PGLib case. Must be a valid PGLib case name.
-pglib_case = "pglib_opf_case1888_rte"
-floating_point_type = "Float32"
-
-[sampler]
-# data sampler options
-[sampler.load]
-noise_type = "ScaledUniform"
-l          = 0.7                # Lower bound of base load factor
-u          = 1.1                # Upper bound of base load factor
-sigma      = 0.20               # Relative (multiplicative) noise level.
-
-
-[OPF]
-
-[OPF.ACOPF]
-type = "ACOPF"
-solver.name = "Ipopt"
-solver.attributes.tol = 1e-6
-solver.attributes.linear_solver = "ma27"
-
-[OPF.DCOPF]
-# Formulation/solver options
-type = "DCOPF"
-solver.name = "HiGHS"
-
-[OPF.SOCOPF]
-type = "SOCOPF"
-solver.name = "Clarabel"
-# Tight tolerances
-solver.attributes.tol_gap_abs    = 1e-6
-solver.attributes.tol_gap_rel    = 1e-6
-solver.attributes.tol_feas       = 1e-6
-solver.attributes.tol_infeas_rel = 1e-6
-solver.attributes.tol_ktratio    = 1e-6
-# Reduced accuracy settings
-solver.attributes.reduced_tol_gap_abs    = 1e-6
-solver.attributes.reduced_tol_gap_rel    = 1e-6
-solver.attributes.reduced_tol_feas       = 1e-6
-solver.attributes.reduced_tol_infeas_abs = 1e-6
-solver.attributes.reduced_tol_infeas_rel = 1e-6
-solver.attributes.reduced_tol_ktratio    = 1e-6

infeasible/ACOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:198c13db2590bdd260dbee140dfe9a938964f9c86c87a1604ef6bdfe24d4b539
-size 4374420083

infeasible/ACOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:df254bdf46c8344aa95f2d8dcf87f3d4aaa749fde1e242c322bc5eeddc5f96e2
-size 1845622252

infeasible/DCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:95109b8bfcfb0d0814c0f4f8469fbb24bf8b4ff8f4fab96bbe2de35488d6d1d3
-size 521286630

infeasible/SOCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a43f76dd4e89bf68e5f68a383ac66a158883b0b728d09a1e77e820801c5d2a87
-size 7345156989

infeasible/SOCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1acaf7a04ce943510515818d3bf59391ea61410c474f6ee71694ea6ead6bf62d
-size 1242153

infeasible/SOCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0e7a6670e7ec7ea3c53c5f099b770b613c139fe634f12b15065bcfd3f7f1de9f
-size 2141884604

infeasible/input.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4468d12fb16dbc0edd81529a0ccdebefa75ebb22216e5230143fd2d1b4bc8615
-size 262430198

test/ACOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:035345139ba10243192f6b59a2333643de031651787b4933ae815431e50ea33d
-size 10176519735

test/ACOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9c56d1db5ade25fc2db37b3ea1b41cb82aefc65a9bcfe4aa5672d70b3d177f6d
-size 3168804

test/ACOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2e2f39aa14286d080209312eccd91986e2c8058ac3ba980a95246e9fc82a2302
-size 4599883525

test/DCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ec2261a7a3d586c1761db089d5455e17031a84251579dc88b6ad71ae0d54a4c8
-size 366996811

test/DCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e4dc1361c5dcc4115d39c6b40ac955916863d7766132e7a34921eeb85030a82
-size 3080732

test/DCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:18e68bfc4ec46c8cd71d15d21fa48958a880ba7232a64a33e9e090bd0ef328f1
-size 1367542973

test/SOCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:20bc17ca1d3f0c812401373324fc1ea41c4eb6bd39f814c572d11b3b866eabc8
-size 19218771667

test/SOCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:52d38ffdacbabc4cbe184e54730eec544a3a8262d01ad9981e8f72f8929b8d21
-size 3164796

test/SOCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:be5607be029b86da66c40c96523b4bae3352ff18ad7b9ad77c73f240972f0ac7
-size 5602374327

test/input.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b980b40b75853cac97771fcda5fdcc0cb4bdcda4ba174cbfa899711397ca115a
-size 686700820

train/ACOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f57ce97db72c9c5a1004f54fafd4a2fa59011faa9f89d9dd5a4303a5bf4f3145
-size 40707507061