Convert dataset to Parquet (part 00011-of-00012) (#12)

- Convert dataset to Parquet (part 00011-of-00012) (d295b55e497a7fc3c6629422be11a8f8ddfd9c45)
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infeasible/ACOPF/meta.h5.gz → 9241_pegase/test-00105-of-00112.parquet

@@ -1,3 +1,3 @@
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+size 495074099

case.json.gz → 9241_pegase/test-00106-of-00112.parquet

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+size 495266802

infeasible/DCOPF/meta.h5.gz → 9241_pegase/test-00107-of-00112.parquet

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+size 495041057

infeasible/SOCOPF/meta.h5.gz → 9241_pegase/test-00108-of-00112.parquet

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9241_pegase/test-00109-of-00112.parquet

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+size 495152140

9241_pegase/test-00110-of-00112.parquet

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9241_pegase/test-00111-of-00112.parquet

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PGLearn-Large-9241_pegase.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 = "9241_pegase"
-SIZES = CaseSizes(n_bus=9241, n_load=4895, n_gen=1445, n_branch=16049)
-NUM_TRAIN = 64309
-NUM_TEST = 16078
-NUM_INFEASIBLE = 19631
-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-Large-9241_pegase"
-DESCRIPTION = """\
-The 9241_pegase 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-9241_pegase/blob/script/case.json.gz
-"""
-USE_ML4OPF_WARNING = """
-================================================================================================
-  Loading PGLearn-Large-9241_pegase 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-9241_pegase#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 PGLearnLarge9241_pegaseConfig(hfd.BuilderConfig):
-    """BuilderConfig for PGLearn-Large-9241_pegase. 
-    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(PGLearnLarge9241_pegaseConfig, 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 PGLearnLarge9241_pegase(hfd.ArrowBasedBuilder):
-    """DatasetBuilder for PGLearn-Large-9241_pegase.
-    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-9241_pegase", 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 = PGLearnLarge9241_pegaseConfig
-    DEFAULT_CONFIG_NAME=CASENAME
-    BUILDER_CONFIGS = [
-        PGLearnLarge9241_pegaseConfig(
-            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: 55615711263
     num_examples: 16078
-  download_size: 242538350400
+  download_size: 278172833149
   dataset_size: 278068178958
+configs:
+- config_name: 9241_pegase
+  data_files:
+  - split: train
+    path: 9241_pegase/train-*
+  - split: test
+    path: 9241_pegase/test-*
+  default: true
 ---

config.toml

@@ -1,54 +0,0 @@
-export_dir = "data/pglearn/9241_pegase"
-# Name of the reference PGLib case. Must be a valid PGLib case name.
-pglib_case = "pglib_opf_case9241_pegase"
-floating_point_type = "Float32"
-
-[sampler]
-# data sampler options
-[sampler.load]
-noise_type = "ScaledUniform"
-l          = 0.6                # Lower bound of base load factor
-u          = 1.0                # 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
-
-[slurm]
-n_samples = 250000
-n_jobs = 44
-minibatch_size = 32
-cpus_per_task = 8
-queue = "embers"
-charge_account = "gts-phentenryck3-ai4opt"
-sysimage_memory = "128G"
-sampler_memory = "8G"
-extract_memory = "500G"

infeasible/ACOPF/dual.h5.gz

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