Convert dataset to Parquet (part 00007-of-00008) (#8)

- Convert dataset to Parquet (part 00007-of-00008) (25ff1fe9b43ffe61556050f9e4e8d4f5028917d8)
- Delete data file (824145edf54b19f4a6de462a8a79d4fdf464a353)
- Delete data file (50839c40ca608e16c7a7385d7b4c5f8e6e726073)
- Delete loading script (b8c967a5e2dfea68a14c9c913b6def6dd44a3c62)
- Delete data file (2bfcb15fc2ea67dc2424e3ce98723d57dadff039)
- Delete data file (359bbb2449ab92e48a588f2f23a048d2c1a2bac1)
- Delete data file (afd36dfbcbe5420bf359da8fa8be59277ee906ff)
- Delete data file (7ad8db0e9a4b674077af30638873b7bff9f2c85b)
- Delete data file (7f525fae35934023d3781551063294c4790ef91d)
- Delete data file (1ef85f106798e619340ec50f695680815700f097)
- Delete data file (ab2a25e4b486bff19420e48f6ea17d6f40e7b49a)
- Delete data file (66e552c3bb94c7824ab9fc0588e7344e27e85281)
- Delete data file (e0f9a557316e5ba0691d164d0e0bf17c0cd74afe)
- Delete data file (2be4e1bec71201bacaef54cb8a002429b04e6ebe)
- Delete data file (6f4192dceadea1b0b32dda4cd09bf43218f7c526)
- Delete data file (0158e9a6766940dfa76dff26007ad83444b29258)
- Delete data file (109003b48932c150af6c845475c07166b941a0c1)
- Delete data file (50f52d2c572d112d874c4c805bf8283dc6ab7bd6)
- Delete data file (2a4cc2c2f59e8abc969ef192b1fc0f99abbb0da2)
- Delete data file (9c69ffe789e3e9b6fc08f1d776739440bc57780f)
- Delete data file (674ba62a55ba8873edd2f247fcdcab71cde35ca6)
- Delete data file (2af0f5fd8eb59bf23e6a08a6eadfcc30d4d4d696)
- Delete data file (29a4ab5650612500a9a3d7a852b5d4f92554140b)
- Delete data file (4183a7d693ecf5e1506e7d47cb36a891fa52c6cc)
- Delete data file (79b9d4c0de1260d4c7244efdae7cb671581efeb1)
- Delete data file (7797b483b88d3e5227404db03d88d51077da8349)
- Delete data file (58de2da26fc695e737e2572888e97bb9b5f3146d)
- Delete data file (86a51a1648abfece4e18d0b5301723561a1e5882)
- Delete data file (a3e4c4b8e5d54257cc55b97abdd351d1d06985fb)
- Delete data file (5aec57e81eb26bacdd7a78d6c1b46457d69b3b5d)
- Delete data file (3399ae2f8157f9288fa1dcde045ae81340de80af)
- Delete data file (efd8c3db3038380c72b62bfe770700b9d7f1638e)
- Delete data file (86b68e310418a107397fc5542038bece01af7121)
- Delete data file (bcbbd7181c8d4f92e2df4248ef4589998cd7cfff)

infeasible/ACOPF/meta.h5.gz → 6470_rte-nminus1/test-00056-of-00074.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f47e96394f3bac4bf7d5ad8a80b3277d0f5adaeb83add364717890bed7f85765
-size 311884
+oid sha256:deb7733aeb25a481207ee39ee8bde53cb73b9d609a1e5094420621f07c391318
+size 496708318

infeasible/DCOPF/dual.h5.gz → 6470_rte-nminus1/test-00057-of-00074.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e25adc84583d72c66b8ba71fb104ba760dab3966f1a6343266a492415fd22e4f
-size 187749566
+oid sha256:1fffaee4d5b187e0c4ecaf4699b7ae2d8b96c8c7a32ac7f4dd754acad7a545a7
+size 496730847

infeasible/DCOPF/meta.h5.gz → 6470_rte-nminus1/test-00058-of-00074.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a303a2b404adcddeb2368574d7b7c134ae89e38c5362f9e47fe606ef55dcf5d8
-size 298019
+oid sha256:b9028a83e379a87fc1673a65765e32d26fe82186b315a04049792c5f283eb850
+size 496843335

6470_rte-nminus1/test-00059-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cbd76f6d0f243ff1641bdeaa8889fa02f9231b9fb3b2f99c1b4aeb62766f50ea
+size 496939869

6470_rte-nminus1/test-00060-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d40d5456520dc8e9cc066c4bb75bbbb967f94dab7df1d2de127740e18ea7046f
+size 495046382

6470_rte-nminus1/test-00061-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c81100166e4c17128e90759943660b3246019bcaa63be7161588ef8645876491
+size 494976200

6470_rte-nminus1/test-00062-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47159dd1548a9e46714b7f4019b166d33053d47b9143494876b979f40717cffc
+size 495025856

6470_rte-nminus1/test-00063-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e97951138847ec15532c0491a274bf46459fa95bd5cd0d7cc5d4522fe9edc02f
+size 494760773

6470_rte-nminus1/test-00064-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b3e99e75076066488712ae08c0c70ae37901e44a7d2898af20c2e89f354b1bd
+size 494897791

6470_rte-nminus1/test-00065-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a791d663706d4251adebb30aec4161da4d29a183407d9109f1eff3d90f62f84b
+size 494933986

6470_rte-nminus1/test-00066-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97e398175dc7284d55037b22c1f03f40e54a9bbf2cc6e9cf6d639bfe5c157681
+size 494960738

6470_rte-nminus1/test-00067-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51077d8b8ef0259514c15addb343028f7cbbbb6b3914ebd9d157872f9553790c
+size 495011752

6470_rte-nminus1/test-00068-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:956ffc9ce3c3d00fdbc3631846dd0becccdd2cc1f98665dcff2ae5817b6c0e4e
+size 494977200

6470_rte-nminus1/test-00069-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce7c40378e928241a8296c987d21bd48c53c5a086830cb14097b008018e207f5
+size 494806089

6470_rte-nminus1/test-00070-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e377077bc431f9938f1081ecf51dcbc31ca8ebe1cae9c1554e7435e0c9f8d18
+size 494859119

6470_rte-nminus1/test-00071-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af6e5e6261d1da997b47390faeb1d08576b66e979a4c2db9221bf24a4dba9c5e
+size 494789072

6470_rte-nminus1/test-00072-of-00074.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bcf98ae6f719baa5ec9ca7c31acd1f3c8b0481f2d67b04fa0a8ae12cd860efa9
+size 494931676

case.json.gz → 6470_rte-nminus1/test-00073-of-00074.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f43d0d0af92a30858a123d9ddbbaa2e2fb5060fb044183a637933b4e1bd00f9b
-size 5183573
+oid sha256:21ce04af8c609a6bb4d7de8babf7cb2fa4cc17379cf01cabc4d456d40226da5e
+size 494780612

PGLearn-Large-6470_rte-nminus1.py

@@ -1,427 +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 = "6470_rte-nminus1"
-SIZES = CaseSizes(n_bus=6470, n_load=3670, n_gen=761, n_branch=9005)
-NUM_TRAIN = 73351
-NUM_TEST = 18338
-NUM_INFEASIBLE = 8311
-SPLITFILES = {}
-
-URL = "https://huggingface.co/datasets/PGLearn/PGLearn-Large-6470_rte-nminus1"
-DESCRIPTION = """\
-The 6470_rte-nminus1 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 and DCOPF 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-nminus1/blob/script/case.json.gz
-"""
-USE_ML4OPF_WARNING = """
-================================================================================================
-  Loading PGLearn-Large-6470_rte-nminus1 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-nminus1#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_rteNminus1Config(hfd.BuilderConfig):
-    """BuilderConfig for PGLearn-Large-6470_rte-nminus1. 
-    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_rteNminus1Config, 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_rteNminus1(hfd.ArrowBasedBuilder):
-    """DatasetBuilder for PGLearn-Large-6470_rte-nminus1.
-    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-nminus1", 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_rteNminus1Config
-    DEFAULT_CONFIG_NAME=CASENAME
-    BUILDER_CONFIGS = [
-        PGLearnLarge6470_rteNminus1Config(
-            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: 36730514291
     num_examples: 18338
-  download_size: 151027913425
+  download_size: 183620602331
   dataset_size: 183650568477
+configs:
+- config_name: 6470_rte-nminus1
+  data_files:
+  - split: train
+    path: 6470_rte-nminus1/train-*
+  - split: test
+    path: 6470_rte-nminus1/test-*
+  default: true
 ---

config.toml

@@ -1,56 +0,0 @@
-export_dir = "/storage/home/hcoda1/3/mklamkin3/qq/proj/OPFGenerator/data/pglearn/6470_rte-n1"
-# Name of the reference PGLib case. Must be a valid PGLib case name.
-pglib_case = "pglib_opf_case6470_rte"
-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.
-
-[sampler.status]
-type = "Nminus1"
-
-[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 = 100000
-n_jobs = 44
-minibatch_size = 16
-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

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:14c28a2f1b0920c5d39d4028c5cd6796cd492d8040fd5ae47ae67d9a218a0592
-size 3577970109

infeasible/ACOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:243035d6edac499e89870ee9ce48e077a76be6b9e95a2d4733f1fa8235563b28
-size 1510053893

infeasible/DCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:43a269e6415f42922ab48409af7f0c942f7ef5e215bb0b8e0a95fb76d8fad8f6
-size 242296626

infeasible/SOCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:122b51c5fb4b6b38f3688ec82d84231fb088e25c2a32a4e8370fa6d62923c93a
-size 6202612499

infeasible/SOCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:13828cf512bb047714cba3eed7160a92468b21fdf4603fc8e8b38539f8f3df9e
-size 296459

infeasible/SOCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:62a23019cd2bf71da40c238450c6b5be5b63ff7c9a4d9c4ebcb50b1f03cbccae
-size 1619455524

infeasible/input.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:80232395dd3c22e27f9c687421f153564f2eec3bce00e345c0c29c7a1362d228
-size 220241221

test/ACOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b2e53bec3448c9e9abf50f5272fbee45a7511217694d37ff870d7a8f347e8798
-size 7250063538

test/ACOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:187094a9308e9b5f7f5b8792cf289f406eabd8590d667422f253ec43ccd7cb3a
-size 629588

test/ACOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eb5e28f9c9f7e980b341d0ea05838bb9ebb5fce6ed8a8ea595482a0959999958
-size 3293762695

test/DCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a489755f9cc4190c3e687c5f1aa874a7cabc3c8e8c96ffe7c05aa6b459dd3837
-size 753235996

test/DCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:458be2d504c69c922a18826c5ba02f5f0fec71c7ab33c1c866b0cdf1de8c9a30
-size 627183

test/DCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a67cf9374913f2a919fd1133b752615e908e027603e751806d805bd32708dd0e
-size 978151537

test/SOCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d9df8b81f808e75838942531519bd506743cc6e7dd295a3ae67c279ceab90005
-size 13390542132

test/SOCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e9aa200fe270a5303e2251a928c97771ffc72043e6858252e7040f03d8121fb4
-size 628600

test/SOCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3eab4022af399689398dd7735d90295e7a3fe9dc6681b05c3b19434125dd0b39
-size 4051988628

test/input.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:60e11feb15311cd80a5a39251aab5f2a32ea0db302279a3bd3e36048b92a983f
-size 485991014

train/ACOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ea9074ed41e71a0906fe1a1e1e52e20c16979f335b758810041cc67e0f55c0a5
-size 29000071052

train/ACOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d84152b04c3103155b370cb6f7956981a56c40a1e9ea63461c87980658bd306e
-size 2488022

train/ACOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1d6c5c7a8d060477ed0c07f8309a04b8088c894e512d91b7c978956d9082cc97
-size 13174657776

train/DCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fcdbba6d5e0119d8ca377d42a2fb3a2bc30c791fac2b0812acf408aa51499cbb
-size 3013205637

train/DCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:baf3539717b05044d48d1c21c3178244c91e2f06f83b19d67c70be7083602e16
-size 2475003

train/DCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7fa0a3ec4c46c5123e4829c417517c6903a9b3f5c47eb3af802a63c3a0630f7c
-size 3912598725

train/SOCOPF/dual.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a90ba8c5252c9fbc94b6a07e6ed3477109f99aa7a21ad3549efeade122fce07e
-size 53562588218

train/SOCOPF/meta.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4a2f32e751e997a3859733f5fe4b8328f0c1de9cab9460e5206ac70a9ae885d2
-size 2480553

train/SOCOPF/primal.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f5f9645fc52bf7c942cbdeb3b1992a6eadd978d9c59a29c827d9ee59039f7379
-size 16207809724

train/input.h5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:27bcc671977c901c14f8f2f9b0f175b37eafd47ed3b35278dcdacabf87c6b1c7
-size 1943917804