Convert dataset to Parquet (part 00002-of-00003)

infeasible/ACOPF/meta.h5.gz → 30_ieee/test-00000-of-00030.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7c87ec1c8b5326fce02d19ed9ce6a9c9489211da93619f97a736421eea41e229
-size 305417
+oid sha256:8c68d288dd453d72417dbac767cf79ced1320876856c03853a4069f637cb1876
+size 82966160

case.json.gz → 30_ieee/test-00001-of-00030.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c284d9a6a0ca63298f98cc8f53892f3f0253b72a8bb181f344c9db88f7a496ee
-size 24607
+oid sha256:db996e7b3771d7b8a6c768e8f0f51a78e562e09f3c045a1926ac04701ccd9b09
+size 82969249

infeasible/ACOPF/primal.h5.gz → 30_ieee/test-00002-of-00030.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:05b37df707e8ed264a8ac73e2b95e6ad4cacf5cdc3520fe745c2a5a870aef4ee
-size 7012506
+oid sha256:2314b5e85d4798f9b5bb7286a6ebeab632d6dece95cc1951e79d74cb4d6407ae
+size 82963556

infeasible/ACOPF/dual.h5.gz → 30_ieee/test-00003-of-00030.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2c20d000d865807da3417cae7ad14c717711c29282fb9b5241c6cad096dd7305
-size 16104833
+oid sha256:55b2fec1246115236e0edc75c7a6a95564eba3ac991e28bf2ee8ccbb1c492de6
+size 82978609

30_ieee/test-00004-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8147222b8cba238d976defa42a6e91c60b200d4f0465b2a6d81e6f1754268c4
+size 82961477

30_ieee/test-00005-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd4841e6e984333c52f3f9a27603281f4b34ee6c04aee939c9224ad67eb02c86
+size 82976841

30_ieee/test-00006-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11b874b2f91ff5b779eefdebfe981493205f64d925eac306cd417304a6c402df
+size 82968276

30_ieee/test-00007-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13813f44a856521a67e7365b900fa5a9c1488274a4bf2ef112590adad0297bf4
+size 82968567

30_ieee/test-00008-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff05cd48d9411db934e32c90c39c6ab3451b7b4801d7d607cb19c7f582e075d8
+size 82967211

30_ieee/test-00009-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:067e8d2bb4987caa3484e1ce5fa108d4f43b1a2ab324087fb4f00c79c1b2cbdb
+size 82964759

30_ieee/test-00010-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:901110aaaa04c092b5d2d201bb328d645d8ea9779c3d9368099affa07181c8fb
+size 82977013

30_ieee/test-00011-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:302e668023d4a8f3f09dd16cfd814dfd205337ad064995bde52ab8ab3d64b34b
+size 82968739

30_ieee/test-00012-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2588773ea56ef6233780ec1f8ad30c4726640a7e3febd17b76713ac0b2437e65
+size 82968617

30_ieee/test-00013-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f4410df2cb11ae489b59baf3adf3dca0422a70283c6c1aa8e26c3f4f90999cd
+size 82972857

30_ieee/test-00014-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3fea0cf958e7d32c6a55110eb686dd37ae9f046bde610c19a24dae08778efc4
+size 82972722

30_ieee/test-00015-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:258290c86c21865db9ddaa6f50e2c1cb160399bc1d4ac70596d41c55844aef81
+size 82971512

30_ieee/test-00016-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f2358041fac5ddf36ae5904c1b98c2c38b10f1b123835db13c12183e1b2c267
+size 82975902

30_ieee/test-00017-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e81e81dd90e641464db03aaa8b2b4070f3817caae3d226b1dec562d672d75517
+size 82967610

30_ieee/test-00018-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf17a2738a18ba8dd6ab13c4090a0e8a4542f79ef9a6d5a9bcade6a1ecaca338
+size 82970111

30_ieee/test-00019-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd3dfab4ea0b9a419946f428cee026ed60d3891def5e6bb36e50f68bd8c6e380
+size 82975068

30_ieee/test-00020-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c68fe5c96565e4055f0d9dcad6bd7efdbc1e58d0b3c09616fa9da954ade4069
+size 82970948

30_ieee/test-00021-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3006b11696b4df694fa9606ab05e9143408e677825ccc49cbbd8f32c445314a7
+size 82971115

30_ieee/test-00022-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef4d93d2e5fcf42d386f69d9159c53a3a553232e36d1e6b39bb25ea11365feb
+size 82967343

30_ieee/test-00023-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7614bcbfda69d888de4e84fe182932f4acf0292f7c13df010706968ebc5816d9
+size 82972218

30_ieee/test-00024-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6fff700edecc09862d8515bfc0c97b543985051b397bd98f2ccae80e6b66b120
+size 82976426

30_ieee/test-00025-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bacc931782c82a07dd3a2e301c2694ef3943f6507618a84594a9b4f414fb9b78
+size 82970641

30_ieee/test-00026-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab93905acd7c7f1ee1711bb277a3c08a725210d50f5411137491a01d23537732
+size 82968940

30_ieee/test-00027-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1ad6c47ea57f6bb4c72c4593f6a7573528426c09b5ab5166fdee2f5090ea21d
+size 82957989

30_ieee/test-00028-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77224a8b18415e7bcded1a5a4e725b3d1da1eb02eebde7973a0987eb95260459
+size 82956175

30_ieee/test-00029-of-00030.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a84056046ed719eccaea78a23f1cef78d3b2a83b718139202b7cb632d6beaa7
+size 82953894

30_ieee/train-00100-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ef969a19fc296e91d505943a872edb225afc53c52a3d43ef61932785b9a7bca
+size 83656831

30_ieee/train-00101-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f217d8016572bcccb1449d128cd9b4ebe702a05bdc5520899bb99dd42eca346c
+size 83656115

30_ieee/train-00102-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ccf2f946be55635fd7fbdf43b277cdd76d9f13b53d37505d8796c949721471e
+size 83663301

30_ieee/train-00103-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:772eb97eac50b08e1c7025215eb294113f4302be246ae99e863f52cb9d8634fd
+size 83664324

30_ieee/train-00104-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfd8f281a066550f31c11dd20275c6b76440556afd2574d1210ad09d657271fb
+size 83661518

30_ieee/train-00105-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a318fbe2157b4b175eb0043f0b729eb16b38f2778a36d22145acbd7294291760
+size 83661440

30_ieee/train-00106-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5dab53cf6c8ec520851e9384c2a6955226f49009633e2f241ec2ad25ead48d5
+size 83665018

30_ieee/train-00107-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:490f4b899d27c94646500a671a9cca241467a2c97b0dde4e8c8eaf5557d2953e
+size 83666776

30_ieee/train-00108-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1822b3c6fec5c084179644020e3827404e937cd94140f69862b8b9f44e38970f
+size 83665168

30_ieee/train-00109-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a83c7580af79bca2c2257abbc19cce1c11c332e6bdd8cc0c318dec5984cf32b8
+size 83661855

30_ieee/train-00110-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db885afccfa17aacfb7a539c2b9810d5665fe9c187b6e0eb12054ace8b826aaf
+size 83669681

30_ieee/train-00111-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6767077628e3d4903ebdab0d60f4b1800983f813c8c5779348d41a705905d696
+size 83665598

30_ieee/train-00112-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e390b3c3f89d0570575747dafdd4daf26cace858bf12cfdd162bcc01dc9d3fe
+size 83668103

30_ieee/train-00113-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12723353d1328011292e7e45be62f812a5c07c8828d6c45e3840e3cc7a859fbf
+size 83665087

30_ieee/train-00114-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cf5b9c3fe48416c5163748a7830f8c88735d8d74f3fa4406dd8e1fd0f123032
+size 83657937

30_ieee/train-00115-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9a4f3b28c8b99d32efa63a371fb9c84c6a8ff7bbeb9db5ba340d472288e7f52
+size 83658259

30_ieee/train-00116-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b0398e3a35381ceca25bd1d858697650ecfa8a693e5159f90b4f38d4e3fdc12
+size 83663130

30_ieee/train-00117-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b84a3e5e377af30a1e0645bca85b518dbe1afce4b05e25890a58fae5aaf0d10d
+size 83669676

30_ieee/train-00118-of-00119.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b37c14829e767649f6ed220136bd1db196d97b8a72eaa84c4e6fa0b0aedf2bc3
+size 83668277

PGLearn-Small-30_ieee.py

@@ -1,397 +0,0 @@
-from __future__ import annotations
-from dataclasses import dataclass
-from pathlib import Path
-import json
-import gzip
-
-import datasets as hfd
-import h5py
-import pyarrow as pa
-
-# ┌──────────────┐
-# │   Metadata   │
-# └──────────────┘
-
-@dataclass
-class CaseSizes:
-    n_bus:    int
-    n_load:   int
-    n_gen:    int
-    n_branch: int
-
-CASENAME = "30_ieee"
-SIZES = CaseSizes(n_bus=30, n_load=21, n_gen=6, n_branch=41)
-NUM_TRAIN = 793067
-NUM_TEST = 198267
-NUM_INFEASIBLE = 8666
-
-URL = "https://huggingface.co/datasets/PGLearn/PGLearn-Small-30_ieee"
-DESCRIPTION = """\
-The 30_ieee PGLearn optimal power flow dataset, part of the PGLearn-Small collection. \
-"""
-VERSION = hfd.Version("1.0.0")
-DEFAULT_CONFIG_DESCRIPTION="""\
-This configuration contains feasible input, metadata, primal solution, and dual solution data \
-for the ACOPF, DCOPF, and SOCOPF formulations on the {case} system.
-"""
-USE_ML4OPF_WARNING = """
-================================================================================================
-  Loading PGLearn-Small-30_ieee 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-Small-30_ieee#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 PGLearnSmall30_ieeeConfig(hfd.BuilderConfig):
-    """BuilderConfig for PGLearn-Small-30_ieee. 
-    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(PGLearnSmall30_ieeeConfig, 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
-
-        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: urls[split].append(f"{split}/{formulation}/primal.h5" + self.gz_ext)
-                if self.dual:   urls[split].append(f"{split}/{formulation}/dual.h5" + self.gz_ext)
-                if self.meta:   urls[split].append(f"{split}/{formulation}/meta.h5" + self.gz_ext)
-        return urls
-
-# ┌────────────────────┐
-# │   DatasetBuilder   │
-# └────────────────────┘
-
-class PGLearnSmall30_ieee(hfd.ArrowBasedBuilder):
-    """DatasetBuilder for PGLearn-Small-30_ieee.
-    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-Small-30_ieee", 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 = PGLearnSmall30_ieeeConfig
-    DEFAULT_CONFIG_NAME=CASENAME
-    BUILDER_CONFIGS = [
-        PGLearnSmall30_ieeeConfig(
-            name=CASENAME, description=DEFAULT_CONFIG_DESCRIPTION.format(case=CASENAME),
-            formulations=list(FORMULATIONS_TO_FEATURES.keys()),
-            primal=True, dual=True, meta=True, input=True, casejson=True,
-            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["case"], 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["case"], 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["case"], 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], n_samples: int):
-        case_data: str | None = json.dumps(json.load(open_maybe_gzip(case_file))) if case_file is not None else None
-
-        opened_files = [open_maybe_gzip(file) for file in data_files]
-        data = {'/'.join(Path(df.get_origin()).parts[-2:]).split('.')[0]: h5py.File(of) for of, df in zip(opened_files, data_files)}
-        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 opened_files:
-            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(path):
-    return gzip.open(path, "rb") if path.endswith(".gz") else open(path, "rb")