Delete multitab.py

multitab.py

@@ -1,168 +0,0 @@
-import h5py
-import datasets
-from typing import List
-
-_CITATION = ""  # paste your paper bibtex here if you want
-_DESCRIPTION = """
-MultiTab benchmark datasets (HIGGS, ACS Income, AliExpress) preprocessed
-into HDF5 with a multi-task tabular structure: categorical + numerical
-features and multiple labels per sample.
-"""
-
-_HOMEPAGE = ""
-_LICENSE = ""
-
-
-class MultitabConfig(datasets.BuilderConfig):
-    """Config for one of the MultiTab datasets (e.g. higgs, acs_income, aliexpress)."""
-
-    def __init__(
-        self,
-        h5_path: str,
-        task_names: List[str],
-        **kwargs,
-    ):
-        """
-        Args:
-            h5_path: Name of the HDF5 file in the repo (e.g. 'higgs.h5').
-            task_names: List of label keys in the HDF5 groups for this dataset
-                        (e.g. ['Target', 'm_bb', ...] or ['click', 'conversion']).
-        """
-        super().__init__(**kwargs)
-        self.h5_path = h5_path
-        self.task_names = task_names
-
-
-class Multitab(datasets.GeneratorBasedBuilder):
-    """MultiTab benchmark datasets hosted under one repo."""
-
-    BUILDER_CONFIG_CLASS = MultitabConfig
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        MultitabConfig(
-            name="higgs",
-            description="HIGGS dataset preprocessed for MultiTab.",
-            h5_path="higgs.h5",
-            task_names=[
-                "Target",
-                "m_bb",
-                "m_jj",
-                "m_jjj",
-                "m_jlv",
-                "m_lv",
-                "m_wbb",
-                "m_wwbb",
-            ],
-        ),
-        MultitabConfig(
-            name="acs_income",
-            description="ACS Income dataset preprocessed for MultiTab.",
-            h5_path="acs_incom.h5",   # matches the upload name
-            task_names=[
-                "MAR",
-                "PINCP",
-            ],
-        ),
-        MultitabConfig(
-            name="aliexpress",
-            description="AliExpress recommendation dataset preprocessed for MultiTab.",
-            h5_path="aliexpress.h5",
-            task_names=[
-                "click",
-                "conversion",
-            ],
-        ),
-    ]
-
-    def _info(self) -> datasets.DatasetInfo:
-        """
-        Schema for each example.
-
-        We keep features as list fields; HF Dataset Viewer will show them as arrays per cell.
-        Both 'features_categorical' and 'features_numerical' exist in the schema; if a given
-        dataset/split doesn't have one of them, we return an empty list.
-        """
-        features_dict = {
-            "features_categorical": datasets.Sequence(datasets.Value("int64")),
-            "features_numerical": datasets.Sequence(datasets.Value("float32")),
-        }
-
-        # One column per task label (float32 for generality; ints are cast)
-        for t in self.config.task_names:
-            features_dict[t] = datasets.Value("float32")
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(features_dict),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        """
-        Map HF splits to the 'train', 'val', 'test' groups in your HDF5 file.
-        """
-        # Download/copy the HDF5 from the repo to the local cache
-        h5_path = dl_manager.download(self.config.h5_path)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": h5_path, "split_name": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": h5_path, "split_name": "val"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": h5_path, "split_name": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath: str, split_name: str):
-        """
-        Iterate over one split in the HDF5 file and yield examples.
-
-        Expected structure per HDF5 group (train/val/test):
-          - features_categorical: (N, C)  [optional]
-          - features_numerical:  (N, D)   [optional]
-          - one array per task in self.config.task_names, shape (N,)
-        """
-
-        cfg = self.config
-
-        with h5py.File(filepath, "r") as f:
-            grp = f[split_name]
-
-            has_cat = "features_categorical" in grp
-            has_num = "features_numerical" in grp
-
-            first_task = cfg.task_names[0]
-            n_samples = grp[first_task].shape[0]
-
-            for idx in range(n_samples):
-                example = {}
-
-                # Categorical features
-                if has_cat:
-                    cat_row = grp["features_categorical"][idx]
-                    example["features_categorical"] = cat_row.astype("int64").tolist()
-                else:
-                    example["features_categorical"] = []
-
-                # Numerical features
-                if has_num:
-                    num_row = grp["features_numerical"][idx]
-                    example["features_numerical"] = num_row.astype("float32").tolist()
-                else:
-                    example["features_numerical"] = []
-
-                # Task labels
-                for t in cfg.task_names:
-                    example[t] = float(grp[t][idx])
-
-                yield idx, example