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 *.jpeg filter=lfs diff=lfs merge=lfs -text
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+higgs.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+tags:
+- higgs
+- tabular_classification
+- binary_classification
+pretty_name: Higgs
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- higgs
+---
+# Higgs
+The [Higgs dataset](https://www.nature.com/articles/ncomms5308/) from "[Searching for exotic particles in high-energy physics with deep learning](https://www.nature.com/articles/ncomms5308/)".
+
+# Configurations and tasks
+- `higgs` Higgs boson identification;
+
+# Features
+|**Feature**                |**Type**   |
+|---------------------------|-----------|
+|`lepton_pT`                |`[float64]`|
+|`lepton_eta`               |`[float64]`|
+|`lepton_phi`               |`[float64]`|
+|`missing_energy_magnitude` |`[float64]`|
+|`missing_energy_phi`       |`[float64]`|
+|`jet1pt`                   |`[float64]`|
+|`jet1eta`                  |`[float64]`|
+|`jet1phi`                  |`[float64]`|
+|`jet1b`                    |`[float64]`|
+|`jet2pt`                   |`[float64]`|
+|`jet2eta`                  |`[float64]`|
+|`jet2phi`                  |`[float64]`|
+|`jet2b`                    |`[float64]`|
+|`jet3pt`                   |`[float64]`|
+|`jet3eta`                  |`[float64]`|
+|`jet3phi`                  |`[float64]`|
+|`jet3b`                    |`[float64]`|
+|`jet4pt`                   |`[float64]`|
+|`jet4eta`                  |`[float64]`|
+|`jet4phi`                  |`[float64]`|
+|`jet4b`                    |`[float64]`|
+|`m_jj`                     |`[float64]`|
+|`m_jjj`                    |`[float64]`|
+|`m_lv`                     |`[float64]`|
+|`m_jlv`                    |`[float64]`|
+|`m_bb`                     |`[float64]`|
+|`m_wbb`                    |`[float64]`|
+|`m_wwbb`                   |`[float64]`|

higgs.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d48bd1d78420400e0ebe7e8b906a74162b3e66b65e98fc73f0729f0a80b57aba
+size 45444445

higgs.py

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+"""Higgs: A Census Dataset"""
+
+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_ORIGINAL_FEATURE_NAMES = [
+    "is_boson",
+    "lepton_pT",
+    "lepton_eta",
+    "lepton_phi",
+    "missing_energy_magnitude",
+    "missing_energy_phi",
+    "jet1pt",
+    "jet1eta",
+    "jet1phi",
+    "jet1b-tag",
+    "jet2pt",
+    "jet2eta",
+    "jet2phi",
+    "jet2b-tag",
+    "jet3pt",
+    "jet3eta",
+    "jet3phi",
+    "jet3b-tag",
+    "jet4pt",
+    "jet4eta",
+    "jet4phi",
+    "jet4b-tag",
+    "m_jj",
+    "m_jjj",
+    "m_lv",
+    "m_jlv",
+    "m_bb",
+    "m_wbb",
+    "m_wwbb"
+]
+
+DESCRIPTION = "Higgs dataset from \"Searching for exotic particles in high-energy physics with deep learning\"."
+_HOMEPAGE = "https://www.nature.com/articles/ncomms5308/"
+_URLS = ("https://www.openml.org/search?type=data&status=active&id=4532")
+_CITATION = """
+@article{baldi2014searching,
+  title={Searching for exotic particles in high-energy physics with deep learning},
+  author={Baldi, Pierre and Sadowski, Peter and Whiteson, Daniel},
+  journal={Nature communications},
+  volume={5},
+  number={1},
+  pages={4308},
+  year={2014},
+  publisher={Nature Publishing Group UK London}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/higgs/raw/main/higgs.csv",
+}
+features_types_per_config = {
+    "higgs": {
+        "lepton_pT": datasets.Value("float64"),
+        "lepton_eta": datasets.Value("float64"),
+        "lepton_phi": datasets.Value("float64"),
+        "missing_energy_magnitude": datasets.Value("float64"),
+        "missing_energy_phi": datasets.Value("float64"),
+        "jet1pt": datasets.Value("float64"),
+        "jet1eta": datasets.Value("float64"),
+        "jet1phi": datasets.Value("float64"),
+        "jet1b-tag": datasets.Value("float64"),
+        "jet2pt": datasets.Value("float64"),
+        "jet2eta": datasets.Value("float64"),
+        "jet2phi": datasets.Value("float64"),
+        "jet2b-tag": datasets.Value("float64"),
+        "jet3pt": datasets.Value("float64"),
+        "jet3eta": datasets.Value("float64"),
+        "jet3phi": datasets.Value("float64"),
+        "jet3b-tag": datasets.Value("float64"),
+        "jet4pt": datasets.Value("float64"),
+        "jet4eta": datasets.Value("float64"),
+        "jet4phi": datasets.Value("float64"),
+        "jet4b-tag": datasets.Value("float64"),
+        "m_jj": datasets.Value("float64"),
+        "m_jjj": datasets.Value("float64"),
+        "m_lv": datasets.Value("float64"),
+        "m_jlv": datasets.Value("float64"),
+        "m_bb": datasets.Value("float64"),
+        "m_wbb": datasets.Value("float64"),
+        "m_wwbb": datasets.Value("float64"),
+        "is_boson": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+    }
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class HiggsConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(HiggsConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Higgs(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "higgs"
+    BUILDER_CONFIGS = [
+        HiggsConfig(name="higgs",
+                    description="Higgs boson binary classification.")
+    ]
+
+
+    def _info(self):
+        if self.config.name not in features_per_config:
+            raise ValueError(f"Unknown configuration: {self.config.name}")
+        
+        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
+                                    features=features_per_config[self.config.name])
+
+        return info
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        downloads = dl_manager.download_and_extract(urls_per_split)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
+        ]
+    
+    def _generate_examples(self, filepath: str):
+        data = pandas.read_csv(filepath)
+        data = self.preprocess(data, config=self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row
+
+
+    def preprocess(self, data: pandas.DataFrame, config: str = "higgs") -> pandas.DataFrame:       
+        if config == "higgs":
+            return data[list(features_types_per_config["higgs"].keys())]
+        else:
+            raise ValueError(f"Unknown config: {config}")