Datasets:
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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://gist.githubusercontent.com/msetzu/99114313deb9cc98318d5940fd536b06/raw/e8a2ebca490b559be84e535b1082ed2356c25be7/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":
print(list(data.columns))
return data[list(features_types_per_config[config].keys())]
else:
raise ValueError(f"Unknown config: {config}")
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