Datasets:
File size: 4,348 Bytes
108946c 3d26742 ca5ca26 3d26742 108946c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
"""Higgs."""
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/7d4cb798cf460aa240b6b92d971009ae6790e3d0/gistfile1.txt",
}
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):
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:
return data[list(features_types_per_config[config].keys())]
|