Hugging Face's logo

Datasets:
lewtun
/
top_quark_tagging

Tags:
License:
Dataset card Viewer Files Community
top_quark_tagging / top_quark_tagging.py
lewtun's picture
lewtun HF staff
Refactor script
972d3d5
raw history blame contribute delete
No virus
16 kB
import datasets
import numpy as np
import pandas as pd
_CITATION = """\
@dataset{kasieczka_gregor_2019_2603256,
author = {Kasieczka, Gregor and
Plehn, Tilman and
Thompson, Jennifer and
Russel, Michael},
title = {Top Quark Tagging Reference Dataset},
month = mar,
year = 2019,
publisher = {Zenodo},
version = {v0 (2018\_03\_27)},
doi = {10.5281/zenodo.2603256},
url = {https://doi.org/10.5281/zenodo.2603256}
}
"""
_DESCRIPTION = """\
Top Quark Tagging is a dataset of Monte Carlo simulated hadronic top and QCD dijet events for the evaluation of top quark tagging architectures. The dataset consists of 1.2M training events, 400k validation events and 400k test events.
"""
_URL = "https://zenodo.org/record/2603256/"
_TRAIN_DOWNLOAD_URL = "data/train-raw.parquet"
_VALIDATION_DOWNLOAD_URL = "data/validation-raw.parquet"
_TEST_DOWNLOAD_URL = "data/test-raw.parquet"
_URLS = {
"raw": {
"train": "data/train-raw.parquet",
"train-labels": "data/train-labels.parquet",
"validation": "data/validation-raw.parquet",
"validation-labels": "data/validation-labels.parquet",
"test": "data/test-raw.parquet",
"test-labels": "data/test-labels.parquet",
},
"nsubjettiness": {
"train": "data/train-nsubjettiness.parquet",
"train-labels": "data/train-labels.parquet",
"validation": "data/validation-nsubjettiness.parquet",
"validation-labels": "data/validation-labels.parquet",
"test": "data/test-nsubjettiness.parquet",
"test-labels": "data/test-labels.parquet",
},
"image": {
"train": "data/train-images.zip",
"train-labels": "data/train-labels.parquet",
"validation": "data/train-images.zip",
"validation-labels": "data/validation-labels.parquet",
"test": "data/train-images.zip",
"test-labels": "data/test-labels.parquet",
},
}
FEATURE_NAMES = [
"E_0",
"PX_0",
"PY_0",
"PZ_0",
"E_1",
"PX_1",
"PY_1",
"PZ_1",
"E_2",
"PX_2",
"PY_2",
"PZ_2",
"E_3",
"PX_3",
"PY_3",
"PZ_3",
"E_4",
"PX_4",
"PY_4",
"PZ_4",
"E_5",
"PX_5",
"PY_5",
"PZ_5",
"E_6",
"PX_6",
"PY_6",
"PZ_6",
"E_7",
"PX_7",
"PY_7",
"PZ_7",
"E_8",
"PX_8",
"PY_8",
"PZ_8",
"E_9",
"PX_9",
"PY_9",
"PZ_9",
"E_10",
"PX_10",
"PY_10",
"PZ_10",
"E_11",
"PX_11",
"PY_11",
"PZ_11",
"E_12",
"PX_12",
"PY_12",
"PZ_12",
"E_13",
"PX_13",
"PY_13",
"PZ_13",
"E_14",
"PX_14",
"PY_14",
"PZ_14",
"E_15",
"PX_15",
"PY_15",
"PZ_15",
"E_16",
"PX_16",
"PY_16",
"PZ_16",
"E_17",
"PX_17",
"PY_17",
"PZ_17",
"E_18",
"PX_18",
"PY_18",
"PZ_18",
"E_19",
"PX_19",
"PY_19",
"PZ_19",
"E_20",
"PX_20",
"PY_20",
"PZ_20",
"E_21",
"PX_21",
"PY_21",
"PZ_21",
"E_22",
"PX_22",
"PY_22",
"PZ_22",
"E_23",
"PX_23",
"PY_23",
"PZ_23",
"E_24",
"PX_24",
"PY_24",
"PZ_24",
"E_25",
"PX_25",
"PY_25",
"PZ_25",
"E_26",
"PX_26",
"PY_26",
"PZ_26",
"E_27",
"PX_27",
"PY_27",
"PZ_27",
"E_28",
"PX_28",
"PY_28",
"PZ_28",
"E_29",
"PX_29",
"PY_29",
"PZ_29",
"E_30",
"PX_30",
"PY_30",
"PZ_30",
"E_31",
"PX_31",
"PY_31",
"PZ_31",
"E_32",
"PX_32",
"PY_32",
"PZ_32",
"E_33",
"PX_33",
"PY_33",
"PZ_33",
"E_34",
"PX_34",
"PY_34",
"PZ_34",
"E_35",
"PX_35",
"PY_35",
"PZ_35",
"E_36",
"PX_36",
"PY_36",
"PZ_36",
"E_37",
"PX_37",
"PY_37",
"PZ_37",
"E_38",
"PX_38",
"PY_38",
"PZ_38",
"E_39",
"PX_39",
"PY_39",
"PZ_39",
"E_40",
"PX_40",
"PY_40",
"PZ_40",
"E_41",
"PX_41",
"PY_41",
"PZ_41",
"E_42",
"PX_42",
"PY_42",
"PZ_42",
"E_43",
"PX_43",
"PY_43",
"PZ_43",
"E_44",
"PX_44",
"PY_44",
"PZ_44",
"E_45",
"PX_45",
"PY_45",
"PZ_45",
"E_46",
"PX_46",
"PY_46",
"PZ_46",
"E_47",
"PX_47",
"PY_47",
"PZ_47",
"E_48",
"PX_48",
"PY_48",
"PZ_48",
"E_49",
"PX_49",
"PY_49",
"PZ_49",
"E_50",
"PX_50",
"PY_50",
"PZ_50",
"E_51",
"PX_51",
"PY_51",
"PZ_51",
"E_52",
"PX_52",
"PY_52",
"PZ_52",
"E_53",
"PX_53",
"PY_53",
"PZ_53",
"E_54",
"PX_54",
"PY_54",
"PZ_54",
"E_55",
"PX_55",
"PY_55",
"PZ_55",
"E_56",
"PX_56",
"PY_56",
"PZ_56",
"E_57",
"PX_57",
"PY_57",
"PZ_57",
"E_58",
"PX_58",
"PY_58",
"PZ_58",
"E_59",
"PX_59",
"PY_59",
"PZ_59",
"E_60",
"PX_60",
"PY_60",
"PZ_60",
"E_61",
"PX_61",
"PY_61",
"PZ_61",
"E_62",
"PX_62",
"PY_62",
"PZ_62",
"E_63",
"PX_63",
"PY_63",
"PZ_63",
"E_64",
"PX_64",
"PY_64",
"PZ_64",
"E_65",
"PX_65",
"PY_65",
"PZ_65",
"E_66",
"PX_66",
"PY_66",
"PZ_66",
"E_67",
"PX_67",
"PY_67",
"PZ_67",
"E_68",
"PX_68",
"PY_68",
"PZ_68",
"E_69",
"PX_69",
"PY_69",
"PZ_69",
"E_70",
"PX_70",
"PY_70",
"PZ_70",
"E_71",
"PX_71",
"PY_71",
"PZ_71",
"E_72",
"PX_72",
"PY_72",
"PZ_72",
"E_73",
"PX_73",
"PY_73",
"PZ_73",
"E_74",
"PX_74",
"PY_74",
"PZ_74",
"E_75",
"PX_75",
"PY_75",
"PZ_75",
"E_76",
"PX_76",
"PY_76",
"PZ_76",
"E_77",
"PX_77",
"PY_77",
"PZ_77",
"E_78",
"PX_78",
"PY_78",
"PZ_78",
"E_79",
"PX_79",
"PY_79",
"PZ_79",
"E_80",
"PX_80",
"PY_80",
"PZ_80",
"E_81",
"PX_81",
"PY_81",
"PZ_81",
"E_82",
"PX_82",
"PY_82",
"PZ_82",
"E_83",
"PX_83",
"PY_83",
"PZ_83",
"E_84",
"PX_84",
"PY_84",
"PZ_84",
"E_85",
"PX_85",
"PY_85",
"PZ_85",
"E_86",
"PX_86",
"PY_86",
"PZ_86",
"E_87",
"PX_87",
"PY_87",
"PZ_87",
"E_88",
"PX_88",
"PY_88",
"PZ_88",
"E_89",
"PX_89",
"PY_89",
"PZ_89",
"E_90",
"PX_90",
"PY_90",
"PZ_90",
"E_91",
"PX_91",
"PY_91",
"PZ_91",
"E_92",
"PX_92",
"PY_92",
"PZ_92",
"E_93",
"PX_93",
"PY_93",
"PZ_93",
"E_94",
"PX_94",
"PY_94",
"PZ_94",
"E_95",
"PX_95",
"PY_95",
"PZ_95",
"E_96",
"PX_96",
"PY_96",
"PZ_96",
"E_97",
"PX_97",
"PY_97",
"PZ_97",
"E_98",
"PX_98",
"PY_98",
"PZ_98",
"E_99",
"PX_99",
"PY_99",
"PZ_99",
"E_100",
"PX_100",
"PY_100",
"PZ_100",
"E_101",
"PX_101",
"PY_101",
"PZ_101",
"E_102",
"PX_102",
"PY_102",
"PZ_102",
"E_103",
"PX_103",
"PY_103",
"PZ_103",
"E_104",
"PX_104",
"PY_104",
"PZ_104",
"E_105",
"PX_105",
"PY_105",
"PZ_105",
"E_106",
"PX_106",
"PY_106",
"PZ_106",
"E_107",
"PX_107",
"PY_107",
"PZ_107",
"E_108",
"PX_108",
"PY_108",
"PZ_108",
"E_109",
"PX_109",
"PY_109",
"PZ_109",
"E_110",
"PX_110",
"PY_110",
"PZ_110",
"E_111",
"PX_111",
"PY_111",
"PZ_111",
"E_112",
"PX_112",
"PY_112",
"PZ_112",
"E_113",
"PX_113",
"PY_113",
"PZ_113",
"E_114",
"PX_114",
"PY_114",
"PZ_114",
"E_115",
"PX_115",
"PY_115",
"PZ_115",
"E_116",
"PX_116",
"PY_116",
"PZ_116",
"E_117",
"PX_117",
"PY_117",
"PZ_117",
"E_118",
"PX_118",
"PY_118",
"PZ_118",
"E_119",
"PX_119",
"PY_119",
"PZ_119",
"E_120",
"PX_120",
"PY_120",
"PZ_120",
"E_121",
"PX_121",
"PY_121",
"PZ_121",
"E_122",
"PX_122",
"PY_122",
"PZ_122",
"E_123",
"PX_123",
"PY_123",
"PZ_123",
"E_124",
"PX_124",
"PY_124",
"PZ_124",
"E_125",
"PX_125",
"PY_125",
"PZ_125",
"E_126",
"PX_126",
"PY_126",
"PZ_126",
"E_127",
"PX_127",
"PY_127",
"PZ_127",
"E_128",
"PX_128",
"PY_128",
"PZ_128",
"E_129",
"PX_129",
"PY_129",
"PZ_129",
"E_130",
"PX_130",
"PY_130",
"PZ_130",
"E_131",
"PX_131",
"PY_131",
"PZ_131",
"E_132",
"PX_132",
"PY_132",
"PZ_132",
"E_133",
"PX_133",
"PY_133",
"PZ_133",
"E_134",
"PX_134",
"PY_134",
"PZ_134",
"E_135",
"PX_135",
"PY_135",
"PZ_135",
"E_136",
"PX_136",
"PY_136",
"PZ_136",
"E_137",
"PX_137",
"PY_137",
"PZ_137",
"E_138",
"PX_138",
"PY_138",
"PZ_138",
"E_139",
"PX_139",
"PY_139",
"PZ_139",
"E_140",
"PX_140",
"PY_140",
"PZ_140",
"E_141",
"PX_141",
"PY_141",
"PZ_141",
"E_142",
"PX_142",
"PY_142",
"PZ_142",
"E_143",
"PX_143",
"PY_143",
"PZ_143",
"E_144",
"PX_144",
"PY_144",
"PZ_144",
"E_145",
"PX_145",
"PY_145",
"PZ_145",
"E_146",
"PX_146",
"PY_146",
"PZ_146",
"E_147",
"PX_147",
"PY_147",
"PZ_147",
"E_148",
"PX_148",
"PY_148",
"PZ_148",
"E_149",
"PX_149",
"PY_149",
"PZ_149",
"E_150",
"PX_150",
"PY_150",
"PZ_150",
"E_151",
"PX_151",
"PY_151",
"PZ_151",
"E_152",
"PX_152",
"PY_152",
"PZ_152",
"E_153",
"PX_153",
"PY_153",
"PZ_153",
"E_154",
"PX_154",
"PY_154",
"PZ_154",
"E_155",
"PX_155",
"PY_155",
"PZ_155",
"E_156",
"PX_156",
"PY_156",
"PZ_156",
"E_157",
"PX_157",
"PY_157",
"PZ_157",
"E_158",
"PX_158",
"PY_158",
"PZ_158",
"E_159",
"PX_159",
"PY_159",
"PZ_159",
"E_160",
"PX_160",
"PY_160",
"PZ_160",
"E_161",
"PX_161",
"PY_161",
"PZ_161",
"E_162",
"PX_162",
"PY_162",
"PZ_162",
"E_163",
"PX_163",
"PY_163",
"PZ_163",
"E_164",
"PX_164",
"PY_164",
"PZ_164",
"E_165",
"PX_165",
"PY_165",
"PZ_165",
"E_166",
"PX_166",
"PY_166",
"PZ_166",
"E_167",
"PX_167",
"PY_167",
"PZ_167",
"E_168",
"PX_168",
"PY_168",
"PZ_168",
"E_169",
"PX_169",
"PY_169",
"PZ_169",
"E_170",
"PX_170",
"PY_170",
"PZ_170",
"E_171",
"PX_171",
"PY_171",
"PZ_171",
"E_172",
"PX_172",
"PY_172",
"PZ_172",
"E_173",
"PX_173",
"PY_173",
"PZ_173",
"E_174",
"PX_174",
"PY_174",
"PZ_174",
"E_175",
"PX_175",
"PY_175",
"PZ_175",
"E_176",
"PX_176",
"PY_176",
"PZ_176",
"E_177",
"PX_177",
"PY_177",
"PZ_177",
"E_178",
"PX_178",
"PY_178",
"PZ_178",
"E_179",
"PX_179",
"PY_179",
"PZ_179",
"E_180",
"PX_180",
"PY_180",
"PZ_180",
"E_181",
"PX_181",
"PY_181",
"PZ_181",
"E_182",
"PX_182",
"PY_182",
"PZ_182",
"E_183",
"PX_183",
"PY_183",
"PZ_183",
"E_184",
"PX_184",
"PY_184",
"PZ_184",
"E_185",
"PX_185",
"PY_185",
"PZ_185",
"E_186",
"PX_186",
"PY_186",
"PZ_186",
"E_187",
"PX_187",
"PY_187",
"PZ_187",
"E_188",
"PX_188",
"PY_188",
"PZ_188",
"E_189",
"PX_189",
"PY_189",
"PZ_189",
"E_190",
"PX_190",
"PY_190",
"PZ_190",
"E_191",
"PX_191",
"PY_191",
"PZ_191",
"E_192",
"PX_192",
"PY_192",
"PZ_192",
"E_193",
"PX_193",
"PY_193",
"PZ_193",
"E_194",
"PX_194",
"PY_194",
"PZ_194",
"E_195",
"PX_195",
"PY_195",
"PZ_195",
"E_196",
"PX_196",
"PY_196",
"PZ_196",
"E_197",
"PX_197",
"PY_197",
"PZ_197",
"E_198",
"PX_198",
"PY_198",
"PZ_198",
"E_199",
"PX_199",
"PY_199",
"PZ_199",
"truthE",
"truthPX",
"truthPY",
"truthPZ",
"ttv",
"is_signal_new",
]
NSUBJETINESS_FEATURE_NAMES = []
for idx in range(4):
NSUBJETINESS_FEATURE_NAMES.extend([f"tau_{idx+1}_0.5", f"tau_{idx+1}_1", f"tau_{idx+1}_2"])
class TopQuarkTagging(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="raw", description="The raw events"),
datasets.BuilderConfig(name="nsubjettiness", description="The N-subjet events"),
datasets.BuilderConfig(name="image", description="The jet image events"),
]
DEFAULT_CONFIG_NAME = "raw"
def _info(self):
if self.config.name == "raw":
features_data = {c: datasets.Value("float32") for c in FEATURE_NAMES[:-2]}
features_data["ttv"] = datasets.Value("int64")
features_data["is_signal_new"] = datasets.ClassLabel(names=["qcd", "top"])
features = datasets.Features(features_data)
elif self.config.name == "nsubjettiness":
features_data = {c: datasets.Value("float32") for c in NSUBJETINESS_FEATURE_NAMES}
features_data["is_signal_new"] = datasets.ClassLabel(names=["qcd", "top"])
features = datasets.Features(features_data)
else:
features = datasets.Features(
{"file": datasets.Value("string"), "is_signal_new": datasets.ClassLabel(names=["qcd", "top"])}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls = _URLS[self.config.name]
data_paths = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": [data_paths["train"], data_paths["train-labels"]], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": [data_paths["validation"], data_paths["validation-labels"]],
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": [data_paths["test"], data_paths["test-labels"]], "split": "test"},
),
]
def _generate_examples(self, filepath, split):
"""Generate examples."""
if self.config.name == "image":
examples = pd.read_csv(f"{filepath[0]}/train-images/train.csv")
for row in examples.itertuples():
yield row.Index, {"file": f"{filepath[0]}/{row.file}", "is_signal_new": row.label}
else:
examples = datasets.load_dataset("parquet", data_files={split: filepath[0]}, split=split)
labels = datasets.load_dataset("parquet", data_files={split: filepath[1]}, split=split)
for id_, (row, label) in enumerate(zip(examples, labels)):
# if self.config.name == "image":
# values = np.fromiter(row.values(), dtype=float)
# image = values.reshape(33, 33)
# yield id_, {"image": image, **label}
# else:
yield id_, {**row, **label}