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}