Add loading script

.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

README.md

@@ -1,3 +1,22 @@
 ---
 license: cc-by-4.0
 ---
+
+# Top Quark Tagging Reference Dataset
+
+A set of MC simulated training/testing events for the evaluation of top quark tagging architectures.
+
+In total 1.2M training events, 400k validation events and 400k test events. Use “train” for training, “val” for validation during the training and “test” for final testing and reporting results.
+
+## Description
+
+* 14 TeV, hadronic tops for signal, qcd diets background, Delphes ATLAS detector card with Pythia8
+* No MPI/pile-up included
+* Clustering of  particle-flow entries (produced by Delphes E-flow) into anti-kT 0.8 jets in the pT range [550,650] GeV
+* All top jets are matched to a parton-level top within ∆R = 0.8, and to all top decay partons within 0.8
+* Jets are required to have |eta| < 2
+* The leading 200 jet constituent four-momenta are stored, with zero-padding for jets with fewer than 200
+* Constituents are sorted by pT, with the highest pT one first
+* The truth top four-momentum is stored as truth_px etc.
+* A flag (1 for top, 0 for QCD) is kept for each jet. It is called is_signal_new
+* The variable "ttv" (= test/train/validation) is kept for each jet. It indicates to which dataset the jet belongs. It is redundant as the different sets are already distributed as different files.

top_quark_tagging.py

@@ -0,0 +1,866 @@
+import datasets
+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.parquet"
+_VALIDATION_DOWNLOAD_URL = "data/validation.parquet"
+_TEST_DOWNLOAD_URL = "data/test.parquet"
+
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+ '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']
+
+
+
+class TopQuarkTagging(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        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)
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        valid_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
+        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        df = pd.read_parquet(filepath)
+        for id_, row in df.iterrows():
+            yield id_, {row.index[idx]: row[idx] for idx in range(len(row))}