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Create paperswithcode-aspects.py

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  1. paperswithcode-aspects.py +156 -0
paperswithcode-aspects.py ADDED
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+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+ import os
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+ import sys
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+
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+ import datasets
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+ from pyarrow import csv
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+
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+ _DESCRIPTION = """Papers with aspects from paperswithcode.com dataset"""
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+
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+ _HOMEPAGE = "https://github.com/malteos/aspect-document-embeddings"
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+
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+ _CITATION = '''@InProceedings{Ostendorff2022,
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+ title = {Specialized Document Embeddings for Aspect-based Similarity of Research Papers},
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+ booktitle = {Proceedings of the {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL})},
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+ author = {Ostendorff, Malte and Blume, Till, Ruas, Terry and Gipp, Bela and Rehm, Georg},
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+ year = {2022},
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+ }'''
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+
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+ DATA_URL = "http://datasets.fiq.de/paperswithcode_aspects.tar.gz"
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+
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+ DOC_A_COL = "from_paper_id"
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+ DOC_B_COL = "to_paper_id"
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+ LABEL_COL = "label"
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+
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+ # binary classification (y=similar, n=dissimilar)
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+ LABEL_CLASSES = labels = ['y', 'n']
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+
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+ ASPECTS = ['task', 'method', 'dataset']
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+
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+
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+ def get_train_split(aspect, k):
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+ return datasets.Split(f'fold_{aspect}_{k}_train')
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+
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+
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+ def get_test_split(aspect, k):
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+ return datasets.Split(f'fold_{aspect}_{k}_test')
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+
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+
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+ class PWCConfig(datasets.BuilderConfig):
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+ def __init__(self, features, data_url, aspects, **kwargs):
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+ super().__init__(version=datasets.Version("0.1.0"), **kwargs)
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+ self.features = features
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+ self.data_url = data_url
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+ self.aspects = aspects
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+
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+
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+ class PWCAspects(datasets.GeneratorBasedBuilder):
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+ """Paper aspects dataset."""
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+
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+ BUILDER_CONFIGS = [
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+ PWCConfig(
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+ name="docs",
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+ description="document text and meta data",
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+ # Metadata format from paperswithcode.com
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+ # see https://github.com/paperswithcode/paperswithcode-data
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+ features={
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+ "paper_id": datasets.Value("string"),
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+ "paper_url": datasets.Value("string"),
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+ "title": datasets.Value("string"),
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+ "abstract": datasets.Value("string"),
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+ "arxiv_id": datasets.Value("string"),
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+ "url_abs": datasets.Value("string"),
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+ "url_pdf": datasets.Value("string"),
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+ "aspect_tasks": datasets.Sequence(datasets.Value('string', id='task')),
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+ "aspect_methods": datasets.Sequence(datasets.Value('string', id='method')),
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+ "aspect_datasets": datasets.Sequence(datasets.Value('string', id='dataset')),
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+ },
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+ data_url=DATA_URL,
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+ aspects=ASPECTS,
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+ ),
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+ PWCConfig(
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+ name="relations",
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+ description=" relation data",
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+ features={
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+ DOC_A_COL: datasets.Value("string"),
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+ DOC_B_COL: datasets.Value("string"),
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+ LABEL_COL: datasets.Value("string"),
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+ },
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+ data_url=DATA_URL,
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+ aspects=ASPECTS,
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+ ),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION + self.config.description,
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+ features=datasets.Features(self.config.features),
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+ homepage=_HOMEPAGE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ arch_path = dl_manager.download_and_extract(self.config.data_url)
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+
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+ if "relations" in self.config.name:
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+ train_file = "train.csv"
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+ test_file = "test.csv"
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+
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+ generators = []
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+
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+ # for k in [1, 2, 3, 4]:
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+ for aspect in self.config.aspects:
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+ for k in ["sample"] + [1, 2, 3, 4]:
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+ folds_path = os.path.join(arch_path, 'folds', aspect, str(k))
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+ generators += [
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+ datasets.SplitGenerator(
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+ name=get_train_split(aspect, k),
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+ gen_kwargs={'filepath': os.path.join(folds_path, train_file)}
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+ ),
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+ datasets.SplitGenerator(
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+ name=get_test_split(aspect, k),
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+ gen_kwargs={'filepath': os.path.join(folds_path, test_file)}
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+ )
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+ ]
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+ return generators
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+
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+ elif "docs" in self.config.name:
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+ # docs
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+ docs_file = os.path.join(arch_path, "docs.jsonl")
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split('docs'), gen_kwargs={"filepath": docs_file}),
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+ ]
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+ else:
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+ raise ValueError()
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+
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+ @staticmethod
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+ def get_dict_value(d, key, default=None):
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+ if key in d:
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+ return d[key]
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+ else:
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+ return default
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+
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+ def _generate_examples(self, filepath):
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+ """Generate docs + rel examples."""
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+
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+ if "relations" in self.config.name:
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+ df = csv.read_csv(filepath).to_pandas()
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+
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+ for idx, row in df.iterrows():
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+ yield idx, {
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+ DOC_A_COL: str(row[DOC_A_COL]),
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+ DOC_B_COL: str(row[DOC_B_COL]),
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+ LABEL_COL: row['label'], # !!! labels != label
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+ }
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+
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+ elif self.config.name == "docs":
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+ with open(filepath, 'r') as f:
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+ for i, line in enumerate(f):
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+ doc = json.loads(line)
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+ # extract feature keys from doc
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+ features = {k: doc[k] if k in doc else None for k in self.config.features.keys()}
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+
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+ yield i, features