Create paperswithcode-aspects.py

paperswithcode-aspects.py

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