upload hubscripts/hallmarks_of_cancer_hub.py to hub from bigbio repo

hallmarks_of_cancer.py

@@ -0,0 +1,214 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pathlib import Path
+
+import datasets
+
+from .bigbiohub import text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{DBLP:journals/bioinformatics/BakerSGAHSK16,
+  author    = {Simon Baker and
+               Ilona Silins and
+               Yufan Guo and
+               Imran Ali and
+               Johan H{\"{o}}gberg and
+               Ulla Stenius and
+               Anna Korhonen},
+  title     = {Automatic semantic classification of scientific literature
+               according to the hallmarks of cancer},
+  journal   = {Bioinform.},
+  volume    = {32},
+  number    = {3},
+  pages     = {432--440},
+  year      = {2016},
+  url       = {https://doi.org/10.1093/bioinformatics/btv585},
+  doi       = {10.1093/bioinformatics/btv585},
+  timestamp = {Thu, 14 Oct 2021 08:57:44 +0200},
+  biburl    = {https://dblp.org/rec/journals/bioinformatics/BakerSGAHSK16.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DATASETNAME = "hallmarks_of_cancer"
+_DISPLAYNAME = "Hallmarks of Cancer"
+
+_DESCRIPTION = """\
+The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication
+abstracts manually annotated by experts according to a taxonomy. The taxonomy
+consists of 37 classes in a hierarchy. Zero or more class labels are assigned
+to each sentence in the corpus. The labels are found under the "labels"
+directory, while the tokenized text can be found under "text" directory.
+The filenames are the corresponding PubMed IDs (PMID).
+"""
+
+_HOMEPAGE = "https://github.com/sb895/Hallmarks-of-Cancer"
+
+_LICENSE = 'GNU General Public License v3.0 only'
+
+_URLs = {
+    "corpus": "https://github.com/sb895/Hallmarks-of-Cancer/archive/refs/heads/master.zip",
+    "split_indices": "https://microsoft.github.io/BLURB/sample_code/data_generation.tar.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+_CLASS_NAMES = [
+    "evading growth suppressors",
+    "tumor promoting inflammation",
+    "enabling replicative immortality",
+    "cellular energetics",
+    "resisting cell death",
+    "activating invasion and metastasis",
+    "genomic instability and mutation",
+    "none",
+    "inducing angiogenesis",
+    "sustaining proliferative signaling",
+    "avoiding immune destruction",
+]
+
+
+class HallmarksOfCancerDataset(datasets.GeneratorBasedBuilder):
+    """Hallmarks Of Cancer Dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="hallmarks_of_cancer_source",
+            version=SOURCE_VERSION,
+            description="Hallmarks of Cancer source schema",
+            schema="source",
+            subset_id="hallmarks_of_cancer",
+        ),
+        BigBioConfig(
+            name="hallmarks_of_cancer_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="Hallmarks of Cancer Biomedical schema",
+            schema="bigbio_text",
+            subset_id="hallmarks_of_cancer",
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = "hallmarks_of_cancer_source"
+
+    def _info(self):
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": [datasets.ClassLabel(names=_CLASS_NAMES)],
+                }
+            )
+
+        elif self.config.schema == "bigbio_text":
+            features = text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URLs)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "corpuspath": Path(data_dir["corpus"]),
+                    "indicespath": Path(data_dir["split_indices"])
+                    / "data_generation/indexing/HoC/train_pmid.tsv",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "corpuspath": Path(data_dir["corpus"]),
+                    "indicespath": Path(data_dir["split_indices"])
+                    / "data_generation/indexing/HoC/test_pmid.tsv",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "corpuspath": Path(data_dir["corpus"]),
+                    "indicespath": Path(data_dir["split_indices"])
+                    / "data_generation/indexing/HoC/dev_pmid.tsv",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, corpuspath: Path, indicespath: Path):
+
+        indices = indicespath.read_text(encoding="utf8").strip("\n").split(",")
+        dataset_dir = corpuspath / "Hallmarks-of-Cancer-master"
+        texts_dir = dataset_dir / "text"
+        labels_dir = dataset_dir / "labels"
+
+        uid = 1
+        for document_index, document in enumerate(indices):
+            text_file = texts_dir / document
+            label_file = labels_dir / document
+            text = text_file.read_text(encoding="utf8").strip("\n")
+            labels = label_file.read_text(encoding="utf8").strip("\n")
+
+            sentences = text.split("\n")
+            labels = labels.split("<")[1:]
+
+            for example_index, example_pair in enumerate(zip(sentences, labels)):
+                sentence, label = example_pair
+
+                label = label.strip()
+
+                if label == "":
+                    label = "none"
+
+                multi_labels = [m_label.strip() for m_label in label.split("AND")]
+                unique_multi_labels = {
+                    m_label.split("--")[0].lower().lstrip()
+                    for m_label in multi_labels
+                    if m_label != "NULL"
+                }
+
+                arrow_file_unique_key = 100 * document_index + example_index
+                if self.config.schema == "source":
+                    yield arrow_file_unique_key, {
+                        "document_id": f"{text_file.name.split('.')[0]}_{example_index}",
+                        "text": sentence,
+                        "label": list(unique_multi_labels),
+                    }
+                elif self.config.schema == "bigbio_text":
+                    yield arrow_file_unique_key, {
+                        "id": uid,
+                        "document_id": f"{text_file.name.split('.')[0]}_{example_index}",
+                        "text": sentence,
+                        "labels": list(unique_multi_labels),
+                    }
+                    uid += 1