upload hubscripts/umnsrs_hub.py to hub from bigbio repo

umnsrs.py

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+# 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.
+
+"""
+UMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness.
+The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch
+a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness. The Intraclass
+Correlation Coefficient (ICC) for the reference standard tagged for similarity was 0.47, and 0.50 for relatedness.
+Therefore, as suggested by Pakhomov and colleagues, the subset below consists of 401 pairs for the similarity set and
+430 pairs for the relatedness set which each have an ICC equal to 0.73.
+"""
+
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@inproceedings{pakhomov2010semantic,
+  title={Semantic similarity and relatedness between clinical terms: an experimental study},
+  author={Pakhomov, Serguei and McInnes, Bridget and Adam, Terrence and Liu, Ying and Pedersen, Ted and Melton, \
+  Genevieve B},
+  booktitle={AMIA annual symposium proceedings},
+  volume={2010},
+  pages={572},
+  year={2010},
+  organization={American Medical Informatics Association}
+}
+"""
+
+_DATASETNAME = "umnsrs"
+_DISPLAYNAME = "UMNSRS"
+
+_DESCRIPTION = """\
+UMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness.
+The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch
+a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness.
+The following subsets are available:
+- similarity: A set of 566 UMLS concept pairs manually rated for semantic similarity (e.g. whale-dolphin) using a
+  continuous response scale.
+- relatedness: A set of 588 UMLS concept pairs manually rated for semantic relatedness (e.g. needle-thread) using a
+  continuous response scale.
+- similarity_mod: Modification of the UMNSRS-Similarity dataset to exclude control samples and those pairs that did not
+  match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus
+  Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan,
+  Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 449 pairs.
+- relatedness_mod: Modification of the UMNSRS-Relatedness dataset to exclude control samples and those pairs that did
+  not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper
+  (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley,
+  Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644).
+  The resulting dataset contains 458 pairs.
+"""
+
+_HOMEPAGE = "https://conservancy.umn.edu/handle/11299/196265/"
+
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+
+_BASE_URL = "https://conservancy.umn.edu/bitstream/handle/11299/196265/"
+
+_URLS = {
+    "umnsrs_similarity": _BASE_URL + "UMNSRS_similarity.csv",
+    "umnsrs_relatedness": _BASE_URL + "UMNSRS_relatedness.csv",
+    "umnsrs_similarity_mod": _BASE_URL + "UMNSRS_similarity_mod449_word2vec.csv",
+    "umnsrs_relatedness_mod": _BASE_URL + "UMNSRS_relatedness_mod458_word2vec.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class UmnsrsDataset(datasets.GeneratorBasedBuilder):
+    """UMNSRS, developed by Pakhomov, et al., contains clinical term pairs whose semantic similarity and
+    relatedness were scored by experts."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for subset in ["similarity", "relatedness"]:
+        for mod in ["_mod", ""]:
+            BUILDER_CONFIGS.append(
+                BigBioConfig(
+                    name=f"umnsrs_{subset}{mod}_source",
+                    version=SOURCE_VERSION,
+                    description=f"UMNSRS {subset}{mod} source schema",
+                    schema="source",
+                    subset_id=f"umnsrs_{subset}{mod}",
+                )
+            )
+            BUILDER_CONFIGS.append(
+                BigBioConfig(
+                    name=f"umnsrs_{subset}{mod}_bigbio_pairs",
+                    version=BIGBIO_VERSION,
+                    description=f"UMNSRS {subset}{mod} BigBio schema",
+                    schema="bigbio_pairs",
+                    subset_id=f"umnsrs_{subset}{mod}",
+                )
+            )
+
+    DEFAULT_CONFIG_NAME = "umnsrs_similarity_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "mean_score": datasets.Value("float32"),
+                    "std_score": datasets.Value("float32"),
+                    "text_1": datasets.Value("string"),
+                    "text_2": datasets.Value("string"),
+                    "code_1": datasets.Value("string"),
+                    "code_2": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_pairs":
+            features = pairs_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[self.config.subset_id]
+        filepath = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        print(filepath)
+        data = pd.read_csv(
+            filepath,
+            sep=",",
+            header=0,
+            names=["mean_score", "std_score", "text_1", "text_2", "code_1", "code_2"],
+        )
+
+        if self.config.schema == "source":
+            for id_, row in data.iterrows():
+                yield id_, row.to_dict()
+        elif self.config.schema == "bigbio_pairs":
+            for id_, row in data.iterrows():
+                yield id_, {
+                    "id": id_,
+                    "document_id": id_,
+                    "text_1": row["text_1"],
+                    "text_2": row["text_2"],
+                    "label": row["mean_score"],
+                }