upload hubscripts/minimayosrs_hub.py to hub from bigbio repo

minimayosrs.py

@@ -0,0 +1,164 @@
+# 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.
+
+"""
+MayoSRS consists of 101 clinical term pairs whose relatedness was determined by
+nine medical coders and three physicians from the Mayo Clinic.
+"""
+
+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 = """\
+@article{pedersen2007measures,
+  title={Measures of semantic similarity and relatedness in the biomedical domain},
+  author={Pedersen, Ted and Pakhomov, Serguei VS and Patwardhan, Siddharth and Chute, Christopher G},
+  journal={Journal of biomedical informatics},
+  volume={40},
+  number={3},
+  pages={288--299},
+  year={2007},
+  publisher={Elsevier}
+}
+"""
+
+_DATASETNAME = "minimayosrs"
+_DISPLAYNAME = "MiniMayoSRS"
+
+_DESCRIPTION = """\
+MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement was
+achieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78.
+"""
+
+_HOMEPAGE = "https://conservancy.umn.edu/handle/11299/196265"
+
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+
+_URLS = {
+    _DATASETNAME: "https://conservancy.umn.edu/bitstream/handle/11299/196265/MiniMayoSRS.csv?sequence=2&isAllowed=y"
+}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MinimayosrsDataset(datasets.GeneratorBasedBuilder):
+    """MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement
+    was achieved. The average correlation between physicians is 0.68. The average correlation between medical coders
+    is 0.78.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="minimayosrs_source",
+            version=SOURCE_VERSION,
+            description="MiniMayoSRS source schema",
+            schema="source",
+            subset_id="minimayosrs",
+        ),
+        BigBioConfig(
+            name="minimayosrs_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="MiniMayoSRS BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="minimayosrs",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "minimayosrs_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text_1": datasets.Value("string"),
+                    "text_2": datasets.Value("string"),
+                    "code_1": datasets.Value("string"),
+                    "code_2": datasets.Value("string"),
+                    "label_physicians": datasets.Value("float32"),
+                    "label_coders": datasets.Value("float32"),
+                }
+            )
+
+        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[_DATASETNAME]
+        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."""
+
+        data = pd.read_csv(
+            filepath,
+            sep=",",
+            header=0,
+            names=[
+                "label_physicians",
+                "label_coders",
+                "code_1",
+                "code_2",
+                "text_1",
+                "text_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": str((row["label_physicians"] + row["label_coders"]) / 2),
+                }