upload hubscripts/medical_data_hub.py to hub from bigbio repo

medical_data.py

@@ -0,0 +1,179 @@
+# 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.
+
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import entailment_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = True
+_CITATION = """\
+@misc{ask9medicaldata,
+  author    = {Khan, Arbaaz},
+  title     = {Sentiment Analysis for Medical Drugs},
+  year      = {2019},
+  url       = {https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment},
+}
+"""
+
+_DATASETNAME = "medical_data"
+_DISPLAYNAME = "Medical Data"
+
+_DESCRIPTION = """\
+    This dataset is designed to do multiclass classification on medical drugs
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {}
+
+_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MedicaldataDatatset(datasets.GeneratorBasedBuilder):
+    """This dataset contains comments about patients and the sentiment in those comments about a specific drug that's mentioned.
+    1 - Negative sentiment
+    2 - Positive sentiment
+    0 - Neutral"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        BigBioConfig(
+            name=f"{_DATASETNAME}_bigbio_te",
+            version=BIGBIO_VERSION,
+            description=f"{_DATASETNAME} BigBio schema",
+            schema="bigbio_te",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "hash": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "drug_name": datasets.Value("string"),
+                    "sentiment": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_te":
+            features = entailment_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."""
+
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+        else:
+            data_dir = self.config.data_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train_F3WbcTw.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test_tOlRoBf.csv"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            csv_reader = pd.read_csv(filepath, dtype="object")
+            # Train and test splits handled differently as test split is missing values
+            if split == "train":
+                for _cols, line in csv_reader.iterrows():
+                    document = {}
+                    document["hash"] = line["unique_hash"]
+                    document["text"] = line["text"]
+                    document["drug_name"] = line["drug"]
+                    document["sentiment"] = line["sentiment"]
+                    yield document["hash"], document
+            else:
+                for _cols, line in csv_reader.iterrows():
+                    document = {}
+                    document["hash"] = line["unique_hash"]
+                    document["text"] = line["text"]
+                    document["drug_name"] = line["drug"]
+                    document["sentiment"] = None
+                    yield document["hash"], document
+
+        elif self.config.schema == "bigbio_te":
+            csv_reader = pd.read_csv(filepath, dtype="object")
+            # Train and test splits handled differently as test split is missing values
+            if split == "train":
+                for _cols, line in csv_reader.iterrows():
+                    document = {}
+                    document["id"] = line["unique_hash"]
+                    document["premise"] = line["text"]
+                    document["hypothesis"] = line["drug"]
+                    document["label"] = line["sentiment"]
+                    yield document["id"], document
+            else:
+                for _cols, line in csv_reader.iterrows():
+                    document = {}
+                    document["id"] = line["unique_hash"]
+                    document["premise"] = line["text"]
+                    document["hypothesis"] = line["drug"]
+                    document["label"] = None
+                    yield document["id"], document