upload hubscripts/evidence_inference_hub.py to hub from bigbio repo

evidence_inference.py

@@ -0,0 +1,293 @@
+# 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.
+
+"""
+The dataset consists of biomedical articles describing randomized control trials (RCTs)
+that compare multiple treatments. Each of these articles will have multiple questions,
+or 'prompts' associated with them. These prompts will ask about the relationship between
+an intervention and comparator with respect to an outcome, as reported in the trial.
+For example, a prompt may ask about the reported effects of aspirin as compared to placebo
+on the duration of headaches.
+For the sake of this task, we assume that a particular article will report that the intervention of interest either
+significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.
+"""
+
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import qa_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{deyoung-etal-2020-evidence,
+    title = "Evidence Inference 2.0: More Data, Better Models",
+    author = "DeYoung, Jay  and
+      Lehman, Eric  and
+      Nye, Benjamin  and
+      Marshall, Iain  and
+      Wallace, Byron C.",
+    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.bionlp-1.13",
+    pages = "123--132",
+}
+"""
+
+_DATASETNAME = "evidence_inference"
+_DISPLAYNAME = "Evidence Inference 2.0"
+
+_DESCRIPTION = """\
+The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple
+treatments. Each of these articles will have multiple questions, or 'prompts' associated with them.
+These prompts will ask about the relationship between an intervention and comparator with respect to an outcome,
+as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared
+to placebo on the duration of headaches. For the sake of this task, we assume that a particular article
+will report that the intervention of interest either significantly increased, significantly decreased
+or had significant effect on the outcome, relative to the comparator.
+"""
+
+_HOMEPAGE = "https://github.com/jayded/evidence-inference"
+
+_LICENSE = 'MIT License'
+
+_URLS = {
+    _DATASETNAME: "http://evidence-inference.ebm-nlp.com/v2.0.tar.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "2.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+QA_CHOICES = [
+    "significantly increased",
+    "no significant difference",
+    "significantly decreased",
+]
+
+# Some examples are removed due to comments on the dataset's github page
+# https://github.com/jayded/evidence-inference/blob/master/annotations/README.md#caveat
+
+INCORRECT_PROMPT_IDS = set([
+    911, 912, 1262, 1261, 3044, 3248, 3111, 3620, 4308, 4490, 4491, 4324,
+    4325, 4492, 4824, 5000, 5001, 5002, 5046, 5047, 4948, 5639, 5710, 5752,
+    5775, 5782, 5841, 5843, 5861, 5862, 5863, 5964, 5965, 5966, 5975, 4807,
+    5776, 5777, 5778, 5779, 5780, 5781, 6034, 6065, 6066, 6666, 6667, 6668,
+    6669, 7040, 7042, 7944, 8590, 8605, 8606, 8639, 8640, 8745, 8747, 8749,
+    8877, 8878, 8593, 8631, 8635, 8884, 8886, 8773, 10032, 10035, 8876, 8875,
+    8885, 8917, 8921, 8118, 10885, 10886, 10887, 10888, 10889, 10890
+])
+
+QUESTIONABLE_PROMPT_IDS = set([
+    7811, 7812, 7813, 7814, 7815, 8197, 8198, 8199,
+    8200, 8201, 9429, 9430, 9431, 8536, 9432
+])
+
+SOMEWHAT_MALFORMED_PROMPT_IDS = set([
+    3514, 346, 5037, 4715, 8767, 9295, 9297, 8870, 9862
+])
+
+SKIP_PROMPT_IDS = INCORRECT_PROMPT_IDS | QUESTIONABLE_PROMPT_IDS | SOMEWHAT_MALFORMED_PROMPT_IDS
+
+
+class EvidenceInferenceDataset(datasets.GeneratorBasedBuilder):
+    f"""{_DESCRIPTION}"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="evidence-inference_source",
+            version=SOURCE_VERSION,
+            description="evidence-inference source schema",
+            schema="source",
+            subset_id="evidence-inference",
+        ),
+        BigBioConfig(
+            name="evidence-inference_bigbio_qa",
+            version=BIGBIO_VERSION,
+            description="evidence-inference BigBio schema",
+            schema="bigbio_qa",
+            subset_id="evidence-inference",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "evidence-inference_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "prompt_id": datasets.Value("int64"),
+                    "pmcid": datasets.Value("int64"),
+                    "label": datasets.Value("string"),
+                    "evidence": datasets.Value("string"),
+                    "intervention": datasets.Value("string"),
+                    "comparator": datasets.Value("string"),
+                    "outcome": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_qa":
+            features = qa_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]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": [
+                        os.path.join(data_dir, "annotations_merged.csv"),
+                        os.path.join(data_dir, "prompts_merged.csv"),
+                    ],
+                    "datapath": os.path.join(data_dir, "txt_files"),
+                    "split": "train",
+                    "datadir": data_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": [
+                        os.path.join(data_dir, "annotations_merged.csv"),
+                        os.path.join(data_dir, "prompts_merged.csv"),
+                    ],
+                    "datapath": os.path.join(data_dir, "txt_files"),
+                    "split": "validation",
+                    "datadir": data_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": [
+                        os.path.join(data_dir, "annotations_merged.csv"),
+                        os.path.join(data_dir, "prompts_merged.csv"),
+                    ],
+                    "datapath": os.path.join(data_dir, "txt_files"),
+                    "split": "test",
+                    "datadir": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepaths, datapath, split, datadir
+    ) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(f"{datadir}/splits/{split}_article_ids.txt", "r") as f:
+            ids = [int(i.strip()) for i in f.readlines()]
+        prompts = pd.read_csv(filepaths[-1], encoding="utf8")
+        prompts = prompts[prompts["PMCID"].isin(ids)]
+
+        annotations = pd.read_csv(filepaths[0], encoding="utf8").set_index("PromptID")
+        evidences = pd.read_csv(filepaths[0], encoding="utf8").set_index("PMCID")
+        evidences = evidences[evidences["Evidence Start"] != -1]
+        uid = 0
+
+        def lookup(df: pd.DataFrame, id, col) -> str:
+            try:
+                label = df.loc[id][col]
+                if isinstance(label, pd.Series):
+                    return label.values[0]
+                else:
+                    return label
+            except KeyError:
+                return -1
+
+        def extract_evidence(doc_id, start, end):
+            p = f"{datapath}/PMC{doc_id}.txt"
+            with open(p, "r") as f:
+                return f.read()[start:end]
+
+
+        for key, sample in prompts.iterrows():
+
+            pid = sample["PromptID"]
+            pmcid = sample["PMCID"]
+            label = lookup(annotations, pid, "Label")
+            start = lookup(evidences, pmcid, "Evidence Start")
+            end = lookup(evidences, pmcid, "Evidence End")
+
+            if pid in SKIP_PROMPT_IDS:
+                continue
+
+            if label == -1:
+                continue
+
+            evidence = extract_evidence(pmcid, start, end)
+
+            if self.config.schema == "source":
+
+                feature_dict = {
+                    "id": uid,
+                    "pmcid": pmcid,
+                    "prompt_id": pid,
+                    "intervention": sample["Intervention"],
+                    "comparator": sample["Comparator"],
+                    "outcome": sample["Outcome"],
+                    "evidence": evidence,
+                    "label": label,
+                }
+
+                uid += 1
+                yield key, feature_dict
+
+            elif self.config.schema == "bigbio_qa":
+
+                context = evidence
+                question = (
+                    f"Compared to {sample['Comparator']} "
+                    f"what was the result of {sample['Intervention']} on {sample['Outcome']}?"
+                )
+                feature_dict = {
+                    "id": uid,
+                    "question_id": pid,
+                    "document_id": pmcid,
+                    "question": question,
+                    "type": "multiple_choice",
+                    "choices": QA_CHOICES,
+                    "context": context,
+                    "answer": [label],
+                }
+
+                uid += 1
+                yield key, feature_dict