upload hubscripts/essai_hub.py to hub from bigbio repo

essai.py

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+import os
+
+import datasets
+import numpy as np
+import pandas as pd
+
+from .bigbiohub import text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['French']
+_PUBMED = False
+_LOCAL = True
+_CITATION = """\
+ @misc{dalloux, title={Datasets – Clément Dalloux}, url={http://clementdalloux.fr/?page_id=28}, journal={Clément Dalloux}, author={Dalloux, Clément}} 
+"""
+
+_DATASETNAME = "essai"
+_DISPLAYNAME = "ESSAI"
+
+_DESCRIPTION = """\
+We manually annotated two corpora from the biomedical field. The ESSAI corpus \
+contains clinical trial protocols in French. They were mainly obtained from the \
+National Cancer Institute The typical protocol consists of two parts: the \
+summary of the trial, which indicates the purpose of the trial and the methods \
+applied; and a detailed description of the trial with the inclusion and \
+exclusion criteria. The CAS corpus contains clinical cases published in \
+scientific literature and training material. They are published in different \
+journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
+African countries, tropical countries) and are related to various medical \
+specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
+gastro-enterology). The purpose of clinical cases is to describe clinical \
+situations of patients. Hence, their content is close to the content of clinical \
+narratives (description of diagnoses, treatments or procedures, evolution, \
+family history, expected audience, etc.). In clinical cases, the negation is \
+frequently used for describing the patient signs, symptoms, and diagnosis. \
+Speculation is present as well but less frequently.
+
+This version only contain the annotated ESSAI corpus
+"""
+
+_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"
+
+_LICENSE = 'Data User Agreement'
+
+_URLS = {
+    "essai_source": "",
+    "essai_bigbio_text": "",
+    "essai_bigbio_kb": "",
+}
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+
+class ESSAI(datasets.GeneratorBasedBuilder):
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    DEFAULT_CONFIG_NAME = "essai_source"
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="essai_source",
+            version=SOURCE_VERSION,
+            description="ESSAI source schema",
+            schema="source",
+            subset_id="essai",
+        ),
+        BigBioConfig(
+            name="essai_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="ESSAI simplified BigBio schema for negation/speculation classification",
+            schema="bigbio_text",
+            subset_id="essai",
+        ),
+        BigBioConfig(
+            name="essai_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="ESSAI simplified BigBio schema for part-of-speech-tagging",
+            schema="bigbio_kb",
+            subset_id="essai",
+        ),
+    ]
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": [datasets.Value("string")],
+                    "lemmas": [datasets.Value("string")],
+                    "POS_tags": [datasets.Value("string")],
+                    "labels": [datasets.Value("string")],
+                }
+            )
+        elif self.config.schema == "bigbio_text":
+            features = text_features
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        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={"datadir": data_dir},
+            ),
+        ]
+
+    def _generate_examples(self, datadir):
+        key = 0
+        for file in ["ESSAI_neg.txt", "ESSAI_spec.txt"]:
+            filepath = os.path.join(datadir, file)
+            label = "negation" if "neg" in file else "speculation"
+            id_docs = []
+            id_words = []
+            words = []
+            lemmas = []
+            POS_tags = []
+
+            with open(filepath) as f:
+                for line in f.readlines():
+                    line_content = line.split("\t")
+                    if len(line_content) > 1:
+                        id_docs.append(line_content[0])
+                        id_words.append(line_content[1])
+                        words.append(line_content[2])
+                        lemmas.append(line_content[3])
+                        POS_tags.append(line_content[4])
+
+            dic = {
+                "id_docs": np.array(list(map(int, id_docs))),
+                "id_words": id_words,
+                "words": words,
+                "lemmas": lemmas,
+                "POS_tags": POS_tags,
+            }
+            if self.config.schema == "source":
+                for doc_id in set(dic["id_docs"]):
+                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    text = [dic["words"][id] for id in idces]
+                    text_lemmas = [dic["lemmas"][id] for id in idces]
+                    POS_tags_ = [dic["POS_tags"][id] for id in idces]
+                    yield key, {
+                        "id": key,
+                        "document_id": doc_id,
+                        "text": text,
+                        "lemmas": text_lemmas,
+                        "POS_tags": POS_tags_,
+                        "labels": [label],
+                    }
+                    key += 1
+            elif self.config.schema == "bigbio_text":
+                for doc_id in set(dic["id_docs"]):
+                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    text = " ".join([dic["words"][id] for id in idces])
+                    yield key, {
+                        "id": key,
+                        "document_id": doc_id,
+                        "text": text,
+                        "labels": [label],
+                    }
+                    key += 1
+            elif self.config.schema == "bigbio_kb":
+                for doc_id in set(dic["id_docs"]):
+                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    text = [dic["words"][id] for id in idces]
+                    POS_tags_ = [dic["POS_tags"][id] for id in idces]
+
+                    data = {
+                        "id": str(key),
+                        "document_id": doc_id,
+                        "passages": [],
+                        "entities": [],
+                        "relations": [],
+                        "events": [],
+                        "coreferences": [],
+                    }
+                    key += 1
+
+                    data["passages"] = [
+                        {
+                            "id": str(key + i),
+                            "type": "sentence",
+                            "text": [text[i]],
+                            "offsets": [[i, i + 1]],
+                        }
+                        for i in range(len(text))
+                    ]
+                    key += len(text)
+
+                    for i in range(len(text)):
+                        entity = {
+                            "id": key,
+                            "type": "POS_tag",
+                            "text": [POS_tags_[i]],
+                            "offsets": [[i, i + 1]],
+                            "normalized": [],
+                        }
+                        data["entities"].append(entity)
+                        key += 1
+
+                    yield key, data