Create PxCorpus.py

PxCorpus.py

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+# pip install bs4 syntok
+
+import os
+import random
+
+import datasets
+
+import numpy as np
+from bs4 import BeautifulSoup, ResultSet
+from syntok.tokenizer import Tokenizer
+
+tokenizer = Tokenizer()
+
+_CITATION = """\
+@InProceedings{Kocabiyikoglu2022,
+  author =     "Alican Kocabiyikoglu and Fran{\c c}ois Portet and Prudence Gibert and Hervé Blanchon and Jean-Marc Babouchkine and Gaëtan Gavazzi",
+  title =     "A Spoken Drug Prescription Dataset in French for Spoken Language Understanding",
+  booktitle =     "13th Language Resources and Evaluation Conference (LREC 2022)",
+  year =     "2022",
+  location =     "Marseille, France"
+}
+"""
+
+_DESCRIPTION = """\
+PxSLU is to the best of our knowledge, the first spoken medical drug prescriptions corpus to be distributed. It contains 4 hours of transcribed
+and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in drug prescriptions.
+
+The automatic transcriptions were verified by human effort and aligned with semantic labels to allow training of NLP models. The data acquisition
+protocol was reviewed by medical experts and permit free distribution without breach of privacy and regulation.
+
+Overview of the Corpus
+
+The experiment has been performed in wild conditions with naive participants and medical experts. In total, the dataset includes 1981 recordings
+of 55 participants (38% non-experts, 25% doctors, 36% medical practitioners), manually transcribed and semantically annotated.
+"""
+
+_URL = "https://zenodo.org/record/6524162/files/pxslu.zip?download=1"
+
+class PxCorpus(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=f"default", version="1.0.0", description=f"PxCorpus data"),
+    ]
+    
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "label": datasets.features.ClassLabel(
+                    names=["medical_prescription", "negate", "none", "replace"],
+                ),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=['O', 'B-A', 'B-cma_event', 'B-d_dos_form', 'B-d_dos_form_ext', 'B-d_dos_up', 'B-d_dos_val', 'B-dos_cond', 'B-dos_uf', 'B-dos_val', 'B-drug', 'B-dur_ut', 'B-dur_val', 'B-fasting', 'B-freq_days', 'B-freq_int_v1', 'B-freq_int_v1_ut', 'B-freq_int_v2', 'B-freq_int_v2_ut', 'B-freq_startday', 'B-freq_ut', 'B-freq_val', 'B-inn', 'B-max_unit_uf', 'B-max_unit_ut', 'B-max_unit_val', 'B-min_gap_ut', 'B-min_gap_val', 'B-qsp_ut', 'B-qsp_val', 'B-re_ut', 'B-re_val', 'B-rhythm_hour', 'B-rhythm_perday', 'B-rhythm_rec_ut', 'B-rhythm_rec_val', 'B-rhythm_tdte', 'B-roa', 'I-cma_event', 'I-d_dos_form', 'I-d_dos_form_ext', 'I-d_dos_up', 'I-d_dos_val', 'I-dos_cond', 'I-dos_uf', 'I-dos_val', 'I-drug', 'I-fasting', 'I-freq_startday', 'I-inn', 'I-rhythm_tdte', 'I-roa'],
+                    ),
+                ),
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            citation=_CITATION,
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        data_dir = dl_manager.download_and_extract(_URL)
+
+        print(data_dir)
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath_1": os.path.join(data_dir, "seq.in"),
+                    "filepath_2": os.path.join(data_dir, "seq.label"),
+                    "filepath_3": os.path.join(data_dir, "PxSLU_conll.txt"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath_1": os.path.join(data_dir, "seq.in"),
+                    "filepath_2": os.path.join(data_dir, "seq.label"),
+                    "filepath_3": os.path.join(data_dir, "PxSLU_conll.txt"),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath_1": os.path.join(data_dir, "seq.in"),
+                    "filepath_2": os.path.join(data_dir, "seq.label"),
+                    "filepath_3": os.path.join(data_dir, "PxSLU_conll.txt"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def getTokenTags(self, document):
+
+        tokens = []
+        ner_tags = []
+
+        for pair in document.split("\n"):
+
+            if len(pair) <= 0:
+                continue
+
+            text, label = pair.split("\t")
+            tokens.append(text)
+            ner_tags.append(label)
+
+        return tokens, ner_tags
+
+    def _generate_examples(self, filepath_1, filepath_2, filepath_3, split):
+
+        key = 0
+        all_res = []
+    
+        f_seq_in = open(filepath_1, "r")
+        seq_in = f_seq_in.read().split("\n")
+        f_seq_in.close()
+
+        f_seq_label = open(filepath_2, "r")
+        seq_label = f_seq_label.read().split("\n")
+        f_seq_label.close()
+
+        f_in_ner = open(filepath_3, "r")
+        docs = f_in_ner.read().split("\n\n")
+        f_in_ner.close()
+
+        for idx, doc in enumerate(docs):
+
+            text = seq_in[idx]
+            label = seq_label[idx]
+
+            tokens, ner_tags = self.getTokenTags(docs[idx])
+
+            if len(text) <= 0 or len(label) <= 0:
+                continue
+
+            all_res.append({
+                "id": key,
+                "text": text,
+                "label": label,
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            })
+            
+            key += 1
+
+        ids = [r["id"] for r in all_res]
+
+        random.seed(4)
+        random.shuffle(ids)
+        random.shuffle(ids)
+        random.shuffle(ids)
+        
+        train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
+
+        if split == "train":
+            allowed_ids = list(train)
+        elif split == "validation":
+            allowed_ids = list(validation)
+        elif split == "test":
+            allowed_ids = list(test)
+        
+        for r in all_res:
+            if r["id"] in allowed_ids:
+                yield r["id"], r