Update MANTRAGSC.py

MANTRAGSC.py

@@ -16,14 +16,16 @@
 import re
 import ast
 import json
+import random
 from pathlib import Path
 from itertools import product
 from dataclasses import dataclass
 from typing import Dict, List, Tuple
 
-
 import datasets
 
+import numpy as np
+
 _CITATION = """\
 @article{10.1093/jamia/ocv037,
 	author = {Kors, Jan A and Clematide, Simon and Akhondi,
@@ -116,30 +118,14 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 
 	def _info(self) -> datasets.DatasetInfo:
 
-		if self.config.schema == "source":
-			features = datasets.Features(
-				{
-					"document_id": datasets.Value("string"),
-					"text": datasets.Value("string"),
-					"entities": [
-						{
-							"entity_id": datasets.Value("string"),
-							"type": datasets.Value("string"),
-							"offsets": datasets.Sequence([datasets.Value("int32")]),
-							"text": datasets.Sequence(datasets.Value("string")),
-							"cui": datasets.Value("string"),
-							"preferred_term": datasets.Value("string"),
-							"semantic_type": datasets.Value("string"),
-							"normalized": [
-								{
-									"db_name": datasets.Value("string"),
-									"db_id": datasets.Value("string"),
-								}
-							],
-						}
-					],
-				}
-			)
+		features = datasets.Features(
+			{
+				"id": datasets.Value("string"),
+				"document_id": datasets.Value("string"),
+				"tokens": [datasets.Value("string")],
+				"ner_tags": [datasets.Value("string")],
+			}
+		)
 
 		return datasets.DatasetInfo(
 			description=_DESCRIPTION,
@@ -151,17 +137,12 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 
 	def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
 
-		print("1 - " + "*"*50)
-		print(_URL)
 		data_dir = dl_manager.download_and_extract(_URL)
 
-		print("2 - " + "*"*50)
 		data_dir = Path(data_dir) / "Mantra-GSC"
 
-		print("3 - " + "*"*50)
 		language, dataset_type = self.config.name.split("_")
 
-		print("4 - " + "*"*50)
 		return [
 			datasets.SplitGenerator(
 				name=datasets.Split.TRAIN,
@@ -169,6 +150,25 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 					"data_dir": data_dir,
 					"language": language,
 					"dataset_type": dataset_type,
+					"split": "train",
+				},
+			),
+			datasets.SplitGenerator(
+				name=datasets.Split.VALIDATION,
+				gen_kwargs={
+					"data_dir": data_dir,
+					"language": language,
+					"dataset_type": dataset_type,
+					"split": "validation",
+				},
+			),
+			datasets.SplitGenerator(
+				name=datasets.Split.TEST,
+				gen_kwargs={
+					"data_dir": data_dir,
+					"language": language,
+					"dataset_type": dataset_type,
+					"split": "test",
 				},
 			),
 		]
@@ -202,24 +202,19 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 
 			return text
 		
-		# print(json.dumps(json_object, sort_keys=True, indent=4))
 		new_json = []
 
 		for ex in [json_object]:
 
-			# print(json.dumps(ex, sort_keys=True, indent=4))
-			
 			text = prepare_split(ex['text'])
 
 			tokenized_text = text.split()
-			print(tokenized_text)
 
 			list_spans = []
 
 			cpt = 0
 
 			for a in ex['entities']:
-			# for a in ex['text_bound_annotations']:
 
 				for o in range(len(a['offsets'])):
 
@@ -262,7 +257,6 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 		return new_json
 
 	def convert_to_hf_format(self, json_object):
-	# def convert_to_hf_format(self, json_object, list_label):
 		"""
 		Le format prends en compte le multilabel en faisant une concaténation avec "_" entre chaque label
 		"""
@@ -271,8 +265,6 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 		
 		for i in json_object:
 
-			print("#"*50)
-			
 			nb_tokens = len(i['tokens'])
 			
 			ner_tags = ['O']*nb_tokens
@@ -281,32 +273,10 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 
 				for j in i['spans']:
 
-					print(j)
-
-					# if j['text'] != ' '.join(i['tokens'][j['token_start']:j['token_end']+1]):
-					# 	print(j)
-					# 	print(j['id'])
-					# 	print(j['text'])
-					# 	print(' '.join(i['tokens'][j['token_start']:j['token_end']+1]))
-					# 	print()
-
-					# for x in range(j['token_start'], j['token_end'], 1):
 					for x in range(j['token_start'], j['token_end']+1, 1):
 
-						# if j['label'] in list_label:
-						# if j['text'] != ' '.join(i['tokens'][j['token_start']:j['token_end']+1]):
-						print("x: ", x)
-						print("t: ", i['tokens'][x])
-						print("n: ", j['label'])
-						print()
-
-						# x -= 1
 						if i['tokens'][x] not in j['text'] and i['tokens'][x] != "Matériovigilance":
-							print("Mots entiers")
-							print("x: ", x-1)
-							print("t: ", i['tokens'][x-1])
-							print("n: ", j['label'])
-							print()
+
 							if ner_tags[x-1] == 'O':
 								ner_tags[x-1] = j['label']
 							else:
@@ -317,8 +287,7 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 							else:
 								# Commenter la ligne et mettre pass si on veut prendre qu'un label par token
 								pass
-							# ner_tags[x] = '_'.join(sorted(list(set(ner_tags[x].split('_')+[j['label']]))))
-			
+
 			dict_out.append({
 				'id': i['id'],
 				'document_id': i['document_id'],
@@ -340,15 +309,11 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 		with txt_file.open() as f:
 			example["text"] = f.read()
 
-		# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
-		# for event extraction
 		if annotation_file_suffixes is None:
 			annotation_file_suffixes = [".a1", ".a2", ".ann"]
 
 		if len(annotation_file_suffixes) == 0:
-			raise AssertionError(
-				"At least one suffix for the to-be-read annotation files should be given!"
-			)
+			raise AssertionError("At least one suffix for the to-be-read annotation files should be given!")
 
 		ann_lines = []
 		for suffix in annotation_file_suffixes:
@@ -436,10 +401,6 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 
 				example["relations"].append(ann)
 
-			# '*' seems to be the legacy way to mark equivalences,
-			# but I couldn't find any info on the current way
-			# this might have to be adapted dependent on the brat version
-			# of the annotation
 			elif line.startswith("*"):
 				ann = {}
 				fields = line.split("\t")
@@ -493,19 +454,18 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 				ann["type"] = info[0]
 				ann["ref_id"] = info[1]
 				example["notes"].append(ann)
-		return example
 
+		return example
 
-	def _generate_examples(
-		self, data_dir: Path, language: str, dataset_type: str
-	) -> Tuple[int, Dict]:
+	def _generate_examples(self, data_dir: Path, language: str, dataset_type: str, split: str):
 		"""Yields examples as (key, example) tuples."""
+
 		data_dir = data_dir / f"{_LANGUAGES_2[language]}"
 
 		if dataset_type in ["patents", "emea"]:
 			data_dir = data_dir / f"{_DATASET_TYPES[dataset_type]}_ec22-cui-best_man"
 		else:
-			# It is Medline now
+			# Medline
 			if language != "en":
 				data_dir = (
 					data_dir
@@ -531,24 +491,31 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 			source_example = self._to_source_example(brat_example)
 
 			prod_format = self.convert_to_prodigy(source_example)
-			# print(prod_format)
-			# print()
 
 			hf_format = self.convert_to_hf_format(prod_format)[0]
-			print(">>> hf_format")
-			print(hf_format)
-			print("*"*50)
-			for a, b in zip(hf_format['tokens'], hf_format['ner_tags']):
-				print(a, " - ", b, end=" || ")
-			print()
-			print("*"*50)
-
-			# yield i, self.convert_to_hf_format(
-			#     self.convert_to_prodigy(source_example),
-			#     _LABELS_BASE,
-			# )
-			yield i, source_example
+			all_res.append(hf_format)
+	
+		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:
+			identifier = r["id"]
+			if identifier in allowed_ids:
+				yield identifier, r 
+		
 	def _to_source_example(self, brat_example: Dict) -> Dict:
 
 		source_example = {
@@ -558,30 +525,29 @@ class MantraGSC(datasets.GeneratorBasedBuilder):
 
 		source_example["entities"] = []
 
-		for entity_annotation, ann_notes in zip(
-			brat_example["text_bound_annotations"], brat_example["notes"]
-		):
+		for entity_annotation, ann_notes in zip(brat_example["text_bound_annotations"], brat_example["notes"]):
+			
 			entity_ann = entity_annotation.copy()
 
-			# Change id property name
 			entity_ann["entity_id"] = entity_ann["id"]
 			entity_ann.pop("id")
 
 			# Get values from annotator notes
 			assert entity_ann["entity_id"] == ann_notes["ref_id"]
 			notes_values = ast.literal_eval(ann_notes["text"])
+			
 			if len(notes_values) == 4:
 				cui, preferred_term, semantic_type, semantic_group = notes_values
 			else:
 				preferred_term, semantic_type, semantic_group = notes_values
 				cui = entity_ann["type"]
+			
 			entity_ann["cui"] = cui
 			entity_ann["preferred_term"] = preferred_term
 			entity_ann["semantic_type"] = semantic_type
 			entity_ann["type"] = semantic_group
 			entity_ann["normalized"] = [{"db_name": "UMLS", "db_id": cui}]
 
-			# Add entity annotation to sample
 			source_example["entities"].append(entity_ann)
 
 		return source_example