DOC : Add example for the loading and pre-processing of corpus

docs/examples/dataset_loader.md

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+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.1
+kernelspec:
+  display_name: .venv
+  language: python
+  name: python3
+---
+
+```{code-cell} ipython3
+from glob import glob
+from medkit.core.text import TextDocument
+from medkit.io.brat import BratInputConverter
+from medkit.text.postprocessing import filter_overlapping_entities, DocumentSplitter
+from statistics import mean
+import pandas as pd
+from medkit.text.segmentation import SentenceTokenizer, SyntagmaTokenizer
+from medkit.core import Pipeline, DocPipeline, PipelineStep
+from pathlib import Path
+from medkit.tools.e3c_corpus import load_data_annotation
+from medkit.io.doccano import DoccanoInputConverter,DoccanoTask
+from sklearn.model_selection import train_test_split
+from medkit.tools.mtsamples import load_mtsamples
+from medkit.io.medkit_json import save_text_documents
+import json
+
+sentence_tok = SentenceTokenizer(output_label="sentence", punct_chars=["."],keep_punct=True,split_on_newlines=True)
+
+pipeline_phrase_creator = Pipeline(steps=[PipelineStep(sentence_tok, input_keys=["full_text"], output_keys=["sentences"])],
+                                          input_keys=["full_text"],
+                                          output_keys=["sentences"])
+
+phrase_creator = DocPipeline(pipeline_phrase_creator)
+
+splitter = DocumentSplitter(segment_label="sentence", attr_labels=[])
+
+def corpus_specs(_corpus, _title, num_docs):
+    doc_data = {}
+    doc_data['Documents'] = num_docs
+    doc_data['Sentences'] = len(_corpus)
+    doc_data['MSL'] = round(mean([len(sen.text) for sen in _corpus]))
+    doc_data['All'] = sum([len(doc.anns.get_entities()) for doc in _corpus])
+
+    labels = []
+    for doc in _corpus:
+        for ent in doc.anns.get_entities():
+            if ent.label not in doc_data:
+                doc_data[ent.label] = 0
+                labels.append(ent.label)
+            doc_data[ent.label] += 1
+
+    for label in labels:
+        doc_data[label] = round(doc_data[label] / doc_data['All'] * 100)
+
+    df = pd.DataFrame(doc_data, index=[_title])
+    return df
+
+def load_quaero_split(_split):
+    QUAERO_DIR = Path.home() / "src/corpus/QUAERO_FrenchMed/corpus"
+    converter = BratInputConverter()
+    raw_docs = []
+
+    for text_file in sorted(QUAERO_DIR.glob(f"{_split}/*/*.txt")):
+        doc = TextDocument.from_file(text_file)
+        ann_file = text_file.with_suffix(".ann")
+        entities = converter.load_annotations(ann_file)
+        entities = filter_overlapping_entities(entities)
+        for ent in entities:
+            doc.anns.add(ent)
+        raw_docs.append(doc)
+
+    phrase_creator.run(raw_docs)
+    splitted_docs = splitter.run(raw_docs)
+
+    return splitted_docs, corpus_specs(splitted_docs, num_docs=len(raw_docs), _title=_split)
+
+def load_e3c_split(_split):
+    data_collection = Path.home() / "src/corpus/E3C_corpus"
+    dir_path = data_collection / _split
+    raw_docs = list(load_data_annotation(dir_path=dir_path, keep_sentences = True))
+
+    for doc in raw_docs:
+        for ent in doc.anns.get_entities():
+            ent.label = "DISO"
+
+    phrase_creator.run(raw_docs)
+    splitted_docs = splitter.run(raw_docs)
+
+    return splitted_docs, corpus_specs(splitted_docs, num_docs=len(raw_docs), _title=_split)
+
+def load_casm2():
+
+    ANNOTATION_DIR = Path.home() / "src/corpus/CasM2_Files/m2annotations"
+    SPLIT_SEEDS = (67, 33)
+    TEST_SIZE = 0.2
+    VALIDATION_SIZE = 0.2
+
+    converter = DoccanoInputConverter(task=DoccanoTask.RELATION_EXTRACTION)
+    raw_documents = converter.load_from_directory_zip(ANNOTATION_DIR)
+
+    phrase_creator.run(raw_documents)
+
+    casm2 = {}
+    casm2['train'], casm2['test'] = train_test_split(raw_documents, random_state=SPLIT_SEEDS[0], test_size=TEST_SIZE)
+    casm2['train'], casm2['val'] = train_test_split(casm2['train'], random_state=SPLIT_SEEDS[1], test_size=VALIDATION_SIZE)
+    casm2_splitter = DocumentSplitter(segment_label="sentence", entity_labels=['treatment', 'test', 'problem'], attr_labels=[])
+
+    remap = {'treatment':'CHEM', 'test':'PROC','problem':'DISO'}
+    casm2_splitted = {}
+    docs_num = {}
+
+    for key, docs in casm2.items():
+        docs_num[key] = len(docs)
+        casm2_splitted[key] = casm2_splitter.run(docs)
+        for doc in casm2_splitted[key]:
+            for ent in doc.anns.get_entities():
+                if ent.label in remap:
+                    ent.label = remap[ent.label]
+
+    specs = pd.concat([corpus_specs(casm2_splitted[key], key, docs_num[key]) for key in casm2.keys()])
+
+    return casm2_splitted, specs
+
+def load_quaero():
+    splits = ["train", "test", "dev"]
+
+    quaero = {}
+    stats = []
+
+    for split in splits:
+        quaero[split], stat = load_quaero_split(split)
+        stats.append(stat)
+
+    specs = pd.concat(stats)
+    quaero['val'] = quaero.pop('dev')
+
+    return quaero, specs
+
+def load_e3c():
+    splits = ["layer1_test", "layer1_train", "layer2_val"]
+
+    e3c = {}
+    stats = []
+
+    for split in splits:
+        e3c[split], stat = load_e3c_split(split)
+        stats.append(stat)
+
+    specs = pd.concat(stats)
+
+    e3c['test'] = e3c.pop('layer1_test')
+    e3c['train'] = e3c.pop('layer1_train')
+    e3c['val'] = e3c.pop('layer2_val')
+
+    return e3c, specs
+
+def load_processed_mtsamples():
+    mt_samples = load_mtsamples()
+    doc_num = len(mt_samples)
+    phrase_creator.run(mt_samples)
+    mt_splitted = splitter.run(mt_samples)
+    specs = corpus_specs(mt_splitted, 'mtsamples', doc_num)
+
+    return mt_splitted, specs
+```
+
+```{code-cell} ipython3
+quaero, specs_quaero = load_quaero()
+e3c, specs_e3c = load_e3c()
+casm2, specs_casm2 = load_casm2()
+#mt, specs_mt = load_processed_mtsamples()
+```
+
+```{code-cell} ipython3
+specs_quaero.T
+```
+
+```{code-cell} ipython3
+specs_e3c.T
+```
+
+```{code-cell} ipython3
+specs_casm2.T
+```
+
+```{code-cell} ipython3
+corpus = {'quaero':quaero, 'e3c':e3c, 'casm2':casm2}
+
+for corpa_name, corpa in corpus.items():
+    for split_name, split in corpa.items():
+        output = f"datasets/{corpa_name}/{split_name}.jsonl"
+        save_text_documents(split, output)
+```
+
+```{code-cell} ipython3
+
+```
+
+```{code-cell} ipython3
+
+```