cantemist/using_dataset_hugginface.py

# -*- coding: utf-8 -*-
"""using_dataset_hugginface.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT
"""

"""**Hugginface loggin for push on Hub**"""
###
#
#  Used bibliografy:
#    https://huggingface.co/learn/nlp-course/chapter5/5
#
###

import os
import time
import math
from huggingface_hub import login
from datasets import load_dataset, concatenate_datasets
from functools import reduce
from pathlib import Path
import pandas as pd

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

HF_TOKEN = ''
DATASET_TO_LOAD = 'bigbio/cantemist'
DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
DATASET_SOURCE_ID = '1'

#Loggin to Huggin Face
login(token = HF_TOKEN)

dataset_CODING = load_dataset(DATASET_TO_LOAD)
royalListOfCode = {}
issues_path = 'dataset'
tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")

#Read current path
path = Path(__file__).parent.absolute()

with open( str(path) + os.sep + 'ICD-O-3_valid-codes.txt',encoding='utf8') as file:
  """
     # Build a dictionary with ICD-O-3 associated with 
     # healtcare problems
  """
  linesInFile = file.readlines()
  for iLine in linesInFile:
    listOfData = iLine.split('\t')

    code = listOfData[0]
    description = reduce(lambda a, b: a + " "+ b, listOfData[1:2], "")
    royalListOfCode[code.strip()] = description.strip()


def getCodeDescription(labels_of_type, royalListOfCode):
  """
    Search description associated with some code 
    in royalListOfCode

  """
  classification = [] 

  for iValue in labels_of_type:
    if iValue in  royalListOfCode.keys():
      classification.append(royalListOfCode[iValue])
  return classification


    # raw_text: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.

    # topic: (puede ser healthcare_treatment, healthcare_diagnosis, tema, respuesta a pregunta, o estar vacío p.ej en el texto abierto)

    # speciality: (especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)

    # raw_text_type: (puede ser caso clínico, open_text, question)

    # topic_type: (puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)

    # source: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.

    # country: Identificador del país de procedencia de la fuente (p.ej.; ch, es) usando el estándar ISO 3166-1 alfa-2 (Códigos de país de dos letras.).
cantemistDstDict = {
  'raw_text': '',
  'topic': '',
  'speciallity': '',
  'raw_text_type': 'clinic_case',
  'topic_type': 'medical_diagnostic',
  'source': DATASET_SOURCE_ID,
  'country': 'es',
  'document_id': ''
}

totalOfTokens = 0
corpusToLoad = []
countCopySeveralDocument = 0
counteOriginalDocument = 0

for iDataset in dataset_CODING:
    if iDataset == 'test':
      for item in dataset_CODING[iDataset]:
        #print ("Element in dataset")
        idFile = item['id']
        text = item['text']
        list_of_type = item['text_bound_annotations']
        labels_of_type = item['labels']

        #Find topic or diagnosti clasification about the text
        diagnostyc_types = getCodeDescription( labels_of_type, royalListOfCode)
        counteOriginalDocument += 1
        classFileSize = len(diagnostyc_types)

        #If there are more clasification about the file
        
        if classFileSize > 1:
         countCopySeveralDocument += classFileSize - 1

        listOfTokens = tokenizer.tokenize(text)
        currentSizeOfTokens = len(listOfTokens)
        totalOfTokens += currentSizeOfTokens
        
        for iTypes in diagnostyc_types:
          #print(iTypes)
          newCorpusRow = cantemistDstDict.copy()

          #print('Current text has ', currentSizeOfTokens)
          #print('Total of tokens is ', totalOfTokens)
          newCorpusRow['raw_text'] = text
          newCorpusRow['document_id'] = str(idFile)
          newCorpusRow['topic'] = iTypes
          corpusToLoad.append(newCorpusRow)
        
df = pd.DataFrame.from_records(corpusToLoad)

if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"):
  os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")

df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True)
print(
        f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl"
)

print(' On dataset there are as document ', counteOriginalDocument)
print(' On dataset there are as copy document ', countCopySeveralDocument)
print(' On dataset there are as size of Tokens ', totalOfTokens)
file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")  # or Path('./doc.txt')
size = file.stat().st_size
print ('File size on Kilobytes (kB)', size >> 10)  # 5242880 kilobytes (kB)
print ('File size on Megabytes  (MB)', size >> 20 ) # 5120 megabytes (MB)
print ('File size on Gigabytes (GB)', size >> 30 ) # 5 gigabytes (GB)

#Once the issues are downloaded we can load them locally using our 
local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train")

##Update local dataset with cloud dataset
try:  
  spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train")
  spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
except Exception:
  spanish_dataset = local_spanish_dataset

spanish_dataset.push_to_hub(DATASET_TO_UPDATE)

print(spanish_dataset)

# Augmenting the dataset

#Importan if exist element on DATASET_TO_UPDATE we must to update element 
# in list, and review if the are repeted elements