class_docstr: This class is an implementation of the original EDA algorithm (2019) [1].
  
  [1] Wei, J. and Zou, K., 2019, November. EDA: Easy Data Augmentation Techniques for Boosting Performance on
  Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing
  and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 6383-6389).
  https://www.aclweb.org/anthology/D19-1670.pdf
  
  Example usage: ::
      >>> from textaugment import EDA
      >>> t = EDA()
      >>> t.synonym_replacement("John is going to town",top_n=3)
      John is give out to town
      >>> t.random_deletion("John is going to town", p=0.2)
      is going to town
      >>> t.random_swap("John is going to town")
      John town going to is
      >>> t.random_insertion("John is going to town")
      John is going to make up town
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docstr: A method to initialize parameters
  
  :type random_state: int
  :param random_state: (optional) Seed
  :type stop_words: list
  :param stop_words: (optional) List of stopwords
  
  :rtype:   None
  :return:  Constructer do not return.
docstr_tok: ["A","method","to","initialize","parameters",":","type","random_state",":","int",":","param","random_state",":","(","optional",")","Seed",":","type","stop_words",":","list",":","param","stop_words",":","(","optional",")","List","of","stopwords",":","rtype",":","None",":","return",":","Constructer","do","not","return","."]
code: def __init__(self, stop_words=None, random_state=1):
      """A method to initialize parameters
  
      :type random_state: int
      :param random_state: (optional) Seed
      :type stop_words: list
      :param stop_words: (optional) List of stopwords
  
      :rtype:   None
      :return:  Constructer do not return.
      """
      self.stopwords = (
          stopwords.words("english")
          if stop_words is None
          else stop_words
      )
      self.sentence = None
      self.p = None
      self.n = None
      self.random_state = random_state
      if isinstance(self.random_state, int):
          random.seed(self.random_state)
      else:
          raise TypeError("random_state must have type int")
  
file_path: textaugment/textaugment/eda.py
imports: import nltk
  from nltk.corpus import wordnet, stopwords
  import random
label_desc: Use image node_id 4 to create a new EDA object  with example: obj = EDA(stop_words, random_state)