lstm_preprocessing.py

import re
import string
import numpy as np
import torch

from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))

def data_preprocessing(text: str) -> str:
    """preprocessing string: lowercase, removing html-tags, punctuation and stopwords

    Args:
        text (str): input string for preprocessing

    Returns:
        str: preprocessed string
    """    

    text = text.lower()
    text = re.sub('<.*?>', '', text) # Remove html tags
    text = re.sub(r'@\w+', " ", text) # Remove usernames
    text = re.sub(r'#\w+', " ", text) #Remove hash tags
    text = re.sub(r'\d+', " ", text) #Remove digits
    text = ''.join([c for c in text if c not in string.punctuation])# Remove punctuation
    text = [word for word in text.split() if word not in stop_words] 
    text = ' '.join(text)
    return text

def get_words_by_freq(sorted_words: list, n: int = 10) -> list:
    return list(filter(lambda x: x[1] > n, sorted_words))

def padding(review_int: list, seq_len: int) -> np.array:
    """Make left-sided padding for input list of tokens

    Args:
        review_int (list): input list of tokens
        seq_len (int): max length of sequence, it len(review_int[i]) > seq_len it will be trimmed, else it will be padded by zeros

    Returns:
        np.array: padded sequences
    """    
    features = np.zeros((len(review_int), seq_len), dtype = int)
    for i, review in enumerate(review_int):
        if len(review) <= seq_len:
            zeros = list(np.zeros(seq_len - len(review)))
            new = zeros + review
        else:
            new = review[: seq_len]
        features[i, :] = np.array(new)
            
    return features

def preprocess_single_string(
    input_string: str, 
    seq_len: int, 
    vocab_to_int: dict,
    ) -> torch.tensor:
    """Function for all preprocessing steps on a single string

    Args:
        input_string (str): input single string for preprocessing
        seq_len (int): max length of sequence, it len(review_int[i]) > seq_len it will be trimmed, else it will be padded by zeros
        vocab_to_int (dict, optional): word corpus {'word' : int index}. Defaults to vocab_to_int.

    Returns:
        list: preprocessed string
    """    

    preprocessed_string = data_preprocessing(input_string)
    result_list = []
    for word in preprocessed_string.split():
        try: 
            result_list.append(vocab_to_int[word])
        except KeyError as e:
            print(f'{e}: not in dictionary!')
    result_padded = padding([result_list], seq_len)[0]

    return torch.tensor(result_padded)