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| import re | |
| import string | |
| import numpy as np | |
| import torch | |
| from nltk.corpus import stopwords | |
| stop_words = set(stopwords.words('russian')) | |
| def data_preprocessing(text: str) -> str: | |
| text = text.lower() | |
| text = re.sub('<.*?>', '', text) | |
| text = ''.join([c for c in text if c not in string.punctuation]) | |
| text = ' '.join([word for word in text.split() if word not in stop_words]) | |
| text = [word for word in text.split() if not word.isdigit()] | |
| text = ' '.join(text) | |
| return text | |
| def get_words_by_freq(sorted_words: list[tuple[str, int]], n: int = 10) -> list: | |
| return list(filter(lambda x: x[1] > n, sorted_words)) | |
| def padding(review_int: list, seq_len: int) -> np.array: | |
| 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, | |
| verbose : bool = False | |
| ) -> torch.tensor: | |
| 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: | |
| if verbose: | |
| print(f'{e}: not in dictionary!') | |
| pass | |
| result_padded = padding([result_list], seq_len)[0] | |
| return torch.tensor(result_padded) | |