"""Contains the text featurizer class.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os class TextFeaturizer(object): """Text featurizer, for processing or extracting features from text. Currently, it only supports char-level tokenizing and conversion into a list of token indices. Note that the token indexing order follows the given vocabulary file. :param vocab_filepath: Filepath to load vocabulary for token indices conversion. :type specgram_type: basestring """ def __init__(self, vocab_filepath): self._vocab_dict, self._vocab_list = self._load_vocabulary_from_file( vocab_filepath) def featurize(self, text): """Convert text string to a list of token indices in char-level.Note that the token indexing order follows the given vocabulary file. :param text: Text to process. :type text: basestring :return: List of char-level token indices. :rtype: list """ tokens = self._char_tokenize(text) return [self._vocab_dict[token] for token in tokens] @property def vocab_size(self): """Return the vocabulary size. :return: Vocabulary size. :rtype: int """ return len(self._vocab_list) @property def vocab_list(self): """Return the vocabulary in list. :return: Vocabulary in list. :rtype: list """ return self._vocab_list def _char_tokenize(self, text): """Character tokenizer.""" return list(text.strip()) def _load_vocabulary_from_file(self, vocab_filepath): """Load vocabulary from file.""" vocab_lines = [] with open(vocab_filepath, 'r') as file: vocab_lines.extend(file.readlines()) vocab_list = [line[:-1] for line in vocab_lines] vocab_dict = dict( [(token, id) for (id, token) in enumerate(vocab_list)]) return vocab_dict, vocab_list